Power management system and method for identity authentication device

Abstract

The application discloses a power consumption management system and method for identity authentication equipment, relates to the technical field of equipment power consumption management, and is characterized in that a user fingerprint database is established according to collected user data, the fingerprint features of the user are reordered, the influence of the power of the smart door lock on the sensitivity of the door lock is analyzed based on the response time length of the smart door lock, when the power of the smart door lock is in a saturated state, user information is matched based on the fingerprint features of the user, when the power of the smart door lock is in an unsaturated state, the number of collected fingerprint features of the user is limited, user information is matched based on the limited fingerprint features, new user information is updated in real time, and the monitoring period is updated. The application adjusts power consumption on demand, reduces invalid power consumption, avoids the need to frequently replace power sources or charge due to frequent power consumption of the equipment, significantly prolongs the use period of the equipment after single charging, and is especially suitable for identity authentication scenes with high demand for endurance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of device power consumption management, in particular to a power consumption management system and method for identity authentication devices. BACKGROUND

[0002] In the fingerprint recognition process of most current identity authentication devices, a fixed full-quantity feature recognition mode is often used, and the recognition strategy is not adjusted in combination with the device's own power state. Whether the device has sufficient power or not, all fingerprint features need to be extracted and matched, resulting in a lot of unnecessary energy consumption in the feature processing and calculation link. This lack of dynamic adjustment of energy consumption management mode easily makes the device quickly consume power, and frequent replacement of power supply or charging is needed, which not only increases the use cost, but also may cause the device to fail to work normally due to power consumption in critical use scenarios, and it is difficult to meet the use demand of identity authentication scenarios with high demand for endurance. In order to reduce energy consumption, some identity authentication devices try to reduce the number of fingerprint feature recognition, but the existing technology lacks a scientific screening mechanism for the importance of fingerprint features, and cannot accurately locate the key features with high user identity discrimination. When simplifying the features, random selection is often used, which easily loses core identification information and causes the identity recognition accuracy to decrease significantly. At the same time, the existing technology does not develop adaptive strategies in combination with the response performance changes of devices in different power stages, and when the power fluctuates, the recognition algorithm cannot be adjusted in time, which easily causes misjudgment, omission and other problems, and it is difficult to guarantee the stability and reliability of the identity recognition result, affecting the safety authentication effect of the device. When the existing identity authentication device responds to the change of the number of users, the user information updating mechanism is not flexible enough, and after adding new users, manual operation is needed to include the information in the database, and the adaptation efficiency is low. And when there is no use record in the device monitoring period, there is no effective processing scheme for invalid data, which easily causes deviation of the analysis result of the power stage and affects the accuracy of the recognition strategy adjustment. In addition, the recognition mode switching of some devices needs to be completed manually by the user, the user needs to learn the operation process additionally, and needs to pay attention to the association between the device power and the recognition mode at all times, which increases the use complexity and is difficult to provide a convenient and smooth use experience. SUMMARY

[0003] The purpose of the present application is to provide a power consumption management system and method for identity authentication devices to solve the problems raised in the background.

[0004] In order to solve the above technical problems, the present application provides the following technical scheme: a power consumption management method for identity authentication devices, comprising the following steps:

[0005] S1, when identity recognition is performed using a smart door lock, a user fingerprint database is established according to the user data collected;

[0006] S2, analyzing the feature discrimination degree of the user's fingerprint features, and reordering the user's fingerprint features;

[0007] S3, dividing the power of the smart door lock from high to low, real-time acquiring the power information and response time of the smart door lock, and analyzing the influence of the power of the smart door lock on the sensitivity of the door lock based on the response time of the smart door lock;

[0008] S4, when the power of the smart door lock is in a saturated state, matching the user information based on the fingerprint features of the user;

[0009] S5, when the power of the smart door lock is in an unsaturated state, analyzing the relationship between the current power and the power stage, limiting the number of collected fingerprint features of the user, and matching the user information based on the limited fingerprint features;

[0010] S6, real-time updating the new user information, and updating the monitoring period, limiting the reference period to the monitoring period of the number of times the smart door lock is used.

[0011] Further, in step S1, when using the smart door lock for identity recognition, the fingerprint information of the user is obtained after authorization, the collected fingerprint information is preprocessed and normalized, the fingerprint features of the user are obtained after preprocessing and normalization, the user information and the fingerprint features of the user are matched, and the record is recorded into the user data file after matching. The number of users is M, and the fingerprint features of the mth user are {Z m_1 ,Z m_2 ,…,Z m_n ,…,Z m_N}, wherein N represents the total number of fingerprint features, Z m_n represents the nth fingerprint feature of the mth user, and then a user fingerprint database is established according to the user data file, the user information and the fingerprint features are associated and matched and recorded into a file, and finally integrated into a user fingerprint database, which not only provides stable data support for subsequent feature analysis and dynamic identification strategy adjustment, avoids process faults caused by missing or chaotic basic data, but also flexibly responds to user increase and decrease scenarios, and subsequent maintenance of user information does not need to reconstruct the database, which guarantees the convenience of data management and lays a foundation for reliable operation and flexible adaptation of the entire system.

