A method and system for remote identity verification of a self-service terminal for enterprise registration

Abstract

This invention relates to the field of digital identity authentication technology, specifically to a remote identity verification method and system for a self-service terminal for enterprise registration. The method involves acquiring identity verification request messages and parsing structured feature parameters, matching additional weights and basic weights to generate initial weights, combining queuing time sorting to form absolute scheduling numbers, verifying matching status in sequence to generate classification labels, extracting identity information based on length verification templates and determining consistency, and outputting the identity verification result. This invention breaks through the processing bottleneck caused by traditional static queuing architectures by extracting structured features and assigning differentiated scheduling weights based on business priorities. It constructs a scheduling sequence based on dynamic weighted arrangement to ensure priority execution of urgent verification tasks. Simultaneously, it deeply mines the differences in location and name attributes to establish flexible classification matching rules, and performs customized discretionary review based on diversified verification upper limits, effectively avoiding the risk of identity judgment errors and rollbacks caused by traditional fixed and rigid mechanisms.

Description

Technical Field

[0001] This invention relates to the field of digital identity authentication technology, and in particular to a remote identity verification method and system for a self-service terminal for enterprise registration. Background Technology

[0002] The field of digital identity authentication technology refers to a system of information processing technologies centered around verifying the authenticity of the identity of natural persons or legal entities. It mainly covers identity information collection, document information reading, biometric comparison, encrypted data transmission, identity data storage and retrieval, online verification, and authentication result recording.

[0003] Among them, the remote identity verification method of enterprise registration self-service terminal refers to the process of using the data interaction between the self-service terminal and the remote server during the enterprise registration business to verify the consistency of the identity information fields entered or read by the applicant with the identity data stored in the authoritative database. Typically, this is done by reading the name field, citizen identity number field and validity period field from the ID card chip in the self-service terminal, encapsulating the above fields into an authentication request message according to a preset data format, and sending it to the remote authentication server through the network interface. The remote server performs length verification, encoding verification and logical relationship verification on the fields in the message according to the preset interface protocol.

[0004] Existing technologies employ fixed-format message encapsulation and conventional verification logic during operation. They directly follow preset interface protocols to process concurrent requests, making it difficult to dynamically adjust resource allocation order when faced with differentiated business demands. The uniform and rigid queuing processing mode is prone to causing delays in urgent verification tasks. At the same time, the fixed verification mechanism ignores the structural differences of multi-dimensional attribute dimensions and cannot adapt to the flexible verification boundaries of diversified origins and special identity types. This can easily lead to misjudgments of legitimate identity data and audit blockages, resulting in the ineffective use of terminal interaction resources and uncontrollable delays in the overall verification cycle. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a remote identity verification method and system for enterprise registration self-service terminals.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a remote identity verification method for a self-service terminal for enterprise registration, comprising the following steps:

[0007] S1: Obtain the authentication request message sent by the terminal, and extract the message structure feature parameters of the authentication request message through encoding conversion and data parsing operations;

[0008] S2: Based on the message structured feature parameters, match the corresponding additional weights in the server, and sum the additional weights with the preset basic weights to obtain the initial weight parameters;

[0009] S3: Extract the time record of the authentication request message entering the gateway waiting queue to generate an initial queue number, perform descending sorting based on the initial queue number and the initial weight parameter, and obtain the absolute scheduling number of the message;

[0010] S4: Retrieve the corresponding authentication request messages in sequence according to the absolute scheduling sequence number of the message, verify the multi-class matching status of the message structured feature parameters, and generate naming rule classification labels;

[0011] S5: Extract the upper limit of the length verification corresponding to the naming rule category label from multiple length verification parameter templates, extract the identity information of enterprise personnel within the upper limit of the length verification, determine the consistency of the corresponding enterprise personnel identity information, and generate the identity verification result.

[0012] The present invention improves upon this invention by including the following structured feature parameters for the message: the enterprise personnel identity type code, the enterprise personnel business urgency identifier, the length of the enterprise personnel name in bytes, and the first six digits of the enterprise personnel's location code; the initial weight parameter is specifically a weight calculated by combining the basic weight, the enterprise personnel identity weight, and the additional weight for urgent business operations; the message absolute scheduling sequence number is specifically a queue position calculated by combining the initial weight parameter with the enterprise personnel's initial queuing sequence number; the naming rule classification label is specifically a template classification calculated by comparing the matching status with the enterprise personnel length; and the identity verification result is specifically a verification conclusion obtained by comparing and verifying the enterprise personnel identity verification request message with the enterprise personnel's underlying basic identity information.

[0013] The present invention is improved in that step S1 is specifically as follows:

[0014] S101: Obtain network data interface to receive enterprise personnel identity verification request messages sent by enterprise self-service registration terminal devices, perform unified character set encoding conversion processing, separate and extract the primary byte data segment inside the message header data, and generate encoding conversion parsing feature set;

[0015] S102: Based on the encoding conversion parsing feature set, separate the enterprise personnel identity type code and the enterprise personnel business emergency identifier based on the message header data, and at the same time obtain the length of the enterprise personnel name bytes inside the message, as well as the first six digits of the enterprise personnel's location code, to obtain the multi-dimensional business extraction identifier code.

[0016] S103: Align the enterprise personnel identity type code and enterprise personnel business emergency identifier execution format in the multi-dimensional business extraction identifier code, and integrate them with the enterprise personnel name byte length and the first six digits of the enterprise personnel's place of origin in a vectorized manner to obtain the message structured feature parameters.

[0017] The present invention is improved in that step S2 is specifically as follows:

[0018] S201: Obtain records from the enterprise remote identity authentication server, extract the enterprise personnel identity type code and enterprise personnel business emergency identifier from the structured feature parameters of the message, collect the enterprise personnel identity mapping relationship table in the enterprise remote identity authentication server, perform a query on the mapping relationship table for the enterprise personnel identity type code, and set the enterprise personnel identity weight.

[0019] S202: Obtain the emergency business additional weight corresponding to the emergency business identifier of the enterprise personnel from the enterprise's remote identity authentication server, extract the preset basic weight from the enterprise's remote identity authentication server, and establish a derived basic weight parameter group.

[0020] S203: Combine the emergency business additional weight and basic weight in the derived basic weight parameter group with the enterprise personnel identity weight and calculate the corresponding arithmetic sum to obtain the initial weight parameters.

[0021] The present invention is improved in that step S3 is specifically as follows:

[0022] S301: Collect the queue data of enterprise personnel authentication request messages recorded in the enterprise's gateway waiting sequence, extract the time sequence records of enterprise personnel authentication request messages entering the enterprise's gateway waiting queue, and assign a ranking label and encoding to each authentication request message based on the timestamp record to generate the initial queue number of enterprise personnel.

