Intelligent security monitoring management system based on Internet of Things

Abstract

The invention discloses an intelligent security and protection monitoring management system based on the Internet of Things, and relates to the technical field of security and protection management and control, and the system comprises a video monitoring module, an access control management module, an environment monitoring module, an intrusion detection module, a response module and a route planning module. The environment monitoring module comprises a region division unit, a risk index evaluation unit, a change rate unit and a risk prediction unit, the risk prediction unit predicts the risk condition in the future time by combining a risk index R and a risk index change rate L, the risk index R is obtained through the risk index evaluation unit, the change rate L is calculated, and the risk index R is obtained through the risk index evaluation unit. The campus security management system can predict a future risk index RP, automatically trigger an alarm and respond to emergencies in time when the risk is too high, and through a region division unit, specific monitoring measures are set for different regions, security resources are reasonably allocated, accurate evacuation is carried out in an emergency, panic is avoided, and the campus security management efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of security management and control technology, and in particular to an intelligent security monitoring and management system based on the Internet of Things. Background Art

[0002] With the rapid development and widespread application of Internet of Things technology, the smart security monitoring and management system realizes real-time monitoring, data collection and remote management of the monitoring area through Internet of Things sensors, smart cameras and network communication technology. Its main purpose is to improve the response speed and accuracy of the security system, thereby improving the efficiency and effectiveness of overall security management. The security monitoring system plays a vital role in various fields, among which the campus smart security system plays an important role in improving the school's safety management level and protecting students' personal safety.

[0003] At present, in the smart security monitoring of campuses, fixed thresholds are usually set for sensors, and alarms are triggered only when environmental data reaches a certain threshold. Risks such as fire and gas leakage often have a certain trend, resulting in untimely response to emergencies, and teachers and students are prone to panic, thus expanding the disaster. In addition, the campus has a complex zoning system, including multiple functional areas, but there is currently a lack of zoning, resulting in poor monitoring results, inconvenient management, and affecting the overall security effect. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent security monitoring and management system based on the Internet of Things, which is capable of monitoring regional temperature and gas concentration through a risk index evaluation unit, integrating the risk index R, calculating the rate of change L, predicting the future risk index RP, combining the high-risk and medium-risk thresholds Z1 and Z2, real-time monitoring of environmental changes, and timely responding to emergencies. When the risk is too high, the alarm is automatically triggered, and the weight is adjusted to improve the monitoring sensitivity, thereby realizing intelligent monitoring. Through the regional division unit, specific monitoring measures are set for different areas, security resources are reasonably allocated, and accurate evacuation is carried out in an emergency to avoid panic, thereby improving the efficiency of campus security management, and solving the problems raised in the above-mentioned background technology.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an intelligent security monitoring and management system based on the Internet of Things, including a video monitoring module, an access control management module, an environmental monitoring module, an intrusion detection module, a response module and a route planning module; The intrusion detection module is used to detect illegal activities on campus, and the access control management module is used to manage the campus access control system and control the access rights of various entrances and exits on campus. The access control management module includes an intelligent access control device and a networking unit; The environmental monitoring module is used to monitor the environmental risks on campus, and the environmental monitoring module includes a region division unit, a risk index assessment unit, a change rate unit and a risk prediction unit; The area division unit is used to divide the campus into different functional areas, specifically including area 1 to area i. The risk index unit is used to detect the temperature and gas concentration of the monitoring area to obtain the risk index R. The change rate unit is used to calculate the risk index change in the area to obtain the risk index change rate L. The risk prediction unit predicts the risk situation in the future by combining the risk index R and the risk index change rate L to obtain the risk index prediction value RP at time t+Δt, and sets the high risk threshold of RP(t+Δt) to Z1 and the medium risk threshold to Z2; The response module is used to send an alarm to relevant personnel in case of an emergency.

[0006] Optionally, the video surveillance module is used to monitor campus images in real time and store monitoring data, and the video surveillance module includes a high-definition IP camera and a network video recorder.

[0007] Optionally, the risk index assessment unit assessment process is as follows: ; Where R i is the risk index of the ith region, and R is the risk index; T is the current ambient temperature; T max is the maximum ambient temperature, T min is the minimum ambient temperature; α is the ambient temperature influence coefficient, which is 0.6; C gas is the concentration of harmful gases; C th is the concentration threshold of harmful gases; β is the influence coefficient of harmful gas concentration, which is 0.6; The larger the risk index R is, the more serious the risk situation in the region is. i The maintenance threshold is Y i , when R i Greater than Y i , it means that the environmental risk of the i-th area is too high. At this time, the response module is triggered to issue an alarm to relevant personnel.

