CN116489176A - Private cloud storage server system of intelligent lock - Google Patents

Private cloud storage server system of intelligent lock Download PDF

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Publication number
CN116489176A
CN116489176A CN202310424274.2A CN202310424274A CN116489176A CN 116489176 A CN116489176 A CN 116489176A CN 202310424274 A CN202310424274 A CN 202310424274A CN 116489176 A CN116489176 A CN 116489176A
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China
Prior art keywords
target
unlocking
access user
target access
personnel
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Inventor
皱异
赵永林
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Suzhou Kunshan General Locks Co ltd
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Suzhou Kunshan General Locks Co ltd
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Priority to CN202310424274.2A priority Critical patent/CN116489176A/en
Publication of CN116489176A publication Critical patent/CN116489176A/en
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H04L67/1097—Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C9/00—Individual registration on entry or exit
    • G07C9/00174—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys
    • G07C9/00563—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys using personal physical data of the operator, e.g. finger prints, retinal images, voicepatterns
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C9/00—Individual registration on entry or exit
    • G07C9/00174—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys
    • G07C9/00571—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys operated by interacting with a central unit
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/26—Speech to text systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/08—Network architectures or network communication protocols for network security for authentication of entities
    • H04L63/0815—Network architectures or network communication protocols for network security for authentication of entities providing single-sign-on or federations
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/10—Network architectures or network communication protocols for network security for controlling access to devices or network resources
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • General Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Security & Cryptography (AREA)
  • Computer Hardware Design (AREA)
  • Computational Linguistics (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Storage Device Security (AREA)

Abstract

The invention discloses a private cloud storage server system of an intelligent lock, and relates to the technical field of intelligent locks; the intelligent lock video storage system comprises a personnel video acquisition module, a personnel information analysis module, a personnel video storage module, an access login information acquisition module, an access login security analysis module, an early warning terminal, a display terminal and a cloud database, wherein personnel unlocking video content acquired by an appointed intelligent lock in an appointed enterprise is analyzed, classified storage is further carried out, safety and access authority levels of an access user are analyzed, the problem of insufficient pertinence of intelligent lock video storage in the prior art is solved, the pertinence of intelligent lock video storage and intelligent authority setting are realized, the rationality, the ordering and the security of intelligent lock video storage are greatly improved, the risk of intelligent lock password leakage is reduced, and the safety of information and property in the enterprise is ensured to a certain extent.

Description

Private cloud storage server system of intelligent lock
Technical Field
The invention relates to the technical field of intelligent locks, in particular to a private cloud storage server system of an intelligent lock.
Background
Along with the continuous development of intelligent house, intelligent lock also is popular in market, because of its advantage that has safety convenient, wide application in all kinds of enterprises, but intelligent lock self-collection user's video in the use contains information such as personnel's face image in the video, has the potential safety hazard, consequently in order to reveal the risk that causes the enterprise because of the video, need manage intelligent lock's information storage safety.
The unlocking video of the user obtained by the intelligent lock in the prior art is mainly stored on a unified cloud storage server provided by a manufacturer, and obviously, the storage method has at least the following problems:
1. the importance of video has been decided to the video content, and the prior art does not carry out the analysis to the content of the user video of unblanking that the intelligent lock gathered, and then can't be accurate to know the privacy degree of user video of unblanking, on the other hand, does not classify the face identification image of user according to the intelligent lock, thereby can't realize the pertinence that different users correspond the video content analysis of unblanking, can't improve the order and the rationality of intelligent latch storage area in the storage process yet, can't improve the accuracy that the user unblanked video carries out the authority setting simultaneously, and can't ensure the security that follow-up user unblanked video was preserved.
2. The login security of the access user is a precondition of the unlocking video storage security of the user, the access security of the access user is not analyzed according to the face image, the work number, the access MAC address and the login time of the access user in the prior art, the identity of the access user cannot be effectively confirmed, the unlocking video storage security is reduced, meanwhile, reliable data cannot be provided for the access authority analysis of the subsequent access user, the risk of unlocking video leakage of the user cannot be effectively reduced, the password leakage of the intelligent lock is caused, and therefore enterprise information or property security cannot be effectively guaranteed.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a private cloud storage server system of an intelligent lock.
In order to solve the technical problems, the invention adopts the following technical scheme: a private cloud storage server system of an intelligent lock comprises a personnel video acquisition module, wherein the personnel video acquisition module is used for acquiring unlocking videos and unlocking modes of target personnel corresponding to the intelligent lock in a specified period in a specified enterprise.
And the personnel information analysis module is used for analyzing the information privacy evaluation coefficients of all the target personnel according to the unlocking video and unlocking mode of all the target personnel.
