CN113591821B - Image identification security system based on big data screening - Google Patents
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Abstract
The invention relates to the technical field of security systems, in particular to an image identification security system based on big data screening. The system comprises a video acquisition module, a face recognition module, a big data analysis module, a behavior feature recognition module, a newly-added special crowd information collection and judgment module, a special crowd behavior guide module and a control terminal. After the video acquisition module gathered facial information, because the dress style is different, lead to facial information probably to be sheltered from, or the facial information of gathering is unclear, carry out the analysis to the behavioral characteristics of gathering the person through behavioral characteristics identification module this moment, and then obtain the behavioral characteristics of gathering the person, when guaranteeing facial information is fuzzy, also can obtain whether the gathering person has the health obstacle through observing the behavioral characteristics of gathering the person, and then carry out special channel to the gathering person and handle, improve the accuracy of public place security protection, make every health obstacle person can both enjoy the service that receives.
Description
Technical Field
The invention relates to the technical field of security systems, in particular to an image identification security system based on big data screening.
Background
Nowadays, with the continuous advance of urbanization, the construction in public places is more and more intensive and complex, and people flow more and more densely, which brings great inconvenience and even danger to the travel of disabled or injured people with behavior disorders; for example, as the number of passengers in the subway is gradually increased, the passengers need to carry an elevator to move up and down when taking the subway due to the fact that the subway is built below the ground, and particularly, due to the fact that a plurality of cross stations for line changing exist, the passengers with behavior disorder can find a correct travel route, and great inconvenience is brought; for example, in a large mall and subway combined dense place, the staff is difficult to quickly and effectively find the behavior-handicapped people needing help due to limited field staff and dense pedestrian flow, so that timely and effective help and guidance cannot be provided, and the people with behavior-handicapped cannot know the route information concerned by the people at the first time.
With the progress of internet science and technology and the popularization of big data analysis technology, technical support is provided for the improvement of security work in public places, and a safety protection system which can quickly identify and quickly discover crowds with behavior disorders in public places with dense personnel and complex lines is urgently needed at present, so that help is provided for the crowds.
Disclosure of Invention
The invention aims to provide an image identification security system based on big data screening to solve the problems in the background technology.
In order to achieve the purpose, the image recognition security system based on big data screening is provided, and comprises a video acquisition module, a face recognition module, a big data analysis module, a behavior characteristic recognition module, a newly-added special crowd information collection and judgment module, a special crowd behavior guide module and a control terminal;
the video acquisition module is used for acquiring the facial information of an acquired person and the behavior characteristic information of the acquired person;
the face recognition module is used for receiving and extracting the face information collected by the video collection module and transmitting the face information to the big data analysis module;
the big data analysis module is used for analyzing and comparing the facial information of the collected person and judging whether the collected person belongs to a behavior-disorder person or not; the big data analysis module can compare the facial information of the acquired person with the facial information in the special crowd information base through big data analysis so as to judge whether the current acquired person belongs to a behavior disorder person;
the behavior feature recognition module is used for receiving and extracting the behavior feature information of the acquired person acquired by the video acquisition module and judging whether the acquired person has behavior disorder or not;
the newly-added special crowd information collecting and judging module is used for receiving a judgment result of whether the acquired person belongs to a behavior obstacle person or not sent by the big data analysis module, receiving a judgment result of whether the acquired person has a behavior obstacle or not sent by the behavior feature identification module, synchronously receiving personnel information of the corresponding acquired person, judging whether the current acquired person is a to-be-newly-added behavior obstacle person which is not input in the special crowd information base at present, and if the current acquired person is the to-be-newly-added behavior obstacle person, sending the newly-added behavior obstacle person information to the special crowd information base to finish personnel information updating of the special crowd information base;
the control terminal can receive judgment results aiming at the collected person and sent by the big data analysis module and the behavior characteristic identification module, and sends a corresponding control instruction to the special crowd behavior guide module according to the judgment results;
the special crowd behavior guiding module is used for guiding the current behavior barrier personnel to advance according to the control instruction of the control terminal.
