Data security transmission method for intelligent biological information collector

Abstract

The present invention discloses a data security transmission method for an intelligent bio-information collector, and relates to the field of data processing technology. The method includes: grading the data collected in real time according to the bio-information sensitivity classification rules, extracting low-sensitivity data packets as transmission verification indicators, and high-sensitivity data packets as key encrypted data packets; performing data dimension reduction and noise encryption on high-sensitivity data packets through edge computing nodes; after link security verification using transmission verification indicators, while performing encrypted data transmission, real-time monitoring of the transmission link and abnormal warning. The present invention solves the technical problem that the prior art lacks graded processing for data of different sensitivities and security verification of the transmission link, which affects the transmission efficiency and security, and achieves the technical effect of graded processing according to the sensitivity of bio-information, and link security verification before transmission, thereby improving the efficiency and security of data transmission.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a data security transmission method for an intelligent biological information collector. Background Art

[0002] With the rapid development of smart devices, biometric information collectors have been widely used in the fields of identity authentication, health monitoring and public safety. These devices collect personal biometric information (such as fingerprints, irises, facial features, etc.) to identify users. However, the transmission of biometric information faces severe security challenges, especially in network transmission environments, where data may be subject to risks such as interception, tampering, and replay attacks.

[0003] Existing data transmission methods often use traditional encryption technology for protection, but lack hierarchical processing for data of different sensitivities, which affects transmission efficiency and increases security risks. In addition, the security detection and abnormal warning functions of the transmission link in the existing technology are not perfect enough, making it difficult to respond to sudden abnormalities in the network in a timely manner, and unable to effectively ensure the security of highly sensitive data during transmission. Summary of the invention

[0004] The present application provides a data security transmission method for an intelligent bio-information collector, which is used to solve the technical problem that the prior art lacks hierarchical processing for data of different sensitivities and security verification of transmission links, which affects transmission efficiency and security.

[0005] The present application provides a data security transmission method for an intelligent bio-information collector, the method comprising: through a data collection module, based on a preset bio-information sensitivity classification rule, performing sensitivity grading on the data collected by the real-time device, and performing graded packaging according to the sensitivity grading results to obtain a plurality of graded data packets; based on the plurality of graded data packets, extracting low-sensitivity data packets as transmission verification indicators, and extracting high-sensitivity data packets as key encrypted data packets; setting an edge computing node in the intelligent device, and performing encryption processing on the key encrypted data packets through the edge computing node, including performing data dimensionality reduction and noise encryption, to obtain an encrypted transmission data set; using the transmission verification indicator to perform link security verification, securely transmitting the encrypted transmission data set, and during the data transmission process, performing real-time monitoring and abnormal warning on the transmission link through a network monitoring module.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The data security transmission method for intelligent bio-information collector provided by the present application relates to the field of data processing technology. The data collected in real time is graded according to the bio-information sensitivity classification rules, and the data is packaged into low-sensitivity and high-sensitivity data packets. The low-sensitivity data packets are extracted as transmission verification indicators, and the high-sensitivity data packets are used as key encrypted data packets. The edge computing nodes in the intelligent device encrypt the high-sensitivity data packets to generate encrypted transmission data sets. After link security verification using the transmission verification indicators, encrypted data transmission is performed. At the same time, the transmission link is monitored in real time through the network monitoring module and abnormal warnings are issued. The technical problem of the lack of graded processing for data of different sensitivities and security verification of the transmission link in the prior art, which affects the transmission efficiency and security, is solved. The graded processing according to the sensitivity of the bio-information is realized, and the link security verification is performed before transmission, so as to improve the technical effect of data transmission efficiency and security. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A schematic diagram of a data security transmission method for an intelligent biological information collector provided in an embodiment of the present application;

[0010] Figure 2 A schematic diagram of a process for obtaining a plurality of graded data packets by performing graded packaging according to sensitivity grading results in a data security transmission method for an intelligent bio-information collector provided in an embodiment of the present application. DETAILED DESCRIPTION

[0011] The present application provides a data security transmission method for an intelligent bio-information collector, which is used to solve the technical problem that the prior art lacks hierarchical processing for data of different sensitivities and security verification of transmission links, which affects transmission efficiency and security.