[0008] Optionally, the change rate unit calculation process is as follows: ; Where L i is the risk index change rate of the ith region, and L is the risk index change rate; R i (t) is the risk index of the ith region at the current time; R i (t-Δt) is the risk index of the ith region at time t-Δt; Δt is the time interval in minutes; The risk index change rate L represents the temperature change and harmful gas concentration change in the area. Relevant personnel estimate the risk situation based on the magnitude of the change.

[0009] Optionally, the risk prediction unit predicts the process as follows: ; RP i (t+Δt) is the predicted value of the risk index of the ith region at time t+Δt; R i (t) is the risk index of the ith region at the current time; L i (t) is the risk index change rate of the ith region at the current time; C gas (t + Δt) is the concentration of harmful gases at time t + Δt; Δt is the time interval in minutes; C gas (t) is the concentration of harmful gases at the current time; ; Among them C gas (t-Δt) is the concentration of harmful gases at time t-Δt; Risk index prediction value RP at time t + Δt i (t+Δt) represents the risk situation in the future time region, RP i The larger the (t+Δt) is, the more serious the risk is, and the RP is set. i The high risk threshold of (t+Δt) is Z1, and the medium risk threshold is Z2. When Z1<RP i (t+Δt)<Z2 is medium risk. i When (t+Δt) is greater than Z1, it means that the risk index of the i-th area in the future time is high risk. At this time, the response module is triggered to remind relevant personnel to take countermeasures to achieve the effect of early warning.

[0010] Optionally, when Z1<RP i When (t+Δt)<Z2, it is a medium risk. At this time, the ambient temperature influence coefficient α and the harmful gas concentration influence coefficient β in the risk index assessment unit are increased, and both α and β are increased to 0.8 to increase the sensitivity to changes in ambient temperature and harmful gas concentrations.

[0011] Optionally, the intrusion detection module includes an infrared sensor, a ground vibration sensor and an alarm unit.

[0012] Optionally, the route planning module is used to plan routes for patrol personnel and risk evacuation, and includes a navigation unit and an indicator light unit. The navigation unit is used to provide teachers and students with campus maps and mark evacuation areas on the maps, which teachers and students can view through smart phones. The indicator light unit is a guide light installed in the school to guide the actions of teachers and students.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention monitors the regional temperature and gas concentration through a risk index evaluation unit for detection, and integrates them to obtain a risk index R. The risk situation of the area can be judged according to the risk index R. Then, the risk index change in the area is calculated by the change rate unit to obtain the risk index change rate L, so that the temperature change and the harmful gas concentration change in the area can be known. Finally, the risk prediction unit predicts the risk situation in the future by combining the risk index R and the risk index change rate L, and obtains the risk index prediction value RP at the future time t+Δt, and sets the high risk threshold of RP(t+Δt) to Z1 and the medium risk threshold to Z2, so that the system can predict the environmental risk of a certain area in the future period of time, so that it can be automatically triggered when the risk is too high. At the medium risk, the weight coefficient in the risk index evaluation unit will be adjusted to improve the sensitivity to the change in ambient temperature and the change in harmful gas concentration, so as to achieve the effect of intelligent monitoring, help schools to prepare in advance, respond to emergencies in a timely manner, and improve the quality of security management of the entire campus.

[0014] 2. The present invention divides the campus into different functional areas through area division units, and can set corresponding monitoring measures according to different areas. For example, in dormitory areas and cafeteria areas, it can focus on security monitoring of fire and gas leakage, so that security resources can be reasonably utilized and easy to manage. At the same time, when an emergency occurs in a certain area, relevant personnel are notified in time to evacuate people in the area without alarming all areas, avoiding panic and improving the overall security monitoring management effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a block diagram of the system module of the present invention; Figure 2 This is a block diagram of the environmental monitoring module of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] For examples, see Figure 1 As shown in the figure, this implementation provides an intelligent security monitoring and management system based on the Internet of Things, including a video monitoring module, an access control management module, an environmental monitoring module, an intrusion detection module, a response module and a route planning module; The intrusion detection module is used to detect illegal activities on campus, and the access control management module is used to manage the campus access control system and control the access rights of various entrances and exits on campus. The access control management module includes an intelligent access control device and a networking unit; The intelligent access control device manages people at various entrances and exits of the campus by collecting fingerprints, faces and identity cards of teachers and students who log in to the system. Only after successful identity authentication can people enter the campus.

