CN117058803A - Intelligent data acquisition method and system based on deep learning - Google Patents

Intelligent data acquisition method and system based on deep learning Download PDF

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CN117058803A
CN117058803A CN202311324315.7A CN202311324315A CN117058803A CN 117058803 A CN117058803 A CN 117058803A CN 202311324315 A CN202311324315 A CN 202311324315A CN 117058803 A CN117058803 A CN 117058803A
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face
module
acquisition
digital signal
access control
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CN117058803B (en
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杨小林
刘超
付金涛
李晨
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Inspur Smart Technology Innovation Shandong Co Ltd
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Inspur Smart Technology Innovation Shandong Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/168—Feature extraction; Face representation
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    • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
    • G—PHYSICS
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    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
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    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
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    • G—PHYSICS
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    • 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
    • G07C9/30—Individual registration on entry or exit not involving the use of a pass
    • G07C9/32—Individual registration on entry or exit not involving the use of a pass in combination with an identity check
    • G07C9/37—Individual registration on entry or exit not involving the use of a pass in combination with an identity check using biometric data, e.g. fingerprints, iris scans or voice recognition

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Abstract

The application relates to an intelligent data acquisition method and system based on deep learning, and relates to the technical field of data processing, wherein the data acquisition method comprises the steps of primary acquisition, feature analysis, formation of feature parameters, face information acquisition, face feature analysis, primary feature comparison, face daily acquisition, primary judgment, secondary judgment, calculation and the like; the data acquisition system comprises an input terminal, an identification terminal and an access control module. The application can lead the facial information parameters of personnel to be compared with the facial information parameters of the last time when the personnel pass through the entrance guard, thereby improving the recognition accuracy of the entrance guard on the facial information and further reducing the refusal rate of the entrance guard.

Description

Intelligent data acquisition method and system based on deep learning
Technical Field
The application relates to the technical field of data processing, in particular to an intelligent data acquisition method and system based on deep learning.
Background
As an essential article for daily life of people, the entrance guard provides excellent protection experience for daily life of people. With the demands of people on intellectualization, convenience and practicability of the access control, various novel intelligent access control systems are emerging in the current market, and the access control system based on face recognition is the most popular among people. The face recognition access control system takes the face of a resident as a switch basis, can recognize resident personnel and non-resident personnel, does not depend on keys in the recognition process, and saves the practical cost of users.
At present, a region feature analysis algorithm widely adopted in the face recognition technology utilizes a computer image processing technology to extract human image feature points from video, and utilizes the principle of biometrical science to analyze and build a mathematical model, namely a face feature template. And carrying out feature analysis by using the built face feature template and the face image of the person to be tested, and giving a similarity value according to the analysis result. From this value it is determined whether the person is the same.
However, when the face information of a person is identified by the existing access control identification system, when the face information of the person is changed (such as fat and thin change, old change and the like), the access control system still compares the face information parameters according to the recorded information, so that the refusal rate of the person is improved, and the person with the recorded face information cannot pass through the access control.
Disclosure of Invention
In order to reduce the rejection rate of the access control system to personnel and improve the probability of the personnel who have recorded face information passing through the access control, the application provides an intelligent data acquisition method and an intelligent data acquisition system based on deep learning.