[0012] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0013] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0014] Examples, such as Figure 1 As shown, the present application provides a data security transmission method for an intelligent biological information collector, the method comprising:

[0015] P10: Through the data acquisition module, based on the preset bio-information sensitivity classification rules, the data collected by the real-time device is graded for sensitivity, and graded and packaged according to the sensitivity grading results to obtain multiple graded data packets.

[0016] Further, such as Figure 2 As shown, step P10 of the embodiment of the present application also includes:

[0017] P11: Read historical transmission data and attack mode data, and preset multiple sensitivity classification rules according to biometric information type, privacy level and usage scenario; P12: Based on the usage scenario of the target information acquisition device, traverse the multiple sensitivity classification rules, match and obtain the target sensitivity classification rules; P13: According to the target sensitivity classification rules, establish a sensitivity classification model, perform sensitivity classification on the real-time device collected data, and obtain sensitivity classification results, which include multiple biometric information data with sensitivity level identification; P14: According to the sensitivity level identification, the real-time device collected data is graded and packaged to obtain multiple graded data packets.

[0018] It should be understood that, through the data acquisition module, based on the preset bio-information sensitivity classification rules, the data collected by the device is graded according to sensitivity, and graded and packaged according to the grading results, thereby obtaining multiple graded data packets.

[0019] First, read the historical transmission data and attack mode data. The historical transmission data can reflect the transmission characteristics and problems that the device has encountered in the past transmission process, while the attack mode data records the possible attack methods and patterns against the biometric information data. By analyzing these data, potential threats in transmission can be effectively predicted, providing an important basis for subsequent sensitivity classification. According to the type of biometric information (such as facial recognition, iris scanning, etc.), privacy level (i.e., the sensitivity of the data) and usage scenarios (such as security inspection sites, medical scenarios, etc.), multiple sensitivity classification rules are preset. These rules define the sensitivity of different types of data for subsequent classification processing.

[0020] Next, in the usage scenario of the target information collection device, by traversing multiple preset sensitivity classification rules, find the target sensitivity classification rule that best suits the current scenario. The target sensitivity classification rule is determined based on the current usage scenario and the need for real-time data collection, ensuring that the classification rule can dynamically adapt to different usage scenarios. For example, the privacy protection strength of facial recognition data in public places may be lower than the protection of iris data in medical scenarios.

[0021] Once the target sensitivity classification rules are determined, a sensitivity classification model will be established based on these rules to process the biometric data collected by the device in real time. The sensitivity classification model classifies the collected data according to the rules, and each data block is assigned a sensitivity level identifier to identify different privacy protection requirements. For example, facial feature data may be marked as low sensitivity, while complete iris data may be marked as high sensitivity. Through this process, the device can perform detailed privacy level classification for different types of biometric information based on predefined sensitivity rules while collecting data.

[0022] Finally, the data collected by the device is packaged in different levels according to the sensitivity level identifier. Each data packet is packaged into different graded data packets according to its sensitivity identifier. For example, a low-sensitivity data packet can contain relatively public information such as facial contours, while a high-sensitivity data packet contains biometric information with a higher privacy level such as fingerprints and irises. The purpose of graded packaging is to ensure that different protection measures can be used for data of different sensitivities during transmission. Data packets with lower sensitivity can be used for link verification, while data packets with higher sensitivity require additional encryption processing to ensure the security of data in transmission.

[0023] P20: Based on the multiple graded data packets, extract low-sensitivity data packets as transmission verification indicators, and extract high-sensitivity data packets as key encryption data sets.

[0024] Furthermore, step P20 of the embodiment of the present application also includes:

[0025] P21: Sort the multiple graded data packets according to the sensitivity level, and extract multiple low-sensitivity data packets; P22: Traverse the multiple graded data packets respectively for the multiple low-sensitivity data packets, perform data association analysis, and based on the association analysis results, extract the low-sensitivity data packets that are weakly associated with the multiple graded data packets as transmission verification indicators; P23: With reference to the transmission verification indicators, use the remaining high-sensitivity data packets as key encryption data sets.

