CN113689648A - Intelligent community security management system and method based on Internet of things - Google Patents
Intelligent community security management system and method based on Internet of things Download PDFInfo
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- CN113689648A CN113689648A CN202110979169.6A CN202110979169A CN113689648A CN 113689648 A CN113689648 A CN 113689648A CN 202110979169 A CN202110979169 A CN 202110979169A CN 113689648 A CN113689648 A CN 113689648A
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
- G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
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- 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
- G07C9/30—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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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
- G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
- G08B13/19639—Details of the system layout
- G08B13/19645—Multiple cameras, each having view on one of a plurality of scenes, e.g. multiple cameras for multi-room surveillance or for tracking an object by view hand-over
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Abstract
The invention discloses an intelligent community security management system and method based on the Internet of things, wherein the management system comprises an authentication database, an access database, a face image acquisition module, a face image verification module, a camera monitoring information acquisition module and a monitoring information analysis module, the authentication database is used for storing resident personnel information in a community, the resident personnel information comprises resident face images, the access database is used for storing external access personnel information in the community, the face image acquisition module is used for acquiring face images of personnel at an entrance of the community and setting the face images as images to be authenticated, the face image verification module compares the images to be authenticated with resident face images in the authentication database, and if a certain resident face image is consistent with the images to be authenticated, the images to be authenticated pass the verification, the person is allowed to enter the community.
Description
Technical Field
The invention relates to the technical field of intelligent community security, in particular to an intelligent community security management system and method based on the Internet of things.
Background
The intelligent community integrates various existing service resources of the community by using various intelligent technologies and modes, and provides multiple convenient services such as government affairs, commerce, entertainment, education, medical care, life mutual assistance and the like for the community masses. The setting of wisdom community provides more swift and comfortable intelligent living environment for the resident of wide residential quarter. In the process of building the intelligent community, the security monitoring system also plays an important role.
In the prior art, the security monitoring of the community is mainly realized by arranging workers to manually observe video monitoring through property companies, and the monitoring mode consumes more manpower and has low accuracy.
Disclosure of Invention
The invention aims to provide an intelligent community security management system and method based on the Internet of things, and aims to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an intelligent community security management system based on the Internet of things comprises an authentication database, an access database, a face image acquisition module, a face image verification module, a camera monitoring information acquisition module and a monitoring information analysis module, wherein the authentication database is used for storing resident personnel information in a community, the resident personnel information comprises resident face images, the access database is used for storing external access personnel information in the community, the face image acquisition module is used for acquiring face images of personnel at an entrance of the community and setting the face images as images to be authenticated, the face image verification module compares images to be authenticated with resident face images in the authentication database, when a certain resident face image is consistent with the images to be authenticated, the images to be authenticated pass verification and allow the personnel to enter the community, when the image to be authenticated is inconsistent with the face images of all residents, the person corresponding to the image to be authenticated is set as the suspect, the suspect is allowed to enter the community after the access information of the suspect is collected, the camera monitoring information acquisition module is made to acquire the camera monitoring information in the community, the activity of the suspect in the community is monitored, the monitoring information analysis module is used for analyzing the content acquired by the camera monitoring information acquisition module and judging whether alarm information needs to be transmitted or not according to the content, wherein m cameras are arranged in the community in advance, and m is a natural number.
Further, the monitoring information analysis module comprises a stay time comparison module, an associated camera selection module, an associated threshold comparison module, a monitoring index acquisition module, a monitoring index comparison module and a deep analysis module, wherein the stay time comparison module acquires the stay time of the suspicious person in the community and compares the stay time with a stay time threshold, when the stay time is more than or equal to the stay time threshold, the associated camera selection module extracts the image information of the suspicious person collected by the cameras in the analysis community, and sets the cameras collecting the images of the suspicious person as the associated cameras, the associated threshold comparison module is used for acquiring the distance between the positions of two associated cameras, and when the distance between the positions of two associated cameras is more than or equal to the associated threshold, the monitoring index acquisition module works, the monitoring index obtaining module is used for obtaining the monitoring index of the doubtful person, the monitoring index comparing module compares the monitoring index of the doubtful person with the monitoring threshold, and when the monitoring index of the doubtful person is larger than or equal to the monitoring threshold, the deep layer analyzing module is further used for analyzing images of the doubtful person collected by the associated camera.