And the personnel video storage module is used for analyzing the storage subareas of the unlocking videos corresponding to each target personnel so as to store and set the authority level.
The access login information acquisition module is used for acquiring login information of a target access user corresponding to the designated intelligent lock.
The access login security analysis module is used for analyzing the login security evaluation coefficient of the target access user corresponding to the designated intelligent lock, judging the login state corresponding to the target access user, analyzing each memory subarea which is corresponding to the target access user and is permitted to be accessed if the login state corresponding to the target access user is in the security state, and sending the memory subarea to the display terminal, otherwise, sending an early warning signal to the early warning terminal.
And the early warning terminal is used for carrying out early warning prompt when the login state corresponding to the target access user is in a dangerous state.
And the display terminal is used for displaying each memory subarea which is corresponding to the access permission of the target access user.
The cloud database is used for storing the face images and the information privacy evaluation coefficient sections of the memory subareas corresponding to the appointed intelligent locks and allowing unlocking, storing the job numbers, the standard face images, the job levels and the job entering time length corresponding to the staff, and storing the MAC addresses corresponding to the computers in the appointed enterprises.
Preferably, the unlocking mode comprises password unlocking, fingerprint identification unlocking and face identification unlocking.
Preferably, the information privacy assessment coefficient of each target person is analyzed, and the specific analysis process is as follows: and acquiring face images corresponding to all target persons from unlocking videos corresponding to all target persons, comparing the face images with face images corresponding to the designated intelligent locks and permitting unlocking in a cloud database, if the face images corresponding to the designated intelligent locks and permitting unlocking are different, marking the target persons as non-associated persons, and if the face images corresponding to the designated intelligent locks and permitting unlocking are the same, marking the target persons as associated persons, and classifying the target persons as non-associated persons and associated persons in this way.
And respectively analyzing information privacy evaluation coefficients corresponding to each non-associated person and each associated person according to the unlocking mode and unlocking video corresponding to each target person.
Preferably, the information privacy assessment coefficient corresponding to each non-associated person is obtained through analysis, and the specific analysis process is as follows: and extracting unlocking modes corresponding to the non-associated persons, and if the unlocking mode corresponding to a certain non-associated person is password unlocking, acquiring the display area of a password input area in the unlocking video corresponding to the non-associated person from the unlocking video corresponding to the non-associated person, and marking the display area as S.
The unlocking audio corresponding to the unassociated personnel is obtained from the unlocking video corresponding to the unassociated personnel, the unlocking text corresponding to the unassociated personnel is obtained through voice recognition, and then each unlocking keyword corresponding to the unassociated personnel is obtained through keyword extraction, so that the unlocking keywords are compared with the set password keywords, if a certain unlocking keyword corresponding to the unassociated personnel is identical to a certain password keyword, the unlocking keywords are marked as target keywords, the number of the target keywords corresponding to the unassociated personnel is counted in the mode, and N is marked.
By calculation formulaObtaining information privacy evaluation coefficients alpha corresponding to each associated person i Wherein Q' is the set reference facial image sharpness, gamma 1 、γ 2 、γ 3 The method comprises the steps of respectively setting target keyword number, face covering area and weight factors corresponding to face image definition of associated personnel, χ' is information confidentiality influence factor corresponding to the associated personnel, and e is a natural constant.
If the unlocking mode corresponding to a certain non-related person is fingerprint identification and resolutionLock is obtained by calculation formulaObtaining information privacy evaluation coefficient corresponding to the non-relevant person>Wherein ε is 3 And e is a correction factor corresponding to the set target keyword number, and e is a natural constant.
In this way, information privacy assessment coefficients corresponding to the non-associated persons are obtained.
Preferably, the information privacy assessment coefficients corresponding to each associated person are analyzed, and the specific calculation process is as follows: dividing unlocking video corresponding to each associated person to obtain each video picture, further obtaining each face image of each associated person from each video picture corresponding to each associated person, extracting the total face area and the face covering area in each face image of each associated person from each face image of each associated person, and marking the total face area and the face covering area as S respectively ij And S' ij Wherein i represents the number corresponding to each associated person, i=1, 2....n, j represents the number corresponding to each face image, j=1, 2. Once again, m is chosen, simultaneously acquiring the definition of each face image from each face image of each associated person, denoted as Q ij 。
Acquiring unlocking audio corresponding to each associated person from unlocking videos of each associated person, and further acquiring the number of target keywords corresponding to each associated person according to an analysis mode of the number of target keywords corresponding to non-associated persons, wherein the number is marked as N i 。
By calculation formulaObtaining information privacy evaluation coefficients alpha corresponding to each associated person i Wherein Q' is the set reference facial image sharpness, gamma 1 、γ 2 、γ 3 The target keyword number, the face covering area and the weight factors corresponding to the face image definition of the set associated personnel are respectively adopted.
