User key information identification processing system based on data real-time interaction

Abstract

The invention relates to the technical field of user information processing, and particularly discloses a user key information identification processing system based on data real-time interaction, which comprehensively analyzes data acquisition process parameters of data interaction equipment in an interaction scene and network parameters of a network environment to which the data interaction equipment belongs, and dynamically matches a data transmission encryption protocol. The security of the user information in the transmission process is ensured, and the data processing optimization coefficient of the data interaction equipment is judged in real time, so that the transmission efficiency of the user key information is improved, the security of the data interaction equipment for processing and optimizing the user key information can be enhanced, the dual improvement of the data security and the transmission efficiency is realized, and the user experience is improved. And a solid technical support is provided for protection and efficient utilization of key information of the user.

Description

Technical Field

[0001] The present invention relates to the technical field of user information processing, and in particular to a user key information identification 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 and important resource in modern society. In all walks of life, real-time interaction and processing of data plays a vital role in improving operational efficiency, optimizing decision-making processes and enhancing user experience. Through secure data collection, real-time analysis and processing, and intelligent recognition technology, key user information can be quickly extracted from massive data to provide strong support for business decisions.

[0003] For example, the invention patent with announcement number CN105956484B announces a secure interaction method and system for a smart terminal, which includes obtaining touch operations on the screen and collecting touch fingerprints of the touch operations; determining whether the instructions corresponding to the touch operations involve security and privacy restrictions, and if not, executing the instructions corresponding to the touch operations; if so, using the touch fingerprints for fingerprint authentication, and processing the instructions corresponding to the touch operations according to the results of the fingerprint authentication.

[0004] For example, the invention patent with announcement number CN117725615B announces a production information recording method, system and medium based on two-way interactive information sharing, which involves the field of shared information recording technology, including the following steps: after the trustee logs into the sharing platform, the trustee's shared information is uploaded and the holder's shared information is viewed; after the holder logs into the sharing platform, the holder's shared information is uploaded and the trustee's shared information is viewed; the uploaded information is encrypted; and the encryption password is input to decrypt the encrypted ciphertext and encrypted image.

[0005] However, in the process of implementing the embodiments of the present application, it was found that the above-mentioned technology has at least the following technical problems: in the existing process of collecting and processing user key information, the user key information is only processed through a unified data processing method, which makes it easy for the user's key information (such as identity information, etc.) to be easily obtained by unauthorized third parties during transmission, storage or processing, resulting in user privacy leakage, thereby infringing the user's legitimate rights and interests. Summary of the invention

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

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a user key information identification and processing system based on real-time data interaction, including a data interaction quality assessment module, which is used for collecting data collection process parameters of data interaction devices in the interaction scenario based on the real-time interaction scenario of user key information, and evaluating 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 comprehensively analyze 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 of the data interaction device; a data interaction optimization determination module, which is used to identify and process the user key information of real-time interaction, generate a user key information set, and execute the data interaction of the data interaction device. The interactive database is used to store the bit error rate limit value, the impact factor corresponding to the unit value of the clock frequency, the impact factor corresponding to the unit value of the average software error recovery time, 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 unit value of the network attack frequency, the impact factor corresponding to the unit value of the number of security vulnerabilities, 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 unit value of the encryption level, 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 collection process parameters of the data interaction device in the collection interaction scenario, wherein the interaction scenario refers to the scenario in which the target user interacts with the data interaction device for data, and the data collection process parameters of the data interaction device within the device detection cycle are collected through this interaction scenario.

[0009] As a further solution, the data transmission risk index of the data interaction device has the following specific analysis process: the result of multiplying the data interaction quality factor of the data interaction device by the weight factor corresponding to the data interaction quality factor preset 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 network risk index preset in the interaction database are subjected to comprehensive data processing 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 belonging to the data interaction device is matched, and the specific matching process is: matching the data transmission risk index of the data interaction device with the data transmission encryption protocol corresponding to each data transmission risk index interval stored in the interaction database, thereby deriving the data transmission encryption protocol of the data interaction belonging to the data interaction device.