[0012] Further, in step S2, for the mth user, the user data file of the mth user is called, and the fingerprint features of the mth user are analyzed, wherein the feature discrimination degree of the nth fingerprint feature is:

[0013] ;

[0014] wherein k represents the kth user, Z k_n represents the nth fingerprint feature of the kth user, and n=1,2,…,N is substituted one by one, for the mth user, the feature discrimination degrees of the N fingerprint features are obtainedm_1 A m_2 ,…,A m_n ,…,A m_N Substitute each value into m = 1, 2, ..., M to obtain the feature discrimination level of the M users, and then obtain the feature explicitness B of the nth fingerprint feature. n The apparent strength of the nth fingerprint feature is the average of the feature discrimination of the nth fingerprint features of M users. The N fingerprint features are then reordered according to their feature discrimination from highest to lowest. This in-depth analysis of the fingerprint feature discrimination capability provides a precise basis for subsequent identification strategy optimization. First, the discrimination level of each user's fingerprint feature is calculated. Then, the apparent strength of each feature is derived from the average value, clearly distinguishing the features most crucial for user identification and sorting them by importance. This process accurately locates core identification features, avoiding the blindness of subsequent feature selection, and provides a scientific standard for feature simplification during low-power phases. It ensures that even when only highly discriminative features are retained, user identification remains effective. This reduces computational load without sacrificing accuracy, while simultaneously improving the system's refined utilization of feature resources, laying a crucial foundation for balancing power management and identity authentication.

[0015] In step S3, after authorization, the battery level and response time of the smart lock are acquired in real time. The battery level of the smart lock is divided into X battery levels, from high to low. The xth battery level is analyzed. In the xth battery level, the number of times the smart lock is used within a reference period is denoted as Y. The reference period is a monitoring period ending at the current time. The response time for Y uses of the smart lock is {C1, C2, ..., C...}. y ,…,C Y}, and thus obtain the average response time D of the smart lock in the xth stage. x D x For {C1,C2,…,C y ,…,C Y Substitute the average of all values ​​in} into x=1,2,…,X to obtain the average response time of the smart lock in the X stages {D1,D2,…,D}. x ,…,D X};

[0016] By consulting the smart lock's manufacturer's specifications, the standard response time D of the smart lock can be obtained, and then the lock's sensitivity E at the xth power level can be calculated. x The door lock sensitivity E during the xth power stage x D x Substituting the ratio of D into x = 1, 2, ..., X, we obtain E.x The minimum value of x, where E is a preset door lock sensitivity threshold value, and x satisfies E x The minimum value of x, where E is a preset door lock sensitivity threshold value, and x satisfies E min As a recognition adjustment critical stage, and then from the x min th power stage to the x min th power stage, F is the number of recognition adjustment stages, F = x min +1, record the recognition adjustment stage, record the recognition adjustment stage in the door lock recognition database, provide accurate data support for the power consumption and performance balance of the intelligent door lock, and avoid the blindness of strategy adjustment. Through real-time monitoring and quantitative analysis, the power state and door lock sensitivity performance are directly related, rather than relying on experience to determine the low power stage. It can accurately find the critical node of performance deficiency caused by power decline, ensure that the adjustment is started only in the stage that really needs to be optimized, avoid the impact of early adjustment on the recognition experience or the waste of energy consumption caused by late adjustment. At the same time, the division of the recognition adjustment stage provides a clear basis for subsequent dynamic adjustment of the recognition strategy, so that the power consumption optimization at low power has a specific target, and there is no need to blindly simplify the recognition process. And the whole process relies on real-time data and factory standard value comparison, the result is objective and reliable, avoiding subjective judgment error, laying a foundation for finding a balance between energy consumption optimization and recognition accuracy, and ensuring that the door lock can operate stably in different power stages.

[0017] Further, in step S4, when the user uses the intelligent door lock, the intelligent door lock queries the current power, and if the current power of the intelligent door lock is in the power stage between the 1st power stage and the x min th power stage, wherein the power stage between the 1st power stage and the x min th power stage includes the 1st power stage but does not include the x min th power stage, the intelligent door lock extracts N fingerprint features of the user, judges the user's identity based on the N fingerprint features of the user, and the method for judging the user's identity is: collecting N fingerprint features of the user as {Z 0_1 ,Z 0_2 ,…,Z 0_n ,…,Z 0_N}, calling the fingerprint features of the mth user in the database {Z m_1 ,Z m_2 ,…,Z m_n ,…,Z m_N}, calculating the fingerprint matching degree G m :

[0018] ;

[0019] Substituting m=1,2,…,M one by one, we obtain the fingerprint matching degree {G1,G2,…,G} between the user and M users in the database. m ,…,G M If there exists m such that G m If the fingerprint match score is greater than G, the user identity corresponding to the highest fingerprint matching score is assigned to the current user, where G is the pre-set fingerprint matching score threshold. Otherwise, the user is determined not to be a user already stored in the database, triggering an alarm for an abnormal door lock. By accurately matching the battery level stage with the recognition strategy, the security and accuracy of identity authentication are fully guaranteed when the door lock has sufficient power. This stage relies on full fingerprint features for identity verification, comprehensively covering key differences in user fingerprints and avoiding misjudgments or omissions that may occur due to feature simplification, making the identification of authorized users more accurate and reliable. At the same time, it clearly sets matching standards and anomaly alarm mechanisms. When the recognition result does not match an authorized user in the database, an alarm can be triggered in time to effectively intercept unauthorized use and improve the door lock's security capabilities. In addition, activating full feature recognition only when the battery is sufficient avoids wasting device performance resources when the battery is high and reserves adjustment space for switching to a low-power strategy in the subsequent low-battery stage, achieving a reasonable balance between security authentication and resource utilization.