Further, the monitoring index obtaining module comprises a correlation camera ratio calculating module, a reference area ratio calculating module, a reference time ratio calculating module and a monitoring index calculating module, wherein the correlation camera ratio calculating module obtains the number n of correlation cameras, and calculates the correlation camera ratio n/m according to the number n, the reference area ratio calculating module connects the correlation cameras through straight lines to obtain the maximum area Sg of a closed image surrounded by all the correlation cameras, and calculates the reference area ratio P ═ Sg/Sz, wherein Sz is the maximum area of the closed image surrounded by all the cameras after the cameras are connected through straight lines, the reference time ratio calculating module collects the time node of the image collected by the first correlation camera as the first time node, and collects the time node of the image collected by the nearest correlation camera as the second time node, and obtaining a time interval between the first time node and the second time node as a reference time interval Tc, and then calculating a reference time ratio Q as Tc/Tz, wherein Tz is the time interval from the first time node to the current time node, and the monitoring index calculation module calculates a monitoring index U of the suspicious person as 0.3 n/m + 0.3P + 0.4Q according to the associated camera ratio, the reference area ratio and the reference time ratio.
Further, the deep analysis module includes a first sorting module, a second sorting module, a priority camera selection module, a reference distance comparison module and a position transmission module, the first sorting module respectively counts the time lengths of images of suspected persons collected by each associated camera and sorts the time lengths in descending order to obtain a first sorting, the second sorting module sequentially calculates the difference value between two adjacent sorting time lengths in the sequence from front to back according to the first sorting, sorts the calculated difference value according to the corresponding first sorting to obtain a second sorting, the priority camera selection module sequentially compares the difference value in the second sorting with a difference threshold value in the sequence from front to back, if a certain difference value is smaller than or equal to the difference threshold value, the next difference value is continuously compared with the difference threshold value, if a certain difference value is larger than the difference value threshold value, stopping comparing the difference value with the difference value threshold value, setting the associated cameras corresponding to the difference values before the difference value sorting as the priority cameras, wherein the reference distance comparison module is used for acquiring the positions of the priority cameras, calculating the average distance between every two adjacent priority cameras, comparing the average distance with the reference distance, transmitting alarm information when the average distance is larger than or equal to the reference distance, enabling the position transmission module to acquire the nearest camera which acquires the person in doubt as a central camera, and transmitting the position of the central camera to the patrol persons in the community.
An intelligent community security management method based on the Internet of things comprises the following steps:
the method comprises the steps that an authentication database and an access database are established in advance, the authentication database is used for storing resident personnel information in a community, the resident personnel information comprises resident face images, and the access database is used for storing external access personnel information of the community;
acquiring a face image of a person at an entrance of a community, setting the face image as an image to be authenticated, comparing the image to be authenticated with a resident face image in an authentication database, and if the face image of a certain resident is consistent with the image to be authenticated, verifying the image to be authenticated and allowing the person to enter the community;
if the image to be authenticated is inconsistent with the face images of all the residents, the person corresponding to the image to be authenticated is a suspector, and the suspector is allowed to enter the community after the access information of the suspector is collected;
gather the camera monitoring information in the community, the monitoring personnel of suspicing should be in the community activity and judge whether will transmit alarm information in view of the above, wherein, set up m cameras in the community in advance, m is the natural number.
Further, the monitoring the activity of the suspect in the community comprises:
obtaining the stay time of the suspicious person in the community, if the stay time is more than or equal to the stay time threshold, extracting the image information of the suspicious person collected by a camera in the community,
the cameras for collecting the images of the suspect are set as the associated cameras, the number of the associated cameras is n,
respectively obtaining the distance between the positions of every two associated cameras, if the distance between the positions of some two associated cameras is larger than or equal to the associated threshold value,
connecting all the associated cameras through straight lines, obtaining the maximum area Sg of a closed image surrounded by all the associated cameras, and calculating a reference area ratio P which is Sg/Sz, wherein Sz is the maximum area of the closed image surrounded by all the cameras after all the cameras are connected through the straight lines;
acquiring a time node of a first acquired image of the suspect in the associated cameras as a first time node, acquiring a time node of a latest acquired image of the suspect in the associated cameras as a second time node, and acquiring a time interval between the first time node and the second time node as a reference time interval Tc, wherein the reference time ratio Q is Tc/Tz, and Tz is a time interval from the first time node to the current time node;
the index U of the suspect is 0.3 n/m + 0.3P + 0.4Q,
and if the monitoring index of the suspect is larger than or equal to the monitoring threshold, further analyzing the image of the suspect collected by the associated camera.
