Enterprise registration self-service terminal and identity verification method and system thereof

Abstract

The invention relates to the technical field of identity verification, in particular to an enterprise registration self-service terminal and an identity verification method and system thereof, and the method comprises the following steps: comparing dynamic two-dimensional code parameters item by item based on the enterprise registration self-service terminal, a verification serial number, a card issuer identifier and a validity period, and judging the consistency of each field; and according to the matching of the result and the registration system information, identifying the offset condition of the handling process, and outputting a sequence offset index. According to the invention, through synchronous acquisition of multiple parameters of the identity card, in combination with two-dimensional code dynamic feature comparison and chip data analysis, real-time association of identity data in three aspects of time, source and content is realized, each node is bound with a unique identifier, an equipment source and time sequence relationship, and a node data abstract series full-process log chain is handled; interrupt, offset and risk handling nodes can be positioned in the whole process, a data chain structure supports afterward tracing and node-level traceability, and handling safety, data integrity and traceability are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of identity authentication, and in particular to an enterprise registration self-service terminal and an identity authentication method and system thereof. Background Art

[0002] The field of identity authentication involves the identification and confirmation of user identities, including the collection of user information, information comparison and verification, identity authentication process management and the formulation of security policies. It systematically covers various devices and methods for identity verification based on biometric recognition, password verification, digital certificates, etc. Among them, the traditional enterprise registration self-service terminal and its identity authentication method refer to the problem of verifying the authenticity of the identity of the person handling the enterprise registration business by setting up a self-service terminal. Usually, the user's identity is confirmed by swiping an ID card, entering a password, fingerprint recognition, face recognition, etc., and the user is allowed to continue with subsequent enterprise registration operations.

[0003] Existing identity authentication is limited to single information comparison, and the parameter verification mode is single. It is difficult to establish multi-level cross-verification for identity card parameters and registration database information during business processing. The process lacks node timing association and data tracking structure, and cannot record the corresponding node of each abnormal event in real time. When the certificate is misused, the registration record is inconsistent, or the key steps are out of order, the identity data and the processing flow cannot form a complete link, the missing nodes and risky behaviors cannot be discovered in time, and it is difficult to trace the identity source and operation records during subsequent investigations. The data security protection and risk prevention and control capabilities are obviously insufficient. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an enterprise registration self-service terminal and an identity verification method and system thereof.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: an enterprise registration self-service terminal and an identity verification method thereof, comprising the following steps: S1: Based on the enterprise registration self-service terminal, the surface of the enterprise personnel ID card is analyzed, the ID card serial number is read in sequence, the issuing authority identification is parsed, the expiration date is verified, and the consistency of the dynamic QR code timestamp and the ID card parameters is compared to obtain the QR code consistency feature; S2: Based on the consistency characteristics of the QR code, determine whether the card issuing authority name matches the authority field in the registration system, analyze the correspondence between the registration date and the database record, compare the expiration date with the card validity, verify the unique identifier, and obtain a structured comparison value; S3: Based on the structured comparison quantity, the data is compared with the authentication trust comparison standard. If there is any inconsistency, the identity recognition status is adjusted, the inconsistent fields are recorded, and the associated parameters are summarized. The source of the abnormal fields is analyzed to obtain interception event statistics; S4: Based on the interception event statistics, collect the operation time sequence, verify the unique identifier, read the terminal hardware number, register the business type, obtain the process sequence number, and analyze the parameter combination correlation to obtain the log chain summary feature.

[0006] The improvements of the present invention are that the QR code consistency features include timestamp consistency status, serial number matching status, and agency identification consistency status; the structured comparison quantity includes agency field matching status, registration time correspondence status, and unique identification verification results; the interception event statistics include interception type, abnormal field category, and verification number; the log chain summary features include node sequence information, data link identification, and business process node identification.

[0007] The present invention is improved in that the steps of obtaining the consistency feature of the QR code are specifically as follows: S111: Based on the enterprise registration self-service terminal, the collected ID card surface information is analyzed, and the card serial number, expiration date, and QR code content read by the scanning unit are compared with the card issuing authority information obtained by the chip to determine the consistency between multiple source data and obtain the multi-source basic parameters of the ID card; S112: Based on the multi-source basic parameters of the ID card, the QR code content is compared with the chip data and the card surface data, and the consistency of the timestamp, serial number and agency identification is analyzed. The equality of each data item is screened and classified to obtain a field consistency status group; S113: Based on the field consistency status group, analyze the timestamp consistency status, serial number matching status and agency identification consistency status, determine the standardized relationship of the comparison items, screen key comparison parameters, and obtain the QR code consistency characteristics.

[0008] The present invention is improved in that the step of obtaining the structured alignment amount is specifically as follows: S211: Based on the consistency feature of the QR code, the timestamp, serial number, and agency identification in the QR code are analyzed to determine if they are consistent with the data read from the ID card chip. The ID card issuing agency name is compared with the agency field content in the enterprise registration system database to generate an agency field comparison status item. S212: Based on the agency field comparison status item, analyze the time format and sequence of the ID card registration date and the enterprise registration system registration time, compare the ID card validity period and the registration card validity period data overlap range, calculate the time intersection, and obtain the registration time overlap parameter; S213: Based on the registration time coincidence parameter, analyze whether the unique identification field of the identity card is completely consistent with the unique field of the person in the system database, calculate the consistency expression amount between the fields, and obtain the structured comparison amount.

[0009] The present invention is improved in that the step of obtaining the interception event statistics is specifically as follows: S311: Based on the structured comparison data, analyze each enterprise registration field, compare the ID card data with the system database registration information item by item, determine the consistency of each corresponding field, filter out the fields with differences, and integrate all inconsistent field contents to obtain a field inconsistency set; S312: Based on the field inconsistency set, determine the status of the identity unit associated with each field, analyze the corresponding enterprise registration parameters, identification number and source category, filter out fields in abnormal operation or interception status, and integrate them into a field abnormality data group; S313: Based on the field abnormality data group, calculate the distribution characteristics of each data in the field group in the corresponding path, call the unit number and the tag type to perform number mapping, and obtain the interception event statistics.

[0010] The present invention is improved in that the steps of obtaining the log chain summary feature are specifically as follows: S411: Based on the interception event statistics, the time, personnel identity, and equipment information involved in each operation in the processing process are sorted out, and the attribution relationship between the process nodes is compared to determine the association status of each node to obtain the node identification association value; S412: Based on the node identification correlation, a cross-analysis is performed on the time sequence, identity identifier, and operation process number of each node. Through sequence matching and information consistency screening, nodes with structural deviation are determined to obtain a node structure deviation index; S413: Based on the node structure offset index, according to the original summary of the node and the summary content of the adjacent nodes, the node summary chain structure is optimized, the link continuity and abnormal marking between the process nodes are analyzed, and the log chain summary feature is obtained.