In a first aspect, the application provides an intelligent data acquisition method based on deep learning, which adopts the following technical scheme:
an intelligent data acquisition method based on deep learning comprises the following steps:
preliminary collection: acquiring face information and identity information of a person needing to pass through the entrance guard, acquiring a face image, and performing image size adjustment and gray scale adjustment operation on the face image;
and (3) feature analysis: performing face detection on the shot face image, positioning the position of the face, performing matching of the face object, and simultaneously performing operation by using data of a database so as to obtain a feature vector matrix of the face;
forming characteristic parameters: the feature vector matrix of the face image is converted into a first digital signal parameter M through the analysis of the face image in the feature analysis step;
face information acquisition: the access control system acquires face information of personnel passing through the access control, acquires face images, and performs image size adjustment and gray scale adjustment operation on the face images;
face feature analysis: the access control system performs face detection on the shot face image, positions the face, performs matching of the face object, and simultaneously performs operation by using data of a database, so that a feature vector matrix of the face is obtained, and the feature vector matrix of the face image is converted into a second digital signal parameter N;
primary feature comparison: setting the maximum allowable error A, ifExecuting an entrance guard opening step and a face daily acquisition step; if->The method comprises the steps of carrying out a first treatment on the surface of the Executing an access control step, and executing a preliminary acquisition step again;
daily face collection: recording face images of persons passing through the entrance guard i times, converting a feature vector matrix of the face images into a third digital signal, and recording the third digital signal as N i ；
And (3) primary judgment: if it isExecuting an entrance guard opening step, executing a secondary judging step, and executing an entrance guard closing step if not;
and (3) secondary judgment: is provided withFix the warning error B, and B satisfiesIf->Executing the calculating step;
and (3) calculating: calculating a fourth digital signal parameter Q, wherein a calculation model of the fourth digital signal parameter Q is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the And m=q;
and (3) opening: controlling the opening of an access control;
door confinement: and controlling the closing of the door control.
Optionally, in the secondary judging step, the warning error B is updated at any time, and a calculation model of the warning error B is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein T is the daily face acquisition period, C is the sensitivity constant, and +.>。
Optionally, determining the value of C according to the identity information of the person in the preliminary acquisition step, where the calculation model of the value of C is:the method comprises the steps of carrying out a first treatment on the surface of the Wherein, the age is the age of the person to be collected in the preliminary collection step.
Optionally, a period threshold T1 is set, and if face information is not acquired in the period threshold T1 in the face information acquisition step, a preliminary acquisition step is performed.
In a second aspect, the present application provides a data acquisition system, which adopts the following technical scheme:
the utility model provides a data acquisition system, includes input terminal, discernment terminal and entrance guard control module, input terminal includes:
acquisition module I: the output end is in electrical signal connection with the input end of the analysis module I and is used for acquiring face information of a person needing to pass through the access control, acquiring a face image and performing image size adjustment and gray scale adjustment operation on the face image;
analysis module I: the output end is electrically connected with the input end of the computing module I and is used for carrying out face detection work on the shot face image, positioning the position of the face and carrying out matching work on the face object, and meanwhile, carrying out operation by using the data of the database so as to obtain a feature vector matrix of the face;
calculation module I: the output end is in electrical signal connection with the input end of the identification terminal, and the characteristic vector matrix of the face image is converted into a first digital signal parameter M through analysis of the face image in the analysis module I, and the first digital signal parameter M is transmitted to the identification terminal;
the identification terminal comprises an acquisition module II, an analysis module II and a comparison module I:
acquisition module II: the output end is electrically connected with the input end of the analysis module II and is used for acquiring face information of personnel passing through the entrance guard, acquiring a face image and performing image size adjustment and gray scale adjustment operation on the face image;
analysis module II: the output end is electrically connected with the input end of the computing module II, the shot face image is subjected to face detection work, the position of the face is positioned, the matching work of the face object is carried out, and meanwhile, the data of the database is used for carrying out operation, so that a feature vector matrix of the face is obtained;
calculation module II: the output end is electrically connected with the input end of the comparison module I, and the characteristic vector matrix of the face image is converted into a first digital signal parameter N through the analysis of the face image in the analysis module II;
comparison module I: the input end is electrically connected with the output end of the computing module I and the output end of the computing module II, the output end is electrically connected with the input end of the access control module, and the maximum allowable error A is obtained and used for comparing the second digital signal parameter N with the first digital signal parameter M;
the access control module: and controlling the opening and closing of the access control system according to the comparison result in the comparison module I.