[0018] The environmental monitoring module is used to monitor the environmental risks on campus, and the environmental monitoring module includes a region division unit, a risk index assessment unit, a change rate unit and a risk prediction unit; The area division unit is used to divide the campus into different areas according to the building types, specifically including area 1 to area i. The risk index unit is used to detect the temperature and gas concentration of the monitoring area to obtain the risk index R. The change rate unit is used to calculate the risk index change in the area to obtain the risk index change rate L. The risk prediction unit predicts the risk situation in the future by combining the risk index R and the risk index change rate L to obtain the risk index prediction value RP at time t+Δt, and sets the high risk threshold of RP(t+Δt) to Z1 and the medium risk threshold to Z2; The response module is used to send an alarm to relevant personnel in case of an emergency.

[0019] Specifically, alarms can be sent to relevant personnel through campus broadcasts, campus apps, etc., enabling them to respond quickly to emergencies.

[0020] In this embodiment, the campus is divided into different functional areas through the area division unit in the environmental monitoring module, specifically including area 1 to area i, where area 1 can be the teaching building area, area 2 can be the cafeteria area, and area i can be the dormitory area. By setting the area division unit, corresponding monitoring measures can be set according to different areas. For example, in the dormitory area and the cafeteria area, the focus can be on security monitoring of fire and gas leakage, and in the teaching building area, the focus can be on intrusion detection, so that security resources can be reasonably utilized, management is convenient, and security monitoring effects are improved. At the same time, when an emergency occurs in a certain area, relevant personnel are notified in time to evacuate people in the area without alarming all areas to avoid causing panic.

[0021] In the actual monitoring process, the risk index evaluation unit monitors the regional temperature and gas concentration for detection, and integrates them to obtain the risk index R. The risk situation of the area can be judged according to the risk index R. Then, the change of the risk index in the area is calculated by the change rate unit to obtain the risk index change rate L, so that the temperature change and the change of harmful gas concentration in the area can be known. The occurrence of risks such as fire and gas leakage often has a certain trend. The temperature and harmful gases will rise sharply during the process. Relevant personnel can estimate the risk situation according to the magnitude of the change. Finally, the risk prediction unit predicts the risk situation in the future by combining the risk index R and the risk index change rate L to obtain the risk index prediction value RP at the future time t+Δt, and sets the high risk threshold of RP(t+Δt) to Z1 and the medium risk threshold to Z2, so that the system can predict the environmental risk of a certain area in the future period of time, and by setting the threshold, it can be automatically triggered when the risk is too high, so as to achieve the effect of intelligent monitoring, help schools prepare in advance, respond to emergencies in a timely manner, and improve the efficiency of the entire campus security management.

[0022] Furthermore, the video monitoring module is used to monitor campus images in real time and store monitoring data, and the video monitoring module includes a high-definition IP camera and a network video recorder.

[0023] Specifically, by setting up a video surveillance module including a high-definition IP camera and a network video recorder, real-time monitoring of various areas within the campus can be achieved. The high-definition images captured by the high-definition IP camera enable security personnel to clearly understand the situation in the area and store the monitoring data for subsequent reference at any time to avoid missing important information.

[0024] Furthermore, the risk index assessment unit assessment process is as follows: ; Where R i is the risk index of the ith region; T is the current ambient temperature; T max is the maximum ambient temperature, T min is the minimum ambient temperature; α is the ambient temperature influence coefficient, which is 0.6; C gas is the concentration of harmful gases; C th is the concentration threshold of harmful gases; β is the influence coefficient of harmful gas concentration, which is 0.6; Specifically, the regional division unit is used to divide the campus into different areas, and then the risk index R of each area is evaluated. The larger the risk index R, the more serious the risk situation in the area, and R is set. i The maintenance threshold is Y i , when R i Greater than Y i , it means that the environmental risk of the i-th area is too high. At this time, the response module is triggered to issue an alarm to relevant personnel.