[0011] The present invention is improved in that the steps further include: S5: Based on the log chain summary characteristics, analyze the node operation sequence, compare the summary content of the current node with that of the previous node, determine the continuity of the log chain, verify the node structure offset, and mark the abnormal node number to obtain the sequence offset index; The sequence offset indicator includes an offset type, a node sequence number, and a link abnormality mark.

[0012] The present invention is improved in that the steps of obtaining the sequence offset index are specifically as follows: S511: Based on the log chain summary feature, the sequence information and data link identifier of each operation node are checked item by item. By comparing the node structure field with the summary content of the previous node, the correspondence between the two in the actual operation sequence is determined to obtain sequence structure correspondence data; S512: Analyze the connection between each set of node structure fields and summary fields based on the sequence structure correspondence data, combined with the business process node identifier and the data link identifier, determine the connection integrity status of the node in the actual link, and obtain the link connection integrity feature; S513: Based on the link connection integrity characteristics, analyze the node sequence numbers and link abnormality marks in the log chain, judge whether the sequence of node structure fields is interrupted, misplaced or jumped, optimize the sequential connection performance of each node, and obtain the sequence offset index.

[0013] A self-service terminal for enterprise registration and an identity verification system thereof, the system comprising: The ID card verification module, based on the enterprise registration self-service terminal, analyzes the surface area of ​​the ID card presented by the enterprise personnel, sequentially calls the scanning unit to read the ID card serial number, interprets the issuing authority identification through chip reading, then compares the ID card expiration date with the card surface information, and compares the timestamp of the dynamic QR code on the terminal screen with the ID card parameters item by item, calculating the equivalence relationship of each set of parameters to obtain the QR code consistency characteristics; Based on the consistency features of the QR code, the data consistency verification module determines the consistency between the name of the issuing authority and the authority field in the enterprise registration system database, analyzes the correspondence between the ID card registration date and the database registration record time, compares the validity period with the registration card validity information, verifies the unique identification of the personnel item by item, calculates the consistency between each field, and obtains the structured comparison quantity; The abnormality tracing module compares the structured comparison quantity with the authentication trusted comparison range benchmark. If the comparison does not meet the standard, it adjusts the terminal identity recognition unit status, records the identity card fields that do not match the comparison, summarizes the related parameters, and then performs source verification on the fields involved in the abnormal event to obtain interception event statistics; The full-process operation tracking module collects the time sequence of operations during the processing based on the interception event statistics, verifies the unique identification of personnel item by item, reads the terminal hardware identification number, registers the business type parameters, obtains the process operation sequence number, analyzes the combination association between parameters, and calculates the node summary chain structure to obtain the log chain summary characteristics; The node link detection module analyzes the operation sequence of the log chain nodes based on the log chain summary characteristics, gradually compares the current node data structure with the previous node summary, determines the continuity of the log chain during the processing, and verifies the node structure offset, marking the node number of each abnormality to obtain the sequence offset index.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, through the synchronous collection of multiple parameters of the identity card, combined with the dynamic feature comparison of the QR code and the analysis of the chip data, the real-time association of the identity data in terms of time, source and content is realized. The registration process cross-checks the agency field, validity period, registration sequence and unique identification and other multi-source information layer by layer. Abnormal parameter labels and event indexes are automatically generated during the processing. Each processing node is bound to the unique identification, equipment source and time sequence relationship. The node data summary is connected in series with the whole process log chain. The processing interruption, offset and risk node can be located throughout the process. The data chain structure supports post-investigation and node-level tracing. The identity verification and process log links form a linkage closed loop, which significantly improves the processing security, data integrity and traceability. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of the main steps of the present invention; Figure 2 This is a flow chart for obtaining the consistency feature of a QR code in the present invention; Figure 3 This is a flow chart for obtaining structured comparison quantities in the present invention; Figure 4 This is a flowchart for obtaining interception event statistics in the present invention; Figure 5 This is a flow chart for obtaining log chain summary features in the present invention; Figure 6 This is a flow chart for obtaining the sequence offset indicator in the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operate in a specific direction, and therefore should not be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0018] Example See also Figure 1 The present invention provides a technical solution: an enterprise registration self-service terminal and an identity verification method thereof, comprising the following steps: S1: Based on the enterprise registration self-service terminal, the surface area of ​​the ID card presented by the enterprise personnel is analyzed. The scanning unit is called in sequence to read the ID card serial number. The issuing authority identification is analyzed through chip reading. The ID card expiration date is then compared with the card surface information. The timestamp of the dynamic QR code on the terminal screen is compared with the ID card parameters item by item. The equivalence relationship of each set of parameters is calculated to obtain the QR code consistency characteristics. S2: Based on the consistency characteristics of the QR code, the consistency of the card issuing authority name content and the authority field in the enterprise registration system database is determined. The correspondence between the ID card registration date and the database registration record time is analyzed. The validity period interval is compared with the validity information of the registered card in sequence. The unique identification of the personnel is verified item by item. The consistency between each field is calculated to obtain the structured comparison quantity. S3: Based on the structured comparison volume, the system compares the data with the authentication trusted comparison range benchmark. If the comparison fails to meet the standard, the terminal identity recognition unit status is adjusted, the ID card fields that do not match the comparison are recorded, and the associated parameters are summarized. The source of the fields involved in the abnormal event is then verified to obtain the interception event statistics. S4: Based on interception event statistics, collect the time sequence of operations during the processing process, verify the unique identification of personnel item by item, read the terminal hardware identification number, register the business type parameters, obtain the process operation sequence number, analyze the combination association between parameters, and calculate the node summary chain structure to obtain the log chain summary feature; S5: Based on the log chain summary characteristics, analyze the operation sequence of the log chain nodes, gradually compare the current node data structure with the previous node summary, determine the continuity of the log chain during the processing, and check the node structure offset. Mark the node number where an abnormality occurs each time to obtain the sequence offset indicator.

[0019] The consistency features of the QR code include the timestamp consistency status, serial number matching status, and agency identification consistency status. The structured comparison quantity includes the agency field matching status, registration time correspondence, and unique identification verification results. The interception event statistics include the interception type, abnormal field category, and verification number. The log chain summary features include node sequence information, data link identification, and business process node identification. The sequence offset indicators include offset type, node sequence number, and link abnormality mark.