Preferably, the memory subareas of the unlocking video corresponding to each target person are analyzed, and the specific analysis process is as follows: based on the information privacy evaluation coefficients corresponding to the non-associated persons and the associated persons, obtaining the information privacy evaluation coefficients corresponding to the target persons, comparing the information privacy evaluation coefficients with the information privacy evaluation coefficient intervals of the memory subareas corresponding to the appointed intelligent locks stored in the cloud database, and judging that the memory subareas corresponding to the appointed intelligent locks are the memory subareas of the unlocking videos corresponding to the target persons if the information privacy evaluation coefficients corresponding to the certain target persons are in the information privacy evaluation coefficient intervals of the memory subareas corresponding to the appointed intelligent locks, so that the memory subareas of the unlocking videos corresponding to the target persons are obtained.
Preferably, the login information of the target access user includes a face image, a job number, an access MAC address, and a login time.
Preferably, the login security evaluation coefficient of the target access user corresponding to the specified intelligent lock is analyzed, and the specific analysis process is as follows: comparing the job number corresponding to the target access user with the job number corresponding to each employee stored in the cloud database to obtain a reference employee corresponding to the target access user, extracting a standard face image corresponding to the reference employee from the cloud database, and comparing the standard face image with the face image corresponding to the target access user to obtain an identity security evaluation coefficient corresponding to the target access user and marking the identity security evaluation coefficient as phi 1.
And comparing and analyzing the access MAC address corresponding to the target access user with the MAC address corresponding to each computer in the appointed enterprise stored in the cloud database to obtain an access address security evaluation coefficient corresponding to the target access user, and marking the access address security evaluation coefficient as phi 2.
Bringing the login time corresponding to the target access user into a calculation formulaObtaining an access time security evaluation coefficient phi 3 corresponding to the target access user, wherein T Upper part 、T Lower part(s) Respectively the working time and the working time of a designated enterprise stored in a cloud database, wherein DeltaT is the set allowable login time difference, and T is a tableAnd showing the login time corresponding to the target access user, wherein mu is a weight factor corresponding to the set access time security evaluation coefficient.
By calculation formulaObtaining login security evaluation coefficient phi, sigma of target access user corresponding to appointed intelligent lock 1 、σ 2 、σ 3 Respectively setting weight factors corresponding to the identity security assessment coefficient, the access address security assessment coefficient and the access time security assessment coefficient.
Preferably, the specific judgment process is as follows: comparing the login security evaluation coefficient of the target access user corresponding to the designated intelligent lock with the set standard login security evaluation coefficient, if the login security evaluation coefficient of the target access user corresponding to the designated intelligent lock is greater than or equal to the standard login security evaluation coefficient, judging that the login state corresponding to the target access user is in a security state, otherwise, judging that the login state corresponding to the target access user is in a dangerous state.
Preferably, each memory subarea which is accessed by the target access user and is correspondingly permitted to be accessed is analyzed, and the specific analysis process is as follows: based on the job number corresponding to the target access user, extracting the job level and the job entering time length corresponding to the target access user from the cloud database, and substituting the job level and the job entering time length into a calculation formulaObtaining an access right evaluation coefficient psi corresponding to the target access user, wherein D, T ' respectively represents the position grade and the time length of entering the position corresponding to the target access user, D ' and T ' are respectively set standard position grade and standard time length of entering the position, and tau 1 、τ 2 Respectively set weight factors corresponding to the job level and the job entering time length.
And comparing the access right evaluation coefficient corresponding to the target access user with the access right evaluation coefficient corresponding to each set access right level to obtain the access right level corresponding to the target access user.
And comparing the access authority level corresponding to the target access user with the authority level corresponding to each memory subarea, and judging that the memory subarea is the memory subarea which is permitted to be accessed by the target access user if the access authority level corresponding to the target access user is greater than or equal to the authority level corresponding to a certain memory subarea, so as to obtain each memory subarea which is permitted to be accessed by the target access user.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the private cloud storage server system of the intelligent lock, provided by the invention, the unlocking video content of the person collected by the appointed intelligent lock in the appointed enterprise is analyzed, so that classified storage is performed, the safety and the access authority level of the access user are analyzed, the problem of intelligent lock video storage in the prior art is solved, the targeted storage and the intelligent authority setting of the intelligent lock video are realized, the rationality, the order and the safety of the intelligent lock video storage are greatly improved, the risk of password leakage of the intelligent lock is reduced, and the safety of information and property in the enterprise is ensured to a certain extent.