Furthermore, the behavior feature recognition module comprises an appearance feature recognition module and an action feature recognition module, the appearance feature recognition module is used for collecting the appearance state of the collected person, and the action feature recognition module is used for collecting the action features of the collected person.
Further, when the big data analysis module determines that the current collected person does not belong to a person with behavioral impairment through big data analysis, but the behavioral characteristic identification module determines that the current same collected person has behavioral impairment, the newly-added special population information collection and determination module determines that the current collected person is a person with behavioral impairment to be newly added, which is not recorded in the special population information base at present.
Furthermore, the video acquisition module comprises a plurality of video acquisition terminals, each video acquisition terminal is connected with a video acquisition terminal switching module, the video acquisition terminal switching module is in signal connection with the control terminal, and the video acquisition terminal switching module is used for switching the video acquisition terminals according to a control instruction of the control terminal or the action track of an acquired person.
Furthermore, the special crowd behavior guidance module comprises a voice prompt module and a traveling line guidance module, wherein the voice prompt module can receive a control instruction of the control terminal and perform voice prompt on field workers or behavior-handicapped people; the advancing route guiding module is used for receiving a control instruction of the control terminal and providing advancing route guidance for field behavior barrier personnel.
Furthermore, the voice prompt module comprises a volume adjustment module, a language switching module and a voice playing unit, wherein the volume adjustment module is used for adjusting the volume of the voice prompted by the voice playing unit; the voice playing unit is used for receiving a control instruction of the control terminal and playing voice; the language switching module is used for switching the languages played by the voice playing unit according to a control instruction sent by the control terminal.
Furthermore, the behavior feature recognition module further comprises a voice feature recognition module, wherein the voice feature recognition module is used for recognizing the language information of the collected person and sending the language information to the control terminal, the control terminal sends a control instruction to the language switching module, and then the language switching module switches the language of the voice played by the voice playing unit.
Furthermore, the big data analysis module comprises a personnel information acquisition unit, a behavior disorder crowd judgment unit and an information receiving unit which are in signal connection with each other;
the information receiving unit is used for receiving the face information of the current acquired person sent by the face recognition module;
the personnel information acquisition unit is used for calling the personnel information in the special crowd information base;
the behavior disorder crowd judgment unit is used for matching and comparing the facial information of the current acquired person received by the information receiving unit with the facial information of the person in the special crowd information base called by the person information acquisition unit, judging that the current acquired person belongs to a behavior disorder person if matching is successful, and judging that the current acquired person does not have behavior disorder if matching is failed.
Further, the system also comprises a non-behavior obstacle personnel information base;
when the behavior disorder crowd judging unit judges that the current person to be collected is a person without behavior disorder and the behavior feature identifying module judges that the same current person to be collected has behavior disorder, the personnel information acquisition unit retrieves personnel information in the behavioral disorder-free personnel information base, the behavioral disorder crowd judgment unit matches and compares the facial information of the current acquired person received by the information receiving unit with the facial information of the persons in the behavioral disorder-free person information base called by the person information acquisition unit, if the matching is successful, the newly added special population information collecting and judging module receives the personnel information in the behavioral disorder-free personnel information base called by the personnel information obtaining unit, and the personnel information is sent to the special crowd information base to complete the personnel information updating of the special crowd information base.
Further, when the behavioral disorder crowd judgment unit judges that the current person to be collected is a person without behavioral disorder and the behavioral feature recognition module judges that the same current person to be collected has behavioral disorder, the behavioral disorder crowd judgment unit performs matching comparison on the facial information of the current person to be collected received by the information receiving unit and the facial information of the person in the person information base without behavioral disorder, which is called by the person information obtaining unit, if the matching is unsuccessful, the behavioral disorder crowd judgment unit issues a disorder person information missing report to the control terminal, the control terminal sends a control instruction to a field worker, and the field worker completes information registration work and/or traveling guidance work on the target person with behavioral disorder according to the control instruction.
