CN117912186A - Intelligent security linkage early warning system based on big data service - Google Patents
Intelligent security linkage early warning system based on big data service Download PDFInfo
- Publication number
- CN117912186A CN117912186A CN202311723439.2A CN202311723439A CN117912186A CN 117912186 A CN117912186 A CN 117912186A CN 202311723439 A CN202311723439 A CN 202311723439A CN 117912186 A CN117912186 A CN 117912186A
- Authority
- CN
- China
- Prior art keywords
- module
- security
- alarm signal
- alarm
- intrusion
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012544 monitoring process Methods 0.000 claims abstract description 154
- 238000004458 analytical method Methods 0.000 claims abstract description 69
- 230000004044 response Effects 0.000 claims abstract description 59
- 238000012545 processing Methods 0.000 claims abstract description 52
- 238000007781 pre-processing Methods 0.000 claims abstract description 10
- 238000001514 detection method Methods 0.000 claims description 30
- 206010000117 Abnormal behaviour Diseases 0.000 claims description 27
- 238000000034 method Methods 0.000 claims description 22
- 238000013210 evaluation model Methods 0.000 claims description 18
- 230000007613 environmental effect Effects 0.000 claims description 15
- 230000006399 behavior Effects 0.000 claims description 10
- 238000007405 data analysis Methods 0.000 claims description 10
- 230000000694 effects Effects 0.000 claims description 10
- 230000002093 peripheral effect Effects 0.000 claims description 10
- 230000005856 abnormality Effects 0.000 claims description 9
- 238000004590 computer program Methods 0.000 claims description 9
- 230000000670 limiting effect Effects 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 7
- 238000004422 calculation algorithm Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
- 230000002159 abnormal effect Effects 0.000 claims description 4
- 230000003213 activating effect Effects 0.000 claims description 2
- 230000000007 visual effect Effects 0.000 claims description 2
- 239000007789 gas Substances 0.000 description 3
- 239000011521 glass Substances 0.000 description 3
- 230000033001 locomotion Effects 0.000 description 3
- 230000002829 reductive effect Effects 0.000 description 3
- 230000001960 triggered effect Effects 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000013500 data storage Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000005286 illumination Methods 0.000 description 2
- 238000003331 infrared imaging Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 239000000779 smoke Substances 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000001931 thermography Methods 0.000 description 2
- 238000004148 unit process Methods 0.000 description 2
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 1
- RWSOTUBLDIXVET-UHFFFAOYSA-N Dihydrogen sulfide Chemical compound S RWSOTUBLDIXVET-UHFFFAOYSA-N 0.000 description 1
- 229910002091 carbon monoxide Inorganic materials 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000001010 compromised effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 229910000037 hydrogen sulfide Inorganic materials 0.000 description 1
- 230000009474 immediate action Effects 0.000 description 1
- 238000007654 immersion Methods 0.000 description 1
- 230000009545 invasion Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000012502 risk assessment Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 239000002341 toxic gas Substances 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Alarm Systems (AREA)
Abstract
The invention discloses an intelligent security linkage early warning system based on big data service, which relates to the technical field of security, and comprises a data processing unit, a monitoring early warning unit and a data processing unit, wherein the data processing unit is used for collecting original data from a plurality of sources and transmitting the original data to the monitoring early warning unit after preprocessing; the monitoring early warning unit is used for monitoring and identifying the security threat in the data of the data processing unit in real time, generating alarm information and transmitting the alarm information to the analysis response unit; the analysis response unit is used for receiving the alarm information, deeply analyzing the alarm information, judging and processing the alarm information according to the priority of the intelligent alarm module, and feeding the alarm information back to the monitoring and early warning unit; the access control unit is used for managing the access authority of the system resource and realizing remote security control through the mobile application and the Internet of things equipment. The invention comprehensively utilizes the multi-source data to monitor and rapidly identify the potential threat in real time, effectively manage the alarm information, improve the overall efficiency of the security system and provide more efficient and comprehensive security for users.
Description
Technical Field
The invention relates to the technical field of security protection, in particular to an intelligent security linkage early warning system based on big data service.
Background
With rapid development of information technology and internet of things, existing security systems have begun to use various sensors, cameras and network devices for real-time monitoring and data collection. These systems typically rely on conventional data processing and analysis methods, as well as predetermined pre-alarm rules, to detect potential security threats and abnormal behavior. At the same time, many existing security systems also provide basic data storage, access control and alarm functions.
However, the existing security system cannot rapidly and accurately monitor and identify potential security threats, and cannot effectively identify abnormal behaviors, intrusion events and environmental changes when monitoring a plurality of data sources in real time, so that the system cannot be ensured to rapidly respond to various alarm information.
Disclosure of Invention
The invention is provided in view of the problems existing in the existing intelligent security linkage early warning system.
Therefore, the invention aims to solve the problem of how to provide an intelligent security linkage early warning system based on big data service, which can effectively identify abnormal behaviors, invasion anomalies and environment change anomalies and rapidly respond to alarm information.
In order to solve the technical problems, the invention provides the following technical scheme:
In a first aspect, an embodiment of the present invention provides an intelligent security linkage early warning system based on big data service, which includes a data processing unit, configured to collect raw data from multiple sources, and transmit the raw data to a monitoring early warning unit after preprocessing; the monitoring early warning unit is used for monitoring and identifying the security threat in the data of the data processing unit in real time, generating alarm information and transmitting the alarm information to the analysis response unit; the monitoring and early warning unit comprises a video monitoring and analyzing module, an intrusion detection module, an environment monitoring module and an intelligent alarm module; the intelligent alarm module receives the video monitoring alarm signal, the intrusion alarm signal and the environment alarm signal from the video monitoring and analyzing module, the intrusion detection module and the environment monitoring module, carries out comprehensive analysis, judges the priority of processing the alarm signal, and generates alarm information from all the alarm signals and transmits the alarm information to the analysis response unit; the security threats include abnormal behavior, intrusion anomalies, and environmental monitoring anomalies; the analysis response unit is used for receiving the alarm information, deeply analyzing the alarm information, judging and processing the alarm information according to the priority of the intelligent alarm module, and feeding the alarm information back to the monitoring and early warning unit; the analysis response unit comprises a data analysis module and an intelligent decision support module; the access control unit is used for managing the access authority of the system resource and realizing remote security control through the mobile application and the Internet of things equipment; the access control unit comprises an access control module, a mobile security application module and an Internet of things module.
As a preferable scheme of the intelligent security linkage early warning system based on big data service, the intelligent security linkage early warning system based on big data service comprises the following steps: the source of the original data comprises a sensor source, a camera video source and other data sources; if the source of the original data is a sensor source or a camera video source, the original data is preprocessed by the data processing unit and then transmitted to the monitoring and early warning unit; if the source of the original data is other data sources, the original data is directly transmitted to the analysis response unit by the data processing unit.