[0011] As a further solution, the identification and processing of the user's key information is fed back, and the specific feedback process is: 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, the user key information is subjected to subsequent data interaction processing; if the data processing optimization coefficient of the data interaction device is less than the data processing optimization threshold, the identification and processing of the user's key information is fed back, that is, the data processing optimization threshold and the data processing optimization coefficient of the data interaction device are subjected to difference processing to obtain the data processing optimization difference of the data interaction device, thereby matching and executing the data processing optimization adjustment set of the data interaction device, thereby completing the identification and processing feedback of the user's 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 identification 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 the transmission process. 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 enhance the security of the data interaction device's optimization of user key information processing, thereby achieving a dual improvement in data security and transmission efficiency, and providing solid technical support for the protection and efficient use of user key information.

[0014] (2) 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 personalizes the data transmission encryption protocol of the data interaction of the data interaction device. This not only significantly enhances the security of the data transmission of user key information and effectively resists potential network threats, but also takes into account the optimization of the data transmission rate of user key information, thereby ensuring the efficient and stable circulation of user key information data.

[0015] (3) The present invention greatly improves the accuracy and efficiency of user key information data processing by determining the data processing optimization coefficient of the data interaction device and providing feedback on the identification and processing of user key information, thereby ensuring the rapid identification and secure transmission of user key information data and providing users with a more reliable and efficient information service experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0017] Figure 1 It is a schematic diagram of system module connection of the present invention. DETAILED DESCRIPTION

[0018] 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 described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

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

[0020] The data interaction quality assessment 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 assessment 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 data collection process parameters of data interaction devices in the real-time interaction scenario based on user key information, and evaluate the data interaction quality factor of the data interaction device.

[0022] In this embodiment, the data collection process parameters of the above-mentioned data interaction device include the number of instructions executed by the central processor of the data interaction device within the device detection cycle, the recovery time of each software error and the bit error rate. In addition to being applicable to the above-mentioned parameters, this example can also be used for other parameters.

[0023] Specifically, the data collection process parameters of the data interaction device in the collection interaction scenario, wherein the interaction scenario refers to the scenario in which the target user interacts with the data interaction device for data, and the data collection process parameters of the data interaction device within the device detection cycle are collected through this interaction scenario; in the present embodiment, the scenario in which the above-mentioned target user interacts with the data interaction device for data, specifically refers to: when the target user is authenticated as a new citizen in the New Citizen Financial Service Zone of Wuxi Branch of Bank of Communications, the target user interacts with the data interaction device of Wuxi Branch of Bank of Communications (such as a smart counter terminal) on the premise of obtaining the target user's authorization, and the target user enters key user information such as name, ID number and mobile phone number into the data interaction device of Wuxi Branch of Bank of Communications (such as a smart counter terminal), and the target user enters key user information into the data interaction device, which is the scenario in which the target user interacts with the data interaction device.

[0024] Furthermore, the data interaction quality factor of the data interaction device is evaluated, and the specific evaluation process is as follows:

[0025] The number of instructions executed by the central processor of the data interaction device during the device detection cycle is extracted from the data collection process parameters of the data interaction device during the device detection cycle, and the number is ratio-processed with the duration corresponding to the device detection cycle to obtain the clock frequency of the central processor of the data interaction device during the device detection cycle; the above-mentioned device detection cycle refers to a cycle for real-time detection of the performance of the data interaction device, which specifically starts from a short period of time after the data interaction device senses the target user input instruction (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 instructions executed by the central processor of the above-mentioned data interaction device during the device detection cycle is extracted from the system log of the data interaction device, wherein the execution instructions include but are not limited to data movement instructions, arithmetic logic instructions and data transmission instructions.

[0026] The software error recovery time of the data interaction device within the device detection cycle is extracted from the data collection process parameters of the data interaction device within the device detection cycle, and the average software error recovery time of the data interaction device within the device detection cycle is obtained by mean processing; the software error recovery time of the above-mentioned data interaction device within the device detection cycle is extracted from the error system log deployed in the data interaction device, wherein the software errors include but are not limited to failure to correctly capture or handle exceptions, slow program running, excessive resource usage and memory leakage.

