User Key Information Recognition and Processing System Based on Real-Time Data Interaction

Abstract

The present invention relates to the technical field of user information processing, and specifically discloses a user key information recognition and processing system based on real-time data interaction. The system comprehensively analyzes the data acquisition process parameters of data interaction devices in the interaction scenario and the network parameters of the network environment to which the data interaction devices belong, dynamically matches the data transmission encryption protocol, ensures the security of user information during transmission, and at the same time determines the data processing optimization coefficient of the data interaction devices in real time, which helps to improve the efficiency of user key information transmission, and can further enhance the security of the data interaction devices in processing and optimizing user key information, achieving a double improvement in data security and transmission efficiency, and providing a solid technical support for the protection and efficient utilization of user key information.

Description

Technical Field

[0001] The present invention relates to the technical field of user information processing, and specifically to a user key information recognition and processing system based on real-time data interaction. Background Art

[0002] With the rapid development of information technology, user key data has become an indispensable important resource in modern society. In all walks of life, the real-time interaction and processing of data play a crucial role in improving operation efficiency, optimizing the decision-making process, and enhancing the user experience. Through secure data collection, real-time analysis and processing, and intelligent recognition technology, the key information of users can be quickly extracted from a large amount of data to provide strong support for business decisions.

[0003] For example, the invention patent with the publication number CN105956484B discloses a secure interaction method and system for intelligent terminals. The method includes obtaining a touch operation on the screen and collecting the touch fingerprint of the touch operation; determining whether the instruction corresponding to the touch operation involves security and privacy restrictions. If not, execute the instruction corresponding to the touch operation. If so, use the touch fingerprint for fingerprint authentication and process the instruction corresponding to the touch operation according to the result of the fingerprint authentication.

[0004] For example, the invention patent with the publication number CN117725615B discloses a production information recording method, system and medium based on two-way interactive information sharing, which relates to the technical field of shared information recording, and includes the following steps: after the trustee logs in to the sharing platform, upload the trustee's shared information and view the holder's shared information; after the holder logs in to the sharing platform, upload the holder's shared information and view the trustee's shared information; encrypt the uploaded information; input the encryption password to decrypt the encrypted ciphertext and the encrypted image.

[0005] However, in the process of implementing the embodiments of the present application, it is found that the above technologies have at least the following technical problems: in the process of collecting and processing user key information, only a unified data processing method is used to process user key information, so that the user's key information (such as identity information, etc.) is extremely easy to be obtained by unauthorized third parties in the transmission, storage or processing links, resulting in the leakage of user privacy and thus infringing on the legitimate rights and interests of users. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention provides a user key information recognition and processing system based on real-time data interaction, which can effectively solve the problems involved in the above background art.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: A user key information recognition and processing system based on real-time data interaction, including a data interaction quality evaluation module, which is used to collect the data acquisition process parameters of the data interaction device in the interaction scenario based on the real-time interaction scenario of user key information, and evaluate the data interaction quality factor of the data interaction device; a data interaction network analysis module, which is used to obtain the network parameters of the network environment to which the data interaction device belongs, analyze the network risk index of the network environment to which the data interaction device belongs, and combine the data interaction quality factor of the data interaction device to obtain the data transmission risk index of the data interaction device, thereby matching the data transmission encryption protocol of the data interaction to which the data interaction device belongs; a data interaction optimization determination module, which is used to identify and process the user key information in real-time interaction, generate a user key information set, and execute the data transmission encryption protocol of the data interaction to which the data interaction device belongs, determine the data processing optimization coefficient of the data interaction device, and thereby provide feedback on the recognition and processing of user key information; an interaction database, which is used to store the error rate boundary value, the influence factor corresponding to the clock frequency unit value, the influence factor corresponding to the average software error recovery duration unit value, the weight factor corresponding to the data interaction quality factor, the weight factor corresponding to the network risk index, the reference usage traffic, the influence factor corresponding to the network attack frequency unit value, the influence factor corresponding to the number of security vulnerabilities unit value, the data transmission encryption protocol corresponding to each data transmission risk index interval, the encryption level corresponding to each data transmission encryption protocol, the reference packet payload, the weight factor corresponding to the data transmission risk index, the influence factor corresponding to the encryption level unit value, the data processing optimization threshold, and the data processing optimization adjustment set corresponding to each data processing optimization difference interval.

[0008] As a further solution, the data acquisition process parameters of the data interaction device in the interaction scenario are collected, where the interaction scenario refers to the scenario in which the target user interacts with the data interaction device, and the data acquisition process parameters of the data interaction device within the device detection period are collected through this interaction scenario.

[0009] As a further solution, the specific analysis process of the data transmission risk index of the data interaction device is as follows: The result of multiplying the data interaction quality factor of the data interaction device by the preset weight factor corresponding to the data interaction quality factor in the interaction database is subjected to comprehensive data processing with the result of multiplying the network risk index of the network environment to which the data interaction device belongs by the preset weight factor corresponding to the network risk index in the interaction database, to obtain the data transmission risk index of the data interaction device.

[0010] As a further solution, the data transmission encryption protocol of the data interaction to which the data interaction device belongs is matched. The specific matching process is as follows: The data transmission risk index of the data interaction device is matched with the data transmission encryption protocols corresponding to the data transmission risk index ranges stored in the interaction database, so as to obtain the data transmission encryption protocol of the data interaction to which the data interaction device belongs.

[0011] As a further solution, feedback is provided for the recognition and processing of user key information. The specific feedback process is as follows: The data processing optimization coefficient of the data interaction device is compared with the data processing optimization threshold. If the data processing optimization coefficient of the data interaction device is greater than or equal to the data processing optimization threshold, subsequent processing of the user key information is performed for data interaction; if the data processing optimization coefficient of the data interaction device is less than the data processing optimization threshold, feedback is provided for the recognition and processing of the user key information, that is, the difference between the data processing optimization threshold and the data processing optimization coefficient of the data interaction device is processed to obtain the data processing optimization difference of the data interaction device, and thus the data processing optimization adjustment set of the data interaction device is matched and executed, thereby completing the feedback on the recognition and processing of the user key information.