[0026] Optionally, low-sensitivity data packets are extracted from multiple graded data packets as transmission verification indicators, while high-sensitivity data packets are used as key encrypted data sets for special protection and processing, aiming to ensure the security and efficiency of data transmission.

[0027] First, sort the data packets based on their sensitivity level. The sensitivity level reflects the privacy protection requirements of the data. The more sensitive the data is, the easier it is to leak user privacy and the more encryption protection is needed. After the sorting is completed, extract multiple low-sensitivity data packets. These low-sensitivity data packets usually contain less privacy information and have less impact on user security, so they are suitable for use as verification data. The extracted low-sensitivity data packets may include less sensitive information such as facial contours and environmental parameters, which will not cause significant privacy risks even if they are intercepted during transmission.

[0028] After extracting low-sensitivity data packets, these low-sensitivity data packets are traversed and further data association analysis is performed with multiple hierarchical data packets. Data association analysis is a method for discovering potential relationships between data packets. By analyzing the dependencies, repetitions, or correlations between data packets, it is ensured that low-sensitivity data packets have no direct or significant associations with other high-sensitivity data packets. This analysis aims to find low-sensitivity data packets that are weakly associated with other data packets. The so-called weak association means that there is no important logical connection between the low-sensitivity data packet and other sensitive data packets. Even if the low-sensitivity data packet is leaked during transmission, the attacker cannot deduce highly sensitive data from it. Therefore, selecting weakly associated low-sensitivity data packets as verification data can not only avoid the exposure of highly sensitive data, but also perform link verification while ensuring transmission security. After completing the extraction of low-sensitivity data packets, the remaining high-sensitivity data packets are extracted and used as the key encrypted data set.

[0029] High-sensitivity data packets contain more privacy-sensitive data, such as complete facial features, iris information, fingerprint data, etc., which directly involve user privacy and security. In order to ensure that these highly sensitive data will not be leaked during transmission, the system will assign them additional encryption protection measures. Key encrypted data sets are usually processed by strong encryption algorithms (such as AES-256 or RSA) and technologies such as differential privacy to ensure that even if intercepted during transmission, attackers cannot recover the original sensitive information. The protection strategy for these highly sensitive data packets needs to take into account the sensitivity and importance of the data, and dynamically adjust according to preset security standards and privacy rules.

[0030] P30: An edge computing node is set in the smart device, and encryption processing of the key encrypted data set is performed through the edge computing node, including data dimension reduction and noise encryption, to obtain an encrypted transmission data set. The edge computing node includes a data dimension reduction channel and a noise encryption channel, and is connected to the data acquisition module.

[0031] Specifically, an edge computing node is set up in the smart device, which is responsible for the localized preliminary processing of the collected highly sensitive data. The edge computing node is directly connected to the data acquisition module of the device and can receive the bioinformatics data collected by the device in real time. Compared with traditional cloud computing methods, edge computing nodes have the advantages of low latency and high efficiency, and are particularly suitable for bioinformatics processing scenarios with high requirements for real-time and privacy.

[0032] The data dimension reduction channel included in the edge computing node is an important part of the system. Its main function is to reduce the dimension of high-dimensional biometric data (such as iris, fingerprint images, and facial features). Dimensionality reduction reduces the dimension of data by extracting key information from the data and removing redundant information. It can not only reduce the amount of data transmitted, but also speed up the efficiency of subsequent encryption processing. Commonly used dimensionality reduction algorithms such as principal component analysis (PCA) and linear discriminant analysis (LDA) can effectively compress high-dimensional data into smaller data sets and ensure that the data still retains enough features for subsequent identification and analysis. After dimensionality reduction processing, not only the amount of data is greatly reduced, but also because dimensionality reduction itself can hide some details, it further improves the privacy protection of the data.

