Identity authentication process adjustment method and device based on self-service depositing and withdrawing equipment

Abstract

The invention discloses an identity authentication process adjusting method and device based on self-service depositing and withdrawing equipment, and relates to the field of financial science and tech, and the method comprises the steps: determining a target interface region according to user historical operation data, and generating simplified interface structure data based on the element distribution characteristics of the interface region, adjusting a to-be-presented identity verification process structure, generating a recombined verification process sequence, embedding operation guidance, generating identity verification process data with guidance information, identifying individual difference characteristics through a preset clustering analysis strategy, generating user behavior classification model data, and displaying the user behavior classification model data. And generating personalized verification step sequence data according to the user behavior classification model data, adjusting the execution sequence of the identity verification process data, analyzing the adaptability of different user groups, and generating multiple identity authentication scripts. According to the invention, the technical problem that the operation becomes difficult because the cognitive load is increased due to the excessive complex user interface of the self-service cash recycling system in the related technology is solved.

Description

Technical Field

[0001] The present invention relates to the field of financial technology or other related fields, and in particular to a method and device for adjusting an identity authentication process based on a self-service deposit and withdrawal device. Background Art

[0002] In the current field of financial technology and smart banking services, automated teller machines (ATMs) serve as a critical bridge connecting financial institutions and the general public. Their security and convenience have always been key considerations in their design and innovation. While the ATM multi-identity authentication methods used in related technologies have ensured transaction security to a certain extent, their inherent limitations—such as complex interface design, lengthy verification processes, and insufficient support for specific user groups such as the elderly and those with special needs—continue to hinder user experience and system penetration. Specifically, overly complex user interfaces increase cognitive load, making operation difficult, especially for elderly users and those with special needs. The lack of intuitive and guided verification processes slows down operations and reduces efficiency. Furthermore, the lack of intelligent adaptation to user behavior patterns and the failure to optimize verification steps based on the habits and needs of different users pose significant challenges to the security and usability of ATMs.

[0003] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0004] The embodiment of the present invention provides a method and apparatus for adjusting the identity authentication process based on a self-service deposit and withdrawal device, so as to at least solve the technical problem in the related art that the overly complex user interface of the self-service deposit and withdrawal machine increases the cognitive load and makes operation difficult.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for adjusting the identity authentication process based on a self-service deposit and withdrawal device is provided, comprising: obtaining user historical operation data of the self-service deposit and withdrawal device, determining a target interface area based on the user historical operation data, and generating simplified interface structure data based on the element distribution characteristics of the target interface area, wherein the user historical operation data at least includes: the number of clicks and the length of stay in each area of ​​the device interface; adjusting the identity authentication process structure to be presented on the self-service deposit and withdrawal device based on the simplified interface structure data, generating a reorganized verification process sequence, and embedding operation instructions in the reorganized verification process sequence to generate identity authentication process data with guidance information; extracting user behavior data from the identity authentication process data, identifying individual difference characteristics through a preset clustering analysis strategy, generating user behavior classification model data, and generating personalized verification step sequence data based on the user behavior classification model data; adjusting the execution order of the identity authentication process data based on the personalized verification step sequence data; analyzing the adaptability of different user groups based on the adjusted identity authentication process data, and generating multiple identity authentication scripts.

[0006] Optionally, the steps of determining the target interface area according to the user's historical operation data and generating simplified interface structure data based on the element distribution characteristics of the target interface area include: analyzing the number of clicks and dwell time of each area of ​​the device interface in the user's historical operation data, and outputting user operation behavior distribution data; using a statistical analysis tool to process the user operation behavior distribution data, and determining a correlation index between interface complexity and cognitive burden; when the correlation index exceeds a preset correlation threshold, confirming the position of the target interface area according to the number of clicks and dwell time; obtaining the element distribution characteristics within the target interface area, and generating a complexity quantification value; and using a preset clustering algorithm to divide the area according to the complexity quantification value to obtain simplified interface structure data.

[0007] Optionally, the steps of adjusting the identity authentication process structure to be presented on the self-service deposit and withdrawal device based on the simplified interface structure data to generate a reorganized verification process sequence include: extracting the original information of the verification process from the simplified interface structure data to obtain a process structure distribution; using a hierarchical decomposition strategy to divide the process structure distribution into multiple single-task units, and extracting independent unit features from the single-task unit division results to generate a single-task unit set; using a preset sorting algorithm to reorganize and adjust the single-task unit set to obtain a reorganized process structure; when the total execution time of the reorganized process structure exceeds a preset time threshold, iteratively optimizing the reorganized process structure, and using a preset clustering algorithm to divide the iteratively optimized process sequence results to generate a target verification process sequence; extracting key features from the target verification process sequence, and judging the process integrity based on the extracted key features; when the judgment result indicates that the target verification process sequence meets the integrity requirements, using the target verification process sequence as the reorganized verification process sequence.

[0008] Optionally, the step of embedding operation instructions in the reorganized verification process sequence to generate identity authentication process data with guidance information includes: generating an initial data set based on the reorganized verification process sequence, adjusting the sequence order using a preset embedding design strategy, and obtaining reorganized sequence data; extracting operation instructions from the reorganized sequence data to generate a preliminary guidance framework; based on the preliminary guidance framework, generating real-time operation instructions using preset dynamic prompt generation rules, and determining the correspondence between the real-time operation instructions and the reorganized sequence data; when it is detected that the correspondence between the real-time operation instructions and the reorganized sequence data is incomplete, extracting key information from the verification process data, adjusting the verification process using the key information, and obtaining optimized verification process data; for the optimized verification process data, clustering the guidance information using a preset clustering algorithm to generate segmented guidance data; extracting prompt generation rules from the segmented guidance data, generating target operation instructions using the prompt generation rules, and confirming target verification process data based on the target operation instructions; verifying the target verification process data using preset integrity judgment rules to generate the identity authentication process data with guidance information.

[0009] Optionally, the steps of extracting user behavior data from the identity authentication process data, identifying individual difference features through a preset cluster analysis strategy, and generating user behavior classification model data include: extracting user behavior data from the identity authentication process data, and organizing the extracted user behavior data into a structured behavior data set according to a preset field mapping rule; performing cluster analysis on the structured behavior data set through a preset cluster analysis strategy, dividing data with similar behavior patterns into the same group, and obtaining an individual difference feature set; for the individual difference feature set, using principal component analysis to calculate the variance contribution rate of each feature in the set, and according to The weight of the feature is adjusted according to the contribution rate to obtain weighted feature data; when it is found that there are data points in the weighted feature data that deviate from the mean by more than a predetermined standard deviation, the weighted feature data is filtered to retain valid feature data; a preset clustering algorithm is used to divide the valid feature data into multiple categories to generate preliminary classification model data; the characteristic distribution pattern of each cluster center point is extracted from the preliminary classification model data to form a dynamic generation rule to obtain optimized user behavior classification data; the optimized user behavior classification data is verified for matching with the original structured behavior data set to generate the user behavior classification model data.

[0010] Optionally, the step of generating personalized verification step sequence data based on the user behavior classification model data includes: extracting user behavior data from the user behavior classification model data, and locating user habit preferences based on the extracted user behavior data using a decision tree algorithm to obtain personality characteristics; adjusting preset rules according to the personality characteristics, determining verification steps, and generating a verification sequence according to the verification steps, and using a data analysis tool to analyze the distribution characteristics of the verification sequence to obtain preliminary sequence data; when the preliminary sequence data matches the user behavior data, adjusting the judgment logic to generate adjusted sequence data, and generating the personalized verification step sequence data based on the adjusted sequence data.

[0011] Optionally, after generating personalized verification step sequence data based on the user behavior classification model data, it also includes: counting the operation time through a timing tool to obtain a time statistics result, and when the time statistics result exceeds a preset time threshold, using a parallel processing algorithm to rearrange the execution order to obtain adjusted sequence data; generating time-optimized process data based on the adjusted sequence data, extracting individual behavior characteristics from the time-optimized process data, using a preset clustering algorithm to divide the behavior distribution, and obtaining user behavior classification data; adjusting the verification step logic based on the user behavior classification data to generate time-optimized verification process data.

[0012] Optionally, after adjusting the verification step logic according to the user behavior classification data and generating time-consuming optimized verification process data, it also includes: using a threat modeling strategy to detect potential vulnerabilities in the time-consuming optimized verification process data, and generating suspected vulnerability link distribution data based on the potential vulnerabilities; extracting key risk points from the suspected vulnerability link distribution data, determining the priority order of the key risk points, and adjusting the execution order of the key risk points when the priority order exceeds a preset priority threshold to generate optimized process data; detecting behavior distribution according to the optimized process data, obtaining dynamic change trend characteristics, adjusting the verification process reinforcement strategy through the dynamic change trend characteristics, and generating security-reinforced verification process data.

[0013] According to another aspect of an embodiment of the present invention, an identity authentication process adjustment device based on a self-service deposit and withdrawal device is provided, comprising: an interface simplification unit for obtaining historical user operation data of the self-service deposit and withdrawal device, determining a target interface area based on the historical user operation data, and generating simplified interface structure data based on element distribution characteristics of the target interface area, wherein the historical user operation data includes at least the number of clicks and dwell time on each area of ​​the device interface; a verification process reorganization unit for adjusting the identity authentication process structure to be presented on the self-service deposit and withdrawal device based on the simplified interface structure data, generating a reorganized verification process sequence, and embedding operation instructions in the reorganized verification process sequence to generate identity authentication process data with guidance information; a personalized verification step sequence generation unit for extracting user behavior data from the identity authentication process data, identifying individual difference characteristics through a preset cluster analysis strategy, generating user behavior classification model data, and generating personalized verification step sequence data based on the user behavior classification model data; a verification process sequence adjustment unit for adjusting the execution order of the identity authentication process data based on the personalized verification step sequence data; and a multiple identity authentication script generation unit for analyzing the adaptability of different user groups based on the adjusted identity authentication process data to generate multiple identity authentication scripts.