[0027] The bit error rate of the data interaction device during the device detection period is extracted from the data collection process parameters of the data interaction device during the device detection period, and the bit error rate boundary value is extracted from the interaction database; the bit error rate of the above-mentioned data interaction device during the device detection period 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, pl 1 It is the influence factor corresponding to the clock frequency unit value preset in the interaction database, which 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] pl 2It is the impact factor corresponding to the unit value of the average software error recovery time preset in the interaction database, which represents the degree of influence of the unit value of the average software error recovery time on the data interaction quality factor of the data interaction device. When used, the impact factor corresponding to the unit value of the average software error recovery time can be directly obtained from the interaction database, and the corresponding relationship can be a preset mapping relationship. For example, the average software error recovery time and the impact factor corresponding to the unit value of the average software error recovery time preset in the interaction database form a mapping set, and the real-time average software error recovery time is input into the mapping set to obtain the impact factor corresponding to the unit value of the average software error recovery time. The mapping relationship can be one-to-one or many-to-one. 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 processor is low, it indicates that the central processor of the data interaction device processes data relatively slowly, which may lead to the response and recovery capabilities of the central processor being limited when facing sudden errors or abnormal situations during the data interaction process. Therefore, a lower clock frequency is often accompanied by a longer average error recovery time, that is, the time required for the system to recover from an error state to a normal state increases, which may cause the loss or damage of data packets transmitted during error recovery, thereby increasing the bit error rate. Under this condition, the interaction of the three parameters jointly leads to a decrease in the data interaction quality of the data interaction device, which reduces the security of the data interaction device when receiving user key information, greatly weakening the credibility and availability of user key 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 comprehensively consider 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 network parameters of the network environment to which the above-mentioned data interaction device belongs include the number of network attacks, the number of security vulnerabilities and the used traffic of the network environment to which the data interaction device belongs within the network performance detection cycle. In addition to being applicable to the above-mentioned parameters, this example can also be used for other parameters.

[0039] In a specific embodiment, the present invention personalizes the data transmission encryption protocol for data interaction of the data interaction device through 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. This not only significantly enhances the security of data transmission of user key information and effectively resists potential network threats, but also takes into account the optimization of the data transmission rate of user key information, thereby ensuring the efficient and stable circulation of user key information data.

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

[0041] The number of network attacks on the network environment to which the data interaction device belongs within the network performance detection period is extracted from the network parameters of the network environment to which the data interaction device belongs, and the ratio is processed with the duration corresponding to the network performance detection period to obtain the frequency of network attacks on the network environment to which the data interaction device belongs within 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 overlaps with the device detection period; the number of network attacks on the network environment to which the data interaction device belongs within the network performance detection period is obtained by querying the alarm records of the intrusion detection system (IDS), wherein the 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] The number of security vulnerabilities of the network environment to which the data interaction device belongs during the network performance detection period and the usage traffic of the network environment to which the data interaction device belongs during the network performance detection period are extracted from the network parameters of the network environment to which the data interaction device belongs, and the reference usage traffic is extracted from the interaction database at the same time; the network parameters of the network environment to which the above-mentioned data interaction device belongs are exhaustively acquired through professional network tools corresponding to each parameter in the network parameters, for example, the number of security vulnerabilities of the network environment to which the above-mentioned data interaction device belongs during the network performance detection period is obtained by comprehensively scanning the network environment through a vulnerability scanning tool (such as AWVS vulnerability scanner), thereby obtaining the number of security vulnerabilities, wherein the security vulnerabilities include but are not limited to remote code execution vulnerabilities, SQL injection vulnerabilities, cross-site scripting (XSS) vulnerabilities, file inclusion vulnerabilities, buffer overflow vulnerabilities and privilege escalation vulnerabilities; the usage traffic of the network environment to which the above-mentioned data interaction device belongs during the network performance detection period is analyzed through network traffic collection technology (such as sampling stream technology).

