Self-adaptive very-low-frequency intelligent perimeter system

Abstract

The invention relates to the technical field of safety monitoring, in particular to a self-adaptive very-low-frequency intelligent perimeter system, which comprises a perimeter monitoring unit used for monitoring a perimeter area in real time; the analysis processing unit is used for carrying out fusion processing on the received data, extracting and analyzing signal features and judging whether the signals are abnormal or not; the wireless transmission unit is used for communicating the perimeter monitoring unit with the analysis processing unit to realize rapid transmission of data; the alarm unit is used for triggering an alarm mechanism to send an alarm signal; wherein the perimeter monitoring unit comprises a signal transmitting module, a signal receiving module, a sound detection module and an infrared sensing module. The combination of the modules provides a plurality of sensing means, the detection capability of the system on intrusion behaviors is enhanced, the adaptive gain algorithm can effectively suppress noise and interference and improve the anti-interference capability and stability of the system, and the infrared sensing module can eliminate nonlinear response and errors through calibration.

Description

Technical Field

[0001] The invention relates to the technical field of security monitoring, and in particular to an adaptive very low frequency intelligent perimeter system. Background Art

[0002] The adaptive very low frequency intelligent perimeter system is an advanced security system used to monitor and protect the boundaries of specific areas. It uses very low frequency electromagnetic wave technology and intelligent algorithms to detect and identify perimeter intrusions in real time and take corresponding measures in a timely manner. Traditional perimeter security systems may be limited by the limitations of technologies such as visible light cameras and infrared sensors, and are easily affected by factors such as natural climate and light. The adaptive very low frequency intelligent perimeter system uses electromagnetic field sensing technology to generate very low frequency electromagnetic fields to form a virtual space barrier and achieve 360-degree azimuth monitoring of the perimeter space. The adaptive very low frequency intelligent perimeter system can generate very low frequency electromagnetic fields to form a virtual space barrier by arranging excitation conductors and receiving conductors in specific areas, and realize the physical characteristics of monitoring the changes in very low frequency electromagnetic fields caused by human disturbances to detect 100% alarms for perimeter human intrusions, and non-biological non-response intelligence. In the prior art, due to the limitations of environmental factors and the physical characteristics of the system itself, and the interaction between the very low frequency electromagnetic field and other electromagnetic field signals and noise in the environment, the perimeter system may have problems such as missed reports and false reports due to interference from non-biological factors, bad weather, and human damage; and the existing infrared sensors have nonlinear responses and errors, which affect their sensitivity and lead to inaccurate collected signals. The present invention proposes an adaptive very low frequency intelligent perimeter system to solve the above problems. Summary of the invention

[0003] The main purpose of the present invention is to provide an adaptive very low frequency intelligent perimeter system to solve the problems raised in the related art.

[0004] In order to achieve the above object, according to one aspect of the present invention, an adaptive very low frequency intelligent perimeter system is provided, comprising:

[0005] Perimeter monitoring unit: used to monitor the perimeter area in real time and transmit data to the analysis and processing unit;

[0006] Analysis and processing unit: used to fuse the received data, extract and analyze signal features, and determine whether it is abnormal;

[0007] Wireless transmission unit: used to communicate between the perimeter monitoring unit and the analysis and processing unit to achieve rapid data transmission;

[0008] Alarm unit: used to trigger the alarm mechanism to send out an alarm signal;

[0009] Among them, the perimeter monitoring unit includes:

[0010] Signal transmission module: used to transmit very low frequency signals to form electromagnetic fields or electromagnetic waves covering the target area;

[0011] Signal receiving module: used to receive and enhance the signal returned from the perimeter;

[0012] Sound detection module: used to detect the sound in the perimeter area in real time;

[0013] Infrared sensor module: used to detect heat changes in the perimeter area and improve the quality of infrared images.