[0025] Furthermore, the change rate unit calculation process is as follows: ; Where L i is the risk index change rate of the ith region; R i (t) is the risk index of the ith region at the current time; R i (t-Δt) is the risk index of the ith region at time t-Δt; Δt is the time interval in minutes; Specifically, by deriving the risk index change rate L, we can know the temperature changes and harmful gas concentration changes in the area. The occurrence of risks such as fire and gas leakage often has a certain trend. During the process, the temperature and harmful gases will rise sharply. Relevant personnel can estimate the risk situation according to the magnitude of the change. In actual use, a change threshold Z3 is set for L. When L is greater than Z3, it means that the risk index change in the current area is too high, that is, the temperature and harmful gas concentration have changed significantly. At this time, the response module can be triggered, so that the system can trigger an earlier warning and take preventive measures in time to avoid accidents.

[0026] Furthermore, the risk prediction unit prediction process is as follows: ; RP i (t+Δt) is the predicted value of the risk index of the ith region at time t+Δt; Ri (t) is the risk index of the ith region at the current time; L i (t) is the risk index change rate of the ith region at the current time; C gas (t + Δt) is the concentration of harmful gases at time t + Δt; Δt is the time interval in minutes; C gas (t) is the concentration of harmful gases at the current time; ; Among them C gas (t-Δt) is the concentration of harmful gases at time t-Δt; Specifically, the risk index prediction value RP at time t + Δt i (t+Δt) represents the risk situation in the future time region, RP i The larger the (t+Δt) is, the more serious the risk is, and the RP is set. i The high risk threshold of (t+Δt) is Z1, the medium risk threshold is Z2, and RP i (t+Δt)<Z1 is low risk, when Z1<RP i When (t+Δt)<Z2, it is a medium risk. At this time, the school will increase the frequency of inspections and inform teachers and students of the risk situation so that they can be prepared. i When (t+Δt)>Z1, it means that the risk index of the i-th area in the future time is high risk. At this time, the response module is triggered to remind relevant personnel to take countermeasures, thereby improving the intelligence, emergency response capability and preventive management level of the system.

[0027] Furthermore, when Z1<RP i When (t+Δt)<Z2, it is medium risk. At this time, the ambient temperature influence coefficient α and the harmful gas concentration influence coefficient β in the risk index assessment unit are increased, and both α and β are increased to 0.8 to increase the sensitivity to changes in ambient temperature and harmful gas concentrations, so that it can be adjusted according to actual conditions.

[0028] Specifically, the ambient temperature influence coefficient α and the harmful gas concentration influence coefficient β in the risk index assessment unit are adjusted in real time with the specific risk index prediction value RP, making the system more sensitive to changes in these key environmental factors. Even smaller environmental changes can have a greater impact on the risk index R, enabling the system to perceive environmental anomalies earlier and improve security quality.

[0029] Furthermore, the intrusion detection module includes an infrared sensor, a ground vibration sensor and an alarm unit.

[0030] Specifically, the intrusion detection module can be set in different areas of the campus and set according to actual conditions. For example, in the teaching building area, the module can be closed during the day and students can attend classes as usual. It can be opened at night to achieve the effect of intrusion detection. More specifically, this module can be set in an important room, such as a computer room, database, etc., and the intrusion detection effect can be achieved through infrared sensors and ground vibration sensors. When an intrusion is detected, the alarm unit is activated, an alarm sound is issued and the security personnel are notified at the same time.

[0031] A further route planning module is used to plan routes for patrol personnel and risk evacuation, including a navigation unit and an indicator light unit. The navigation unit is used to provide teachers and students with campus maps and mark evacuation areas on the maps, which teachers and students can view through smartphones. The indicator light unit is a guide light installed in the school to guide the actions of teachers and students.

[0032] Specifically, when inspectors are inspecting on campus, they are likely to miss some important areas. The navigation unit in the route planning module can plan inspection routes for them according to their specific locations. Specifically, the school sets up check-in points in important areas on campus. When inspecting, they only need to go to each check-in point to locate themselves and then open it. This can standardize the routes of inspectors and ensure the quality of inspections. In an emergency, the navigation unit marks refuge areas on the campus map, which are usually open open-air places such as playgrounds, and sets up guide lights in various areas on campus to provide guidance when vision is obstructed and reduce losses.