[0043] Thus, a method for analyzing the network risk index of the network environment to which the data interaction device belongs is comprehensively obtained. In this embodiment, the network risk index of the network environment to which the data interaction device belongs is obtained by comprehensively analyzing the network attack frequency, the number of security vulnerabilities and the usage flow of the network environment to which the data interaction device belongs during the network performance detection cycle, and is used to evaluate the value of the network risk degree of the network environment to which the data interaction device belongs. The specific analysis method is:

[0044]

[0045] FCA is the frequency of network attacks on the network environment to which the data interaction device belongs during the network performance detection cycle. It refers to the frequency of network attacks on the network environment to which the data interaction device belongs during a specific network performance detection cycle.

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

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

[0048] ΔNT is a reference usage flow preset in the interaction database, which represents a reference value of the usage flow of the network environment to which the data interaction device belongs during the network performance detection cycle.

[0049] e is a natural constant, zp 1 It is the impact factor corresponding to the unit value of the network attack frequency preset in the interactive database, which represents the numerical value of the impact degree of the unit value of the network attack frequency on the network risk index of the network environment to which the data interactive device belongs. When used, the impact factor corresponding to the unit value of the network attack frequency can be directly obtained from the interactive database, and the corresponding relationship can be a preset mapping relationship. For example, the network attack frequency and the impact factor corresponding to the unit value of the network attack frequency preset in the interactive database form a mapping set, and the real-time network attack frequency is input into the mapping set to obtain the impact factor corresponding to the unit value of the network attack frequency. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0050] z 2 It is the impact factor corresponding to the unit value of the number of security vulnerabilities preset in the interaction database, which represents the degree of influence 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 used, the impact factor corresponding to the unit value of the number of security vulnerabilities can be directly obtained from the interaction database, and the corresponding relationship can be a preset mapping relationship. For example, the number of security vulnerabilities and the impact factors corresponding to the unit values ​​of the number of security vulnerabilities preset in the interaction database form a mapping set, and the real-time number of security vulnerabilities is input into the mapping set to obtain the impact factor corresponding to the unit value of the number of security vulnerabilities. The mapping relationship can be one-to-one or many-to-one. 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 frequency of network attacks and the number of security vulnerabilities. Security vulnerabilities are the main entry points for data security intrusions and malicious attacks. The more vulnerabilities there are, the wider the potential attack surface is, thereby attracting 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 frequency of network attacks. Security vulnerabilities may lead to serious consequences such as data leakage, tampering or denial of service. These consequences may also affect the normal use of network traffic, that is, the deviation of traffic from the reference traffic is large. A high-traffic environment may increase the risk of vulnerabilities being exploited. The above parameters together constitute a comprehensive network risk assessment framework, which not only reveals the degree of network risk, but also conducts an in-depth analysis of the network environment from two important dimensions: stability and sustainability. It helps to fully understand the current status of the network environment and provide strong support for the formulation of effective secure data transmission strategies.

[0052] In this exemplary embodiment, a table showing changes in 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. Changes in network risk index and corresponding parameters of the network environment to which data interaction equipment belongs

[0054]

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

[0056] Specifically, the data transmission risk index of the data interaction device is analyzed in the following process:

[0057] The result of multiplying the data interaction quality factor of the data interaction device by the weight factor corresponding to the data interaction quality factor preset 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 network risk index preset in the interaction database are subjected to comprehensive data processing to obtain the data transmission risk index of the data interaction device, wherein the data transmission risk index of the data interaction device is used for subsequent matching of the data transmission encryption protocol.

[0058] Specifically, the data transmission risk index of the data interaction device, in this embodiment, is obtained by 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 used to determine the value of the data transmission risk degree of the data interaction device. The specific data processing method is:

[0059]

[0060] QUA is the data interaction quality factor of the data interaction device. It is obtained by comprehensive analysis of the clock frequency of the central processor of the data interaction device during the device detection cycle, the average software error recovery time of the data interaction device during the device detection cycle, and the bit error rate. It 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. It is obtained by comprehensively analyzing the frequency of network attacks, the number of security vulnerabilities, and the usage traffic of the network environment to which the data interaction device belongs during the network performance detection cycle. It is a numerical value used to evaluate the degree of network risk of the network environment to which the data interaction device belongs.