[0023] S302: Call the initial weight parameter and the initial queuing number of the enterprise personnel, collect the preset time slice allocation constant in the network configuration parameter set, calculate the product value of the initial weight parameter and the time slice allocation constant, and obtain the enterprise personnel's computing share value.

[0024] S303: Perform normalization mapping processing on the initial weight parameters, the enterprise personnel operation share values ​​and the initial queue number of the enterprise personnel and input them into the weighted round-robin scheduling algorithm to perform descending order sorting operation based on the initial weight parameters and the initial queue number of the enterprise personnel, and calculate the absolute scheduling sequence number of the message.

[0025] The present invention is improved in that step S4 is specifically as follows:

[0026] S401: Based on the absolute scheduling sequence number of the message, look up the enterprise's gateway waiting queue list item, retrieve the corresponding enterprise personnel authentication request message from the enterprise's gateway waiting queue in sequence, extract the message structured feature parameter related comparison field associated with the authentication request message, and obtain the feature dataset to be verified.

[0027] S402: Call the message structured feature parameters associated with the target scheduling message data, extract the first six digits of the enterprise personnel's place of origin and the length of the enterprise personnel's name bytes, input the first six digits of the enterprise personnel's place of origin into the first branch node of the decision tree algorithm, obtain the preset designated area code set at the first branch node, verify the matching status of the first six digits of the enterprise personnel's place of origin and the designated area code set, and generate an area matching judgment status value;

[0028] S403: Input the length of the enterprise personnel's name in bytes into the second branch node of the decision tree algorithm, obtain the preset standard length threshold of the enterprise personnel's name at the second branch node, compare the length of the enterprise personnel's name in bytes with the standard length threshold of the enterprise personnel's name, and combine the zoning matching judgment status value to divide the naming rule classification label.

[0029] The present invention is improved in that step S5 is specifically as follows:

[0030] S501: Obtain the preset enterprise personnel name length verification parameter template, and extract the corresponding enterprise personnel length verification upper limit value from the enterprise personnel name length verification parameter template based on the naming rule classification label;

[0031] S502: Calculate the difference between the current enterprise personnel name byte length and the enterprise personnel length verification upper limit value, evaluate the overflow data range of the storage space occupied by the enterprise personnel name field, and obtain the enterprise personnel byte length deviation value;

[0032] S503: When the byte length deviation value of the enterprise personnel is determined to be less than or equal to zero, the underlying basic identity information of the enterprise personnel is extracted, and the consistency comparison and verification operation is performed on the enterprise personnel identity verification request message using the underlying basic identity information of the enterprise personnel to obtain the identity verification result.

[0033] A remote identity verification system for a self-service terminal for enterprise registration, the system comprising:

[0034] The message feature parsing module obtains the authentication request message sent by the terminal, and extracts the message structure feature parameters of the authentication request message through encoding conversion and data parsing operations.

[0035] The weight matching calculation module matches the corresponding additional weights in the server based on the message structured feature parameters, and sums the additional weights with the preset basic weights to obtain the initial weight parameters.

[0036] The scheduling and sorting control module extracts the time record of the authentication request message entering the gateway waiting queue to generate an initial queuing number, and performs a descending sort based on the initial queuing number and the initial weight parameter to obtain the absolute scheduling number of the message.

[0037] The rule matching and classification module retrieves the corresponding authentication request messages sequentially based on the absolute scheduling sequence number of the messages, verifies the multi-class matching status of the message structured feature parameters, and generates named rule classification labels.

[0038] The identity verification and determination module extracts the upper limit of the length verification corresponding to the naming rule category label from multiple length verification parameter templates, extracts the identity information of enterprise personnel within the upper limit of the length verification, determines the consistency of the corresponding enterprise personnel identity information, and generates the identity verification result.

[0039] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0040] In this invention, by extracting structured features and assigning differentiated scheduling weights based on business priorities, the processing bottleneck caused by the traditional static queuing architecture is broken. A scheduling sequence based on dynamic weighted arrangement is constructed to ensure that urgent verification tasks are executed first. At the same time, the differences between the location and name attributes are deeply mined to establish flexible classification and matching rules. Customized discretionary review is performed based on the upper limit of diversified verification, which effectively avoids the risk of identity judgment error and return caused by the traditional fixed and rigid mechanism. The accuracy of resource allocation and circulation is comprehensively optimized and the time consumption of high-concurrency business interaction is stably reduced. Attached Figure Description

[0041] Figure 1 This is a flowchart of the method of the present invention;

[0042] Figure 2 This is a detailed flowchart of step S1 of the present invention;

[0043] Figure 3 This is a detailed flowchart of step S2 of the present invention;

[0044] Figure 4 This is a detailed flowchart of step S3 of the present invention;

[0045] Figure 5 This is a detailed flowchart of step S4 of the present invention;

[0046] Figure 6 This is a detailed flowchart of step S5 of the present invention;

[0047] Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the 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 merely illustrative and not intended to limit the invention.

[0049] Please see Figure 1This invention provides a technical solution, a method for remote identity verification of a self-service terminal for enterprise registration, comprising the following steps:

[0050] S1: Obtain the authentication request message sent by the terminal, and extract the message structure feature parameters of the authentication request message through encoding conversion and data parsing operations;

[0051] S2: Based on the message structure feature parameters, match the corresponding additional weights in the server, sum the additional weights with the preset basic weights to obtain the initial weight parameters;

[0052] S3: Extract the time record of the authentication request message entering the gateway waiting queue to generate the initial queue number, perform descending sorting based on the initial queue number and the initial weight parameter, and obtain the absolute scheduling number of the message;

[0053] S4: Retrieve the corresponding authentication request messages in sequence according to the absolute scheduling sequence number of the messages, verify the multi-class matching status of the message structured feature parameters, and generate naming rule classification labels;

[0054] S5: Extract the upper limit of the length verification corresponding to the naming rule category label from multiple length verification parameter templates, extract the identity information of enterprise personnel within the upper limit of the length verification, determine the consistency of the corresponding enterprise personnel identity information, and generate the identity verification result.

[0055] The message structured feature parameters include the enterprise personnel identity type code, the enterprise personnel business urgency identifier, the length of the enterprise personnel name in bytes, and the first six digits of the enterprise personnel's location code. The initial weight parameter is specifically calculated by the basic weight, the enterprise personnel identity weight, and the additional weight for urgent business. The message absolute scheduling sequence number is specifically calculated by the queue position calculated by the initial weight parameter and the enterprise personnel's initial queuing sequence number. The naming rule classification label is specifically calculated by the template classification calculated by the matching status and the enterprise personnel length comparison result. The identity verification result is specifically the verification conclusion of the enterprise personnel identity verification request message performed by comparing and verifying the enterprise personnel's underlying basic identity information.