As a preferable scheme of the intelligent security linkage early warning system based on big data service, the intelligent security linkage early warning system based on big data service comprises the following steps: the video monitoring and analyzing module utilizes the camera to carry out real-time video monitoring, analyzes video content and identifies abnormal behaviors, and specifically comprises the following steps of installing the camera at a key position, configuring the camera, and setting proper visual angles and resolution; the data processing unit acquires real-time video data of the camera, performs preprocessing, and uploads the real-time video data to the video monitoring and analyzing module; the video monitoring and analyzing module applies an image recognition algorithm to recognize the real-time video data and recognizes abnormal behaviors through a behavior analysis algorithm; if abnormal behaviors are identified, generating a video monitoring alarm signal and transmitting the video monitoring alarm signal to the intelligent alarm module.
As a preferable scheme of the intelligent security linkage early warning system based on big data service, the intelligent security linkage early warning system based on big data service comprises the following steps: the key positions comprise an access opening, a public area, a sensitive area and a peripheral area, and the personnel-intensive area; the setting of the appropriate viewing angle and resolution includes: a wide-angle lens is arranged at the entrance and exit to cover the whole entrance area, and high resolution is set to identify the face and the license plate; setting a middle angle lens in the public area, and setting middle resolution to balance definition and storage requirements; setting a high-resolution camera in the sensitive area, focusing on an article or an area, and ensuring detailed capture; setting a wide angle or a panoramic lens in the peripheral area to cover a wide area, and setting medium resolution to monitor the flow of personnel and vehicles; the medium-to-high resolution cameras are arranged in the personnel-intensive area, so that the crowd activity area is covered, and the personnel safety is ensured; the abnormal behavior includes any one of the following: the unauthorized person/vehicle is attempting to enter or leave at the entrance at the non-working/regular time, the unauthorized person/vehicle stays at the entrance for a long time, the person gathers at the public area at the non-working/regular time, the person stays at the public area or takes away the unknown object, the unauthorized person stays for a long time after entering the sensitive area or entering the sensitive area, the unauthorized person moves or operates at the sensitive area, the vehicle/person stays at the peripheral area at the non-working/regular time, and the abnormal group/individual behavior occurs in the person-dense area.
As a preferable scheme of the intelligent security linkage early warning system based on big data service, the intelligent security linkage early warning system based on big data service comprises the following steps: the intrusion detection module is used for detecting whether unauthorized access or authorized out-of-range exists, generating an intrusion alarm signal if the unauthorized access or authorized out-of-range exists, and transmitting the intrusion alarm signal to the intelligent alarm module. The intrusion detection module comprises a security level assessment model for constructing the key area according to the asset value, the potential threat and the historical security event of the key area, wherein the expression of the security level assessment model is as follows:
Wherein S represents the final calculated security level; v i represents the asset value of the i-th zone; A weight corresponding to the asset value representing the i-th region; t j represents a potential threat to the jth zone; /(I) Representing the weight corresponding to the potential threat of the jth region; h k represents a historical security event for the kth zone; /(I)Representing the weight corresponding to the historical security event of the kth region; a. b and c represent indexes for adjusting the influence intensity; alpha, beta, gamma denote coefficients that balance the effects of the scores.
Calculating the security level S of each key area through the security level evaluation model, and dividing the security level S into a low-level security area, a medium-level security area and a high-level security area; if the security level S of the key area is less than or equal to a first security threshold P 1, determining that the key area is a low-level security area; if the first safety threshold P 1 is smaller than the safety level S of the key area and smaller than the second safety threshold P 2, judging that the key area is a middle-level safety area; and if the security level S of the key area is more than or equal to the second security threshold P 2, judging that the key area is an advanced security area.
The intrusion detection module divides unauthorized access or authorized out-of-range into a high intrusion anomaly, a medium intrusion anomaly and a light intrusion anomaly according to the security level S of the key area, and specifically comprises the following steps:
If the unauthorized person/vehicle accesses in the non-working/regular time or the authorized person/vehicle crosses the boundary in the critical area in the working/regular time, the intrusion detection module immediately generates an intrusion alarm signal and transmits the intrusion alarm signal to the intelligent alarm module, the intelligent alarm module skips the video monitoring and analysis module and the environment monitoring module after receiving the intrusion alarm signal, directly sends the intrusion alarm signal to the analysis response unit, the intelligent decision support module in the analysis response unit notifies the access control module in the access control unit to immediately close the access right, limits or prevents further unauthorized access, simultaneously, the mobile security application module immediately notifies the security team to arrive at the site, provides real-time updating and remote control options, and activates the instant security measures of the internet of things module.
If unauthorized personnel/vehicles access in the non-working/conventional time or authorized personnel/vehicles cross the boundary in the critical area in the working/conventional time, the medium-level security area is regarded as medium-level intrusion abnormality, and an intrusion alarm signal is generated in a first limiting time t 1 of the intrusion detection module and is transmitted to the intelligent alarm module; if the abnormal behavior in the video monitoring and analyzing module corresponds to the moderate intrusion abnormal behavior, an intrusion alarm signal is sent to the analysis response unit, the access control module in the access control unit is guided by the intelligent decision support module to improve the access right, the security team is notified by the mobile security application module, and meanwhile, the Internet of things module is activated to take emergency security measures.
If the unauthorized person/vehicle accesses in the non-working/regular time or the authorized person/vehicle crosses the boundary in the critical area in the working/regular time, the advanced security area is regarded as a slight intrusion anomaly, at this time, the intrusion detection module generates an intrusion alarm signal in the second limiting time t 2 and transmits the intrusion alarm signal to the intelligent alarm module, if the slight intrusion anomaly is associated with the video monitoring alarm signal or the environment alarm signal, the intrusion alarm signal is sent to the analysis response unit, and if the unauthorized access of the unauthorized person/vehicle is an occasional event and no anomaly is generated in the video monitoring and analysis module, the intelligent alarm module does not need to generate the intrusion alarm signal; wherein the first defined time t 1 < the second defined time t 2.
The video monitoring and analyzing module is used for detecting whether the unauthorized person/vehicle enters or leaves the entrance and exit at the non-working/normal time, enters the sensitive area after staying at the entrance and exit for a long time, or enters the sensitive area after crossing all key areas at the non-working/normal time, if the video monitoring and analyzing module detects that the unauthorized person/vehicle is out of range at all key areas, the intrusion detecting module is combined with the monitoring data of the intrusion detecting module to further confirm whether the actual intrusion abnormality is formed.