2. According to the invention, the information privacy evaluation coefficients of all target persons are analyzed in the personnel information analysis module, so that data is provided for the storage and authority level setting of the unlocking videos corresponding to all subsequent target persons, meanwhile, the classified storage of the videos is realized, and the ordering and efficiency of the storage space in the storage process are greatly improved.
3. According to the access login security analysis module, the login security evaluation coefficient of the target access user corresponding to the designated intelligent lock is analyzed, so that the security of the access user is effectively ensured, and the security guarantee is provided for the storage space of the designated intelligent lock.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram showing the connection of the system modules according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a private cloud storage server system of an intelligent lock includes a personnel video acquisition module, a personnel information analysis module, a personnel video storage module, an access login information acquisition module, an access login security analysis module, an early warning terminal, a display terminal and a cloud database.
The cloud database is respectively connected with a personnel information analysis module, a personnel video storage module and an access login security analysis module, the personnel information analysis module is also connected with a personnel video acquisition module and a personnel video storage module, the access login information acquisition module is respectively connected with the personnel video storage module and the access login security analysis module, and the access login security analysis module is also connected with an early warning terminal and a display terminal.
And the personnel video acquisition module is used for acquiring unlocking videos and unlocking modes of target personnel corresponding to the specified intelligent locks in a specified period in a specified enterprise.
In a specific embodiment, the unlocking mode includes password unlocking, fingerprint identification unlocking and face identification unlocking.
In the above, the unlocking video corresponding to each target person is collected by designating the camera in the intelligent lock, and the unlocking mode corresponding to each target person is obtained from the intelligent lock management background.
And the personnel information analysis module is used for analyzing the information privacy evaluation coefficients of all the target personnel according to the unlocking video and unlocking mode of all the target personnel.
In a specific embodiment, the information privacy assessment coefficients of each target person are analyzed, and the specific analysis process is as follows: and acquiring face images corresponding to all target persons from unlocking videos corresponding to all target persons, comparing the face images with face images corresponding to the designated intelligent locks and permitting unlocking in a cloud database, if the face images corresponding to the designated intelligent locks and permitting unlocking are different, marking the target persons as non-associated persons, and if the face images corresponding to the designated intelligent locks and permitting unlocking are the same, marking the target persons as associated persons, and classifying the target persons as non-associated persons and associated persons in this way.
And respectively analyzing information privacy evaluation coefficients corresponding to each non-associated person and each associated person according to the unlocking mode and unlocking video corresponding to each target person.
In another specific embodiment, the information privacy assessment coefficients corresponding to the non-relevant persons are obtained through analysis, and the specific analysis process is as follows: and extracting unlocking modes corresponding to the non-associated persons, and if the unlocking mode corresponding to a certain non-associated person is password unlocking, acquiring the display area of a password input area in the unlocking video corresponding to the non-associated person from the unlocking video corresponding to the non-associated person, and marking the display area as S.
The unlocking audio corresponding to the unassociated personnel is obtained from the unlocking video corresponding to the unassociated personnel, the unlocking text corresponding to the unassociated personnel is obtained through voice recognition, and then each unlocking keyword corresponding to the unassociated personnel is obtained through keyword extraction, so that the unlocking keywords are compared with the set password keywords, if a certain unlocking keyword corresponding to the unassociated personnel is identical to a certain password keyword, the unlocking keywords are marked as target keywords, the number of the target keywords corresponding to the unassociated personnel is counted in the mode, and N is marked.
By calculation formulaObtaining information privacy evaluation coefficient corresponding to the non-relevant person>S 'and N' are respectively the display area of the set reference password input area and the number of the reference target keywords epsilon 1 、ε 2 And (3) respectively setting a weight factor corresponding to the display area of the password input area and the number of target keywords, wherein χ is an information confidentiality influence factor corresponding to the set non-related personnel, and e represents a natural constant.
If the unlocking mode corresponding to a certain non-associated person is fingerprint identification unlocking, the unlocking is performed according to a calculation formulaObtaining information privacy evaluation coefficient corresponding to the non-relevant person>Wherein ε is 3 And e is a correction factor corresponding to the set target keyword number, and e is a natural constant.
In this way, information privacy assessment coefficients corresponding to the non-associated persons are obtained.