[0056] Please see Figure 2 Step S1 is as follows:

[0057] S101: Obtain network data interface to receive enterprise personnel identity verification request messages sent by enterprise self-service registration terminal devices, perform unified character set encoding conversion processing, separate and extract the primary byte data segment inside the message header data, and generate encoding conversion parsing feature set;

[0058] The system receives enterprise personnel authentication request messages from enterprise self-service registration terminals via network data interfaces, and performs pre-validation and standardization parsing operations on the data format of these messages. It obtains the data format category identifier of the enterprise personnel authentication request message and determines its matching status with a preset standard message format identifier. The preset standard message format identifier is set based on message format encoding characteristics collected from historical compliant communication transmission records. The distribution frequency of format encoding characteristics of multiple historical compliant messages is statistically analyzed, and the format encoding characteristic with the highest frequency and no transmission corruption records is established as the preset standard message format identifier. The system extracts the underlying byte stream data of the current enterprise personnel authentication request message, performs a byte-by-byte scan of the underlying byte stream data, and identifies the original character set encoding type. Based on the identified original character set encoding type, it calls the corresponding character mapping lookup table in the underlying configuration and performs unified character set encoding conversion processing. The specific execution process of the unified character set encoding conversion is as follows: Each character encoding in the original byte stream data is replaced one by one with the corresponding basic pixel stream in the international standard format according to a character mapping table. This completely eliminates encoding garbled characters or incompatibility caused by differences in operating systems across different terminal devices, outputting a standardized unified character set encoding sequence. After obtaining the unified character set encoding sequence, the start and end positions of the message header data are located according to the preset message structure definition specifications, and the message header data is completely separated from the unified character set encoding sequence. Further, within the message header data, the primary byte data segment is extracted according to a preset fixed byte offset and truncation length. The preset fixed byte offset and truncation length are determined based on the densely distributed intervals of key authentication information in historical test messages, selecting the first 16 consecutive bytes covering the core identification information as the truncation target. Extract the data of each adjacent byte within the primary byte data segment, perform cross-product operation on the adjacent byte data, obtain the numerical set of all product operation results, and record these product results one by one into the pre-allocated array memory space according to the original byte reading order, generating an encoding conversion parsing feature set containing multiple key features.

[0059] S102: Based on the encoding conversion parsing feature set, separate the enterprise personnel identity type code and the enterprise personnel business emergency identifier based on the message header data, and at the same time obtain the length of the enterprise personnel name bytes in the message and the first six digits of the enterprise personnel's location code to obtain the multi-dimensional business extraction identifier code;

[0060] Enterprise personnel identification type codes include enterprise formal employee identification codes, enterprise temporary visitor identification codes, and enterprise outsourced maintenance personnel identification codes;

[0061] The emergency status indicator for enterprise personnel is determined based on the preset processing time limit data for each business and the access priority level characteristics of enterprise personnel's appointment registration.

[0062] The preprocessed encoding conversion and parsing feature set is retrieved. The overall byte arrangement structure within the feature set is analyzed, and field stripping is performed according to preset fixed byte offset rules to separate and extract the enterprise personnel identity type code and the enterprise personnel business urgency identifier. The enterprise personnel identity type code is specifically divided into enterprise formal employee identity codes, enterprise temporary visitor identity codes, and enterprise outsourced maintenance personnel identity codes within the permission system. Different categories of personnel identity codes strictly correspond to different security access permission benchmarks within the enterprise. The establishment of the enterprise personnel business urgency identifier relies on reading preset processing time limit data for each business transaction and the access priority level characteristics of enterprise personnel appointment registration. The processing time limit data for each business transaction is set based on the standard response time regulations for various business transactions in the enterprise's administrative management system. All standard response times are uniformly converted into their corresponding reciprocal forms to intuitively reflect the business data characteristic that shorter time requirements indicate higher urgency. The access priority level characteristics of enterprise personnel appointment registration are calculated based on the statistical analysis weights of the personnel's department level and the historical criticality of their business. Extract the processing time limit requirements for each business transaction and the corresponding priority level features for enterprise personnel appointment registration. Using the processing time limit requirements as the dividend and the priority level features as the divisor, perform a division operation to obtain the quotient record. This quotient record is directly set and fixed as the enterprise personnel business emergency identifier for the current message. Simultaneously, perform a full traversal and occupancy statistics operation on the region corresponding to the name field in the encoding conversion and parsing feature set, calculate the total number of storage bytes actually occupied by the name field, and obtain and record the byte length of the enterprise personnel name. Following a fixed-bit truncation logic, extract the first six consecutive bytes of the location parameter within the encoding conversion and parsing feature set to obtain the precise first six digits of the enterprise personnel's location code. Extract the enterprise personnel identity type code, enterprise personnel business emergency identifier, enterprise personnel name byte length, and the first six digits of the enterprise personnel's location code obtained from the above process. Sequentially push these four key feature parameters into the same feature data packet structure according to a preset data concatenation order to form a multi-dimensional business extraction identifier code.

[0063] S103: Align the enterprise personnel identity type code and enterprise personnel business emergency identifier execution format in the multi-dimensional business extraction identifier code, and integrate them with the enterprise personnel name byte length and the first six digits of the enterprise personnel location code in a vectorized manner to obtain the message structured feature parameters.

[0064] The system retrieves the multi-dimensional business extraction identifier code and extracts the enterprise personnel identity type code and enterprise personnel business emergency identifier stored within it. It then checks the current data bit length of both the enterprise personnel identity type code and the enterprise personnel business emergency identifier. A standard alignment bit length benchmark value is retrieved from the local configuration security library. This benchmark value is calculated based on a comprehensive statistical analysis of the maximum single-field length limit that the underlying database storage engine can accommodate, ensuring absolute compatibility for persistent data storage. This benchmark value is fixed at 8 bits. The system compares the current data bit length of the enterprise personnel identity type code with the benchmark value. If the current data bit length is insufficient, it continuously adds zero-value data blocks to the end of the enterprise personnel identity type code. This process is repeated until the total length fully reaches the benchmark value. The same length comparison and data block addition mechanism is used to synchronously add zeros to the enterprise personnel business emergency identifier, ensuring that the data length specifications of the enterprise personnel identity type code and the enterprise personnel business emergency identifier are completely consistent, successfully completing the format alignment processing of the core fields. Subsequently, the length of the employee's name (byte length) and the first six digits of the employee's location code, stored independently in the multi-dimensional business extraction identifier, are obtained. The format-aligned employee identity type code and the format-aligned employee business emergency identifier are extracted, along with the employee's name (byte length) and the first six digits of the employee's location code. These four feature parameters are then transferred and transformed into a one-dimensional row vector structure for vectorization integration. Specifically, the vectorization integration process involves sequentially mapping and filling each format-aligned feature parameter into a preset index column of the one-dimensional row vector, forming a compact and coherent structured numerical sequence. Finally, the entire row vector is packaged to obtain the message's structured feature parameters.