Compared with the prior art, the language switching module has the beneficial effects that:
1. in this image identification security protection system based on big data screening, gather after the facial information and the action characteristic information of gathering the people when video acquisition module, because everyone dresses the style different, lead to facial information probably to be sheltered from or can't be gathered by the accuracy, lead to gathering facial information unclear or inaccurate, carry out the analysis to gathering people's action characteristic through action characteristic identification module this moment, reach gathering people's action characteristic, when guaranteeing that facial information is fuzzy, also can obtain gathering people whether there is the health obstacle through the action characteristic who observes gathering people, improve the timely effective acquisition of public place to this type of special crowd information again, guarantee that the staff can have the obstacle personnel to every health and can provide timely effective service.
2. The system has the advantages that the data updating is carried out on the special crowd information base in time by arranging the newly-added special crowd information collecting and judging module, the effective information capacity of the whole system is improved, and the behavior obstacle personnel needing help on site can be guided to advance effectively and most in time by arranging the special crowd behavior guiding module.
3. By arranging the video acquisition terminal switching module, real-time switching of a plurality of video acquisition terminal devices on site is realized, so that behavior information and behavior tracks of information of an acquired person can be timely and effectively acquired, and a system is helped to timely and effectively acquire the acquired information.
4. Based on the data analysis capability of the big data analysis module, the effective cooperation of the personnel information acquisition unit, the behavior disorder crowd judgment unit and the information receiving unit can quickly and accurately judge whether the current collected person belongs to the behavior disorder personnel according to the facial information of the current collected person, and the working efficiency of the system is improved.
Drawings
FIG. 1 is an overall flow chart of example 1;
FIG. 2 is a flow chart of a video capture module of embodiment 1;
FIG. 3 is a flow chart of a big data analysis module of example 1;
FIG. 4 is a flowchart of a behavior feature recognition module according to embodiment 1;
FIG. 5 is a flowchart of the voice guidance module according to embodiment 1;
FIG. 6 is a block flow diagram of example 1;
FIG. 7 is a characteristic identification effect diagram of embodiment 1;
the various reference numbers in the figures mean:
100. a video acquisition module; 110. a video acquisition terminal;
200. a face recognition module;
300. a big data analysis module; 301. a person information acquisition unit; 302. a behavior disorder population judgment unit; 303. an information receiving unit;
400. a behavior feature identification module; 410. a appearance characteristic identification module; 420. an action characteristic identification module; 430. a voice feature recognition module;
500. a special crowd information base;
600. a behavioral disorder-free personnel information base;
700. the video acquisition terminal switching module;
800. a voice prompt module; 810. a volume adjustment module; 820. a language switching module; 830. a voice playing unit;
900. a special crowd information collecting and judging module is added;
1000. a special crowd behavior guide module;
1100. a control terminal;
1200. and a travel route guide module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
Referring to fig. 1 to 7, an object of the present embodiment is to provide an image recognition security system based on big data screening, which includes a video acquisition module 100, a face recognition module 200, a big data analysis module 300, a behavior feature recognition module 400, a newly added special population information collection and determination module 900, a special population behavior guidance module 1000, and a control terminal 1100;
the video acquisition module 100 is used for acquiring the face information of the acquired person and the behavior characteristic information of the acquired person; specifically, the facial information includes facial information of the human face, and the behavior information includes information of walking posture, sitting posture, limb movement of limbs, and the like of the human;
the face recognition module 200 is configured to receive and extract face information acquired by the video acquisition module 100, and transmit the face information to the big data analysis module 300;
the big data analysis module 300 is used for analyzing and comparing the facial information of the person to be collected and judging whether the person to be collected belongs to the person with the behavioral disorder; the big data analysis module 300 can compare the facial information of the person to be collected with the facial information in the special crowd information base 500 through big data analysis to judge whether the current person to be collected belongs to a behavior-disorder person;
