WO2018232846A1

WO2018232846A1 - Large-scale peripheral security monitoring method and system

Info

Publication number: WO2018232846A1
Application number: PCT/CN2017/096024
Authority: WO
Inventors: 杜光东
Original assignee: 深圳市盛路物联通讯技术有限公司
Priority date: 2017-06-23
Filing date: 2017-08-04
Publication date: 2018-12-27
Also published as: CN107277443B; CN107277443A

Abstract

The present invention relates to a large-scale peripheral security monitoring method and system. The method comprises: creating an intrusion feature library; collecting perturbation data of the boundaries of a monitored area; quantifying the perturbation data so as to obtain a quantized amplitude, and comparing the quantized amplitude and intrusion data features, so as to determine whether the monitored area has been intruded into and determine a corresponding type of the intrusion event; analyzing intrusion information about the intruder according to the perturbation data; and determining a processing scheme according to the intrusion event type and the intrusion information. The system comprises: a monitoring sensing belt and a monitoring centre, the monitoring centre comprising an intrusion feature library creation module, a quantization analysis module, an intrusion information analysis module, and a scheme determination module. By acquiring perturbation data and processing same by means of the method and system above, whether a monitored area has been intruded into, the intrusion event type and intrusion information can be determined, thereby ensuring the security of the monitored area, having advantages such as high accuracy, good monitoring effect and low maintenance cost, being particularly suitable for large-scale security monitoring such as border.

Description

Wide-range peripheral security monitoring method and system

Technical field

The present invention relates to the field of Internet of Things, and in particular to a wide-range peripheral security monitoring method and system.

Background technique

The security issue is a big issue of common concern to the whole society and involves people's lives and property. The development and progress of the security industry depends not only on the development and progress of science and technology, but also on providing and creating a good social environment for the advancement and development of science and technology.

From the application scenario, the security monitoring system can be divided into small-scale systems and large-scale systems. Small-scale security monitoring systems, such as security systems in residential areas, private areas, and public places, are based on video capture and infrared intrusion alarms and other front-end devices to collect data and send out alarm information. Monitoring and control are easier to implement. A wide range of safety monitoring systems, such as safety monitoring systems applied to the boundary line, if still monitored and controlled by the above systems and methods, a large number of active devices will greatly increase the system construction cost and operation and maintenance costs, especially considering China's land area is vast, and it is not realistic to place traditional security monitoring systems on such long borders. In addition, there are also problems of wasted manpower and inefficiency through manual duty.

Summary of the invention

To solve the above technical problems, the present invention provides a wide range of perimeter security monitoring methods and systems.

In a first aspect, the present invention provides a wide range of perimeter security monitoring methods, the method specifically comprising the steps of:

S1. Creating an intrusion feature library, where the intrusion feature library includes an intrusion event type and an intrusion data feature;

S2. collecting perturbation data of the boundary of the monitored area;

S3. Quantify the perturbation data to obtain the quantized amplitude, compare the quantized amplitude and the intrusion data characteristics, determine whether the monitored area is invaded, return to S2 when the monitored area is not invaded, and further determine the corresponding intrusion event when the monitored area is invaded. Types of;

S4. analyzing the intruder's intrusion information according to the perturbative data, where the intrusion information includes an intruder number, a moving track, and a moving speed;

S5. Determine a processing scheme according to the type of the intrusion event and the intrusion information.

The invention provides a large-scale perimeter security monitoring method, pre-creates an intrusion feature database, collects the perturbation data and obtains the quantized amplitude, and compares with the intrusion data features in the intrusion feature database to determine whether the monitored area is invaded and determined. Corresponding types of intrusion events; further, through the perturbation data, the intrusion information such as the number of intruders, the movement trajectory and the moving speed are analyzed, and the intrusion is combined. The piece type and intrusion information determine the processing scheme. The intruder generates a vibration signal during the traveling process, and monitors the boundary of the monitored area in real time, so that the perturbation data brought by the vibration signal can be collected when the invading. By acquiring and analyzing the perturbation data by the above method, only the perturbation data needs to be collected and processed to determine whether the monitored area is intruded, the type of intrusion event, and the intrusion information, thereby ensuring the security of the monitored area, and having high accuracy. The advantages of good monitoring effect and low operation and maintenance cost are especially applicable to a wide range of safety monitoring such as border lines.

Further, creating an intrusion feature library specifically includes:

S101. A preset intrusion event type table, where the intrusion event type table includes one or more intrusion event types;

S102. Presetting a corresponding intrusion data feature table according to the intrusion event type table, where the intrusion data feature table includes intrusion data features corresponding to each intrusion event type;

S103. Establish a mapping relationship between the intrusion event type table and the intrusion data feature table, and each intrusion event type matches the corresponding intrusion data feature.

In the foregoing embodiment, after the intrusion event type table and the intrusion data feature table are preset and the mapping relationship between the intrusion event type table and the intrusion data feature table is established, after the subsequent acquisition of the perturbation data and determining whether the monitored area is invaded The type of intrusion event can be determined by the mapping relationship between the intrusion event type table and the intrusion data feature table.

In a second aspect, the present invention provides a wide range of perimeter security monitoring systems, the apparatus comprising:

Monitoring a sensing strip for collecting perturbation data at a boundary of the monitored area, the monitoring sensing strip being parallel to a boundary of the monitored area, the monitoring sensing strip comprising a plurality of vibration sensing nodes;

Monitoring center for data processing and control, the monitoring center includes:

An intrusion feature library creation module, configured to create an intrusion feature library, where the intrusion feature library includes an intrusion event type and an intrusion data feature;

a quantization analysis module for quantizing the perturbation data to obtain a quantized amplitude, comparing the quantized amplitude and the intrusion data characteristics, determining whether the monitored area is invaded, and further determining a corresponding intrusion event type when the monitored area is invaded;

An intrusion information analysis module, configured to analyze intruder intrusion information according to the perturbative data, where the intrusion information includes an intruder number, a moving track, and a moving speed;

The solution determining module is configured to determine a processing plan according to the type of the intrusion event and the intrusion information.