Optionally, the identification terminal further comprises an acquisition module III, a judgment module I, a judgment module II and a calculation module;
acquisition module III: the input end is electrically connected with the output end of the comparison module I, the output end is electrically connected with the input end of the judgment module I, and the comparison module I is used for recording face images of I times of personnel passing through the entrance guard, converting a feature vector matrix of the face images into a third digital signal and recording the third digital signal as N i；
Judging module I: the output end is electrically connected with the input end of the access control module and the input end of the judging module II and is used for outputting a third digital signal N i Make a judgment whenWhen the door control system is in a closed state, the judging module transmits a judging result to the door control module and the judging module II;
judging module II: the output end is electrically connected with the input end of the calculation module, the warning error B is input in the judgment module II, and B meets the requirementIf->When the judgment result is received, the judgment result is transmitted to a calculation module;
the calculation module: a fourth digital signal parameter Q is calculated,and allows M to be overlaid.
Optionally, the judgment module II also updates the warning error B in real time through a formulaDetermining the value of B, wherein T is the daily face acquisition period, C is the sensitivity constant, and +.>。
Optionally, determining the value of C according to the identity information of the person in the acquisition module I, where the value calculation model of C is:the method comprises the steps of carrying out a first treatment on the surface of the Wherein, the age is the age of the person to be collected in the preliminary collection step.
Optionally, the period threshold T1 is included in the digital data in the acquisition module II, and if the acquisition module II does not acquire the face information in the period of the period threshold T1, the signal is transmitted to the acquisition module I.
In summary, the present application includes at least one of the following beneficial technical effects:
1. and when the person passes through the entrance guard, the maximum allowable error is set, so that whether the face data of the person passing through the entrance guard exceeds the range of the maximum allowable error is judged, and the probability that the person cannot pass through the entrance guard system when the face of the person is slightly decorated is reduced, and the rejection rate of the bedding entrance guard system is further reduced.
2. When the face information is input into the access control system, the access control system can recognize the face information of the person entering and exiting the access control according to the input face, and the face of the person can be changed along with the increase of time, so that the access control system records the face information N of the person passing through the access control for i times by setting the warning error value B i Judgment of N i Whether the face information parameters are in the range of the warning error B or not, and reassigning the initial face information parameters M, so that the face information parameters of a person when passing through the entrance guard are compared with the face information parameters of the last time, the recognition accuracy of the entrance guard on the face information is improved, and the refusal rate of the entrance guard is reduced.
3. Because the face information of young people and old people changes greatly, the face information of young people changes less, and the value of C is determined according to people in preliminary information acquisition by setting the sensitivity constant C, so that the accuracy of the face recognition of the people with large changes by the access control system is improved.
Drawings
FIG. 1 is a flow chart of embodiment 1 of the present application;
FIG. 2 is a schematic diagram of a system structure according to embodiment 2 of the present application;
Detailed Description
The application is described in further detail below in connection with fig. 1-2.
Example 1: the embodiment discloses an intelligent data acquisition method based on deep learning, referring to fig. 1, the intelligent data acquisition method based on deep learning comprises the following steps:
preliminary collection: acquiring face information and identity information of a person needing to pass through the entrance guard, acquiring a face image, and performing operations such as image size adjustment, gray scale adjustment and the like;
and (3) feature analysis: performing face detection on the shot face image, positioning the position of the face, performing matching of the face object, and simultaneously performing operation by using data of a database so as to obtain a feature vector matrix of the face;
forming characteristic parameters: the feature vector matrix of the face image is converted into a first digital signal parameter M through the analysis of the face image in the feature analysis step;
face information acquisition: the access control system acquires face information of personnel passing through the access control, acquires face images, and performs operations such as image size adjustment, gray scale adjustment and the like;
face feature analysis: the access control system performs face detection on the shot face image, positions the face, performs matching of the face object, and simultaneously performs operation by using data of a database, so that a feature vector matrix of the face is obtained, and the feature vector matrix of the face image is converted into a second digital signal parameter N;
primary feature comparison: setting the maximum allowable error A, ifExecuting an entrance guard opening step and a face daily acquisition step; if->The method comprises the steps of carrying out a first treatment on the surface of the Executing an access control step, and executing a preliminary acquisition step again;
daily face collection: recording face images of persons passing through the entrance guard i times, converting a feature vector matrix of the face images into a third digital signal, and recording the third digital signal as N i ；
And (3) primary judgment: if it isExecuting an entrance guard opening step, executing a secondary judging step, and executing an entrance guard closing step if not;
and (3) secondary judgment: setting a warning error B, wherein B meets the following conditionIf->Executing a calculation step, wherein the warning error B is updated in real time, and a calculation model of the warning error B is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein T is the daily face acquisition period, C is the sensitivity constant, and +.>The value of C in the formula is assigned according to the crowd to which the acquired personnel belong in the preliminary acquisition step, and the value calculation model of C is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein, the age is the age of the person to be collected in the preliminary collection step; (e.g., if age is 20, then by the formula +.>Calculating the value of C, wherein C is about 0.866, and if the age is 50, the value of C is directly 0.998).