[0020] In S1, the surface area of ​​the identity card refers to the machine-readable area on the physical card of the corporate identity card used by corporate personnel for registration, which usually contains visible and machine-readable information such as card number, name, expiration date, QR code, etc.; the scanning unit refers to the dedicated hardware module in the self-service terminal used to read the corporate identity card information, which can be a contact or contactless IC card reader, QR code scanner, etc., used to capture various parameter data of the identity card; the issuing agency identification refers to the independent identification marked or embedded on the corporate identity card to indicate the card issuing agency, which can be obtained through card printing or chip data; item-by-item comparison refers to the one-to-one comparison of similar parameters from different sources (such as QR code, card chip, card printing), such as serial number, expiration date, agency identification, etc., rather than an overall comparison; the equivalence relationship refers to whether the contents of two or more compared parameters are exactly the same. For example, if the serial number data in the card serial number and the serial number in the QR code parameter are exactly the same, it is "equivalent".

[0021] In S2, field consistency refers to the state in which the content of a certain data item (field) is completely consistent between two data sources (such as the enterprise registration system and the enterprise identity card); corresponding situation refers to the comparison relationship between two data items in terms of content, format or time sequence, such as whether the registration date is consistent with the system record time; validity information refers to the data content on the identity card or system regarding the start and end dates of card use or registration, status code, etc., which can describe the current status of the identity card; verification personnel unique identification refers to the one-by-one comparison and verification of the enterprise personnel unique code (such as employee number, work number, UID, etc.) on the enterprise identity card with the same field in the system database; inter-field consistency refers to the overall consistency status of multiple parameters (such as card number, registration authority, validity period, unique identifier) ​​when compared between different data sources, usually as a consistent or inconsistent ratio or situation description.

[0022] In S3, the trusted comparison range benchmark for identity authentication refers to the judgment criteria set by the system for identity authentication, such as the consistent items reaching a certain proportion, all of which must be consistent, etc., which is used to determine whether the comparison is "passed", but does not limit the specific value; the terminal identity recognition unit status refers to the operating status of the hardware, software or module used for identity authentication in the enterprise registration self-service terminal during the verification process, including normal, abnormal, interrupted, intercepted and other status information; the identity card field refers to the content of various comparable parameters in the identity card, such as card number, validity period, issuing authority, unique identifier, etc.; the summary of associated parameters refers to the collection and archiving of all identity card data parameters directly related to the abnormality when an interception event occurs, so as to facilitate subsequent tracing or analysis; abnormal events refer to situations in which the process is interrupted, suspended or recorded due to parameter inconsistency, data abnormality, system warnings, etc. during the identity authentication process; source verification refers to the analysis of the causes and links of the abnormality of the specific field that caused the abnormality through system logs, card historical data and other channels.

[0023] In S4, the operation time sequence refers to the specific time and sequence of each operation event in the enterprise registration business process, which is usually used to verify whether the process is compliant; the terminal hardware identification number refers to the unique number or hardware ID of the self-service terminal device itself, which is used to mark the specific device source where the operation occurs; the business type parameter refers to the type code or name of the current matter being handled during the business process, such as establishment, change, cancellation, etc.; the process operation sequence number refers to the serial number automatically assigned by the system to each operation node in the business process, which is used to trace the sequence logic of each node; combination association refers to the logical combination and association of parameters such as operation time, personnel unique identification, terminal identification number, business type, sequence number, etc., which is used for subsequent process tracing and data structure analysis; the node summary chain structure refers to the summary extraction of key information of each operation node in the order of the business process, and the association of the summary data from beginning to end to form an unalterable data chain structure.

[0024] In S5, the node data structure refers to the set of data records containing all key information generated by each business process node; the previous node summary refers to the data summary content generated by the previous node in the process corresponding to the current node, which is used for sequential comparison between nodes; continuity refers to the integrity of the node summary chain in terms of arrangement order and content links, without broken links, missing links or abnormal jumps; node structure offset refers to the abnormal order of business process nodes in the summary chain, mismatched data content or misplaced links between nodes; abnormal node number refers to the identification or number of the specific process node that is detected to be offset, broken or abnormal during process tracing or node comparison.

[0025] See also Figure 2 , the specific steps for obtaining the consistency features of the QR code are: S111: Based on the enterprise registration self-service terminal, the collected ID card surface information is analyzed, and the card serial number, expiration date, and QR code content read by the scanning unit are compared with the card issuing authority information obtained by the chip to determine the consistency between multiple source data and obtain the multi-source basic parameters of the ID card; Activate its configured contact reader module to read the serial number, expiration date and issuing agency information from the corporate identity card chip. The reading action includes initializing the data channel through the chip interface electrical contacts, then calling the card basic information file from the chip, extracting each data content and caching it into the system memory; then start the QR code scanning module to identify the QR code pattern on the card surface, extract the QR code information content through image positioning, parse the serial number, QR code generation time and issuing agency field, and generate a QR code data set; then call the image acquisition module to capture the card surface image, use the image text recognition algorithm to convert the card number, expiration date, and issuing agency name in the card surface printed text into structured text, and establish a card surface data set; uniformly name the three key fields involved in the chip, QR code, and card surface data as serial number, expiration date, and agency identification, and establish a three-party comparison list, locate the three-party data position by the field name, and compare whether the serial number is in the three sources. If the chip and the QR code are consistent but the card surface data is inconsistent, the serial number consistency is considered incomplete. Then determine whether the three sources of the validity period field are the same expiration time. If only the time printed on the card surface is earlier than the time recorded in the chip and the QR code, it is marked as a discrepancy in the validity period. Continue to determine whether the issuing agency field is completely consistent. If the QR code matches the agency code displayed by the chip, but there is a difference in the abbreviation or identification of the agency name printed on the card surface, it is identified as an inconsistency in the agency field. In actual operation, if the serial number in the ID card chip is "JZ20250013", the expiration date is "December 31, 2030", the agency identification is "450100XZ01", the QR code information matches it, but the serial number printed on the card surface is "JZ20250012", it is considered that there is a difference in one of the data sources, record the field inconsistency, and summarize and construct a list of multi-source basic parameters of the ID card for subsequent structural consistency analysis.