As a preferable scheme of the intelligent security linkage early warning system based on big data service, the intelligent security linkage early warning system based on big data service comprises the following steps: the environment monitoring module receives the environment monitoring sensor data collected and preprocessed by the data processing unit, confirms the upper and lower bounds of the environment monitoring abnormality by using an IQR calculation method, and generates an environment alarm signal and sends the environment alarm signal to the intelligent alarm module if the calculation exceeds the upper and lower bounds; the comprehensive analysis of the intelligent alarm module comprises the steps of receiving a video monitoring alarm signal, an intrusion alarm signal and an environment alarm signal, setting the highest priority of the intrusion alarm signal, and constructing a priority evaluation model for the video monitoring alarm signal and the environment alarm signal; comparing according to the output result of the priority evaluation model, wherein the output result is higher in priority; if the intelligent alarm module receives three alarm signals in unit time, triggering red alarm at the moment, requesting an analysis response unit to process the intrusion alarm signals preferentially, and then analyzing the priorities of the video monitoring alarm signals and the environment alarm signals according to a priority evaluation model to determine the processing sequence; if the intelligent alarm module receives the two alarm signals in unit time, judging whether the intelligent alarm module contains an intrusion alarm signal, if the intelligent alarm module contains the intrusion alarm signal, triggering a red alarm, and requesting an analysis response unit to preferentially process the intrusion alarm signal; if the intrusion alarm signal is not contained, triggering yellow alarm, and analyzing the priority of the video monitoring alarm signal and the environment alarm signal through a priority evaluation model to determine the processing sequence; if the intelligent alarm module receives an alarm signal in unit time, the alarm signal is directly processed by the analysis response unit; if the alarm signal is an intrusion alarm signal, triggering a red alarm; if the signal is a video monitoring alarm signal or an environment alarm signal, the signal is analyzed by the data analysis module and then is processed by the intelligent decision support module.
As a preferable scheme of the intelligent security linkage early warning system based on big data service, the intelligent security linkage early warning system based on big data service comprises the following steps: the other data sources are received and processed by a data analysis module in an analysis response unit, and the other data sources comprise social media, weather forecast and public transportation information; and the intelligent decision support module performs priority processing on the alarm signal by the linkage access control unit according to comprehensive analysis of the intelligent alarm module.
In a second aspect, an embodiment of the present invention provides an intelligent security linkage early warning method based on big data service, which includes: collecting original data from a plurality of sources and preprocessing the original data by a data processing unit; transmitting the preprocessed data to a monitoring and early warning unit, and monitoring and early warning the data and identifying security threats by the monitoring and early warning unit; generating an alarm signal based on the security threat detected by the monitoring and early warning unit; the intelligent alarm module receives the alarm signal and judges the priority, and forwards the alarm signal to the analysis response unit for further processing; the analysis response unit receives the alarm information, performs deep analysis, and combines the access control unit to formulate and execute a response strategy.
In a third aspect, embodiments of the present invention provide a computer apparatus comprising a memory and a processor, the memory storing a computer program, wherein: and the processor realizes any step of the intelligent security linkage early warning method based on the big data service when executing the computer program.
In a fourth aspect, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: and the computer program is executed by the processor to realize any step of the intelligent security linkage early warning method based on the big data service.
The intelligent security system has the beneficial effects that the monitoring and early warning capability of the intelligent security system is improved, real-time monitoring and rapid identification of potential threats are realized by comprehensively utilizing multi-source data, and alarm information is effectively managed. The system integrates a plurality of modules such as video monitoring, intrusion detection, environment monitoring, data analysis and the like, and improves the accuracy and response speed of early warning. In addition, the invention optimizes the data processing flow, enhances the automation and intelligent degree of the system, greatly improves the overall efficiency of the security system, and provides more efficient and comprehensive security for users.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a block diagram of an intelligent security linkage early warning system based on big data services.
Fig. 2 is an overall flowchart of an intelligent security linkage early warning method based on big data service.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present invention have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the invention. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1 and fig. 2, in a first embodiment of the present invention, an intelligent security linkage early warning system based on big data service is provided, where the system is composed of a data processing unit, a monitoring early warning unit, an access control unit, and an analysis response unit.
In particular, the data processing unit collects data through a variety of channels, such as sensor data, camera video sources, and other data. The camera monitors and generates video data in real time and is used for monitoring and analyzing environment or behavior patterns. Other data sources include, but are not limited to, social media, weather forecast, public transportation information. ( A data source of social media refers to publicly available social media data, possibly including information about specific events or trends. The data source of weather forecast refers to data from weather service, which is important for predicting environmental changes and coping with emergency. The data source of public traffic information refers to real-time traffic conditions and scheduling information, which are helpful for analyzing and predicting the traffic flow and the traffic flow. )
In this embodiment, the data processing unit performs preprocessing on the collected multi-source original data, and then transmits the preprocessed multi-source original data to the monitoring and early warning unit. Sources of raw data include sensor sources, camera video sources, and other data sources. Other data sources include social media, weather forecast, and public transportation information.
If the source of the original data is a sensor source or a camera video source, the original data is preprocessed by a data processing unit and then transmitted to the monitoring and early warning unit; if the source of the original data is other data sources, the original data is directly transmitted to the analysis response unit by the data processing unit.
In addition, the specific process of pretreatment is as follows: identifying and classifying raw data from sensor sources, camera video sources, and other data sources; performing data cleansing to remove invalid, erroneous or incomplete data points; converting all data into a unified format and coding; synchronizing time stamps of different data sources to ensure data consistency; performing scale and range standardization treatment on the data; identifying and processing or excluding outliers in the data; the cleaned and formatted data is fused into a single, consistent data set. It should be noted that, in the present invention, the data preprocessing adopts the prior art, which is common knowledge of those skilled in the art, so that the description is not excessive in this embodiment.
Furthermore, the monitoring and early warning unit comprises a video monitoring and analyzing module, an intrusion detection module, an environment monitoring module and an intelligent alarm module. In the monitoring and early warning unit, the data sources of the video monitoring and analyzing module are camera video sources, and the data sources of the intrusion detection module and the environment monitoring module are sensor sources. It should be noted that all raw data from the sensor source, the camera video source and other data sources are preprocessed in the data processing unit and then transmitted to the corresponding modules in the monitoring and early warning unit by the data processing unit.
