Biological characteristic data management system and method based on multi-source stereoscopic perception

Abstract

The invention belongs to the technical field of data management, and particularly relates to a biological characteristic data management system and method based on multi-source stereoscopic perception, and the system comprises a multi-source data collection module, a data preprocessing fusion module, a biological characteristic database, a data security management module and a background supervision terminal. According to the invention, the multi-source data acquisition module performs high-precision and multi-dimensional acquisition on the biological characteristic data, and the data preprocessing fusion module performs preprocessing and effective integration on the multi-source biological characteristic data to form a uniform biological characteristic vector and send the uniform biological characteristic vector to the biological characteristic database for storage. The data security management module ensures the security and privacy protection of the biological characteristic data in the storage, transmission and processing processes, carries out risk analysis on the biological characteristic database through the data security management module, and carries out management auxiliary analysis when a data low-risk signal is generated, thereby being beneficial to ensuring the storage security of the biological characteristic data, and improving the safety of the biological characteristic data. And the workload and supervision difficulty of an administrator are obviously reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a biometric data management system and method based on multi-source stereoscopic perception. Background Art

[0002] Biometric data is a unique identifier that everyone is born with, carrying our identity, privacy and rights. With the rapid development of information technology, biometric recognition technology has become a key technology in the fields of identity authentication and security monitoring. Traditional biometric recognition mostly relies on a single data source, such as fingerprint recognition and facial recognition, which is susceptible to environmental interference and deception attacks, resulting in limited recognition accuracy and security.

[0003] At present, identity verification is performed by combining multiple biometric data to ensure identification accuracy and security. However, the processing and management of multiple biometric data faces the problems of large data volume and high processing complexity. In addition, it is impossible to reasonably analyze the data security of the biometric database and the equipment risks of the storage devices involved and to provide timely warnings, which is not conducive to ensuring the safe storage of biometric data.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a biometric data management system and method based on multi-source stereoscopic perception, which solves the problem that the prior art is unable to reasonably analyze and timely warn the data security of the biometric database and the equipment risks of the storage devices involved, which is not conducive to ensuring the safe storage of biometric data and has a low level of intelligence.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The biometric data management system based on multi-source stereoscopic perception includes a multi-source data acquisition module, a data preprocessing and fusion module, a biometric database, a data security management module and a background supervision terminal; the multi-source data acquisition module integrates a variety of biometric acquisition devices and introduces stereoscopic perception technology to collect biometric data with high precision and multi-dimensionality, and sends the collected multi-source biometric data to the data preprocessing and fusion module;

[0008] The data preprocessing and fusion module preprocesses the collected multi-source biometric data, uses the fusion algorithm to effectively integrate the biometric data from different sources to form a unified biometric vector, and sends the fused biometric vector to the biometric database for storage;

[0009] The data security management module uses encryption technology, access control mechanism and data desensitization strategy to perform security management on biometric vectors, and performs risk analysis on the biometric database to generate a database high-risk signal or a database low-risk signal, and sends the database high-risk signal or the database low-risk signal to the background supervision end. When the background supervision end receives the database high-risk signal, it will issue a corresponding warning.

[0010] Furthermore, the biometric feature collection equipment integrated in the multi-source data collection module includes a fingerprint scanner, an iris recognizer, a high-definition camera and a voiceprint collection device.

[0011] Furthermore, the biometric database is communicated with the intelligent verification and identification module. When identity authentication or identity identification is required, the intelligent identification and verification module obtains the biometric data that needs to be verified, and compares it with the biometric vectors stored in advance in the biometric database. By calculating the similarity between the biometrics, it is determined whether the match is successful; if the similarity reaches a preset threshold or meets specific rules, it is determined that the match is successful, and the identity authentication or identity identification is successful.

[0012] Furthermore, the specific analysis process of the data security management module includes:

[0013] Conduct real-time monitoring of the biometric database to identify illegal intrusions and network attacks, and issue an early warning when an illegal intrusion or network attack is identified; set a detection period, obtain the amount of data lost or leaked in the biometric database due to illegal intrusions and network attacks during the detection period and mark it as a leakage detection value, compare the leakage detection value with the preset leakage detection threshold, and generate a database high-risk signal if the leakage detection value exceeds the preset leakage detection threshold.