[0026] S112: Based on the multi-source basic parameters of the ID card, the QR code content is compared with the chip data and the card surface data, and the consistency of the timestamp, serial number and agency identification is analyzed. The equality of each data item is screened and classified to obtain a field consistency status group; Set the QR code information as the main reference source, and compare the field content with the chip data and the card surface data item by item. First, retrieve the generation time, serial number and issuing agency name recorded in the QR code as the reference field content, and then search the corresponding time, serial number, and agency identification in the chip data respectively, and compare them one by one with the QR code field content. If the time information is the same as the chip, mark it as consistent. If it is different, record the difference. Then find the time field printed in the card surface data and continue to compare it with the QR code. For example, if the QR code records the time as "July 23, 2025 13:05", the chip shows the same time, and the time printed on the card surface is "July 22, 2025", then the field is marked as consistent with the chip but inconsistent with the card surface. Then compare the serial number field. If the QR code is exactly the same as the chip serial number, and the card surface serial number has a slight error, it will be identified as part of the serial number. If the QR code is the same as the chip agency identification, but the agency printed on the card is only an abbreviation or missing characters, it is identified as only partially consistent. The number of times each field appears consistent in the three data items is classified and marked as four states: complete consistency, partial consistency, weak consistency, and inconsistency. For example, the QR code of a card shows the serial number as "ZJ88800005", the time is "13:30 on July 23, 2025", and the agency identification is "450100XZ01". The chip contains exactly the same content, but the last digit of the serial number on the card is "6", the time is consistent, and the agency is displayed as the abbreviation "450100". In this case, the serial number status is partially consistent, the time status is completely consistent, and the agency status is partially consistent. This status is recorded as a field consistency status group to determine the acceptability of the identity card in the subsequent verification process.

[0027] S113: Based on the field consistency status group, analyze the timestamp consistency status, serial number matching status, and agency identification consistency status, determine the standardized relationship of the comparison items, and screen key comparison parameters using the formula: ; Get the QR code consistency feature ,in, Indicates the serial number parameter content recorded in the QR code. Indicates the serial number parameter content read by the chip. Indicates the card issuing authority identification code recorded in the QR code. Indicates the card issuing authority identification code read by the chip. Indicates the The matching number of the item, Indicates the Item compares the parameter number of the item, Indicates the number of consistent comparison items.

[0028] The QR code consistency feature refers to the comparison of key parameters in the QR code (such as serial number and issuing authority identification) with the chip reading parameters when the corporate identity card is authenticated at the corporate registration self-service terminal, and the quantitative description and standardized measurement of the matching of core items. It reflects the overall consistency of the structure and content of the QR code data with other data sources. The feature is used to automatically determine whether there are any anomalies or discrepancies in the data of the identity card, improve the accuracy and security of identity recognition at the self-service terminal, and provide a standardized and consistent verification data foundation for subsequent business processes.

[0029] The original data content of the serial number field in the QR code and the chip is "9812736512" and "9812736512", the difference is 0, and the normalized difference is , then extract the card issuing agency identification field in the QR code and chip as "GDIC2022" and "GDIC2021". The difference can be normalized by character bit difference conversion and expressed as , and then read the matching identifier of each matching item in the field consistency status group in turn With parameter number , suppose this analysis involves 5 comparison fields, corresponding to parameter numbers 、 、 、 、 , the matching status are 、 、 、 、 , substitute the consistency feature calculation formula as follows: ; ; ; In the formula, the numerator is used to accumulate the normalized differences between key fields, and the denominator is calculated by matching the status Corresponding field intensity number The square results of the weighted summation are taken, and the square root is taken to form the normalization factor. This structure reflects the synthetic influence of the consistency of multiple fields on the overall comparison result. The result shows that the QR code consistency feature result calculated by the formula is , the value is within the preset consistency deviation range It belongs to the section with relatively small deviation, indicating that the serial number in the QR code and the card issuing agency identification are highly consistent as a whole when compared with the chip reading data, reflecting that the degree of consistency between the fields has reached the credible range of judgment. Therefore, it can be deduced that the consistency status of the identity card in the multi-source data fusion comparison is "valid", and the result is included in the structured comparison chain for continuity verification of subsequent identity recognition.

[0030] See also Figure 3 , the steps for obtaining the structured alignment are as follows: S211: Based on the consistency characteristics of the QR code, the timestamp, serial number, and agency identification in the QR code are analyzed to determine if they are consistent with the data read from the ID card chip. The ID card issuing agency name is compared with the agency field content in the enterprise registration system database to generate an agency field comparison status item. The timestamp, serial number and agency identification extracted from the QR code are respectively used to establish field lists, and the timestamp field, serial number field and agency code field recorded in the chip data are called one by one. Strict matching is performed through character length, encoding rules and content to determine whether they are completely consistent. If any field is found to have inconsistent number of characters, letters or numbers, it is marked as inconsistent. The agency name corresponding to the agency code read from the identity card chip is then reversely mapped through the agency dictionary preset in the terminal to obtain the standard agency name text, and then the standard record value of the agency field corresponding to the current registration item is retrieved from the enterprise registration database for field content. Comparison: Identify the content consistency between two fields through word segmentation and full character comparison. For example, the agency identification extracted from the QR code is "450100XZ01", the same code is recorded in the chip, the agency name is "XXXXXX Approval Department", and the registration agency field in the registration system is "XXXXX Approval Service Department". There are three characters different between the two texts. If the character similarity is less than 80%, it is marked as inconsistent. If the agency code is exactly the same as the registration agency field or matches the same after dictionary conversion, it is considered a successful match. The comparison results of each field are registered as the agency field comparison status item to generate the agency field comparison status item.

[0031] S212: Based on the agency field comparison status item, analyze the time format and sequence of the ID card registration date and the enterprise registration system registration time, compare the ID card validity period and the registration card validity period data overlap range, calculate the time intersection, and obtain the registration time overlap parameter; The registration date field in the chip data is called, and the format is unified and the order is judged with the registration time of the enterprise registration record. First, the registration date format in the chip is converted to a unified year-month-day format. For example, the chip registration time is "2025 / 06 / 15", and the registration system record is "June 15, 2025". After unifying it to "2025-06-15", the content is compared. If the two are exactly the same, they are registered as time consistency. If there is a reversal of the month and day or a deviation before and after the date, the deviation days need to be calculated and registered as the time difference. Then judge whether the difference is less than or equal to 1 day. If it is less than, it is classified as nearly consistent, otherwise it is inconsistent. Then the validity period recorded in the chip, that is, the start date and end date, is compared. Date, for example, the chip record starts from "2023-07-01" and ends at "2030-06-30", and the enterprise registration system records the card validity period from "2024-01-01" to "2029-12-31". It is necessary to determine the intersection range of the two time intervals. If the number of overlapping days is greater than 180 days, it is judged as high overlap. If it is between 30 and 180 days, it is moderate overlap. If it is less than 30 days or there is no intersection, it is low overlap. Then, the correspondence between the registration time and the card registration date is summarized and the overlap ratio is recorded. The total number of days of the overlapping time period and the length of the longest interval are converted to form a unified ratio value, and finally this ratio value is used as the registration time overlap parameter.

[0032] S213: Based on the registration time overlap parameter, analyze whether the unique identification field of the ID card is completely consistent with the unique field of the person in the system database, using the formula: ; Calculate the consistent performance between fields , and obtain the structured alignment, where Indicates the number of consistent fields, Indicates the status of the agency field comparison item. Indicates the registration time overlap parameter, Indicates the total number of fields.