[0014] Furthermore, the signal receiving module enhances the signal returned from the perimeter based on an adaptive gain algorithm, which is used to adjust according to the dynamic range of the received signal to maintain the appropriate amplitude of the signal, and the expression is:

[0015] y(n)=G(n)*x(n)

[0016] Wherein, y(n) is the signal sample sequence after gain adjustment; x(n) is the received signal sample sequence; G(n) is the gain coefficient at each sample;

[0017] The gain factor is calculated based on the RMS energy and is expressed as:

[0018]

[0019] Where N is the number of samples for calculating energy, k = 0, 1, 2, ..., N-1, and the gain coefficient is:

[0020]

[0021] Where A(n) is the expected energy of the target signal;

[0022] Introducing the noise level into the gain coefficient, the expression is:

[0023]

[0024] Among them, E noise is the noise energy, z(k) is the known signal sample sequence, and the gain coefficient is:

[0025]

[0026] Furthermore, the infrared sensor module is first calibrated and calibrated to obtain the response characteristics of the infrared sensor module. The calculation steps of the calibration and calibrating are:

[0027] S11: Obtain reference temperature source;

[0028] S12: Measure the output voltage of the infrared sensor at the reference temperature;

[0029] S13: Measure the output voltage at other temperature points

[0030] S14: Calculate the calibration coefficient;

[0031] S15: Convert the output voltage of the infrared sensor into a temperature value to obtain the response characteristics of the infrared sensor module.

[0032] Furthermore, the expression for converting the output voltage of the infrared sensor into a temperature value is:

[0033]

[0034] Where, T is the temperature value; V is the voltage output by the infrared sensor module; V0 is the reference voltage; a and b are calibration coefficients.

[0035] Furthermore, the humidity calibration coefficient is introduced into the response characteristics, and the calculation steps are:

[0036] Given a set of humidity calibration data points (H1,C h1 ),(H2,C h2 ),…,(H n ,C hn ), where H i represents the humidity measurement value of the i-th group; C hi Indicates the corresponding humidity calibration coefficient;

[0037] According to the measured humidity value H, find the two closest calibration data points, recorded as (H i ,C hi ) and (H i+1 ,C hi+1 ), where H i ≤H≤H i+1 ;

[0038] The humidity calibration coefficient is calculated using linear interpolation and is given by:

[0039]

[0040] Among them, C h is the humidity calibration factor;

[0041] The expression for converting the output voltage of the infrared sensor into a temperature value is:

[0042]

[0043] V c =V*C h

[0044] Where, V is the voltage output by the infrared sensor module; V cIt is the voltage output after humidity calibration.

[0045] Furthermore, the infrared sensing module improves the quality of infrared images based on histogram equalization and contrast enhancement, and the expression is:

[0046]

[0047] Among them, I a (x, y) is the image pixel value after histogram equalization; I in (x, y) is the pixel value of the input image; CDF(·) is the cumulative distribution function; maxCDF and minCDF are the maximum cumulative distribution function value of the input image and the maximum cumulative distribution function value of the input image, respectively; maxG is the maximum pixel value of the output image;

[0048] I b (x,y)=mI a (x,y)+n

[0049] Among them, I b (x, y) is the pixel value of the image after contrast enhancement; m and n are adjustment parameters.

[0050] Furthermore, the analysis and processing unit includes a data processing module, a data fusion module, and an analysis and judgment module;

[0051] Among them, the data processing module is used to pre-process the collected data to ensure the quality of the data;

[0052] The data fusion module is used to fuse different data in the perimeter monitoring unit;

[0053] The analysis and judgment module is used to determine whether there is an abnormal event based on the results of data fusion.

[0054] Furthermore, the preprocessing in the data processing module includes data denoising, data filtering, data calibration and data time synchronization.

[0055] Furthermore, the wireless transmission unit switches channels according to channel capacity, transmission rate, and signal quality.

[0056] Furthermore, the alarm signal includes a voice alarm and a flash alarm.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] The perimeter monitoring unit in the present invention includes a signal transmission module, a signal receiving module, a sound detection module and an infrared sensor module. The combination of these modules can provide a variety of sensing means, thereby enhancing the system's ability to detect intrusion behavior. The adaptive gain algorithm in the signal receiving module can be adjusted according to the dynamic range of the received signal to maintain the appropriate amplitude of the signal. This can effectively suppress noise and interference and improve the anti-interference ability and stability of the system. The infrared sensor module can eliminate nonlinear responses and errors through calibration and calibration, thereby improving the accuracy of the system to temperature changes, and can also ensure the accurate correspondence between the voltage output by the infrared sensor module and the actual temperature. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 The figure is a system block diagram of the present invention as a whole.