In another specific embodiment, the information privacy assessment coefficients corresponding to each associated person are analyzed, and the specific calculation process is as follows: dividing unlocking video corresponding to each associated person to obtain each video picture, further obtaining each face image of each associated person from each video picture corresponding to each associated person, extracting the total face area and the face covering area in each face image of each associated person from each face image of each associated person, and marking the total face area and the face covering area as S respectively ij And S is i ′ j Wherein i represents the number corresponding to each associated person, i=1, 2....n, j represents the number corresponding to each face image, j=1, 2. Once again, m is chosen, simultaneously acquiring the definition of each face image from each face image of each associated person, denoted as Q ij 。
Obtaining unlocking video of each associated personTaking unlocking audio corresponding to each associated person, further obtaining the number of target keywords corresponding to each associated person according to the analysis mode of the number of target keywords corresponding to the non-associated person, and marking the number as N i 。
By calculation formulaObtaining information privacy evaluation coefficients alpha corresponding to each associated person i Wherein Q' is the set reference facial image sharpness, gamma 1 、γ 2 、γ 3 The method comprises the steps of respectively setting target keyword number, face covering area and weight factors corresponding to face image definition of associated personnel, χ' is information confidentiality influence factor corresponding to the associated personnel, and e is a natural constant.
It should be noted that, the information privacy influencing factor corresponding to the associated person is smaller than the information privacy influencing factor corresponding to the non-associated person, and when the information privacy influencing factor is larger, the information privacy evaluation coefficient is smaller.
According to the embodiment of the invention, through analyzing the information privacy evaluation coefficients of all target personnel, data is provided for the storage and authority level setting of the unlocking video corresponding to each subsequent target personnel, and meanwhile, the classified storage of the video is realized, and the ordering and efficiency of the storage space in the storage process are greatly improved;
and the personnel video storage module is used for analyzing the storage subareas of the unlocking videos corresponding to each target personnel so as to store and set the authority level.
It should be noted that, the storage area of the designated intelligent lock is a fixed storage area in the designated cloud storage server, and the storage area of the designated intelligent lock is divided into storage subareas according to a preset storage space, and the designated intelligent lock manufacturer has no access right.
It should be further noted that, the authority level corresponding to each memory subarea is fixedly set, if the authority level corresponding to the memory subarea a is 1 level and the memory subarea of the unlocking video corresponding to a certain target person is the memory subarea a, the authority level of the unlocking video corresponding to the target person is set to 1 level.
It should be noted that the higher the authority level, the higher the confidentiality degree of the memory subarea.
In a specific embodiment, the memory subarea of the unlocking video corresponding to each target person is analyzed, and the specific analysis process is as follows: based on the information privacy evaluation coefficients corresponding to the non-associated persons and the associated persons, obtaining the information privacy evaluation coefficients corresponding to the target persons, comparing the information privacy evaluation coefficients with the information privacy evaluation coefficient intervals of the memory subareas corresponding to the appointed intelligent locks stored in the cloud database, and judging that the memory subareas corresponding to the appointed intelligent locks are the memory subareas of the unlocking videos corresponding to the target persons if the information privacy evaluation coefficients corresponding to the certain target persons are in the information privacy evaluation coefficient intervals of the memory subareas corresponding to the appointed intelligent locks, so that the memory subareas of the unlocking videos corresponding to the target persons are obtained.
The access login information acquisition module is used for acquiring login information of a target access user corresponding to the designated intelligent lock.
In the above description, the login information of the target access user includes a face image, a job number, an access MAC address and a login time.
It should be noted that, the MAC address of each computer in the designated enterprise is fixed, and the storage area of the designated smart lock cannot be accessed by any computer outside the designated enterprise.
The access login security analysis module is used for analyzing the login security evaluation coefficient of the target access user corresponding to the designated intelligent lock, judging the login state corresponding to the target access user, analyzing each memory subarea which is corresponding to the target access user and is permitted to be accessed if the login state corresponding to the target access user is in the security state, and sending the memory subarea to the display terminal, otherwise, sending an early warning signal to the early warning terminal.
In a specific embodiment, the login security assessment coefficient of the target access user corresponding to the specified intelligent lock is analyzed, and the specific analysis process is as follows: comparing the job number corresponding to the target access user with the job number corresponding to each employee stored in the cloud database to obtain a reference employee corresponding to the target access user, extracting a standard face image corresponding to the reference employee from the cloud database, and comparing the standard face image with the face image corresponding to the target access user to obtain an identity security evaluation coefficient corresponding to the target access user and marking the identity security evaluation coefficient as phi 1.
In the above, the specific analysis process is as follows: and if the job number corresponding to the target access user is the same as the job number corresponding to a certain employee stored in the cloud database, taking the certain employee as a reference employee corresponding to the target access user.