The invention provides a wide-range perimeter security monitoring system, and the monitoring induction belt is used as the front end of the system for data collection; the monitoring center is used as the back end of the system for data processing and control. During a wide range of perimeter security monitoring, the monitoring sensing strip is disposed at a boundary of the monitored area, and the monitoring sensing strip includes a plurality of vibration sensing nodes. The process of data processing is: monitoring the inductive band to collect the perturbative data, sending it to the monitoring center for data analysis and processing, determining the type of intrusion event and intrusion information, and determining the processing plan according to the type of intrusion event and the intrusion information. By acquiring and analyzing the perturbation data through the above system, it is possible to determine whether the monitored area is intruded, the type of intrusion event, and the intrusion information, thereby ensuring The security of the monitoring area has the advantages of high accuracy, good monitoring effect and low operation and maintenance cost, and realizes a wide range of perimeter security monitoring functions.

Further, the monitoring sensing strip includes a plurality of vibration sensing nodes, and the perturbation data of the boundary of the monitored area is collected by the vibration sensing node, and the perturbation data is accessed by the fusion gateway to the monitoring center;

The converged gateway is configured to collect the received perturbation data, and forward the perturbed data to the monitoring center after performing protocol conversion;

The vibration sensing node includes an FFD device and a RFD device, the FFD device has a data collection function, a data transmission function, and a routing function, the RFD device has a data collection function and a data transmission function, and the RFD device is composed of an FFD device. a sensing network, the FFD device and the RFD device are connected through a ZigBee protocol;

After collecting the perturbation data, the RFD device sends the perturbation data to the FFD device, and the FFD device accesses the perturbation data to the monitoring center through the converged gateway, where the FFD device is connected to the converged gateway through a wired or wireless manner;

After collecting the perturbation data, the FFD device directly accesses the perturbation data to the monitoring center through the converged gateway;

The working state of the vibration sensing node includes a sleep state and an awake state. When the vibration sensing node is in a sleep state, only data acquisition is performed, and when the vibration sensing node is in an awake state, data acquisition and data transmission are simultaneously performed. The sleep sensor node in the sleep state switches to the awake state after collecting the perturbation data.

In the above embodiments, the networking form of the vibration sensing node is essential for comprehensive data acquisition, and the networking of the vibration sensing node should be both effective and practical. The vibration sensing node includes an FFD device and an RFD device. The RFD device forms a sensing network around the FFD device, and can provide multiple data delivery routes, and select other suitable routes when the optimal delivery route fails, which is beneficial to shorten the transmission. Delay, improve communication reliability. Specifically, the plurality of vibration sensing nodes form a sensing network of a mesh structure, and the perturbation data of the boundary of the monitored area is collected, and the FFD devices are connected to each other, and the RFD device is also connected to a plurality of FFD devices around it. When an FFD device cannot send data, the RFD device can continue to send data through other FFD devices. In addition, protocol conversion is performed through the converged gateway, and the monitoring sensor band 3 and the monitoring center 1 are connected to bridge the Internet of Things and the traditional Internet.

DRAWINGS

1 is a structural diagram of a wide-area perimeter security monitoring system according to the present invention;

2 is a schematic diagram of interaction of a wide-area perimeter security monitoring system according to the present invention;

3 is a schematic diagram of interaction of a response calibration of a monitoring sensing belt according to the present invention;

4 is a schematic flow chart of a wide-range perimeter security monitoring method according to the present invention;

FIG. 5 is a schematic flowchart of response calibration of a monitoring induction belt according to the present invention; FIG.

FIG. 6 is a diagram of the present invention for determining whether a monitored area is intruded and determining a corresponding type of intrusion event Schematic diagram of the process;

7 is a schematic flowchart of analyzing intruder intrusion information according to the present invention;

FIG. 8 is a schematic flow chart of determining a processing scheme according to the present invention.

Detailed ways

In the following description, for purposes of illustration and description However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, circuits, and methods are omitted so as not to obscure the description of the invention.

As shown in FIG. 1, FIG. 1 is a structural diagram of a wide-area perimeter security monitoring system according to the present invention. A wide range of perimeter security monitoring systems, including:

Monitoring the sensing strip 3 for collecting perturbative data at the boundary of the monitored area;

Monitoring Center 1, for data processing and control;

The monitoring sensor strip 3 is connected to the monitoring center 1 through the converged gateway 2.

The sensing strip 3 is used as the front end of the system for data acquisition. In the process of extensive perimeter security monitoring, the monitoring sensing strip 3 is disposed at the boundary of the monitored area, and the monitoring sensing strip 3 includes a plurality of vibration sensing nodes. The function of the converged gateway 2 is to collect the received perturbation data and forward the perturbed data to the monitoring center 1. As the back end of the system, Monitoring Center 1 is the core of the entire system for data processing and control.

The monitoring sensing strip 3 includes a plurality of vibration sensing nodes, and the perturbation data of the boundary of the monitored area is collected by the vibration sensing node, and the perturbation data is accessed to the monitoring center 1 through the converged gateway 2;

From the perspective of the Internet of Things, the monitoring sensor strip 3, the converged gateway 2 and the monitoring center 1 together constitute a three-tier architecture of the Internet of Things. The monitoring sensing strip 3 is set as the data collecting device at the forefront of the Internet of Things. The data collecting function of the monitoring sensing strip 3 is mainly realized by the vibration sensing node, and the plurality of vibration sensing nodes cover the monitored area as completely as possible. The boundary ensures that no areas are missed, which requires the number of vibration sensing nodes to be as high as possible. However, on the other hand, the amount of data collected by a large number of vibration sensing nodes is also very large. If the data structure sent by the vibration sensing node is complicated, it will bring great pressure to the data processing of the monitoring center 1. This requires the data structure of the vibration sensing node to be as simple as possible. Traditional Internet protocols require data to be accurate and secure. These features are negligible in the Internet of Things. The Internet of Things is more concerned with the number of samples sampled. In this embodiment, it is represented by the number of vibration sensing nodes and whether it covers the entire area. . In the present invention, the monitoring sensing band 3 only needs to continuously collect and transmit the perturbative data, and the implementation of such a function by the traditional Internet protocol is too inefficient. Therefore, the monitoring sensing band 3 as the front end of the system can adopt other networking protocols. Simplify the data structure of the vibration sensing node. The data of the vibration sensing node only needs to include the address code and the sensing data, wherein the address code records the position of the vibration sensing node; the sensing data is the perturbation data. The number of front-end devices of the Internet of Things is huge. Considering the overhead and efficiency of data, the vibration sensing node only carries the most useful information for the system, which is convenient for large-scale implementation.