[0020] Furthermore, in step S5, when the user uses the smart lock, the smart lock queries the current battery level. If the current battery level of the smart lock is at the xth position... min The energy phase between the first energy phase and the Xth energy phase, where the xth energy phase... min The energy phases between the xth energy phase and the xth energy phase include the xth energy phase. min In the first and Xth power levels, the collected fingerprint features are adjusted to determine the current power level of the smart lock in the x0th stage. I restricted fingerprint features of the user are collected, where I is [(X-x0) / (Xx...]. min The maximum value between N and N0, where N0 is the preset minimum number of fingerprint features to be collected, and I restricted fingerprint features are represented by N fingerprint features {Z 0_1 Z 0_2 ,…,Z 0_n ,…,Z 0_N From the I fingerprint features with the highest explicit intensity in the {H}, I restricted fingerprint features of the user are collected, and these I restricted fingerprint features are denoted as {H}. 0_1 H 0_2 ,…,H 0_i ,…,H 0_I}, where H 0_i This represents the i-th restricted fingerprint feature of a user. It retrieves the i-th restricted fingerprint feature of the m-th user from the database, denoted as {H}. m_1 H m_2 ,…,Hm_i ,…,H m_I}, where H m_i This represents the i-th restricted fingerprint feature of the m-th user in the database, and it calls the feature explicitness strength corresponding to the I restricted fingerprint features, denoted as {J1, J2, ..., J...}. i ,…,J I}, where J i This represents the feature explicitness corresponding to the i-th restricted fingerprint feature, and then the degree of matching g between the user and the m-th user in the database is calculated. m :

[0021] ;

[0022] Substituting m=1,2,…,M one by one, we obtain the degree of restricted fingerprint matching between the user and the m-th user in the database {g1,g2,…,g m ,…,g M If there exists m such that g m If the fingerprint match score is greater than g, the user identity corresponding to the highest fingerprint matching score is assigned to the current user, where g is the preset fingerprint matching score threshold. Otherwise, if the user is not a user already stored in the database, an alarm is triggered due to a door lock malfunction. This achieves a balance between power consumption optimization and recognition accuracy when the door lock battery is low. By dynamically adjusting the number of fingerprint features collected, only the key features with the highest apparent strength are selected, significantly reducing feature processing and computation during low battery conditions, reducing energy consumption to extend device battery life, and avoiding functional failure due to insufficient power. At the same time, the matching score is calculated based on the weighted average of apparent strength of features, strengthening the recognition role of core features. Even with a reduced number of features, the user identity can still be accurately matched, avoiding misjudgments caused by simplified features. Combined with a preset minimum number of features and an anomaly alarm mechanism, it maintains the minimum recognition accuracy while promptly blocking unauthorized use, balancing device battery life, authentication reliability, and usage security in low battery scenarios.

[0023] Furthermore, in step S6, when adding user information, the user information is stored in the user fingerprint database. When a user uses the smart lock, the monitoring period is updated in real time. If the number of times the smart lock is used within the reference period is 0, the monitoring period β ending at the current time point is ignored, and the reference period is replaced with the monitoring period before β.

[0024] The power management system for identity authentication devices includes: a user fingerprint database construction module, a fingerprint feature identification and sorting module, a power sensitivity correlation analysis module, a fingerprint matching strategy operation module, a restricted fingerprint matching strategy operation module, and a fingerprint data security management module.

[0025] The user fingerprint library establishing module is used for establishing a user fingerprint database according to the collected user data when identity recognition is performed by using the smart door lock;

[0026] The fingerprint feature discrimination sorting module is used for analyzing the feature discrimination degree of the fingerprint features of the user, and re-sorting the fingerprint features of the user;

[0027] The power sensitivity correlation analysis module is used for acquiring the power information and the response time length of the smart door lock in real time, and analyzing the influence of the power of the smart door lock on the sensitivity of the door lock based on the response time length of the smart door lock;

[0028] The fingerprint matching strategy running module is used for matching the user information based on the fingerprint features of the user when the power of the smart door lock is in a saturated state.

[0029] The limited fingerprint matching strategy running module is used for limiting the number of the fingerprint features of the user collected, and matching the user information based on the limited fingerprint features when the power of the smart door lock is in an unsaturated state.

[0030] The fingerprint data security management module is used for updating the new user information in real time, updating the monitoring period, and limiting the reference period to the monitoring period in which the smart door lock is used.

[0031] Compared with the prior art, the beneficial effects achieved by the present application are as follows: on the one hand, the number of fingerprint feature recognitions is dynamically adjusted according to the change of the power of the device, thereby avoiding unnecessary energy consumption caused by full-range full-quantity feature recognition. When the power of the device is sufficient, full-quantity features can be used to ensure comprehensive recognition. When the power enters the performance decline stage, only the key fingerprint features with high user discrimination degree are selected for recognition, thereby reducing the calculation amount in the feature extraction and matching process and reducing the energy consumption per unit time. This on-demand adjustment mode can minimize invalid energy consumption, avoid the need to frequently replace the power supply or charge the device due to frequent power consumption, significantly prolong the use period of the device after single charging, and is particularly suitable for identity authentication scenarios with high demand for endurance.