Specifically, the video monitoring and analyzing module utilizes a camera to perform real-time video monitoring, analyzes video content through an image recognition technology to recognize abnormal behaviors or potential threats, and specifically comprises the following steps:
First, cameras are installed and configured in strategic locations (including doorways, public areas such as halls, hallways, elevators and stairways, sensitive areas such as warehouses, server rooms or valuables storage areas, peripheral areas such as fences, parking lots and other peripheral areas, and personnel intensive areas such as restaurants, meeting rooms or rest areas), to set the proper viewing angle and resolution. Specifically, a wide-angle lens is arranged at an entrance and an exit to cover the whole entrance area, and high resolution is set to identify a face and a license plate; setting a medium-angle lens in a public area to cover a wider area, and setting medium resolution to balance definition and storage requirements; providing a high resolution camera in a sensitive area that focuses on a particular item or area (e.g., a vault, data center, or valuables storage area where the camera needs to focus on these particular items of interest or critical areas-for example, in a data center, the camera may be specifically aimed at a server rack and access door to capture any activity approaching or operating these devices at high resolution), ensuring detailed capture; setting a wide angle or a panoramic lens in the peripheral area to cover a wide area, and setting medium resolution to monitor the flow of personnel and vehicles; in a personnel intensive area setting to high resolution cameras, the cameras should be mounted in a position to cover the entire area and adjusted to a proper angle to ensure that all active areas and access paths are included in the field of view for monitoring (in a personnel intensive area such as a mall hall, conference room or rest area, for example.) the cameras may be mounted in the center of the ceiling and looking down to cover the entire open space.) to ensure personnel safety.
And secondly, the data processing unit acquires real-time video data of the camera, performs preprocessing and uploads the real-time video data to a video monitoring and analyzing module in the monitoring and early warning unit.
Then, the video monitoring and analyzing module applies an image recognition algorithm (such as CNN, etc.) to recognize the real-time video data, and recognizes abnormal behaviors through a behavior analysis algorithm. If abnormal behaviors are identified, generating a video monitoring alarm signal and transmitting the video monitoring alarm signal to the intelligent alarm module. (abnormal behavior includes any of the following behaviors that an unauthorized person or a vehicle tries to enter or leave at an entrance and exit at the non-working/regular time, the unauthorized person or the vehicle stays at the entrance and exit for a long time, the person gathers or collides in a public area at the non-working/regular time, the person stays in or takes away an unknown object in the public area, the unauthorized person enters or enters the sensitive area and stays for a long time, the unauthorized person moves or operates in the sensitive area, the vehicle or the person stays in or crosses the peripheral area at the non-working/regular time, abnormal group/individual behaviors appear in the personnel intensive area.) the intelligent alarm module is combined with the information comprehensive judgment provided by the video monitoring and analyzing module, the intrusion detection module and the environment monitoring module, the color (red alarm or yellow alarm) for triggering an alarm is decided, and all alarm signals are generated to alarm information to be transmitted to the analysis response unit. The comprehensive processing of the information ensures the accuracy and timeliness of the alarm and reduces the occurrence of false alarm and missing alarm.
Further, the intrusion detection module is used for detecting whether unauthorized access or boundary crossing exists, if so, an intrusion alarm signal is generated and transmitted to the intelligent alarm module.
The intrusion detection module is in particular a physical intrusion detection, meaning the detection of data transmitted by an associated intrusion sensor in the sensor source, which data has been preprocessed in the data processing unit. In this embodiment, the intrusion sensor includes a door and window switch sensor, a motion detector, a glass breakage sensor, a pressure pad or floor sensor, and an infrared or thermal imaging sensor. The door and window switch sensor is used for detecting whether the door and window is illegally opened. The motion detector is used to detect unintended motion within a particular region. The glass breaking sensor is used for monitoring the breaking sound of the glass and detecting the breaking of windows and the like. Pressure pads or floor sensors are used to detect changes in step or weight on the ground. Infrared or thermal imaging sensors are used to detect human heat or infrared activity.
Specifically, the physical intrusion detection includes the following: firstly, constructing a security level assessment model of the key area according to the asset value, the potential threat and the historical security event of the key area. The expression of the security level assessment model is as follows:
Wherein S represents the final calculated security level; v i represents the asset value of the i-th zone; A weight corresponding to the asset value representing the i-th region; t j represents a potential threat to the jth zone; /(I) Representing the weight corresponding to the potential threat of the jth region; h k represents a historical security event for the kth zone; /(I)Representing the weight corresponding to the historical security event of the kth region; a. b and c represent indexes for adjusting the influence intensity; alpha, beta, gamma denote coefficients that balance the effects of the scores.
It should be noted that, compared with the prior art, the security level evaluation model provided by the invention can evaluate the security risks of different areas more accurately by integrating a plurality of factors and operations, thereby avoiding the problems of erroneous judgment, misjudgment and missed judgment caused by artificial subjective judgment of the security level of the key area; also, the multiple variables and different weights allow for customized risk assessment according to specific security needs and regional characteristics; in addition, the model has more dynamic adaptability, variables and parameters in the formula can be adjusted according to real-time data and long-term trends, and the adaptability and the flexibility of the evaluation model are improved.
And secondly, calculating the security level S of each key area through a security level evaluation model, and dividing the security level S into a low-level security area, a medium-level security area and a high-level security area. If the security level S of the key area is less than or equal to the first security threshold P 1, determining that the key area is a low-level security area; if the first safety threshold P 1 is smaller than the safety level S of the key area and smaller than the second safety threshold P 2, judging that the key area is a medium-level safety area; and if the security level S of the key area is more than or equal to the second security threshold P 2, judging that the key area is an advanced security area. In the present embodiment, the first safety threshold P 1 and the second safety threshold P 2 are 30 and 60, respectively.
If the unauthorized person/vehicle accesses in the non-working/regular time or the authorized person/vehicle crosses the boundary in the critical area in the working/regular time, the intrusion detection module immediately generates an intrusion alarm signal and transmits the intrusion alarm signal to the intelligent alarm module, the intelligent alarm module skips the video monitoring and analysis module and the environment monitoring module after receiving the intrusion alarm signal and directly sends the intrusion alarm signal to the analysis response unit, the intelligent decision support module in the analysis response unit informs the access control module in the access control unit to immediately close the access authority, limit or prevent further unauthorized access, simultaneously, the mobile security application module immediately informs the security team to arrive at the site, provides real-time updating and remote control options, and activates the instant security measures of the internet of things module (for example, by using internet of things equipment such as an automatic door lock, illumination control and the like, implements the instant security measures such as locking the access control or opening the emergency illumination).