[0014] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is further provided, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned identity authentication process adjustment methods based on self-service deposit and withdrawal devices.

[0015] According to another aspect of an embodiment of the present invention, an electronic device is also provided, comprising one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any one of the above-mentioned identity authentication process adjustment methods based on self-service deposit and withdrawal devices.

[0016] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the computer program implements the steps of any one of the above-mentioned methods for adjusting the identity authentication process based on the self-service deposit and withdrawal device.

[0017] In the present disclosure, user historical operation data of a self-service deposit and withdrawal device is obtained, a target interface area is determined based on the user historical operation data, and simplified interface structure data is generated based on the element distribution characteristics of the target interface area. The user historical operation data includes at least: the number of clicks and the length of stay in each area of ​​the device interface. Based on the simplified interface structure data, the identity authentication process structure to be presented on the self-service deposit and withdrawal device is adjusted to generate a reorganized verification process sequence, and an operation guide is embedded in the reorganized verification process sequence to generate identity authentication process data with guidance information. User behavior data is extracted from the identity authentication process data, and individual difference characteristics are identified through a preset clustering analysis strategy to generate user behavior classification model data. Personalized verification step sequence data is generated based on the user behavior classification model data, and the execution order of the identity authentication process data is adjusted based on the personalized verification step sequence data. The adaptability of different user groups is analyzed based on the adjusted identity authentication process data to generate multiple identity authentication scripts.

[0018] Based on the above-mentioned public content, by comprehensively considering factors such as interface complexity, user behavior, and operational efficiency, the identity authentication process is comprehensively optimized, the system's usability and adaptability are improved, and security is guaranteed at the same time. It can effectively improve the overall performance and user experience of multiple identity authentications, thereby solving the technical problem in related technologies that the overly complex user interface of self-service ATMs increases cognitive load and makes operation difficult. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0020] Figure 1 A hardware structure block diagram of a computer terminal (or mobile device) for implementing an identity authentication process adjustment method based on a self-service deposit and withdrawal device is shown;

[0021] Figure 2 This is a flowchart of an optional method for adjusting an identity authentication process based on a self-service deposit and withdrawal device according to an embodiment of the present invention;

[0022] Figure 3 1 is a schematic diagram of an optional identity authentication process adjustment device based on a self-service deposit and withdrawal device according to an embodiment of the present invention;

[0023] Figure 4 This is a structural block diagram of an electronic device that executes an identity authentication process adjustment method based on a self-service deposit and withdrawal device according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] To facilitate those skilled in the art to understand the present invention, some of the terms or nouns involved in the embodiments of the present invention are explained below:

[0027] The K-means Clustering Algorithm, or K-means for short, is a commonly used unsupervised machine learning algorithm used for cluster analysis of datasets. It determines the optimal clustering result by minimizing the sum of the squared distances between each sample and its cluster center. In this paper, K-means is used to perform cluster analysis on user operation areas, verification steps, guidance information, and user behavior after time-consuming optimization, in order to more effectively manage and optimize the user verification process and identify the unique behavioral patterns of different user groups.

[0028] Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a clustering algorithm suitable for processing noisy data. It does not require a predefined number of clusters, but instead automatically discovers cluster boundaries based on the density distribution of data points. In this paper, DBSCAN is used to identify groups with similar operating patterns in user behavior data, providing a basis for the subsequent design of personalized verification processes.

[0029] The AutoRegressive Integrated Moving Average (ARIMA) model is a statistical model used for time series forecasting and is applicable to stationary time series data. It combines autoregressive (AR), differencing (I), and moving average (MA) techniques to analyze trends, seasonality, and random fluctuations in time series data, thereby predicting future data trends. In this paper, ARIMA is used to analyze and predict future trends in key indicators such as user authentication success rate, time consumption, and coverage, assisting decision makers in making reasonable design adjustments.

[0030] It should be noted that the identity authentication process adjustment method and device based on the self-service deposit and withdrawal device in the present disclosure can be used in the field of financial technology to achieve the optimization of the identity authentication process of the self-service deposit and withdrawal device and multiple identity authentication. It can also be used in any field other than the field of financial technology to achieve the optimization of the identity authentication process of the self-service deposit and withdrawal device and multiple identity authentication. The present disclosure does not limit the application field of the identity authentication process adjustment method and device based on the self-service deposit and withdrawal device.

[0031] It should be noted that the information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) collected by this disclosure are information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with the relevant laws, regulations and standards of the relevant regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation portals for users to choose to authorize or refuse. For example, an interface is set up between this system and relevant users or institutions. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or institution through the interface, and obtain relevant information after receiving the consent information fed back by the aforementioned user or institution.

[0032] It should be noted that in this disclosure, when collecting and analyzing customer information, the corresponding operation entrance is provided for users to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered.

[0033] The following embodiments of the present invention can be applied to various systems, applications, and devices that adjust the identity authentication process based on self-service deposit and withdrawal devices. This invention can be applied to financial technology scenarios, particularly multi-identity authentication systems deployed in automated teller machines (ATMs). It is particularly suitable for banks and financial institutions seeking to improve transaction security and user experience. It can be widely used in various financial transaction scenarios, such as ATM deposit and withdrawal operations, account inquiries, and transfers, to enhance higher-level security measures while maintaining operational convenience.

[0034] The present invention effectively identifies and resists potential security risks through user behavior pattern analysis and threat modeling, reducing the probability of identity theft and fraud. Interface complexity and cognitive burden analysis ensure the intuitiveness and simplicity of the user interface. The embedded operation guidance design, combined with personalized verification step sequencing, makes the operation process more user-friendly, especially for the elderly and special groups.

[0035] At the same time, the verification process is dynamically adjusted according to user behavior characteristics, achieving a highly personalized identity authentication experience that can cover a wider range of user groups, including but not limited to young users, middle-aged users, and elderly users, and adapt to different operating habits and needs.

[0036] The present invention will be described in detail below with reference to various embodiments.

[0037] Example 1

[0038] According to an embodiment of the present invention, an embodiment of a method for adjusting an identity authentication process based on a self-service deposit and withdrawal device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0039] The embodiment of the identity authentication process adjustment method based on the self-service deposit and withdrawal device provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal (or mobile device) for implementing an identity authentication process adjustment method based on a self-service deposit and withdrawal device is shown. Figure 1 As shown, the computer terminal 10 (or mobile device) may include one or more ( Figure 1The computer system includes a processor 102 (shown as 102a, 102b, ..., 102n) (the processor 102 may include but is not limited to a microcontroller unit (MCU) or a field programmable gate array (FPGA)), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, the computer system may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS), a network interface, a power supply, and / or a camera. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0040] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuitry may be a single independent processing module, or may be incorporated in whole or in part into any of the other components of the computer terminal 10 (or mobile device). As described in the embodiments of the present application, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0041] The memory 104 can be used to store software programs and modules for application software, such as the program instructions / data storage device corresponding to the method for adjusting the identity authentication process for a self-service deposit and withdrawal device in the embodiments of the present application. The processor 102 executes the software programs and modules stored in the memory 104 to perform various functional applications and data processing, thereby implementing the aforementioned method for adjusting the identity authentication process for a self-service deposit and withdrawal device. The memory 104 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory remotely located relative to the processor 102, and such remote memory may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0042] The transmission device 106 is configured to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by the communications provider of the computer terminal 10. In one embodiment, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission device 106 may be a radio frequency (RF) module, which is configured to communicate with the Internet wirelessly.

[0043] The display may be, for example, a touch screen liquid crystal display (LCD), which enables a user to interact with a user interface of the computer terminal 10 (or mobile device).

[0044] Under the above operating environment, this application provides Figure 2 The illustrated method for adjusting the identity authentication process based on a self-service deposit and withdrawal device. Figure 2 Flowchart of an optional method for adjusting the identity authentication process based on a self-service deposit and withdrawal device according to an embodiment of the present invention. Figure 2 As shown, the method includes the following steps:

[0045] Step S201: Obtain historical user operation data of the self-service deposit and withdrawal device, determine the target interface area based on the historical user operation data, and generate simplified interface structure data based on the element distribution characteristics of the target interface area, wherein the historical user operation data at least includes: the number of clicks and the length of stay in each area of ​​the device interface.

[0046] In this embodiment, step S201 collects historical user operation data from the self-service deposit and withdrawal device, and analyzes the relationship between the complexity of the interface and the user's cognitive burden. Based on this, the target interface area is simplified to generate optimized interface structure data. By obtaining detailed records of the user's interaction with the device interface, including but not limited to the number of clicks and dwell time in each area, the user's operation mode and interface usage efficiency are deeply understood.

[0047] This embodiment begins with a comprehensive analysis of the user's historical operation data. This data covers the click frequency and user attention span of all areas of the device interface. By recording and analyzing it, the user's preferences and pain points during operation can be captured, providing empirical evidence for interface optimization. Optionally, the target interface area is determined based on the user's historical operation data, and the simplified interface structure data is generated based on the element distribution characteristics of the target interface area. The steps include: analyzing the number of clicks and dwell time of each area of ​​the device interface in the user's historical operation data, and outputting the user operation behavior distribution data; processing the user operation behavior distribution data using a statistical analysis tool to determine the correlation index between interface complexity and cognitive burden; when the correlation index exceeds a preset correlation threshold, confirming the location of the target interface area based on the number of clicks and dwell time; obtaining the element distribution characteristics within the target interface area to generate a complexity quantification value; and dividing the area using a preset clustering algorithm based on the complexity quantification value to obtain the simplified interface structure data.