[0033] The noise encryption channel is another key component of the edge computing node. The data after dimensionality reduction will enter the noise encryption channel for encryption based on differential privacy. The core idea of ​​noise encryption is to add random noise to the original data to cover up sensitive information in the data. Differential privacy technology introduces randomness to ensure that even if an attacker obtains a part of the data, the original data cannot be accurately inferred. Commonly used noise encryption mechanisms include Laplace mechanism and Gaussian mechanism. The appropriate noise generation algorithm is selected according to different data types and sensitivities. During the noise encryption process, the system adjusts the noise intensity according to the sensitivity of each data packet to balance data privacy and availability.

[0034] Furthermore, the data dimension reduction of the key encrypted data set is performed through the data dimension reduction channel. Step P30 of the embodiment of the present application further includes:

[0035] P31: Determine the key identification features based on preset key biometric indicators; P32: Generate a first dimensionality reduction coefficient based on the data types of multiple high-sensitivity data packets in the key encrypted data set; P33: Generate a second dimensionality reduction coefficient based on the sensitivity levels of multiple high-sensitivity data packets in the key encrypted data set; P34: According to the first dimensionality reduction coefficient, the second dimensionality reduction coefficient, and the key identification features, match the corresponding dimensionality reduction sub-units to perform data dimensionality reduction processing on multiple high-sensitivity data packets to obtain a dimensionality reduction data set.

[0036] In a possible embodiment of the present application, the dimension reduction process of the key encrypted data set is further optimized. This step performs a refined dimension reduction operation on the highly sensitive data packet through a data dimension reduction channel to improve the efficiency of data transmission and the strength of privacy protection.

[0037] First, based on the preset key biometric indicators, the system automatically determines the key identification features in the biometric information. Key identification features refer to feature points or feature areas with high recognition capabilities in biometric information data, such as the feature points of the eyes, nose and mouth in facial recognition, the pattern features in fingerprint recognition, or the unique texture information in iris recognition. Through the preset biometric indicators, the system can filter out these key features, thereby ensuring that the data after dimensionality reduction still retains the most recognizable parts, providing basic support for subsequent data processing and analysis.

[0038] Next, based on the data types of multiple highly sensitive data packets in the key encrypted data set, a first dimensionality reduction coefficient is generated. The data type refers to the structure and form of different biometric data packets, such as image data (such as fingerprints, facial images) or sequence data (such as heart rate or brain wave data). The first dimensionality reduction coefficient is a dimensionality reduction parameter generated based on the characteristics of the data type, and is mainly used to control the compression ratio of the data. For example, image-type biometric data packets usually have high dimensions and require more dimensionality reduction processing; while for time series data, it may be necessary to maintain its complete timing information, so the dimensionality reduction coefficient is smaller. By generating the first dimensionality reduction coefficient based on the data type, the system can flexibly adjust the degree of dimensionality reduction to ensure that different types of data are properly processed.

[0039] On the basis of generating the first dimensionality reduction coefficient, the second dimensionality reduction coefficient is generated according to the sensitivity level of each highly sensitive data packet. The sensitivity level is used to measure the privacy protection requirements and security risk level of the data packet. The second dimensionality reduction coefficient reflects the impact of the sensitivity of the data packet on the dimensionality reduction process. For highly sensitive data packets (such as complete iris data), dimensionality reduction should be more cautious, with a smaller dimensionality reduction coefficient to avoid excessive compression and loss of key information; for low-sensitivity data packets, a larger dimensionality reduction coefficient can be selected to increase the proportion of data compression and further improve transmission efficiency. By comprehensively considering the sensitivity level, the second dimensionality reduction coefficient helps the system achieve the best balance between privacy protection and dimensionality reduction efficiency.

[0040] After the two dimensionality reduction coefficients are generated, the appropriate dimensionality reduction subunits are matched to carry out dimensionality reduction processing of multiple high-sensitivity data packets in combination with key identification features. The dimensionality reduction subunit is a computing module that implements specific dimensionality reduction operations. Different data types and dimensionality reduction coefficients will correspond to different dimensionality reduction algorithms. Common dimensionality reduction algorithms such as principal component analysis (PCA) and linear discriminant analysis (LDA) can extract key features and remove redundant information based on the characteristics of bioinformatics data. When matching the dimensionality reduction subunit, the most suitable dimensionality reduction method is automatically selected based on the first and second dimensionality reduction coefficients and the key identification features in the data packet to ensure that the data dimension is minimized without losing key information, thereby generating the final dimensionality reduction data set. This method greatly improves the efficiency of data transmission while ensuring data privacy and security, and provides strong technical support for subsequent data use.