Further, the further analyzing the image of the person in doubt acquired by the associated camera includes:
respectively counting the time length of images of the suspect collected by each associated camera, and sequencing the time lengths according to a sequence from big to small to obtain a first sequence,
sequentially calculating the difference between the time lengths of two adjacent sequences according to the sequence from front to back of the first sequence, sequencing the calculated difference according to the corresponding first sequence to obtain a second sequence,
comparing the difference values in the second sequence with the difference threshold values in sequence from front to back, if a certain difference value is smaller than or equal to the difference threshold value, continuing to compare the next difference value with the difference threshold value, if a certain difference value is larger than the difference threshold value, stopping comparing the difference value with the difference threshold value, and setting the associated camera corresponding to the difference value before the difference value sequence as a priority camera,
and acquiring the position of each priority camera, calculating the average distance between two adjacent priority cameras, comparing the average distance with a reference distance, and transmitting alarm information if the average distance is greater than or equal to the reference distance.
Further, the management method further includes:
when alarm information is transmitted, a camera which collects the person in doubt recently is obtained as a central camera, and the position of the central camera is transmitted to community patrol personnel.
Compared with the prior art, the invention has the following beneficial effects: the invention deduces the moving activity condition of the suspect in the community by acquiring the camera of the suspect, judges whether the suspect is a lawbreaker who wants to perform point-stepping stealing in the community according to the moving activity condition of the suspect, and transmits alarm information when judging that the suspect is a lawbreaker who possibly performs point-stepping stealing, thereby improving the safety performance of the community.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic block diagram of an Internet of things-based intelligent community security management system according to the present invention.
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.
Referring to fig. 1, the present invention provides a technical solution: an intelligent community security management system based on the Internet of things comprises an authentication database, an access database, a face image acquisition module, a face image verification module, a camera monitoring information acquisition module and a monitoring information analysis module, wherein the authentication database is used for storing resident personnel information in a community, the resident personnel information comprises resident face images, the access database is used for storing external access personnel information in the community, the face image acquisition module is used for acquiring face images of personnel at an entrance of the community and setting the face images as images to be authenticated, the face image verification module compares images to be authenticated with resident face images in the authentication database, when a certain resident face image is consistent with the images to be authenticated, the images to be authenticated pass verification and allow the personnel to enter the community, when the image to be authenticated is inconsistent with the face images of all residents, the person corresponding to the image to be authenticated is set as the suspect, the suspect is allowed to enter the community after the access information of the suspect is collected, the camera monitoring information acquisition module is made to acquire the camera monitoring information in the community, the activity of the suspect in the community is monitored, the monitoring information analysis module is used for analyzing the content acquired by the camera monitoring information acquisition module and judging whether alarm information needs to be transmitted or not according to the content, wherein m cameras are arranged in the community in advance, and m is a natural number.
The monitoring information analysis module comprises a stay time comparison module, an associated camera selection module, an associated threshold comparison module, a monitoring index acquisition module, a monitoring index comparison module and a deep analysis module, wherein the stay time comparison module acquires the stay time of the suspicious person in the community and compares the stay time with a stay time threshold, when the stay time is more than or equal to the stay time threshold, the associated camera selection module is used for extracting and analyzing the image information of the suspicious person acquired by the cameras in the community, and setting the cameras acquiring the images of the suspicious person as the associated cameras, the associated threshold comparison module is used for acquiring the distance between the positions of two associated cameras, and when the distance between the positions of two associated cameras is more than or equal to the associated threshold, the monitoring index acquisition module works, the monitoring index obtaining module is used for obtaining the monitoring index of the doubtful person, the monitoring index comparing module compares the monitoring index of the doubtful person with the monitoring threshold, and when the monitoring index of the doubtful person is larger than or equal to the monitoring threshold, the deep layer analyzing module is further used for analyzing images of the doubtful person collected by the associated camera.