specifically, the video acquisition module 100 sends the face information of the acquired person acquired in real time to the face recognition module 200, the face recognition module 200 further extracts and processes the received face information to obtain a clearer and more accurate face image, and sends the face image to the big data analysis module 300, the big data analysis module 300 extracts the face information of the person existing in the special crowd information base 500, and compares the face information with the similarity of the target face image, and when the similarity is greater than or equal to 95%, the big data analysis module 300 judges that the acquired person currently compared is a person with behavior disorder;
the behavior feature identification module 400 is configured to receive and extract the behavior feature information of the person to be collected, which is collected by the video collection module 100, and determine whether the person to be collected has a behavior disorder, for example, when the person to be collected has abnormal walking behaviors such as leaning, crippling, sitting in a wheelchair, and the like, the behavior feature identification module 400 determines that the person to be collected currently has the behavior disorder;
the newly-added special crowd information collecting and judging module 900 is configured to receive a judgment result of whether the acquired person belongs to a behavior-disorder person sent by the big data analyzing module 300, receive a judgment result of whether the acquired person has a behavior disorder or not sent by the behavior feature identifying module 400, synchronously receive staff information of a corresponding acquired person, judge whether a current acquired person is a to-be-newly-added behavior-disorder person not entered in the special crowd information base 500 at present, and send newly-added behavior-disorder staff information to the special crowd information base 500 if the current acquired person is the to-be-newly-added behavior-disorder person, so as to complete staff information update of the special crowd information base 500;
the control terminal 1100 is capable of receiving the judgment results for the acquired person sent by the big data analysis module 300 and the behavior feature recognition module 400, and sending a corresponding control instruction to the special crowd behavior guidance module 1000 according to the judgment results;
the special crowd behavior guidance module 1000 is configured to guide the current behavior obstacle person to travel according to the control instruction of the control terminal 1100. Specifically, the special crowd behavior guidance module 1000 includes a line guidance lamp, a prompting horn/identification, and/or a field worker, and after the special crowd behavior guidance module 1000 is issued by a control instruction, the current behavior barrier worker receives voice prompting guidance information or is arranged on the ground to advance guidance information for disabled people or directly arrive at the field to assist or guide, so as to provide convenience for the target worker to advance.
Preferably, the behavior feature identification module 400 includes an appearance feature identification module 410 and an action feature identification module 420, the appearance feature identification module 410 is configured to collect an appearance state of the collected person, the action feature identification module 420 is configured to collect an action feature of the collected person, specifically, the appearance feature includes, for example, appearance information of the existence of limb deformity, such as appearance information of the existence of wheelchair travel, and the action feature includes, but is not limited to, abnormal behavior action of walking with crippling, crawling action, and the like.
Preferably, when the big data analysis module 300 determines, through big data analysis, that the current person to be collected does not belong to a person with behavioral impairment, but at the same time the behavioral impairment recognition module 400 determines that the current same person to be collected has behavioral impairment, the newly added special population information collection and determination module 900 determines that the current person to be collected is a person with behavioral impairment to be newly added, which is not currently recorded in the special population information base 500. The main purpose of the arrangement is to comprehensively, accurately and timely find the crowd with behavior disorder and provide help and rescue for the crowd.
Preferably, the video capture module 100 includes a plurality of video capture terminals 110, each video capture terminal 110 is connected to a video capture terminal switching module 700, the video capture terminal switching module 700 is in signal connection with the control terminal 1100, and the video capture terminal switching module 700 is configured to switch the video capture terminals 110 according to a control instruction of the control terminal 1100 or according to a motion trajectory of a captured person. Specifically, the video capture terminal can be a camera, and the control terminal issues a camera switching instruction to the video capture terminal switching module 700 in real time according to the action track of the target people on the spot shot by the front-end camera, so that the action route of the current target person can be timely and accurately known, and meanwhile, the operator of the control terminal can also obtain the real-time image information to be acquired from the required angle.