In the above, the identity security assessment coefficient corresponding to the target access user is obtained, and the specific analysis process is as follows: if the standard face image corresponding to the reference staff is the same as the face image corresponding to the target access user, the identity security evaluation coefficient corresponding to the target access user is marked as b1, and if the standard face image corresponding to the reference staff is not the same as the face image corresponding to the target access user, the identity security evaluation coefficient corresponding to the target access user is marked as b2, so that the identity security evaluation coefficient phi 1 corresponding to the target access user is obtained, wherein the value of phi 1 is b1 or b2, both b1 and b2 are natural numbers, and b1 is more than b2.
And comparing and analyzing the access MAC address corresponding to the target access user with the MAC address corresponding to each computer in the appointed enterprise stored in the cloud database to obtain an access address security evaluation coefficient corresponding to the target access user, and marking the access address security evaluation coefficient as phi 2.
In the above, the access address security evaluation coefficient corresponding to the target access user is obtained, and the specific analysis process is as follows: if the access MAC address corresponding to the target access user is the same as the MAC address corresponding to a certain computer in the appointed enterprise, the access address security evaluation coefficient corresponding to the target access user is marked as f1, if the access MAC address corresponding to the target access user is different from the MAC address corresponding to each computer in the appointed enterprise, the access address security evaluation coefficient corresponding to the target access user is marked as f2, and therefore the access address security evaluation coefficient phi 2 corresponding to the target access user is obtained, wherein the phi 2 takes the value of f1 or f2, and f1 and f2 are natural numbers, and f1 is more than f2.
Bringing the login time corresponding to the target access user into a calculation formulaObtaining an access time security evaluation coefficient phi 3 corresponding to the target access user, wherein T Upper part 、T Lower part(s) The cloud database is used for storing the working time and the working time of a designated enterprise, delta T is a set allowable login time difference, T represents the login time corresponding to a target access user, and mu is a weight factor corresponding to a set access time security evaluation coefficient.
T is the same as Upper part The sign-on time corresponding to the target access user is longer than the working time of the appointed enterprise, T Lower part(s) < T represents that the login time corresponding to the target access user is later than the working time of the appointed enterprise, T Upper part ≤T≤T Lower part(s) And indicating that the login time corresponding to the target access user is between the working time and the working time of the appointed enterprise.
By calculation formulaObtaining login security evaluation coefficient phi, sigma of target access user corresponding to appointed intelligent lock 1 、σ 2 、σ 3 Respectively setting weight factors corresponding to the identity security assessment coefficient, the access address security assessment coefficient and the access time security assessment coefficient.
According to the embodiment of the invention, the login security evaluation coefficient of the target access user corresponding to the designated intelligent lock is analyzed, so that the security of the access user is effectively ensured, and the security guarantee is provided for the storage space of the designated intelligent lock.
And the early warning terminal is used for carrying out early warning prompt when the login state corresponding to the target access user is in a dangerous state.
And the display terminal is used for displaying each memory subarea which is corresponding to the access permission of the target access user.
The cloud database is used for storing the face images and the information privacy evaluation coefficient sections of the memory subareas corresponding to the appointed intelligent locks and allowing unlocking, storing the job numbers, the standard face images, the job levels and the job entering time length corresponding to the staff, and storing the MAC addresses corresponding to the computers in the appointed enterprises.
According to the embodiment of the invention, the unlocking video content of the person collected by the appointed intelligent lock in the appointed enterprise is analyzed, so that classified storage is performed, and the security and access authority level of the access user are analyzed, so that the problems of intelligent lock video storage in the prior art are solved, the targeted storage and intelligent authority setting of the intelligent lock video are realized, the rationality, the order and the security of intelligent lock video storage are greatly improved, the risk of intelligent lock password leakage is reduced, and the security of information and property in the enterprise is ensured to a certain extent.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims (10)

1. A private cloud storage server system of an intelligent lock, comprising:
the personnel video acquisition module is used for acquiring unlocking videos and unlocking modes of target personnel corresponding to the specified intelligent locks in a specified period in a specified enterprise;
the personnel information analysis module is used for analyzing information privacy assessment coefficients of all target personnel according to unlocking videos and unlocking modes of all target personnel;
the personnel video storage module is used for analyzing storage subareas of unlocking videos corresponding to all target personnel so as to store and set authority levels;
the access login information acquisition module is used for acquiring login information of a target access user corresponding to the designated intelligent lock;
the access login security analysis module is used for analyzing the login security evaluation coefficient of the target access user corresponding to the appointed intelligent lock, judging the login state corresponding to the target access user, analyzing each memory subarea which is corresponding to the target access user and is permitted to be accessed if the login state corresponding to the target access user is in a security state, and sending to the display terminal, otherwise, sending an early warning signal to the early warning terminal;
the early warning terminal is used for carrying out early warning prompt when the login state corresponding to the target access user is in a dangerous state;
the display terminal is used for displaying each memory subarea which is corresponding to the target access user and is permitted to be accessed;
the cloud database is used for storing the face images and the information privacy evaluation coefficient sections of the memory subareas corresponding to the appointed intelligent locks and allowing unlocking, storing the job numbers, the standard face images, the job levels and the job entering time length corresponding to the staff, and storing the MAC addresses corresponding to the computers in the appointed enterprises.