[0032] On the one hand, by analyzing the discrimination degree and the explicit intensity of the fingerprint features, the most critical features for distinguishing the user identity are selected. Even if the number of recognition features is reduced in the low-power stage, accurate matching can still be achieved based on the key features. At the same time, in combination with the response performance data of the device in different power stages, a reasonable recognition adjustment threshold is set to ensure that the recognition accuracy does not decrease due to feature simplification while optimizing energy consumption. In addition, the feature explicit intensity weighting calculation is introduced in the matching process, which further improves the role of key features in recognition, effectively avoids misjudgment and omission problems caused by power changes or feature quantity adjustment, and maintains the stability and reliability of the identity recognition result.

[0033] On the other hand, it has dynamic updating and adaptive ability, and when a new user is added, the user information can be timely included in the database, so that the device can adapt to the change of the number of users; when the device has no use record in the monitoring period, the reference period is automatically adjusted to avoid the interference of invalid data on the analysis of the power stage, and the accuracy of the identification strategy adjustment is ensured. At the same time, the switching of the whole identification strategy does not need manual operation of the user, and the device can automatically complete the mode conversion according to the power state of itself, so that the user does not need to pay attention to the association between the power of the device and the identification mode in the use process, and does not need to learn the operation process additionally, thereby reducing the use complexity. Whether the number of users increases or decreases, or the use frequency of the device changes, the device can keep stable operation, and provides convenient and smooth use experience for the user. BRIEF DESCRIPTION OF DRAWINGS

[0034] The accompanying drawings are used to provide a further understanding of the present application, and constitute a part of the specification, and are used to explain the present application together with embodiments of the present application, and do not constitute a limitation to the present application. In the drawings:

[0035] Figure 1 is a structure diagram of the power consumption management system for the identity authentication device of the present application;

[0036] Figure 2 is a flowchart of the power consumption management method for the identity authentication device of the present application. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0038] Please refer to Figure 1 and Figure 2 , the present application provides a technical solution: a power consumption management method for an identity authentication device, comprising the following steps:

[0039] S1, when identity recognition is performed by using a smart door lock, a user fingerprint database is established according to the user data collected and obtained;

[0040] S2, the feature recognition degree of the fingerprint features of the user is analyzed, and the fingerprint features of the user are reordered;

[0041] S3, the power of the smart door lock is divided into stages from high to low according to the power of the smart door lock, the power information and the response time length of the smart door lock are acquired in real time, and based on the response time length of the smart door lock, the influence of the power of the smart door lock on the sensitivity of the door lock is analyzed;

[0042] S4, when the power of the smart door lock is in a saturated state, matching the user information based on the fingerprint features of the user;

[0043] S5, when the power of the smart door lock is in an unsaturated state, analyzing the relationship between the current power and the power stage, limiting the number of collected fingerprint features of the user, and matching the user information based on the limited fingerprint features;

[0044] S6, updating the new user information in real time, updating the monitoring period, and limiting the reference period to the monitoring period of the number of times the smart door lock is used.

[0045] In step S1, when using the smart door lock for identity recognition, the fingerprint information of the user is obtained after authorization, the collected fingerprint information is preprocessed and normalized, the fingerprint features of the user are obtained after preprocessing and normalization, the user information and the fingerprint features of the user are matched, and the record is recorded into the user data file after matching. The number of users is M, and the fingerprint features of the mth user are {Z m_1 ,Z m_2 ,…,Z m_n ,…,Z m_N}, wherein N represents the total number of fingerprint features, Z m_n represents the nth fingerprint feature of the mth user, and then a user fingerprint database is established according to the user data file, the user information and the fingerprint features are associated and matched and recorded into a file, and finally integrated into a user fingerprint database, which not only provides stable data support for subsequent feature analysis and dynamic identification strategy adjustment, avoids process faults caused by missing or chaotic basic data, but also flexibly responds to user increase and decrease scenarios, and subsequent maintenance of user information does not need to reconstruct the database, thereby guaranteeing the convenience of data management and laying a foundation for reliable operation and flexible adaptation of the entire system.

[0046] In step S2, for the mth user, the user data file of the mth user is called, and the fingerprint features of the mth user are analyzed, wherein the feature discrimination degree of the nth fingerprint feature is:

[0047]

[0048] wherein k represents the kth user, Z k_n represents the nth fingerprint feature of the kth user, and n=1, 2, …, N is substituted one by one, for the mth user, the feature discrimination degrees of the N fingerprint features are obtained {A m_1 ,A m_2 ,…,A m_n ,…,A m_N}, and m=1, 2, …, M is substituted one by one to obtain the feature discrimination degrees of the M users, and then the feature explicit intensity B n ​The feature explicit strength of the nth fingerprint feature is the average value of the feature discrimination degree of the nth fingerprint feature of the M-bit user, the N fingerprint features are reordered, and the rule of the sorting is that the N fingerprint features are sorted according to the feature discrimination degree of the fingerprint feature from large to small; the discrimination ability of the fingerprint feature is analyzed in depth, and accurate basis is provided for subsequent identification strategy optimization. It firstly calculates the discrimination degree of the fingerprint feature of each user, then obtains the explicit strength of each feature through the average value, clearly distinguishes the features which are more critical to distinguish the user identity, and sorts them according to the importance. This process not only accurately locates the core identification feature, avoids the blindness of subsequent feature screening, but also provides a scientific standard for simplifying the features in the low power stage, ensures that only the high discrimination degree features are retained, and still effectively identifies the user, reduces the amount of calculation without sacrificing accuracy, and improves the fine utilization ability of the system to feature resources, which lays a key foundation for the balance of the whole power consumption management and identity authentication.