[0062] T 1 It is the weight factor corresponding to the data interaction quality factor preset in the interaction database, which indicates the proportion of the data interaction quality factor to the data transmission risk index of the data interaction device. When in use, the weight factor corresponding to the data interaction quality factor can be directly obtained from the interaction database, and the corresponding relationship can be a preset mapping relationship. For example, the clock frequency, the average time for software error recovery, and the bit error rate form a mapping set with the weight factor corresponding to the data interaction quality factor preset in the interaction database. The real-time clock frequency, the average time for 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 can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0063] T 2 It is the weight factor corresponding to the network risk index preset in the interactive database, which represents the proportion of the network risk index to the data transmission risk index of the data interactive device. When in use, the weight factor corresponding to the network risk index can be directly obtained from the interactive database, and the corresponding relationship can be a preset mapping relationship. For example, the network attack frequency, the number of security vulnerabilities, and the usage flow are used with the weight factors corresponding to the network risk index preset in the interactive database to form a mapping set. The real-time network attack frequency, the number of security vulnerabilities, and the usage flow are input into the mapping set to obtain the weight factor corresponding to the network risk index. The mapping relationship can be one-to-one or many-to-one. 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 correlation between the data interaction quality and the network risk. They work together in the data transmission process and determine the level of the data transmission risk index. Specifically, when the data interaction quality factor is low, the data is more prone to errors, delays or losses during the transmission process, thereby increasing the risk of data transmission. Low-quality data interaction may aggravate the risk of the network environment. For example, frequent data transmission errors may cause network congestion or retransmission, thereby increasing the network burden and potential security risks. In addition, security vulnerabilities or improper operations in the data interaction process may also provide opportunities for external attackers, thereby increasing the risk index of the network environment. At the same time, if the network environment risk index is high, it means that the data may face more external attacks, internal leaks and other security threats during the transmission process, further aggravating the risk of data transmission. In the complex environment of data transmission, the data interaction quality factor and the network risk index together constitute a multi-dimensional data basis for risk assessment and identification. This comprehensive consideration method improves the transparency and controllability of the data transmission process, thereby improving the efficiency and effectiveness of data transmission risk prevention and control.

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

[0066] The data transmission risk index of the data interaction device is matched with the data transmission encryption protocol corresponding to each data transmission risk index interval stored in the interaction database, thereby obtaining the data transmission encryption protocol of the data interaction to which the data interaction device belongs; the specific corresponding rules and values ​​of the data transmission encryption protocol corresponding to each of the above-mentioned data transmission risk index intervals are formulated by the data security industry standards; in an example embodiment, assuming 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 for 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 user key information of real-time interaction, generate a user key information set, and execute the data transmission encryption protocol of the data interaction belonging to the data interaction device, determine the data processing optimization coefficient of the data interaction device, and thereby provide feedback on the identification and processing of user key information.

[0068] In a specific embodiment, the present invention greatly improves the accuracy and efficiency of user key information data processing by determining the data processing optimization coefficient of the data interaction device and providing feedback on the identification and processing of user key information, thereby ensuring the rapid identification 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 of the data interaction device is executed in the following specific execution process:

[0070] 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 of the data interaction to which the data interaction device belongs, to obtain a user key information encryption set, thereby completing the data transmission encryption protocol of the 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 key user information entered by the target user

[0072]

[0073]

[0074] Table 2 shows the fields of user key information that the target user needs to enter. When the target user enters the data corresponding to the fields involved in Table 2 into the data interaction device, the data interaction device identifies 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","isNewCit i zens":"1"}. The user key information set is encrypted by matching the data transmission encryption protocol of the data interaction to which the data interaction device belongs to obtain a user key information encryption set. An example of encrypted data processing (taking the conversion of binary data encrypted by AES into 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 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 a Base64 encoded string after AES encryption)

[0077] This completes the data transmission encryption protocol for data interaction of the data interaction device.