If unauthorized personnel/vehicles access in the non-working/conventional time or authorized personnel/vehicles cross the boundary in the critical area in the working/conventional time, the medium-level security area is regarded as medium-level intrusion abnormality, and an intrusion alarm signal is generated in a first limiting time t 1 of the intrusion detection module and is transmitted to the intelligent alarm module; if the video monitoring and analyzing module is corresponding to the abnormal behavior of the medium intrusion, including the video monitoring and analyzing module detecting that if the unauthorized person/vehicle enters or leaves the entrance and exit at the non-working/normal time, the unauthorized person/vehicle enters the sensitive area after staying at the entrance and exit for a long time, or the vehicle/person crosses the boundary in all key areas at the non-working/normal time, generating an intrusion alarm signal and sending the intrusion alarm signal to the intrusion detecting module, receiving and processing the intrusion alarm signal by the intrusion detecting module, and simultaneously combining the monitoring data of the intrusion alarm signal to further confirm whether the actual intrusion security threat is formed, sending the intrusion alarm signal to the analysis response unit, guiding the access control module in the access control unit to improve the access authority by the intelligent decision support module, notifying the security by the mobile security application module, and simultaneously activating the internet of things module to take emergency security measures.
If the unauthorized person/vehicle accesses in the non-working/regular time or the authorized person/vehicle crosses the boundary in the critical area in the working/regular time, the advanced security area is regarded as a slight intrusion anomaly, at this time, the intrusion detection module generates an intrusion alarm signal in the second limiting time t 2 and transmits the intrusion alarm signal to the intelligent alarm module, if the slight intrusion anomaly is associated with the video monitoring alarm signal or the environment alarm signal, the intrusion alarm signal is sent to the analysis response unit, and if the unauthorized access of the unauthorized person/vehicle is an occasional event and no anomaly is generated in the video monitoring and analysis module, the intelligent alarm module does not need to generate the intrusion alarm signal; wherein the first defined time t 1 < the second defined time t 2. The first limit time t 1 and the second limit time t 2 may be adjusted according to actual situations, and in this embodiment, the first limit time t 1 =5 minutes and the second limit time t 2 =3 minutes are set.
It should be noted that closing the access rights in the event of a high intrusion anomaly is because this typically implies a serious security risk, such as unauthorized personnel entering the critical area at sensitive times. The closing authority is used for immediately preventing risk expansion and protecting regional safety. In the case of moderate intrusion anomalies, only the access rights are raised because this situation may be less urgent or severe, the rights are raised to enhance security control, while the normal workflow is not completely blocked. The intelligent security linkage early warning system based on the big data service balances the requirements on safety and operation efficiency in the intelligent security linkage early warning system based on the big data service.
Further, in the present embodiment, the environmental monitoring sensor includes a temperature sensor, a smoke sensor, a harmful gas sensor, a humidity sensor, a water immersion sensor, and the like. The temperature sensor is used for monitoring the ambient temperature and early warning fire or overheat conditions; smoke sensors detect fire or combustion products; the harmful gas sensor detects leakage of harmful gases such as carbon monoxide, hydrogen sulfide and the like; the humidity sensor monitors the air humidity and prevents the growth of mould or the damage of electrical equipment; the water logging sensor detects water leakage or flood.
The environment monitoring module receives the environment monitoring sensor data collected and preprocessed by the data processing unit, and confirms the upper and lower bounds of the environment monitoring abnormality by using an IQR method. First, a first quartile Q1 and a third quartile Q3 of data are calculated; then, calculating the difference between the two, namely IQR; then, determine a range of outliers using the factor 1.5 multiplied by IQR; finally, any value below the lower bound L B or above the upper bound U B is considered an outlier and the environmental monitoring module sends a generated environmental alarm signal to the intelligent alarm module. The specific formula is as follows:
IQR＝Q3-Q1
LB＝Q1-1.5×IQR
UB＝Q3+1.5×IQR
wherein IQR represents a quartile range; q1 represents a first quartile of the environmental monitoring sensor data, i.e., 25% of the data is less than or equal to this value; q3 represents the third quartile of the environmental monitoring sensor data, i.e., 75% of the data is less than or equal to this value; l B denotes the lower bound and U B denotes the upper bound.
The comprehensive analysis of the intelligent alarm module comprises the steps of receiving a video monitoring alarm signal, an intrusion alarm signal and an environment alarm signal, setting the highest priority of the intrusion alarm signal, and constructing a priority evaluation model for the video monitoring alarm signal and the environment alarm signal; and comparing according to the output result of the priority evaluation model, wherein the output result with higher priority is higher.
Specifically, the construction process of the priority evaluation model is as follows:
Defining a scoring system: each alert signal is assigned a base score: the video monitor alarm signal (yellow) is initially divided into 10 minutes and the ambient alarm signal (orange) is initially divided into 10 minutes.
The score is increased or decreased according to the specific situation. If a plurality of abnormal behaviors are identified in the video monitoring alarm, adding 2 points every time one abnormal behavior is added; the more data detected in the environmental alarm exceeds the upper and lower bounds, the more 1 point is added for each data addition.
Calculating the total score: the base score plus the score added by the additional factors gives the total score. Determining priority: the higher the total score, the higher the alarm priority. For example, 3 abnormal behaviors are identified in the video monitoring alarm, and the total score is 10+3×2=16; 2 data anomalies were detected in the environmental alarm, with a total score of 10+2×1=12. It is known that the video surveillance alarm has a higher priority than the environmental alarm.
Further, if the intelligent alarm module receives three alarm signals (intrusion alarm signal > video monitoring alarm signal > environment alarm signal, or intrusion alarm signal > environment alarm signal > video monitoring alarm signal) within a unit time, a red alarm is triggered at this time, the request analysis response unit processes the intrusion alarm signal preferentially, and then the priority of the video monitoring alarm signal and the environment alarm signal is analyzed according to the priority evaluation model, so as to determine the processing sequence.
If the intelligent alarm module receives two alarm signals in unit time, judging whether the intelligent alarm module contains an intrusion alarm signal, and if the intelligent alarm module contains the intrusion alarm signal (the intrusion alarm signal is > an environment alarm signal or the intrusion alarm signal is > a video monitoring alarm signal), triggering a red alarm, and requesting an analysis response unit to preferentially process the intrusion alarm signal; if the intrusion alarm signal is not included (the video monitoring alarm signal > the environment monitoring alarm signal or the environment monitoring alarm signal > the video monitoring alarm signal), a yellow alarm is triggered, and the priority of the video monitoring alarm signal and the environment alarm signal is analyzed through a priority evaluation model to determine the processing sequence.
If the intelligent alarm module receives an alarm signal (an intrusion alarm signal, a video monitoring alarm signal or an environment alarm signal) in unit time, the intelligent alarm module is directly processed by the analysis response unit; if the alarm signal is an intrusion alarm signal, triggering a red alarm; if the signal is a video monitoring alarm signal or an environment alarm signal, the signal is analyzed by the data analysis module and then is processed by the intelligent decision support module.
If no alarm signal exists in unit time, no alarm is triggered.