[0012] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0013] (1) The present invention provides a user key information recognition and processing system based on real-time data interaction. By comprehensively analyzing the data acquisition process parameters of the data interaction device in the interaction scenario and the network parameters of the network environment to which the data interaction device belongs, the data transmission encryption protocol is dynamically matched to ensure the security of user information during transmission. At the same time, the data processing optimization coefficient of the data interaction device is determined in real time, which helps to improve the efficiency of user key information transmission, and can further enhance the security of the data interaction device for processing and optimizing user key information, achieving a double improvement in data security and transmission efficiency, and providing a solid technical support for the protection and efficient utilization of user key information.

[0014] (2) Through the integrated analysis of the data interaction quality factor of the data interaction device and the network risk index of the network environment to which the data interaction device belongs, the present invention matches the data transmission encryption protocol of the data interaction to which the data interaction device belongs in a personalized manner, not only significantly enhancing the security of user key information data transmission and effectively resisting potential network threats, but also taking into account the optimization of the data transmission rate of user key information to ensure the high efficiency and stability of the data circulation of user key information.

[0015] (3) By determining the data processing optimization coefficient of the data interaction device and providing feedback on the recognition and processing of user key information, the present invention greatly improves the accuracy and efficiency of user key information data processing, ensures the rapid recognition and secure transmission of user key information data, and provides users with a more reliable and efficient information service experience. Description of the Drawings

[0016] The present invention will be further described with reference to the accompanying drawings. However, the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the following drawings.

[0017] Figure 1 It is a schematic diagram of the connection of the system modules of the present invention. Detailed Embodiments

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0019] Refer to Figure 1 As shown, an embodiment of the present invention provides a technical solution: a user key information recognition and processing system based on real-time data interaction, including a data interaction quality evaluation module, a data interaction network analysis module, a data interaction optimization determination module, and an interaction database.

[0020] The data interaction quality evaluation module is connected to the data interaction network analysis module, the data interaction network analysis module is connected to the data interaction optimization determination module, and the data interaction quality evaluation module, the data interaction network analysis module, and the data interaction optimization determination module are all connected to the interaction database.

[0021] The data interaction quality evaluation module is used to collect the data acquisition process parameters of the data interaction device in the interaction scenario based on the real-time interaction scenario of user key information, and evaluate the data interaction quality factor of the data interaction device.

[0022] In this embodiment, the data acquisition process parameters of the above data interaction device include the number of execution instructions of the central processing unit to which the data interaction device belongs within the device detection period, the error recovery duration of each software, and the bit error rate. In addition to the above parameters, this example can also be applied to other parameters.

[0023] Specifically, the data acquisition process parameters of the data interaction device in the acquisition interaction scenario, where the interaction scenario refers to the scenario in which the target user interacts with the data interaction device, and the data acquisition process parameters of the data interaction device within the device detection cycle are acquired through this interaction scenario; in this embodiment, the scenario in which the above-mentioned target user interacts with the data interaction device specifically refers to: when the target user conducts new citizen authentication in the new citizen financial service area of Bank of Communications Wuxi Branch, on the premise of obtaining the authorization of the target user, the target user interacts with the data interaction device (such as an intelligent counter terminal) of Bank of Communications Wuxi Branch, and the target user inputs user key information such as name, ID number, and mobile phone number to the data interaction device (such as an intelligent counter terminal) of Bank of Communications Wuxi Branch. The target user inputs user key information to the data interaction device, which is the scenario of the target user interacting with the data interaction device.

[0024] Further, the process of evaluating the data interaction quality factor of the data interaction device is specifically as follows:

[0025] Extract the number of execution instructions of the central processing unit to which the data interaction device belongs within the device detection cycle from the data acquisition process parameters of the data interaction device within the device detection cycle, and perform a ratio process with the duration corresponding to the device detection cycle to obtain the clock frequency of the central processing unit to which the data interaction device belongs within the device detection cycle; the above-mentioned device detection cycle refers to the cycle for real-time detection of the performance of the data interaction device, specifically starting from a short period after the data interaction device senses the input instruction of the target user (for example, the target user touches the screen of the data interaction device), and the specific duration is set and managed by the device administrator; the number of execution instructions of the central processing unit to which the data interaction device belongs within the device detection cycle is extracted from the system log of the data interaction device, and the execution instructions include but are not limited to data movement instructions, arithmetic logic instructions, and data transmission instructions.

[0026] Extract the software error recovery durations of the data interaction device within the device detection cycle from the data acquisition process parameters of the data interaction device within the device detection cycle, and perform an average process to obtain the average software error recovery duration of the data interaction device within the device detection cycle; the software error recovery durations of the data interaction device within the device detection cycle are extracted from the error system log deployed in the data interaction device, and the software errors include but are not limited to failure to correctly capture or handle exceptions, slow program operation, excessive resource occupation, and memory leakage.

[0027] Extract the bit error rate of the data interaction device within the device detection cycle from the data acquisition process parameters of the data interaction device within the device detection cycle, and extract the bit error rate threshold value from the interaction database; the bit error rate of the data interaction device within the device detection cycle is extracted from the error system log deployed in the data interaction device.

[0028] Thus, a method for evaluating the data interaction quality factor of the data interaction device is comprehensively obtained. In this embodiment, the data interaction quality factor of the above-mentioned data interaction device is obtained by comprehensively analyzing the clock frequency of the central processor of the data interaction device in the device detection cycle, the average software error recovery time of the data interaction device in the device detection cycle, and the bit error rate, and is used to evaluate the data interaction quality of the data interaction device. The specific evaluation method is:

[0029]

[0030] PN is the clock frequency of the central processor of the data interaction device in the device detection cycle, indicating the frequency of instructions that the central processor of the data interaction device can execute per unit time in the device detection cycle.