The monitoring index obtaining module comprises a correlation camera ratio calculating module, a reference area ratio calculating module, a reference time ratio calculating module and a monitoring index calculating module, wherein the correlation camera ratio calculating module obtains the number n of correlation cameras and calculates the correlation camera ratio n/m according to the number n, the reference area ratio calculating module connects the correlation cameras through straight lines to obtain the maximum area Sg of a closed image surrounded by all the correlation cameras, and calculates the reference area ratio P which is Sg/Sz, wherein Sz is the maximum area of the closed image surrounded by all the cameras after the cameras are connected through straight lines, the time node for collecting the image of the doubtful person collected by the first correlation camera in the correlation cameras is a first time node, and the time node for collecting the image of the doubtful person collected by the latest correlation camera is a second time node, and obtaining a time interval between the first time node and the second time node as a reference time interval Tc, and then calculating a reference time ratio Q as Tc/Tz, wherein Tz is the time interval from the first time node to the current time node, and the monitoring index calculation module calculates a monitoring index U of the suspicious person as 0.3 n/m + 0.3P + 0.4Q according to the associated camera ratio, the reference area ratio and the reference time ratio.
The deep analysis module comprises a first sequencing module, a second sequencing module, a priority camera selection module, a reference distance comparison module and a position transmission module, wherein the first sequencing module respectively counts the time length of images of suspicious persons collected by each associated camera and sequences the time lengths in a descending order to obtain a first sequence, the second sequencing module sequentially calculates the difference value between two adjacent sequenced time lengths in a front-to-back order, sequences the calculated difference value according to the corresponding first sequence to obtain a second sequence, the priority camera selection module sequentially compares the difference value in the second sequence with a difference threshold value in a front-to-back order, if a certain difference value is smaller than or equal to the difference threshold value, continues to compare the next difference value with the difference threshold value, if a certain difference value is greater than the difference threshold value, and stopping comparing the difference value with the difference value threshold value, setting the associated cameras corresponding to the difference values before the difference value sorting as the priority cameras, wherein the reference distance comparison module is used for acquiring the positions of the priority cameras, calculating the average distance between every two adjacent priority cameras, comparing the average distance with the reference distance, transmitting alarm information when the average distance is larger than or equal to the reference distance, enabling the position transmission module to acquire the camera which is closest to the person who acquires the doubt as a central camera, and transmitting the position of the central camera to the patrol persons in the community.
An intelligent community security management method based on the Internet of things comprises the following steps:
the method comprises the steps that an authentication database and an access database are established in advance, the authentication database is used for storing resident personnel information in a community, the resident personnel information comprises resident face images, and the access database is used for storing external access personnel information of the community;
acquiring a face image of a person at an entrance of a community, setting the face image as an image to be authenticated, comparing the image to be authenticated with a resident face image in an authentication database, and if the face image of a certain resident is consistent with the image to be authenticated, verifying the image to be authenticated and allowing the person to enter the community;
if the image to be authenticated is inconsistent with the face images of all the residents, the person corresponding to the image to be authenticated is a suspector, and the suspector is allowed to enter the community after the access information of the suspector is collected; the access information of the suspect comprises the identity card information of the suspect and the contact information of the suspect,
gather the camera monitoring information in the community, the monitoring personnel of suspicing should be in the community activity and judge whether will transmit alarm information in view of the above, wherein, set up m cameras in the community in advance, m is the natural number. The cameras in the community are connected with the network, the cameras refer to cameras inside the community and outside the building, and the cameras are mainly used for collecting the conditions of coming and going of people inside the community and outside the building;
the monitoring of the activity of the suspect in the community comprises the following steps:
obtaining the stay time of the suspicious person in the community, if the stay time is more than or equal to the stay time threshold, extracting the image information of the suspicious person collected by a camera in the community,
the cameras for collecting the images of the suspect are set as the associated cameras, the number of the associated cameras is n,
respectively obtaining the distance between the positions of every two associated cameras, if the distance between the positions of some two associated cameras is larger than or equal to the associated threshold value,
connecting all the associated cameras through straight lines, obtaining the maximum area Sg of a closed image surrounded by all the associated cameras, and calculating a reference area ratio P which is Sg/Sz, wherein Sz is the maximum area of the closed image surrounded by all the cameras after all the cameras are connected through the straight lines;
acquiring a time node of a first acquired image of the suspect in the associated cameras as a first time node, acquiring a time node of a latest acquired image of the suspect in the associated cameras as a second time node, and acquiring a time interval between the first time node and the second time node as a reference time interval Tc, wherein the reference time ratio Q is Tc/Tz, and Tz is a time interval from the first time node to the current time node;