It should be noted that the priority of the intrusion alert signal is always highest, and the intrusion detection module determines whether to generate the intrusion alert signal and send the intrusion alert signal to the intelligent alert module according to the intrusion anomaly determination rule (i.e., the high intrusion anomaly, the medium intrusion anomaly and the light intrusion anomaly) mentioned above, and then the intelligent alert module generates the alert information and transmits the alert information to the analysis response unit and the analysis response unit processes the alert information. The priority of intrusion alert signals is always highest because intrusion behavior often represents a direct and urgent security threat, as compared to environmental monitoring or video monitoring alerts (video monitoring and analysis modules provide real-time monitoring of abnormal behavior, such as activities of unauthorized personnel or suspicious behavior, which can intervene in time and avoid causing more serious security threats), while environmental alert signals concern abnormal changes in environmental factors, such as toxic gas leaks, which can also pose a threat to personnel security, but can timely detect anomalies by the IQR method and intervene.) intrusion alerts often mean significant security breaches or impending danger, such as unauthorized personnel entering a sensitive area or illegal intrusion, once intrusion alert has occurred, indicating that personal and property security may have been compromised. This situation requires immediate action to prevent potential damage or to prevent further safety risks. Thus, prioritizing intrusion alarms ensures timely response to the most severe security events, thereby maximizing personnel and property security.
In this embodiment, the unit time means 1 hour. It should be noted that the unit time is set according to the actual situation, and is not a fixed value, for example, the security level is at a middle level or a low level, and the unit time can be set to be shorter.
And the intelligent decision support module performs priority processing on the alarm signal by the linkage access control unit according to the comprehensive analysis of the intelligent alarm module. The access control unit is used for managing the access authority of the system resource, realizing remote security control through the mobile application and the Internet of things equipment, and consists of an access control module, a mobile security application module and an Internet of things module. Further, the access control module is used for identity authentication and regional security control; the mobile security application module is used for remote monitoring and management of mobile equipment; the internet of things module is used for connecting various security devices to realize information sharing and cooperative work among the devices.
The analysis response unit is used for receiving the alarm information, deeply analyzing the alarm information, judging and processing the alarm information according to the priority of the intelligent alarm module, and feeding the alarm information back to the monitoring and early warning unit. The analysis response unit comprises a data analysis module and an intelligent decision support module.
Further, the embodiment also provides an intelligent security linkage early warning method based on big data service, which comprises the following steps:
s1: raw data is collected from a plurality of sources and preprocessed by a data processing unit.
S2: and transmitting the preprocessed data to a monitoring and early warning unit, and monitoring and early warning the data and identifying the security threat by the monitoring and early warning unit.
S3: an alarm signal is generated based on the security threat detected by the monitoring and early warning unit.
S4: the intelligent alarm module receives the alarm signal and judges the priority, and forwards the alarm signal to the analysis response unit for further processing.
S5: the analysis response unit receives the alarm information, performs deep analysis, and combines the access control unit to formulate and execute a response strategy.
The embodiment also provides a computer device, which is suitable for the situation of the anti-disability safety regulation and control method of the civil aviation vehicle driver, and comprises the following steps: a memory and a processor; the memory is used for storing computer executable instructions and the processor is used for executing the computer executable instructions to implement all or part of the steps of the method according to the embodiments of the present invention as set forth in the embodiments above.
The present embodiment also provides a storage medium having stored thereon a computer program which, when executed by a processor, performs the method of any of the alternative implementations of the above embodiments. The storage medium may be implemented by any type of volatile or non-volatile memory device or combination thereof, such as static random access memory (Static Random Access Memory, SRAM), electrically erasable programmable read-only memory (ELECTRICALLY ERASABLE PROGRAMMABLE READ-only memory, EEPROM), erasable programmable read-only memory (Erasable Programmable Read OnlyMemory, EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.
The storage medium according to the present embodiment belongs to the same inventive concept as the data storage method according to the above embodiment, and technical details not described in detail in the present embodiment can be seen in the above embodiment, and the present embodiment has the same advantageous effects as the above embodiment.
In conclusion, the invention realizes real-time monitoring, rapid identification of potential threats and effective management of alarm information by comprehensively utilizing multi-source data. The system integrates a plurality of modules such as video monitoring, intrusion detection, environment monitoring, data analysis and the like, and improves the accuracy and response speed of early warning. In addition, the invention optimizes the data processing flow, enhances the automation and intelligent degree of the system, greatly improves the overall efficiency of the security system, and provides more efficient and comprehensive security for users.
Example 2
Referring to table 1, for the third embodiment of the present invention, based on the first two embodiments, the embodiment provides an intelligent security linkage early warning system based on big data service, and in order to verify the beneficial effects of the present invention, scientific demonstration is performed through economic benefit calculation and simulation experiments.
First, different types of environments, such as business, residential, industrial, are selected, then the prior art and the present solution are deployed simultaneously in each environment, and other variables (e.g., weather, traffic) are ensured to be the same or similar.
Second, monitoring is continued for at least 1 month to ensure validity of the data. During this time, various security threats (e.g., intrusion, abnormal behavior, fire, etc.) and daily activities are simulated while the response time, alarm accuracy, system load, etc. of each event are recorded in detail.
The performance of the two schemes in different scenarios is then compared.
Table 1 comparison with the prior art table
| Scene(s) | Index (I) | Prior Art | The technical proposal | Statistical significance |
| Commercial district | Response time | 12 Seconds | 6 Seconds | p<0.05 |
| Residential area | False alarm rate | 20％ | 3％ | p<0.01 |
| Industrial area | Rate of missing report | 15％ | 2％ | p<0.05 |
| ... | ... | ... | ... | ... |
As shown in table 1, in the commercial area, the response time was reduced from 12 seconds to 6 seconds, showing a faster response capability. The false alarm rate of the residential area is greatly reduced from 20% to 3%, and higher accuracy is shown. In the industrial area, the rate of missing report is also reduced from 15% to 2%, meaning a more reliable safety monitoring. The data show that the performance of the technical scheme under different scenes is obviously superior to that of the prior art through statistical analysis, and particularly, the technical scheme is in response speed and alarm accuracy.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. An wisdom security protection linkage early warning system based on big data service, its characterized in that: comprising the steps of (a) a step of,
The data processing unit is used for collecting original data from a plurality of sources and transmitting the original data to the monitoring and early warning unit after preprocessing;
The monitoring early warning unit is used for monitoring and identifying the security threat in the data of the data processing unit in real time, generating alarm information and transmitting the alarm information to the analysis response unit; the monitoring and early warning unit comprises a video monitoring and analyzing module, an intrusion detection module, an environment monitoring module and an intelligent alarm module; the intelligent alarm module receives the video monitoring alarm signal, the intrusion alarm signal and the environment alarm signal from the video monitoring and analyzing module, the intrusion detection module and the environment monitoring module, carries out comprehensive analysis, judges the priority of processing the alarm signal, and generates alarm information from all the alarm signals and transmits the alarm information to the analysis response unit; the security threats include abnormal behavior, intrusion anomalies, and environmental monitoring anomalies;
The analysis response unit is used for receiving the alarm information, deeply analyzing the alarm information, judging and processing the alarm information according to the priority of the intelligent alarm module, and feeding the alarm information back to the monitoring and early warning unit; the analysis response unit comprises a data analysis module and an intelligent decision support module;
The access control unit is used for managing the access authority of the system resource and realizing remote security control through the mobile application and the Internet of things equipment; the access control unit comprises an access control module, a mobile security application module and an Internet of things module.