[0031] TIE is the average time for data interaction equipment to recover from software errors within the equipment detection cycle. It refers to the average time required from the occurrence of a software error to the repair of the error and restoration of normal operation after a software error occurs in the data interaction equipment within the equipment detection cycle.

[0032] OL is the bit error rate of the data interaction device within the device detection cycle, which refers to the ratio of the number of bits received erroneously to the total number of bits during the data transmission process of the data interaction device within the device detection cycle. In the data interaction device, the bit error rate is used to measure the accuracy of data transmission by the device within the device detection cycle. If the bit error rate is high, it means that there are many errors in the data transmission process of the device, which may affect the integrity and reliability of the data. The bit error rate is affected by many factors, such as signal transmission distance and device performance.

[0033] JOL is the bit error rate limit value, which indicates the maximum bit error rate of the analyzed data interaction device within the device detection cycle.

[0034] e is a natural constant, pl1 is an influence factor corresponding to the clock frequency unit value preset in the interaction database, and represents the influence degree of the clock frequency unit value on the data interaction quality factor of the data interaction device. When used, the influence factor corresponding to the clock frequency unit value can be directly obtained from the interaction database, and the corresponding relationship can be a preset mapping relationship. For example, the clock frequency and the influence factor corresponding to the clock frequency unit value preset in the interaction database form a mapping set, and the real-time clock frequency is input into the mapping set to obtain the influence factor corresponding to the clock frequency unit value. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0035] Pl2 is the influence factor corresponding to the preset average software error recovery duration unit value in the interactive database, representing the numerical value of the influence degree of the average software error recovery duration unit value on the data interaction quality factor of the data interaction device. When in use, the influence factor corresponding to the average software error recovery duration unit value can be directly obtained from the interactive database, and its corresponding relationship can be a pre-set mapping relationship. For example, the average software error recovery duration and the influence factor corresponding to the preset average software error recovery duration unit value in the interactive database form a mapping set. Inputting the real-time average software error recovery duration into the mapping set to obtain the influence factor corresponding to the average software error recovery duration unit value, and the mapping relationship therein can be a one-to-one or many-to-one relationship. In this example, its value range is [0, 1].

[0036] Among them, QUA is the data interaction quality factor of the data interaction device. When the clock frequency of the central processing unit is relatively low, it indicates that the data processing speed of the central processing unit of the data interaction device is relatively slow. This may lead to limited response and recovery capabilities of the central processing unit during data interaction in the face of sudden errors or abnormal situations. Therefore, a lower clock frequency is often accompanied by a longer average error recovery duration, that is, the time required for the system to recover from an error state to a normal state increases. This may result in the loss or corruption of data packets transmitted during error recovery, thereby increasing the bit error rate. Under these conditions, the interaction of the three parameters jointly leads to a decrease in the data interaction quality of the data interaction device, reducing the security of the data interaction device when receiving critical user information and greatly weakening the credibility and availability of critical user information.

[0037] The data interaction network analysis module is used to obtain the network parameters of the network environment to which the data interaction device belongs, analyze the network risk index of the network environment to which the data interaction device belongs, and integrate the data interaction quality factor of the data interaction device to obtain the data transmission risk index of the data interaction device, thereby matching the data transmission encryption protocol of the data interaction to which the data interaction device belongs.

[0038] The above-mentioned network parameters of the network environment to which the data interaction device belongs include the number of network attacks, the number of security vulnerabilities, and the usage traffic in the network performance detection period of the network environment to which the data interaction device belongs. In this example, in addition to being applicable to the above parameters, it can also be used for other parameters.

[0039] In a specific embodiment, the present invention integrates and analyzes the data interaction quality factor of the data interaction device and the network risk index of the network environment to which the data interaction device belongs, and personalized matches the data transmission encryption protocol of the data interaction to which the data interaction device belongs. This not only significantly enhances the security of the transmission of critical user information data, effectively resists potential network threats, but also takes into account the optimization of the data transmission rate of critical user information data, ensuring the efficiency and stability of the circulation of critical user information data.

[0040] Specifically, the network risk index of the network environment to which the data interaction device belongs is analyzed as follows:

[0041] Extract the number of network attacks in the network performance detection period of the network environment to which the data interaction device belongs from the network parameters of the network environment to which the data interaction device belongs, and perform a ratio process with the duration corresponding to the network performance detection period, thereby obtaining the network attack frequency of the network environment to which the data interaction device belongs in the network performance detection period; the above-mentioned network performance detection period refers to the time period for monitoring and detecting the network environment to which the data interaction device belongs, which completely coincides with the device detection period; the number of network attacks in the network performance detection period of the network environment to which the data interaction device belongs is obtained by querying the alarm records of the intrusion detection system (IDS), where network attacks include but are not limited to distributed denial of service attacks, challenge-response attacks, SQL injection attacks, cross-site scripting attacks, and phishing attacks.

[0042] Extract the number of security vulnerabilities in the network performance detection period of the network environment to which the data interaction device belongs and the traffic used in the network performance detection period of the network environment to which the data interaction device belongs from the network parameters of the network environment to which the data interaction device belongs, and at the same time extract the reference traffic from the interaction database; the network parameters of the network environment to which the data interaction device belongs are obtained in detail through professional network tools corresponding to each parameter in the network parameters. For example, the number of security vulnerabilities in the network performance detection period of the network environment to which the data interaction device belongs is obtained by comprehensively scanning the network environment with a vulnerability scanning tool (such as the AWVS vulnerability scanner), thereby obtaining the number of security vulnerabilities, where security vulnerabilities include but are not limited to remote code execution vulnerabilities, SQL injection vulnerabilities, cross-site scripting attack (XSS) vulnerabilities, file inclusion vulnerabilities, buffer overflow vulnerabilities, and privilege escalation vulnerabilities; the traffic used in the network performance detection period of the network environment to which the data interaction device belongs is analyzed through network traffic collection technology (such as sampling flow technology).