The calculation steps are as follows: calculating a fourth digital signal parameter Q, wherein a calculation model of the fourth digital signal parameter Q is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the And M=Q (for example, the value of the initial value M is 100, the ith identification N of the entrance guard i 94, then M and N i Taking the average value as Q to be 97, enabling Q to cover M, and when the next time the access control system recognizes the face, obtaining the compared data as Q).
And (3) opening: and controlling the door control to be opened.
Door confinement: and controlling the closing of the door control.
The implementation principle of the intelligent data acquisition method based on deep learning in the embodiment is as follows: when the access control data acquisition is executed, personnel needing to pass through the access control firstly need to go to a face acquisition position to acquire faces, the personnel acquire face information and identity information of the personnel needing to pass through the access control, face images are acquired, and the operations such as image size adjustment, gray scale adjustment and the like are carried out on the face images; performing face detection on the shot face image, positioning the position of the face, performing matching of the face object, and simultaneously performing operation by using data of a database so as to obtain a feature vector matrix of the face; the feature vector matrix of the face image is converted into a digital signal parameter M through the analysis of the feature analysis on the face image, after the face information acquisition is finished, the face information is transmitted to an access control system, the access control system carries out face detection on the shot face image, positions the face, carries out matching on the face object, carries out operation by using the data of a database, and therefore the feature vector matrix of the face is obtained, the feature vector matrix of the face image is converted into a second digital signal parameter N, and the maximum allowable error A is set ifThe entrance guard is opened, and the face daily acquisition step is executed; if it isThe method comprises the steps of carrying out a first treatment on the surface of the Executing the entrance guard closing step and executing the preliminary acquisition step (re-entering the face information) again, when the entrance guard is opened, recording the face images of the personnel passing through the entrance guard i times by the entrance guard system, converting the feature vector matrix of the face images into a third digital signal, and converting the third numberThe word signal is recorded as N i If->The entrance guard is opened, a secondary judging step is executed, and if not, the entrance guard is closed; setting a warning error B, and B satisfies +.>If (if)Executing a calculation step, wherein the warning error B is updated in real time, and a calculation model of the warning error B is as follows: />The method comprises the steps of carrying out a first treatment on the surface of the Wherein T (unit: month) is the daily face acquisition period, C is the sensitivity constant, andthe value of C in the formula is assigned according to the age of the person to be collected in the preliminary collection step; (e.g., if age is 20, then by the formula +.>Calculating the value of C, wherein the value of C is about 0.866, if the age is 50, the value of C is directly 0.998), and replacing the first digital signal parameter M with the fourth digital signal parameter Q, wherein the entrance guard system records the face information N of i times of personnel passing through the entrance guard i Judgment of N i Whether the face information parameters are in the range of the warning error B or not, and reassigning the initial face information parameters M, so that the face information parameters of a person when passing through the entrance guard are compared with the face information parameters of the last time, the recognition accuracy of the entrance guard on the face information is improved, and the refusal rate of the entrance guard is reduced.
Example 2: the embodiment discloses a data acquisition system, referring to fig. 2, the data acquisition system includes an input terminal, an identification terminal and an access control module.
