CN112002100A - Intelligent security system based on 5G - Google Patents
Intelligent security system based on 5G Download PDFInfo
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- CN112002100A CN112002100A CN202010913123.XA CN202010913123A CN112002100A CN 112002100 A CN112002100 A CN 112002100A CN 202010913123 A CN202010913123 A CN 202010913123A CN 112002100 A CN112002100 A CN 112002100A
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B19/00—Alarms responsive to two or more different undesired or abnormal conditions, e.g. burglary and fire, abnormal temperature and abnormal rate of flow
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/13—Satellite images
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- G06V20/40—Scenes; Scene-specific elements in video content
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
- G08B25/01—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium
- G08B25/08—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium using communication transmission lines
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
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Abstract
The invention discloses a 5G-based intelligent security system, which comprises an AI cloud platform, an active monitoring module, a passive monitoring module, a 5G wireless communication module, an intelligent wearing module, a track monitoring module, a comprehensive control module, a data storage module, a plurality of inspection unmanned aerial vehicles and a plurality of high-definition cameras; the invention is provided with the 5G wireless communication module, and the arrangement ensures the speed of data transmission and improves the processing capacity of the system; the passive monitoring module is arranged, and the high-definition video of the target area is acquired aiming at the passive monitoring signal, so that the working content of the system is effectively reduced, and the working efficiency of the system is improved; the active monitoring module is arranged, the target area is monitored in real time according to the active monitoring scheme, and the target area is monitored through the preset monitoring scheme, so that the overall safety of a city is improved.
Description
Technical Field
The invention belongs to the technical field of security, and particularly relates to a 5G-based intelligent security system.
Background
The security system provides a safe living and working environment system for people by using various scientific technologies such as audio and video, infrared, detection, microwave, control, communication and the like and adopting various security products and equipment, thereby achieving the effects of early warning, control and timely processing and protecting the safety of human bodies and lives and property.
The intelligent security monitoring system and method with the publication number of CN107301753A detect security state information in a defense area through a security monitoring device, a server group generates a security state grading early warning result according to the security state information, acquires a rejection mode corresponding to the security state grading early warning result, and a control platform generates a rejection instruction according to the rejection mode to control the rejection device to operate so as to achieve the purpose of preventing an intruder from continuously intruding, thereby realizing the intelligent security monitoring system from monitoring to actively rejecting the intruder.
Above-mentioned scheme has realized intelligent security's function to a certain extent, but this scheme has following shortcoming: the method has the advantages of small application range, slow data transmission and signal feedback and single data acquisition means, so the scheme still has room for improvement.
Disclosure of Invention
In order to solve the problems existing in the scheme, the invention provides a 5G-based intelligent security system.
The purpose of the invention can be realized by the following technical scheme: A5G-based intelligent security system comprises an AI cloud platform, an active monitoring module, a passive monitoring module, a 5G wireless communication module, an intelligent wearing module, a comprehensive control module, a data storage module, a plurality of inspection unmanned aerial vehicles and a plurality of high-definition cameras;
the AI cloud platform is used for realizing information interaction of each module in the system, the AI cloud platform comprises a signal analysis unit, a data processing unit and a cloud storage unit, the signal analysis unit is used for receiving alarm signals and carrying out analysis processing, the alarm signals comprise alarm keywords, alarm levels and GPS coordinates, the alarm levels comprise I-level alarm and II-level alarm, and the specific analysis processing steps are as follows:
z1: receiving an alarm signal through a signal analysis unit, separating an alarm keyword, an alarm grade and a GPS (global positioning system) address in the alarm signal, and simultaneously generating a passive monitoring signal;
z2: sending the GPS coordinates and the passive monitoring signals to a passive monitoring module through an AI cloud platform;
the passive monitoring module monitors the traffic accident of a specific area in real time according to the passive monitoring signal, and the specific monitoring steps are as follows:
x1: after the passive monitoring module receives the passive monitoring signals, the AI cloud platform acquires a plurality of specific area RE from the data storage moduleiAnd comparing and positioning the coordinate range with the GPS coordinate received by the passive monitoring module, wherein the specific positioning steps are as follows:
x11: acquiring longitude and latitude from the GPS coordinates, and respectively marking the longitude and latitude as LON and LAT;