[0048] Specifically, when acquiring historical user operation data, front-end tracking technology can be used to record the number of clicks and dwell time of users on the self-service deposit and withdrawal device interface. For example, on a product details page on an e-commerce platform, suppose a user clicks the "Add to Cart" button three times, dwelling for a total of 15 seconds, and spends 40 seconds in the "Product Description" area without clicking. This data reflects the distribution characteristics of user operation behavior. The number of clicks may indicate the clarity of the operation intention, while the dwell time may indicate the difficulty of understanding the content or the user's level of interest.

[0049] After analyzing historical user operation data, the system generates a detailed user operation behavior distribution report. This report details the number of clicks and average dwell time for each interface area, helping to pinpoint which areas present additional challenges for users. For example, the report might indicate that the "Account Information Modification" page receives more clicks and longer dwell time than the "Balance Inquiry" page, suggesting that this area may require simplification. Next, this embodiment uses professional statistical analysis tools to process the operation behavior distribution data and calculate a correlation index between interface complexity and user cognitive burden. By setting a preset correlation threshold, when the calculated correlation index exceeds this threshold, the system can identify that a specific area of ​​the interface design may be placing excessive cognitive strain on users. For example, if the correlation index for the "Account Information Modification" page is as high as 0.85, far exceeding the standard threshold of 0.6, this indicates that this area is highly complex and significantly correlated with user cognitive burden, and that the page requires adjustments to reduce cognitive burden. Once the correlation index exceeds the threshold, this embodiment pinpoints target interface areas (i.e., high-complexity areas) that require optimization based on the number of clicks and dwell time of users in specific areas. For example, assuming that the "Personal Information Editing" section of the "Account Information Modification" page is frequently clicked and stays there the longest, then this section becomes the primary target for optimization in order to reduce the user's operational burden. For example, in the "Product Description" area, a stay time of up to 40 seconds without a click may indicate that the content is lengthy or the layout is chaotic, making it difficult for users to quickly extract information. After locking the problem area in this way, the element distribution characteristics in the area are obtained, such as the number of text paragraphs is 5, the number of pictures is 3, and the number of buttons is 1, and a complexity quantification value is generated. Assume that the quantification value is 8, which is higher than the average value of 5.

[0050] To further analyze the complexity of the target interface area, the system extracts element distribution features from that area, such as text length, number of buttons, and image resolution. Using a specific quantification method, it assigns each feature a corresponding numerical value, ultimately summarizing the complexity quantification value. This value quantifies the cognitive challenge users may encounter when accessing that area. For example, if the "Personal Information Editing" section includes multiple required fields, multiple optional buttons, and complex validation logic, its complexity quantification value may be very high. Based on the complexity quantification value, this embodiment uses a preset clustering algorithm, such as K-means, to segment the target interface area and group similar interface elements. Clustering can clearly identify which elements, when combined, pose the greatest operational obstacles, allowing targeted simplification. For example, the clustering results may show that adjusting "Name," "Address," and "Contact Information" are in the same high-complexity group, indicating that entering and editing this information is a difficult task for users. For another example, suppose the "Product Description" area is divided into three categories: text-intensive area, image display area, and interactive area. After clustering, simplified interface structure data is obtained. Text-dense areas may be merged into a collapsed text box to reduce visual interference. These data can be used to generate an optimized interface to present data, such as displaying images and text in separate columns and placing interactive buttons in prominent positions.

[0051] It's important to note that when extracting key features from optimized interface presentation data, consider factors such as uniform element spacing and clear information hierarchy. For example, if text spacing is adjusted to 10 pixels and button response time is reduced to 0.5 seconds after optimization, the completeness of the final output data can be assessed. If all key features are present and user testing indicates a decrease in dwell time to 25 seconds and a 20% increase in click efficiency, the optimization is considered effective.

[0052] Through the above steps, this embodiment can accurately identify and simplify the target interface areas in the self-service deposit and withdrawal device that cause cognitive pressure on users, and generate simplified interface structure data, which not only improves the fluency and efficiency of user operations, but also significantly reduces user distress and operational errors caused by improper interface design. At the same time, it enhances the user experience of the self-service deposit and withdrawal machine, especially for elderly users and special groups who may be confused by the complexity of the interface, providing a friendlier and more intuitive operation interface, thereby promoting the popularization and inclusiveness of financial services.

[0053] Step S202: Based on the simplified interface structure data, the identity authentication process structure to be presented on the self-service deposit and withdrawal device is adjusted to generate a reorganized verification process sequence, and an operation guide is embedded in the reorganized verification process sequence to generate identity authentication process data with guidance information.

[0054] In this embodiment, step S202 can adjust the identity authentication process structure presented on the self-service deposit and withdrawal device based on the analysis results of the user operation data, that is, the simplified interface structure data, to generate a reorganized verification process sequence.

[0055] Optionally, the steps of adjusting the identity authentication process structure to be presented on the self-service deposit and withdrawal device based on the simplified interface structure data to generate a reorganized verification process sequence include: extracting the original information of the verification process from the simplified interface structure data to obtain the process structure distribution; using a hierarchical decomposition strategy to divide the process structure distribution into multiple single-task units, and extracting independent unit features from the single-task unit division results to generate a single-task unit set; using a preset sorting algorithm to reorganize and adjust the single-task unit set to obtain a reorganized process structure; when the total execution time of the reorganized process structure exceeds a preset time threshold, iteratively optimizing the reorganized process structure, and using a preset clustering algorithm to divide the iteratively optimized process sequence results to generate a target verification process sequence; extracting key features from the target verification process sequence, and judging the process integrity based on the extracted key features; when the judgment result indicates that the target verification process sequence meets the integrity requirements, the target verification process sequence is used as the reorganized verification process sequence.

[0056] First, it's necessary to extract the raw information of the verification process from the simplified interface structure data. This allows us to understand the composition and structure of the current verification process and obtain the process structure distribution. This includes, but is not limited to, each authentication step required by the user, the logical relationships between steps, and the order of operations. Based on the extracted process structure distribution, a hierarchical decomposition strategy is used to decompose the complex process into smaller, more manageable units based on function or operation type. This allows the verification process to be divided into multiple single-task units, each representing an independent verification action or function, such as password entry or biometric recognition. For example, in the payment verification process on an e-commerce platform, the raw information may include steps such as user password entry, verification code recognition, and fingerprint confirmation. The timestamp and operation status of each step are recorded. For example, suppose logs show that users spend an average of 20 seconds on verification code recognition, while password entry takes only 5 seconds. This reflects the preliminary structure of the steps in the process. Based on the process structure distribution, a hierarchical decomposition method is used to divide it into multiple single-task units. In one possible implementation, payment verification can be decomposed into three units: "password entry," "verification code recognition," and "fingerprint confirmation." The decomposition can be determined based on the independence of the operations and the clarity of the objectives. Specifically, "verification code recognition," as a single-task unit, may be separated because image loading and user identification take a long time. When extracting independent unit features from the segmentation results of single-task units, we can focus on the operation duration and interaction frequency of each unit. For example, the unit features of "verification code recognition" may be an operation duration of 20 seconds and an interaction frequency of 1, while the unit features of "fingerprint confirmation" may be an operation duration of 2 seconds and an interaction frequency of 1.

[0057] Next, independent unit features are extracted from the division results of the hierarchical decomposition strategy. These features may include the execution time of a single-task unit, the number of attempts made by the user to complete the task, the acceptance of the unit by specific user groups (such as the elderly and the visually impaired), etc. Based on these features, a set of single-task units is generated to provide a basis for subsequent process reorganization.

[0058] Furthermore, this embodiment uses a preset sorting algorithm to reorganize and adjust the set of single-task units, with the goal of optimizing the user verification experience and shortening the total operation time. The sorting algorithm can be based on user preferences, task execution efficiency, or dependencies between units. For example, the unit with the shortest execution time or the highest user completion rate is placed at the beginning of the process to quickly filter out unauthorized users or quickly confirm authorized users, reduce waiting time, and improve verification fluency. In one embodiment, if efficiency is prioritized, "fingerprint confirmation" can be ranked first because it takes the shortest time. The reorganized process becomes "fingerprint confirmation-password input-verification code recognition." It should be noted that if the total duration of the reorganized process exceeds a preset threshold, such as 15 seconds, iterative optimization is required.

[0059] If the total execution time of the restructured process exceeds a preset threshold—for example, if the overall process duration exceeds the system-defined average user-acceptable time—the restructured process structure undergoes iterative optimization. This optimization involves reevaluating the ordering logic of the unit set, adjusting the connections between single-task units, or improving efficiency through parallel processing strategies. After optimization, the iteratively optimized process sequence is segmented using a pre-defined clustering algorithm, such as K-means clustering, to identify the most efficient and intuitive verification process pattern and generate a target verification process sequence. Key features are extracted from the target verification process sequence, such as unit execution order, user completion time distribution, and error rate. Based on these extracted key features, the process completeness is assessed to ensure that all necessary verification steps are properly included and that the process is logically coherent, without omissions or redundancies. For example, the verification code loading time can be reduced to 10 seconds, generating an optimized sequence. Based on the optimized sequence results, the K-means clustering algorithm can be used to segment the sequence into two categories: "fast verification" and "complex verification." For example, "fingerprint confirmation" and "password input" are classified as quick verification, and "verification code recognition" is classified as complex verification, which ultimately generates a verification process sequence.