[0033] The inter-field consistency performance is a comprehensive parameter that measures the overall consistency of multiple comparison fields during the enterprise registration and identity verification process. It reflects the degree of synchronization and consistency between the identity card and the enterprise registration system database in multiple key fields such as agency name, registration time, and unique identifier. The higher the value of this quantity, the more consistent the comparison results of the inspected data in terms of field content, timing, and relevance, and the stronger the data relevance. : The number of fields that are consistent among all the fields involved in the comparison ( ) and the comparison status item of the agency field ( ) are added together to reflect the consistency of the overall field comparison, and multiplied by the coincidence parameter of the registration time ( ), incorporating temporal consistency into the overall measurement; the denominator : The total number of all fields involved in the comparison is used to normalize the overall number of comparisons; square root operation: nonlinear normalization is performed on the comparison results to highlight the hierarchy and distribution characteristics of the consistency between fields; the formula outputs a performance quantity that can intuitively reflect the overall consistency level through weighted normalization of comparison field consistency, agency name comparison, and registration time overlap, providing a quantitative basis for identity authentication decisions.

[0034] Convert all matching data fields into a standard format according to their attribute types and perform consistency checks to obtain the number of matching fields. , the total number of statistical comparison fields is , compare the agency field to the status item Directly set to 1 for consistency and 0 for inconsistency. The ID card and the database compare a total of 9 fields, including card number, name, issuing authority, registration time, validity start and end time, unique identifier, organization, personnel role, filing record, etc. Among them, the number of fields that match the comparison results is 7, then there is 、 , the agency field comparison status item is set to The registration time overlap parameter is calculated in the previous sub-step and the ratio of the intersection interval to the reference interval is 0.86, which is set as , normalize the above-mentioned participating items, and get 、 、 、 , substitute into the formula to calculate the sum in brackets: ; Multiply by the registration time overlap parameter: ; Divide by the normalized total number of fields: ; Take the square root: ; The preset range of consistent performance between fields can be divided into three levels: Low consensus interval:

[0035] Moderate agreement interval:

[0036] High agreement interval:

[0037] The result of the formula Compared with the above interval, it is in the high consistency interval, meeting the high field consistency requirements, indicating that the information contained in the ID card and the relevant records in the enterprise registration database are highly consistent in multiple dimensions such as structural hierarchy, field content, and registration time. During the information verification process, it can be directly confirmed as field consistency and can serve as the basis for subsequent generation of structured comparison volume. Specifically, In this step, the value of reflects the overall identity data matching strength by normalizing and integrating multiple field comparison results, agency comparison status and registration time overlap, thereby providing an effective basis for downstream anomaly interception, log chain recording, etc.

[0038] See also Figure 4 , the specific steps for obtaining interception event statistics are: S311: Based on the structured comparison data, analyze each enterprise registration field. By comparing the ID card data with the registration information in the system database item by item, determine the consistency of each corresponding field, filter out the fields with differences, and integrate all inconsistent field contents to obtain a field inconsistency set. First, read the field contents involved in the current business in the enterprise registration database one by one, including the enterprise name, unified social credit code, legal representative name, ID number, registration date, registration authority name, business type, validity period, etc., and then extract the corresponding field information from the enterprise identity card, and match each group of fields according to the field name to establish a one-to-one comparison table. When performing the content comparison operation, first compare the text type field with character-level accuracy. For the enterprise name field, compare the card record "XXXXX Co., Ltd." with the database field "XXXXX Co., Ltd." for length comparison, vocabulary comparison, and suffix word judgment. If the number of characters is not equal or the suffix words are inconsistent, they are directly marked as inconsistent. Then perform full-character position judgment on the ID number field. For example, if the code in the identity card is "45012319891212XX XX", the database is "45012319891212XXXX", and the last check code is determined to be different, and it is marked as a verification failure field. The date fields such as registration date and validity period are format-standardized and then compared at the value level. For example, the card registration start date is "July 1, 2023", and the database record is "July 1, 2023". After the format is unified, it is judged to be consistent, otherwise it is identified as an inconsistent field. For special cases such as vacancies, garbled characters, and unrecognized field names in the field, it is marked as "unable to compare" and the field index position is recorded. Subsequently, all field contents that have not reached a consistent state during the comparison process are integrated and recorded in a field inconsistency set. Each item in the set is accompanied by parameter information such as field name, source location, comparison content, and difference description for subsequent abnormality screening and processing.

[0039] S312: Based on the field inconsistency set, determine the status of the identity unit associated with each field, analyze the corresponding enterprise registration parameters, identification number and source category, filter out fields in abnormal operation or interception status, and integrate them into a field abnormality data group; Retrieve the module status of the identity recognition unit involved in each field in turn, call the terminal record log to obtain the hardware identification number, software module version, and operation status code used when reading the corresponding field, and determine whether the status is a standard operation state. If the status code record shows module delayed response, data loss, card reading failure, etc., then mark the identification unit as abnormal operation. Further analyze the enterprise registration parameters associated with the field during the data extraction process. For example, if the business type associated with the field is establishment registration, the identification number is "REG-04500120250723-0012", and the source category is marked as "card chip data", then combine the identification The unit operation status and the content of the field are marked as abnormal. For the content of the source category of the QR code parsing field and the image recognition field, the corresponding sub-module log records are also retrieved for status judgment. If there are records such as blurred QR codes, unclear image acquisition, and high OCR recognition error rate during the recognition process, the field is also marked as abnormal. Then all fields marked as reading source abnormalities, parsing process errors, inconsistent comparisons, and recognition units in abnormal or intercepted states are collected to generate a field abnormality data group. The data group retains structured content such as field name, field value, reading time, terminal number, status code, error description, etc., and is integrated into a field abnormality data group.

[0040] S313: Based on the field abnormality data group, calculate the distribution characteristics of each data in the field group in the corresponding path, call the unit number and tag type to perform number mapping, using the formula: ; Get interception event statistics ,in, Indicates the total number of fields in the field exception data group. Indicates the The source number of the item field, Indicates the The unit number to which the item field belongs, Indicates the The source category mapping dimension of the item field, Indicates the The identification tag type of the item field, Indicates the The intercept frequency of the item field, Indicates the The unit transaction number of the item field.

[0041] Interception event statistics refer to the comprehensive statistical characteristics of the interception, marking, and processing of abnormal fields in the identity authentication process of the enterprise registration self-service terminal, which are reflected after normalization, offset analysis, and distribution feature superposition for all fields determined to be abnormal after structured comparison based on multi-dimensional information such as field source number, module number, source category mapping, identification mark type, interception frequency, and module processing number. It is used to quantify and present the overall distribution and degree of abnormal performance of all fields determined to be abnormal and intercepted in this registration process. It can reflect core data such as the frequency of occurrence, distribution structure, source path, and processing nodes of field anomalies in the identity authentication process, and provide a basis for subsequent process tracing, anomaly investigation, and data analysis of the terminal system.