Although the vibration sensing nodes in the monitoring sensor strip 3 are not suitable for the conventional Internet protocol, today's Internet of Things is still based on the traditional Internet to varying degrees. In the present invention, the traditional Internet protocol is still followed between the converged gateway 2 and the monitoring center 1. The converged gateway 2 provides the data transmission and gateway functions of the traditional Internet, that is, performs protocol conversion, and connects the networking protocol of the monitoring sensing band 3 with the networking protocol of the traditional Internet.

As shown in FIG. 1, the leftmost dotted line is the monitored area boundary, the right side of the dotted line is the monitored area, and the front end for collecting data includes three side-by-side monitoring sensing strips 3 disposed in the monitored area. In fact, in the process of application, the monitoring sensing strips 3 may be provided in plurality, and a plurality of vibration sensing nodes in the plurality of monitoring sensing strips 3 constitute a sensing network. Once there is an intruder, the sensor network collects the perturbation data. The monitoring center 1 sequentially analyzes and processes the perturbation data through the intrusion signature database creation module 11, the quantization analysis module 12, the intrusion information analysis module 13, and the scheme determination module 14, and implements a large-scale peripheral security monitoring function. The specific process is shown in FIG. 2 .

2 is a schematic diagram of interaction of a wide-area perimeter security monitoring system according to the present invention. The monitoring center 1 includes:

The intrusion feature library creation module 11 is configured to create an intrusion feature library, where the intrusion feature library includes an intrusion event type and an intrusion data feature;

The quantization analysis module 12 is configured to quantize the perturbation data to obtain a quantized amplitude, compare the quantized amplitude and the intrusion data feature, determine whether the monitored area is invaded, and further determine a corresponding intrusion event type when the monitored area is invaded;

The intrusion information analysis module 13 is configured to analyze intruder intrusion information according to the perturbation data, where the intrusion information includes an intruder number, a moving track, and a moving speed;

The solution determining module 14 is configured to determine a processing scheme according to the type of the intrusion event and the intrusion information. Specifically, the processing scheme includes material scheduling, personnel scheduling, configuration of combat equipment, and release of warnings, and the like.

The intrusion signature database is first created by the intrusion signature database creation module 11 as a criterion for determining whether the monitored region is intruded and the corresponding intrusion event type. After the monitoring sensor strip 3 collects the perturbation data, the quantization data is quantized by the quantization analysis module 12 to obtain the quantized amplitude, and the quantized amplitude and the intrusion data feature are further compared to determine whether the monitored area is invaded and the corresponding is determined. The type of intrusion event. The intrusion information analysis module 13 analyzes the intruder information such as the number of intruders, the moving track, and the moving speed through the perturbative data. Finally, the solution determining module 14 determines the processing plan according to the type of the intrusion event and the intrusion information, completes the basic security monitoring process, and is applicable to a wide range of security monitoring such as border lines.

As shown in FIG. 4, FIG. 4 is a schematic flowchart of a wide-range peripheral security monitoring method according to the present invention. A large-scale perimeter security monitoring method corresponding to a large-scale perimeter security monitoring system, specifically comprising:

S1. Creating an intrusion feature database in the monitoring center 1 in advance, where the intrusion feature database includes an intrusion event type and an intrusion data feature;

S2. collecting the perturbation data of the boundary of the monitored area in real time by monitoring the sensing strip 3;

S3. The monitoring center 1 quantizes the perturbative data after receiving the perturbation data sent by the monitoring sensing strip 3, quantizes the perturbative data to obtain the quantized amplitude, compares the quantized amplitude and the intrusion data characteristics, and determines whether the monitored area is invaded. Returning to S2 when the monitored area is not invaded, and further determining the corresponding type of intrusion event when the monitored area is invaded;

S4. The monitoring center 1 analyzes the intruder's intrusion information according to the perturbative data, where the intrusion information includes the number of intruders, the moving track, and the moving speed;

S5. The monitoring center 1 determines a processing scheme according to the type of the intrusion event and the intrusion information.

The traditional security monitoring method and system mainly collect real-time video/image data of the monitored area by monitoring the camera at all times, and the attendant needs to pay attention to the video/image data on multiple displays at all times to pay attention to abnormal situations; or, by being monitored The area is equipped with an infrared sensing device that triggers an infrared sensing device when the monitored area is invaded, and then issues an alarm. Regardless of the above methods, there are disadvantages such as high cost, waste of manpower and inefficiency: the layout and operation and maintenance required for surveillance cameras and infrared sensing devices require a lot of cost; the role of the surveillance camera is only to collect real-time video of the monitored area/ Image data and real-time situations are gathered into the monitoring room, and still need to be on duty to keep an eye on real-time trends.

Unlike traditional monitoring methods that rely on front-end equipment such as video capture and infrared-sensing alarms, the front-end equipment of the present invention only needs to have simple data acquisition and transmission functions; the implementation of the monitoring function mainly depends on the back-end equipment, and the back-end equipment accepts The data collected by the front-end equipment is analyzed and discovered in time. It does not require traditional manual guards and manual judgments. It has the advantages of high accuracy, good monitoring effect and low operation and maintenance cost, and is especially suitable for a wide range of security such as border lines. monitor.

In S1, creating an intrusion feature library specifically includes:

The perturbation data collected by a single vibration sensing node is too one-sided and has no reference value. Therefore, the effect achieved by a single vibration sensing node in the monitoring process is quite limited. Multiple monitoring sensors The vibration sensing node in 3 collects the perturbation data in the form of a sensing network to ensure the validity of the front-end data.

The S2 specifically includes:

Collecting perturbation data at the boundary of the monitored area in the form of a sensor network;

When a single vibration sensing node collects the perturbative data, the monitoring center 1 determines that the perturbation data is an abnormal signal;

Control the adjacent vibration sensing node to work, collect the perturbation data of the boundary of the monitored area, and the adjacent vibration sensing nodes send the respective perturbation data to the monitoring center 1;

The monitoring center 1 performs abnormality verification, and the abnormality verification process is to compare the perturbation data of the adjacent vibration sensing node with the abnormal signal;

When the perturbation data collected by the adjacent vibration sensing node matches the abnormal signal, it is determined that the perturbation data collected by the single vibration sensing node is effective, and the perturbation data collected by the adjacent vibration sensing node does not match the abnormal signal. At the same time, it is judged that the perturbation data collected by the single vibration sensing node is invalid.