[0060] Illustration Description:

[0061] 1. Perimeter monitoring unit;

[0062] 11. Signal transmitting module; 12. Signal receiving module; 13. Sound detection module; 14. Infrared sensor module;

[0063] 2. Analysis and processing unit;

[0064] 21. Data processing module; 22. Data fusion module; 23. Analysis and judgment module;

[0065] 3. Wireless transmission unit;

[0066] 4. Alarm unit. DETAILED DESCRIPTION

[0067] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation mode, structure, characteristics and effects of the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0068] Reference Figure 1 The present invention provides an adaptive very low frequency intelligent perimeter system, comprising:

[0069] Perimeter monitoring unit 1: used to monitor the perimeter area in real time and transmit data to the analysis and processing unit 2;

[0070] Analysis and processing unit 2: used to perform fusion processing on the received data, extract and analyze signal features, and determine whether it is abnormal;

[0071] Wireless transmission unit 3: used to communicate between the perimeter monitoring unit 1 and the analysis and processing unit 2 to achieve rapid data transmission;

[0072] Alarm unit 4: used to trigger the alarm mechanism to send out an alarm signal;

[0073] Wherein, the perimeter monitoring unit 1 includes:

[0074] Signal transmitting module 11: used to transmit very low frequency signals to form an electromagnetic field or electromagnetic waves covering the target area;

[0075] Signal receiving module 12: used to receive and enhance the signal returned from the perimeter;

[0076] Sound detection module 13: used for real-time detection of sounds in the perimeter area;

[0077] Infrared sensor module 14: used to detect heat changes in the peripheral area and improve the quality of infrared images.

[0078] The signal receiving module 12 enhances the signal returned from the perimeter based on an adaptive gain algorithm, which is used to adjust according to the dynamic range of the received signal to maintain the appropriate amplitude of the signal, and the expression is:

[0079] y(n)=G(n)*x(n)

[0080] Wherein, y(n) is the signal sample sequence after gain adjustment; x(n) is the received signal sample sequence; G(n) is the gain coefficient at each sample;

[0081] The gain factor is calculated based on the RMS energy and is expressed as:

[0082]

[0083] Where N is the number of samples for calculating energy, k = 0, 1, 2, ..., N-1, and the gain coefficient is:

[0084]

[0085] Where A(n) is the expected energy of the target signal;

[0086] Introducing the noise level into the gain coefficient, the expression is:

[0087]

[0088] Among them, E noise is the noise energy, z(k) is the known signal sample sequence, and the gain coefficient is:

[0089]

[0090] The infrared sensor module 14 is first calibrated and calibrated to obtain the response characteristics of the infrared sensor module 14. The calculation steps of the calibration and calibrating are as follows:

[0091] S11: Obtain reference temperature source;

[0092] S12: Measure the output voltage of the infrared sensor at the reference temperature;

[0093] S13: Measure the output voltage at other temperature points

[0094] S14: Calculate the calibration coefficient;

[0095] S15: Convert the output voltage of the infrared sensor into a temperature value to obtain the response characteristics of the infrared sensor module.

[0096] The expression for converting the output voltage of the infrared sensor into a temperature value is:

[0097]

[0098] Where, T is the temperature value; V is the voltage output by the infrared sensor module; V0 is the reference voltage; a and b are calibration coefficients.

[0099] Introducing the humidity calibration coefficient into the response characteristics, the calculation steps are:

[0100] Given a set of humidity calibration data points (H1,C h1 ),(H2,C h2 ),…,(H n ,C h n), where H i represents the humidity measurement value of the i-th group; C hi Indicates the corresponding humidity calibration coefficient;

[0101] According to the measured humidity value H, find the two closest calibration data points, recorded as (H i ,C hi ) and (H i+1 ,C hi+1 ), where H i ≤H≤H i+1 ;

[0102] The humidity calibration coefficient is calculated using linear interpolation and is given by:

[0103]

[0104] Among them, C h is the humidity calibration factor;

[0105] The expression for converting the output voltage of the infrared sensor into a temperature value is:

[0106]

[0107] V c=V*C h

[0108] Where, V is the voltage output by the infrared sensor module; V c It is the voltage output after humidity calibration.