The input terminal includes:
acquisition module I: the output end is in electrical signal connection with the input end of the analysis module I and is used for acquiring face information of a person needing to pass through the access control, acquiring a face image, and performing operations such as image size adjustment, gray scale adjustment and the like;
analysis module I: the output end is electrically connected with the input end of the computing module I and is used for carrying out face detection work on the shot face image, positioning the position of the face and carrying out matching work on the face object, and meanwhile, carrying out operation by using the data of the database so as to obtain a feature vector matrix of the face;
calculation module I: the output end is in electrical signal connection with the input end of the identification terminal, and the characteristic vector matrix of the face image is converted into a first digital signal parameter M through analysis of the face image in the analysis module I, and the first digital signal parameter M is transmitted to the identification terminal;
the identification terminal comprises an acquisition module II, an analysis module II, a comparison module I, an acquisition module III, a judgment module I, a judgment module II and a calculation module:
acquisition module II: the output end is electrically connected with the input end of the analysis module II and is used for acquiring face information of personnel passing through the entrance guard, acquiring a face image, and performing operations such as image size adjustment, gray scale adjustment and the like; the digital of the acquisition module II is provided with a period threshold T1, and if the acquisition module II does not acquire the face information in the period of the period threshold T1, a signal is transmitted to the acquisition module I.
Analysis module II: the output end is electrically connected with the input end of the computing module II, the shot face image is subjected to face detection work, the position of the face is positioned, the matching work of the face object is carried out, and meanwhile, the data of the database is used for carrying out operation, so that a feature vector matrix of the face is obtained;
calculation module II: the output end is electrically connected with the input end of the comparison module I, and the characteristic vector matrix of the face image is converted into a first digital signal parameter N through the analysis of the face image in the analysis module II;
comparison module I: the input end is electrically connected with the output end of the computing module I and the output end of the computing module II, the output end is electrically connected with the input end of the access control module, and the maximum allowable error A is obtained and used for comparing the second digital signal parameter N with the first digital signal parameter M;
acquisition module III: the input end is electrically connected with the output end of the comparison module I, the output end is electrically connected with the input end of the judgment module I, and the comparison module I is used for recording face images of I times of personnel passing through the entrance guard, converting a feature vector matrix of the face images into a third digital signal and recording the third digital signal as N i 。
Judging module I: the output end is electrically connected with the input end of the access control module and the input end of the judging module II and is used for outputting a third digital signal N i Make a judgment whenWhen the door control system is in a closed state, the judging module transmits a judging result to the door control module and the judging module II;
judging module II: the output end is electrically connected with the input end of the calculation module, the warning error B is input in the judgment module II, and B meets the requirementIf->When the judgment result is received, the judgment result is transmitted to a calculation module; the warning error B is updated in real time by the formula +.>Determining the value of B, wherein T is the daily face acquisition period, C is the sensitivity constant, and +.>Determining the value of C according to the identity information of the personnel in the acquisition module I, wherein the value calculation model of C is as follows: />The method comprises the steps of carrying out a first treatment on the surface of the Wherein, the age is the age of the person to be collected in the preliminary collection step.
The calculation module: a fourth digital signal parameter Q is calculated,and allows M to be overlaid.
The access control module: and controlling the opening and closing of the access control system according to the comparison result in the comparison module I and the judgment module I.
The implementation principle of the data acquisition system of the embodiment is as follows: the acquisition module I acquires face information of personnel needing access control, acquires a face image, performs operations such as image size adjustment and gray level adjustment, and the like, then transmits the acquired and processed face information to the analysis module I, the analysis module I performs face detection on the shot face image, positions the face, performs matching of face objects, and simultaneously performs operation by using data of a database, so that a feature vector matrix of the face is obtained, the analysis module I transmits the obtained feature vector matrix information of the face to the calculation module I, and the calculation module I converts the feature vector matrix of the face image into a first digital signal parameter M through analysis of the face image in the analysis module I and transmits the first digital signal parameter M to the comparison module I in the identification terminal. When an acquired person passes through an entrance guard test for the first time, the acquisition module II acquires facial information of the person passing through the entrance guard, acquires a face image, performs operations such as image size adjustment and gray scale adjustment, and the like, transmits the information to the analysis module II, a period threshold T1 is arranged in the acquisition module II, if the acquisition module II does not acquire the face information within the period of the period threshold T1, a signal is transmitted to the acquisition module I, the analysis module II performs face detection on the shot face image, positions the face, performs matching of the face object, and performs operation by using data of a database, so that a feature vector matrix of the face image is obtained, the feature vector matrix of the face image is transmitted to the calculation module II, the calculation module II converts the feature vector matrix of the face image into a second digital signal parameter N, the comparison module I obtains a maximum allowable error A, the second digital signal parameter N shot by the entrance guard system is compared with the first digital signal parameter M, and the comparison result is transmitted to the entrance guard control module, and whether the entrance guard control module opens the door according to the entrance guard control result.