[0049] In step S3, after authorization, the power information and response time of the intelligent door lock are acquired in real time, the power of the intelligent door lock is evenly divided, the power of the intelligent door lock is divided into X power stages from high to low according to the power of the intelligent door lock, the xth power stage is analyzed, the number of times of using the intelligent door lock in the reference period is Y in the xth power stage, the reference period is a monitoring period with the current time point as the end point, the response time of Y times of using the intelligent door lock is {C1, C2, …, C y ,…,C Y}, and then the stage average response time D x of the intelligent door lock in the xth stage is obtained, D x is the average value of all values in {C1, C2, …, C y ,…,C Y}, x=1, 2, …, X is substituted one by one to obtain the stage average response time {D1, D2, …, D x ,…,D X} of the intelligent door lock in X stages.

[0050] The specification book of the intelligent door lock is inquired to obtain the standard response time D of the intelligent door lock, and then the door lock sensitivity E x of the xth power stage is obtained, the door lock sensitivity E x of the xth power stage is the ratio of D x and D, x=1, 2, …, X is substituted one by one to obtain the value of x that satisfies E x <E, wherein E is a preset door lock sensitivity threshold, the minimum value x x of x that satisfies E min <E is selected as the identification adjustment critical stage, and then the xth minF = X - x min +1 stage as an identification adjustment stage, where F is the number of identification adjustment stages, F = X - x min +1, record the identification adjustment stage, record the identification adjustment stage in the door lock identification database, provide accurate data support for the power consumption and performance balance of the intelligent door lock, and avoid the blindness of strategy adjustment. Through real-time monitoring and quantitative analysis, the power state is directly related to the sensitivity performance of the door lock, rather than relying on experience to determine the low power stage. It can accurately find the critical node of performance deficiency caused by power decline, ensure that the adjustment is started only in the stage that really needs to be optimized, avoid the impact of early adjustment on the identification experience or the waste of energy consumption caused by late adjustment. At the same time, the identification adjustment stage provides a clear basis for subsequent dynamic adjustment of identification strategy, so that the power consumption optimization at low power has a specific target, and there is no need to blindly simplify the identification process. And the whole process relies on real-time data and factory standard value comparison, the result is objective and reliable, avoiding subjective judgment error, laying a foundation for finding a balance between energy consumption optimization and identification accuracy, and ensuring that the door lock can operate stably in different power stages.

[0051] In step S4, when the user uses the intelligent door lock, the intelligent door lock queries the current power, if the current power of the intelligent door lock is in the power stage between the first power stage and the x min th power stage, where the power stage between the first power stage and the x min th power stage includes the first power stage but does not include the x min th power stage, the intelligent door lock extracts N fingerprint features of the user, judges the user's identity based on the N fingerprint features of the user, and the method for judging the user's identity is: collecting N fingerprint features of the user {Z 0_1 ,Z 0_2 ,…,Z 0_n ,…,Z 0_N}, calling the fingerprint features of the mth user in the database {Z m_1 ,Z m_2 ,…,Z m_n ,…,Z m_N}, calculating the fingerprint matching degree G m between the user and the mth user in the database:

[0052] ;

[0053] Substitute m = 1, 2, …, M one by one to get the fingerprint matching degrees {G1, G2, …, G m ,…,G M} of the user and the M users in the database, if there is m such that G mIf the fingerprint match score is greater than G, the user identity corresponding to the highest fingerprint matching score is assigned to the current user, where G is the pre-set fingerprint matching score threshold. Otherwise, the user is determined not to be a user already stored in the database, triggering an alarm for an abnormal door lock. By accurately matching the battery level stage with the recognition strategy, the security and accuracy of identity authentication are fully guaranteed when the door lock has sufficient power. This stage relies on full fingerprint features for identity verification, comprehensively covering key differences in user fingerprints and avoiding misjudgments or omissions that may occur due to feature simplification, making the identification of authorized users more accurate and reliable. At the same time, it clearly sets matching standards and anomaly alarm mechanisms. When the recognition result does not match an authorized user in the database, an alarm can be triggered in time to effectively intercept unauthorized use and improve the door lock's security capabilities. In addition, activating full feature recognition only when the battery is sufficient avoids wasting device performance resources when the battery is high and reserves adjustment space for switching to a low-power strategy in the subsequent low-battery stage, achieving a reasonable balance between security authentication and resource utilization.