[0065] Please see Figure 3 Step S2 is as follows:

[0066] S201: Obtain records from the enterprise remote identity authentication server, extract the enterprise personnel identity type code and enterprise personnel business emergency identifier from the message structured feature parameters, collect the enterprise personnel identity mapping relationship table within the enterprise remote identity authentication server, perform a query on the mapping relationship table for the enterprise personnel identity type code, and set the enterprise personnel identity weight;

[0067] Establish a secure communication channel with the enterprise's remote identity authentication server and send a standardized data retrieval command request to obtain records from the enterprise's remote identity authentication server. Parse the underlying structure of the received enterprise remote identity authentication server records, extracting the rigorously processed message structured feature parameters. Perform precise slicing and separation operations on the message structured feature parameters to accurately extract the enterprise personnel identity type codes and enterprise personnel business urgency identifiers contained within. In the internal storage space of the enterprise remote identity authentication server, send an advanced query command to collect the full enterprise personnel identity mapping table. The enterprise personnel identity mapping table consists of detailed identity category identifier columns and corresponding identity permission score columns. The values ​​of the corresponding score columns are set according to the personnel permission level point conversion standard in the enterprise's highest security control regulations, assigning different basic restriction scores based on the historical violation review rate and long-term trust level of different identities. For the extracted enterprise personnel identity type codes, perform a row-by-row precise matching scan query operation within the enterprise personnel identity mapping table. Compare the specific values ​​of the enterprise personnel identity type code with the values ​​in the identity category identifier column, read the specific values ​​in the identity permission score column corresponding to the completely matching row, extract the score value and set it directly as the enterprise personnel identity weight of the current processing object.

[0068] S202: Obtain the emergency business additional weight corresponding to the emergency business identifier of the enterprise personnel from the enterprise's remote identity authentication server, extract the preset basic weight from the enterprise's remote identity authentication server, and establish a derived basic weight parameter group.

[0069] Maintain high-speed data interaction with the enterprise's remote identity authentication server, and retrieve the enterprise personnel's business emergency identifier from the corresponding data interface of the enterprise's remote identity authentication server. Trigger a fast retrieval mechanism in the emergency business additional weight reference library stored internally by the enterprise's remote identity authentication server. The emergency business additional weight reference library is established based on the statistical archived data of the impact factors of the enterprise's emergency handling time over the years. It is divided into multiple continuous time intervals for different handling time requirements, and the arithmetic mean of the corresponding historical impact factors is calculated for each independent time interval as a fixed weight reference value. Input the obtained enterprise personnel's business emergency identifier as the core search keyword into the emergency business additional weight reference library. Through numerical interval matching and comparison, obtain the specific factor value corresponding to the time interval to which the enterprise personnel's business emergency identifier belongs, and securely extract and formally establish the specific factor value as the emergency business additional weight. Then, send a status data retrieval request independently to the enterprise's remote identity authentication server to extract the preset basic weight from its core network configuration area. The preset base weights are set with reference to the historical maximum carrying capacity parameters of the enterprise's conventional network backbone bandwidth. Specifically, the process involves retrieving the arithmetic mean of the bandwidth idle rate of all network nodes within a set monitoring period, multiplying it by a preset fixed network expansion constant, and setting the product as the preset base weight reflecting the current network idle level. After obtaining the emergency service additional weights and the preset base weights, these weights are written together into a pre-allocated independent memory space data set container according to a tight data aggregation rule, establishing a derived base weight parameter group containing multiple related weights.

[0070] S203: Combine the emergency business additional weight and basic weight in the derived basic weight parameter group with the enterprise personnel identity weight and calculate the corresponding arithmetic sum to obtain the initial weight parameters;

[0071] Access the data set container established in the upstream operation process to extract the emergency business additional weights and preset basic weights securely stored within the derived basic weight parameter group. Simultaneously, invoke the enterprise personnel identity weights precisely set according to the mapping relationship in step 201. Perform corresponding arithmetic summation calculations on the extracted emergency business additional weights, preset basic weights, and enterprise personnel identity weights in sequence. The specific arithmetic summation progression process is as follows: first, perform a first round of addition numerical operation on the emergency business additional weights and the preset basic weights; the sum generated is set as the transition weight sum. Then, perform a second round of addition numerical operation on the transition weight sum and the enterprise personnel identity weights; the final cumulative sum output from the two rounds of addition is directly defined as the initial weight parameter.

[0072] Please see Figure 4Step S3 is as follows:

[0073] S301: Collect the queue data of enterprise personnel authentication request messages recorded in the enterprise's gateway waiting sequence, extract the time sequence records of enterprise personnel authentication request messages entering the enterprise's gateway waiting queue, and assign a ranking label and encoding to each authentication request message based on the timestamp record to generate the initial queue number of enterprise personnel.

[0074] By proactively connecting to the enterprise's gateway core device through a secure, isolated network interface, and issuing underlying queue read commands, the system comprehensively collects queuing data of enterprise personnel authentication request messages recorded within the enterprise's gateway waiting sequence. The acquired queuing data is then subjected to structured parsing to extract the time sequence record generated when each enterprise personnel authentication request message entered the enterprise's gateway waiting queue. This time sequence record exists in the underlying cache as a high-precision, non-repeating numerical timestamp. For all extracted timestamp records, a comparison and judgment operation is performed according to their time sequence, and a ranking and encoding operation is performed on each authentication request message. Specifically, the comparison and encoding process involves iterating through all timestamp values ​​and comparing their differences. A smaller timestamp value indicates that the corresponding message entered the network queue earlier. The record at the very front of the comparison results (i.e., the record with the absolute smallest timestamp value) is assigned an initial ranking code. For the next timestamp record, the code is incremented by 1 sequentially based on the previous encoded value. This process is strictly recursively applied to all message records to uniquely number them, ultimately generating the initial queuing sequence number for each message.

[0075] S302: Call the initial weight parameters and the initial queuing number of the enterprise personnel, collect the preset time slice allocation constant inside the network configuration parameter set, calculate the product value of the initial weight parameters and the time slice allocation constant, and obtain the calculation share value of the enterprise personnel.

[0076] The system reads the pre-calculated initial weight parameters and the initial queuing numbers of enterprise personnel. It establishes a high-speed data transmission channel with the network configuration parameter management set, issues a security parameter read request, and collects the preset time slice allocation constant within the network configuration parameter set. The preset time slice allocation constant is strictly based on the average actual processing capacity of the CPU core within a single instruction clock cycle. Specifically, it is determined by collecting massive amounts of historical data on the actual time consumed by the CPU core in processing standard protocol messages. All consumed times are accumulated and their arithmetic mean is calculated. This arithmetic mean, which objectively reflects the hardware processing limit, is established as the preset time slice allocation constant, ensuring that the subsequently allocated time slice resources perfectly match the actual throughput capacity of the underlying physical hardware. The system extracts the initial weight parameter values ​​and the preset time slice allocation constant values, performs a multiplication operation on the initial weight parameter values ​​and the preset time slice allocation constant values ​​within the arithmetic logic unit, and records the product as the initial value of the enterprise personnel's computing share. Subsequently, the preliminary value of the enterprise personnel's calculation share is extracted from the records, and the initial queuing number of the enterprise personnel of the current processing object is retrieved. The preliminary value of the enterprise personnel's calculation share is used as the dividend, and the initial queuing number of the enterprise personnel is used as the divisor. The division calculation operation is strictly performed, and the final quotient result obtained by the division calculation is directly defined as the enterprise personnel's calculation share value.

[0077] S303: Perform normalization mapping on the initial weight parameters, the enterprise personnel's calculation share value, and the enterprise personnel's initial queuing number, and input them into the weighted round-robin scheduling algorithm. Perform descending sorting operation based on the initial weight parameters and the enterprise personnel's initial queuing number to calculate the absolute scheduling number of the message.

[0078] The system retrieves the initial weight parameters, enterprise personnel's computing share values, and initial queue numbers temporarily stored in system memory. Normalization mapping is performed on these three numerical features to completely eliminate computational bias caused by different parameter units. The specific process of normalization mapping involves retrieving the maximum limit values ​​of the initial weight parameters, enterprise personnel's computing share values, and initial queue numbers from historical processing records. The current initial weight parameter is divided by its maximum limit value to obtain the initial weight normalized value; the current enterprise personnel's computing share value is divided by its maximum limit value to obtain the share normalized value; and the current enterprise personnel's initial queue number is divided by its maximum limit value to obtain the queue normalized value. These three normalized values ​​are then input into a preset weighted round-robin scheduling algorithm. The weighted round-robin scheduling algorithm calculates the comprehensive scheduling score for each message using a comprehensive scheduling formula, which is set as follows: , in the formula, This represents the index of the message's position in the processing queue. The smaller the value, the earlier the message entered the waiting queue. Representing the The overall scheduling score of a message indicates that the higher the score value, the higher the scheduling priority of the message in the queue. Representing the The initial weight normalization value of the message is obtained by step 203 through progressive calculation and normalization. The larger the value, the greater the comprehensive weight of the business urgency and personnel identity security level. Representing the The share normalization value of the message is derived and normalized in step 302. The larger the value, the larger the computing share that the message obtains based on the hardware throughput capacity. Representing the The queuing normalization value of a message is obtained by normalizing the initial queuing number. The smaller the value, the earlier the message is in the queue. This represents the preset primary proportion coefficient, which is set based on the decisive impact of the initial weight on overall security and scheduling priority. The greater the importance of the indicator determined by the system for the initial weight, the larger the value of this primary proportion coefficient will be. This represents the preset secondary proportion coefficient, which is set based on the system's requirements for balanced allocation of computing resources. The smoother the resource allocation requirements, the more central the value of this coefficient will be. This represents the preset last-place percentage coefficient, which is set based on the queuing tolerance limit. The shorter the allowed queuing time, the larger the value of this coefficient. This represents the dynamic balance constant. This parameter is introduced to compensate for the weight loss of messages at the end of the queue due to the low queuing normalization value. The setting is based on the maximum allowable starvation waiting time set at the underlying level. The longer the starvation waiting time, the larger this constant is set. Representing the smallest positive constant, this parameter is introduced to normalize the value in the queue. In the extreme case of 0, the mathematical compliance of the division operation is guaranteed and the system does not crash. All parameters and coefficients involved in the calculation are mapped to a unified dimensionless interval to ensure that the physical meaning of each accumulation calculation is completely consistent and can be calculated together. The corresponding parameter value processed in the current scenario is set as: ranking index subscript. The value is 1, which is the preset primary proportion coefficient. Set to 0.4, initial weight normalization value The value is 0.8, and the preset secondary proportion coefficient is [missing information]. Set to 0.3, the share normalization value. The value is 0.7, and the preset last digit percentage coefficient is used. Set to 0.2, queue normalization value The value is 0.5, the dynamic equilibrium constant. Set to 0.05, the smallest positive constant. Set it to 0.001. Substitute the above-set parameters directly into the comprehensive scheduling formula of the weighted round-robin scheduling algorithm to list the numerical calculation formulas for unified calculation, that is... The overall scheduling score for the first message is calculated to be 0.7298. All calculated overall scheduling scores are then sorted in descending order. The final sequence number generated by this descending order is assigned a consecutive numerical number, which is the calculated absolute scheduling sequence number of the message.

[0079] Please see Figure 5 Step S4 is as follows:

[0080] S401: Based on the absolute scheduling sequence number of the message, look up the enterprise's gateway waiting queue list item, retrieve the corresponding enterprise personnel authentication request message from the enterprise's gateway waiting queue in sequence, extract the message structured feature parameter related comparison fields associated with the authentication request message, and obtain the feature dataset to be verified.

[0081] Obtain the absolute scheduling sequence number of the message output by the scheduling calculation engine. Use this absolute scheduling sequence number as the core lookup index and perform a line-by-line precise matching and lookup operation on the underlying list items of the enterprise's gateway waiting queue. Based on the matching and lookup feedback, retrieve the enterprise personnel authentication request messages that precisely correspond to the physical location of the absolute scheduling sequence number from the enterprise's gateway waiting queue in a determined absolute order. Use the system's built-in message parsing protocol stack to strip away the outer encapsulation communication shell of the enterprise personnel authentication request message and deeply analyze its underlying payload file structure. From the underlying payload file structure, extract the message structured feature parameter comparison fields associated with the enterprise personnel authentication request message. In the initial encapsulation state, the message structured feature parameter comparison fields necessarily contain three consecutive blocks: a field identifier header, the data body content, and a checksum suffix. Perform noise reduction and trimming processing on the extracted full content, accurately locate and completely remove the special escape boundary markers of the field identifier header and checksum suffix, and retain only the core data body content with actual comparison value. The extracted and successfully cropped data body content is securely stored in a temporary cache data pool dedicated to system memory. The data body content of multiple different messages is aggregated to form an efficient feature dataset to be verified.

[0082] S402: Call the message structured feature parameters associated with the target scheduling message data, extract the first six digits of the enterprise personnel's place of origin and the length of the enterprise personnel's name bytes, input the first six digits of the enterprise personnel's place of origin into the first branch node of the decision tree algorithm, obtain the preset specified area code set at the first branch node, verify the matching status of the first six digits of the enterprise personnel's place of origin and the specified area code set, and generate the area matching judgment status value;

[0083] The system triggers internal security access control commands, accesses high-speed storage sectors in the temporary cache data pool, and invokes the message structured feature parameters closely associated with the target scheduling message data. It accurately parses the internal data block division boundaries of the message structured feature parameters, extracting the first six digits of the enterprise personnel's place of origin and the length of the enterprise personnel's name bytes according to a fixed data dictionary index. The system then initiates a pre-loaded and deployed decision tree algorithm model, using the extracted first six digits of the enterprise personnel's place of origin as the primary input variable, inputting it into the first branch node of the decision tree algorithm model. The decision tree algorithm model guides subsequent data flow to the execution path branching through a rigorous hierarchical condition judgment mechanism. At the first branch node, the program accesses a preset local security administrative rule base to obtain a preset set of designated administrative region codes. The preset set of designated administrative region codes is based on a compilation and review of national standard administrative region codes for the cities where multiple legally established branches and trusted long-term partners of the enterprise are located. Within the logical operation unit of the first branch node, the compliance matching status of the first six digits of the enterprise personnel's place of origin and each element within the set of designated administrative region codes is rigorously verified. The specific verification process is as follows: It iterates through all valid code data stored in the specified administrative division code set. The first six digits of the current employee's location code are compared with each element in the set using a difference operation. If an element exists during the iteration that results in an exact difference of 0, the first six digits of the current employee's location code successfully matches the whitelist in the set, and the matching status is output as "Complete Match". If, after completely iterating through all elements in the specified administrative division code set, all difference comparison results show a non-zero offset, then no valid rule is matched, and the matching status is output as "Mismatch". Based on the final matching status, a corresponding numeric administrative division matching judgment status value is generated. If the verification match is successful, a positive logical value is assigned; if the verification match fails, a negative logical value is assigned.

[0084] S403: Input the length of the enterprise personnel name in bytes into the second branch node of the decision tree algorithm, obtain the preset standard length threshold of the enterprise personnel name at the second branch node, compare the length of the enterprise personnel name in bytes with the standard length threshold of the enterprise personnel name, combine the division matching to determine the status value, and divide the naming rule classification label.

[0085] The process retrieves the byte length of the company personnel names extracted in parallel from the structured feature parameters in step 402. This byte length is then imported into the subsequent deep processing layer of the decision tree algorithm model, seamlessly inputting it into the second branch node. At the second branch node, the overflow prevention security threshold file configured in the local core system is quickly retrieved to obtain the preset standard byte length threshold for company personnel names. The preset standard byte length threshold is set based on the actual number of storage bytes occupied by the real, valid, and legal names of company employees in the national public household registration system, collected on a large scale with desensitization. The global arithmetic mean of the number of bytes occupied by these massive numbers of names is calculated, and then a statistical standard deviation of the number of bytes in a specific interval is added to this global arithmetic mean with fault tolerance. Finally, a standard byte length threshold for company personnel names with strong universal adaptability and fault tolerance is scientifically derived and calculated. Within the mathematical logic processing unit of the second branch node, a subtraction operation is performed between the currently input byte length of the company personnel name and the preset standard byte length threshold for company personnel names. The specific subtraction process is as follows: the actual extracted length of the company personnel's name in bytes is used as the minuend, and the preset standard length threshold for company personnel's names is used as the subtrahend. The difference data output after the subtraction operation is obtained, and the positive or negative sign characteristic of the difference result is evaluated. When the difference result is determined to be less than or equal to 0, it is confirmed that the current length of the company personnel's name in bytes does not exceed the limit standard of the underlying storage and belongs to the data stream that meets the standard size requirements. Next, the area matching judgment status value calculated in step 402 is retrieved synchronously. The positive or negative status represented by the area matching judgment status value is strictly combined with the standard judgment result of the name length comparison of this node. When the area matching judgment status value indicates that the place of origin is successfully matched and the length comparison result indicates that it meets the standard size requirements, the current processing object is classified and officially assigned the naming rule classification label as a highly reliable compliant internal personnel label. Conversely, if the zoning matching status value indicates that the location matching has failed, or if the length comparison result indicates that the difference exceeds the standard limit, the process will be blocked and the applicant will be assigned the naming rule classification label as an abnormal person awaiting review with risks.

[0086] Please see Figure 6 Step S5 is as follows:

[0087] S501: Obtain the preset enterprise personnel name length verification parameter template, and extract the corresponding enterprise personnel length verification upper limit value from the enterprise personnel name length verification parameter template based on the naming rule classification label;

[0088] Establish a secure dedicated data interface with the enterprise's internal human resource management system to reliably read the pre-deployed enterprise personnel name length verification parameter template at the system's underlying layer. Retrieve naming rule category tags from the upstream business segmentation output from the processing task queue. Use this retrieved naming rule category tag as the unique search key and perform a high-precision string consistency comparison with the tree-structured search directory within the pre-configured enterprise personnel name length verification parameter template. The enterprise personnel name length verification parameter template is pre-configured with strict limit verification values ​​corresponding to different risk category tags, using a structured two-dimensional table underlying structure. The specific setting of these pre-configured limit verification values ​​is based on the maximum character limit record in the database of all legal and valid documents provided by personnel groups corresponding to each tag during historical onboarding and long-term registration security audits. The historical highest extreme value corresponding to each tag is securely extracted as the absolute verification benchmark for future entry of personnel in that category. After confirming a successful string comparison between the input search key and the template directory item, instantly lock the read-only data row containing the corresponding risk tag in the enterprise personnel name length verification parameter template. Extract the corresponding specific length limit value from the locked data row. Set this extracted specific length limit value directly as the upper limit value for enterprise personnel length verification required for the current underlying overflow prevention judgment.

[0089] S502: Calculate the difference between the current length of the enterprise personnel name in bytes and the upper limit of the enterprise personnel length verification value, assess the overflow data range of the storage space occupied by the enterprise personnel name field, and obtain the deviation value of the enterprise personnel byte length;

[0090] The system retrieves the actual byte length of the enterprise personnel name corresponding to the currently processed enterprise personnel authentication request message in the queue, and simultaneously extracts the upper limit value for the corresponding category of enterprise personnel length verification. Using the current enterprise personnel name byte length as the minuend and the upper limit value for enterprise personnel length verification as the subtrahend, a fast difference calculation operation is performed in the arithmetic logic unit of the central processing unit. By performing this simple difference calculation operation, the core of the system aims to quantitatively assess the potential risk of overflow data in the current enterprise personnel name field during actual network packet transmission and final database persistence. The result of the subtraction is obtained, and regardless of whether the result is positive or negative, it is uniformly extracted and recorded as the enterprise personnel byte length deviation value reflecting the current verification status. When the calculation result is positive due to an excessively large minuend, the value directly and accurately reflects the absolute excess length of the name bytes in the current message that exceeds the system's security margin configuration, indicating a high risk of memory overflow attacks or abnormal data entry. When the calculation result is negative, the value safely reflects the remaining buffer storage space of the current name bytes before the system's highest security margin collapse limit.