Correspondingly, in a large-scale peripheral security monitoring system, the monitoring sensing belt 3 collects the perturbation data of the boundary of the monitored area in the form of a sensing network and sends it to the monitoring center 1, and the monitoring center 1 determines that the perturbative data is The abnormal signal, and control the adjacent vibration sensing node to work, collect the perturbation data of the boundary of the monitored area, and the monitoring center 1 performs abnormality verification by comparing the perturbation data and the abnormal signal of the adjacent vibration sensing nodes.

When the monitored area is invaded, there should be multiple seismic sensing nodes in the sensing network to collect matching perturbation data. The above matching specifically means that the perturbation data and the abnormal signal of the adjacent vibration sensing nodes are the same. The perturbation data and the abnormal signal of the adjacent vibration sensing nodes are the same, and the single vibration sensing node that collects the perturbative data is excluded from failure. The perturbation data of the boundary of the monitored area is collected in the form of a sensing network, which improves the effectiveness of the data collected by the front end.

Simply by monitoring the vibration sensing nodes in the sensing strip 3 to form a sensing network for data acquisition, real-time monitoring of the boundary of the monitored area, there will be problems of inconsistent response, thereby affecting the accuracy of the acquisition of the perturbative data, the monitoring center of the back end 1 The accurate analysis and processing of the perturbation data cannot be performed, which causes deviations in the process of judging whether the monitored area is invaded, the type of intrusion event and the intrusion information, which seriously affects the monitoring effect. In order to solve the above problem, the present invention enables the monitoring inductive strip 3 to output a stable response output data to the shock signal by means of soft calibration. The soft calibration process is to perform calibration conversion on the perturbation data by the calibration adjustment parameter.

As shown in FIG. 3, FIG. 3 is an interaction diagram of response monitoring of the monitoring sensor strip 3 according to the present invention. A wide range of perimeter security monitoring systems, including:

Monitoring the sensing strip 3, the monitoring sensing strips 3 are provided in plurality, the monitoring sensing strips 3 are arranged along the boundary of the monitored area, and the monitoring sensing strips 3 are parallel to each other;

Calibrating a test strip disposed adjacent to the monitor strip 3, the calibration strip being parallel to the monitor strip 3;

a manual disturbance point for moving along the calibration test strip and generating a vibration signal, and each monitoring sensing strip 3 collecting the perturbation data;

The monitoring center 1 includes:

a response output module 15 for converting the perturbative data into response output data;

The calibration parameter module 16 is configured to compare corresponding response output data when the artificial disturbance point is in different positions, and obtain calibration adjustment parameters;

The calibration module 17 is configured to perform soft calibration on the monitoring sensing strip 3 according to the calibration adjustment parameter, so that the monitoring sensing strip 3 outputs a stable response output data to the vibration signal. The soft calibration process is to perform the perturbation data through the calibration adjustment parameter. Calibration conversion.

Correspondingly, as shown in FIG. 5, FIG. 5 is a schematic flowchart of response calibration of the monitoring sensor strip 3 according to the present invention. In the S2, a plurality of mutually parallel monitoring sensing strips 3 are arranged along the boundary of the monitored area, and the perturbation data is collected by the monitoring sensing strip 3, the monitoring sensing strip 3 is parallel to the boundary of the monitored area, and the monitoring sensing The belt 3 includes a plurality of vibration sensing nodes;

In the process of arranging the monitoring sensor strip 3 along the boundary of the monitored area, the monitoring sensing strip 3 needs to be responsively calibrated, and the response calibration specifically includes:

S201. Arranging a calibration test strip next to the monitoring sensing strip 3, the calibration test strip being parallel to the monitoring sensing strip 3;

S202. setting a manual disturbance point, controlling the artificial disturbance point to move along the calibration test belt and generating a vibration signal, and each monitoring induction belt 3 collects the perturbation data generated by the vibration signal;

S203. Converting the perturbation data into response output data by the calibration parameter module 16 in response to the output module 15, specifically, the response output data includes a time domain characteristic and a frequency domain characteristic of the vibration signal;

S204. obtaining a calibration adjustment parameter by comparing corresponding response output data when the artificial disturbance point is at different positions;

S205. Perform soft calibration of the monitoring sensor strip 3 by the calibration module 17 according to the calibration adjustment parameter.

An example is given to the specific principle of soft calibration: controlling the artificial disturbance point to move along the calibration test belt and generating a vibration signal. In this process, the three vibration sensing nodes in the monitoring induction belt 3 successively collect the perturbative data generated by the vibration signal. . When the artificial disturbance point is the same distance from the three vibration sensing nodes, the three vibration sensing nodes sequentially collect the perturbation data, and the response output module 15 converts the perturbation data into the response output data. Ideally, the response output data corresponding to the three vibration sensing nodes should be the standard response output data. Since different vibration sensing nodes are affected by various factors in the sensing network, the response output generated is quite different. For example, the ratio of the response output data corresponding to the three vibration sensing nodes to the standard response output data is 1.2. 1.5 and 0.8, and the ratio of the response output data corresponding to the three response sensor nodes to the standard response output data should be 1 in the ideal state. Therefore, the calibration adjustment parameters of the three vibration sensing nodes are 1.2, 1.5, and 0.8, respectively. The process of soft calibration of the monitoring sensor strip 3 according to the calibration adjustment parameter by the calibration module 17 is corresponding to dividing the respective response output data by 1.2, 1.5, and 0.8, respectively, thereby causing three vibration sensing nodes. When the same intrusion is detected, the response output data received by the monitoring center 1 is the same, and the problem that the sensor network node cannot achieve consistency is removed.