[0060] When the judgment result indicates that the target verification process sequence meets the integrity requirements, for example, all necessary verification steps are covered, and there are no major logical errors or execution time exceeding the threshold in the process, the target verification process sequence is used as the reorganized verification process sequence, and is prepared to further embed operation instructions to generate identity authentication process data with guidance information, ensuring that each identity authentication step is arranged reasonably, and at the same time the process structure is stable, efficient and user-friendly, making the identity authentication process both safe and convenient.

[0061] Finally, operational guidance is embedded within the restructured verification process sequence. This process involves identifying potential user confusion or operational difficulties during the verification process and providing clear, timely guidance to enhance the interactivity and usability of the user interface. For example, before the fingerprint recognition step, the system can display prompts such as "Please keep your finger flat; avoid tilting or moving it" to improve recognition success rates. Generating authentication process data with guidance in this way not only helps reduce user errors but also improves the user experience, especially for users who are less familiar with technical operations, allowing them to easily complete the authentication process and increase the popularity and usability of self-service deposit and withdrawal devices.

[0062] In this embodiment, in order to further improve the operational intuitiveness and user-friendliness of the identity authentication process, steps are provided for embedding operational guidance in the reorganized verification process sequence, which includes multiple detailed and orderly links, aiming to ensure that each user can clearly understand and successfully complete the verification process. Optionally, the steps of embedding operation instructions in the reorganized verification process sequence to generate identity authentication process data with guidance information include: generating an initial data set based on the reorganized verification process sequence, adjusting the sequence order using a preset embedding design strategy, and obtaining reorganized sequence data; extracting operation instructions from the reorganized sequence data to generate a preliminary guidance framework; based on the preliminary guidance framework, generating real-time operation instructions using preset dynamic prompt generation rules, and determining the correspondence between the real-time operation instructions and the reorganized sequence data; when it is detected that the correspondence between the real-time operation instructions and the reorganized sequence data is incomplete, extracting key information from the verification process data, adjusting the verification process using the key information, and obtaining optimized verification process data; for the optimized verification process data, clustering the guidance information using a preset clustering algorithm to generate segmented guidance data; extracting prompt generation rules from the segmented guidance data, generating target operation instructions using the prompt generation rules, and confirming the target verification process data based on the target operation instructions; verifying the target verification process data using preset integrity judgment rules to generate identity authentication process data with guidance information.

[0063] This embodiment first starts with optimizing the reorganized verification process sequence and constructs an initial data set that includes all verification steps and their related attributes. These attributes can include step identification, execution time, required user input type, etc. Specifically, this embodiment uses a pre-defined embedded design strategy to adjust the presentation method and order of the verification steps to ensure that the guidance is naturally integrated into the process and that the order of appearance of each step is reasonable. For example, in an e-commerce payment scenario, the initial data set may include three steps: "password input", "verification code verification" and "fingerprint recognition", each with a timestamp and operation status. For this initial data set, when the embedded design method adjusts the sequence order, weights can be embedded according to user operating habits. For example, assuming that the user prefers to complete fingerprint recognition quickly, this step can be placed in front, and the adjusted sequence becomes "fingerprint recognition-password input-verification code verification" to generate reorganized sequence data. This adjustment can make the process more in line with user habits.

[0064] For example, if it is found that "fingerprint recognition" is the verification method that users are most accustomed to and fastest, the policy may place it at the beginning of the process to guide users to quickly enter the verification state and generate recombinant sequence data.

[0065] This embodiment then identifies the user instructions required for each verification step from the recombined sequence data. These instructions may include specific instructions such as "touch the screen," "enter your password," or "scan your fingerprint." At this stage, based on the preliminary guidance framework and pre-set dynamic prompt generation rules, this embodiment generates more detailed real-time guidance for each verification step. For example, for the "fingerprint recognition" step, in addition to the basic instruction "scan your fingerprint," specific instructions such as "place your finger flat on the sensor and hold it steady until confirmed" may be provided. Furthermore, each real-time guidance establishes a precise one-to-one mapping with the corresponding verification step, ensuring that users receive timely and effective guidance throughout the process. If the system detects that the generated real-time guidance fails to fully cover or match the verification steps in the recombined sequence data, this embodiment immediately initiates a correction process. If the user guidance information for a key verification step is missing or inadequate, the system extracts key information such as common user errors and operational difficulties from the verification process data. Based on this information, the verification process is adjusted, adding or modifying necessary guidance to generate new, optimized verification process data. This ensures clear guidance for each step, eliminating blind spots and obstacles in user operation.

[0066] This embodiment uses a preset clustering algorithm to group the guidance information in the optimized process, with the aim of identifying verification steps with similar guidance requirements for unified management. For example, all steps involving touch screen operations may be classified into one category, and all password input-related operations may be classified into another category to generate segmented guidance data. From the segmented guidance data, this embodiment further extracts general prompt generation rules. For example, for touch screen operation steps, the rules may require "ensuring that the guidance includes finger placement and pressure requirements"; for password input steps, it may emphasize "providing clear prompts that distinguish between case, numbers and characters." These rules will be used to generate more personalized and refined target operation guidance to ensure that each guidance can help users operate smoothly to the greatest extent. On this basis, it is confirmed again whether the entire process data covers all necessary steps to generate target verification process data.

[0067] Ultimately, this embodiment conducts a comprehensive review of the target verification process data to ensure that all steps are not only logically continuous and complete, but also that there are no omissions or conflicts in user guidance information. Pre-set integrity judgment rules may include checking whether each step has corresponding guidance information, whether all guidance information complies with the principle of consistency, and whether there are redundant or invalid guidance items. Only when the target verification process data passes all integrity verification conditions can the identity authentication process data with guidance information be formally generated, ensuring that users receive the most direct and effective operational guidance at each verification step.

[0068] Step S203: extract user behavior data from the identity authentication process data, identify individual difference characteristics through a preset cluster analysis strategy, generate user behavior classification model data, and generate personalized verification step sequence data based on the user behavior classification model data.

[0069] Optionally, the steps of extracting user behavior data from the identity authentication process data, identifying individual difference features through a preset cluster analysis strategy, and generating user behavior classification model data include: extracting user behavior data from the identity authentication process data, organizing the extracted user behavior data into a structured behavior data set according to a preset field mapping rule; performing cluster analysis on the structured behavior data set through a preset cluster analysis strategy, dividing data with similar behavior patterns into the same group, and obtaining an individual difference feature set; for the individual difference feature set, using a principal component analysis method to calculate the variance contribution rate of each feature in the set, adjusting the feature weight according to the contribution rate, and obtaining weighted feature data; when comparing the weighted feature data, if there are data points that deviate from the mean by more than a predetermined standard deviation, filtering the weighted feature data and retaining valid feature data; using a preset clustering algorithm to divide the valid feature data into multiple categories to generate preliminary classification model data; extracting the feature distribution pattern of each cluster center point from the preliminary classification model data to form a dynamic generation rule to obtain optimized user behavior classification data; and verifying the matching degree between the optimized user behavior classification data and the original structured behavior data set to generate user behavior classification model data.

[0070] First, this embodiment extracts user behavior data from the identity authentication process data. This user behavior data includes but is not limited to: operation time, operation frequency, input error rate, and the verification method selected by the user; then, according to the preset field mapping rules, the extracted user behavior data is organized into a structured behavior data set to ensure the uniformity and analyzability of the data. The field mapping rules generally define how to map the original behavior data to specific data fields, such as mapping the operation time to the "timestamp" field, and the input error rate to the "error count" field. The above-mentioned structured behavior data set is then processed using a preset clustering analysis strategy, and data with similar behavior patterns are divided into the same group to obtain a set of individual difference features, that is, the behavior characteristics of different user groups.

[0071] An optional embodiment, when extracting user behavior data from verification process data with guidance information, it can be regarded as capturing the user's operating habits and preferences during the verification process. For example, in an e-commerce payment scenario, user behavior data may include "fingerprint recognition completion time", "number of password inputs" and "pause duration during verification code verification". For example, assuming that a user takes an average of 1 second for fingerprint recognition, 2 password input attempts, and a 5-second pause for verification code verification, these data are organized into a structured behavior data set through preset field mapping rules. Specifically, the field mapping rules can be defined as mapping "time consumption" to a time field and "number of attempts" to a count field, forming a set containing multi-dimensional information. When using the DBSCAN algorithm for cluster analysis, similar patterns can be grouped together according to the density of user behavior data.

[0072] In one possible implementation, assuming that the variance contribution rate of "fingerprint recognition time" is 40% and the variance contribution rate of "password input times" is 30%, after adjusting the weights according to the contribution rates, the weighted feature data will highlight the impact of key behaviors.

[0073] It should be noted that if a user's data deviates from the mean by more than three standard deviations, such as when fingerprint recognition takes 10 seconds, these outliers are filtered out and valid feature data is retained to ensure analysis accuracy. Using the K-means algorithm based on valid feature data, users can be divided into multiple categories.

[0074] For example, data might be categorized into three groups: "Quick Completion," "Repeated Attempts," and "Pauses," generating preliminary classification model data. Specifically, the cluster center characteristics for "Quick Completion" might be short processing time and a low number of attempts, while "Repeated Attempts" might indicate multiple inputs. By extracting the distribution patterns of these cluster centers, dynamic generation rules can be formed, such as "Prioritize users who take less time to complete the process and simplify the process," resulting in optimized user behavior classification data. When verifying the matching degree of this optimized user behavior classification data against the original data set, the goal is to ensure that the classification results are consistent with the actual behavior.