[0091] S503: When the byte length deviation of the enterprise personnel is determined to be less than or equal to zero, the underlying basic identity information of the enterprise personnel is extracted. The underlying basic identity information of the enterprise personnel is used to perform a consistency comparison and verification operation on the enterprise personnel identity verification request message to obtain the identity verification result.

[0092] The system reads the byte length deviation value record of the enterprise personnel stored in the storage register. It then performs a rigorous numerical comparison with the zero value of the absolute security judgment benchmark of the system's preset underlying defense. When the comparison result determines that the specific value of the enterprise personnel byte length deviation value is less than or equal to the zero value, it is confirmed that the length of the name submitted by the person is within the absolute security range of the underlying storage medium, thereby triggering the security gateway to generate an advanced query authorization instruction allowing cross-segment access to the enterprise's underlying human resources database. Upon receiving this advanced query authorization instruction, the system retrieves the basic identity information of the enterprise personnel associated with the person from the highly encrypted and isolated human resources database segment. This basic identity information is composed of highly sensitive multimodal information, including the ID card number string sequence collected and verified during the rigorous onboarding process, multi-dimensional facial feature point array data, and biometric fingerprint topology image data. After extracting this basic identity information, it performs a bit-by-bit consistency comparison and verification operation with the claimed authentication features explicitly stated in the initially received enterprise personnel identity verification request message. The specific deep verification process involves extracting the core ID number string arrays from the verification data packets of both parties. By calculating the Hamming distance between the two string matrices, or performing a low-level binary bit-level XOR checksum, the system precisely scans and compares the differences at every bit. When the total number of differences is statistically analyzed and shows absolutely no difference (i.e., the Hamming distance is strictly 0), the multimodal comparison result is determined to be completely consistent. After determining complete consistency and without any abnormal interruption alarms throughout the review process, the system core generates an absolute authorization instruction representing final identity verification. This absolute authorization instruction is encapsulated and output as core Boolean data, thus obtaining the final identity verification result. Using a large-scale, real-machine, high-frequency verification request sample library containing maliciously tampered and ambiguous data for empirical anti-counterfeiting attack testing, and after introducing a pre-screening and interception mechanism for name length deviation and a subsequent multi-modal consistency deep comparison and verification operation, the final identity verification result shows that the overall system's anti-counterfeiting false recognition rate has been effectively suppressed and reduced to nearly zero at 0.01%. The overall system's anti-counterfeiting false recognition and missed detection rate has been significantly reduced, fully ensuring the secure and reliable operation of the enterprise's remote digital identity access control.

[0093] Please see Figure 7 A remote identity verification system for enterprise registration self-service terminals, the system comprising:

[0094] The message feature parsing module obtains the authentication request message sent by the terminal, and extracts the message structure feature parameters of the authentication request message through encoding conversion and data parsing operations.

[0095] The weight matching and calculation module matches the corresponding additional weights in the server based on the message structure feature parameters, and sums the additional weights with the preset basic weights to obtain the initial weight parameters.

[0096] The scheduling and sorting control module extracts the time record of the authentication request message entering the gateway waiting queue to generate an initial queue number, and performs a descending sort based on the initial queue number and the initial weight parameter to obtain the absolute scheduling number of the message.

[0097] The rule matching and classification module retrieves the corresponding authentication request messages sequentially based on the absolute scheduling sequence number of the messages, verifies the multi-class matching status of the message structured feature parameters, and generates named rule classification labels.

[0098] The identity verification and determination module extracts the upper limit of the length verification corresponding to the naming rule category label from multiple length verification parameter templates, extracts the identity information of enterprise personnel within the upper limit of the length verification, determines the consistency of the corresponding enterprise personnel identity information, and generates the identity verification result.

[0099] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for remote identity verification on a self-service terminal for enterprise registration, characterized in that, Includes the following steps: S1: Obtain the authentication request message sent by the terminal, and extract the message structure feature parameters of the authentication request message through encoding conversion and data parsing operations; S2: Based on the message structured feature parameters, match the corresponding additional weights in the server, and sum the additional weights with the preset basic weights to obtain the initial weight parameters; S3: Extract the time record of the authentication request message entering the gateway waiting queue to generate an initial queue number, perform descending sorting based on the initial queue number and the initial weight parameter, and obtain the absolute scheduling number of the message; S4: Retrieve the corresponding authentication request messages in sequence according to the absolute scheduling sequence number of the message, verify the multi-class matching status of the message structured feature parameters, and generate naming rule classification labels; S5: Extract the upper limit of the length verification corresponding to the naming rule category label from multiple length verification parameter templates, extract the enterprise personnel identity information within the upper limit of the length verification, determine the consistency of the corresponding enterprise personnel identity information, and generate the identity verification result; Step S4 is as follows: S401: Based on the absolute scheduling sequence number of the message, look up the enterprise's gateway waiting queue list item, retrieve the corresponding enterprise personnel authentication request message from the enterprise's gateway waiting queue in sequence, extract the message structured feature parameter related comparison field associated with the authentication request message, and obtain the feature dataset to be verified. S402: Call the message structured feature parameters associated with the target scheduling message data, extract the first six digits of the enterprise personnel's place of origin and the length of the enterprise personnel's name bytes, input the first six digits of the enterprise personnel's place of origin into the first branch node of the decision tree algorithm, obtain the preset designated area code set at the first branch node, verify the matching status of the first six digits of the enterprise personnel's place of origin and the designated area code set, and generate an area matching judgment status value; S403: Input the length of the enterprise personnel's name in bytes into the second branch node of the decision tree algorithm, obtain the preset standard length threshold of the enterprise personnel's name at the second branch node, compare the length of the enterprise personnel's name in bytes with the standard length threshold of the enterprise personnel's name, and combine the zoning matching judgment status value to divide the naming rule classification label.

2. The remote identity verification method for enterprise registration self-service terminals according to claim 1, characterized in that, The message structured feature parameters include the enterprise personnel identity type code, the enterprise personnel business urgency identifier, the length of the enterprise personnel name in bytes, and the first six digits of the enterprise personnel's location code. The initial weight parameter is specifically a weight calculated by the basic weight, the enterprise personnel identity weight, and the emergency business additional weight. The message absolute scheduling sequence number is specifically a queue position calculated by the initial weight parameter and the enterprise personnel's initial queuing sequence number. The naming rule classification label is specifically a template classification calculated by comparing the matching status with the enterprise personnel length. The identity verification result is specifically a verification conclusion performed by comparing and verifying the enterprise personnel identity verification request message with the enterprise personnel's underlying basic identity information.