2. The intelligent security linkage early warning system based on big data service according to claim 1, wherein: the source of the original data comprises a sensor source, a camera video source and other data sources;
If the source of the original data is a sensor source or a camera video source, the original data is preprocessed by the data processing unit and then transmitted to the monitoring and early warning unit;
If the source of the original data is other data sources, the original data is directly transmitted to the analysis response unit by the data processing unit.
3. The intelligent security linkage early warning system based on big data service according to claim 2, wherein: the video monitoring and analyzing module utilizes a camera to carry out real-time video monitoring, analyzes video content and identifies abnormal behaviors, and comprises the following specific steps,
Installing cameras at key positions, configuring the cameras, and setting proper visual angles and resolutions;
the data processing unit acquires real-time video data of the camera, performs preprocessing, and uploads the real-time video data to the video monitoring and analyzing module;
the video monitoring and analyzing module applies an image recognition algorithm to recognize the real-time video data and recognizes abnormal behaviors through a behavior analysis algorithm;
If abnormal behaviors are identified, generating a video monitoring alarm signal and transmitting the video monitoring alarm signal to the intelligent alarm module.
4. The intelligent security linkage early warning system based on big data service of claim 3, wherein: the key positions comprise an access opening, a public area, a sensitive area and a peripheral area, and the personnel-intensive area;
The setting of the appropriate viewing angle and resolution includes:
A wide-angle lens is arranged at the entrance and exit to cover the whole entrance area, and high resolution is set to identify the face and the license plate;
setting a middle angle lens in the public area, and setting middle resolution to balance definition and storage requirements;
Setting a high-resolution camera in the sensitive area, focusing on an article or an area, and ensuring detailed capture;
setting a wide angle or a panoramic lens in the peripheral area to cover a wide area, and setting medium resolution to monitor the flow of personnel and vehicles;
The medium-to-high resolution cameras are arranged in the personnel-intensive area, so that the crowd activity area is covered, and the personnel safety is ensured;
The abnormal behavior includes any one of the following: the unauthorized person/vehicle is attempting to enter or leave at the entrance at the non-working/regular time, the unauthorized person/vehicle stays at the entrance for a long time, the person gathers at the public area at the non-working/regular time, the person stays at the public area or takes away the unknown object, the unauthorized person stays for a long time after entering the sensitive area or entering the sensitive area, the unauthorized person moves or operates at the sensitive area, the vehicle/person stays at the peripheral area at the non-working/regular time, and the abnormal group/individual behavior occurs in the person-dense area.
5. The intelligent security linkage early warning system based on big data service according to claim 4, wherein: the intrusion detection module is used for detecting whether unauthorized access or authorized boundary crossing exists, generating an intrusion alarm signal if the unauthorized access or authorized boundary crossing exists, and transmitting the intrusion alarm signal to the intelligent alarm module;
the intrusion detection module comprises a processor configured to,
Constructing a security level assessment model of the key area according to the asset value, the potential threat and the historical security event of the key area, wherein the expression of the security level assessment model is as follows:
Wherein S represents the final calculated security level; v i represents the asset value of the i-th zone; A weight corresponding to the asset value representing the i-th region; t j represents a potential threat to the jth zone; /(I) Representing the weight corresponding to the potential threat of the jth region; h k represents a historical security event for the kth zone; /(I)Representing the weight corresponding to the historical security event of the kth region; a. b and c represent indexes for adjusting the influence intensity; alpha, beta, gamma represent coefficients that balance the effects of each score;
calculating the security level S of each key area through the security level evaluation model, and dividing the security level S into a low-level security area, a medium-level security area and a high-level security area;
If the security level S of the key area is less than or equal to a first security threshold P 1, determining that the key area is a low-level security area; if the first safety threshold P 1 is smaller than the safety level S of the key area and smaller than the second safety threshold P 2, judging that the key area is a middle-level safety area; if the security level S of the key area is more than or equal to a second security threshold P 2, judging that the key area is an advanced security area;
the intrusion detection module divides unauthorized access or authorized out-of-range into a high intrusion anomaly, a medium intrusion anomaly and a light intrusion anomaly according to the security level S of the key area, and specifically comprises the following steps:
If the access of unauthorized personnel/vehicles occurs in the low-level safety area in non-working/normal time or the access of authorized personnel/vehicles exceeds the boundary in the critical area in working/normal time, the high-level safety area is regarded as high-intrusion abnormality, at the moment, an intrusion detection module immediately generates an intrusion alarm signal and transmits the intrusion alarm signal to an intelligent alarm module, the intelligent alarm module skips a video monitoring and analyzing module and an environment monitoring module after receiving the intrusion alarm signal, directly sends the intrusion alarm signal to an analysis response unit, an intelligent decision-making support module in the analysis response unit informs an access control module in the access control unit to immediately close the access authority, limits or prevents further unauthorized access, and simultaneously, a mobile security application module immediately informs a security team to arrive at the scene to provide real-time updating and remote control options and activate instant security measures of an internet of things module;
If unauthorized personnel/vehicles access in the non-working/conventional time or authorized personnel/vehicles cross the boundary in the critical area in the working/conventional time, the medium-level security area is regarded as medium-level intrusion abnormality, and an intrusion alarm signal is generated in a first limiting time t 1 of the intrusion detection module and is transmitted to the intelligent alarm module; if the abnormal behavior in the video monitoring and analyzing module corresponds to the moderate intrusion abnormal behavior, sending an intrusion alarm signal to an analysis response unit, guiding an access control module in the access control unit to improve access rights through an intelligent decision-making support module, notifying a security team through a mobile security application module, and activating an Internet of things module to take emergency security measures;
If the unauthorized person/vehicle accesses in the non-working/regular time or the authorized person/vehicle crosses the boundary in the critical area in the working/regular time, the advanced security area is regarded as a slight intrusion anomaly, at this time, the intrusion detection module generates an intrusion alarm signal in the second limiting time t 2 and transmits the intrusion alarm signal to the intelligent alarm module, if the slight intrusion anomaly is associated with the video monitoring alarm signal or the environment alarm signal, the intrusion alarm signal is sent to the analysis response unit, and if the unauthorized access of the unauthorized person/vehicle is an occasional event and no anomaly is generated in the video monitoring and analysis module, the intelligent alarm module does not need to generate the intrusion alarm signal; wherein the first defined time t 1 < the second defined time t 2;
the video monitoring and analyzing module is used for detecting whether the unauthorized person/vehicle enters or leaves the entrance and exit at the non-working/normal time, enters the sensitive area after staying at the entrance and exit for a long time, or enters the sensitive area after crossing all key areas at the non-working/normal time, if the video monitoring and analyzing module detects that the unauthorized person/vehicle is out of range at all key areas, the intrusion detecting module is combined with the monitoring data of the intrusion detecting module to further confirm whether the actual intrusion abnormality is formed.