[0043] Thus, an analysis method for comprehensively obtaining the network risk index of the network environment to which the data interaction device belongs is obtained. In this embodiment, the network risk index of the network environment to which the data interaction device belongs is obtained through comprehensive analysis of the network attack frequency, the number of security vulnerabilities, and the traffic used in the network performance detection period of the network environment to which the data interaction device belongs, and is a numerical value used to evaluate the network risk degree of the network environment to which the data interaction device belongs. The specific analysis method is as follows:

[0044]

[0045] FCA is the network attack frequency of the network environment to which the data interaction device belongs during the network performance detection period, which refers to the network attack frequency against the network environment to which the data interaction device belongs during a specific network performance detection period.

[0046] VC is the number of security vulnerabilities in the network environment to which the data interaction device belongs during the network performance detection period, which refers to the number of known security vulnerabilities existing in the network environment to which the data interaction device belongs during a specific network performance detection period. These vulnerabilities may be caused by software, hardware, or configuration errors, etc., and can be exploited by attackers to invade the system or steal users' key information.

[0047] NT is the usage traffic of the network environment to which the data interaction device belongs during the network performance detection period, which refers to the amount of data transmitted in the network environment to which the data interaction device belongs during a specific network performance detection period. This includes all data traffic in and out of the network, reflecting the activity and usage of the network environment.

[0048] ΔNT is the reference usage traffic preset in the interaction database, representing the reference value for analyzing the usage traffic of the network environment to which the data interaction device belongs during the network performance detection period.

[0049] e is the natural constant, and zp1 is the influence factor corresponding to the unit value of the network attack frequency preset in the interaction database, representing the value of the influence degree of the unit value of the network attack frequency on the network risk index of the network environment to which the data interaction device belongs. When using it, the influence factor corresponding to the unit value of the network attack frequency can be directly obtained from the interaction database, and its corresponding relationship can be a pre-set mapping relationship. For example, the network attack frequency and the influence factor corresponding to the unit value of the network attack frequency preset in the interaction database form a mapping set. Input the real-time network attack frequency into the mapping set to obtain the influence factor corresponding to the unit value of the network attack frequency. The mapping relationship therein can be a one-to-one or many-to-one relationship. In this example, its value range is [0, 1].

[0050] zp2 is the influence factor corresponding to the unit value of the number of security vulnerabilities preset in the interaction database, representing the value of the influence degree of the unit value of the number of security vulnerabilities on the network risk index of the network environment to which the data interaction device belongs. When using it, the influence factor corresponding to the unit value of the number of security vulnerabilities can be directly obtained from the interaction database, and its corresponding relationship can be a pre-set mapping relationship. For example, the number of security vulnerabilities and the influence factor corresponding to the unit value of the number of security vulnerabilities preset in the interaction database form a mapping set. Input the real-time number of security vulnerabilities into the mapping set to obtain the influence factor corresponding to the unit value of the number of security vulnerabilities. The mapping relationship therein can be a one-to-one or many-to-one relationship. In this example, its value range is [0, 1].

[0051] Among them, CRI is the network risk index of the network environment to which the data interaction device belongs. There is a direct positive correlation between the network attack frequency and the number of security vulnerabilities. Security vulnerabilities are the main entry points for data security intrusion and malicious attacks. The larger the number of vulnerabilities, the wider the potential attack surface, which attracts more attackers to try to exploit these vulnerabilities. Therefore, an increase in the number of security vulnerabilities is often accompanied by an increase in the network attack frequency. Security vulnerabilities may lead to serious consequences such as data leakage, tampering, or denial of service, which may also affect the normal use of network traffic, that is, the usage traffic deviates significantly from the reference usage traffic. A high-traffic environment may exacerbate the risk of vulnerability exploitation. The above parameters jointly constitute a comprehensive network risk assessment framework, which not only reveals the degree of network risk but also deeply analyzes the network environment from two important dimensions of stability and sustainability, helping to comprehensively understand the current situation of the network environment and providing strong support for formulating effective secure data transmission strategies.

[0052] In this exemplary embodiment, the change table of the network risk index of the network environment to which the above data interaction device belongs and its corresponding parameters is shown in Table 1:

[0053] Table 1 Change Table of the Network Risk Index of the Network Environment to which the Data Interaction Device Belongs and Its Corresponding Parameters

[0054]

[0055] In this exemplary embodiment, it is set that the value of the influence factor corresponding to the unit value of the network attack frequency is 0.9, the value of the influence factor corresponding to the unit value of the number of security vulnerabilities is 0.85, and the reference usage traffic is set to 1450 megabytes. It can be obtained from Table 1 that as the network attack frequency increases (from 0.2 times per second to 0.41 times per second), the number of security vulnerabilities increases (from 8 to 10), and the deviation of the usage traffic from the reference usage traffic also increases (from a deviation of 150 megabytes to a deviation of 400 megabytes). At this time, the network risk index also gradually increases (from 273% to 275%).

[0056] Specifically, the data transmission risk index of the data interaction device is analyzed as follows:

[0057] The result of multiplying the data interaction quality factor of the data interaction device by the weight factor corresponding to the preset data interaction quality factor in the interaction database is subjected to comprehensive data processing with the result of multiplying the network risk index of the network environment to which the data interaction device belongs by the weight factor corresponding to the preset network risk index in the interaction database, to obtain the data transmission risk index of the data interaction device, where the data transmission risk index of the data interaction device is used to subsequently match the data transmission encryption protocol.