[0041] Further, the noise encryption of the dimension reduction data set is performed through the noise encryption channel, and step P30 of the embodiment of the present application further includes:

[0042] P35: Receive the reduced dimension data set through the noise encryption channel, the reduced dimension data set includes multiple high-sensitivity data packets after dimensionality reduction; P36: Match the corresponding noise encryption mechanism based on the multiple high-sensitivity data packets after dimensionality reduction; P37: Generate multiple groups of random noises according to the sensitivity levels of the multiple high-sensitivity data packets after dimensionality reduction in combination with the noise encryption mechanism; P38: Add the multiple groups of random noises to the multiple high-sensitivity data packets after dimensionality reduction to obtain the encrypted transmission data set.

[0043] Optionally, the high-sensitivity data packets after dimensionality reduction are noise encrypted through a noise encryption channel to protect data privacy by adding random noise.

[0044] First, the noise encryption channel receives the reduced-dimensionality data sets that have been processed. These reduced-dimensionality data sets contain multiple high-sensitivity data packets after dimensionality reduction. The data in each data packet has been processed by the previous stage of dimensionality reduction to remove redundant information, but still retains key identification features, such as feature points in facial recognition or feature lines in fingerprint images. The purpose of the reduced-dimensionality data set is to ensure that important biometric information is retained while reducing the data volume and optimizing transmission efficiency, providing a basis for the subsequent encryption processing.

[0045] Next, based on multiple highly sensitive data packets after dimensionality reduction, the corresponding noise encryption mechanism is automatically matched. The noise encryption mechanism is a technology used for data privacy protection. By adding random noise, it ensures that even if the data is intercepted during transmission, it is difficult to restore the original sensitive information. Commonly used noise encryption mechanisms include the Laplace mechanism and the Gaussian mechanism. For example, for image data with higher dimensions (such as fingerprints or irises), the Gaussian mechanism can add noise more smoothly and maintain the overall structure of the data. For feature data with lower dimensions (such as facial feature points), the Laplace mechanism can cover up potential privacy risks by adding stronger randomness.

[0046] Finally, according to the sensitivity levels of multiple high-sensitivity data packets after dimensionality reduction, combined with the previously matched noise encryption mechanism, multiple sets of random noise are generated. The sensitivity level determines the intensity of the added noise: for high-sensitivity data packets (such as complete iris data), stronger random noise needs to be generated to ensure high privacy protection of the data; for low-sensitivity dimensionality reduction data packets, weaker noise can be used to reduce the impact on data accuracy. The noise generation process is based on the encryption mechanism and is usually calculated through mathematical formulas.

[0047] After generating multiple sets of random noise, these noises are added to each highly sensitive data packet after dimensionality reduction to obtain an encrypted transmission data set. The noise addition operation directly superimposes the generated random noise into the data packet, so that even if each data packet is intercepted during transmission, the attacker cannot accurately restore the original data. Noise conceals potential privacy risks by destroying detailed information in the data. By encrypting the highly sensitive data packets after dimensionality reduction with noise and generating an encrypted transmission data set, it is ensured that sensitive information is fully protected during the data transmission process, while taking into account the efficiency and availability of data transmission.

[0048] P40: Use the transmission verification indicator to perform link security verification, securely transmit the encrypted transmission data set, and during the data transmission process, perform real-time monitoring and abnormal warning of the transmission link through the network monitoring module.