x12: associating longitudes LON and latitudes LAT with specific regions REiComparing the coordinate ranges one by one;
x13: when the longitude LON and the latitude LAT are both within the coordinate range of the specific area, marking the longitude LON and the latitude LAT as target areas and marking the target areas as RE 1; when a plurality of specific areas REiWhen the coordinate ranges can not contain longitude LON and latitude LAT at the same time, an alarm signal error instruction is sent to the comprehensive control module through the AI cloud platform;
x2: after acquiring the target area, the passive monitoring module controls the patrol unmanned aerial vehicle and the high definition camera to acquire the high definition video of the GPS coordinate area according to the alarm level, and the specific acquisition steps are as follows:
x21: when the alarm level is I-level alarm, determining the number SS of high-definition cameras within the range of L1 m according to the GPS coordinates and the GPS positioning unit, and sending high-definition videos shot by the high-definition cameras to an AI cloud platform; if the SS of the high-definition cameras in the range of the square circle L1 of the GPS coordinate is less than L2, the GPS coordinate is sent to a main inspection unmanned aerial vehicle of the target area through a passive monitoring module to shoot the GPS coordinate area, and passive monitoring videos shot by the main inspection unmanned aerial vehicle and the high-definition cameras are sent to an AI cloud platform, wherein L1 and L2 are preset threshold values;
x22: when the alarm level is II-level alarm, the passive detection module simultaneously controls a high-definition camera in the range of L1 m of the GPS coordinate square circle and an inspection unmanned aerial vehicle of a target area where the GPS coordinate is located to shoot a GPS coordinate area, and sends a shot passive monitoring video to an AI cloud platform;
x3: the AI cloud platform receives the passive monitoring video and then respectively sends the passive monitoring video to the data storage module and the data processing unit;
the active monitoring module monitors a target area in real time according to an active monitoring scheme, and the specific monitoring steps are as follows:
c1: the AI cloud platform acquires an active monitoring scheme through the data storage module and sends the active monitoring scheme to the signal analysis unit; the signal analysis unit analyzes scheme contents in the active monitoring scheme, wherein the scheme contents comprise frequency and frame number; the scheme content is sent to an active monitoring module through an AI cloud platform;
c2: the active monitoring module acquires seasons and time through the AI cloud platform, inspects a target area according to a received active monitoring scheme, and sends an active monitoring video shot in the inspection process to the AI cloud platform;
c3: the AI cloud platform receives the active monitoring video and then respectively sends the active monitoring video to the data storage module and the data processing unit;
the data processing unit is used for analyzing and processing monitoring videos, the monitoring videos comprise active monitoring videos and passive monitoring videos, and the specific analyzing and processing steps are as follows:
b1: the data processing unit is used for preprocessing the monitoring video;
b2: performing feature extraction on the preprocessed monitoring video, wherein the specific extraction steps are as follows:
BB 1: extracting the characteristics of the natural disasters in the active monitoring video, calculating the occurrence area of the natural disasters, and marking the occurrence area as ZM; at that time, the AI cloud platform does not send instructions to the integrated control module and the intelligent wearing module; at the moment, the AI cloud platform sends a low-risk disaster instruction to the comprehensive control module and the intelligent wearing module, and simultaneously sends the central coordinate of the natural disaster to the intelligent wearing module; at that time, the AI cloud platform sends a high-risk disaster instruction to the integrated control module and the intelligent wearing module, and simultaneously sends the central coordinates of the natural disaster to the intelligent wearing module, wherein L5 and L6 are preset threshold values; the feature extraction of natural disasters from videos is the prior art, and the method based on dual-density dual-tree complex wavelet transform in a natural disaster monitoring method based on dual-density dual-tree complex wavelet transform and SAR images can realize the extraction of natural disaster information from images, and the feature extraction of natural disasters can be realized by frame-by-frame analysis of monitoring videos by an AI cloud platform;
BB 2: extracting the characteristics of the traffic accident in the passive monitoring video, calculating the occurrence area of the traffic accident, marking the occurrence area as JM, sending a low-risk event instruction to the comprehensive control module and the intelligent wearing module by the AI cloud platform, and sending the coordinate of the traffic accident to the intelligent wearing module; at that time, the AI cloud platform sends a high-risk event instruction to the integrated control module and the intelligent wearing module, and simultaneously sends the coordinates of the traffic accident to the intelligent wearing module, wherein L7 is a preset threshold value; the detection and identification of traffic accidents from videos is the prior art, and the detection method provided in the thesis of traffic accident detection method based on videos can realize the detection and identification of traffic accidents from videos;
b3: and the data processing unit sends the natural disaster occurrence area ZM and the traffic accident area JM to the data storage module for storage.