[0109] The infrared sensor module 14 improves the quality of the infrared image based on histogram equalization and contrast enhancement, and the expression is:

[0110]

[0111] Among them, I a (x, y) is the image pixel value after histogram equalization; I in (x, y) is the pixel value of the input image; CDF(·) is the cumulative distribution function; maxCDF and minCDF are the maximum cumulative distribution function value of the input image and the maximum cumulative distribution function value of the input image, respectively; maxG is the maximum pixel value of the output image;

[0112] I b (x,y)=mI a (x,y)+n

[0113] Among them, I b (x, y) is the pixel value of the image after contrast enhancement; m and n are adjustment parameters.

[0114] The analysis and processing unit 2 includes a data processing module 21, a data fusion module 22, and an analysis and judgment module 23;

[0115] The data processing module 21 is used to pre-process the collected data to ensure the quality of the data;

[0116] The data fusion module 22 is used to fuse different data in the perimeter monitoring unit 1;

[0117] The analysis and judgment module 23 is used to judge whether there is an abnormal event according to the result of data fusion.

[0118] The preprocessing in the data processing module 21 includes data denoising, data filtering, data calibration and data time synchronization.

[0119] The wireless transmission unit 3 switches channels according to channel capacity, transmission rate and signal quality.

[0120] The alarm signals include voice alarm and flash alarm.

[0121] In this embodiment, the perimeter monitoring unit 1 includes a signal transmitting module 11, a signal receiving module 12, a sound detection module 13 and an infrared sensor module 14. The combination of these modules can provide a variety of sensing means and enhance the ability to detect human intrusion behavior. First, each module needs to collect data from the perimeter area. The transmitting module, the sound detection module, the infrared sensor module and the receiving module collect relevant signals, sounds and infrared data respectively. These data are transmitted to the analysis and processing unit 2 through the wireless transmission unit 3. The analysis and processing unit 2 identifies abnormal events through preprocessing, data fusion, analysis and judgment, thereby triggering the alarm mechanism in the alarm unit 4 to send an alarm signal. The specific method is as follows:

[0122] First, the signal transmitting module 11 transmits a very low frequency signal, usually in the range of several hundred hertz to several thousand hertz, to form an electromagnetic field or electromagnetic wave covering the target area; the signal receiving module 12 receives and enhances the signal returned from the perimeter in real time; wherein, the enhanced signal is based on an adaptive gain algorithm, which is a signal processing technology used to adjust according to the dynamic range of the received signal to maintain the appropriate amplitude of the signal, and the expression is:

[0123] y(n)=G(n)*x(n)

[0124] Wherein, y(n) is the signal sample sequence after gain adjustment; x(n) is the received signal sample sequence; G(n) is the gain coefficient at each sample;

[0125] The gain factor is calculated based on the RMS energy and is expressed as:

[0126]

[0127] Where N is the number of samples for calculating energy, k = 0, 1, 2, ..., N-1, and the gain coefficient is:

[0128]

[0129] Where A(n) is the expected energy of the target signal;

[0130] The noise level is introduced into the gain coefficient. Noise refers to interference signals that are unrelated to the target signal. A higher noise level may require a larger gain adjustment to strengthen the target signal. Therefore, the higher the noise level, the greater the impact on the gain coefficient. The calculation of the noise level can be achieved by estimating the noise energy in the signal. The expression is:

[0131]

[0132] The noise energy is calculated by taking the difference between the signal and the estimated noise when the signal is known; where E noiseis the noise energy, z(k) is the known signal sample sequence, and the gain coefficient is:

[0133]

[0134] Therefore, through the above formula, we can use the noise energy as an adjustment factor for the gain coefficient. When the noise energy is high, the gain coefficient will be reduced accordingly to reduce the impact of the noise; when the noise energy is low, the gain coefficient will be increased accordingly to enhance the signal.

[0135] It should be noted that the received signal sample sequence x[n] is the actual received signal, which may contain noise and other interference. In the adaptive gain algorithm, we hope to reduce the impact of noise and enhance the quality of the signal by adjusting the gain coefficient. Therefore, the received signal sample sequence x[n] is the object that needs to be adjusted. The known signal sample sequence z[n] is a pure signal known in advance and does not contain noise and other interference. In the adaptive gain algorithm, the known pure signal z[n] is used as a reference signal and compared with the received signal sample sequence x[n] to estimate the noise level and adjust the gain coefficient.

[0136] The sound detection module 13 is used to detect relatively obvious human voices. It detects and identifies the existence and characteristics of sounds by analyzing audio signals. A microphone or other audio equipment can be used to collect sound signals in the environment. These signals can be real-time recorded audio data or audio data read from a file. The collected audio signals are pre-processed to extract useful features and reduce the impact of noise. Finally, the sound is judged to determine whether it is the sound of human footsteps or walking.

[0137] The infrared sensor module 14 is calibrated and calibrated before detection to obtain the response characteristics of the infrared sensor module 14, which can eliminate nonlinear response and error, thereby improving the accuracy of the system to temperature changes. The calculation steps of calibration and calibration are:

[0138] S11: Obtain a reference temperature source; use a reference source with a known temperature, such as a black body radiation source, and place it within the field of view of the infrared sensor.

[0139] S12: Measure the output voltage V0 of the infrared sensor at the reference temperature;

[0140] S13: Measure the output voltage at other temperature points; at different temperatures, use a suitable temperature measuring device to measure the output voltage V of the infrared sensor;

[0141] S14: Calculate the calibration coefficient; use the measured output voltage V and the corresponding temperature value T to calculate the calibration coefficients a and b by fitting method;

[0142] S15: Convert the output voltage of the infrared sensor into a temperature value to obtain the response characteristics of the infrared sensor module.

[0143] The expression for converting the output voltage of the infrared sensor into a temperature value is:

[0144]

[0145] Where, T is the temperature value; V is the voltage output by the infrared sensor module; V0 is the reference voltage; a and b are calibration coefficients.

[0146] Humidity is one of the environmental factors that affect the response of infrared sensors. During the calibration and calibration process, it is necessary to consider the impact of humidity on the output of infrared sensors and introduce a humidity calibration coefficient. The calculation steps are:

[0147] Given a set of humidity calibration data points (H1,C h1 ),(H2,C h2 ),…,(H n ,C hn ), where H i represents the humidity measurement value of the i-th group; C hi Indicates the corresponding humidity calibration coefficient;

[0148] According to the measured humidity value H, find the two closest calibration data points, recorded as (H i ,C hi ) and (H i+1 ,C hi+1 ), where H i ≤H≤H i+1 ; Select a calibration point near the measured value H for difference calculation;

[0149] The humidity calibration coefficient is calculated using linear interpolation, and the expression is:

[0150]

[0151] Among them, C h is the humidity calibration factor;

[0152] The numerator part of the interpolation formula (HH i )*(C hi+1 -C hi ) represents the distance difference between the measured value H and the two nearest calibration points. The denominator H i+1 -H i Represents the humidity difference between the two nearest calibration points. The corresponding humidity correction coefficient C can be calculated by multiplying the ratio of the distance difference to the humidity difference by the correction coefficient difference between the correction data points. hLinear interpolation is based on the straight line relationship between two known points, and an estimate of the correction factor is calculated proportionally to the position of the measured value between the two known points.

[0153] The expression for converting the output voltage of the infrared sensor into a temperature value is:

[0154]

[0155] V c =V*C h

[0156] Where, V is the voltage output by the infrared sensor module; V c It is the voltage output after humidity calibration.

[0157] The sensitivity of the infrared sensor is increased through the above calibration and calibration, ensuring that the output value of the infrared sensor module 14 is consistent with the actual environmental conditions, and improving the accuracy of the measurement. Further, in order to improve the image quality collected by the infrared sensor, histogram equalization and contrast enhancement are used. Through histogram equalization, the brightness distribution of the image is more balanced, the details are clearer, and the contrast of the image is enhanced. Through contrast enhancement, the brightness and contrast of the image are further adjusted to make the image clearer and more vivid. The expression is:

[0158]

[0159] Among them, I a (x, y) is the image pixel value after histogram equalization; I in (x, y) is the pixel value of the input image; CDF(·) is the cumulative distribution function; maxCDF and minCDF are the maximum cumulative distribution function value of the input image and the maximum cumulative distribution function value of the input image respectively; maxG is the maximum pixel value of the output image; the cumulative distribution function is a function that describes the probability distribution of a random variable. In histogram equalization, the cumulative distribution function is used to calculate the cumulative probability of each pixel value in the input image, thereby achieving pixel value mapping and equalization;

[0160] Then

[0161] I b (x,y)=mI a (x,y)+n

[0162] Among them, I b (x, y) is the pixel value of the image after contrast enhancement; m and n are adjustment parameters.