2. The private cloud storage server system of an intelligent lock according to claim 1, wherein the unlocking means comprises password unlocking, fingerprint identification unlocking and face identification unlocking.
3. The private cloud storage server system of the intelligent lock according to claim 2, wherein the analyzing the information privacy evaluation coefficients of each target person comprises the following specific analysis process:
acquiring face images corresponding to all target persons from unlocking videos corresponding to all target persons, comparing the face images with face images corresponding to the designated intelligent locks and permitting unlocking in a cloud database, if the face images corresponding to the designated intelligent locks and permitting unlocking are different, marking the target persons as non-associated persons, and if the face images corresponding to the designated intelligent locks and permitting unlocking are the same, marking the target persons as associated persons, and classifying the target persons as non-associated persons and associated persons in the mode;
and respectively analyzing information privacy evaluation coefficients corresponding to each non-associated person and each associated person according to the unlocking mode and unlocking video corresponding to each target person.
4. The private cloud storage server system of the intelligent lock according to claim 3, wherein the analysis obtains information privacy assessment coefficients corresponding to each non-associated person, and the specific analysis process is as follows:
extracting unlocking modes corresponding to each non-associated person, if the unlocking mode corresponding to a certain non-associated person is password unlocking, acquiring the display area of a password input area in the unlocking video corresponding to the non-associated person from the unlocking video corresponding to the non-associated person, and marking the display area as S;
acquiring unlocking audio corresponding to the unassociated personnel from unlocking video corresponding to the unassociated personnel, obtaining unlocking text corresponding to the unassociated personnel through voice recognition, further obtaining unlocking keywords corresponding to the unassociated personnel through keyword extraction, comparing the unlocking keywords with the set password keywords, and if a certain unlocking keyword corresponding to the unassociated personnel is identical to a certain password keyword, marking the unlocking keyword as a target keyword, counting the number of the target keywords corresponding to the unassociated personnel in the mode, and marking the number as N;
by calculation formulaObtaining information privacy evaluation coefficient corresponding to the non-relevant person>S 'and N' are respectively the display area of the set reference password input area and the number of the reference target keywords epsilon 1 、ε 2 Respectively setting a weight factor corresponding to the display area of a password input area and the number of target keywords, wherein χ is an information confidentiality influence factor corresponding to a set non-related person, and e represents a natural constant;
if the unlocking mode corresponding to a certain non-associated person is fingerprint identification unlocking, the unlocking is performed according to a calculation formulaObtaining information privacy evaluation coefficient corresponding to the non-relevant person>Wherein ε is 3 E represents a natural constant for a correction factor corresponding to the set target keyword number;
in this way, information privacy assessment coefficients corresponding to the non-associated persons are obtained.
5. The private cloud storage server system of the intelligent lock according to claim 4, wherein the information privacy assessment coefficients corresponding to each associated person are analyzed, and the specific calculation process is as follows:
dividing unlocking video corresponding to each associated person to obtain each video picture, further obtaining each face image of each associated person from each video picture corresponding to each associated person, extracting the total face area and the face covering area in each face image of each associated person from each face image of each associated person, and marking the total face area and the face covering area as S respectively ij And S' ij Wherein i represents the number corresponding to each associated person, i=1, 2....n, j represents the number corresponding to each face image, j=1, 2. Once again, m is chosen, simultaneously acquiring the definition of each face image from each face image of each associated person, denoted as Q ij ；
Acquiring unlocking audio corresponding to each associated person from unlocking videos of each associated person, and further acquiring the number of target keywords corresponding to each associated person according to an analysis mode of the number of target keywords corresponding to non-associated persons, wherein the number is marked as N i ；
By calculation formulaObtaining information privacy evaluation coefficients alpha corresponding to each associated person i Wherein Q' is the set reference facial image sharpness, gamma 1 、γ 2 、γ 3 Target keywords of associated personnel respectively setThe number, the face covering area and the weight factor corresponding to the definition of the face image are that χ' is the information confidentiality influence factor corresponding to the set associated person, and e is a natural constant.