Preferably, the intelligent wearable module is used for commanding the staff to carry out work, the staff comprises police and firefighters, the intelligent wearable module comprises a storage unit, a GPS positioning unit, a temporary display unit and an identity verification unit, the identity verification unit comprises a fingerprint verification node, a facial image verification node and a voice verification node, and the specific commanding steps are as follows:
n1: acquiring fingerprint characteristics, facial characteristics and voice characteristics of a current person through an intelligent wearing module, respectively comparing and matching the fingerprint characteristics, facial characteristics and voice characteristics with the fingerprint characteristics, facial characteristics and voice characteristics stored in a storage unit, marking the fingerprint matching degree as ZW, the facial matching degree as MB and the voice matching degree as YY, acquiring a matching degree coefficient PP through a formula, and then successfully matching; wherein alpha, beta and gamma are preset proportionality coefficients, and L8 is a preset threshold;
n2: when the intelligent wearable module receives a high-risk disaster instruction, the AI cloud platform positions and dispatches workers within an L9 meter square circle of a natural disaster center coordinate to a disaster occurrence place through the GPS positioning unit; when the intelligent wearable module receives a low-risk disaster instruction, the AI cloud platform dispatches workers in a square circle L10 m of a center coordinate of the natural disaster to go to a disaster place, wherein L9 and L10 are preset threshold values;
n3: when the intelligent wearable module receives a high-risk event instruction, the AI cloud platform positions and dispatches workers within an L11 meter traffic accident coordinate square circle to an accident site through the GPS positioning unit; when the intelligent wearable module receives a low-risk event instruction, the AI cloud platform dispatches a worker within an L12 meter traffic accident coordinate square circle to an accident site, wherein L11 and L12 are preset threshold values, and L11 is more than L12;
n4: and meanwhile, the monitoring video is sent to the temporary display unit through the AI cloud platform.
Preferably, the natural disasters include flooding, drought, typhoon and fog.
Preferably, the preprocessing includes band-limited filtering and down-sampling, noise removal, image enhancement and special processing, the special processing includes rain fog processing, dim light processing, auto-exposure and focus processing and backlight compensation processing, and the preprocessing has two purposes, namely, improving the quality of decoded images, especially the video quality under adverse conditions, and improving the efficiency of video coding and reducing the quality fluctuation of transmitted video.
Preferably, the active monitoring scheme is edited by the integrated control module and sent to the data storage module through the AI cloud platform, and the specific contents of the scheme are as follows:
v1: when the current season is spring or autumn, a main inspection unmanned aerial vehicle dispatching a target area performs full-coverage inspection on the target area every L3 hours, wherein L3 is a preset threshold value, the spring starts from spring to finish the day before spring, and the autumn starts from autumn to finish the day before winter;
v2: when the current season is summer or winter, all the patrol inspection unmanned aerial vehicles dispatching the target area perform full-coverage patrol inspection on the target area every L4 hours, wherein L4 is a preset threshold value, the summer is finished from the beginning of summer to the day before the beginning of autumn, and the winter is finished from the beginning of winter to the day before the beginning of spring of the second year.
Preferably, the inspection unmanned aerial vehicle is used for dynamically inspecting a specific area, and the specific area can be divided into a plurality of specific areas according to the urban administrative area and marked as REiI is 1, 2, … … n, and a number of specific regions REiCoordinate range storage is in data storage module, and every specific area contains two at least and patrols and examines unmanned aerial vehicle, and every specific area includes that a owner patrols and examines unmanned aerial vehicle, and is a plurality of it all embeds there is GPS positioning unit to patrol and examine unmanned aerial vehicle and high definition digtal camera.
Preferably, the 5G wireless communication module connects the AI cloud platform with the emergency monitoring module, the public security monitoring module and the intelligent wearing module through 5G wireless signals, and the data storage module and the comprehensive control module are linearly connected with the AI cloud platform.
Preferably, the integrated control module is used for displaying the received active monitoring video and passive monitoring video and giving an alarm according to the received instruction.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention is provided with a 5G wireless communication module, and the AI cloud platform is connected with an emergency monitoring module, a public security monitoring module and an intelligent wearing module through 5G wireless signals; the arrangement ensures the speed of data transmission and improves the processing capacity of the system;
2. the passive monitoring module is arranged, the traffic accident in a specific area is monitored in real time according to the passive monitoring signal, the target area where the received GSP coordinate is located is determined, the patrol unmanned aerial vehicle and the high-definition camera are controlled according to the alarm level to acquire the high-definition video of the GPS coordinate area, the passive monitoring video is sent to the AI cloud platform, the high-definition video of the target area is acquired according to the passive monitoring signal, the working content of the system is effectively reduced, and the working efficiency of the system is improved;
3. the invention is provided with an active monitoring module which monitors a target area in real time according to an active monitoring scheme, and an AI cloud platform acquires the active monitoring scheme through a data storage module and sends the active monitoring scheme to a signal analysis unit; the signal analysis unit analyzes scheme contents in the active monitoring scheme, wherein the scheme contents comprise frequency and frame number; the scheme content is sent to an active monitoring module through an AI cloud platform; the active monitoring module acquires seasons and time through the AI cloud platform, patrols and examines the target area according to the received active monitoring scheme, sends the active monitoring video shot in the patrolling and examining process to the AI cloud platform for processing, monitors the target area through the monitoring scheme set in advance, and contributes to improving the overall safety of the city.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic diagram of the principle of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood 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, a 5G-based intelligent security system includes an AI cloud platform, an active monitoring module, a passive monitoring module, a 5G wireless communication module, an intelligent wearable module, a trajectory monitoring module, a comprehensive control module, a data storage module, a plurality of inspection unmanned aerial vehicles, and a plurality of high definition cameras;
the AI cloud platform is used for realizing the information interaction of each module in the system, and the AI cloud platform includes signal analysis unit, data processing unit and cloud memory cell, and signal analysis unit is used for receiving alarm signal and carries out analysis processes, and alarm signal includes warning keyword, warning grade and GPS coordinate, and the warning grade includes I level warning and II level warning, and concrete analysis processes step is:
z1: receiving an alarm signal through a signal analysis unit, separating an alarm keyword, an alarm grade and a GPS (global positioning system) address in the alarm signal, and simultaneously generating a passive monitoring signal;
z2: sending the GPS coordinates and the passive monitoring signals to a passive monitoring module through an AI cloud platform;
the passive monitoring module carries out real-time monitoring on the traffic accident of the specific area according to the passive monitoring signal, and the specific monitoring steps are as follows:
x1: after the passive monitoring module receives the passive monitoring signals, the AI cloud platform acquires a plurality of specific area RE from the data storage moduleiAnd comparing and positioning the coordinate range with the GPS coordinate received by the passive monitoring module, wherein the specific positioning steps are as follows:
x11: acquiring longitude and latitude from the GPS coordinates, and respectively marking the longitude and latitude as LON and LAT;
x12: associating longitudes LON and latitudes LAT with specific regions REiComparing the coordinate ranges one by one;
x13: coordinates when longitude LON and latitude LAT are both in a specific areaWithin range, it is marked as target area and marked as RE 1; when a plurality of specific areas REiWhen the coordinate ranges can not contain longitude LON and latitude LAT at the same time, an alarm signal error instruction is sent to the comprehensive control module through the AI cloud platform;
x2: after acquiring the target area, the passive monitoring module controls the patrol unmanned aerial vehicle and the high definition camera to acquire the high definition video of the GPS coordinate area according to the alarm level, and the specific acquisition steps are as follows:
x21: when the alarm level is I-level alarm, determining the number SS of high-definition cameras within the range of L1 m according to the GPS coordinates and the GPS positioning unit, and sending high-definition videos shot by the high-definition cameras to an AI cloud platform; if the SS of the high-definition cameras in the range of the square circle L1 of the GPS coordinate is less than L2, the GPS coordinate is sent to a main inspection unmanned aerial vehicle of the target area through a passive monitoring module to shoot the GPS coordinate area, and passive monitoring videos shot by the main inspection unmanned aerial vehicle and the high-definition cameras are sent to an AI cloud platform, wherein L1 and L2 are preset threshold values;
x22: when the alarm level is II-level alarm, the passive detection module simultaneously controls a high-definition camera in the range of L1 m of the GPS coordinate square circle and an inspection unmanned aerial vehicle of a target area where the GPS coordinate is located to shoot a GPS coordinate area, and sends a shot passive monitoring video to an AI cloud platform;
x3: the AI cloud platform receives the passive monitoring video and then respectively sends the passive monitoring video to the data storage module and the data processing unit;
the active monitoring module monitors a target area in real time according to an active monitoring scheme, and the specific monitoring steps are as follows:
c1: the AI cloud platform acquires an active monitoring scheme through the data storage module and sends the active monitoring scheme to the signal analysis unit; the signal analysis unit analyzes scheme contents in the active monitoring scheme, wherein the scheme contents comprise frequency and frequency; the scheme content is sent to an active monitoring module through an AI cloud platform;
c2: the active monitoring module acquires seasons and time through the AI cloud platform, inspects a target area according to a received active monitoring scheme, and sends an active monitoring video shot in the inspection process to the AI cloud platform;
c3: the AI cloud platform receives the active monitoring video and then respectively sends the active monitoring video to the data storage module and the data processing unit;
the data processing unit is used for analyzing and processing the monitoring video, the monitoring video comprises an active monitoring video and a passive monitoring video, and the specific analyzing and processing steps are as follows:
b1: the data processing unit is used for preprocessing the monitoring video;
b2: performing feature extraction on the preprocessed monitoring video, wherein the specific extraction steps are as follows:
BB 1: extracting the characteristics of the natural disasters in the active monitoring video, calculating the occurrence area of the natural disasters, and marking the occurrence area as ZM; at that time, the AI cloud platform does not send instructions to the integrated control module and the intelligent wearing module; at the moment, the AI cloud platform sends a low-risk disaster instruction to the comprehensive control module and the intelligent wearing module, and simultaneously sends the central coordinate of the natural disaster to the intelligent wearing module; at that time, the AI cloud platform sends a high-risk disaster instruction to the integrated control module and the intelligent wearing module, and simultaneously sends the central coordinates of the natural disaster to the intelligent wearing module, wherein L5 and L6 are preset threshold values; the feature extraction of natural disasters from videos is the prior art, and the method based on dual-density dual-tree complex wavelet transform in a natural disaster monitoring method based on dual-density dual-tree complex wavelet transform and SAR images can realize the extraction of natural disaster information from images, and the feature extraction of natural disasters can be realized by frame-by-frame analysis of monitoring videos by an AI cloud platform;
BB 2: extracting the characteristics of the traffic accident in the passive monitoring video, calculating the occurrence area of the traffic accident, marking the occurrence area as JM, sending a low-risk event instruction to the comprehensive control module and the intelligent wearing module by the AI cloud platform, and sending the coordinate of the traffic accident to the intelligent wearing module; at that time, the AI cloud platform sends a high-risk event instruction to the integrated control module and the intelligent wearing module, and simultaneously sends the coordinates of the traffic accident to the intelligent wearing module, wherein L7 is a preset threshold value; the detection and identification of traffic accidents from videos is the prior art, and the detection method provided in the thesis of traffic accident detection method based on videos can realize the detection and identification of traffic accidents from videos;
b3: and the data processing unit sends the natural disaster occurrence area ZM and the traffic accident area JM to the data storage module for storage.
Be used for the commander staff to carry out work with intelligent wearing module, the staff includes police and fire fighter, and intelligent wearing module includes memory cell, GPS positioning unit, temporary display unit and authentication unit, and the authentication unit includes fingerprint verification node, facial image verification node and pronunciation verification node, and concrete commander step is:
n1: acquiring fingerprint characteristics, facial characteristics and voice characteristics of a current person through an intelligent wearing module, respectively comparing and matching the fingerprint characteristics, facial characteristics and voice characteristics with the fingerprint characteristics, facial characteristics and voice characteristics stored in a storage unit, marking the fingerprint matching degree as ZW, the facial matching degree as MB and the voice matching degree as YY, acquiring a matching degree coefficient PP through a formula, and then successfully matching; wherein alpha, beta and gamma are preset proportionality coefficients, and L8 is a preset threshold;
n2: when the intelligent wearable module receives a high-risk disaster instruction, the AI cloud platform positions and dispatches workers within an L9 meter square circle of a natural disaster center coordinate to a disaster occurrence place through the GPS positioning unit; when the intelligent wearable module receives a low-risk disaster instruction, the AI cloud platform dispatches workers in a square circle L10 m of a center coordinate of the natural disaster to go to a disaster place, wherein L9 and L10 are preset threshold values;
n3: when the intelligent wearable module receives a high-risk event instruction, the AI cloud platform positions and dispatches workers within an L11 meter traffic accident coordinate square circle to an accident site through the GPS positioning unit; when the intelligent wearable module receives a low-risk event instruction, the AI cloud platform dispatches a worker within an L12 meter traffic accident coordinate square circle to an accident site, wherein L11 and L12 are preset threshold values, and L11 is more than L12;
n4: and meanwhile, the monitoring video is sent to the temporary display unit through the AI cloud platform.
Natural disasters include flooding, drought, typhoons and fog.
The preprocessing comprises band-limited filtering and down-sampling, noise removal, image enhancement and special processing, wherein the special processing comprises rain fog processing, dark light processing, automatic exposure and focusing processing and backlight compensation processing, and the preprocessing has two purposes, namely, the quality of a decoded image is improved, particularly, the video quality under adverse conditions is improved, the efficiency of video coding is improved, and the quality fluctuation of a transmitted video is reduced.
The active monitoring scheme is edited by the integrated control module and is sent to the data storage module through the AI cloud platform, and the specific contents of the scheme are as follows:
v1: when the current season is spring or autumn, a main inspection unmanned aerial vehicle dispatching a target area performs full-coverage inspection on the target area every L3 hours, wherein L3 is a preset threshold value, the spring starts from spring to end the day before spring, and the autumn starts from autumn to end the day before winter;
v2: when the current season is summer or winter, all the patrol inspection unmanned aerial vehicles dispatching the target area perform full-coverage patrol inspection on the target area every L4 hours, wherein L4 is a preset threshold value, the summer is finished from the beginning of summer to the day before the beginning of autumn, and the winter is finished from the beginning of winter to the day before the beginning of spring of the second year.