[0054] In step S5, when the user uses the smart lock, the smart lock queries the current battery level. If the current battery level of the smart lock is at the xth position... min The energy phase between the first energy phase and the Xth energy phase, where the xth energy phase... min The energy phases between the xth energy phase and the xth energy phase include the xth energy phase. min In the first and Xth power levels, the collected fingerprint features are adjusted to determine the current power level of the smart lock in the x0th stage. I restricted fingerprint features of the user are collected, where I is [(X-x0) / (Xx...]. min The maximum value between N and N0, where N0 is the preset minimum number of fingerprint features to be collected, and I restricted fingerprint features are represented by N fingerprint features {Z 0_1 Z 0_2 ,…,Z 0_n ,…,Z 0_N From the I fingerprint features with the highest explicit intensity in the {H}, I restricted fingerprint features of the user are collected, and these I restricted fingerprint features are denoted as {H}. 0_1 H 0_2 ,…,H 0_i ,…,H 0_I}, where H 0_i This represents the i-th restricted fingerprint feature of a user. It retrieves the i-th restricted fingerprint feature of the m-th user from the database, denoted as {H}. m_1 H m_2 ,…,H m_i ,…,H m_I}, where H m_i This represents the i-th restricted fingerprint feature of the m-th user in the database, and it calls the feature explicitness strength corresponding to the I restricted fingerprint features, denoted as {J1, J2, ..., J...}.i ,…,J I}, wherein J i represents the feature explicit intensity corresponding to the ith restriction fingerprint feature, and then the restriction fingerprint matching degree g m of the user with the mth user in the database is calculated.

[0055] ;

[0056] Substitute m = 1, 2, …, M, respectively, to obtain the restriction fingerprint matching degree {g1, g2, …, g m ,…,g M} of the user with the mth user in the database, if there is m such that g m > g, the largest restriction fingerprint matching degree corresponding to the user identity is selected to be given to the current user, and g is the preset restriction fingerprint matching degree threshold; otherwise, it is judged that the user is not the user stored in the database, and the door lock abnormal trigger alarm is triggered; in the low power stage of the door lock, the balance between power consumption optimization and recognition accuracy is realized. By dynamically adjusting the number of fingerprint feature collection, only the key features with the highest feature explicit intensity are selected, the feature processing and calculation amount at low power is greatly reduced, the energy consumption is reduced to prolong the device endurance, and the function failure caused by insufficient power is avoided. At the same time, relying on the feature explicit intensity weighted calculation of the matching degree, the recognition effect of the core feature is strengthened, and even if the number of features is reduced, the user identity can still be accurately matched, avoiding the misjudgment problem caused by simplifying the features. Combined with the preset minimum feature number and the abnormal alarm mechanism, the bottom line of recognition accuracy is maintained, and unauthorized use can be intercepted in time, and the device endurance, authentication reliability and use safety are considered in the low power scene.

[0057] In step S6, when the user information is added, the user information is stored in the user fingerprint database, and when the user uses the intelligent door lock, the monitoring period is updated in real time. If the number of times the intelligent door lock is used in the reference period is 0, one monitoring period β ending at the current time point is ignored, and the reference period is replaced by one monitoring period before β.

[0058] The power consumption management system for identity authentication device, the system comprises: a user fingerprint library construction module, a fingerprint feature discrimination sorting module, a power sensitive correlation analysis module, a fingerprint matching strategy running module, a restriction fingerprint matching strategy running module and a fingerprint data security management module;

[0059] The user fingerprint library construction module is used for constructing a user fingerprint database according to the user data collected when the intelligent door lock is used for identity recognition;

[0060] The fingerprint feature discrimination sorting module is used for analyzing the feature discrimination degree of the fingerprint features of the user, and reordering the fingerprint features of the user;

[0061] The power sensitive correlation analysis module is used to obtain the power information and response time of the smart door lock in real time, and analyze the influence of the power of the smart door lock on the sensitivity of the door lock based on the response time of the smart door lock;

[0062] The fingerprint matching strategy running module is used to match the user information based on the fingerprint features of the user when the power of the smart door lock is in a saturated state.

[0063] The limited fingerprint matching strategy running module is used to limit the number of fingerprint features collected from the user when the power of the smart door lock is in an unsaturated state, and match the user information based on the limited fingerprint features.

[0064] The fingerprint data security management module is used to update the new user information in real time, update the monitoring period, and limit the reference period to the monitoring period of the number of times the smart door lock is used.

[0065] Embodiment 1: In the initial deployment stage of the system, the property handles the entry authorization for the residents, and starts the fingerprint information collection process. The collection device first removes the interference information such as stains and noise points in the fingerprint image, and then unifies the fingerprint features of different residents to the same extraction standard to ensure consistent feature format. After processing, the fingerprint features are associated with the resident information and stored as a data file separately. After all the resident files are summarized, a complete fingerprint database is formed to provide basic data for subsequent identification.

[0066] During system operation, the fingerprint features in the database are automatically analyzed. For each fingerprint feature of each resident, the system compares the difference between the feature and the features of other residents at the same position, evaluates the effect of the feature on distinguishing identity, and then combines the evaluation results of all residents to obtain the general distinguishing ability of each feature, and sorts them according to importance to determine which features are more critical to identity recognition.

[0067] At the same time, the system monitors the door lock power and use response time in real time. The power is divided into several intervals from full power to low power, the average response speed of the door lock in each interval is recorded, and compared with the standard response speed at the factory, to judge the sensitivity of the door lock under different power. When the response speed of a certain power interval is lower than the preset standard, mark it and lower power intervals as the stage that needs to adjust the identification strategy, and store it in the system database.