[0163] By first performing histogram equalization and then contrast enhancement, the contrast and detail information of the image can be improved at the same time. It is necessary to select appropriate parameters m and n according to specific applications and requirements to achieve the best enhancement effect.

[0164] In order to ensure that the data of the perimeter monitoring unit 1 can be quickly transmitted to the analysis and processing unit 2, the system is provided with a wireless transmission unit 3, which will switch channels according to the channel capacity, transmission rate, and signal quality. In wireless communication, selecting a suitable transmission channel is essential to ensure reliable data transmission and optimize performance. Among them, the channel capacity refers to the maximum amount of data that can be transmitted per unit time. A wider channel bandwidth and a higher signal power to noise power ratio will increase the channel capacity; the transmission rate refers to the amount of data transmitted per unit time. A higher channel capacity and more modulation methods will increase the transmission rate; the signal quality includes signal power and signal-to-noise ratio. The signal power refers to the power of the transmitted signal. A higher signal power can provide a longer transmission distance and better signal quality; the signal-to-noise ratio refers to the ratio between the signal power and the noise power. A higher signal-to-noise ratio indicates better signal quality.

[0165] In the analysis and processing unit 2, the data processing module 21 first performs secondary denoising, data filtering, and data calibration on the transmitted data. Through secondary denoising, random noise, interference or outliers in the data can be reduced or eliminated, making the data cleaner and more reliable; through data filtering, fluctuations in the data can be smoothed, high-frequency noise or interference can be removed, making the data more stable and more readable. Through data calibration, deviations or errors in the data collection or transmission process can be eliminated, making the data more consistent and comparable with the actual situation; the data processing module 21 also includes data time synchronization, ensuring that the data collected by multiple data sources or devices have the same timestamp, so that the data are consistent in time for subsequent data fusion and analysis.

[0166] Then, the data fusion module 22 fuses the processed different data; this is achieved through methods such as time alignment and space alignment. Time alignment refers to the synchronization of the data collected by different modules in time so that they can be compared and analyzed at the same time point. Spatial alignment refers to the spatial correspondence of the data collected by different modules so that the data of different modules can be compared and analyzed at the same position. The aligned data is fused using a model, and then feature extraction is performed on the fused data. The signal receiving module 12 can extract the amplitude of signal fluctuations; the sound detection module 13 can extract the spectral characteristics and time domain characteristics of the sound; the infrared sensor module 14 can extract features such as the amplitude and distribution of heat changes.

[0167] Finally, the analysis and judgment module 23 judges whether there is an abnormal event based on the characteristic results after data fusion. The judgment rule is that as long as there is an abnormal data in the signal receiving module 12, the sound detection module 13 and the infrared sensor module 14, it is judged as an abnormal event. If there is an abnormal event, the alarm unit 4 will execute a voice alarm and a flash alarm.

[0168] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. An adaptive very low frequency intelligent perimeter system, characterized in that: include: Perimeter monitoring unit (1): used for real-time monitoring of the perimeter area and transmitting data to the analysis and processing unit (2); Analysis and processing unit (2): used to perform fusion processing on the received data, extract and analyze signal features, and determine whether there is an abnormality; A wireless transmission unit (3) is used to enable the perimeter monitoring unit (1) to communicate with the analysis and processing unit (2) to achieve rapid data transmission; Alarm unit (4): used to trigger the alarm mechanism to send out an alarm signal; The perimeter monitoring unit (1) comprises: Signal transmitting module (11): used for transmitting very low frequency signals to form an electromagnetic field or electromagnetic waves covering the target area; Signal receiving module (12): used to receive and enhance the signal returned from the perimeter; Sound detection module (13): used for real-time detection of sounds in the perimeter area; Infrared sensor module (14): used to detect heat changes in the peripheral area and improve the quality of infrared images.