The inspection unmanned aerial vehicle is used for dynamically inspecting a specific area, the specific area can be divided according to the urban administrative area and is marked as REiI is 1, 2, … … n, and a number of specific regions REiCoordinate range storage is in data storage module, and every specific area contains two at least and patrols and examines unmanned aerial vehicle, and every specific area includes that a owner patrols and examines unmanned aerial vehicle, and a plurality of unmanned aerial vehicle and the high definition digtal camera of patrolling and examining all embed GPS positioning unit.
The 5G wireless communication module connects the AI cloud platform with the emergency monitoring module, the public security monitoring module and the intelligent wearing module through 5G wireless signals, and the data storage module and the comprehensive control module are linearly connected with the AI cloud platform.
The comprehensive control module is used for displaying the received active monitoring video and passive monitoring video and giving an alarm according to the received instruction.
The trajectory monitoring module is used for tracking the trajectory of the vehicle which escapes from the hit-and-run accident, and the specific tracking steps are as follows:
n1: after the AI cloud platform acquires a high-definition video of a GPS coordinate area, analyzing the high-definition video frame by frame, analyzing and identifying accident vehicles in a traffic accident scene by a mode identification technology, and tracking the accident vehicles by a track tracking technology; the method for tracking the track of the target in the video is the prior art, and the HMM clustering algorithm in a video target track analysis method based on HMM clustering can track the track of the target in the video;
n2: when the accident vehicle moves within TX time after the accident, the license plate number of the accident vehicle is identified through an AI cloud platform, and the license plate number is marked to be CP, wherein TX is a preset waiting time threshold, and TX is a preset escape time threshold;
n3: acquiring license plate numbers stored in the data storage module through an AI cloud platform, comparing and matching the license plate numbers of accident vehicles with the license plate numbers in the database one by one, and if the matching is unsuccessful, sending a forced interception instruction to the intelligent wearing module; if the matching is successful, acquiring the mobile phone number of the owner of the accident vehicle from the data storage module, sending a 'parking near side' to the mobile phone number by the AI cloud platform, and if the accident vehicle still parks near side within the TX1 time, sending a forced interception instruction to the intelligent wearing module, wherein the TX1 is a preset warning time threshold value;
n4: after the intelligent wearable module receives the forced interception instruction, the AI cloud platform sends R workers nearest to the accident vehicle to intercept the accident vehicle through the positioning of the GPS positioning unit, wherein R is a preset number threshold.
The above formulas are all quantitative calculation, the formula is a formula obtained by acquiring a large amount of data and performing software simulation to obtain the latest real situation, and the preset parameters in the formula are set by the technical personnel in the field according to the actual situation.
The working principle of the invention is as follows:
receiving an alarm signal through a signal analysis unit, separating an alarm keyword, an alarm grade and a GPS (global positioning system) address in the alarm signal, and simultaneously generating a passive monitoring signal; sending the GPS coordinates and the passive monitoring signals to a passive monitoring module through an AI cloud platform;
after the passive monitoring module receives the passive monitoring signals, the AI cloud platform acquires a plurality of specific area RE from the data storage moduleiComparing and positioning the coordinate range with the GPS coordinate received by the passive monitoring module; after the target area is obtained, the passive monitoring module controls the inspection unmanned aerial vehicle and the high-definition camera to obtain a high-definition video of the GPS coordinate area according to the alarm level; the AI cloud platform receives the passive monitoring video and then respectively sends the passive monitoring video to the data storage module and the data processing unit;
the AI cloud platform acquires an active monitoring scheme through the data storage module and sends the active monitoring scheme to the signal analysis unit; the signal analysis unit analyzes scheme contents in the active monitoring scheme, wherein the scheme contents comprise frequency and frame number; the scheme content is sent to an active monitoring module through an AI cloud platform; the active monitoring module acquires seasons and time through the AI cloud platform, inspects a target area according to a received active monitoring scheme, and sends an active monitoring video shot in the inspection process to the AI cloud platform; and the AI cloud platform receives the active monitoring video and then respectively sends the active monitoring video to the data storage module and the data processing unit.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (8)
1. The intelligent security system based on 5G is characterized by comprising an AI cloud platform, an active monitoring module, a passive monitoring module, a 5G wireless communication module, an intelligent wearing module, a comprehensive control module, a data storage module, a plurality of inspection unmanned aerial vehicles and a plurality of high-definition cameras;
the AI cloud platform is used for realizing information interaction of all modules in the system, the AI cloud platform comprises a signal analysis unit, a data processing unit and a cloud storage unit, the signal analysis unit is used for receiving alarm signals and carrying out analysis processing, the alarm signals comprise alarm keywords, alarm levels and GPS coordinates, the alarm levels comprise I-level alarm and II-level alarm, and the specific analysis processing steps are as follows:
z1: receiving an alarm signal through a signal analysis unit, separating an alarm keyword, an alarm grade and a GPS (global positioning system) address in the alarm signal, and simultaneously generating a passive monitoring signal;
z2: sending the GPS coordinates and the passive monitoring signals to a passive monitoring module through an AI cloud platform;
the passive monitoring module monitors the traffic accident of a specific area in real time according to the passive monitoring signal, and the specific monitoring steps are as follows:
x1: after the passive monitoring module receives the passive monitoring signals, the AI cloud platform acquires a plurality of specific area RE from the data storage moduleiAnd comparing and positioning the coordinate range with the GPS coordinate received by the passive monitoring module, wherein the specific positioning steps are as follows:
x11: acquiring longitude and latitude from the GPS coordinates, and respectively marking the longitude and latitude as LON and LAT;
x12: associating longitudes LON and latitudes LAT with specific regions REiComparing the coordinate ranges one by one;
x13: when the longitude LON and the latitude LAT are both within the coordinate range of the specific area, marking the longitude LON and the latitude LAT as target areas and marking the target areas as RE 1; when a plurality of specific areas REiWhen the coordinate ranges can not contain longitude LON and latitude LAT at the same time, an alarm signal error instruction is sent to the comprehensive control module through the AI cloud platform;
x2: after acquiring the target area, the passive monitoring module controls the patrol unmanned aerial vehicle and the high definition camera to acquire the high definition video of the GPS coordinate area according to the alarm level, and the specific acquisition steps are as follows:
x21: when the alarm level is I-level alarm, determining the number SS of high-definition cameras within the range of L1 m according to the GPS coordinates and the GPS positioning unit, and sending high-definition videos shot by the high-definition cameras to an AI cloud platform; if the SS of the high-definition cameras in the range of the square circle L1 of the GPS coordinate is less than L2, the GPS coordinate is sent to a main inspection unmanned aerial vehicle of the target area through a passive monitoring module to shoot the GPS coordinate area, and passive monitoring videos shot by the main inspection unmanned aerial vehicle and the high-definition cameras are sent to an AI cloud platform, wherein L1 and L2 are preset threshold values;
x22: when the alarm level is II-level alarm, the passive detection module simultaneously controls a high-definition camera in the range of L1 m of the GPS coordinate square circle and an inspection unmanned aerial vehicle of a target area where the GPS coordinate is located to shoot the GPS coordinate area, and sends a shot passive monitoring video to an AI cloud platform;
x3: the AI cloud platform receives the passive monitoring video and then respectively sends the passive monitoring video to the data storage module and the data processing unit;
the active monitoring module monitors a target area in real time according to an active monitoring scheme, and the specific monitoring steps are as follows:
c1: the AI cloud platform acquires an active monitoring scheme through the data storage module and sends the active monitoring scheme to the signal analysis unit; the signal analysis unit analyzes scheme contents in the active monitoring scheme, wherein the scheme contents comprise frequency and frame number; the scheme content is sent to an active monitoring module through an AI cloud platform;
c2: the active monitoring module acquires seasons and time through the AI cloud platform, inspects a target area according to a received active monitoring scheme, and sends an active monitoring video shot in the inspection process to the AI cloud platform;
c3: the AI cloud platform receives the active monitoring video and then respectively sends the active monitoring video to the data storage module and the data processing unit;
the data processing unit is used for analyzing and processing monitoring videos, the monitoring videos comprise active monitoring videos and passive monitoring videos, and the specific analyzing and processing steps are as follows:
b1: the data processing unit is used for preprocessing the monitoring video;
b2: performing feature extraction on the preprocessed monitoring video, wherein the specific extraction steps are as follows:
BB 1: extracting the characteristics of the natural disasters in the active monitoring video, calculating the occurrence area of the natural disasters, and marking the occurrence area as ZM; at that time, the AI cloud platform does not send instructions to the integrated control module and the intelligent wearing module; at the moment, the AI cloud platform sends a low-risk disaster instruction to the comprehensive control module and the intelligent wearing module, and simultaneously sends the central coordinate of the natural disaster to the intelligent wearing module; at that time, the AI cloud platform sends a high-risk disaster instruction to the integrated control module and the intelligent wearing module, and simultaneously sends the central coordinates of the natural disaster to the intelligent wearing module, wherein L5 and L6 are preset threshold values;
BB 2: extracting the characteristics of the traffic accident in the passive monitoring video, calculating the occurrence area of the traffic accident, marking the occurrence area as JM, sending a low-risk event instruction to the comprehensive control module and the intelligent wearing module by the AI cloud platform, and sending the coordinate of the traffic accident to the intelligent wearing module; at that time, the AI cloud platform sends a high-risk event instruction to the integrated control module and the intelligent wearing module, and simultaneously sends the coordinates of the traffic accident to the intelligent wearing module, wherein L7 is a preset threshold value;
b3: and the data processing unit sends the natural disaster occurrence area ZM and the traffic accident area JM to the data storage module for storage.