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

[0079] 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, the user key information is subjected to subsequent data interaction processing. 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, which 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 the 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 key information to the Wuxi Public Credit Information Center, applies for relevant target user information, and feeds it back to the Wuxi Branch of Bank of Communications. The Wuxi Branch of Bank of Communications assigns new citizen labels to the target users who meet the new citizen conditions of the Wuxi Branch of Bank of Communications in the internal system, and then accurately matches financial services for new citizen customers, thereby completing the subsequent data interaction processing of the user key information. 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-related target user information

[0081]

[0082] Table 4 New Citizens' Conditions of Bank of Communications Wuxi Branch

[0083]

[0084] If the data processing optimization coefficient of the data interaction device is less than the data processing optimization threshold, feedback is given to the recognition processing of the user's key information, that is, the data processing optimization threshold and the data processing optimization coefficient of the data interaction device are subjected to difference processing to obtain the data processing optimization difference of the data interaction device, thereby matching and executing the data processing optimization adjustment set of the data interaction device, thereby completing the recognition processing feedback of the user's key information; if the data processing optimization coefficient of the data interaction device is less than the data processing optimization threshold, it indicates that the data processing optimization degree of the data interaction device does not meet the data processing optimization threshold requirement, and feedback is required. In an example embodiment, assuming that the data processing optimization difference of the data interaction device is 50%, the data processing optimization adjustment set corresponding to each data processing optimization difference interval is stored in the interaction database, and the data processing optimization difference of 50% is stored in the interaction database within the data processing optimization difference interval [30%, 60%], and the data corresponding to the data processing optimization difference interval [30%, 60%] The processing optimization adjustment set includes replacing the data transmission encryption protocol so that the encryption level of the replaced data transmission encryption protocol is two levels higher than the encryption level of the existing data transmission encryption protocol. The data processing optimization adjustment set of the data interaction device includes the data transmission encryption protocol, so that the encryption level of the replaced data transmission encryption protocol is two levels higher than the encryption level of the existing data transmission encryption protocol. The data transmission encryption protocol of the user key information set is replaced. The security of the banking system itself is usually high, and a relatively complete security protection system has been established. Therefore, when faced with 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. Therefore, 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 data processing optimization threshold requirements, thereby completing the identification, processing and feedback of user key information, and continuing to perform subsequent data interaction processing on user key information.

[0085] Furthermore, the data processing optimization coefficient of the data interaction device is determined by:

[0086] The data transmission encryption protocol of the data interaction belonging to the data interaction device is matched with the encryption level corresponding to each data transmission encryption protocol stored in the interaction database, thereby obtaining the encryption level of the data interaction belonging to the data interaction device; the encryption level corresponding to each data transmission encryption protocol stored in the above-mentioned interaction database, the specific corresponding values ​​and rules are formulated by the data security industry standards. In an example embodiment, the data transmission encryption protocol of the data interaction belonging to the data interaction device 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 belonging to the data interaction device is level 5.

[0087] The data packet payload of the user key information encryption set belonging to the data interaction device is obtained, and the data packet reference payload is extracted from the interaction database; the data packet payload of the user key information encryption set belonging to the above data interaction device is measured by a network data packet analysis tool (such as Wi reshark).

[0088] A method for determining a data processing optimization coefficient of a data interaction device is obtained by comprehensively analyzing the data transmission risk index of the data interaction device, the data packet payload of the user key information encryption set of the data interaction device, and the encryption level of the data interaction of the data interaction device. The method is used to determine the value of the degree of optimization of data processing by 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 by 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 data packet payload of the user key information encryption set belonging to the data interaction device, which refers to the amount of data content actually transmitted in the user key information encryption set belonging to the data interaction device.