then the index U of the suspect is 0.3 × n/m +0.3 × P +0.4 × Q; in practical situations, the moving track of the person in doubt cannot be easily obtained, and the moving situation of the visiting person in the community is simulated and inferred through a camera in the application; when more cameras are collected for the doubtful persons, and the positions of the cameras collected for the doubtful persons are scattered, the range is higher, the time for the doubtful persons to stay outside a building to walk is longer, the doubtful persons possibly observe and step on the community environment, because the normal visitors outside the community usually visit the community for a certain purpose, generally, the visitors run straight at the destination and stay around rarely, if the external visitors move around in the community, the visitors possibly observe and step on the community environment, and are accurately stolen, but the visitors possibly do not know the internal environment of the community and look for the destination around;
and if the monitoring index of the suspect is larger than or equal to the monitoring threshold, further analyzing the image of the suspect collected by the associated camera.
The further analysis of the image of the person in doubt acquired by the associated camera comprises:
respectively counting the time length of images of the suspect collected by each associated camera, and sequencing the time lengths according to a sequence from big to small to obtain a first sequence,
sequentially calculating the difference between the time lengths of two adjacent sequences according to the sequence from front to back of the first sequence, sequencing the calculated difference according to the corresponding first sequence to obtain a second sequence,
and comparing the difference values in the second sequence with the difference threshold values in sequence from front to back, if a certain difference value is smaller than or equal to the difference threshold value, continuing to compare the next difference value with the difference threshold value, if a certain difference value is larger than the difference threshold value, stopping comparing the difference value with the difference threshold value, setting the associated camera corresponding to the difference value before the difference value sequence as a priority camera, comparing the difference value with the difference threshold value in sequence to select the priority camera, and stopping continuing to compare when the difference value is larger than the difference threshold value for the first time, so that the selected priority camera is more accurate.
And acquiring the position of each priority camera, calculating the average distance between two adjacent priority cameras, comparing the average distance with a reference distance, and transmitting alarm information if the average distance is greater than or equal to the reference distance. Selecting a camera with a longer time length when the doubtful person is acquired by sequentially comparing the difference value with the difference value threshold value, judging according to the position distance of the camera, if the distance between the prior cameras is smaller, the length of the stay time of the doubtful person at a certain position is relatively longer, the doubtful person is probably unknown about the internal environment of the community, and searching for destinations at four positions, if the distance between the prior cameras is larger, and the difference of the stay time lengths of the doubtful person at each position is not large, the doubtful person is trampled, the possibility of being prepared for theft is higher, wherein the prior camera is the camera with the longer time length when the image of the doubtful person is acquired; the method further judges the stay time lengths of the suspect in each place inside the community and outside the building through the time length of the image of the suspect collected by each associated camera, if the suspect is trampled and is prepared for stealing, under the condition, the suspect stays everywhere for observation, the stay time lengths everywhere are not greatly different, if the suspect is not aware of the internal environment of the community and finds destinations everywhere, the stay time lengths of all places should be greatly different, the stay time lengths of places closer to the destinations are relatively longer, and the stay time lengths of places farther away from the destinations are relatively shorter;
the management method further comprises the following steps:
when alarm information is transmitted, a camera which collects the person in doubt recently is obtained as a central camera, and the position of the central camera is transmitted to community patrol personnel.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. An intelligent community security management system based on the Internet of things is characterized by comprising an authentication database, an access database, a face image acquisition module, a face image verification module, a camera monitoring information acquisition module and a monitoring information analysis module, wherein the authentication database is used for storing resident personnel information in a community, the resident personnel information comprises resident face images, the access database is used for storing external access personnel information in the community, the face image acquisition module is used for acquiring face images of personnel at an entrance of the community and setting the face images as images to be authenticated, the face image verification module compares the images to be authenticated with the resident face images in the authentication database, and if a certain resident face image is consistent with the images to be authenticated, the images to be authenticated pass the authentication, the person is allowed to enter the community, when the image to be authenticated is inconsistent with the face images of all residents, the person corresponding to the image to be authenticated is set as the person in doubt, the access information of the person in doubt is collected, the person in doubt is allowed to enter the community, the camera monitoring information acquisition module is made to acquire the camera monitoring information in the community, the activity of the person in doubt in the community is monitored, the monitoring information analysis module is used for analyzing the content acquired by the camera monitoring information acquisition module and judging whether alarm information needs to be transmitted or not according to the content, wherein m cameras are arranged in the community in advance, and m is a natural number.