[0042] Call the source number of each exception field , Unit number , Source Category Mapping Dimension , Identification mark type , intercept frequency , unit processing number , mapping it to a parameter group for number analysis and path calculation. Assume that the field exception data group contains field A and field B, where the original parameter of field A is: 、 、 、 、 、 ; The original parameters of field B are: 、 、 、 、 、 ; After normalizing the above parameters according to a unified dimension, they are converted into normalized value groups as follows: 、 、 、 、 、 ; 、 、 、 、 、 , substitute this set of parameters into the formula: ; ; ; ; Numerical results Compared with the benchmark identification interval of the set interception event statistics, the preset interval is , which is used to represent the range of normal interference levels of field anomalies in the structural path. Results exceeding the upper limit of this interval indicate that there is a high-frequency, multi-module, and strongly correlated accumulation of interception events between field structures. This result shows that the interception event statistics have exceeded the set upper limit of the normal identification interval, indicating that the current field anomaly distribution is no longer a single-point burst, but a systematic anomaly state with concentrated paths and aggregated identifiers, indicating that the field anomaly has met the judgment conditions for being marked as a structural interception event in the registration process. Subsequently, the source verification mechanism can be started based on the statistic and written into the log chain node for further tracing and source tracing.

[0043] See also Figure 5 ,The specific steps for obtaining the log chain summary feature are: S411: Based on the interception event statistics, the time, personnel identity, and equipment information involved in each operation in the processing process are sorted out. By comparing the attribution links between process nodes, the association status of each node is determined to obtain the node identification association value; First, extract the timestamp, initiator identity and terminal device number recorded for each abnormal event from the interception log, and establish an event index list. Then, according to the order of the business processing flow, retrieve all the operation record nodes in the entire process, extract and standardize the operation time, executor number and device unique identification corresponding to each node, and build a node field table. Unify the time field into the format of "year, month, day, hour, minute and second", the identity field into the form of enterprise personnel number, and the device information field into the hardware identification number encoding format. Then judge the ownership relationship between the nodes, that is, by comparing whether the identity identification of two adjacent operation nodes is consistent, whether the execution time is continuous, and whether the device number comes from the same terminal device, judge whether the node can be attributed to the same business operator. If If three consecutive operation nodes have the same identity number, occur within 5 minutes of each other, and have the same equipment number, then these three nodes are marked as having a consistent attribution relationship. If a node is inconsistent with the previous node in personnel number or equipment switching, it is recorded as an attribution-broken node, and the difference between the node number and the corresponding field is registered. After comparing all nodes pairwise, nodes with strong attribution relationships and stable attribution are marked as high-association levels, and nodes with weak attribution relationships and complex information intersections are marked as low-association levels. A three-stage scoring interval is set, with a correlation above 80% considered to be fully attributable, the middle section is 60%~80%, and nodes below 60% are marked as weakly associated or broken-link nodes. Finally, by summarizing the comparison results between all nodes, the judgment information on the attribution connection of each process operation node is generated and summarized as the node identification association amount.

[0044] S412: Based on the node identification correlation, a cross-analysis is performed on the time sequence, identity identifier, and operation process number of each node. Through sequence matching and information consistency screening, nodes with structural deviation are determined to obtain a node structure deviation index; The timestamp, identity, and process number are retrieved and compared node by node. All operation nodes are first sorted in ascending order by the time field and a sequential index value is established. Each node is then used as a reference node to determine whether the time interval between it and the previous node meets the execution rhythm threshold set for the process. If the time interval is less than the preset standard upper limit of 5 minutes, the time sequence is considered reasonable. If the interval exceeds 5 minutes, the node is marked as a time deviation node. The reference node identity is then compared item by item with the identifiers of the previous and next nodes. If the identifiers are inconsistent, the node is registered as an identity deviation node. The current node process number is then checked to ensure that it is logically continuous, with a number difference of 1. If there is a skipped number, an incorrect number, or a duplicate number, the node is registered as a process number deviation node. These three types of deviation items are then cross-judged. If two or more of the three items, time, identity, and number, are marked as abnormal for a node, the node is marked as a structural deviation node. The deviation category, occurrence time, associated node number, and deviation location are also recorded. All structural deviation nodes are then summarized to generate a structural deviation list. The deviation items of each node in the list are classified and counted to form a node structural deviation index.

[0045] S413: Based on the node structure offset index and the summary content of the node original summary and adjacent nodes, the node summary chain structure is optimized, and the link continuity and abnormal marking between the process nodes are analyzed to obtain the log chain summary feature; The original summary content recorded in each node in the process log is extracted, and the summary data block of each node is called in turn to match and compare with the summary data of its adjacent nodes. The timestamp, operation type, identity information, and device number contained in the current node summary content are compared and judged with the summary parameters registered in the adjacent nodes. If there are information breakpoints such as time reversal, identity jump or device conversion in the summaries of the previous and next nodes, it is marked as a link interruption state. Further judgment is made as to whether there is a situation in the summary chain structure where the summary value cannot inherit the content of the previous node. For example, if the previous node number is missing in the current node summary or the summary reference field is empty, it is recorded as a chain break. Then all node sequence fields are checked. If there are non-sequential numbers, missing numbers or repeated numbers in three consecutive nodes, they are registered as structural dislocation nodes. All abnormal structural positions are screened out through the above method, and markers are inserted in the chain structure in turn, and the offset position and node number are recorded. Finally, all summary contents are rearranged and a chain summary table is generated, which is integrated into the log chain summary feature.

[0046] See also Figure 6 , the steps for obtaining the sequence offset index are as follows: S511: Based on the log chain summary characteristics, the sequence information and data link identifier of each operation node are checked item by item. By comparing the node structure field with the summary content of the previous node, the correspondence between the two in the actual operation sequence is determined to obtain the sequence structure correspondence data; Extract the sequence number field, timestamp field and link identification field of each operation node from the log chain structure in sequence, build a node information sequence, and then call the structure field content of each node one by one, and obtain the summary field recorded in the log of its predecessor node, and establish a corresponding comparison table. The specific operation is as follows: the operation time recorded in the current node structure field is "2025-07-14, 10:15:32", the node number is "N048", and the link identification is "REG4501". The time recorded in the reference field in the predecessor node summary is "2025-07-14, 10:13:15", and the number is "N047". The link identification is the same and the time is earlier than the current node. It is preliminarily judged that the order is correct, and then the link identification field is matched at the character level to confirm whether it is For the same link number, if there is a character mismatch, for example, the current node identifier is "REG4501" and the previous node is "REG4502", the link is considered discontinuous. Further analysis is performed to determine whether the operation type field in the structure field and the referenced field in the summary belong to the same business process branch. If the summary type of the previous node is "identity authentication" and the current node type is "certificate comparison", and the business logic sequence is continuous, the connection is considered valid. If the summary field shows "process termination" and the current node type is "initiate operation", there is a chain jump operation. Such jump events are recorded. By counting the matching status of the sequence number, time sequence, link identifier and business type of each node and its previous summary field, the comparison results are integrated and inconsistent items are marked to form sequential structure correspondence data.