[0075] In one embodiment, if the match degree reaches 90% or above, final classification model data is generated. For example, a user's behavioral data showing "fingerprint recognition takes 1 second, password entry takes 1 time, and verification code verification takes 3 seconds" is classified as "Quick Completion," which aligns with the original data trends. Preferably, this classification model data can provide a basis for subsequent process optimization, enhancing the targeted user experience. It can be understood that the entire process, from extracting behavioral data to generating the final classification model, forms a complete analytical chain.

[0076] For example, by identifying "repeated users," the verification code input interface design can be adjusted to improve prompt clarity and reduce interruptions. In one embodiment, the waiting time for "pause" users is extended to reduce the timeout rate. These data-driven adjustments can effectively improve the smoothness of the verification process.

[0077] The characteristic distribution patterns of each cluster center point are extracted from the preliminary classification model data to form dynamic generation rules. These rules guide the system in dynamically adjusting the verification process based on the behavioral characteristics of different users, achieving personalized authentication. The optimized user behavior classification data is then verified for matching with the original structured behavior data set to ensure that the classification results are highly consistent with actual user behavior, thus generating user behavior classification model data.

[0078] Optionally, the step of generating personalized verification step sequence data based on user behavior classification model data includes: extracting user behavior data from the user behavior classification model data, and locating user habit preferences based on the extracted user behavior data using a decision tree algorithm to obtain personality characteristics; adjusting preset rules according to the personality characteristics, determining verification steps, and generating a verification sequence according to the verification steps, using a data analysis tool to analyze the distribution characteristics of the verification sequence to obtain preliminary sequence data; when the preliminary sequence data matches the user behavior data, adjusting the judgment logic to generate adjusted sequence data, and generating personalized verification step sequence data based on the adjusted sequence data.

[0079] The process of performing multi-factor authentication at an ATM involves analyzing, but is not limited to, the authentication method selected, the speed of the operation, the frequency of authentication, and the specific interaction methods and time spent during each authentication process. Based on the extracted user behavior data, this embodiment employs a decision tree algorithm to analyze and determine the user's habits and preferences, thereby generating personalized features. The decision tree algorithm described in this embodiment is a supervised learning method that learns the relationship between features and labels in a dataset to generate a series of rules for classifying or predicting unknown data. In this scenario, the decision tree is constructed to identify whether the user prefers a fast and convenient authentication process or a more secure and detailed verification process. This preference is determined based on the user's historical operation records, such as the frequency and time spent on fingerprint recognition and password entry, as well as whether the user has selected additional security measures in specific situations. This information is then converted into personalized features. For example, users who frequently and quickly complete fingerprint recognition might be labeled as "efficiency-oriented," while those who prefer password and fingerprint dual verification might be classified as "security-oriented." Based on the acquired personality traits, this embodiment dynamically adjusts the preset verification rules to more closely match individual habits. For users who prefer efficient operations, the system automatically optimizes the verification process, reducing steps or streamlining the process to shorten authentication time. For users who prefer higher security, additional verification layers may be added, such as SMS verification codes or facial recognition. Next, the verification steps are determined based on the adjusted rules, and the system generates a corresponding verification sequence. It should be noted that this verification sequence can include a series of identity authentication operations customized based on user behavior and preferences, such as "fingerprint recognition - confirmation" or "password entry - SMS verification - confirmation."

[0080] In addition, if the preliminary sequence data matches the user behavior data, that is, the design of the verification sequence well reflects user habits, this embodiment will further adjust the judgment logic to generate more refined sequence data. For example, if data analysis finds that the success rate and speed of the user's fingerprint scan immediately after entering the password are higher than scanning the fingerprint first and then entering the password, the system will adjust the order to prioritize password entry. This adjustment ensures that the verification process is more in line with personal habits, reducing user waiting time and erroneous operations during the authentication process, thereby generating adjusted sequence data. Subsequently, personalized verification step sequence data is generated based on the adjusted sequence data. This data will be directly applied to the user's next identity authentication, providing a customized operation experience.

[0081] Optionally, after generating personalized verification step sequence data based on user behavior classification model data, it also includes: using a timing tool to count the operation time to obtain a time statistics result, and when the time statistics result exceeds a preset time threshold, using a parallel processing algorithm to rearrange the execution order to obtain adjusted sequence data; generating time-optimized process data based on the adjusted sequence data, extracting individual behavior characteristics from the time-optimized process data, using a preset clustering algorithm to divide the behavior distribution, and obtaining user behavior classification data; adjusting the verification step logic based on the user behavior classification data to generate time-optimized verification process data.

[0082] In this embodiment, a timing tool is used to monitor and record the time it takes for users to perform each key step in the identity authentication process, aiming to quantify the efficiency of the verification process. The timing tool can be integrated into the software interface of the self-service teller machine to automatically start and stop timing. Once the identity authentication process begins, the start and end time of each verification activity is immediately recorded, including but not limited to password input, biometric matching, verification code confirmation and other steps. The introduction of the timing tool provides objective and quantitative data support for subsequent process optimization and performance evaluation. The time-consuming statistical results reflect the average time, standard deviation and possible operational bottlenecks of users to complete the verification. This embodiment accurately identifies which steps take the longest time, so as to make targeted optimization adjustments.

[0083] In this embodiment, once it is found that the operation time exceeds the preset threshold standard, it indicates that the current verification process may cause unnecessary time pressure on the user and affect the overall user experience. For this reason, this embodiment adopts a parallel processing algorithm to adjust the execution order of the verification steps, and execute independent and concurrent verification steps in parallel to shorten the total verification time. For example, this embodiment may allow the loading of a verification code image at the same time as fingerprint recognition, or pre-process biometric data during password input. In this way, while the user is waiting for one step to be completed, other verification steps are also carried out synchronously in the background, thereby achieving time overlap and significantly reducing the user's waiting time during the verification process. The adjusted sequential data not only optimizes the execution efficiency of the process, but also improves the user experience. Especially in time-sensitive scenarios, such as peak hours or emergency withdrawals, this optimization can effectively alleviate user anxiety and improve the overall operational smoothness of the system.

[0084] Furthermore, based on the optimization of the parallel processing algorithm, this embodiment converts the adjusted step execution sequence into a complete set of time-optimized verification process data to ensure that the process achieves the shortest total operation time while maintaining logical consistency. After the time-optimized process is implemented, this embodiment further analyzes the personalized behavioral characteristics of user operations through data mining technology. Here, it not only focuses on the average time and overall efficiency, but also focuses on identifying the differentiated behaviors of different users when executing the verification process. For example, some users may prefer fast fingerprint verification, while other users may prefer redundant security steps, such as dual authentication of password confirmation and facial recognition. By adopting a preset clustering algorithm, such as K-means clustering, users can be classified according to their operating habits and preferences. These classification results form the basis of user behavior distribution.

[0085] Optionally, after adjusting the verification step logic according to the user behavior classification data and generating the time-consuming and optimized verification process data, it also includes: using a threat modeling strategy to detect potential vulnerabilities in the time-consuming and optimized verification process data, and generating suspected vulnerability link distribution data based on the potential vulnerabilities; extracting key risk points from the suspected vulnerability link distribution data, determining the priority order of the key risk points, and when the priority order exceeds the preset priority threshold, adjusting the execution order of the key risk points to generate optimized process data; detecting the behavior distribution according to the optimized process data, obtaining dynamic change trend characteristics, adjusting the verification process reinforcement strategy through the dynamic change trend characteristics, and generating security-reinforced verification process data.

[0086] In this embodiment, a threat modeling strategy is introduced to evaluate the security weaknesses of the time-consuming and optimized verification process data, and to comprehensively examine potential security risks from the dimensions of tampering, denial of service, information leakage, denial of service, and privilege escalation. By analyzing the data flow and control flow in the verification process, threat modeling can identify weak links that may be exploited by attackers. The obtained distribution data of suspected vulnerability links not only includes the specific vulnerability location, but also involves the type of vulnerability and the possible scope of impact, providing precise targets for subsequent reinforcement strategies. Based on the distribution of weak links obtained by threat modeling, this embodiment can further screen out key points with higher risks, namely key risk points, to determine the priority order of each risk point. The priority judgment criteria may be based on a preset threshold, such as the number of affected users exceeding a certain proportion or the possibility of a successful attack exceeding a preset probability.

[0087] For the identified key risk points, if their priority order exceeds the preset threshold, it indicates that their security threat level is high and immediate action is required to strengthen them. In this embodiment, the system can adjust the execution order of these key risk points in the verification process to reduce the risk exposure time or facilitate earlier identification of potential attacks. For example, assuming that the facial recognition step is identified as high risk, it may be because it is susceptible to ambient light. The system may advance this step to perform more stringent identity verification at an early stage, or perform it in parallel with other verification steps (such as fingerprint recognition) to improve the overall verification security. The generated optimized process data will focus more on the effectiveness of security and protective measures to ensure that the overall security level of the system is not reduced while improving process efficiency.

[0088] By monitoring the impact of the optimized verification process on user behavior during actual execution, we can capture dynamic trends in behavioral patterns through continuous data analysis. Detecting behavioral distribution may involve measuring metrics such as the average time it takes for users to complete verification, success rates, and the acceptance of the new process by different user groups. These dynamic trends reflect the actual performance of the system after the verification process adjustments, such as user operation fluency, responsiveness, and potential changes in security threats.

[0089] In an alternative implementation, extracting behavioral data from user behavior can be viewed as capturing the user's operational habits during the verification process. For example, in an e-commerce payment verification scenario, behavioral data might include the frequency with which users select verification methods, input speed, and the time between clicks to confirm.

[0090] For example, if a user prefers fingerprint verification, has an average input time of 2 seconds, and a click confirmation interval of 3 seconds, this data can be formed into a structured set through field extraction. Specifically, "Verification method selected" and "input time" can be used as feature inputs to train the model to identify whether the user prefers a fast or cautious operation style.