[0068] When the resident uses the door lock, the system first detects the current power: if it is in the high power interval, start full feature recognition, extract all the fingerprint features of the resident, and compare them one by one with the complete features of all residents in the database. If the matching is successful and the resident information meets the standard, the door is unlocked; otherwise, an abnormal alarm is triggered to prevent unauthorized use.

[0069] If the current power is in the marked adjustment stage, the system adjusts the identification strategy: according to the real-time front power level, the part of features with the strongest distinguishing ability is selected from the sorted features, and the number of selected features is ensured to be not less than the minimum value to guarantee the basic identification accuracy. During matching, the key features are emphasized for comparison, and the feature weights with stronger distinguishing ability are mainly referred to, so as to ensure the identification accuracy, reduce the calculation amount of feature processing, and reduce the energy consumption.

[0070] When a new resident moves in, the system automatically processes the fingerprint information and adds it to the database; if the door lock is not used for a period of time, the system will skip this invalid monitoring period and call the previous valid use data to analyze the relationship between power and response performance, ensuring that the identification strategy adjustment is always based on valid information, maintaining the stable operation of the system under different use frequencies.

[0071] It will be obvious to a person skilled in the art that the application is not limited to the details of the exemplary sensor device embodiments described above, but that the application can be implemented in other concrete forms without departing from the spirit or essential characteristics of the application. Therefore, the embodiments should be considered in all respects as illustrative and not restrictive, and the scope of the application is defined by the appended claims and not by the above description, and all changes falling within the meaning and range of equivalents of the essential features of the claims are intended to be embraced therein. Any reference signs in the claims should not be considered as limiting the claims involved.

Claims

1. A method for power consumption management of an identity authentication device, characterized in that: The method comprises the following steps: S1, when identity recognition is performed using the smart door lock, a user fingerprint database is established according to user data collected; S2, a characteristic discrimination degree of the fingerprint features of the user is analyzed, and the fingerprint features of the user are reordered; S3, the power of the smart door lock is divided into stages from high to low according to the power of the smart door lock, the power information and the response time of the smart door lock are acquired in real time, and the influence of the power of the smart door lock on the sensitivity of the door lock is analyzed based on the response time of the smart door lock; S4, when the power of the smart door lock is in a saturated state, user information is matched based on the fingerprint features of the user; S5, when the power of the smart door lock is in an unsaturated state, the relationship between the current power and the power stage is analyzed, the number of collected fingerprint features of the user is limited, and user information is matched based on the limited fingerprint features; S6, newly added user information is updated in real time, and a monitoring period is updated, and a reference period is limited to a monitoring period in which the smart door lock is used.

2. The power management method for an identity authentication device according to claim 1, wherein: In step S1, when identity recognition is performed using the smart door lock, the fingerprint information of the user is obtained after authorization, the collected fingerprint information is preprocessed and normalized, the fingerprint features of the user are obtained after preprocessing and normalization, the user information and the user fingerprint features are matched, and the user data file is recorded after matching. The number of users is M, and the fingerprint features of the mth user are {Z m_1 , Z m_2 , …, Z m_n , …, Z m_N} wherein N represents the total number of fingerprint features, Z m_n represents the nth fingerprint feature of the mth user, and a user fingerprint database is further established according to the user data file.

3. The power management method for an identity authentication device according to claim 2, wherein: In step S2, for the mth user, the user data file of the mth user is called to analyze the fingerprint features of the mth user, and the characteristic discrimination degree of the nth fingerprint feature is: ； wherein k represents the kth user, Z k_n represents the nth fingerprint feature of the kth user, substituting n = 1, 2, …, N, for the mth user, the feature discrimination degree of the N fingerprint features is obtained as {A m_1 , A m_2 , …, A m_n , …, A m_N}, substituting m = 1, 2, …, M, the feature discrimination degree of the M users is obtained, and then the feature explicit intensity B n of the nth fingerprint feature is obtained, which is the average of the feature discrimination degree of the nth fingerprint feature of the M users, and the N fingerprint features are reordered according to the rule that the N fingerprint features are sorted in descending order of the feature discrimination degree of the fingerprint features.

4. The power management method for an identity authentication device according to claim 3, wherein: In step S3, after authorization, the power information and response time of the smart door lock are acquired in real time, the power of the smart door lock is evenly divided, the power of the smart door lock is divided into X power stages from high to low according to the power of the smart door lock, the xth power stage is analyzed, the number of times the smart door lock is used in the reference period is Y in the xth power stage, the reference period is a monitoring period with the current time point as the end point, the response time of the smart door lock for Y times of use is {C1, C2, …, C y ,…,C Y}, and then the stage average response time D x of the smart door lock in the xth stage is obtained, D x is the average value of all values in {C1, C2, …, C y ,…,C Y}, and x=1, 2, …, X is substituted to obtain the stage average response time {D1, D2, …, D x ,…,D X} of the smart door lock in X stages.