When a person passes through the entrance guard for many times, the acquisition module III records face images of the person passing through the entrance guard for i times, converts a feature vector matrix of the face images into a third digital signal, and records the third digital signal as N i The judging module I receives the third digital signal data Ni transmitted by the acquisition module and transmits a third digital signal N i Make a judgment whenWhen the warning error B is input into the judging module II, when B meets +.>If->When the warning error B is detected, the judgment result is transmitted to a calculation module, the warning error B is updated in real time, and the warning error B is judged to be the same as the warning error B according to the formula ∈>Determining the value of B, wherein T is the daily face acquisition period, C is the sensitivity constant, and +.>Determining the value of C according to the identity information of the personnel in the acquisition module I, wherein the value calculation model of C is as follows: />The method comprises the steps of carrying out a first treatment on the surface of the Wherein, age is the age of the person to be collected in the preliminary collection step, and the calculation module calculates the fourth digital signal parameter Q,/-for the person to be collected>And allows M to be overlaid.
The above embodiments are not intended to limit the scope of the present application, so: all equivalent changes in structure, shape and principle of the application should be covered in the scope of protection of the application.

Claims (8)

1. An intelligent data acquisition method based on deep learning is characterized in that: the method comprises the following steps:
preliminary collection: acquiring face information and identity information of a person needing to pass through the entrance guard, acquiring a face image, and performing image size adjustment and gray scale adjustment operation on the face image;
and (3) feature analysis: performing face detection on the shot face image, positioning the position of the face, performing matching of the face object, and simultaneously performing operation by using data of a database so as to obtain a feature vector matrix of the face;
forming characteristic parameters: the feature vector matrix of the face image is converted into a first digital signal parameter M through the analysis of the face image in the feature analysis step;
face information acquisition: the access control system acquires face information of personnel passing through the access control, acquires face images, and performs image size adjustment and gray scale adjustment operation on the face images;
face feature analysis: the access control system performs face detection on the shot face image, positions the face, performs matching of the face object, and simultaneously performs operation by using data of a database, so that a feature vector matrix of the face is obtained, and the feature vector matrix of the face image is converted into a second digital signal parameter N;
primary feature comparison: setting the maximum allowable error A, ifExecuting an entrance guard opening step and a face daily acquisition step; if->The method comprises the steps of carrying out a first treatment on the surface of the Executing an access control step, and executing a preliminary acquisition step again;
daily face collection: recording face images of i times of personnel passing through the entrance guard, converting a feature vector matrix of the face images into a third digital signal, and converting the third digital signal into a fourth digital signalThree digital signals are recorded as N i ；
And (3) primary judgment: if it isExecuting an entrance guard opening step, executing a secondary judging step, and executing an entrance guard closing step if not;
and (3) secondary judgment: setting a warning error B, wherein B meets the following conditionIf->Executing the calculating step;
and (3) calculating: calculating a fourth digital signal parameter Q, wherein a calculation model of the fourth digital signal parameter Q is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the And m=q;
and (3) opening: controlling the opening of an access control;
door confinement: and controlling the closing of the door control.