[0091] In one possible implementation, the model output indicates an 85% probability that a user prefers fingerprint verification, reflecting a high-efficiency tendency in their personality traits. After extracting habitual preferences from personality traits, adjusting preset rules is key to personalized design. For example, if a user prefers quick verification, fingerprint verification can be set as the default option to reduce unnecessary steps.

[0092] Preferably, this adjustment can shorten verification time. After determining the intelligent steps, a verification sequence is generated. It is understandable that the verification sequence may be "fingerprint verification-confirmation" or "password input-verification code verification." When using the describe function in Pandas to analyze sequence distribution, specifically, the mean and distribution range of the time taken for each step in the sequence can be obtained. For example, the average time taken for the fingerprint verification step is 1.5 seconds, with a standard deviation of 0.5 seconds, which provides data support for subsequent optimization. If the preliminary sequence data matches the behavioral data, the decision logic is optimized using DecisionTreeClassifier.

[0093] In one embodiment, by adding "user abort frequency" as a new feature, the model can more accurately predict whether a user is likely to abandon verification midway. Adjusted sequence data may shorten the time window for certain steps. The correlation between individual characteristics and pre-set rules is then obtained for the adjusted sequence data. For example, if efficient users are highly correlated with simplified rules, an optimized verification sequence, such as "single-step fingerprint verification," can be generated. After extracting the sequence distribution from the optimized verification sequence, KMeans in Scikit-learn is used to segment user behavior.

[0094] Specifically, let's assume the data is divided into an "efficient group" and a "multi-step trial group." The former is characterized by a shorter time and fewer steps, while the latter is the opposite. Once the final categorized data is obtained, comparing it with habitual preferences can reveal the dynamic changes in user behavior.

[0095] For example, a user who was originally in the "high-efficiency group" may have recently taken longer to type, which may indicate a change in habits. In one possible implementation, when generating personalized sequence data, auxiliary prompts can be added for the user to reduce the difficulty of operation.

[0096] In one embodiment, if users of the "multi-step attempt group" frequently terminate, the sequence can be optimized to "step-by-step guided verification" to reduce the interruption rate. Based on the characteristics of dynamic change trends, this embodiment dynamically adjusts the reinforcement strategy of the verification process to generate process data that is safer and more adaptable to user needs. It should be noted that the adjustment here may include enhancing the security verification mechanism of certain key steps, such as by adding additional authentication factors or strengthening data encryption to ensure that even in high-risk environments, the verification process can still effectively resist attacks. At the same time, considering the user experience, the reinforcement strategy must also avoid excessively increasing the complexity of verification and try to find a balance between security and ease of use. The verification process data after security reinforcement will not only include the original verification steps, but may also be embedded with intelligent alarm systems, real-time threat perception mechanisms, and more detailed user behavior monitoring functions to ensure that the identity authentication system of self-service ATMs can continue to provide efficient and secure services in future use.

[0097] Obtain data on the sequence of personalized verification steps and use a timing tool to calculate the time taken to complete the operation, generating a timing result. If the timing result exceeds a preset threshold, the execution order is rearranged. For example, in an e-commerce payment verification scenario, the timing tool records the time taken for each step from selecting a verification method to clicking Confirm. For example, a user selects fingerprint verification, which takes 1 second, enters a password, and the entire process takes 3 seconds, resulting in a timing result of 5 seconds. If the preset threshold is 4 seconds, this result exceeds the threshold.

[0098] Step S204: adjusting the execution order of the identity verification process data based on the personalized verification step sequence data.

[0099] The personalized verification step sequence may include the user's most frequently selected verification methods and their order. For example, for a user who frequently uses fingerprint recognition and quickly completes password entry, the personalized verification step sequence may be "fingerprint recognition," then "password entry." This embodiment not only generates a personalized verification step sequence, but also adjusts the execution order of the entire identity verification process data based on the time required, operational difficulty, and security level of each step in the sequence. Based on a "user-centric" design concept, this ensures a balance between process efficiency and security. For example, for users who demonstrate high operational proficiency in the personalized verification step sequence, the system may place verification steps with shorter durations and lower error rates, such as fingerprint recognition, at the beginning of the process, while placing verification steps with longer durations and higher security levels, such as facial recognition or signature confirmation, at the end or as backup steps.

[0100] In the process of adjusting the execution order, this embodiment combines parallel processing technology and serial processing logic. For verification steps that take a long time but can be performed simultaneously (such as sending and receiving SMS verification codes), the system adopts parallel processing, thereby saving overall verification time; and for key verification steps that must be completed in sequence (such as dynamic password verification after password input), the serial processing principle is followed to ensure the continuity and security of the process.

[0101] Step S205 , analyzing the adaptability of different user groups based on the adjusted identity authentication process data, and generating a multi-identity authentication script.

[0102] In step S205, this embodiment conducts an in-depth analysis of the adjusted identity authentication process data to evaluate the degree to which different user groups (e.g., young people, the elderly, and people with disabilities) adapt to the new execution order. Analysis metrics may include, but are not limited to, operation success rate, average completion time, and user satisfaction ratings to fully understand the effectiveness of the process optimization. For example, for the elderly, analysis may show that they react more slowly to touch screens. Therefore, when generating multi-authentication scripts, the system will appropriately delay the touch response time to ensure that each elderly user has sufficient time to operate.

[0103] Based on the results of the adaptability analysis, this embodiment generates a multi-identity authentication script. The script includes an identity authentication process optimized for different user groups. The generation of the script not only takes into account the security and efficiency of identity authentication, but also incorporates humanized design ideas. For example, it provides larger fonts and high-contrast interfaces for users with poor eyesight, and provides vibration feedback instead of sound prompts for users with hearing impairments, ensuring that every user can feel convenient and respected when performing identity authentication.

[0104] By generating multiple authentication scripts, this embodiment significantly improves the coverage and inclusiveness of the identity verification process. Compared to traditional authentication methods that ignore individual differences between users, which can cause some users to abandon self-service deposit and withdrawal devices due to operational inconvenience, this embodiment, through meticulous adaptability assessments combined with advanced clustering algorithms and statistical analysis tools, provides the most appropriate authentication solution for each user group. This not only enhances the user experience but also expands the service scope of self-service deposit and withdrawal devices, making financial services more accessible and equitable.

[0105] Through the above steps, the user's historical operation data of the self-service deposit and withdrawal device can be obtained, the target interface area can be determined based on the user's historical operation data, and the simplified interface structure data can be generated based on the element distribution characteristics of the target interface area. The user's historical operation data at least includes: the number of clicks and the length of stay in each area of ​​the device interface. Based on the simplified interface structure data, the identity authentication process structure to be presented on the self-service deposit and withdrawal device is adjusted to generate a reorganized verification process sequence, and the operation instructions are embedded in the reorganized verification process sequence to generate identity authentication process data with guidance information. User behavior data is extracted from the identity authentication process data, and individual difference characteristics are identified through a preset clustering analysis strategy to generate user behavior classification model data. Personalized verification step sequence data is generated based on the user behavior classification model data, and the execution order of the identity authentication process data is adjusted based on the personalized verification step sequence data. The adaptability of different user groups is analyzed based on the adjusted identity authentication process data to generate multiple identity authentication scripts. In this embodiment, by comprehensively considering factors such as interface complexity, user behavior, and operational efficiency, a comprehensive optimization of the identity authentication process is achieved, which improves the usability and adaptability of the system while ensuring security. It can effectively improve the overall performance and user experience of multiple identity authentications, thereby solving the technical problem in related technologies that the overly complex user interface of self-service ATMs increases cognitive load and makes operation difficult.

[0106] The following describes it in detail with reference to another embodiment.

[0107] Example 2

[0108] The identity authentication process adjustment device based on the self-service deposit and withdrawal device provided in this embodiment includes multiple implementation units, each implementation unit corresponds to each implementation step in the above-mentioned embodiment 1. Its specific implementation method and beneficial effects can refer to the above-mentioned method embodiment and will not be repeated here.

[0109] Figure 3 Schematic diagram of an optional identity authentication process adjustment device based on a self-service deposit and withdrawal device according to an embodiment of the present invention. Figure 3 As shown, the identity authentication process adjustment device based on the self-service deposit and withdrawal device can include: an interface simplification unit 31, a verification process reorganization unit 32, a personalized verification step sequence generation unit 33, a verification process sequence adjustment unit 34, and a multiple identity authentication script generation unit 35.

[0110] Among them, the interface simplification unit 31 is used to obtain the user's historical operation data of the self-service deposit and withdrawal device, determine the target interface area based on the user's historical operation data, and generate simplified interface structure data based on the element distribution characteristics of the target interface area, wherein the user's historical operation data at least includes: the number of clicks and the length of stay in each area of ​​the device interface.

[0111] The verification process reorganization unit 32 is used to adjust the identity authentication process structure to be presented on the self-service deposit and withdrawal device based on the simplified interface structure data, generate a reorganized verification process sequence, and embed operation instructions in the reorganized verification process sequence to generate identity authentication process data with guidance information.

[0112] The personalized verification step sequence generation unit 33 is used to extract user behavior data from the identity authentication process data, identify individual difference characteristics through a preset cluster analysis strategy, generate user behavior classification model data, and generate personalized verification step sequence data based on the user behavior classification model data.

[0113] The verification process sequence adjustment unit 34 is used to adjust the execution sequence of the identity verification process data based on the personalized verification step sequence data.

[0114] The multiple identity authentication script generating unit 35 is used to analyze the adaptability of different user groups according to the adjusted identity authentication process data and generate a multiple identity authentication script.