It should be noted that on a large-scale periphery such as a borderline of a country, the boundary of the monitored area is mostly a curve. The monitoring sensing strip 3 is parallel to the boundary of the monitored area, the monitoring sensing strips 3 are parallel to each other, and the calibration test strip is parallel to the monitoring sensing strip 3. The above parallel means that each tangent is parallel. In the present invention, the front end is arranged to monitor the sensing strip 3 and the purpose of monitoring the sensing strip 3 parallel to the boundary of the monitored area is to arrange a sensing network capable of uniformly collecting signals; and the purpose of calibrating the test strip parallel to the monitoring sensing strip 3 In the process of moving the artificial disturbance point, it is ensured that the artificial disturbance point can be the same distance from each vibration sensing node at different time points.

In order to determine whether the monitored area is intruded and determine the corresponding type of intrusion event, the type of intrusion event includes personnel intrusion and vehicle intrusion, and the quantitative analysis module 12 includes:

a threshold obtaining unit, configured to acquire an intrusion determination threshold and an intrusion type determination threshold, where the intrusion determination threshold is used to determine whether the monitored area is intruded, and the intrusion type determination threshold is used to determine an intrusion event type;

a pre-processing unit, configured to pre-process the perturbation data to remove noise in the perturbation data;

a quantization unit, configured to quantize the pre-processed perturbation data to obtain a quantized amplitude of the perturbed data;

The intrusion determination unit is configured to compare the quantized amplitude of the perturbation data with the intrusion determination threshold, determine that the monitored area is safe when the quantized amplitude is smaller than the intrusion determination threshold, and determine that the monitored area is invaded when the quantized amplitude is greater than or equal to the intrusion determination threshold;

The type determining unit is configured to compare the quantized amplitude of the perturbation data and the intrusion type decision threshold after determining that the monitored area is invaded, and determine that the intrusion is when the quantized amplitude is smaller than the intrusion type determining threshold, and the quantized amplitude is greater than or equal to the intrusion type. When the threshold is determined, it is determined that the vehicle has entered the vehicle.

The intrusion data feature includes an intrusion determination threshold and an intrusion type determination threshold, and the intrusion determination threshold and the intrusion type determination threshold are acquired in advance by the threshold acquisition unit to prepare for data processing. In the natural environment in which the monitored area is in the wild, in addition to the potential intruders, there are many small animals or other noise sources in the monitored area, which will stimulate the monitoring sensor belt 3 to generate perturbative data. Therefore, after the acquired perturbation data, the noise introduced in the process of collecting the perturbative data needs to be removed by the pre-processing unit to provide more accurate perturbative data for subsequent data processing. Further, the quantized unit quantizes the perturbed data to obtain a quantized amplitude, and finally the intrusion determining unit determines whether the monitored area is invaded by comparing the quantized amplitude and the intrusion determination threshold, and the type determining unit judges by comparing the quantized amplitude and the intrusion type determining threshold. Is it a human invasion or a vehicle invasion?

Specifically, for example, the intrusion determination threshold is A, the intrusion type decision threshold is B, and the quantization amplitude is C, where A must be smaller than B. When C is less than A, it is determined that the monitored area is not invaded, otherwise the monitored area is determined to be invaded; when it is determined that C is not less than A, C is further compared with B; When C is less than B, it is judged as a person intrusion, and vice versa, it is judged as a vehicle invasion To put it simply, it is divided into three cases by A and B. One is that there is no invasion, the other is the invasion of personnel, and the third is the invasion of vehicles. When the quantized amplitude of the perturbation data is smaller than the intrusion determination threshold, it indicates that the perturbation data may be introduced by a non-invasive disturbance source, such as animals living in the monitored area, and the characteristics of the perturbative data introduced by these animals are different from those of human beings. The quantized amplitude is smaller than the quantized amplitude of the intruder, indicating that the monitored area is not invaded; when the quantized amplitude of the perturbed data is larger than the intrusion determination threshold or equal to the intrusion determination threshold, the monitored area is invaded. Since the quantization amplitude corresponding to the personnel invasion is smaller than the quantization amplitude corresponding to the vehicle intrusion, it is determined by analysis that the intrusion type determination threshold can be used as the criterion for the intrusion type determination, and the determination process is the same as above.

Correspondingly, the process of determining whether the monitored area is invaded and determining the corresponding intrusion event type is as shown in FIG. 6. FIG. 6 is a schematic flowchart of determining whether the monitored area is intruded and determining the corresponding intrusion event type according to the present invention. The intrusion data feature includes an intrusion determination threshold and an intrusion type determination threshold, and the S3 specifically includes:

S301. Acquire an intrusion determination threshold and an intrusion type determination threshold, where the intrusion determination threshold is used to determine whether the monitored area is intruded, and the intrusion type determination threshold is used to determine an intrusion event type, where the intrusion event type includes a personnel intrusion and a vehicle. Intrusion

S302. Perform pre-processing on the perturbation data to remove noise in the perturbation data;

S303. Quantizing the pre-processed perturbation data to obtain a quantized amplitude of the perturbed data;

S304. Comparing the quantized amplitude of the perturbation data with the intrusion determination threshold, determining that the monitored area is safe when the quantized amplitude is smaller than the intrusion determination threshold, and determining that the monitored area is invaded when the quantized amplitude is greater than or equal to the intrusion determination threshold;

S305. When it is determined that the monitored area is invaded, comparing the quantized amplitude of the perturbation data with the intrusion type determination threshold, when the quantization amplitude is smaller than the intrusion type determination threshold, it is determined as a human invasion, and when the quantization amplitude is greater than or equal to the intrusion type determination threshold, determining Invade for vehicles.

Through the analysis and comparison of the intrusion decision threshold, the intrusion type decision threshold and the quantization amplitude, the security monitoring of the monitored area is realized. The traditional monitoring system and method mainly rely on front-end equipment such as video capture and infrared-sensing alarm. Unlike the traditional monitoring system and method monitoring method, the present invention passes the monitoring center 1 comparison ratio intrusion determination threshold after the front-end data acquisition is completed. The intrusion type decision threshold and the quantization amplitude are used to determine whether the monitored area is intruded and the type of intrusion. Therefore, the process of implementing security monitoring according to the present invention is mainly a process of big data processing. The massive perturbation data collected by the front end is used as the basis of big data processing, and combined with the big data processing method, the human being can be liberated to the greatest extent in the security field, and Compared with traditional monitoring systems and methods, the effect is better and the accuracy is higher.