[0014] Furthermore, if the leakage detection value does not exceed the preset leakage detection threshold, a timer is started when an illegal intrusion or network attack is identified until the illegal intrusion or network attack ends, thereby obtaining the duration of the intrusion attack;

[0015] A detection period is set, and the total intrusion attack duration of the biometric database within the detection period is summed up to obtain the total intrusion attack duration value, and the intrusion attack duration is numerically compared with the preset intrusion attack duration threshold. If the intrusion attack duration exceeds the preset intrusion attack duration threshold, the corresponding intrusion attack duration is marked as a risk duration, and the number of risk durations within the detection period is marked as a risk frequency value;

[0016] The database risk coefficient is obtained by numerically calculating the total duration of intrusion attacks, the risk frequency value and the leakage detection value, and the database risk coefficient is numerically compared with the preset database risk coefficient threshold. If the database risk coefficient exceeds the preset database risk coefficient threshold, a database high-risk signal is generated; if the database risk coefficient does not exceed the preset database risk coefficient threshold, a database low-risk signal is generated.

[0017] Furthermore, the data security management module communicates with the management auxiliary analysis module, which monitors all storage devices involved in the biometric database. The data security management module sends the database low-risk signal to the hidden danger auxiliary analysis module. When the management auxiliary analysis module receives the database low-risk signal, it performs management auxiliary analysis, generates an auxiliary analysis qualified signal or an auxiliary analysis abnormal signal through analysis, and sends the auxiliary analysis qualified signal or the auxiliary analysis abnormal signal to the background supervision end. When the background supervision end receives the auxiliary analysis abnormal signal, it issues a corresponding warning.

[0018] Furthermore, the specific analysis process of management-assisted analysis includes:

[0019] Through analysis, it is determined whether there is a service timeout device. If there is a service timeout device, an auxiliary analysis abnormal signal is generated; if there is no service timeout device, the average storage speed when storing data in the corresponding storage device and the average call speed of data call to the corresponding storage device during the detection period are collected and marked as storage efficiency characteristic value and adjustment efficiency characteristic value respectively;

[0020] And obtain all scanning information of vulnerability virus scanning for storage devices during the detection period, calculate the ratio of the number of scans for vulnerability viruses in the corresponding storage device to the total number of scans during the detection period to obtain the scanning risk value; obtain the management alarm coefficient by numerically calculating the storage effective characteristic value, the adjustment efficiency characteristic value and the scanning risk value, and numerically compare the management alarm coefficient with the preset management alarm coefficient threshold; if the management alarm coefficient exceeds the preset management alarm coefficient threshold, mark the corresponding storage device as an alarm device; if an alarm device exists, generate an auxiliary analysis abnormality signal; if an alarm device does not exist, generate an auxiliary analysis qualified signal.

[0021] Furthermore, the specific analysis process for determining whether there is a device with service timeout is as follows:

[0022] Obtain the vibration amplitude of the corresponding storage device and the dust concentration of the environment in which it is located and mark them as vibration condition value and dust condition value, mark the deviation value of the humidity of the environment in which the corresponding storage device is located compared to the preset suitable humidity value as humidity condition value, and mark the deviation value of the temperature of the corresponding storage device compared to the preset suitable temperature value as temperature condition value;

[0023] The life impact value is calculated by weighted summing the vibration condition value, the ash condition value, the humidity condition value and the temperature condition value, and the life impact value is numerically compared with the preset life impact threshold value. If the life impact value exceeds the preset life impact threshold value, it is determined that the corresponding storage device is in a life vulnerable state, and the total time length of the corresponding storage device in the life vulnerable state in the historical stage is obtained and marked as the life loss time value;

[0024] The production date of the corresponding storage device is obtained, and the interval between the current date and the production date is marked as the service time table value; the life loss time value and the service time table value are numerically compared with the preset life loss time threshold and the preset service time table threshold respectively; if the life loss time value or the service time table value exceeds the corresponding preset threshold, the corresponding storage device is marked as a service timeout device.

[0025] Furthermore, the intelligent identification and verification module is connected to the application stability analysis module in communication. The application stability analysis module generates an application stability qualified signal or an application stability abnormality signal through analysis, and sends the application stability qualified signal or the application stability abnormality signal to the background supervision end. When the background supervision end receives the application stability abnormality signal, it issues a corresponding warning. The specific analysis process of the application stability analysis module is as follows:

[0026] The number of occurrences of the system crashing and failing to perform identity authentication and identification during the detection period and the duration of each crash are obtained and marked as application suspension value and application stop time value respectively, and all application stop time values ​​during the detection period are summed up to obtain the application crash time value;

[0027] Compare the application pause value and the application crash time value with the preset application pause threshold and the preset application crash time threshold respectively, and if the application pause value or the application crash time value exceeds the corresponding preset threshold, generate an application stability abnormality signal;

[0028] If both the application suspension value and the application crash time value do not exceed the corresponding preset thresholds, the average processing time of the intelligent recognition and verification module for identity recognition and verification during the detection period is obtained and marked as the recognition efficiency value, and the proportion of the number of occurrences in which the processing time of the intelligent recognition and verification module for identity recognition and verification during the detection period exceeds the preset processing time threshold is marked as the recognition inefficiency value;

[0029] The application stability coefficient is obtained by numerically calculating the application suspension value, application crash value, verification efficiency value and verification inefficiency value, and the application stability coefficient is numerically compared with the preset application stability coefficient threshold. If the application stability coefficient exceeds the preset application stability coefficient threshold, an application stability abnormality signal is generated; if the application stability coefficient does not exceed the preset application stability coefficient threshold, an application stability qualified signal is generated.

[0030] Furthermore, the present invention also proposes a biometric data management method based on multi-source stereoscopic perception, comprising the following steps:

[0031] Step 1: By integrating multiple biometric collection devices and introducing stereoscopic sensing technology, high-precision and multi-dimensional collection of biometric data is performed;

[0032] Step 2: preprocessing the collected multi-source biometric data;

[0033] Step 3: Use a fusion algorithm to effectively integrate biometric data from different sources to form a unified biometric vector, and store the fused biometric vector in a biometric database;

[0034] Step 4: Use encryption technology, access control mechanisms and data desensitization strategies to securely manage biometric vectors;

[0035] Step 5: Conduct risk analysis on the biometric database and have the backend supervisor issue an early warning when a high-risk database signal is generated.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. In the present invention, multi-source biometric data is collected, preprocessed and effectively integrated through a multi-source data collection module and a data preprocessing and fusion module, the biometric database stores the biometric vectors, and the data security management module ensures the security and privacy protection of the biometric data during storage, transmission and processing. The data security management module performs risk analysis on the biometric database and performs management auxiliary analysis when generating data low-risk signals, which is conducive to ensuring the storage security of biometric data and significantly reducing the workload and supervision difficulty of administrators;

[0038] 2. In the present invention, identity authentication is performed through an intelligent identification and verification module, which solves the security risk problems caused by limited identification accuracy, susceptibility to forgery attacks and single data during the identity identification process. The application stability analysis module analyzes the identity authentication performance to generate an application stability qualification signal or an application stability abnormality signal. When an application stability abnormality signal is generated, the administrator is reminded to investigate the cause and strengthen subsequent application supervision to ensure subsequent application efficiency and application stability, further reduce the administrator's workload and management difficulty, and has a high level of intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0040] Figure 1 It is a system block diagram of Embodiment 1 and Embodiment 2 of the present invention;

[0041] Figure 2 This is a system block diagram of Embodiment 3 of the present invention;

[0042] Figure 3 This is a flow chart of the method of Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] Embodiment 1: Figure 1 As shown, the biometric data management system based on multi-source stereoscopic perception proposed by the present invention includes a multi-source data acquisition module, a data preprocessing and fusion module, a biometric database, a data security management module and a background supervision terminal;

[0045] The multi-source data acquisition module integrates a variety of biometric acquisition devices (including fingerprint scanners, iris recognizers, high-definition cameras and voiceprint acquisition devices, etc.), and introduces stereoscopic perception technology (such as structured light, ToF depth cameras, etc.) to collect biometric data with high precision and multi-dimensionality, and send the collected multi-source biometric data to the data preprocessing and fusion module.

[0046] The data preprocessing and fusion module preprocesses the collected multi-source biometric data, including denoising, enhancement, feature extraction and other steps, and uses the fusion algorithm to effectively integrate the biometric data from different sources to form a unified biometric vector. The fused biometric vector is sent to the biometric database for storage, so that it can be subsequently compared and verified with the biometric data collected on site.

[0047] The data security management module uses encryption technology, access control mechanism and data desensitization strategy to manage biometric vectors securely, ensuring the security and privacy protection of biometric data during storage, transmission and processing. The data security management module performs risk analysis on the biometric database to generate a database high-risk signal or a database low-risk signal, and sends the database high-risk signal or database low-risk signal to the background supervision end. When the background supervision end receives the database high-risk signal, it issues a corresponding warning to remind the administrator to strengthen the security supervision of the biometric database in the future to ensure the data security of the biometric database. The specific analysis process of the data security management module is as follows:

[0048] Conduct real-time monitoring of the biometric database to identify illegal intrusions and network attacks, issue warnings when illegal intrusions or network attacks are identified, and achieve effective monitoring of the biometric database and threat identification and warning; set a detection period, obtain the amount of data lost or leaked in the biometric database due to illegal intrusions and network attacks during the detection period and mark it as a leakage detection value, compare the leakage detection value with the preset leakage detection threshold, if the leakage detection value exceeds the preset leakage detection threshold, indicating that the data protection performance during the detection period is poor, then a high-risk signal for the database is generated.

[0049] Furthermore, if the leakage detection value does not exceed the preset leakage detection threshold, a timer is started when an illegal intrusion or network attack is identified until the corresponding illegal intrusion or network attack ends, thereby obtaining the intrusion attack duration;

[0050] A detection period is set, preferably, the detection period is twenty-five days; the total intrusion attack duration of all intrusion attack durations of the biometric database within the detection period is calculated by summing up, and the intrusion attack duration is compared with a preset intrusion attack duration threshold. If the intrusion attack duration exceeds the preset intrusion attack duration threshold, indicating that the threat posed by the corresponding illegal intrusion or network attack is relatively large, the corresponding intrusion attack duration is marked as a risk duration, and the number of risk durations within the detection period is marked as a risk frequency value;

[0051] By formula The total intrusion attack time value LP, the risk frequency value NF and the leakage detection value TS are numerically calculated to obtain the database risk coefficient GX; wherein wq, hu, and my are preset weight coefficients with values ​​greater than zero, and the larger the value of the database risk coefficient GX, the greater the security threat to the biometric database in the detection period is in general;

[0052] The database risk coefficient GX is numerically compared with the preset database risk coefficient threshold. If the database risk coefficient GX exceeds the preset database risk coefficient threshold, it indicates that the security threats existing in the biometric database during the detection period are generally large, and a high-risk database signal is generated; if the database risk coefficient GX does not exceed the preset database risk coefficient threshold, it indicates that the security threats existing in the biometric database during the detection period are generally small, and a low-risk database signal is generated.

[0053] Embodiment 2: Figure 2As shown, the difference between this embodiment and the first embodiment is that the data security management module is connected to the management auxiliary analysis module in communication, the management auxiliary analysis module monitors all storage devices involved in the biometric database, the data security management module sends the database low-risk signal to the hidden danger auxiliary analysis module, and the management auxiliary analysis module performs management auxiliary analysis when receiving the database low-risk signal, and generates an auxiliary analysis qualified signal or an auxiliary analysis abnormal signal through analysis;

[0054] The auxiliary analysis qualified signal or auxiliary analysis abnormal signal is sent to the background supervision end. When the background supervision end receives the auxiliary analysis abnormal signal, it issues a corresponding warning to remind the administrator to check and repair the corresponding storage device in time, back up the data of the corresponding storage device as needed and eliminate the corresponding storage device, further ensure the storage security of biometric data, and significantly reduce the workload and supervision difficulty of the administrator; the specific analysis process of management auxiliary analysis is as follows:

[0055] Obtain the vibration amplitude of the corresponding storage device and the dust concentration of the environment in which it is located and mark them as vibration condition value and dust condition value, mark the deviation value of the humidity of the environment in which the corresponding storage device is located compared to the preset suitable humidity value as humidity condition value, and mark the deviation value of the temperature of the corresponding storage device compared to the preset suitable temperature value as temperature condition value;

[0056] By formula The life impact value YL is calculated by weighted summing the vibration condition value SW, the gray condition value TL, the humidity condition value WF and the temperature condition value ZS; wherein c1, c2, c3 and c4 are preset weight coefficients with values ​​greater than zero, and the larger the value of the life impact value YL is, the greater the overall real-time operation risk of the corresponding storage device is, and the greater the damage to the life of the corresponding storage device is;

[0057] Compare the life impact value YL of the corresponding storage device with the preset life impact threshold. If the life impact value YL exceeds the preset life impact threshold, it indicates that the real-time operation risk of the corresponding storage device is relatively large, and the damage to the life of the corresponding storage device is relatively large. Then, the corresponding storage device is judged to be in a life vulnerable state, and the total time length of the corresponding storage device in the history stage in the life vulnerable state is obtained and marked as the life loss time value.

[0058] The production date of the corresponding storage device is obtained, and the interval between the current date and the production date is marked as the service time table value; the life loss value and the service time table value are numerically compared with the preset life loss threshold and the preset service time table threshold, respectively. If the life loss value or the service time table value exceeds the corresponding preset threshold, it indicates that the corresponding storage device has reached the service life and the corresponding storage device should be eliminated in time, and the corresponding storage device is marked as a service-overdue device;

[0059] If there is a device that has been out of service, it indicates that there is a security risk in the storage of biometric data, and an auxiliary analysis abnormality signal is generated; if there is no device that has been out of service, the average storage speed when storing data in the corresponding storage device and the average call speed when calling data from the corresponding storage device during the detection period are collected and marked as storage efficiency feature values ​​and adjustment efficiency feature values ​​respectively;

[0060] And obtain all scanning information of vulnerability virus scanning for storage devices during the detection period (scan each storage device regularly or irregularly), calculate the ratio of the number of scans for vulnerability viruses in the corresponding storage device (i.e., the number of times the vulnerability virus is scanned in the corresponding storage device during the detection period) to the total number of scans during the detection period to obtain the scanning risk value;

[0061] By formula The storage efficiency characteristic value ZW, the adjustment efficiency characteristic value HP and the scanning risk value FX are numerically calculated to obtain the management alarm coefficient XW; wherein up, rg, eq are preset weight coefficients with values ​​greater than zero, and the larger the value of the management alarm coefficient XW is, the worse the overall operation performance of the corresponding storage device during the detection period is;

[0062] The management alarm coefficient XW is numerically compared with the preset management alarm coefficient threshold. If the management alarm coefficient XW exceeds the preset management alarm coefficient threshold, it indicates that the operating performance of the corresponding storage device during the detection period is generally poor, and the corresponding storage device is marked as an alarm device; if an alarm device exists, it indicates that it is not conducive to the safe and efficient operation of the biometric database, and an auxiliary analysis abnormality signal is generated; if no alarm device exists, it indicates that it is conducive to the safe and efficient operation of the biometric database, and an auxiliary analysis qualified signal is generated.

[0063] Embodiment 3: Figure 2 As shown, the difference between this embodiment and the first and second embodiments is that the biometric database is communicatively connected to the intelligent verification and identification module. When identity authentication or identity identification is required, the intelligent identification and verification module obtains the biometric data to be verified, and compares it with the biometric vectors pre-stored in the biometric database, and determines whether the match is successful by calculating the similarity between the biometrics; if the similarity reaches a preset threshold or meets specific rules, it is determined that the match is successful, that is, the identity authentication or identity identification is successful, which solves the security risks caused by limited recognition accuracy, susceptibility to forgery attacks and single data in the identity identification process.

[0064] It should be noted that the application scenarios include airports, financial institutions, etc. For example, the present invention is applied in the passenger security inspection system of an international airport to collect passengers' biometric data such as fingerprints, irises, and facial features through multi-source stereoscopic sensing technology, and compare them with the data in the biometric database to achieve fast and accurate identity recognition and security inspection process optimization, while ensuring the security of passengers' personal information;

[0065] Alternatively, the present invention is integrated into an online payment platform of a financial institution, and multi-source biometric data is used to perform user identity authentication, effectively preventing fraud and improving payment security. At the same time, data encryption and access control are used to ensure the secure storage and access of user biometric data.

[0066] Furthermore, the intelligent identification and verification module is connected to the application stability analysis module in communication. The application stability analysis module generates an application stability qualified signal or an application stability abnormal signal through analysis, and sends the application stability qualified signal or the application stability abnormal signal to the background supervision end. When the background supervision end receives the application stability abnormal signal, it issues a corresponding warning, which can reasonably analyze the performance of the identity recognition application and issue a timely warning to remind the administrator to investigate the cause and strengthen subsequent application supervision, ensure the subsequent application efficiency and application stability, further reduce the workload of the administrator and the difficulty of management, and have a high level of intelligence; the specific analysis process of the application stability analysis module is as follows:

[0067] The number of occurrences of the system crashing and failing to perform identity authentication and identification during the detection period and the duration of each crash are obtained and marked as application suspension value and application stop time value respectively, and all application stop time values ​​during the detection period are summed up to obtain the application crash time value;

[0068] Compare the application suspension value and the application crash time value with the preset application suspension threshold and the preset application crash time threshold, respectively. If the application suspension value or the application crash time value exceeds the corresponding preset threshold, indicating that the identity verification application performs poorly during the detection period, an application stability abnormality signal is generated;

[0069] If both the application suspension value and the application crash time value do not exceed the corresponding preset thresholds, the average processing time of the intelligent recognition and verification module for identity recognition and verification during the detection period is obtained and marked as the recognition efficiency value, and the proportion of the number of occurrences in which the processing time of the intelligent recognition and verification module for identity recognition and verification during the detection period exceeds the preset processing time threshold is marked as the recognition inefficiency value;

[0070] By formula The application pause value GY, the application collapse time value FL, the verification efficiency value QM and the verification inefficiency value PN are numerically calculated to obtain the application stability coefficient HX; wherein kp, eu, sq, ng are preset weight coefficients with values ​​greater than zero, and the larger the value of the application stability coefficient HX, the worse the overall performance of the identity verification application during the detection period;

[0071] The application stability coefficient HX is numerically compared with the preset application stability coefficient threshold. If the application stability coefficient HX exceeds the preset application stability coefficient threshold, it indicates that the performance of the identity recognition and verification application during the detection period is generally poor, and an application stability abnormality signal is generated; if the application stability coefficient HX does not exceed the preset application stability coefficient threshold, it indicates that the performance of the identity recognition and verification application during the detection period is generally good, and an application stability qualified signal is generated.

[0072] Embodiment 4: Figure 3 As shown, the difference between this embodiment and the first, second and third embodiments is that the biometric data management method based on multi-source stereoscopic perception proposed in the present invention includes the following steps:

[0073] Step 1: By integrating multiple biometric collection devices and introducing stereoscopic sensing technology, high-precision and multi-dimensional collection of biometric data is performed;

[0074] Step 2: preprocessing the collected multi-source biometric data;

[0075] Step 3: Use a fusion algorithm to effectively integrate biometric data from different sources to form a unified biometric vector, and store the fused biometric vector in a biometric database;

[0076] Step 4: Use encryption technology, access control mechanisms and data desensitization strategies to securely manage biometric vectors;

[0077] Step 5: Conduct risk analysis on the biometric database and have the backend supervisor issue an early warning when a high-risk database signal is generated.

[0078] The working principle of the present invention is as follows: when in use, a variety of biometric feature collection devices are integrated through a multi-source data collection module and stereoscopic perception technology is introduced to collect biometric feature data with high precision and multi-dimensionality. The data preprocessing and fusion module preprocesses and effectively integrates the collected multi-source biometric feature data to form a unified biometric feature vector and sends it to the biometric feature database for storage. The data security management module uses encryption technology, access control mechanism and data desensitization strategy to safely manage the biometric feature vector to ensure the security and privacy protection of biometric feature data during storage, transmission and processing. The data security management module performs risk analysis on the biometric feature database. When a high-risk database signal is generated, the security supervision of the biometric feature database is subsequently strengthened to ensure the data security of the biometric feature database. When a low-risk data signal is generated, the management auxiliary analysis module performs management auxiliary analysis. When an auxiliary analysis abnormal signal is generated, the corresponding storage device is inspected, repaired and selectively eliminated, further ensuring the storage security of the biometric feature data and significantly reducing the workload and supervision difficulty of the administrator.

[0079] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A biometric data management system based on multi-source stereoscopic perception, characterized in that: It includes multi-source data collection module, data preprocessing and fusion module, biometric database, data security management module and background supervision terminal; the multi-source data collection module integrates a variety of biometric collection equipment and introduces stereoscopic perception technology to collect biometric data with high precision and multi-dimensionality; The data preprocessing and fusion module preprocesses the collected multi-source biometric data, uses the fusion algorithm to effectively integrate the biometric data from different sources to form a unified biometric vector, and sends the fused biometric vector to the biometric database for storage; The data security management module uses encryption technology, access control mechanism and data desensitization strategy to manage biometric vectors securely, and performs risk analysis on the biometric database to generate a database high-risk signal or a database low-risk signal, and sends the database high-risk signal or the database low-risk signal to the background supervision end.

2. The biometric data management system based on multi-source stereoscopic perception according to claim 1 is characterized in that: The biometric acquisition equipment integrated in the multi-source data acquisition module includes a fingerprint scanner, an iris recognizer, a high-definition camera and a voiceprint acquisition device.

3. The biometric data management system based on multi-source stereoscopic perception according to claim 1 is characterized in that: The biometric database is communicated with the intelligent verification and identification module. When identity authentication or identification is required, the intelligent identification and verification module obtains the biometric data that needs to be verified and compares it with the biometric vectors stored in the biometric database in advance. If the similarity reaches a preset threshold or meets specific rules, it is determined to be a successful match.

4. The biometric data management system based on multi-source stereoscopic perception according to claim 1 is characterized in that: The specific analysis process of the data security management module includes: A detection period is set, and the amount of data lost or leaked in the biometric database due to illegal intrusion and network attacks during the detection period is obtained and marked as a leakage detection value. If the leakage detection value exceeds the preset leakage detection threshold, a database high-risk signal is generated.

5. The biometric data management system based on multi-source stereoscopic perception according to claim 4 is characterized in that: If the leakage detection value does not exceed the preset leakage detection threshold, the database risk coefficient is obtained by numerically calculating the total intrusion attack time value, the risk frequency value and the leakage detection value. If the database risk coefficient exceeds the preset database risk coefficient threshold, a database high-risk signal is generated; If the database risk factor does not exceed the preset database risk factor threshold, a database low risk signal is generated.

6. The biometric data management system based on multi-source stereoscopic perception according to claim 5 is characterized in that: The data security management module communicates with the management auxiliary analysis module. The data security management module sends the database low-risk signal to the hidden danger auxiliary analysis module. When the management auxiliary analysis module receives the database low-risk signal, it performs management auxiliary analysis to generate an auxiliary analysis qualification signal or an auxiliary analysis abnormal signal through analysis, and sends the auxiliary analysis qualification signal or the auxiliary analysis abnormal signal to the background supervision end. When the background supervision end receives the auxiliary analysis abnormal signal, it issues a corresponding warning.

7. The biometric data management system based on multi-source stereoscopic perception according to claim 6 is characterized in that: The specific analysis process of management-assisted analysis includes: Analysis is performed to determine whether there is a service timeout device. If so, an auxiliary analysis abnormality signal is generated. If there is no service timeout device, a management alarm coefficient is obtained by numerically calculating the storage effective characteristic value, the adjustment efficiency characteristic value and the scanning risk value. If the management alarm coefficient exceeds the preset management alarm coefficient threshold, the corresponding storage device is marked as an alarm device. If an alarm device exists, an auxiliary analysis abnormality signal is generated. If an alarm device does not exist, an auxiliary analysis qualified signal is generated.

8. The biometric data management system based on multi-source stereoscopic perception according to claim 7 is characterized in that: The specific analysis process to determine whether there is a device with service timeout is as follows: The total duration of the corresponding storage device in the life-vulnerable state in the historical stage is obtained and marked as the life-loss time value, and the life-loss time value and the service time table value are numerically compared with the preset life-loss time threshold and the preset service time table threshold respectively; if the life-loss time value or the service time table value exceeds the corresponding preset threshold, the corresponding storage device is marked as a service-timed device.

9. The biometric data management system based on multi-source stereoscopic perception according to claim 3 is characterized in that: The intelligent identification and verification module is communicated with the application stability analysis module, which generates an application stability qualified signal or an application stability abnormal signal through analysis, and sends the application stability qualified signal or the application stability abnormal signal to the background supervision end; the specific analysis process of the application stability analysis module is as follows: If both the application suspension value and the application collapse time value do not exceed the corresponding preset thresholds, the application stability coefficient is obtained by numerically calculating the application suspension value, the application collapse time value, the verification efficiency value and the verification inefficiency value. If the application stability coefficient exceeds the preset application stability coefficient threshold, an application stability abnormality signal is generated; if the application stability coefficient does not exceed the preset application stability coefficient threshold, an application stability qualified signal is generated.

10. A biometric data management method based on multi-source stereoscopic perception, characterized in that: The method adopts the biometric data management system based on multi-source stereoscopic perception as described in any one of claims 1-9.

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