[0115] The above-mentioned identity authentication process adjustment device based on the self-service deposit and withdrawal device can obtain the user's historical operation data of the self-service deposit and withdrawal device through the interface simplification unit 31, determine the target interface area based on the user's historical operation data, and generate simplified interface structure data based on the element distribution characteristics of the target interface area. The verification process reorganization unit 32 adjusts the identity authentication process structure to be presented on the self-service deposit and withdrawal device based on the simplified interface structure data, generates a reorganized verification process sequence, and embeds operation instructions in the reorganized verification process sequence to generate identity authentication process data with guidance information. The personalized verification step sequence generation unit 33 extracts user behavior data from the identity authentication process data, identifies individual difference characteristics through a preset clustering analysis strategy, generates user behavior classification model data, generates personalized verification step sequence data based on the user behavior classification model data, adjusts the execution order of the identity authentication process data based on the personalized verification step sequence data through the verification process sequence adjustment unit 34, analyzes the adaptability of different user groups according to the adjusted identity authentication process data through the multiple identity authentication script generation unit 35, and generates multiple identity authentication scripts. In this embodiment, by comprehensively considering factors such as interface complexity, user behavior, and operational efficiency, a comprehensive optimization of the identity authentication process is achieved, which improves the usability and adaptability of the system while ensuring security. It can effectively improve the overall performance and user experience of multiple identity authentications, thereby solving the technical problem in related technologies that the overly complex user interface of self-service ATMs increases cognitive load and makes operation difficult.

[0116] Optionally, the interface simplification unit includes: an interface analysis module, which is used to analyze the number of clicks and dwell time of each area of ​​the device interface in the user's historical operation data, and output user operation behavior distribution data; a first processing module, which is used to use a statistical analysis tool to process the user operation behavior distribution data, and determine the correlation index between interface complexity and cognitive burden; an interface area position confirmation module, which is used to confirm the position of the target interface area according to the number of clicks and dwell time when the correlation index exceeds a preset correlation threshold; an interface element feature acquisition module, which is used to obtain the element distribution characteristics in the target interface area and generate a complexity quantification value; a clustering division module, which is used to divide the area according to the complexity quantification value using a preset clustering algorithm to obtain simplified interface structure data.

[0117] Optionally, the verification process reorganization unit includes: a verification process extraction module, which is used to extract the original information of the verification process from the simplified interface structure data to obtain the process structure distribution; a process structure decomposition module, which is used to divide the process structure distribution into multiple single-task units using a hierarchical decomposition strategy, and extract independent unit features from the single-task unit division results to generate a single-task unit set; a unit reorganization module, which is used to reorganize and adjust the single-task unit set using a preset sorting algorithm to obtain a reorganized process structure; a process structure optimization module, which is used to iteratively optimize the reorganized process structure when the total execution time of the reorganized process structure exceeds a preset time threshold, and use a preset clustering algorithm to divide the iteratively optimized process sequence results to generate a target verification process sequence; a process sequence feature extraction module, which is used to extract key features from the target verification process sequence and judge the process integrity based on the extracted key features; a verification process sequence confirmation module, which is used to use the target verification process sequence as the reorganized verification process sequence when the judgment result indicates that the target verification process sequence meets the integrity requirements.

[0118] Optionally, the verification process reorganization unit also includes: a sequence adjustment module for generating an initial data set based on the reorganized verification process sequence, adjusting the sequence order using a preset embedding design strategy, and obtaining reorganized sequence data; an operation instruction extraction module for extracting operation instructions from the reorganized sequence data to generate a preliminary guidance framework; an operation instruction generation module for generating real-time operation instructions based on the preliminary guidance framework using preset dynamic prompt generation rules, and determining the correspondence between the real-time operation instructions and the reorganized sequence data; a verification process key information extraction module for extracting key information from the verification process data when it is detected that the correspondence between the real-time operation instructions and the reorganized sequence data is incomplete, adjusting the verification process using the key information, and obtaining optimized verification process data; a guidance information clustering module for clustering the guidance information using a preset clustering algorithm for the optimized verification process data to generate segmented guidance data; a prompt generation rule extraction module for extracting prompt generation rules from the segmented guidance data, generating target operation instructions using the prompt generation rules, and confirming the target verification process data based on the target operation instructions; a verification process integrity judgment module for verifying the target verification process data using preset integrity judgment rules to generate identity authentication process data with guidance information.

[0119] Optionally, the personalized verification step sequence generation unit includes: a user behavior data extraction module, which is used to extract user behavior data from the identity authentication process data, and organize the extracted user behavior data into a structured behavior data set according to a preset field mapping rule; a structured clustering module, which is used to perform cluster analysis on the structured behavior data set through a preset clustering analysis strategy, and divide data with similar behavior patterns into the same group to obtain an individual difference feature set; a principal component analysis module, which is used to calculate the variance contribution rate of each feature in the set using the principal component analysis method for the individual difference feature set, and adjust the feature weight according to the contribution rate to obtain weighted feature data; feature The filtering module is used to filter the weighted feature data and retain the valid feature data when there are data points in the weighted feature data that deviate from the mean by more than a predetermined standard deviation. The feature clustering module is used to divide the valid feature data into multiple categories using a preset clustering algorithm to generate preliminary classification model data. The feature distribution law extraction module is used to extract the feature distribution law of each cluster center point from the preliminary classification model data to form a dynamic generation rule to obtain optimized user behavior classification data. The data matching verification module is used to verify the matching degree of the optimized user behavior classification data with the original structured behavior data set to generate user behavior classification model data.

[0120] Optionally, the personalized verification step sequence generation unit also includes: a user habit preference judgment module, which is used to extract user behavior data from user behavior classification model data, and based on the extracted user behavior data, use a decision tree algorithm to locate user habit preferences and obtain personality characteristics; a verification step determination module, which is used to adjust preset rules according to personality characteristics, determine verification steps, and generate a verification sequence according to the verification steps, use data analysis tools to analyze the distribution characteristics of the verification sequence, and obtain preliminary sequence data; a judgment logic adjustment module, which is used to adjust the judgment logic when the preliminary sequence data matches the user behavior data, generate adjusted sequence data, and generate personalized verification step sequence data based on the adjusted sequence data.

[0121] Optionally, the identity authentication process adjustment device based on the self-service deposit and withdrawal device also includes: an operation time statistics unit, which is used to generate personalized verification step sequence data according to the user behavior classification model data, and then use a timing tool to count the operation time to obtain a time statistics result. When the time statistics result exceeds a preset time threshold, a parallel processing algorithm is used to rearrange the execution order to obtain adjusted sequence data; a personalized behavior feature extraction unit, which is used to generate time-optimized process data according to the adjusted sequence data, extract personalized behavior features from the time-optimized process data, and use a preset clustering algorithm to divide the behavior distribution to obtain user behavior classification data; a time adjustment unit, which is used to adjust the verification step logic according to the user behavior classification data to generate time-optimized verification process data.

[0122] Optionally, the identity authentication process adjustment device based on the self-service deposit and withdrawal device also includes: a potential vulnerability detection unit, which is used to adjust the verification step logic according to the user behavior classification data, generate time-optimized verification process data, and then use a threat modeling strategy to detect potential vulnerabilities in the time-optimized verification process data, and generate suspected vulnerability link distribution data; a risk point priority determination unit, which is used to extract key risk points from the suspected vulnerability link distribution data based on the potential vulnerability generation, determine the priority order of the key risk points, and adjust the execution order of the key risk points when the priority order exceeds the preset priority threshold to generate optimized process data; a verification process reinforcement unit, which is used to detect behavior distribution according to the optimized process data, obtain dynamic change trend characteristics, adjust the verification process reinforcement strategy through the dynamic change trend characteristics, and generate security-reinforced verification process data.

[0123] The above-mentioned identity authentication process adjustment device based on the self-service deposit and withdrawal device can also include a processor and a memory. The above-mentioned interface simplification unit 31, verification process reorganization unit 32, personalized verification step sequence generation unit 33, verification process sequence adjustment unit 34, multiple identity authentication script generation unit 35, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize the corresponding functions.

[0124] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the identity authentication process based on the self-service deposit and withdrawal device can be optimized by adjusting the kernel parameters.

[0125] The above-mentioned memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0126] Example 3

[0127] An embodiment of the present application may provide an electronic device, Figure 4 This is a structural block diagram of an electronic device that executes an identity authentication process adjustment method based on a self-service deposit and withdrawal device according to an embodiment of the present application. Figure 4 As shown, the electronic device may include: one or more ( Figure 4 Only one is shown) processor 402, memory 404, storage controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0128] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the method and apparatus for adjusting the identity authentication process based on a self-service deposit and withdrawal device in the embodiments of the present application. The processor executes the software programs and modules stored in the memory to perform various functional applications and data processing, thereby implementing the aforementioned method for adjusting the identity authentication process based on a self-service deposit and withdrawal device. The memory can include high-speed random access memory (RAM) and can also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory can further include memory remotely located relative to the processor, and such remote memory can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0129] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: obtain the user's historical operation data of the self-service deposit and withdrawal device, determine the target interface area based on the user's historical operation data, and generate simplified interface structure data based on the element distribution characteristics of the target interface area. The user's historical operation data at least includes: the number of clicks and the length of stay in each area of ​​the device interface; based on the simplified interface structure data, adjust the identity authentication process structure to be presented on the self-service deposit and withdrawal device, generate a reorganized verification process sequence, and embed operation instructions in the reorganized verification process sequence to generate identity authentication process data with guidance information; extract user behavior data from the identity authentication process data, identify individual difference characteristics through a preset clustering analysis strategy, generate user behavior classification model data, generate personalized verification step sequence data based on the user behavior classification model data, adjust the execution order of the identity authentication process data based on the personalized verification step sequence data, analyze the adaptability of different user groups based on the adjusted identity authentication process data, and generate multiple identity authentication scripts.