In order to analyze the intruder's intrusion information, the intrusion information analysis module 13 includes:

a marking unit, configured to mark the vibration sensing node and the corresponding time stamp collected to the perturbative data;

A patterning unit for marking a seismic sensing node that acquires perturbative data on an electronic map Relative position

a sorting unit for sorting timestamps;

The quantity analysis unit is configured to count the number of the vibration sensing nodes that collect the perturbative data at the same time stamp, and estimate the number of the intruders according to the number of the vibration sensing nodes that collect the perturbative data in the same time stamp;

The dynamic tracking unit is configured to integrate the time stamp of the perturbation data and the relative position of the vibration sensing node on the electronic map, and analyze the movement track and the moving speed of the intruder according to the time stamp and the relative position.

Intrusion information includes the number of intruders, moving trajectories, and moving speed. Determining intrusion information is a critical step in the security monitoring process, and the results determine what countermeasures to take to address the intrusion to ensure the security of the monitored area. In the present invention, the vibration sensing node that collects the perturbation data and the time stamp of the acquired perturbation data are first marked by the marking unit; then the vibration of the collected perturbative data is collected by the graphical unit using dynamic topology discovery technology. The relative position of the sensing nodes is marked on the electronic map for visual intelligent management. The operator can visually see where the abnormal signals are collected through the electronic map, that is, where the objects are being invaded. At the same time, the timestamps are sorted by the sorting unit, and the number of the seismic sensor nodes that collect the perturbative data at the same timestamp is counted by the quantity analysis unit to determine the number of intruders; finally, the most important process in analyzing the intrusion information is In one step, the dynamic tracking unit integrates the time stamp of the perturbation data and the relative position of the vibration sensing node on the electronic map, and analyzes the intruder's moving trajectory and moving speed according to the time stamp and the relative position.

Correspondingly, as shown in FIG. 7, FIG. 7 is a schematic flowchart of analyzing intruder intrusion information according to the present invention. In the S4, analyzing the intruder's intrusion information according to the perturbation data specifically includes:

S401. The vibration sensing node in the plurality of monitoring sensing strips 3 collects the perturbation data, and marks the vibration sensing node and the corresponding time stamp collected to the perturbative data;

S402. Mark the relative position of the vibration sensing node that collects the perturbation data on the electronic map;

S403. Sort the timestamps;

S404. Counting the number of the vibration sensing nodes that collect the perturbation data at the same time stamp, and determining the number of the intruders according to the number of the vibration sensing nodes that collect the perturbative data in the same time stamp;

S405. Integrating the timestamp of the perturbation data with the relative position of the vibration sensing node on the electronic map, and analyzing the moving trajectory and moving speed of the intruder according to the timestamp and the relative position.

The vibration sensing nodes in the plurality of monitoring sensing strips 3 collect the perturbative data in the form of a sensing network. When the perturbative data is collected, the corresponding vibration sensing nodes and time stamps are marked; further, the electronic map display is used to collect The relative position of the vibration sensing node to the perturbation data. At this time, the electronic map displays the point at which the abnormal signal is collected in real time, that is, where is being invaded; further, the time stamp is sorted and counted in the same time stamp. The number of vibration sensing nodes that collect the perturbation data determines the number of intruders based on the number of vibration sensing nodes that collect the perturbative data in the same time stamp; finally, by integrating the perturbation data, displaying on the electronic map Out of the intruder’s trajectory and Moving speed.

In the above S404, the monitoring sensing strip 3 is parallel to the boundary of the monitored area, and the sensing network uniformly collects the perturbation signal of the monitored area. Therefore, the number of the vibration sensing nodes that collect the perturbative data in the same time stamp is The number of intruders. For example, within the same timestamp, five seismic sensing nodes in the sensing network collect perturbation data, indicating that there are five intruders in the monitored area.

Through the above-mentioned intrusion information analysis module 13 and the specific steps of analyzing the intrusion information, the massive perturbation data is integrated into information that can be intuitively recognized by the operator, which is an important step in the field of Internet of Things and big data processing. The operator does not need to participate in the process of data processing, and only needs to receive the intruder's intrusion information through the human-computer interaction interface, further liberating manpower.

To determine a processing scheme, the solution determination module 14 includes:

a parameter preset unit, configured to preset a threat radius, a warning level, and a processing scheme corresponding to the warning level;

A threat area defining unit is configured to take the invaded monitored area as an origin, divide the threatened area according to the threat radius, and determine a key protection target;

a tracking lock unit for locking the intruder's whereabouts by continuously collecting and storing the intruder's image and/or video data;

The early warning and grading unit is configured to determine an early warning level according to the type of the intrusion event and the intrusion information, and start a corresponding processing scheme according to the early warning level.

The system determines the processing scheme by the cooperation of the parameter preset unit, the threat area defining unit, the track locking unit and the early warning grading unit. Firstly, the parameter of the threat radius, the warning level and the processing scheme corresponding to the warning level are preset by the parameter preset unit; then the threat area is divided by the threat area defining unit and the key protection target is determined; then, the tracking unit is continuously collected. And storing the intruder's image and/or video data to lock the intruder's whereabouts; finally, the early warning unit determines the alert level according to the intrusion event type and the intrusion information, and starts the corresponding processing scheme according to the alert level.

Correspondingly, as shown in FIG. 8, FIG. 8 is a schematic flowchart of determining a processing scheme according to the present invention. In the S5, determining the processing scheme according to the type of the intrusion event and the intrusion information specifically includes:

S501. Predetermined criteria for threat radius, early warning level, and processing scheme corresponding to the warning level;

S502. Taking the invaded monitored area as the origin, dividing the threatened area according to the threat radius, and determining the key protection target;

S503. Continuously collecting and storing the image and/or video data of the intruder, and locking the whereabouts of the intruder;

S504. Determine an early warning level according to the type of the intrusion event and the intrusion information, and start a corresponding processing plan according to the early warning level;

S505. Issue instructions according to the treatment plan to arm the key protection targets and output the invaders' whereabouts.