[0049] Specifically, the link security is verified by using transmission verification indicators to ensure that the encrypted transmission data set can be transmitted in a secure environment. Transmission verification indicators are usually extracted from low-sensitivity data packets and used to test whether the link is secure and stable. Since these data packets are less sensitive and do not involve the user's core privacy information, their exposure on the network link does not pose a serious risk. After the link verification is passed, the encrypted transmission data set begins to be transmitted. These data sets have been processed by data dimension reduction and noise encryption. To further ensure transmission security, the data set will use encrypted transmission protocols such as Transport Layer Security (TLS) or Virtual Private Network (VPN) during transmission. At the same time, the network monitoring module monitors the data transmission process in real time and provides abnormal warnings to ensure that potential security threats are discovered and responded to in a timely manner during the transmission process.

[0050] Further, the transmission verification indicator is used to perform link security verification. Step P40 of the embodiment of the present application further includes:

[0051] P41: Use the low-sensitivity data packet as test data, perform small-scale transmission verification through the transmission link, and perform transmission consistency check during the link verification process; P42: If the consistency verification result is passed, generate a formal transmission instruction to securely transmit the encrypted transmission data set. If it fails, issue a transmission abnormality warning.

[0052] It should be understood that the use of transmission verification indicators for link security verification ensures the security and stability of the transmission link. Through small-scale transmission verification of low-sensitivity data packets combined with transmission consistency checks, the system can ensure the reliability of the data transmission link and issue an alarm in abnormal situations.

[0053] First, low-sensitivity data packets extracted from the classified data packets are used as test data, and these data packets are used for small-scale transmission verification. Low-sensitivity data packets usually contain insensitive or non-privacy data, such as environmental parameters, basic biometric information, etc. Even if these data are intercepted or tampered with, the impact on user privacy and security is minimal. The purpose of small-scale transmission verification is to test the security and reliability of the current transmission link by testing low-sensitivity data packets before officially transmitting high-sensitivity data packets, so as to avoid unverified links directly carrying highly sensitive data, thereby reducing the risk of leakage of highly sensitive data.

[0054] During the transmission process, the test data packets are subjected to transmission consistency check. Consistency check is to ensure that the data has not been tampered with or lost in the transmission link by calculating and comparing the hash value or checksum of the data packet. Hash value check can effectively detect abnormal situations such as man-in-the-middle attacks or data packet tampering that may occur during the transmission process. If the transmission consistency check result meets expectations, it means that the link is secure under the current conditions and the transmission is stable.

[0055] If the result of the consistency verification is passed, that is, the transmitted data packets remain consistent in the network link, the system will generate a formal transmission instruction to allow the encrypted transmission data set to be transmitted securely. This means that the link has passed the security test and the system can safely start transmitting highly sensitive data packets that have been processed by dimensionality reduction and noise encryption. After the formal transmission instruction is initiated, the encrypted transmission data set will be transmitted through the verified link and further protected by an encrypted transmission protocol (such as TLS / SSL or VPN) to ensure that the data will not be intercepted or tampered with during the transmission process.

[0056] If the transmission consistency verification fails, it indicates that there is a problem with the link. It may be that the data packet is lost, tampered with, or attacked during the transmission process. At this time, the system will immediately issue a transmission abnormality warning. The transmission abnormality warning refers to the alarm notification automatically generated by the system when a link abnormality is detected. The system will send a warning to the administrator and terminate the transmission of highly sensitive data packets. In abnormal situations, it may also enable a backup transmission link or retry the transmission to ensure the security of the final data. In high-risk scenarios, it may be recommended to further strengthen network protection measures or select a safer backup link.

[0057] Furthermore, the transmission link is monitored in real time and abnormal warning is provided through the network monitoring module. Step P40 of the embodiment of the present application further includes:

[0058] P43: Integrated network monitoring module, which includes but is not limited to a transmission rate monitoring unit, a delay and time delay jitter monitoring unit and a data packet loss rate monitoring unit; P44: Data transmission anomaly monitoring is performed through the transmission rate monitoring unit, the delay and time delay jitter monitoring unit and the data packet loss rate monitoring unit, and a multi-level early warning mechanism is activated according to the abnormal monitoring results to issue a transmission anomaly early warning.

[0059] Optionally, the integrated network monitoring module can monitor key network performance indicators during data transmission in real time and trigger a multi-level early warning mechanism when anomalies occur, ensuring the security and stability of the data transmission process.