[0092] BE is the encryption level of data interaction of the data interaction device, which refers to the encryption strength level of the data transmission encryption protocol. The encryption level is positively correlated with the data processing optimization coefficient.

[0093] ΔLN is a reference payload of a data packet preset in the interaction database, and represents a reference value of the payload of a data packet of a user key information encryption set to which the analysis data interaction device belongs.

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

[0095] pm is the impact factor corresponding to the encryption level unit value preset in the interaction database, which represents the degree of influence of the encryption level unit value on the data processing optimization coefficient of the data interaction device. When used, the impact factor corresponding to the encryption level unit value can be directly obtained from the interaction database, and the corresponding relationship can be a preset mapping relationship. For example, the encryption level and the impact factor corresponding to the encryption level unit value preset in the interaction database form a mapping set, and the real-time encryption level is input into the mapping set to obtain the impact factor corresponding to the encryption level unit value. The mapping relationship can be one-to-one or many-to-one. 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 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 capability of data during transmission, it is also necessary to pay attention to the impact of encryption operations on the data packet payload. Specifically, if the encrypted data packet payload significantly deviates from the preset data packet reference payload in the interaction database, it may increase the burden on the data transmission link, reduce transmission efficiency, and even cause network congestion or delay. Secondly, abnormal changes in data packet payload may also become a potential security threat indicator, which can be used by malicious attackers to identify or launch attacks against encrypted data streams. In addition, for the receiving end, processing a large number of data packets that deviate from the standard payload may also increase the computational burden and affect data processing efficiency and real-time performance. Therefore, while improving the data encryption level to cope with high data transmission risks, it is necessary to pay close attention to the impact of encryption operations on data packet payload to ensure that the data packet payload remains within a reasonable range to ensure the security of user key data transmission and improve the data processing performance of data interaction equipment.

[0097] The interactive database is used to store the bit error rate limit value, the impact factor corresponding to the unit value of the clock frequency, the impact factor corresponding to the unit value of the average software error recovery time, 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 unit value of the network attack frequency, the impact factor corresponding to the unit value of the number of security vulnerabilities, 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 unit value of the encryption level, 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 identification 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 the transmission process. 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 enhance the security of the data interaction device's optimization of user key information processing, thereby achieving a dual improvement in data security and transmission efficiency, and providing solid technical support for the protection and efficient use of user key information.

[0099] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A user key information identification and processing system based on real-time data interaction, characterized in that: include: The data interaction quality evaluation module is used to collect data collection process parameters of data interaction devices in real-time interaction scenarios based on key user information, and evaluate the data interaction quality factors of data interaction devices; 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 comprehensively consider 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; The data interaction optimization determination module is used to identify and process the key information of users in real-time interaction, generate a set of key user information, and execute the data transmission encryption protocol of the data interaction of the data interaction device, determine the data processing optimization coefficient of the data interaction device, and thus provide feedback on the identification and processing of the key user information; An interactive database is used to store bit error rate limit values, impact factors corresponding to clock frequency unit values, impact factors corresponding to software error recovery average duration unit values, weight factors corresponding to data interaction quality factors, weight factors corresponding to network risk indexes, reference usage traffic, impact factors corresponding to network attack frequency unit values, impact factors corresponding to security vulnerability quantity unit values, data transmission encryption protocols corresponding to each data transmission risk index interval, encryption levels corresponding to each data transmission encryption protocol, reference data packet payload, weight factors corresponding to data transmission risk index, impact factors corresponding to encryption level unit values, data processing optimization thresholds, and data processing optimization adjustment sets corresponding to each data processing optimization difference interval.

2. The user key information identification and processing system based on real-time data interaction according to claim 1 is characterized by: The data collection process parameters of the data interaction device in the collection interaction scenario, wherein the interaction scenario refers to a scenario in which a target user interacts with the data interaction device for data, and the data collection process parameters of the data interaction device within a device detection cycle are collected through this interaction scenario.