Preferably, the special crowd behavior guidance module 1000 includes a voice prompt module 800 and a travel route guidance module 1200, the voice prompt module 800 can receive a control instruction of the control terminal 1100, and specifically, the voice prompt module 800 may be a speaker placed in a public place to perform voice prompt on field workers or behavior-handicapped people; the travel route guidance module 1200 is configured to receive a control instruction of the control terminal 1100, and provide travel route guidance for a person with a field behavior disorder.
Preferably, the voice prompt module 800 includes a volume adjustment module 810, a language switching module 820 and a voice playing unit 830, where the volume adjustment module 810 is configured to adjust the volume of the voice prompted by the voice playing unit 830; the voice playing unit 830 is configured to receive a control instruction of the control terminal 1100 and play a voice; the language switching module 820 is configured to switch the languages played by the voice playing unit 830 according to a control instruction sent by the control terminal.
Preferably, the behavior feature recognition module 400 further includes a speech feature recognition module 430, where the speech feature recognition module 430 is configured to recognize language information of a collected person and send the language information to the control terminal 1100, and the control terminal 1100 sends a control instruction to the language switching module 820, so that the language switching module 820 switches languages of speech played by the speech playing unit 830. Specifically, when the person with behavioral impairment needs help, the person can call for help through shouting, when the person who seeks help is a foreigner and the person in the field cannot understand the words of the person who seeks help, the speech feature recognition module 430 (specifically, the speech recognizer) can recognize the language of the person in time at the moment, and transmits the information technology back to the control terminal, and the control terminal performs language communication and communication on the person who seeks help through the language conversion device (translator).
Preferably, the big data analysis module 300 includes a personnel information acquisition unit 301, a behavior disorder crowd determination unit 302 and an information receiving unit 303, which are in signal connection with each other;
the information receiving unit 303 is configured to receive the face information of the current person to be collected, which is sent by the face recognition module 200;
the personnel information obtaining unit 301 obtains the personnel information in the special crowd information base 500;
the behavioral disorder crowd determination unit 302 performs matching comparison between the facial information of the current person to be collected received by the information receiving unit 303 and the facial information of the person in the special crowd information base 500 called by the person information obtaining unit 301, determines that the current person to be collected belongs to a behavioral disorder person if matching is successful, and determines that the current person to be collected does not have a behavioral disorder if matching is failed.
Preferably, a non-behavior-obstacle personnel information base 600 is also included;
when the behavioral disorder crowd determination unit 302 determines that the current person to be collected is a person without behavioral disorders and the behavioral feature recognition module 400 determines that the same current person to be collected has behavioral disorders, it indicates that the person to be collected belongs to a person who is not recorded in the current special crowd information base 500, at this time, the person information acquisition unit 301 acquires the person information in the person information base 600 without behavioral disorders, specifically, the person information base 600 without behavioral disorders can be networked with a public security system to acquire the most complete person data information, the behavioral disorder crowd determination unit 302 performs matching comparison between the face information of the current person to be collected received by the information reception unit 303 and the face information of the person in the person information base 600 without behavioral disorders, which is acquired by the person information acquisition unit 301, and if the matching is successful, the newly added special crowd information collection and determination module 900 receives the face information of the person without behavioral disorders, which is acquired by the person information acquisition unit 301 And updating the personnel information of the special crowd information base 500 for the personnel information in the barrier personnel information base 600 and sending the personnel information to the special crowd information base 500. In this way, the specific crowd information base 500 can be continuously refined and updated to improve the processing capability of the whole system.