[0047] S512: Analyze the connection between each set of node structure fields and summary fields based on the sequence structure correspondence data, combined with the business process node identifier and the data link identifier, determine the connection integrity status of the node in the actual link, and obtain the link connection integrity feature; A mapping group is established for the structure field and summary field between each group of nodes, and the time interval, number difference and link identification between the two fields are judged group by group to see whether they are logically continuous. First, a time sequence screening is performed to compare the timestamps of adjacent nodes. If the current node operation time is earlier than the previous node time recorded in the summary, it is registered as a sequence error, and then the node number connection is judged. If the previous node number is "N056" and the current node number should be "N057", but it is actually "N059", the number jump status is recorded, and the link identification field is continued to be compared for consistency. If the summary field is "REG-GX4501" and the current structure field is "REG-GX4501", it is judged to be consistent. If there is a suffix or prefix difference , it is recorded as a link disconnection item. The connection complete items are screened by combining the three dimensions of time, number and identification. If all three items are in normal state, it is marked as a complete connection node. If any item is broken, jumped, empty or cross-sequence, it is uniformly marked as an incomplete node. Then, all discontinuous, inconsistent and mismatched node numbers and connection status descriptions between the structure fields and summary fields in the process are summarized to generate a structural connection record table. The proportion of complete nodes and abnormal nodes is counted. If the total number of process nodes is 80 and the number of complete connection nodes is 73, the connection completeness ratio is 91.25%. According to the set interval judgment threshold, if the completeness rate is higher than 90%, it is considered a normal link, and if it is lower than 70%, it is considered a serious loss. The connection integrity characteristics of each link are output.

[0048] S513: Based on the link connection integrity characteristics, the node sequence numbers and link anomaly marks in the log chain are analyzed to determine if the sequence of node structure fields is interrupted, misplaced, or jumped. The sequential connection performance of each node is optimized to obtain a sequence offset indicator. Read the node number, sequence identification field and abnormal mark field content from the log chain one by one. First, sort all node numbers in ascending order to confirm the continuity of the sequence. If a number jump is found, such as "N062" followed by "N064", it is determined that "N063" is missing. Then screen the abnormal mark field corresponding to each node to identify whether there is an "interrupted", "repeated" or "missing" state. If a node is marked as "interrupted" and its previous node is "normal", then the node is recorded as a broken node. If the subsequent node does not continue the link identifier of the interrupted node, the entire link is determined to be interrupted. Continue to determine whether there is a misalignment in the sequence field, that is, the node number is logically continuous but its time is not. The stamp is earlier than the previous node. For example, the operation time of the node numbered "N066" is "10:11:00", and its predecessor "N065" is "10:12:32". In this case, there is a reverse order behavior here, and the node is recorded as a time dislocation. Then check the link jump situation. If the summary chain of a node points to the link identifier "REG-XZ01", but the next node actually uses "REG-XZ03", "REG-XZ02" is missing in the middle and no termination or transfer status is displayed, it is determined to be a link jump error. All offset information such as sequence field interruption, dislocation, and jump are classified and summarized. Each type of offset type is accompanied by parameters such as node number, time, and offset status to form a sequential offset indicator.

[0049] A self-service terminal for enterprise registration and its identity verification system, the system comprising: The ID card verification module, based on the enterprise registration self-service terminal, analyzes the surface area of ​​the ID card presented by the enterprise personnel, sequentially calls the scanning unit to read the ID card serial number, interprets the issuing authority identification through chip reading, then compares the ID card expiration date with the card surface information, and compares the timestamp of the dynamic QR code on the terminal screen with the ID card parameters item by item, calculating the equivalence relationship of each set of parameters to obtain the QR code consistency characteristics; The data consistency verification module uses the consistency characteristics of the QR code to determine the consistency between the card issuing authority name and the authority field in the enterprise registration system database. It analyzes the correspondence between the ID card registration date and the database registration record time, compares the validity period with the registration card validity information, verifies the unique identification of the personnel item by item, calculates the consistency between each field, and obtains the structured comparison quantity. The anomaly tracing module compares the structured comparison volume with the authentication trusted comparison range benchmark. If the comparison fails to meet the standard, the terminal identity recognition unit status is adjusted, the ID card fields that do not match the comparison are recorded, and the associated parameters are summarized. The source of the fields involved in the anomaly event is then verified to obtain interception event statistics. The full-process operation tracking module collects the time sequence of operations during the processing based on interception event statistics, verifies the unique identification of personnel item by item, reads the terminal hardware identification number, registers the business type parameters, obtains the process operation sequence number, analyzes the combination association between parameters, and calculates the node summary chain structure to obtain the log chain summary characteristics; The node link detection module is based on the log chain summary characteristics, analyzes the operation sequence of the log chain nodes, gradually compares the current node data structure with the previous node summary, determines the continuity of the log chain during the processing process, and verifies the node structure offset, marking the node number of each abnormality to obtain the sequence offset indicator.

[0050] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. An enterprise registration self-service terminal and identity verification method thereof, characterized in that: The following steps are involved: S1: Based on the enterprise registration self-service terminal, the surface of the enterprise personnel ID card is analyzed, the ID card serial number is read in sequence, the issuing authority identification is parsed, the expiration date is verified, and the consistency of the dynamic QR code timestamp and the ID card parameters is compared to obtain the QR code consistency feature; S2: Based on the consistency characteristics of the QR code, determine whether the card issuing authority name matches the authority field in the registration system, analyze the correspondence between the registration date and the database record, compare the expiration date with the card validity, verify the unique identifier, and obtain a structured comparison value; S3: Based on the structured comparison quantity, the data is compared with the authentication trust comparison standard. If there is any inconsistency, the identity recognition status is adjusted, the inconsistent fields are recorded, and the associated parameters are summarized. The source of the abnormal fields is analyzed to obtain interception event statistics; S4: Based on the interception event statistics, collect the operation time sequence, verify the unique identifier, read the terminal hardware number, register the business type, obtain the process sequence number, and analyze the parameter combination correlation to obtain the log chain summary feature.