[0060] Specifically, a network monitoring module is integrated into the transmission link, which is responsible for continuously monitoring multiple key parameters of the data transmission link. The network monitoring module may include the following core units: a transmission rate monitoring unit, which is used to track the data transmission rate in real time to ensure that the data packets can be transmitted at the expected speed. If the transmission rate drops suddenly, it may indicate that the link is congested or other abnormal conditions occur. A delay and delay jitter monitoring unit, which is used to track the delay (round-trip time RTT) and delay jitter (the amplitude of the delay change during the transmission process) of the transmission link to ensure that the link maintains low latency and stability. High latency and increased delay jitter may be a manifestation of unstable network links, and may even indicate attacks or other network anomalies. A data packet loss rate monitoring unit, which is responsible for monitoring the proportion of data packets lost during transmission. A high data packet loss rate may cause transmission failure, or indicate that the network link is interfered with or attacked (such as a DDoS attack). Through the above three monitoring units, the network monitoring module can fully capture the real-time status of the transmission link to ensure that any potential problems can be quickly identified and handled.

[0061] During the real-time monitoring process, each unit of the network monitoring module will continuously track and analyze the operating status of the data transmission link. When the network monitoring module detects problems such as abnormal transmission rate, increased delay or delay jitter, high data packet loss rate, etc., the system will activate the multi-level warning mechanism. The multi-level warning mechanism is divided into different levels according to the severity of the abnormality. For example, when a slight drop in transmission rate or a small range of delay jitter is detected, a low-level alarm is issued to notify the administrator to pay attention to the link status, but it does not affect the continued transmission of data. When the packet loss rate increases or the delay increases significantly, an intermediate warning is issued, and it may be recommended to switch to an alternate link for transmission to avoid the impact of highly sensitive data packets. When the monitoring module detects a major abnormality (such as severe packet loss or transmission interruption), a high-level warning is issued, the transmission is immediately terminated, the emergency response procedure is triggered, and the administrator is notified to take emergency measures. Through this multi-level warning mechanism, the system can take appropriate countermeasures according to different levels of network abnormalities to ensure the security and stability of the transmission link.

[0062] Through the cooperation of these monitoring units, the status of the transmission link can be monitored in real time, and a multi-level early warning mechanism can be activated according to different degrees of abnormalities. This allows preventive measures to be taken when minor abnormalities occur, and transmission can be quickly terminated and data security can be protected when major abnormalities occur.

[0063] In summary, the embodiments of the present application have at least the following technical effects:

[0064] The present application classifies the data collected in real time through biometric information sensitivity classification rules, and packages the data into low-sensitivity and high-sensitivity data packets, extracts the low-sensitivity data packets as transmission verification indicators, and uses the high-sensitivity data packets as key encrypted data packets. The edge computing nodes in the smart device encrypt the high-sensitivity data packets to generate an encrypted transmission data set, and after link security verification using the transmission verification indicators, encrypted data transmission is performed. At the same time, the transmission link is monitored in real time through the network monitoring module and abnormal warnings are issued.

[0065] The technical effect of performing graded processing according to the sensitivity of biometric information and performing link security verification before transmission has been achieved, thereby improving the efficiency and security of data transmission.