2. The intelligent security and protection system based on 5G as claimed in claim 1, wherein the intelligent wearable module is used for commanding workers to work, the workers comprise police and firefighters, the intelligent wearable module comprises a storage unit, a GPS positioning unit, a temporary display unit and an identity verification unit, the identity verification unit comprises a fingerprint verification node, a facial image verification node and a voice verification node, and the specific commanding steps are as follows:
n1: acquiring fingerprint characteristics, facial characteristics and voice characteristics of a current person through an intelligent wearing module, respectively comparing and matching the fingerprint characteristics, facial characteristics and voice characteristics with the fingerprint characteristics, facial characteristics and voice characteristics stored in a storage unit, marking the fingerprint matching degree as ZW, the facial matching degree as MB and the voice matching degree as YY, acquiring a matching degree coefficient PP through a formula, and then successfully matching; wherein alpha, beta and gamma are preset proportionality coefficients, and L8 is a preset threshold;
n2: when the intelligent wearable module receives a high-risk disaster instruction, the AI cloud platform positions and dispatches workers within an L9 meter square circle of a natural disaster center coordinate to a disaster occurrence place through the GPS positioning unit; when the intelligent wearable module receives a low-risk disaster instruction, the AI cloud platform dispatches workers in a square circle L10 m of a center coordinate of the natural disaster to go to a disaster place, wherein L9 and L10 are preset threshold values;
n3: when the intelligent wearable module receives a high-risk event instruction, the AI cloud platform positions and dispatches workers within an L11 meter traffic accident coordinate square circle to an accident site through the GPS positioning unit; when the intelligent wearable module receives a low-risk event instruction, the AI cloud platform dispatches a worker within an L12 meter traffic accident coordinate square circle to an accident site, wherein L11 and L12 are preset threshold values, and L11 is more than L12;
n4: and meanwhile, the monitoring video is sent to the temporary display unit through the AI cloud platform.
3. The intelligent security system based on 5G, according to claim 1, wherein the natural disasters include flooding, drought, typhoon and fog.
4. The 5G-based smart security system according to claim 1, wherein the preprocessing comprises band-limited filtering and down-sampling, noise removal, image enhancement and special processing, and the special processing comprises rain fog processing, dim light processing, auto-exposure and focusing processing and backlight compensation processing.
5. The intelligent security system based on 5G according to claim 1, wherein the active monitoring scheme is edited by the integrated control module and sent to the data storage module through an AI cloud platform, and the specific contents of the scheme are as follows:
v1: when the current season is spring or autumn, a main inspection unmanned aerial vehicle dispatching a target area performs full-coverage inspection on the target area every L3 hours, wherein L3 is a preset threshold value, the spring starts from spring to finish the day before spring, and the autumn starts from autumn to finish the day before winter;
v2: when the current season is summer or winter, all the patrol inspection unmanned aerial vehicles dispatching the target area perform full-coverage patrol inspection on the target area every L4 hours, wherein L4 is a preset threshold value, the summer is finished from the beginning of summer to the day before the beginning of autumn, and the winter is finished from the beginning of winter to the day before the beginning of spring of the second year.
6. The intelligent security and protection system based on 5G according to claim 1, wherein the inspection unmanned aerial vehicle is used for dynamically inspecting a specific area, the specific area can be divided according to a city administrative area and is marked as REiI is 1, 2, … … n, and a number of specific regions REiCoordinate range storage is in data storage module, and every specific area contains two at least and patrols and examines unmanned aerial vehicle, and every specific area includes that a owner patrols and examines unmanned aerial vehicle, and is a plurality of it all embeds there is GPS positioning unit to patrol and examine unmanned aerial vehicle and high definition digtal camera.
7. The intelligent security system based on 5G according to claim 1, wherein the 5G wireless communication module connects the AI cloud platform with the emergency monitoring module, the public security monitoring module and the intelligent wearable module through 5G wireless signals, and the data storage module and the comprehensive control module are linearly connected with the AI cloud platform.
8. The intelligent security and protection system based on 5G as claimed in claim 1, wherein the integrated control module is used for displaying the received active monitoring video and passive monitoring video, and alarming according to the received instruction.
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| CN112511541A (en) * | 2020-12-01 | 2021-03-16 | 万申科技股份有限公司 | Intelligent park emergency early warning management system based on cloud computing |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112511541A (en) * | 2020-12-01 | 2021-03-16 | 万申科技股份有限公司 | Intelligent park emergency early warning management system based on cloud computing |
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