Preferably, when the behavioral disorder crowd determination unit 302 determines that the current person to be collected is a person without behavioral disorder and the behavioral feature recognition module 400 determines that the same current person to be collected has behavioral disorder, the behavioral disorder crowd determination unit 302 matches and compares the facial information of the current person to be collected received by the information receiving unit 303 with the facial information of the person in the person-without-behavioral disorder person information base 600 retrieved by the person information retrieving unit 301, if the matching is unsuccessful, the behavioral disorder crowd determination unit 302 issues a disorder person information missing report to the control terminal 1100, the control terminal 1100 sends a control instruction to a field worker, and the field worker completes information registration work and/or traveling guidance work for the target person with behavioral disorder according to the control instruction. The operation is to deal with the situation that when the information of the target person is not recorded in the information base 600 for the persons without behavioral impairment and the information base 500 for the special population, for example, the target person is a foreign person or other persons not recorded in the information base 600 for the persons without behavioral impairment and/or the information base 500 for the special population, a field worker needs to perform the work of collecting the information of the target person, which is very important for the self-improvement of the whole security system.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and the preferred embodiments of the present invention are described in the above embodiments and the description, and are not intended to limit the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. Image identification security protection system based on big data screening, its characterized in that: comprises a video acquisition module (100), a face recognition module (200), a big data analysis module (300), a behavior feature recognition module (400), a newly added special crowd information collection and judgment module (900), a special crowd behavior guide module (1000) and a control terminal (1100),
the video acquisition module (100) is used for acquiring the face information of the acquired person and the behavior characteristic information of the acquired person;
the face recognition module (200) is used for receiving and extracting face information collected by the video collection module (100) and transmitting the face information to the big data analysis module (300);
the big data analysis module (300) is used for analyzing and comparing the facial information of the collected person and judging whether the collected person belongs to a behavior-disorder person; the big data analysis module (300) can compare the facial information of the acquired person with the facial information in the special crowd information base (500) through big data analysis to judge whether the current acquired person belongs to a behavior disorder person;
the behavior feature recognition module (400) is used for receiving and extracting the behavior feature information of the acquired person acquired by the video acquisition module (100) and judging whether the acquired person has behavior disorder or not;
the newly-added special crowd information collecting and judging module (900) is used for receiving a judgment result of whether an acquired person belongs to a behavior obstacle person or not sent by the big data analyzing module (300), receiving a judgment result of whether the acquired person has a behavior obstacle or not sent by the behavior feature identifying module (400), synchronously receiving personnel information of a corresponding acquired person, judging whether the current acquired person is a person who is not recorded in the special crowd information base (500) and is to be newly added, and if the current acquired person is the person who is to be newly added, sending the newly-added person with the behavior obstacle to the special crowd information base (500) to complete the personnel information updating of the special crowd information base (500);
the control terminal (1100) can receive judgment results aiming at the acquired person and sent by the big data analysis module (300) and the behavior feature recognition module (400), and send corresponding control instructions to the special crowd behavior guide module (1000) according to the judgment results;
the special crowd behavior guiding module (1000) is used for guiding the current behavior barrier personnel to advance according to the control instruction of the control terminal (1100).
2. The image recognition security system based on big data screening of claim 1, wherein: the behavior feature recognition module (400) comprises a appearance feature recognition module (410) and an action feature recognition module (420), wherein the appearance feature recognition module (410) is used for collecting the appearance state of a person to be collected, and the action feature recognition module (420) is used for collecting the action features of the person to be collected.
3. The image recognition security system based on big data screening of claim 1, wherein: when the big data analysis module (300) judges that the current collected person does not belong to a person with behavioral impairment through big data analysis, but the behavior feature recognition module (400) judges that the same current collected person has behavioral impairment, the newly-added special population information collection and judgment module (900) judges that the current collected person is a person with behavioral impairment which is not recorded in the special population information base (500) and is to be newly added.
4. The image recognition security system based on big data screening of claim 1, wherein: the video acquisition module (100) comprises a plurality of video acquisition terminals (110), each video acquisition terminal (110) is connected with a video acquisition terminal switching module (700), the video acquisition terminal switching module (700) is in signal connection with the control terminal (1100), and the video acquisition terminal switching module (700) is used for switching the video acquisition terminals (110) according to a control instruction of the control terminal (1100) or the action track of an acquired person.