[0130] It can be understood by those skilled in the art that Figure 4 The structure shown is for illustration only, and the electronic device may also be a terminal device such as a smart phone, a tablet computer, a PDA, a mobile Internet device (MID), or a PAD. Figure 4 It does not limit the structure of the above electronic device. For example, the electronic device may also include Figure 4 More or fewer components (such as network interfaces, display devices, etc.) shown in, or with Figure 4 Different configurations shown.

[0131] A person skilled in the art will understand that all or part of the steps in the various identity authentication process adjustment methods based on self-service deposit and withdrawal devices in the above-mentioned embodiments can be completed by instructing the hardware related to the terminal device through a program. The program can be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0132] Example 4

[0133] The embodiment of the present application further provides a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the identity authentication process adjustment method based on the self-service deposit and withdrawal device provided in the first embodiment.

[0134] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is further provided, the computer-readable storage medium including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the identity authentication process adjustment method based on the self-service deposit and withdrawal device according to any one of the above-mentioned embodiments one.

[0135] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0136] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the identity authentication process adjustment method based on the self-service deposit and withdrawal device described in each embodiment of the present application.

[0137] The present application also provides a computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the identity authentication process adjustment method based on the self-service deposit and withdrawal device described in each embodiment of the present application are implemented.

[0138] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0139] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0140] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0141] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0142] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0143] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0144] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for adjusting the identity authentication process based on a self-service deposit and withdrawal device, characterized in that: include: Obtaining historical user operation data of the self-service deposit and withdrawal device, determining a target interface area based on the historical user operation data, and generating simplified interface structure data based on element distribution characteristics of the target interface area, wherein the historical user operation data includes at least: the number of clicks and dwell time on each area of ​​the device interface; Adjusting the identity authentication process structure to be presented on the self-service deposit and withdrawal device based on the simplified interface structure data to generate a reorganized authentication process sequence, and embedding operation instructions in the reorganized authentication process sequence to generate identity authentication process data with guidance information; Extracting user behavior data from the identity verification process data, identifying individual difference characteristics through a preset cluster analysis strategy, generating user behavior classification model data, and generating personalized verification step sequence data based on the user behavior classification model data; adjusting the execution order of the identity verification process data based on the personalized verification step sequence data; The adaptability of different user groups is analyzed based on the adjusted identity authentication process data, and a multi-identity authentication script is generated.

2. The method according to claim 1, characterized in that The steps of determining a target interface area according to the user historical operation data and generating simplified interface structure data based on element distribution characteristics of the target interface area include: Analyze the number of clicks and dwell time on each area of ​​the device interface in the user's historical operation data, and output user operation behavior distribution data; Using statistical analysis tools to process the user operation behavior distribution data to determine the correlation index between interface complexity and cognitive load; When the correlation index exceeds a preset correlation threshold, determining the location of the target interface area according to the number of clicks and the dwell time; Obtaining element distribution characteristics within the target interface region and generating a complexity quantification value; According to the complexity quantification value, a preset clustering algorithm is used to divide the region to obtain simplified interface structure data.

3. The method according to claim 1, characterized in that The steps of adjusting the identity authentication process structure to be presented on the self-service deposit and withdrawal device based on the simplified interface structure data to generate a reorganized authentication process sequence include: Extracting original information of the verification process from the simplified interface structure data to obtain a process structure distribution; A hierarchical decomposition strategy is adopted to divide the process structure distribution into multiple single-task units, and independent unit features are extracted from the single-task unit division results to generate a single-task unit set; Reorganize and adjust the set of single-task units using a preset sorting algorithm to obtain a reorganized process structure; When the total execution time of the reorganized process structure exceeds a preset time threshold, iteratively optimizing the reorganized process structure, and dividing the process sequence results after iterative optimization using a preset clustering algorithm to generate a target verification process sequence; Extracting key features from the target verification process sequence, and determining process integrity based on the extracted key features; In a case where the judgment result indicates that the target verification process sequence meets the integrity requirement, the target verification process sequence is used as the reorganized verification process sequence.

4. The method according to claim 1, wherein The steps of embedding operation instructions into the reorganized verification process sequence to generate identity authentication process data with guidance information include: generating an initial data set based on the reorganized verification process sequence, adjusting the sequence order using a preset embedding design strategy, and obtaining reorganized sequence data; extracting operational instructions from the recombinant sequence data to generate a preliminary guide framework; generating a real-time operation guide according to the preliminary guidance framework using a preset dynamic prompt generation rule, and determining a corresponding relationship between the real-time operation guide and the recombinant sequence data; extracting key information from the verification process data when it is detected that the correspondence between the real-time operation guide and the recombinant sequence data is incomplete, and adjusting the verification process using the key information to obtain optimized verification process data; For the optimized verification process data, clustering the guidance information using a preset clustering algorithm to generate segmented guidance data; Extracting prompt generation rules from the segmented guidance data, generating target operation instructions using the prompt generation rules, and confirming target verification process data based on the target operation instructions; The target verification process data is verified using a preset integrity judgment rule to generate the identity authentication process data with the guidance information.

5. The method according to claim 1, wherein The steps of extracting user behavior data from the identity authentication process data, identifying individual difference characteristics through a preset cluster analysis strategy, and generating user behavior classification model data include: Extracting user behavior data from the identity authentication process data, and organizing the extracted user behavior data into a structured behavior data set according to a preset field mapping rule; Performing cluster analysis on the structured behavior data set using a preset cluster analysis strategy, dividing data with similar behavior patterns into the same group, and obtaining a set of individual difference features; For the individual difference feature set, the principal component analysis method is used to calculate the variance contribution rate of each feature in the set, and the weight of the feature is adjusted according to the contribution rate to obtain weighted feature data; When the comparison shows that there are data points in the weighted feature data that deviate from the mean by more than a predetermined standard deviation, filtering the weighted feature data and retaining valid feature data; Using a preset clustering algorithm to divide the effective feature data into multiple categories to generate preliminary classification model data; Extracting the characteristic distribution pattern of each cluster center point from the preliminary classification model data to form a dynamic generation rule to obtain optimized user behavior classification data; The optimized user behavior classification data is verified for matching with the original structured behavior data set to generate the user behavior classification model data.

6. The method according to claim 1, characterized in that The step of generating personalized verification step sequence data according to the user behavior classification model data includes: Extracting user behavior data from the user behavior classification model data, and using a decision tree algorithm to locate user habit preferences based on the extracted user behavior data to obtain personality characteristics; Adjusting preset rules according to the individual characteristics, determining verification steps, generating a verification sequence according to the verification steps, and using a data analysis tool to analyze the distribution characteristics of the verification sequence to obtain preliminary sequence data; In the case where the preliminary sequence data matches the user behavior data, the judgment logic is adjusted to generate adjusted sequence data, and the personalized verification step sequence data is generated based on the adjusted sequence data.

7. The method according to claim 1, characterized in that After generating personalized verification step sequence data according to the user behavior classification model data, the method further includes: Counting the operation time using a timing tool to obtain a time statistics result; if the time statistics result exceeds a preset time threshold, using a parallel processing algorithm to rearrange the execution order to obtain adjusted sequence data; Generating time-optimized process data based on the adjusted sequential data, extracting individual behavior features from the time-optimized process data, and dividing the behavior distribution using a preset clustering algorithm to obtain user behavior classification data; The verification step logic is adjusted according to the user behavior classification data to generate time-optimized verification process data.

8. The method according to claim 7, characterized in that After adjusting the verification step logic according to the user behavior classification data and generating time-optimized verification process data, the method further includes: Using threat modeling strategies to detect potential vulnerabilities in the time-consuming and optimized verification process data, and generating distribution data of suspected vulnerability links; Extracting key risk points from the distribution data of the suspected vulnerability links, determining the priority order of the key risk points, and adjusting the execution order of the key risk points when the priority order exceeds a preset priority threshold to generate optimized process data; According to the optimized process data, the behavior distribution is detected to obtain dynamic change trend characteristics, and the verification process reinforcement strategy is adjusted according to the dynamic change trend characteristics to generate security-reinforced verification process data.

9. An identity authentication process adjustment device based on a self-service deposit and withdrawal device, characterized in that: include: An interface simplification unit is configured to obtain historical user operation data of the self-service deposit and withdrawal device, determine a target interface area based on the historical user operation data, and generate simplified interface structure data based on element distribution characteristics of the target interface area, wherein the historical user operation data includes at least the number of clicks and dwell time on each area of ​​the device interface; a verification process reorganization unit, configured to adjust the identity authentication process structure to be presented on the self-service deposit and withdrawal device based on the simplified interface structure data, generate a reorganized verification process sequence, and embed an operation guide into the reorganized verification process sequence to generate identity authentication process data with guidance information; a personalized verification step sequence generation unit, configured to extract user behavior data from the identity verification process data, identify individual difference characteristics through a preset cluster analysis strategy, generate user behavior classification model data, and generate personalized verification step sequence data based on the user behavior classification model data; a verification process sequence adjustment unit, configured to adjust the execution sequence of the identity verification process data based on the personalized verification step sequence data; The multiple identity authentication script generating unit is used to analyze the adaptability of different user groups according to the adjusted identity authentication process data and generate multiple identity authentication scripts.

10. An electronic device, characterized in that: It includes one or more processors and a memory, the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the identity authentication process adjustment method based on the self-service deposit and withdrawal device as described in any one of claims 1 to 8.

11. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for adjusting the identity authentication process based on a self-service deposit and withdrawal device according to any one of claims 1 to 8 are implemented.

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