3. The remote identity verification method for enterprise registration self-service terminals according to claim 1, characterized in that, Step S1 is as follows: S101: Obtain network data interface to receive enterprise personnel identity verification request messages sent by enterprise self-service registration terminal devices, perform unified character set encoding conversion processing, separate and extract the primary byte data segment inside the message header data, and generate encoding conversion parsing feature set; S102: Based on the encoding conversion parsing feature set, separate the enterprise personnel identity type code and the enterprise personnel business emergency identifier based on the message header data, and at the same time obtain the length of the enterprise personnel name bytes inside the message, as well as the first six digits of the enterprise personnel's location code, to obtain the multi-dimensional business extraction identifier code. S103: Align the enterprise personnel identity type code and enterprise personnel business emergency identifier execution format in the multi-dimensional business extraction identifier code, and integrate them with the enterprise personnel name byte length and the first six digits of the enterprise personnel's place of origin in a vectorized manner to obtain the message structured feature parameters.

4. The remote identity verification method for enterprise registration self-service terminals according to claim 1, characterized in that, Step S2 is as follows: S201: Obtain records from the enterprise remote identity authentication server, extract the enterprise personnel identity type code and enterprise personnel business emergency identifier from the structured feature parameters of the message, collect the enterprise personnel identity mapping relationship table in the enterprise remote identity authentication server, perform a query on the mapping relationship table for the enterprise personnel identity type code, and set the enterprise personnel identity weight. S202: Obtain the emergency business additional weight corresponding to the emergency business identifier of the enterprise personnel from the enterprise's remote identity authentication server, extract the preset basic weight from the enterprise's remote identity authentication server, and establish a derived basic weight parameter group. S203: Combine the emergency business additional weight and basic weight in the derived basic weight parameter group with the enterprise personnel identity weight, and calculate the corresponding arithmetic sum to obtain the initial weight parameters.

5. The remote identity verification method for enterprise registration self-service terminals according to claim 1, characterized in that, Step S3 is as follows: S301: Collect the queue data of enterprise personnel authentication request messages recorded in the enterprise's gateway waiting sequence, extract the time sequence records of enterprise personnel authentication request messages entering the enterprise's gateway waiting queue, and assign a ranking label and encoding to each authentication request message based on the timestamp record to generate the initial queue number of enterprise personnel. S302: Call the initial weight parameter and the initial queuing number of the enterprise personnel, collect the preset time slice allocation constant in the network configuration parameter set, calculate the product value of the initial weight parameter and the time slice allocation constant, and obtain the enterprise personnel's computing share value. S303: Perform normalization mapping processing on the initial weight parameters, the enterprise personnel operation share values ​​and the initial queue number of the enterprise personnel and input them into the weighted round-robin scheduling algorithm to perform descending order sorting operation based on the initial weight parameters and the initial queue number of the enterprise personnel, and calculate the absolute scheduling sequence number of the message.

6. The remote identity verification method for enterprise registration self-service terminals according to claim 1, characterized in that, Step S5 is as follows: S501: Obtain the preset enterprise personnel name length verification parameter template, and extract the corresponding enterprise personnel length verification upper limit value from the enterprise personnel name length verification parameter template based on the naming rule classification label; S502: Calculate the difference between the current enterprise personnel name byte length and the enterprise personnel length verification upper limit value, evaluate the overflow data range of the storage space occupied by the enterprise personnel name field, and obtain the enterprise personnel byte length deviation value; S503: When the byte length deviation value of the enterprise personnel is determined to be less than or equal to zero, the underlying basic identity information of the enterprise personnel is extracted, and the consistency comparison and verification operation is performed on the enterprise personnel identity verification request message using the underlying basic identity information of the enterprise personnel to obtain the identity verification result.

7. The remote identity verification method for enterprise registration self-service terminals according to claim 3, characterized in that, The enterprise personnel identity type codes include the enterprise's formal employee identity codes, enterprise temporary visitor identity codes, and enterprise outsourced maintenance personnel identity codes.

8. The remote identity verification method for enterprise registration self-service terminals according to claim 3, characterized in that, The emergency business identifier for enterprise personnel is determined based on the preset processing time limit data for each business and the access priority level characteristics of enterprise personnel's appointment registration.

9. A remote identity verification system for a self-service terminal for enterprise registration, characterized in that, The system comprises: (1) the remote authentication method for enterprise registration self-service terminal according to any one of claims 1-8; (2) the system comprising: The message feature parsing module obtains the authentication request message sent by the terminal, and extracts the message structure feature parameters of the authentication request message through encoding conversion and data parsing operations. The weight matching calculation module matches the corresponding additional weights in the server based on the message structured feature parameters, and sums the additional weights with the preset basic weights to obtain the initial weight parameters. The scheduling and sorting control module extracts the time record of the authentication request message entering the gateway waiting queue to generate an initial queuing number, and performs a descending sort based on the initial queuing number and the initial weight parameter to obtain the absolute scheduling number of the message. The rule matching and classification module retrieves the corresponding authentication request messages sequentially based on the absolute scheduling sequence number of the messages, verifies the multi-class matching status of the message structured feature parameters, and generates named rule classification labels, including: According to the absolute scheduling sequence number of the message, the enterprise's gateway waiting queue list item is retrieved in sequence from the enterprise's gateway waiting queue. The corresponding enterprise personnel authentication request message is retrieved in sequence, and the message structured feature parameter related comparison field associated with the authentication request message is extracted to obtain the feature dataset to be verified. The message structured feature parameters associated with the target scheduling message data are called to extract the first six digits of the enterprise personnel's place of origin and the length of the enterprise personnel's name bytes. The first six digits of the enterprise personnel's place of origin are input into the first branch node of the decision tree algorithm. At the first branch node, a preset set of designated area codes is obtained. The matching status of the first six digits of the enterprise personnel's place of origin and the set of designated area codes is verified, and an area matching judgment status value is generated. The length of the company personnel's name in bytes is input into the second branch node of the decision tree algorithm. At the second branch node, the preset standard length threshold of the company personnel's name is obtained. The length of the company personnel's name in bytes is compared with the standard length threshold of the company personnel's name. Combined with the zoning matching judgment status value, the naming rule classification label is divided. The identity verification and determination module extracts the upper limit of the length verification corresponding to the naming rule category label from multiple length verification parameter templates, extracts the identity information of enterprise personnel within the upper limit of the length verification, determines the consistency of the corresponding enterprise personnel identity information, and generates the identity verification result.

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