[0033] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The intelligent security monitoring and management system based on the Internet of Things is characterized by: It includes video surveillance module, access control management module, environmental monitoring module, intrusion detection module, response module and route planning module; The intrusion detection module is used to detect illegal activities on campus, and the access control management module is used to manage the campus access control system and control the access rights of various entrances and exits on campus. The access control management module includes an intelligent access control device and a networking unit; The environmental monitoring module is used to monitor the environmental risks on campus, and the environmental monitoring module includes a region division unit, a risk index assessment unit, a change rate unit and a risk prediction unit; The area division unit is used to divide the campus into different functional areas, specifically including area 1 to area i. The risk index unit is used to detect the temperature and gas concentration of the monitoring area to obtain the risk index R. The change rate unit is used to calculate the risk index change in the area to obtain the risk index change rate L. The risk prediction unit predicts the risk situation in the future by combining the risk index R and the risk index change rate L to obtain the risk index prediction value RP at time t+Δt, and sets the high risk threshold of RP(t+Δt) to Z1 and the medium risk threshold to Z2; The response module is used to send an alarm to relevant personnel in case of an emergency.

2. The smart security monitoring and management system based on the Internet of Things according to claim 1 is characterized by: The video monitoring module is used to monitor campus images in real time and store monitoring data. The video monitoring module includes a high-definition IP camera and a network video recorder.

3. The smart security monitoring and management system based on the Internet of Things according to claim 2 is characterized in that: The risk index assessment unit assessment process is as follows: ； Where R i is the risk index of the ith region, and R is the risk index; T is the current ambient temperature; T max is the maximum ambient temperature, T min is the minimum ambient temperature; α is the ambient temperature influence coefficient, which is 0.6; C gas is the concentration of harmful gases; C th is the concentration threshold of harmful gases; β is the influence coefficient of harmful gas concentration, which is 0.6; The larger the risk index R is, the more serious the risk situation in the region is. i The maintenance threshold is Y i , when R i Greater than Y i , it means that the environmental risk of the i-th area is too high. At this time, the response module is triggered to issue an alarm to relevant personnel.

4. The smart security monitoring and management system based on the Internet of Things according to claim 3 is characterized by: The change rate unit calculation process is as follows: ； Where L i is the risk index change rate of the ith region, and L is the risk index change rate; R i (t) is the risk index of the ith region at the current time; R i (t-Δt) is the risk index of the ith region at time t-Δt; Δt is the time interval in minutes; The risk index change rate L represents the temperature change and harmful gas concentration change in the area, and the risk situation is estimated based on the magnitude of the change.

5. The smart security monitoring and management system based on the Internet of Things according to claim 4 is characterized in that: The risk prediction unit prediction process is as follows: ； RP i (t+Δt) is the predicted value of the risk index of the ith region at time t+Δt; R i (t) is the risk index of the ith region at the current time; L i (t) is the risk index change rate of the ith region at the current time; C gas (t + Δt) is the concentration of harmful gases at time t + Δt; Δt is the time interval in minutes; C gas (t) is the concentration of harmful gases at the current time; ； Among them C gas (t-Δt) is the concentration of harmful gases at time t-Δt; Risk index prediction value RP at time t + Δt i (t+Δt) represents the risk situation in the future time region, RP i The larger the (t+Δt) is, the more serious the risk is, and the RP is set. i The high risk threshold of (t+Δt) is Z1, and the medium risk threshold is Z2. When Z1<RP i (t+Δt)<Z2 is medium risk. i When (t+Δt) is greater than Z1, it means that the risk index of the i-th area in the future time is high risk, and the response module is triggered at this time.

6. The smart security monitoring and management system based on the Internet of Things according to claim 5 is characterized by: When Z1<RP i When (t+Δt)<Z2, it is a medium risk. At this time, the ambient temperature influence coefficient α and the harmful gas concentration influence coefficient β in the risk index assessment unit are increased, and both α and β are increased to 0.8 to increase the sensitivity to changes in ambient temperature and harmful gas concentrations.

7. The smart security monitoring and management system based on the Internet of Things according to claim 1 is characterized by: The intrusion detection module includes an infrared sensor, a ground vibration sensor and an alarm unit.

8. The smart security monitoring and management system based on the Internet of Things according to claim 1 is characterized by: The route planning module is used to plan routes for patrol personnel and risk evacuation, and includes a navigation unit and an indicator light unit. The navigation unit is used to provide campus maps to teachers and students and mark evacuation areas on the maps, which teachers and students can view through smartphones. The indicator light unit is a guide light installed in the campus.

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