[0058] Specifically, the data transmission risk index of the data interaction device. In this embodiment, the data transmission risk index of the data interaction device is obtained through comprehensive analysis of the data interaction quality factor of the data interaction device and the network risk index of the network environment to which the data interaction device belongs, and is a numerical value used to determine the data transmission risk degree of the data interaction device. The specific data processing method is as follows:

[0059]

[0060] QUA is the data interaction quality factor of the data interaction device, which is obtained through comprehensive analysis of the clock frequency of the central processing unit to which the data interaction device belongs during the device detection period, the average duration of software error recovery of the data interaction device during the device detection period, and the bit error rate, and is a numerical value used to evaluate the data interaction quality of the data interaction device.

[0061] CRI is the network risk index of the network environment to which the data interaction device belongs, which is obtained through comprehensive analysis of the network attack frequency, the number of security vulnerabilities, and the traffic used in the network performance detection period of the network environment to which the data interaction device belongs, and is a numerical value used to evaluate the network risk degree of the network environment to which the data interaction device belongs.

[0062] T1 is the weight factor corresponding to the preset data interaction quality factor in the interaction database, indicating the proportion of the data interaction quality factor in the data transmission risk index of the data interaction device. When used, the weight factor corresponding to the data interaction quality factor can be directly obtained from the interaction database, and its corresponding relationship can be a pre-set mapping relationship. For example, the clock frequency, the average duration of software error recovery, and the bit error rate form a mapping set with the weight factor corresponding to the preset data interaction quality factor in the interaction database, and the real-time clock frequency, the average duration of software error recovery, and the bit error rate are input into the mapping set to obtain the weight factor corresponding to the data interaction quality factor. The mapping relationship therein can be a one-to-one or many-to-one relationship. In this example, its value range is [0, 1].

[0063] T2 is the weight factor corresponding to the preset network risk index in the interaction database, indicating the proportion of the network risk index in the data transmission risk index of the data interaction device. When used, the weight factor corresponding to the network risk index can be directly obtained from the interaction database, and its corresponding relationship can be a pre-set mapping relationship. For example, the network attack frequency, the number of security vulnerabilities, and the traffic used form a mapping set with the weight factor corresponding to the preset network risk index in the interaction database, and the real-time network attack frequency, the number of security vulnerabilities, and the traffic used are input into the mapping set to obtain the weight factor corresponding to the network risk index. The mapping relationship therein can be a one-to-one or many-to-one relationship. In this example, its value range is [0, 1].

[0064] Among them, μ is the data transmission risk index of the data interaction device. There is a close association between data interaction quality and network risk. They jointly act on the data transmission process and determine the level of the data transmission risk index. Specifically, when the data interaction quality factor is low, data is more likely to have errors, delays, or losses during transmission, thus increasing the risk of data transmission. Low-quality data interaction may exacerbate the risk of the network environment. For example, frequent data transmission errors may lead to network congestion or retransmission, thereby increasing the network burden and potential security risks. In addition, security vulnerabilities or improper operations during the data interaction process may also provide opportunities for external attackers, further increasing the risk index of the network environment. At the same time, if the network environment risk index is high, it means that data may face more security threats such as external attacks and internal leaks during transmission, further exacerbating the risk of data transmission. In the complex environment of data transmission, the data interaction quality factor and the network risk index jointly constitute the multi-dimensional data basis for risk assessment and identification. This comprehensive consideration method improves the transparency and controllability of the data transmission process, thereby enhancing the efficiency and effectiveness of data transmission risk prevention and control.

[0065] Furthermore, the data transmission encryption protocol of the data interaction to which the data interaction device belongs is matched. The specific matching process is as follows:

[0066] Match the data transmission risk index of the data interaction device with the data transmission encryption protocols corresponding to each data transmission risk index interval stored in the interaction database, and thus obtain the data transmission encryption protocol of the data interaction to which the data interaction device belongs; the specific corresponding rules and values of the above data transmission encryption protocols corresponding to each data transmission risk index interval are formulated by the data security industry standard; in an exemplary embodiment, assume that the data transmission risk index of the data interaction device is 120%. Within the data transmission risk index interval [100%, 150%] stored in the interaction database, the data transmission encryption protocol corresponding to the data transmission risk index interval [100%, 150%] stored in the interaction database is the AES-256 encryption algorithm. Then, the data transmission encryption protocol of the data interaction to which the data interaction device belongs is the AES-256 encryption algorithm.

[0067] The data interaction optimization determination module is used to identify and process the user's key information in real-time interaction, generate a set of user key information, and execute the data transmission encryption protocol of the data interaction to which the data interaction device belongs, and determine the data processing optimization coefficient of the data interaction device, thereby providing feedback on the identification and processing of the user's key information.

[0068] In a specific embodiment, the present invention determines the data processing optimization coefficient of the data interaction device and provides feedback on the recognition and processing of user key information, greatly improving the accuracy and efficiency of user key information data processing, ensuring the rapid recognition and secure transmission of user key information data, and providing users with a more reliable and efficient information service experience.

[0069] Specifically, the data transmission encryption protocol for data interaction to which the execution data interaction device belongs is specifically executed as follows:

[0070] The data interaction device receives the user key information input by the target user, recognizes and processes the user key information of the real-time interaction, thereby generating a user key information set, and encrypts the user key information set through the data transmission encryption protocol for data interaction to which the data interaction device belongs to obtain a user key information encrypted set, thereby completing the execution of the data transmission encryption protocol for data interaction to which the data interaction device belongs; the fields corresponding to the user key information input by the target user are shown in Table 2:

[0071] Table 2 Fields corresponding to the user key information input by the target user

[0072]

[0073]

[0074] Table 2 shows the fields of the user key information that the target user needs to input. When the target user inputs the data corresponding to the fields involved in Table 2 into the data interaction device, the data interaction device recognizes and processes it to generate a user key information set corresponding to the target user. The user key information set is in JSON format, such as: {"Name": "Zhang San", "idNo": "123456789012345678", "mobNo": "13800000000", "isNewCitizens": "1"}. The user key information set is encrypted through the data transmission encryption protocol for data interaction to which the data interaction device belongs obtained by matching to obtain a user key information encrypted set. Encryption data processing example (taking the conversion of binary data after AES encryption to Base64 encoding as an example):

[0075] Since the encrypted data is usually in binary format, in order to be transmitted in a text environment (such as an HTTP request body), it is usually converted into a text encoding format, such as Base64, as follows:

[0076] "SGVsbG8gV29ybGQ I SGVsbG8gV29ybGQ I SGVsbG8gV29ybGQ=". (Assume this is the Base64 encoded string after AES encryption)

[0077] Thus, the data transmission encryption protocol for the data interaction to which the data interaction device belongs is completed.