2. The intelligent community security management system based on the internet of things according to claim 1, wherein: the monitoring information analysis module comprises a stay time comparison module, an associated camera selection module, an associated threshold comparison module, a monitoring index acquisition module, a monitoring index comparison module and a deep analysis module, wherein the stay time comparison module acquires the stay time of the suspicious person in the community and compares the stay time with a stay time threshold, when the stay time is more than or equal to the stay time threshold, the associated camera selection module is used for extracting and analyzing the image information of the suspicious person acquired by the cameras in the community, and setting the cameras acquiring the images of the suspicious person as the associated cameras, the associated threshold comparison module is used for acquiring the distance between the positions of two associated cameras, and when the distance between the positions of two associated cameras is more than or equal to the associated threshold, the monitoring index acquisition module works, the monitoring index obtaining module is used for obtaining the monitoring index of the doubtful person, the monitoring index comparing module compares the monitoring index of the doubtful person with the monitoring threshold, and when the monitoring index of the doubtful person is larger than or equal to the monitoring threshold, the deep layer analyzing module is further used for analyzing images of the doubtful person collected by the associated camera.
3. The intelligent community security management system based on the internet of things as claimed in claim 2, wherein: the monitoring index obtaining module comprises a correlation camera ratio calculating module, a reference area ratio calculating module, a reference time ratio calculating module and a monitoring index calculating module, wherein the correlation camera ratio calculating module obtains the number n of correlation cameras and calculates the correlation camera ratio n/m according to the number n, the reference area ratio calculating module connects the correlation cameras through straight lines to obtain the maximum area Sg of a closed image surrounded by all the correlation cameras, and calculates the reference area ratio P which is Sg/Sz, wherein Sz is the maximum area of the closed image surrounded by all the cameras after the cameras are connected through straight lines, the time node for collecting the image of the doubtful person collected by the first correlation camera in the correlation cameras is a first time node, and the time node for collecting the image of the doubtful person collected by the latest correlation camera is a second time node, and obtaining a time interval between the first time node and the second time node as a reference time interval Tc, and then calculating a reference time ratio Q as Tc/Tz, wherein Tz is the time interval from the first time node to the current time node, and the monitoring index calculation module calculates a monitoring index U of the suspicious person as 0.3 n/m + 0.3P + 0.4Q according to the associated camera ratio, the reference area ratio and the reference time ratio.
4. The intelligent community security management system based on the internet of things according to claim 3, wherein: the deep analysis module comprises a first sequencing module, a second sequencing module, a priority camera selection module, a reference distance comparison module and a position transmission module, wherein the first sequencing module respectively counts the time length of images of suspicious persons collected by each associated camera and sequences the time lengths in a descending order to obtain a first sequence, the second sequencing module sequentially calculates the difference value between two adjacent sequenced time lengths in a front-to-back order, sequences the calculated difference value according to the corresponding first sequence to obtain a second sequence, the priority camera selection module sequentially compares the difference value in the second sequence with a difference threshold value in a front-to-back order, if a certain difference value is smaller than or equal to the difference threshold value, continues to compare the next difference value with the difference threshold value, if a certain difference value is greater than the difference threshold value, and stopping comparing the difference value with the difference value threshold value, setting the associated cameras corresponding to the difference values before the difference value sorting as the priority cameras, wherein the reference distance comparison module is used for acquiring the positions of the priority cameras, calculating the average distance between every two adjacent priority cameras, comparing the average distance with the reference distance, transmitting alarm information when the average distance is larger than or equal to the reference distance, enabling the position transmission module to acquire the camera which is closest to the person who acquires the doubt as a central camera, and transmitting the position of the central camera to the patrol persons in the community.