The threat radius is set in advance in the parameter preset unit, and the judgment standard of the preset warning level and the processing scheme corresponding to the warning level are used, and the threat radius is used to determine whether an area is in the threatened area by combining the invaded points, and the warning is The level corresponds to the processing scheme; the key protection target is determined by the threat area defining unit, and the key protection target is preferentially protected in the process of security monitoring; the intruder's whereabouts are locked by the tracking unit, and the specific location of the intruder is grasped in real time, and more Conducive to security protection; through the early warning and rating unit to determine the early warning level according to the type of intrusion event and intrusion information, and start the corresponding treatment plan according to the early warning level; finally, the operator implements the defense against the key protection target according to the treatment plan, and according to the intruder's whereabouts Alert the intruder. In addition, the storage of images and / or video data facilitates investigation and forensics after the completion of security monitoring.

The process of determining the processing scheme is still implemented by the monitoring center 1. The operator only needs to pass the indication of the monitoring center 1 and attack the specific location through a specific processing scheme, so that a wide range of security monitoring can be realized efficiently.

In S503, the method for locking the intruder's whereabouts includes:

Set up multiple fixed monitoring devices in the monitored area;

When the monitored area is invaded, record the location where the perturbation data is collected, and analyze the intruder's movement trajectory and moving speed;

The acquisition of the perturbation data location fixed monitoring device is sequentially enabled, and the fixed monitoring device collects images and/or video data of the intruder.

In addition, according to the intruder's movement trajectory and moving speed, the cruising route of the drone can be set, and the intruder's image and/or video data can be collected by the drone to lock the intruder's whereabouts.

Correspondingly, in a wide range of perimeter security monitoring systems, fixed monitoring devices and/or drones are also included. When the monitored area is invaded, the monitoring center 1 records the location where the perturbation data is collected and analyzes the intruder's moving trajectory and moving speed, and then sequentially enables the acquisition of the perturbative data location fixed monitoring device, and the fixed monitoring device collects the intrusion. Image and/or video data; or, the monitoring center 1 sets the cruise route of the drone according to the intruder's movement trajectory and moving speed, and collects the intruder's image and/or video data through the drone to lock the intruder. Whereabouts.

The neural network algorithm is used to coordinate and direct all fixed monitoring devices. When a fixed monitoring device records the image and/or video data of the intruder, the monitoring center 1 transmits signals to other fixed monitoring devices in the vicinity, and adjusts to enter the working state. . By enabling specific fixed monitoring equipment or setting the cruise route of the drone for image and/or video data collection, it is possible to lock the intruder's whereabouts, grasp the intruder's movements in real time, and improve the efficiency of the police.

The reader should understand that in the description of the present specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" and the like means that the embodiment or example is incorporated. The specific features, structures, materials, or characteristics described are included in at least one embodiment or example of the invention. In the present specification, the schematic representation of the above terms is not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be Any one or more of the embodiments or examples are combined in a suitable manner. In addition, various embodiments or examples described in the specification and features of various embodiments or examples may be combined and combined without departing from the scope of the invention.

A person skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working process of the device and the unit described above can refer to the corresponding process in the foregoing method embodiment, and details are not described herein again.

In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the device embodiments described above are merely illustrative. For example, the division of cells is only a logical function division. In actual implementation, there may be another division manner. For example, multiple units or components may be combined or integrated. Go to another system, or some features can be ignored or not executed.

The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the embodiments of the present invention.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

An integrated unit, if implemented in the form of a software functional unit and sold or used as a standalone product, can be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention contributes in essence or to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium. A number of instructions are included to cause a computer device (which may be a personal computer, server, or network device, etc.) to perform all or part of the steps of the various embodiments of the present invention. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like. .

The above is only the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any equivalent modification or can be easily conceived by those skilled in the art within the technical scope of the present disclosure. Such modifications or substitutions are intended to be included within the scope of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

A large-scale perimeter security monitoring method, which is characterized in that:

S1. Creating an intrusion feature library, where the intrusion feature library includes an intrusion event type and an intrusion data feature;

S2. collecting perturbation data of the boundary of the monitored area;

S3. Quantizing the perturbation data to obtain a quantized amplitude, comparing the quantized amplitude and the intrusion data feature, determining whether the monitored area is intruded, and returning to S2 when the monitored area is not invaded, when Further determining the corresponding type of the intrusion event when the monitored area is invaded;

S4. analyzing the intruder's intrusion information according to the perturbative data, where the intrusion information includes an intruder number, a moving track, and a moving speed;

S5. Determine a processing scheme according to the type of the intrusion event and the intrusion information.
A large-scale perimeter security monitoring method according to claim 1, wherein in the S2, a plurality of mutually parallel monitoring sensing strips are arranged along the boundary of the monitored area, and the monitoring sensing strip is collected by the monitoring area. The perturbation data, each of the monitoring sensing strips is parallel to a boundary of the monitored area, and the monitoring sensing strip includes a plurality of vibration sensing nodes;

During the process of arranging the monitoring sensing strip along the boundary of the monitored area, the monitoring sensing strip is responsively calibrated, and the response calibration specifically includes:

S201. arranging the calibration test strip next to the monitoring sensing strip, the calibration test strip being parallel to the monitoring sensing strip;

S202. Setting a manual disturbance point, controlling the manual disturbance point to move along the calibration test zone and generating a vibration signal, and each of the monitoring induction bands collects the perturbation data generated by the vibration signal;

S203. Convert the perturbation data into response output data.

S204. Comparing the response output data corresponding to the manual disturbance point in different positions, and obtaining a calibration adjustment parameter;

S205. Perform soft calibration on the monitoring sensing strip according to the calibration adjustment parameter.
The method for monitoring a large-scale perimeter security according to claim 1, wherein the intrusion data feature comprises an intrusion determination threshold and an intrusion type determination threshold, and the intrusion event type includes a personnel intrusion and a vehicle intrusion, and the S3 The process of determining whether the monitored area is invaded and determining the corresponding type of the intrusion event specifically includes:

S301. Acquire the intrusion determination threshold and the intrusion type determination threshold.