5. The power management method for an identity authentication device according to claim 4, wherein: By consulting the smart lock's manufacturer's specifications, the standard response time D of the smart lock can be obtained, and then the lock's sensitivity E at the xth power level can be calculated. x The door lock sensitivity E during the xth power stage x D x Substituting the ratio of D into x = 1, 2, ..., X, we obtain E. x The value of x < E, where E is the preset threshold for door lock sensitivity, and the value that satisfies E is selected. x The minimum value of x < E min As a critical stage for identification and regulation, it will then proceed from the xth stage. min The F stages from the start of the first energy level stage to the Xth energy level stage are designated as the identification and adjustment stages, where F is the number of identification and adjustment stages, F = Xx. min +1, record the recognition and adjustment phase, and record the recognition and adjustment phase in the door lock recognition database.

6. The power management method for an identity authentication device according to claim 5, wherein: In step S4, when the user uses the smart door lock, the smart door lock queries the current power, if the current power of the smart door lock is in the power stage between the first power stage and the x min th power stage, wherein the power stage between the first power stage and the x min th power stage includes the first power stage but does not include the x min th power stage, the smart door lock extracts N fingerprint features of the user, judges the user identity based on the N fingerprint features of the user, and the method for judging the user identity is: collecting N fingerprint features of the user as {Z 0_1 ,Z 0_2 ,…,Z 0_n ,…,Z 0_N}, calling the fingerprint features of the mth user in the database {Z m_1 ,Z m_2 ,…,Z m_n ,…,Z m_N}, and calculating the fingerprint matching degree G m between the user and the mth user in the database. ； Substitute m = 1, 2, …, M, to get the user and the database M user fingerprint matching degree {G1, G2, …, GM}, if there is m makes G m … M}, if there is m makes G m > G, select the largest fingerprint matching degree corresponding to the user identity to give the current user, G is the fingerprint matching degree threshold; otherwise, the user is not stored in the database, trigger the door lock trigger alarm.

7. The power management method for an identity authentication device according to claim 6, wherein: In step S5, when the user uses the smart lock, the smart lock queries the current battery level. If the current battery level of the smart lock is at the xth position... min The energy phase between the first energy phase and the Xth energy phase, where the xth energy phase... min The energy phases between the xth energy phase and the xth energy phase include the xth energy phase. min In the first and Xth power levels, the collected fingerprint features are adjusted to determine the current power level of the smart lock in the x0th stage. I restricted fingerprint features of the user are collected, where I is [(X-x0) / (Xx...]. min The maximum value between N and N0, where N0 is the preset minimum number of fingerprint features to be collected, and I restricted fingerprint features are represented by N fingerprint features {Z 0_1 Z 0_2 ,…,Z 0_n ,…,Z 0_N From the I fingerprint features with the highest explicit intensity in the {H}, I restricted fingerprint features of the user are collected, and these I restricted fingerprint features are denoted as {H}. 0_1 H 0_2 ,…,H 0_i ,…,H 0_I }, where H 0_i This represents the i-th restricted fingerprint feature of a user. It retrieves the i-th restricted fingerprint feature of the m-th user from the database, denoted as {H}. m_1 H m_2 ,…,H m_i ,…,H m_I }, where H m_i This represents the i-th restricted fingerprint feature of the m-th user in the database, and it calls the feature explicitness strength corresponding to the I restricted fingerprint features, denoted as {J1, J2, ..., J...}. i ,…,J I }, where J i This represents the feature explicitness corresponding to the i-th restricted fingerprint feature, and then the degree of matching g between the user and the m-th user in the database is calculated. m : ； Substituting m=1,2,…,M one by one, we obtain the degree of restricted fingerprint matching between the user and the m-th user in the database {g1,g2,…,g m ,…,g M If there exists m such that g m If the value is greater than g, then the user identity corresponding to the highest fingerprint matching degree is selected and assigned to the current user, where g is the pre-set fingerprint matching degree threshold; otherwise, it is determined that the user is not a user already stored in the database, and an alarm is triggered due to a door lock malfunction.

8. The power management method for an identity authentication device according to claim 6, wherein: In step S6, when new user information is added, the user information is stored in the user fingerprint database, and when the user uses the smart door lock, the monitoring period is updated in real time, and if the number of times the smart door lock is used in the reference period is 0, one monitoring period β ending at the current time point is ignored, and the reference period is replaced by one monitoring period before β.

9. A power consumption management system for an identity authentication device, the system being applied to the power consumption management method for an identity authentication device according to any one of claims 1 to 8, characterized by: The system comprises a user fingerprint library establishment module, a fingerprint feature discrimination sorting module, a power sensitivity correlation analysis module, a fingerprint matching strategy running module, a limited fingerprint matching strategy running module, and a fingerprint data security management module; The user fingerprint library establishment module is used to establish a user fingerprint database according to user data collected when identity recognition is performed using the smart door lock; The fingerprint feature discrimination sorting module is used to analyze the characteristic discrimination degree of the fingerprint features of the user, and reorder the fingerprint features of the user; The power sensitivity correlation analysis module is used to acquire the power information and the response time of the smart door lock in real time, and analyze the influence of the power of the smart door lock on the sensitivity of the door lock based on the response time of the smart door lock; The fingerprint matching strategy running module is used to match user information based on the fingerprint features of the user when the power of the smart door lock is in a saturated state; The limited fingerprint matching strategy running module is used to limit the number of collected fingerprint features of the user when the power of the smart door lock is in an unsaturated state, and match user information based on the limited fingerprint features; The fingerprint data security management module is used to update newly added user information in real time, update a monitoring period, and limit a reference period to a monitoring period in which the smart door lock is used.

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