2. The enterprise registration self-service terminal and identity verification method according to claim 1, characterized in that: The QR code consistency features include timestamp consistency status, serial number matching status, and agency identification consistency status; the structured comparison quantity includes agency field matching status, registration time correspondence status, and unique identification verification results; the interception event statistics include interception type, abnormal field category, and verification number; the log chain summary features include node sequence information, data link identification, and business process node identification.

3. The enterprise registration self-service terminal and identity verification method according to claim 1, characterized in that: The steps for obtaining the consistency feature of the QR code are specifically as follows: S111: Based on the enterprise registration self-service terminal, the collected ID card surface information is analyzed, and the card serial number, expiration date, and QR code content read by the scanning unit are compared with the card issuing authority information obtained by the chip to determine the consistency between multiple source data and obtain the multi-source basic parameters of the ID card; S112: Based on the multi-source basic parameters of the ID card, the QR code content is compared with the chip data and the card surface data, and the consistency of the timestamp, serial number and agency identification is analyzed. The equality of each data item is screened and classified to obtain a field consistency status group; S113: Based on the field consistency status group, analyze the timestamp consistency status, serial number matching status and agency identification consistency status, determine the standardized relationship of the comparison items, screen key comparison parameters, and obtain the QR code consistency characteristics.

4. The enterprise registration self-service terminal and identity verification method according to claim 1, characterized in that: The steps for obtaining the structured alignment amount are specifically as follows: S211: Based on the consistency feature of the QR code, the timestamp, serial number, and agency identification in the QR code are analyzed to determine if they are consistent with the data read from the ID card chip. The ID card issuing agency name is compared with the agency field content in the enterprise registration system database to generate an agency field comparison status item. S212: Based on the agency field comparison status item, analyze the time format and sequence of the ID card registration date and the enterprise registration system registration time, compare the ID card validity period and the registration card validity period data overlap range, calculate the time intersection, and obtain the registration time overlap parameter; S213: Based on the registration time overlap parameter, analyze whether the unique identification field of the identity card is completely consistent with the unique field of the person in the system database, calculate the consistency expression amount between the fields, and obtain the structured comparison amount.

5. The enterprise registration self-service terminal and identity verification method according to claim 1, characterized in that: The steps for obtaining the interception event statistics are specifically as follows: S311: Based on the structured comparison data, analyze each enterprise registration field, compare the ID card data with the system database registration information item by item, determine the consistency of each corresponding field, filter out the fields with differences, and integrate all inconsistent field contents to obtain a field inconsistency set; S312: Based on the field inconsistency set, determine the status of the identity unit associated with each field, analyze the corresponding enterprise registration parameters, identification number and source category, filter out fields in abnormal operation or interception status, and integrate them into a field abnormality data group; S313: Based on the field abnormality data group, calculate the distribution characteristics of each data in the field group in the corresponding path, call the unit number and the tag type to perform number mapping, and obtain the interception event statistics.

6. The enterprise registration self-service terminal and identity verification method thereof according to claim 1, characterized in that: The steps for obtaining the log chain summary feature are as follows: S411: Based on the interception event statistics, the time, personnel identity, and equipment information involved in each operation in the processing process are sorted out, and the attribution relationship between the process nodes is compared to determine the association status of each node to obtain the node identification association value; S412: Based on the node identification association amount, a cross-analysis is performed on the time sequence, identity identifier, and operation process number of each node. Through sequence matching and information consistency screening, nodes with structural deviation are determined to obtain a node structure deviation index; S413: Based on the node structure offset index, according to the original summary of the node and the summary content of the adjacent nodes, the node summary chain structure is optimized, the link continuity and abnormal marking between the process nodes are analyzed, and the log chain summary feature is obtained.

7. The enterprise registration self-service terminal and identity verification method according to claim 1, characterized in that: The steps also include: S5: Based on the log chain summary characteristics, analyze the node operation sequence, compare the summary content of the current node with that of the previous node, determine the continuity of the log chain, verify the node structure offset, and mark the abnormal node number to obtain the sequence offset index; The sequence offset indicator includes an offset type, a node sequence number, and a link abnormality mark.

8. The enterprise registration self-service terminal and identity verification method thereof according to claim 7, characterized in that: The steps for obtaining the sequence offset index are specifically as follows: S511: Based on the log chain summary feature, the sequence information and data link identifier of each operation node are checked item by item. By comparing the node structure field with the summary content of the previous node, the correspondence between the two in the actual operation sequence is determined to obtain sequence structure correspondence data; S512: Analyze the connection between each set of node structure fields and summary fields based on the sequence structure correspondence data, combined with the business process node identifier and the data link identifier, determine the connection integrity status of the node in the actual link, and obtain the link connection integrity feature; S513: Based on the link connection integrity characteristics, analyze the node sequence numbers and link abnormality marks in the log chain, judge whether the sequence of node structure fields is interrupted, misplaced or jumped, optimize the sequential connection performance of each node, and obtain the sequence offset index.

9. An enterprise registration self-service terminal and identity verification system thereof, characterized in that: The system is used to implement the enterprise registration self-service terminal and identity authentication method thereof according to any one of claims 1 to 8, and the system includes: The ID card verification module, based on the enterprise registration self-service terminal, analyzes the surface area of ​​the ID card presented by the enterprise personnel, sequentially calls the scanning unit to read the ID card serial number, interprets the issuing authority identification through chip reading, then compares the ID card expiration date with the card surface information, and compares the timestamp of the dynamic QR code on the terminal screen with the ID card parameters item by item, calculating the equivalence relationship of each set of parameters to obtain the QR code consistency characteristics; Based on the consistency features of the QR code, the data consistency verification module determines the consistency between the name of the issuing authority and the authority field in the enterprise registration system database, analyzes the correspondence between the ID card registration date and the database registration record time, compares the validity period with the registration card validity information, verifies the unique identification of the personnel item by item, calculates the consistency between each field, and obtains the structured comparison quantity; The abnormality tracing module compares the structured comparison quantity with the authentication trusted comparison range benchmark. If the comparison does not meet the standard, it adjusts the terminal identity recognition unit status, records the identity card fields that do not match the comparison, summarizes the related parameters, and then performs source verification on the fields involved in the abnormal event to obtain interception event statistics; The full-process operation tracking module collects the time sequence of operations during the processing based on the interception event statistics, verifies the unique identification of personnel item by item, reads the terminal hardware identification number, registers the business type parameters, obtains the process operation sequence number, analyzes the combination association between parameters, and calculates the node summary chain structure to obtain the log chain summary characteristics; The node link detection module analyzes the operation sequence of the log chain nodes based on the log chain summary characteristics, gradually compares the current node data structure with the previous node summary, determines the continuity of the log chain during the processing, and verifies the node structure offset, marking the node number of each abnormality to obtain the sequence offset index.

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