[0078] Specifically, the identification and processing of the user's key information is fed back. The specific feedback process is as follows:

[0079] Compare the data processing optimization coefficient of the data interaction device with the data processing optimization threshold. If the data processing optimization coefficient of the data interaction device is greater than or equal to the data processing optimization threshold, subsequent data interaction processing is performed on the user's key information. The above data processing optimization threshold represents the minimum value of the reasonable range of the data processing optimization coefficient of the data interaction device and is extracted from the interaction database. If the data processing optimization coefficient of the data interaction device is greater than or equal to the data processing optimization threshold, it indicates that the data processing optimization degree of the data interaction device meets the requirements of the data processing optimization threshold, and subsequent data interaction processing can be directly performed. The specific subsequent data interaction processing is as follows: The data interaction device of the Wuxi Branch of Bank of Communications sends the encrypted set of user's key information to the Wuxi Public Credit Information Center to apply for relevant target user information, which is then fed back to the Wuxi Branch of Bank of Communications. The Wuxi Branch of Bank of Communications assigns a new citizen label to the target users who meet the new citizen conditions of the Wuxi Branch of Bank of Communications in the in-house system, and then accurately matches financial services for new citizen customers. Thus, the subsequent data interaction processing of the user's key information is completed. The fields corresponding to the application for relevant target user information are shown in Table 3, and the new citizen conditions of the Wuxi Branch of Bank of Communications are shown in Table 4:

[0080] Table 3 Fields Corresponding to the Application for Relevant Target User Information

[0081]

[0082] Table 4 New Citizen Conditions of the Wuxi Branch of Bank of Communications

[0083]

[0084] If the data processing optimization coefficient of the data interaction device is less than the data processing optimization threshold, feedback is provided for the recognition and processing of user critical information, that is, the difference between the data processing optimization threshold and the data processing optimization coefficient of the data interaction device is calculated to obtain the data processing optimization difference of the data interaction device. Then, the data processing optimization adjustment set of the data interaction device is matched and executed, thereby completing the feedback of the recognition and processing of user critical information. The above situation where the data processing optimization coefficient of the data interaction device is less than the data processing optimization threshold indicates that the data processing optimization degree of the data interaction device does not meet the requirements of the data processing optimization threshold, and feedback is required. In an exemplary embodiment, assume that the data processing optimization difference of the data interaction device is 50%. The data processing optimization adjustment sets corresponding to each data processing optimization difference interval are stored in the interaction database. The data processing optimization difference of 50% is within the data processing optimization difference interval [30%, 60%] stored in the interaction database. The data processing optimization adjustment set corresponding to the data processing optimization difference interval [30%, 60%] includes replacing the data transmission encryption protocol, such that the encryption level of the replaced data transmission encryption protocol is two levels higher than that of the existing data transmission encryption protocol. Then, the data processing optimization adjustment set of the data interaction device includes the data transmission encryption protocol, such that the encryption level of the replaced data transmission encryption protocol is two levels higher than that of the existing data transmission encryption protocol. Thus, the data transmission encryption protocol of the user critical information set is replaced. The security of the banking system itself is usually relatively high, and a relatively complete security protection system has been established. Therefore, in the face of the problem of insufficient data processing optimization, based on the existing security foundation, through targeted adjustment and optimization measures, the security and efficiency of data processing can be rapidly improved. Thus, after executing the data processing optimization adjustment set of the data interaction device, the data processing optimization degree of the data interaction device can meet the requirements of the data processing optimization threshold, thereby completing the feedback of the recognition and processing of user critical information and continuing the subsequent data interaction processing of user critical information.

[0085] Further, the specific determination process for the data processing optimization coefficient of the data interaction device is as follows:

[0086] Match the data transmission encryption protocol of the data interaction to which the data interaction device belongs with the encryption levels corresponding to each data transmission encryption protocol stored in the interaction database, thereby obtaining the encryption level of the data interaction to which the data interaction device belongs. The encryption levels corresponding to each data transmission encryption protocol stored in the above interaction database are specifically determined by the data security industry standard in terms of corresponding values and rules. In an exemplary embodiment, the data transmission encryption protocol of the data interaction to which the data interaction device belongs is the AES-256 encryption algorithm, and the encryption level corresponding to the AES-256 encryption algorithm is level 5. Then, the encryption level of the data interaction to which the data interaction device belongs is level 5.

[0087] Obtain the packet payload of the encrypted set of the key information of the user to which the data interaction device belongs, and at the same time extract the reference packet payload from the interaction database; the packet payload of the encrypted set of the key information of the user to which the data interaction device belongs is measured by a network packet analysis tool (such as Wireshark).

[0088] Based on the data transmission risk index of the data interaction device, a method for determining the data processing optimization coefficient of the data interaction device is obtained through comprehensive analysis. In this embodiment, the data processing optimization coefficient of the data interaction device is obtained through comprehensive analysis of the data transmission risk index of the data interaction device, the packet payload of the encrypted set of the key information of the user to which the data interaction device belongs, and the encryption level of the data interaction to which the data interaction device belongs. It is a numerical value used to determine the degree of data processing optimization of the data interaction device. The specific determination method is as follows:

[0089]

[0090] μ is the data transmission risk index of the data interaction device, which is obtained through comprehensive analysis of the data interaction quality factor of the data interaction device and the network risk index of the network environment to which the data interaction device belongs. It is a numerical value used to determine the degree of data transmission risk of the data interaction device.