2. The intelligent data acquisition method based on deep learning as claimed in claim 1, wherein: in the secondary judging step, the warning error B is updated at any time, and the calculation model of the warning error B is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein T is the daily face acquisition period, C is the sensitivity constant, and +.>。
3. The intelligent data acquisition method based on deep learning as claimed in claim 2, wherein: according to personnel in the preliminary collecting stepThe value of C is determined according to the identity information of C, and the value calculation model of C is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein, the age is the age of the person to be collected in the preliminary collection step.
4. The intelligent data acquisition method based on deep learning as claimed in claim 1, wherein: setting a period threshold T1, and if the face information is not acquired in the period threshold T1 in the face information acquisition step, executing a preliminary acquisition step.
5. A data acquisition system applying the intelligent data acquisition method based on deep learning as claimed in any one of claims 1 to 4, characterized in that: including input terminal, identification terminal and entrance guard control module, input terminal includes:
acquisition module I: the output end is in electrical signal connection with the input end of the analysis module I and is used for acquiring face information of a person needing to pass through the access control, acquiring a face image and performing image size adjustment and gray scale adjustment operation on the face image;
analysis module I: the output end is electrically connected with the input end of the computing module I and is used for carrying out face detection work on the shot face image, positioning the position of the face and carrying out matching work on the face object, and meanwhile, carrying out operation by using the data of the database so as to obtain a feature vector matrix of the face;
calculation module I: the output end is in electrical signal connection with the input end of the identification terminal, and the characteristic vector matrix of the face image is converted into a first digital signal parameter M through analysis of the face image in the analysis module I, and the first digital signal parameter M is transmitted to the identification terminal;
the identification terminal comprises an acquisition module II, an analysis module II, a calculation module II, a comparison module I, an acquisition module III, a judgment module I, a judgment module II and a calculation module:
acquisition module II: the output end is electrically connected with the input end of the analysis module II and is used for acquiring face information of personnel passing through the entrance guard, acquiring a face image and performing image size adjustment and gray scale adjustment operation on the face image;
analysis module II: the output end is electrically connected with the input end of the computing module II, the shot face image is subjected to face detection work, the position of the face is positioned, the matching work of the face object is carried out, and meanwhile, the data of the database is used for carrying out operation, so that a feature vector matrix of the face is obtained;
calculation module II: the output end is electrically connected with the input end of the comparison module I, and the characteristic vector matrix of the face image is converted into a first digital signal parameter N through the analysis of the face image in the analysis module II;
comparison module I: the input end is electrically connected with the output end of the computing module I and the output end of the computing module II, the output end is electrically connected with the input end of the access control module, and the maximum allowable error A is obtained and used for comparing the second digital signal parameter N with the first digital signal parameter M;
acquisition module III: the input end is electrically connected with the output end of the comparison module I, the output end is electrically connected with the input end of the judgment module I, and the comparison module I is used for recording face images of I times of personnel passing through the entrance guard, converting a feature vector matrix of the face images into a third digital signal and recording the third digital signal as N i；
Judging module I: the output end is electrically connected with the input end of the access control module and the input end of the judging module II and is used for outputting a third digital signal N i Make a judgment whenWhen the door control system is in a closed state, the judging module transmits a judging result to the door control module and the judging module II;
judging module II: the output end is electrically connected with the input end of the calculation module, the warning error B is input in the judgment module II, and B meets the requirementIf->When the judgment result is received, the judgment result is transmitted to a calculation module;
the calculation module: a fourth digital signal parameter Q is calculated,and allowing M to be covered;
the access control module: and controlling the opening and closing of the access control system according to the comparison result in the comparison module I.
6. A data acquisition system according to claim 5, wherein: the judgment module II also updates the warning error B in real time through a formulaDetermining the value of B, wherein T is the daily face acquisition period, C is the sensitivity constant, and +.>。
7. A data acquisition system according to claim 6, wherein: determining the value of C according to the identity information of the personnel in the acquisition module I, wherein the value calculation model of C is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein, the age is the age of the person to be collected in the preliminary collection step.
8. A data acquisition system according to claim 7, wherein: the number of the acquisition module II is provided with a period threshold T1, and if the acquisition module II does not acquire the face information in the period of the period threshold T1, a signal is transmitted to the acquisition module I.
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