5. An intelligent community security management method based on the Internet of things is characterized by comprising the following steps: the management method comprises the following steps:
the method comprises the steps that an authentication database and an access database are established in advance, the authentication database is used for storing resident personnel information in a community, the resident personnel information comprises resident face images, and the access database is used for storing external access personnel information of the community;
acquiring a face image of a person at an entrance of a community, setting the face image as an image to be authenticated, comparing the image to be authenticated with a resident face image in an authentication database, and if the face image of a certain resident is consistent with the image to be authenticated, verifying the image to be authenticated and allowing the person to enter the community;
if the image to be authenticated is inconsistent with the face images of all the residents, the person corresponding to the image to be authenticated is a suspector, and the suspector is allowed to enter the community after the access information of the suspector is collected;
gather the camera monitoring information in the community, the monitoring personnel of suspicing should be in the community activity and judge whether will transmit alarm information in view of the above, wherein, set up m cameras in the community in advance, m is the natural number.
6. The intelligent community security management method based on the internet of things as claimed in claim 5, wherein: the monitoring of the activity of the suspect in the community comprises the following steps:
obtaining the stay time of the suspicious person in the community, if the stay time is more than or equal to the stay time threshold, extracting the image information of the suspicious person collected by a camera in the community,
the cameras for collecting the images of the suspect are set as the associated cameras, the number of the associated cameras is n,
respectively obtaining the distance between the positions of every two associated cameras, if the distance between the positions of some two associated cameras is larger than or equal to the associated threshold value,
connecting all the associated cameras through straight lines, obtaining the maximum area Sg of a closed image surrounded by all the associated cameras, and calculating a reference area ratio P which is Sg/Sz, wherein Sz is the maximum area of the closed image surrounded by all the cameras after all the cameras are connected through the straight lines;
acquiring a time node of a first acquired image of the suspect in the associated cameras as a first time node, acquiring a time node of a latest acquired image of the suspect in the associated cameras as a second time node, and acquiring a time interval between the first time node and the second time node as a reference time interval Tc, wherein the reference time ratio Q is Tc/Tz, and Tz is a time interval from the first time node to the current time node;
the index U of the suspect is 0.3 n/m + 0.3P + 0.4Q,
and if the monitoring index of the suspect is larger than or equal to the monitoring threshold, further analyzing the image of the suspect collected by the associated camera.
7. The intelligent community security management method based on the internet of things as claimed in claim 6, wherein: the further analysis of the image of the person in doubt acquired by the associated camera comprises:
respectively counting the time length of images of the suspect collected by each associated camera, and sequencing the time lengths according to a sequence from big to small to obtain a first sequence,
sequentially calculating the difference between the time lengths of two adjacent sequences according to the sequence from front to back of the first sequence, sequencing the calculated difference according to the corresponding first sequence to obtain a second sequence,
comparing the difference values in the second sequence with the difference threshold values in sequence from front to back, if a certain difference value is smaller than or equal to the difference threshold value, continuing to compare the next difference value with the difference threshold value, if a certain difference value is larger than the difference threshold value, stopping comparing the difference value with the difference threshold value, and setting the associated camera corresponding to the difference value before the difference value sequence as a priority camera,
and acquiring the position of each priority camera, calculating the average distance between two adjacent priority cameras, comparing the average distance with a reference distance, and transmitting alarm information if the average distance is greater than or equal to the reference distance.
8. The intelligent community security management method based on the internet of things as claimed in claim 7, wherein: the management method further comprises the following steps:
when alarm information is transmitted, a camera which collects the person in doubt recently is obtained as a central camera, and the position of the central camera is transmitted to community patrol personnel.
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