[0091] LN is the packet payload of the encrypted set of the key information of the user to which the data interaction device belongs, which refers to the amount of the actual transmitted data content in the encrypted set of the key information of the user to which the data interaction device belongs.

[0092] BE is the encryption level of the data interaction to which the data interaction device belongs, which refers to the encryption strength level of the data transmission encryption protocol. The encryption level has a positive correlation with the data processing optimization coefficient.

[0093] ΔLN is the preset packet reference payload in the interaction database, which represents the reference value for analyzing the packet payload of the encrypted set of the key information of the user to which the data interaction device belongs.

[0094] e is the natural constant, and F is the weight factor corresponding to the preset data transmission risk index in the interaction database, representing the proportion of the data transmission risk index in the data processing optimization coefficient of the data interaction device. When in use, the weight factor corresponding to the data transmission risk index can be directly obtained from the interaction database, and its corresponding relationship can be a pre-set mapping relationship. For example, the data interaction quality factor of the data interaction device and the network risk index of the network environment to which the data interaction device belongs form a mapping set with the weight factor corresponding to the preset data transmission risk index in the interaction database. By inputting the real-time data interaction quality factor of the data interaction device and the network risk index of the network environment to which the data interaction device belongs into the mapping set, the weight factor corresponding to the data transmission risk index can be obtained. The mapping relationship therein can be a one-to-one or many-to-one relationship. In this example, its value range is [0, 1].

[0095] pm is the influence factor corresponding to the preset encryption level unit value in the interaction database, representing the value of the influence degree of the encryption level unit value on the data processing optimization coefficient of the data interaction device. When in use, the influence factor corresponding to the encryption level unit value can be directly obtained from the interaction database, and its corresponding relationship can be a pre-set mapping relationship. For example, the encryption level forms a mapping set with the influence factor corresponding to the preset encryption level unit value in the interaction database. By inputting the real-time encryption level into the mapping set, the influence factor corresponding to the encryption level unit value can be obtained. The mapping relationship therein can be a one-to-one or many-to-one relationship. In this example, its value range is [0, 1].

[0096] Among them, β is the data processing optimization coefficient of the data interaction device. When the data transmission risk index is relatively large, in order to ensure the security of data transmission, a data transmission encryption protocol with a higher encryption level will be adopted. Although this encryption measure effectively enhances the protection ability of data during transmission, at the same time, attention also needs to be paid to the impact of the encryption operation on the packet payload. Specifically, if the packet payload after encryption significantly deviates from the preset packet reference payload in the interaction database, it may increase the burden on the data transmission link, reduce the transmission efficiency, and even cause network congestion or delay. Secondly, the abnormal change of the packet payload may also become a potential security threat indicator, which can be exploited by malicious attackers to identify or target the encrypted data stream for attack. In addition, for the receiving end, processing a large number of packets with a payload deviating from the standard payload may also increase the computational burden, affecting the data processing efficiency and real-time performance. Therefore, while enhancing the data encryption level to cope with high data transmission risks, it is necessary to closely pay attention to the impact of the encryption operation on the packet payload, ensure that the packet payload remains within a reasonable range, so as to guarantee the security of the user's critical data transmission and also improve the data processing efficiency of the data interaction device.

[0097] The interactive database is used to store the bit error rate definition value, the impact factor corresponding to the clock frequency unit value, the impact factor corresponding to the average software error recovery duration unit value, the weight factor corresponding to the data interaction quality factor, the weight factor corresponding to the network risk index, the reference usage traffic, the impact factor corresponding to the network attack frequency unit value, the impact factor corresponding to the number of security vulnerabilities unit value, the data transmission encryption protocol corresponding to each data transmission risk index interval, the encryption level corresponding to each data transmission encryption protocol, the reference payload of the data packet, the weight factor corresponding to the data transmission risk index, the impact factor corresponding to the encryption level unit value, the data processing optimization threshold, and the data processing optimization adjustment set corresponding to each data processing optimization difference interval.

[0098] In a specific embodiment, the present invention provides a user key information recognition and processing system based on real-time data interaction. By comprehensively analyzing the data acquisition process parameters of the data interaction device in the interaction scenario and the network parameters of the network environment to which the data interaction device belongs, the data transmission encryption protocol is dynamically matched to ensure the security of user information during transmission. At the same time, the data processing optimization coefficient of the data interaction device is determined in real time, which helps to improve the efficiency of user key information transmission and further enhance the security of the data interaction device for processing and optimizing user key information, achieving a double improvement in data security and transmission efficiency, and providing a solid technical support for the protection and efficient utilization of user key information.

[0099] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of the present technology can make various modifications or supplements to the described specific embodiments or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should fall within the protection scope of the present invention.