[0066] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0067] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0068] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A data security transmission method for an intelligent biological information collector, characterized in that: The method is applied to an intelligent biological information collector, wherein the intelligent biological information collector is embedded with a data collection module and a network monitoring module, and the method comprises: Through the data acquisition module, based on the preset bio-information sensitivity classification rules, the real-time device collected data is graded for sensitivity, and graded and packaged according to the sensitivity classification results to obtain multiple graded data packets; According to the multiple graded data packets, extracting low-sensitivity data packets as transmission verification indicators and extracting high-sensitivity data packets as key encryption data sets; An edge computing node is set in the smart device, and encryption processing of the key encrypted data set is performed through the edge computing node, including data dimension reduction and noise encryption, to obtain an encrypted transmission data set, wherein the smart device is the smart bio-information collector; Use the transmission verification indicator to perform link security verification, securely transmit the encrypted transmission data set, and during the data transmission process, use the network monitoring module to perform real-time monitoring and abnormal warning of the transmission link; Wherein, the edge computing node includes a data dimension reduction channel and a noise encryption channel, and is connected to the data acquisition module; The data dimension reduction of the key encrypted data set is performed through the data dimension reduction channel, including: Determine key identification features based on preset key biometric indicators; Generate a first dimensionality reduction coefficient based on the data types of multiple high-sensitivity data packets in the key encrypted data set; Generating a second dimensionality reduction coefficient based on the sensitivity levels of multiple high-sensitivity data packets in the key encrypted data set; According to the first dimensionality reduction coefficient, the second dimensionality reduction coefficient, and the key identification feature, matching corresponding dimensionality reduction subunits to perform data dimensionality reduction processing on multiple high-sensitivity data packets to obtain a dimensionality reduction data set; Performing noise encryption on the dimension reduction data set through the noise encryption channel includes: Receiving the dimension reduction data set through the noise encryption channel, wherein the dimension reduction data set includes a plurality of high-sensitivity data packets after dimension reduction; Based on the multiple high-sensitivity data packets after dimensionality reduction, matching corresponding noise encryption mechanisms; Generate multiple groups of random noise according to the sensitivity levels of the multiple high-sensitivity data packets after dimensionality reduction and in combination with the noise encryption mechanism; The multiple groups of random noise are added to the multiple high-sensitivity data packets after dimensionality reduction to obtain the encrypted transmission data set.

2. The data security transmission method for an intelligent biological information collector according to claim 1, characterized in that: Through the data collection module, based on the preset bio-information sensitivity classification rules, the real-time device collected data is graded for sensitivity, and graded and packaged according to the sensitivity classification results, including: Read historical transmission data and attack pattern data, and preset multiple sensitivity classification rules based on biometric information type, privacy level, and usage scenario; Based on the usage scenario of the target information collection device, traverse the multiple sensitivity classification rules, and match and obtain the target sensitivity classification rules; According to the target sensitivity classification rule, a sensitivity classification model is established to perform sensitivity classification on the data collected by the real-time device to obtain a sensitivity classification result, wherein the sensitivity classification result includes a plurality of biometric information data with sensitivity level identifiers; According to the sensitivity level identifier, the data collected by the real-time device is packaged in a graded manner to obtain a plurality of graded data packets.

3. The data security transmission method for an intelligent biological information collector according to claim 1, characterized in that: According to the multiple graded data packets, extracting low-sensitivity data packets as transmission verification indicators and extracting high-sensitivity data packets as key encryption data sets includes: sorting the plurality of graded data packets according to the sensitivity levels, and extracting a plurality of low-sensitivity data packets; The multiple low-sensitivity data packets are respectively traversed through the multiple hierarchical data packets to perform data association analysis, and according to the association analysis results, the low-sensitivity data packets that are weakly associated with the multiple hierarchical data packets are extracted as transmission verification indicators; With reference to the transmission verification index, the remaining high-sensitivity data packets are used as key encrypted data sets.

4. The data security transmission method for an intelligent biological information collector according to claim 1, characterized in that: Using the transmission verification indicator to perform link security verification includes: Using the low-sensitivity data packet as test data, performing small-scale transmission verification through a transmission link, and performing transmission consistency verification during the link verification process; If the consistency verification result is passed, a formal transmission instruction is generated to securely transmit the encrypted transmission data set. If it is not passed, a transmission abnormality warning is issued.

5. The data security transmission method for an intelligent biological information collector according to claim 1, characterized in that: The network monitoring module provides real-time monitoring and abnormal warning of transmission links, including: An integrated network monitoring module, the network monitoring module includes but is not limited to a transmission rate monitoring unit, a delay and delay jitter monitoring unit and a data packet loss rate monitoring unit; The transmission rate monitoring unit, the delay and time delay jitter monitoring unit and the data packet loss rate monitoring unit are used to monitor data transmission anomalies, and a multi-level early warning mechanism is activated according to the abnormal monitoring results to issue transmission anomaly early warnings.

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