5. The image recognition security system based on big data screening of claim 1, wherein: the special crowd behavior guiding module (1000) comprises a voice prompting module (800) and a traveling route guiding module (1200), wherein the voice prompting module (800) can receive a control instruction of the control terminal (1100) and perform voice prompting on field workers or behavior barrier personnel; the travel route guiding module (1200) is used for receiving a control instruction of the control terminal (1100) and providing travel route guidance for field behavior barrier personnel.
6. The image recognition security system based on big data screening of claim 5, wherein: the voice prompt module (800) comprises a volume adjusting module (810), a language switching module (820) and a voice playing unit (830), wherein the volume adjusting module (810) is used for adjusting the volume of voice prompted by the voice playing unit (830); the voice playing unit (830) is used for receiving a control instruction of the control terminal (1100) and playing voice; the language switching module (820) is used for switching the languages played by the voice playing unit (830) according to a control instruction sent by the control terminal.
7. The image recognition security system based on big data screening of claim 6, wherein: the behavior feature recognition module (400) further comprises a voice feature recognition module (430), the voice feature recognition module (430) is used for recognizing the language information of the collected person and sending the language information to the control terminal (1100), the control terminal (1100) sends a control instruction to the language switching module (820), and then the language switching module (820) switches the language of the voice played by the voice playing unit (830).
8. The image recognition security system based on big data screening of claim 1, wherein: the big data analysis module (300) comprises a personnel information acquisition unit (301), a behavior disorder crowd judgment unit (302) and an information receiving unit (303) which are in signal connection with each other;
the information receiving unit (303) is used for receiving the face information of the current person to be collected, which is sent by the face recognition module (200);
the personnel information acquisition unit (301) calls the personnel information in the special crowd information base (500);
the behavior disorder crowd judgment unit (302) performs matching comparison on the face information of the current acquired person received by the information receiving unit (303) and the face information of the person in the special crowd information base (500) called by the person information acquisition unit (301), if matching is successful, the current acquired person is judged to belong to a behavior disorder person, and if matching is failed, the current acquired person is judged to be absent of a behavior disorder.
9. The image recognition security system based on big data screening of claim 8, wherein: the system also comprises a non-behavior-obstacle personnel information base (600);
when the behavioral disorder crowd judgment unit (302) judges that the current person to be collected is a person without behavioral disorder and the behavioral characteristic identification module (400) judges that the same current person to be collected has behavioral disorder, the personnel information acquisition unit (301) calls the personnel information in the person information base without behavioral disorder (600), the behavioral disorder crowd judgment unit (302) matches and compares the facial information of the current person to be collected received by the information reception unit (303) with the facial information of the personnel in the person information base without behavioral disorder (600) called by the personnel information acquisition unit (301), if the matching is successful, the newly added special crowd information collection and judgment module (900) receives the personnel information in the person information base without behavioral disorder (600) called by the personnel information acquisition unit (301), and the personnel information is sent to the special crowd information base (500), and the personnel information of the special crowd information base (500) is updated.
10. The image recognition security system based on big data screening of claim 9, wherein: when the behavioral disorder crowd determination unit (302) determines that the current person to be collected is a non-behavioral disorder person, and when the behavior characteristic identification module (400) judges that the same current collected person has behavior disorder, the behavioral disorder crowd judgment unit (302) matches and compares the facial information of the current acquired person received by the information receiving unit (303) with the facial information of the person in the behavioral disorder-free person information base (600) called by the person information acquisition unit (301), if the matching is unsuccessful, the behavior disorder crowd determination means (302) issues a report of the missing of the information of the disabled person to the control terminal (1100), the control terminal (1100) sends a control instruction to a field worker, and the field worker completes information registration work and/or traveling guidance work on the target behavior barrier worker according to the control instruction.
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