Claims

1. A user key information recognition and processing system based on real-time data interaction, characterized in that, Including: A data interaction quality assessment module, which is used to collect the data acquisition process parameters of data interaction devices in the real-time interaction scenario based on the user's key information, and evaluate the data interaction quality factor of the data interaction devices; A data interaction network analysis module, which is used to obtain the network parameters of the network environment to which the data interaction device belongs, analyze the network risk index of the network environment to which the data interaction device belongs, and comprehensively obtain the data transmission risk index of the data interaction device based on the data interaction quality factor of the data interaction device, so as to match the data transmission encryption protocol of the data interaction to which the data interaction device belongs; A data interaction optimization determination module, which is used to identify and process the user's key information in real-time interaction, generate a user key information set, and execute the data transmission encryption protocol of the data interaction to which the data interaction device belongs, and determine the data processing optimization coefficient of the data interaction device, so as to feedback on the identification and processing of the user's key information; An interaction database, which is used to store the error rate boundary value, the influencing factor corresponding to the clock frequency unit value, the influencing factor corresponding to the average software error recovery duration unit value, the weight factor corresponding to the data interaction quality factor, the weight factor corresponding to the network risk index, the reference usage traffic, the influencing factor corresponding to the network attack frequency unit value, the influencing factor corresponding to the number of security vulnerabilities unit value, the data transmission encryption protocol corresponding to each data transmission risk index interval, the encryption level corresponding to each data transmission encryption protocol, the packet reference payload, the weight factor corresponding to the data transmission risk index, the influencing factor corresponding to the encryption level unit value, the data processing optimization threshold, and the data processing optimization adjustment set corresponding to each data processing optimization difference interval; The specific determination process of the data processing optimization coefficient of the said data interaction device is as follows: Match the data transmission encryption protocol of the data interaction to which the data interaction device belongs with the encryption levels corresponding to the data transmission encryption protocols stored in the interaction database, so as to obtain the encryption level of the data interaction to which the data interaction device belongs; Obtain the packet payload of the user key information encryption set to which the data interaction device belongs, and at the same time extract the packet reference payload from the interaction database, and comprehensively obtain the data transmission risk index of the data interaction device, so as to comprehensively obtain the determination method of the data processing optimization coefficient of the data interaction device; The specific feedback process of the said feedback on the identification and processing of the user's key information is as follows: Compare the data processing optimization coefficient of the data interaction device with the data processing optimization threshold. If the data processing optimization coefficient of the data interaction device is greater than or equal to the data processing optimization threshold, perform subsequent data interaction processing on the user's key information; If the data processing optimization coefficient of the data interaction device is less than the data processing optimization threshold, feedback on the identification and processing of the user's key information, that is, perform a difference process on the data processing optimization threshold and the data processing optimization coefficient of the data interaction device to obtain the data processing optimization difference of the data interaction device, so as to match and execute the data processing optimization adjustment set of the data interaction device, thus completing the feedback on the identification and processing of the user's key information.

2. The user key information recognition and processing system based on real-time data interaction according to claim 1, wherein: Collect the data acquisition process parameters of the data interaction device in the acquisition interaction scenario, where the interaction scenario refers to the scenario in which the target user interacts with the data interaction device, and the data acquisition process parameters of the data interaction device within the device detection period are collected through this interaction scenario.

3. The user key information recognition and processing system based on real-time data interaction according to claim 1, wherein: Evaluate the data interaction quality factor of the data interaction device. The specific evaluation process is as follows: Extract the number of execution instructions of the central processing unit to which the data interaction device belongs within the device detection period from the data acquisition process parameters of the data interaction device within the device detection period, and perform a ratio process with the duration corresponding to the device detection period to obtain the clock frequency of the central processing unit to which the data interaction device belongs within the device detection period. Extract the software error recovery durations of the data interaction device within the device detection period from the data acquisition process parameters of the data interaction device within the device detection period, and perform an average process to obtain the average software error recovery duration of the data interaction device within the device detection period. Extract the bit error rate of the data interaction device within the device detection period from the data acquisition process parameters of the data interaction device within the device detection period, and extract the bit error rate threshold value from the interaction database, thereby obtaining the evaluation method for the data interaction quality factor of the data interaction device.

4. The user key information recognition and processing system based on real-time data interaction according to claim 1, characterized in that: Analyze the data transmission risk index of the data interaction device. The specific analysis process is as follows: Perform comprehensive data processing on the result of multiplying the data interaction quality factor of the data interaction device by the weight factor corresponding to the preset data interaction quality factor in the interaction database and the result of multiplying the network risk index of the network environment to which the data interaction device belongs by the weight factor corresponding to the preset network risk index in the interaction database to obtain the data transmission risk index of the data interaction device.

5. The user key information recognition and processing system based on real-time data interaction according to claim 4, wherein: The specific data processing method for the data transmission risk index of the data interaction device is as follows: Where, μ is the data transmission risk index of the data interaction device, QUA is the data interaction quality factor of the data interaction device, CRI is the network risk index of the network environment to which the data interaction device belongs, T1 is the weight factor corresponding to the preset data interaction quality factor in the interaction database, and T2 is the weight factor corresponding to the preset network risk index in the interaction database.

6. The user key information recognition and processing system based on real-time data interaction according to claim 5, wherein: Analyze the network risk index of the network environment to which the data interaction device belongs. The specific analysis process is as follows: Extract the number of network attacks of the network environment to which the data interaction device belongs within the network performance detection period from the network parameters of the network environment to which the data interaction device belongs, and perform a ratio process with the duration corresponding to the network performance detection period to obtain the network attack frequency of the network environment to which the data interaction device belongs within the network performance detection period. Extract the number of security vulnerabilities of the network environment to which the data interaction device belongs within the network performance detection period and the traffic used by the network environment to which the data interaction device belongs within the network performance detection period from the network parameters of the network environment to which the data interaction device belongs, and at the same time extract the reference traffic from the interaction database, thereby obtaining the analysis method for the network risk index of the network environment to which the data interaction device belongs.

7. The user key information recognition and processing system based on real-time data interaction according to claim 1, characterized in that: Match the data transmission encryption protocol to which the data interaction device belongs. The specific matching process is as follows: Match the data transmission risk index of the data interaction device with the data transmission encryption protocols corresponding to the data transmission risk index ranges stored in the interaction database, thereby obtaining the data transmission encryption protocol for the data interaction to which the data interaction device belongs.

8. The user key information recognition and processing system based on real-time data interaction according to claim 1, characterized in that: The specific execution process of implementing the data transmission encryption protocol for the data interaction to which the data interaction device belongs is as follows: The data interaction device receives the user key information input by the target user, identifies and processes the user key information of the real-time interaction, thereby generating a user key information set, and encrypts the user key information set through the data transmission encryption protocol for the data interaction to which the data interaction device belongs to obtain a user key information encrypted set, thereby completing the implementation of the data transmission encryption protocol for the data interaction to which the data interaction device belongs.

Images