2. The adaptive very low frequency intelligent perimeter system according to claim 1, characterized in that: The signal receiving module (12) enhances the signal returned from the perimeter based on an adaptive gain algorithm, which is used to adjust according to the dynamic range of the received signal to maintain the appropriate amplitude of the signal, and the expression is: y(n)=G(n)*x(n) Wherein, y(n) is the signal sample sequence after gain adjustment; x(n) is the received signal sample sequence; G(n) is the gain coefficient at each sample; The gain factor is calculated based on the RMS energy and is expressed as: Where N is the number of samples for calculating energy, k = 0, 1, 2, ..., N-1, and the gain coefficient is: Where A(n) is the expected energy of the target signal; Introducing the noise level into the gain coefficient, the expression is: Among them, E noise is the noise energy, z(k) is the known signal sample sequence, and the gain coefficient is:

3. The adaptive very low frequency intelligent perimeter system according to claim 1, characterized in that: The infrared sensor module (14) is first calibrated and calibrated to obtain the response characteristics of the infrared sensor module (14). The calculation steps of the calibration and calibrating are as follows: S11: Obtain reference temperature source; S12: Measure the output voltage of the infrared sensor at the reference temperature; S13: Measure the output voltage at other temperature points; S14: Calculate the calibration coefficient; S15: Convert the output voltage of the infrared sensor into a temperature value to obtain the response characteristics of the infrared sensor module.

4. The adaptive very low frequency intelligent perimeter system according to claim 3, characterized in that: The expression for converting the output voltage of the infrared sensor into a temperature value is: Where, T is the temperature value; V is the voltage output by the infrared sensor module; V0 is the reference voltage; a and b are calibration coefficients.

5. The adaptive very low frequency intelligent perimeter system according to claim 4, characterized in that: Introducing the humidity calibration coefficient into the response characteristics, the calculation steps are: Given a set of humidity calibration data points (H1, C h1 ), (H2, C h2 ),...,(H n , C hn ), where H i represents the humidity measurement value of the i-th group; C hi Indicates the corresponding humidity calibration coefficient; According to the measured humidity value H, find the two closest calibration data points, recorded as (H i , C hi ) and (H i+1 , C hi+1 ), where H i ≤H≤H i+1 ; The humidity calibration coefficient is calculated using linear interpolation, and the expression is: Among them, C h is the humidity calibration factor; The expression for converting the output voltage of the infrared sensor into a temperature value is: In c =V*C h Where, V is the voltage output by the infrared sensor module; V c It is the voltage output after humidity calibration.

6. The adaptive very low frequency intelligent perimeter system according to claim 1, characterized in that: The infrared sensor module (14) improves the quality of the infrared image based on histogram equalization and contrast enhancement, and the expression is: Among them, I a (x, y) is the image pixel value after histogram equalization; in (x, y) is the pixel value of the input image; CDF(·) is the cumulative distribution function; maxCDF and minCDF are the maximum cumulative distribution function value of the input image and the maximum cumulative distribution function value of the input image, respectively; maxG is the maximum pixel value of the output image; Yo b (x, y)=mI a (x, y)+n Among them, I b (x, y) is the pixel value of the image after contrast enhancement; m and n are adjustment parameters.

7. The adaptive very low frequency intelligent perimeter system according to claim 1, characterized in that: The analysis and processing unit (2) comprises a data processing module (21), a data fusion module (22), and an analysis and judgment module (23); The data processing module (21) is used to pre-process the collected data to ensure the quality of the data; The data fusion module (22) is used to fuse different data in the perimeter monitoring unit (1); The analysis and judgment module (23) is used to judge whether there is an abnormal event based on the result of data fusion.

8. The adaptive very low frequency intelligent perimeter system according to claim 1, characterized in that: The preprocessing in the data processing module (21) includes data denoising, data filtering, data calibration and data time synchronization.

9. The adaptive very low frequency intelligent perimeter system according to claim 1, characterized in that: The wireless transmission unit (3) switches channels according to channel capacity, transmission rate and signal quality.

10. The adaptive very low frequency intelligent perimeter system according to claim 1, characterized in that: The alarm signals include voice alarm and flash alarm.

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