6. The private cloud storage server system of the intelligent lock according to claim 5, wherein the analyzing the memory subarea of the unlocking video corresponding to each target person comprises the following specific analysis process:
based on the information privacy evaluation coefficients corresponding to the non-associated persons and the associated persons, obtaining the information privacy evaluation coefficients corresponding to the target persons, comparing the information privacy evaluation coefficients with the information privacy evaluation coefficient intervals of the memory subareas corresponding to the appointed intelligent locks stored in the cloud database, and judging that the memory subareas corresponding to the appointed intelligent locks are the memory subareas of the unlocking videos corresponding to the target persons if the information privacy evaluation coefficients corresponding to the certain target persons are in the information privacy evaluation coefficient intervals of the memory subareas corresponding to the appointed intelligent locks, so that the memory subareas of the unlocking videos corresponding to the target persons are obtained.
7. The private cloud storage server system of claim 1, wherein said login information of said target access user includes face image, job number, access MAC address and login time.
8. The private cloud storage server system of claim 7, wherein the analysis specifies a login security assessment factor of a target access user for the smart lock, and the specific analysis process is as follows:
comparing the job number corresponding to the target access user with the job number corresponding to each employee stored in the cloud database to obtain a reference employee corresponding to the target access user, extracting a standard face image corresponding to the reference employee from the cloud database, and further comparing the standard face image with the face image corresponding to the target access user to obtain an identity security evaluation coefficient corresponding to the target access user and marking the identity security evaluation coefficient as phi 1;
comparing and analyzing the access MAC address corresponding to the target access user with the MAC address corresponding to each computer in the appointed enterprise stored in the cloud database to obtain an access address security evaluation coefficient corresponding to the target access user, and marking the access address security evaluation coefficient as phi 2;
bringing the login time corresponding to the target access user into a calculation formulaObtaining an access time security evaluation coefficient phi 3 corresponding to the target access user, wherein T Upper part 、T Lower part(s) The cloud database is used for storing the working time and the working time of a designated enterprise respectively, wherein DeltaT is a set allowable login time difference, T represents the login time corresponding to a target access user, and mu is a weight factor corresponding to a set access time security evaluation coefficient;
by calculation formulaObtaining login security evaluation coefficient phi, sigma of target access user corresponding to appointed intelligent lock 1 、σ 2 、σ 3 Respectively setting weight factors corresponding to the identity security assessment coefficient, the access address security assessment coefficient and the access time security assessment coefficient.
9. The private cloud storage server system of claim 8, wherein the determining target accesses a login state corresponding to the user, and the specific determining process is as follows: comparing the login security evaluation coefficient of the target access user corresponding to the designated intelligent lock with the set standard login security evaluation coefficient, if the login security evaluation coefficient of the target access user corresponding to the designated intelligent lock is greater than or equal to the standard login security evaluation coefficient, judging that the login state corresponding to the target access user is in a security state, otherwise, judging that the login state corresponding to the target access user is in a dangerous state.
10. The private cloud storage server system of the intelligent lock according to claim 9, wherein the analysis target accesses each memory subarea which is correspondingly permitted to be accessed by the user, and the specific analysis process is as follows:
based on the job number corresponding to the target access user, extracting the job level and the job entering time length corresponding to the target access user from the cloud database, and substituting the job level and the job entering time length into a calculation formulaObtaining an access right evaluation coefficient psi corresponding to the target access user, wherein D, T ' respectively represents the position grade and the time length of entering the position corresponding to the target access user, D ' and T ' are respectively set standard position grade and standard time length of entering the position, and tau 1 、τ 2 Respectively setting weight factors corresponding to the job level and the job entering time length;
comparing the access right evaluation coefficient corresponding to the target access user with the access right evaluation coefficient corresponding to each set access right level to obtain the access right level corresponding to the target access user;
and comparing the access authority level corresponding to the target access user with the authority level corresponding to each memory subarea, and judging that the memory subarea is the memory subarea which is permitted to be accessed by the target access user if the access authority level corresponding to the target access user is greater than or equal to the authority level corresponding to a certain memory subarea, so as to obtain each memory subarea which is permitted to be accessed by the target access user.
CN202310424274.2A 2023-04-20 2023-04-20 Private cloud storage server system of intelligent lock Pending CN116489176A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117560423A (en) * 2023-10-07 2024-02-13 上海琨山智能科技有限公司 Cloud storage node-based intelligent lock cloud storage resource scheduling system
CN117560423B (en) * 2023-10-07 2024-07-26 苏州琨山通用锁具有限公司 Cloud storage node-based intelligent lock cloud storage resource scheduling system

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117560423A (en) * 2023-10-07 2024-02-13 上海琨山智能科技有限公司 Cloud storage node-based intelligent lock cloud storage resource scheduling system
CN117560423B (en) * 2023-10-07 2024-07-26 苏州琨山通用锁具有限公司 Cloud storage node-based intelligent lock cloud storage resource scheduling system

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