3. The user key information identification and processing system based on real-time data interaction according to claim 1 is characterized by: The specific evaluation process of evaluating the data interaction quality factor of the data interaction device is as follows: Extract the number of instructions executed by the central processor of the data interaction device in the device detection cycle from the data collection process parameters of the data interaction device in the device detection cycle, perform ratio processing with the duration corresponding to the device detection cycle, and obtain the clock frequency of the central processor of the data interaction device in the device detection cycle; Extracting the duration of software error recovery of the data interaction device in the device detection cycle from the data collection process parameters of the data interaction device in the device detection cycle, and performing mean processing to obtain the average duration of software error recovery of the data interaction device in the device detection cycle; The bit error rate of the data interaction device during the equipment detection period is extracted from the data collection process parameters of the data interaction device during the equipment detection period, and the bit error rate boundary value is extracted from the interaction database, thereby comprehensively obtaining an evaluation method for the data interaction quality factor of the data interaction device.

4. The user key information identification and processing system based on real-time data interaction according to claim 1 is characterized in that: The data transmission risk index of the data interaction device is specifically analyzed as follows: The data interaction quality factor of the data interaction device is multiplied by the weight factor corresponding to the data interaction quality factor preset in the interaction database, and the network risk index of the network environment to which the data interaction device belongs is multiplied by the weight factor corresponding to the network risk index preset in the interaction database, and comprehensive data processing is performed to obtain the data transmission risk index of the data interaction device.

5. The user key information identification and processing system based on real-time data interaction according to claim 4 is characterized in that: The data transmission risk index of the data interaction device, and the specific data processing method are as follows: Among them, 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 data interaction quality factor preset in the interaction database, and T2 is the weight factor corresponding to the network risk index preset in the interaction database.

6. The user key information identification and processing system based on real-time data interaction according to claim 5 is characterized by: The network risk index of the network environment to which the data interaction device belongs is specifically analyzed as follows: Extracting 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 performing ratio processing 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 within the network performance detection period; The number of security vulnerabilities of the network environment to which the data interaction device belongs during the network performance detection period and the usage traffic of the network environment to which the data interaction device belongs during the network performance detection period are extracted from the network parameters of the network environment to which the data interaction device belongs, and the reference usage traffic is extracted from the interaction database, thereby comprehensively obtaining an analysis method for the network risk index of the network environment to which the data interaction device belongs.

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

8. The user key information identification and processing system based on real-time data interaction according to claim 1 is characterized by: The data transmission encryption protocol for data interaction of the execution data interaction device is specifically executed as follows: The data interaction device receives user key information input by the target user, identifies and processes the user key information of real-time interaction, thereby generating a user key information set, and encrypts the user key information set through the data transmission encryption protocol of the 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 of the data interaction to which the data interaction device belongs.

9. The user key information identification and processing system based on real-time data interaction according to claim 1 is characterized by: The data processing optimization coefficient of the data interaction device is determined by the following specific process: Matching the data transmission encryption protocol of the data interaction to which the data interaction device belongs with the encryption level 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 data packet payload of the encrypted set of user key information belonging to the data interaction device is obtained, and the reference data packet payload is extracted from the interaction database. The data transmission risk index of the data interaction device is comprehensively calculated, thereby comprehensively obtaining a method for determining the data processing optimization coefficient of the data interaction device.

10. The user key information identification and processing system based on real-time data interaction according to claim 1 is characterized in that: The feedback of the identification and processing of the key information of the user is performed, and the specific feedback process is as follows: Compare the data processing optimization coefficient of the data interaction device with the data processing optimization threshold, and if the data processing optimization coefficient of the data interaction device is greater than or equal to the data processing optimization threshold, perform data interaction follow-up processing on the user key information; If the data processing optimization coefficient of the data interaction device is less than the data processing optimization threshold, the recognition processing of the user's key information is fed back, that is, the data processing optimization threshold and the data processing optimization coefficient of the data interaction device are subjected to difference processing to obtain the data processing optimization difference of the data interaction device, thereby matching and executing the data processing optimization adjustment set of the data interaction device, thereby completing the recognition processing feedback of the user's key information.

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