S302. Perform pre-processing on the perturbation data to remove noise in the perturbation data;

S303. Quantizing the pre-processed the perturbation data to obtain a quantized amplitude of the perturbed data;

S304. Comparing the quantized amplitude of the perturbation data with the intrusion determination threshold, determining that the monitored area is safe when the quantized amplitude is less than the intrusion determination threshold, when the quantized amplitude is greater than or equal to the Determining that the monitored area is invaded when the threshold is determined by the intrusion;

S305. When it is determined that the monitored area is invaded, comparing the quantized amplitude of the perturbation data with the intrusion type determination threshold, when the quantized amplitude is less than the intrusion type determination threshold It is determined that the person intrusion is determined to be a vehicle intrusion when the quantization amplitude is greater than or equal to the intrusion type determination threshold.
The method for monitoring a large-scale perimeter security according to claim 2, wherein the analyzing the intrusion information of the intruder according to the perturbation data in the S4 includes:

S401. The vibration sensing node in the plurality of monitoring sensing bands collects the perturbation data, and marks the vibration sensing node and the corresponding time stamp collected to the perturbation data;

S402. Mark, on an electronic map, a relative position of the vibration sensing node that collects the perturbation data;

S403. Sort the timestamps;

S404. Count the number of the vibration sensing nodes that collect the perturbation data in the same timestamp, and determine the number of intruders according to the number of the vibration sensing nodes;

S405. Integrate a timestamp of the perturbation data and a relative position of the vibration sensing node on the electronic map, and analyze an intruder's moving trajectory and moving speed according to the timestamp and the relative position.
The method for monitoring a large-scale perimeter security according to any one of claims 1 to 4, wherein the S5 specifically includes:

S501. Predetermined criteria for threat radius, early warning level, and processing scheme corresponding to the warning level;

S502. Taking the invaded monitored area as an origin, dividing the threatened area according to the threatened radius, and determining a key protection target;

S503. Continuously collecting and storing the image and/or video data of the intruder, and locking the whereabouts of the intruder;

S504. Determine the alert level according to the type of the intrusion event and the intrusion information, and start a corresponding processing solution according to the alert level;

S505. Issue an instruction according to the processing scheme to arm the key protection target, and output the intruder's whereabouts.
A wide range of perimeter security monitoring system, characterized in that it comprises:

Monitoring the sensing strip for collecting perturbation data of the boundary of the monitored area, each of the monitoring sensing strips being parallel to a boundary of the monitored area, the monitoring sensing strip comprising a plurality of vibration sensing nodes;

Monitoring center for data processing and control, the monitoring center includes:

An intrusion feature library creation module, configured to create an intrusion feature library, where the intrusion feature library includes an intrusion event type and an intrusion data feature;

a quantization analysis module, configured to quantize the perturbation data to obtain a quantized amplitude, compare the quantized amplitude and the intrusion data feature, determine whether the monitored area is invaded, and further determine a corresponding when the monitored area is invaded Type of the intrusion event;

An intrusion information analysis module, configured to analyze intruder intrusion information according to the perturbative data, where the intrusion information includes an intruder number, a moving track, and a moving speed;

And a solution determining module, configured to determine a processing solution according to the type of the intrusion event and the intrusion information.
A large-scale perimeter security monitoring system according to claim 6, comprising:

Monitoring the sensing strip, the monitoring sensing strip is disposed in plurality, the monitoring sensing strip is disposed along a boundary of the monitored area, and the monitoring sensing strips are parallel to each other;

Calibrating a test strip disposed adjacent to the monitor strip, the calibration strip being parallel to the monitor strip;

a manual disturbance point for moving along the calibration test strip and generating a vibration signal, each of the monitoring sensing strips collecting the perturbation data;

The monitoring center includes:

a response output module, configured to convert the perturbation data into response output data;

a calibration parameter module, configured to compare the response output data corresponding to the manual disturbance point in different positions, and obtain a calibration adjustment parameter;

a calibration module, configured to perform soft calibration on the monitoring sensing band according to the calibration adjustment parameter, so that the monitoring sensing band outputs a stable response output data to the vibration signal, and the soft calibration process is The calibration adjustment parameter performs calibration conversion on the perturbation data.
A large-scale perimeter security monitoring system according to claim 6, wherein the intrusion data feature comprises an intrusion decision threshold and an intrusion type decision threshold, the intrusion event type including a person intrusion and a vehicle intrusion, the quantification The analysis module includes:

a threshold obtaining unit, configured to acquire the intrusion determination threshold and the intrusion type determination threshold;

a pre-processing unit, configured to pre-process the perturbation data to remove noise in the perturbation data;

a quantization unit, configured to quantize the pre-processed perturbation data, to obtain a quantized amplitude of the perturbed data;

An intrusion determination unit, configured to compare the quantized amplitude of the perturbation data with the intrusion determination threshold, and determine that the monitored area is safe when the quantized amplitude is smaller than the intrusion determination threshold, when the quantized amplitude is greater than Or determining that the monitored area is invaded when the intrusion determination threshold is equal to;

a type determining unit, configured to compare the quantized amplitude of the perturbation data with the intrusion type determination threshold after determining that the monitored area is invaded, and determine when the quantized amplitude is smaller than the intrusion type determination threshold For personnel intrusion, it is determined that the vehicle invades when the quantized amplitude is greater than or equal to the intrusion type determination threshold.
A large-scale perimeter security monitoring system according to claim 7, wherein the intrusion information analysis module comprises:

a marking unit, configured to mark the vibration sensing node and corresponding corresponding to the perturbation data Timestamp

a patterning unit, configured to mark, on an electronic map, a relative position of the vibration sensing node that collects the perturbation data;

a sorting unit, configured to sort the timestamps;

a quantity analysis unit, configured to count the number of the vibration sensing nodes that collect the perturbation data at the same time stamp, and determine the number of intruders according to the number of the vibration sensing nodes;

a dynamic tracking unit, configured to integrate a timestamp of the perturbation data and a relative position of the vibration sensing node on the electronic map, and analyze the movement track of the intruder according to the timestamp and the relative position And moving speed.
A large-scale perimeter security monitoring system according to any one of claims 6-9, wherein the solution determining module comprises:

a parameter preset unit, configured to preset a threat radius, a warning level, and a processing scheme corresponding to the warning level;

a threat area defining unit, configured to use the invaded monitored area as an origin, divide the threatened area according to the threatened radius, and determine a key protection target;

a tracking lock unit for locking the intruder's whereabouts by continuously collecting and storing the intruder's image and/or video data;

The alarm grading unit is configured to determine an early warning level according to the type of the intrusion event and the intrusion information, and start a corresponding processing scheme according to the early warning level.