6. The intelligent security linkage early warning system based on big data service according to claim 5, wherein: the environment monitoring module receives the environment monitoring sensor data collected and preprocessed by the data processing unit, confirms the upper and lower bounds of the environment monitoring abnormality by using an IQR calculation method, and generates an environment alarm signal and sends the environment alarm signal to the intelligent alarm module if the calculation exceeds the upper and lower bounds;
The comprehensive analysis of the intelligent alarm module comprises the steps of receiving a video monitoring alarm signal, an intrusion alarm signal and an environment alarm signal, setting the highest priority of the intrusion alarm signal, and constructing a priority evaluation model for the video monitoring alarm signal and the environment alarm signal; comparing according to the output result of the priority evaluation model, wherein the output result is higher in priority;
if the intelligent alarm module receives three alarm signals in unit time, triggering red alarm at the moment, requesting an analysis response unit to process the intrusion alarm signals preferentially, and then analyzing the priorities of the video monitoring alarm signals and the environment alarm signals according to a priority evaluation model to determine the processing sequence;
if the intelligent alarm module receives the two alarm signals in unit time, judging whether the intelligent alarm module contains an intrusion alarm signal, if the intelligent alarm module contains the intrusion alarm signal, triggering a red alarm, and requesting an analysis response unit to preferentially process the intrusion alarm signal; if the intrusion alarm signal is not contained, triggering yellow alarm, and analyzing the priority of the video monitoring alarm signal and the environment alarm signal through a priority evaluation model to determine the processing sequence;
If the intelligent alarm module receives an alarm signal in unit time, the alarm signal is directly processed by the analysis response unit; if the alarm signal is an intrusion alarm signal, triggering a red alarm; if the signal is a video monitoring alarm signal or an environment alarm signal, the signal is analyzed by the data analysis module and then is processed by the intelligent decision support module.
7. The intelligent security linkage early warning system based on big data service of claim 6, wherein: the other data sources are received and processed by a data analysis module in an analysis response unit, and the other data sources comprise social media, weather forecast and public transportation information;
and the intelligent decision support module performs priority processing on the alarm signal by the linkage access control unit according to comprehensive analysis of the intelligent alarm module.
8. The intelligent security linkage early warning method based on the big data service is based on the intelligent security linkage early warning system based on the big data service according to any one of claims 1 to 7, and is characterized in that: comprises the following steps of the method,
Collecting original data from a plurality of sources and preprocessing the original data by a data processing unit;
transmitting the preprocessed data to a monitoring and early warning unit, and monitoring and early warning the data and identifying security threats by the monitoring and early warning unit;
Generating an alarm signal based on the security threat detected by the monitoring and early warning unit;
the intelligent alarm module receives the alarm signal and judges the priority, and forwards the alarm signal to the analysis response unit for further processing;
the analysis response unit receives the alarm information, performs deep analysis, and combines the access control unit to formulate and execute a response strategy.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that: the steps of the intelligent security linkage early warning method based on big data service in claim 8 are realized when the processor executes the computer program.
10. A computer-readable storage medium having stored thereon a computer program, characterized by: the steps of the intelligent security linkage early warning method based on big data service of claim 8 are realized when the computer program is executed by a processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311723439.2A CN117912186A (en) | 2023-12-15 | 2023-12-15 | Intelligent security linkage early warning system based on big data service |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311723439.2A CN117912186A (en) | 2023-12-15 | 2023-12-15 | Intelligent security linkage early warning system based on big data service |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117912186A true CN117912186A (en) | 2024-04-19 |
Family
ID=90684501
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311723439.2A Pending CN117912186A (en) | 2023-12-15 | 2023-12-15 | Intelligent security linkage early warning system based on big data service |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117912186A (en) |
-
2023
- 2023-12-15 CN CN202311723439.2A patent/CN117912186A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11645904B2 (en) | Drone-augmented emergency response services | |
| CN111144291B (en) | Video monitoring area personnel intrusion discrimination method and device based on target detection | |
| CN112233300A (en) | Community passing epidemic prevention monitoring joint defense system and method based on artificial intelligence | |
| CN110349360B (en) | Building window area defense system and monitoring method | |
| KR102036856B1 (en) | Integrated facility management system and method | |
| US20230070772A1 (en) | Active threat tracking and response | |
| CN116457851B (en) | System and method for real estate monitoring | |
| US20220139199A1 (en) | Accurate digital security system, method, and program | |
| KR20180118979A (en) | Method and apparatus for risk detection, prediction, and its correspondence for public safety based on multiple complex information | |
| CN201974677U (en) | Port restricted area safety monitoring system | |
| CN114926314A (en) | Ubiquitous autonomous controllable Internet of things equipment system and method | |
| KR20180066688A (en) | Digital Social Shield System using Deep Active Semi-Supervised Learning | |
| JP2006120084A5 (en) | ||
| JP2006120084A (en) | Dubious person report system | |
| CN117912186A (en) | Intelligent security linkage early warning system based on big data service | |
| CN115762046A (en) | Early warning and intervention device for building top light life person | |
| CN112885013A (en) | Monitoring and early warning method and device and readable storage medium | |
| CN112102583A (en) | Intelligent building system based on big data | |
| Berberova et al. | Risk-informed security system. The use of surveillance cameras for the particularly hazardous facilities safety | |
| JP2006146432A (en) | Security system | |
| WO2011061767A1 (en) | Smart security-supervision system | |
| KR102452589B1 (en) | Integrated management system for security information inside the building | |
| KR102326193B1 (en) | CCTV-integrated security system for emergency door | |
| CN117542155A (en) | Security monitoring system with recognition function | |
| CN115767017A (en) | Wisdom sentry monitored control system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |