Vibration photoelectric fusion type unattended perimeter alarm system

Abstract

The invention relates to a vibration photoelectric fusion type unattended perimeter alarm system, belongs to the technical field of detection alarm, and solves the problems that an existing video monitoring system lacks a real-time early warning function and cannot achieve full coverage. The system comprises at least one unattended perimeter alarm device which comprises a photoelectric control module, an image acquisition device, a communication antenna, a GPS antenna, a signal processing module, a power supply module, a sound alarm module, a flashing light alarm module, a GPS positioning module, an Internet of Things communication module and a vibration sensor. The vibration sensor and the image acquisition device respectively output vibration data and image data to the signal processing module, and the signal processing module determines a final intrusion early warning result by adopting a vibration photoelectric fusion strategy and respectively sends a control command to the sound alarm module and the flashing alarm module according to the intrusion early warning result. And uploading the intrusion early warning result, the image and the position information to a cloud server. The system integrates multiple functions, is suitable for a complex environment, and is high in early warning accuracy.

Description

Technical Field

[0001] The present invention relates to the field of detection alarm technology, and in particular to a vibration photoelectric fusion type unmanned perimeter alarm system. Background Art

[0002] At present, the perimeter security system mainly relies on the video surveillance system. Although the video surveillance system has played its due role, it has also exposed some problems and shortcomings, such as the lack of more professional on-duty personnel, no one to check the video surveillance system regularly, and the system cannot play its maximum use efficiency; the amount of video data is huge, and the burden of manual review is heavy. At the same time, with the continuous increase in the number of video surveillance, each level needs to store and review massive video surveillance data, which is a huge workload. It is impossible to check and review it in the first time by manpower alone. The system application efficiency is not high, and it lacks real-time warning function. The existing video surveillance system cannot effectively realize real-time warning of suspicious targets in key areas. It can only trace back clues through the system after the case (incident) occurs, which cannot meet the actual work needs. The video surveillance system cannot achieve full coverage. Affected by natural conditions, some areas do not have infrastructure such as electricity and network. It is difficult and costly to build a video surveillance system, and there are blind spots and dead ends in management and control.

[0003] In summary, the existing video surveillance system cannot meet the requirements of perimeter protection. The development of low-power intelligent perimeter warning technology is of great significance to promote the practical application of perimeter security system, and provides scientific and technological means to establish a digital and modern perimeter security system, ensure perimeter security, prevent illegal intrusion, and ensure perimeter stability. Summary of the invention

[0004] In order to solve the problems of lack of real-time warning function and inability to achieve full coverage in existing video surveillance systems, the present invention provides a vibration-photoelectric fusion unmanned perimeter alarm system, which can collect and analyze vibration signals generated by moving targets, and reduce the false alarm rate by cooperating with photoelectric information. It has the advantages of diversified communication means and the ability to achieve all-weather prevention and control.

[0005] In order to solve the above problems, the present invention adopts the following technical solutions:

[0006] A vibration photoelectric fusion type unmanned perimeter alarm system, comprising at least one unmanned perimeter alarm device, wherein the unmanned perimeter alarm device comprises a multi-sensor load unit, a main control communication processing unit and a signal acquisition unit, wherein the multi-sensor load unit comprises a photoelectric control module, an image acquisition device, a communication antenna and a GPS antenna, the main control communication processing unit comprises a signal processing module, a power module, a sound alarm module, a flashing light alarm module, a GPS positioning module and an Internet of Things communication module, and the signal acquisition unit comprises a vibration sensor;

[0007] The vibration sensor senses vibration in real time. When the vibration sensor detects a target, it outputs vibration data to the signal processing module. The photoelectric control module is synchronously triggered and controls the image acquisition device to start. The image acquisition device transmits the acquired image data to the signal processing module. The signal processing module uses a vibration photoelectric fusion strategy to determine the final intrusion warning result, and sends control commands to the sound alarm module and the flashing light alarm module according to the intrusion warning result, so that the sound alarm module emits an alarm sound and the flashing light alarm module emits light. At the same time, the signal processing module also uploads the intrusion warning results and the captured images to the cloud server through the Internet of Things communication module and the communication antenna. The GPS positioning module synchronously uploads the current location information to the cloud server through the GPS antenna. The vibration photoelectric fusion strategy refers to the use of a multimodal information fusion method to combine the dual-dimensional information of vibration and image, and uses a lightweight edge-end neural network to extract features with high discrimination to form a high-dimensional space vector to identify and classify targets.

[0008] Compared with the prior art, the present invention has the following beneficial effects:

[0009] (1) The vibration photoelectric fusion unmanned perimeter alarm system of the present invention adopts a multifunctional design, integrating detection, evidence collection (photography), and warning (warning light + sound) into one;

[0010] (2) The hardware components of the present invention have good environmental adaptability and can work in humid, high temperature / low temperature outdoor environments in addition to indoor and outdoor environments;

[0011] (3) The hardware components of the present invention have a long battery life. By using passive seismic detection technology, the longest battery life of a single deployment can reach 6 months;

[0012] (4) The present invention adopts a unique multi-source sensor fusion technology, combining seismic, infrared detection, target recognition and other sensor technologies, effectively reducing the false alarm rate and improving the accuracy of the alarm;

[0013] (5) The present invention has the function of Internet of Things and can realize the push of alarm information;

[0014] (6) The present invention has an automatic positioning function, supports GPS and Beidou multi-positioning, and can automatically report device location information. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0016] Figure 1 This is the hardware principle architecture diagram of a single unattended perimeter alarm device;

[0017] Figure 2 This is a networking diagram of an unattended perimeter alarm device;

[0018] Figure 3 This is the schematic diagram of the ground multiple moving target recognition model.

[0019] Description of reference numerals:

[0020] 10. Multi-sensor load unit; 11. Photoelectric control module; 12. Image acquisition device; 13. Communication antenna; 14. GPS antenna;

[0021] 20. Main control communication processing unit; 21. Signal processing module; 22. Power supply module; 23. Sound alarm module; 24. Flashing light alarm module; 25. GPS positioning module; 26. Control button module; 27. Internet of Things communication module;

[0022] 30. Signal acquisition unit; 31. Vibration sensor. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0024] See also Figure 1-Figure 3 This embodiment provides a vibration photoelectric fusion type unmanned perimeter alarm system, which includes at least one unmanned perimeter alarm device, such as Figure 1 As shown, the unmanned perimeter alarm device includes a multi-sensor load unit 10, a main control communication processing unit 20 and a signal acquisition unit 30, wherein the multi-sensor load unit 10 includes a photoelectric control module 11 and an image acquisition device 12 connected to the photoelectric control module 11, the image acquisition device 12 can be a high-definition camera or a COMS camera, etc., both have functions such as infrared detection and taking pictures, and the multi-sensor load unit 10 also includes a communication antenna 13 and a GPS antenna 14.

[0025] The main control communication processing unit 20 includes a signal processing module 21, a power module 22, a sound alarm module 23, a flashing light alarm module 24, a GPS positioning module 25 and an Internet of Things communication module 27. The GPS positioning module 25 is connected to the GPS antenna 14, and the GPS positioning module 25 can support GPS and Beidou multiple positioning. The Internet of Things communication module 27 is connected to the communication antenna 13. The power module 22 is respectively connected to the signal processing module 21 and the photoelectric control module 11, and is used to power the multi-sensor load unit 10 and the main control communication processing unit 20. Optionally, the power module 22 is provided with power by the battery compartment. The Internet of Things communication module 27 can adopt any one of the 2G module, 4G module, NB-IoT module, LoRa module, Wi-Fi module and BlueTooth module, or any combination of several of them.

[0026] The signal acquisition unit 30 includes a vibration sensor 31, and the vibration sensor 31 is connected to the signal processing module 21. Optionally, the vibration sensor 31 adopts a dynamic vibration sensor, which is a passive sensor and can convert the vibration of the shallow surface into an electrical signal by cutting the magnetic flux line through the internal coil without the need for additional power input, and transmit it to the signal processing module 21 after internal filtering and amplification.

[0027] The main control communication processing unit 20 also includes a control button module 26, which is connected to the signal processing module 21. The control button module 26 is used to select the working mode of the alarm system (warning mode, sentry mode, duty mode). Each time the control button module 26 is triggered, it outputs a corresponding switching signal to the signal processing module 21, and the signal processing module 21 controls the different working states of the sound alarm module 23 and the flashing light alarm module 24.

[0028] The three working modes in this embodiment are as follows:

[0029] (1) Warning mode:

[0030] a) The flashing light of the flashing light alarm module 24 keeps flashing slowly. After an intrusion warning occurs, i.e. after the signal processing module 21 determines the intrusion warning result, the signal processing module 21 controls the flashing light alarm module 24 to flash faster from far to near according to the target distance. After the intrusion warning is lifted, the flashing light alarm module 24 is turned off;

[0031] b) After determining the intrusion warning result, the signal processing module 21 controls the sound alarm module 23 to sound an alarm and speed up the alarm from far to near according to the target distance.

[0032] (2) Sentinel mode:

[0033] The flashing light alarm module 24 is closed by default. After the intrusion warning result is determined, the flashing light of the flashing light alarm module 24 starts to flash and the flashing frequency increases from far to near according to the target distance. The sound alarm module 23 sounds an alarm. After the intrusion warning is lifted, the flashing light alarm module 24 is closed again.

[0034] (3) On-duty mode:

[0035] The flashing light alarm module 24 is turned off by default and will not be turned on even after the intrusion warning result is determined.

[0036] After selecting the working mode (warning mode, sentry mode, duty mode) through the control button module 26, the photoelectric control module 11 sends a working signal to the image acquisition device 12, completes the parameter configuration, and enters the standby state. The signal processing module 21 sends a working signal to the vibration sensor 31 for real-time vibration perception. When a person or vehicle intrudes and passes by, the vibration sensor 31 detects the target, and the vibration sensor 31 collects the vibration data generated by the target on the shallow surface in real time and transmits it to the signal processing module 21. The photoelectric control module 11 is triggered synchronously, and the image acquisition device 12 is controlled to start, infrared detection begins, images are taken, and the collected image data is transmitted to the signal processing module 21.

[0037] Based on the received vibration data and image data, the signal processing module 21 adopts the vibration photoelectric fusion strategy to determine the final intrusion warning result, and sends control commands to the sound alarm module 23 and the flashing light alarm module 24 respectively according to the intrusion warning result, so that the sound alarm module 23 emits an alarm sound and the flashing light alarm module 24 emits a light. At the same time, the signal processing module 21 also uploads the intrusion warning result and the captured image to the cloud server through the Internet of Things communication module 27 and the communication antenna 13, and the GPS positioning module 25 uploads the current location information to the cloud server synchronously through the GPS antenna 14. Among them, the vibration photoelectric fusion strategy refers to combining the dual-dimensional information of vibration (microseism) and image (i.e., vibration data and image data) by using a multimodal information fusion method, using a lightweight edge-end neural network to extract features with high discrimination, forming a high-dimensional space vector, and using the high-dimensional space vector to identify and classify the target to reduce the false alarm rate and false alarm rate.

[0038] The multimodal information fusion method in this embodiment specifically includes the following steps:

[0039] Firstly, the feature vectors of the original vibration data and image data are trained by edge neural network and fine-grained target recognition technology respectively, and the target classification results and probability distribution of vibration data and image data are obtained by lightweight artificial neural network respectively;

[0040] Secondly, a weighted fusion method is used at the decision level to obtain the final target classification result and its probability. The formula of the weighted fusion method is as follows:

[0041] (1)

[0042] in, Indicates that the target is The probability weighted normalized value of the class result; is the weighted value of vibration data, using the normalized vibration energy ratio; Identify the target for vibration data as The probability of the class, The target for image data recognition is Probability of class;

[0043] The calculation formula is as follows:

[0044] (2)

[0045] in, is the number of points of all vibration data, Indicates The amplitude of the vibration data, is the number of points of valid vibration data, Indicates The amplitude of the effective vibration data, Represents the sum of the energy of the total vibration data, Represents the sum of the energy of effective vibration data. Among them, effective vibration data refers to the vibration data obtained by capturing only the vibration waves generated by the target and discarding the vibration waves caused by environmental noise in the collected vibration waves.

[0046] When the signal processing module 21 performs target classification and recognition on the original vibration data, it first uses a modal decomposition method to perform noise reduction processing on the vibration data collected by the vibration sensor 31. The noise reduction processing process specifically includes the following steps:

[0047] The vibration signal is transformed into Decompose into modes, as shown in formula (3):

[0048] (3)

[0049] After decomposition, In the mode, The target vibration signal contained in The effective component of the target vibration signal contained in is the least.

[0050] Next, for the decomposition mode, The modes are divided into information-dominated mode, noise-dominated mode and pure noise mode. For the mode dominated by noise components, wavelet threshold denoising is used to process the mode to suppress the noise component; for the pure noise mode, the entire mode is discarded. The information-dominated mode in each mode is The 1st to 2nd before the modality modes, the noise dominant mode is mode and several modes after it; keep the previous mode, and use wavelet threshold denoising to process the Mode to modal, processed The mode is denoted as , ,……, , the Euclidean distance is used to determine the pure noise mode in the decomposed mode. When the Euclidean distance meets a specific judgment condition, the mode obtained after decomposition can be considered as a pure noise mode. The judgment condition is shown in formula (4):

[0051] (4)

[0052] in, is the original vibration signal and the Mode The Euclidean distance between ; is the original vibration signal and the Mode The Euclidean distance between is a scaling factor that can be adjusted according to the degree of limitation of the noise component, e.g. Set to 1.1. The mode that satisfies the above equation (4) is determined as the dividing point between the pure noise mode and the noise-dominated mode, The subsequent modes are considered to be pure noise modes, and the pure noise modes are discarded, and finally all modes after noise reduction processing are obtained. , ,……, . Using the noise-reduced mode , ,……, Constructing the denoised signal , as shown in formula (5):

[0053] (5)

[0054] in, is the number of remaining modes after denoising.

[0055] After getting the denoised signal After that, the denoised signal The target is identified by inputting multiple ground moving targets into the recognition model, and the target classification result and probability distribution of the vibration data are obtained.

[0056] In order to achieve rapid identification of ground moving targets, this embodiment chooses to use edge neural network training feature vectors to build a lightweight human-vehicle classification model, and then constructs a variety of vehicle classification models. The lightweight human-vehicle classification model and the multiple vehicle classification models are combined into a ground multiple moving target recognition model, and the accuracy of the ground multiple moving target recognition model is verified using multiple moving target sample data and verification data. The misclassified samples are analyzed, and the ground multiple moving target recognition model is improved in combination with the machine learning algorithm. The model parameters are adjusted to seek the optimal solution of the ground multiple moving target recognition model, thereby improving the target classification accuracy and achieving high-accuracy rapid recognition of multiple types of targets at a long distance.

[0057] The process of building a lightweight multi-vehicle classification model includes: analyzing the characteristics of vibration signals generated by different vehicles driving in the same environment, extracting and screening the characteristic values ​​of pedestrian vibration signals and vehicle vibration signals; combining edge neural networks and fine-grained target recognition technology to train feature vectors, using edge neural networks to process feature data, combining fine-grained target recognition technology to process image feature data, and using local area information to capture subtle differences to achieve target recognition. Select a suitable algorithm based on training accuracy and algorithm stability to build a lightweight multi-vehicle classification model. The process of building a lightweight human-vehicle classification model is similar to that of a lightweight multi-vehicle classification model, so it will not be repeated here.

[0058] Optionally, the target recognition process can also extract the characteristic values ​​of pedestrian vibration signals and vehicle vibration signals by analyzing the differences between different moving target signals, the time domain characteristics and frequency domain distribution of different target vibration signals, for example Figure 3 As shown in the figure, the LSTM network is used to extract the time domain waveform features of the signal to obtain the time domain features; at the same time, the short-time Fourier transform is used to characterize the time-frequency characteristics of the signal to obtain the time-frequency diagram, and then CNN is used to extract the features of the low-frequency part of the signal to obtain the time-frequency domain features; the two feature extraction networks are combined to build a ground moving target recognition model; because the time domain waveform and frequency domain energy information of the signal contain features that can characterize the target category, the CNN and LSTM networks are combined to build a target recognition model, extract features from the target's time domain signal and time-frequency domain, perform feature combination, and finally perform Softmax classification through the recognition layer to obtain the recognition result.

[0059] When the signal processing module 21 performs target classification and recognition on the original image data, it can adopt an existing image target recognition algorithm, such as the YOLOv5 algorithm, etc., which will not be described in detail here.

[0060] To further improve the imaging quality of the image acquisition device 12, the image acquisition device 12 may perform fill-in light shooting by incorporating a climate compensation algorithm and surrounding environment data acquired by a vibration sensor.

[0061] When the alarm system is working, the vibration sensor 31 senses vibration in real time. After the vibration sensor 31 detects the target, it is amplified and filtered by the low-pass filter of the signal conditioning circuit. Then, the signal processing module 21 calculates and analyzes to accurately and quickly identify various moving targets such as people (walking, running, jumping), vehicles, etc., and triggers the sound alarm module 23 and the flash alarm module 24. At the same time as the vibration sensor 31 detects the target, the image acquisition device 12 simultaneously performs real-time image capture.

[0062] This embodiment uses multiple feature selections, combined with edge neural networks and fine-grained target recognition technology, to build a lightweight ground multiple moving target recognition model, and combines machine learning to tune the model to achieve ground multiple moving target recognition, and then determine whether the target is a person, a car, or other. Turn on the sound warning, flash the light warning, and provide a trigger signal to the multi-sensor load unit 10 to take pictures of the ground moving target to provide a basis for subsequent tracking.

[0063] The alarm system of this embodiment can provide all-weather prevention and control, collect and analyze vibration signals generated by moving targets, and reduce the false alarm rate by cooperating with the multi-sensor load unit 10 and the signal acquisition unit 30, and the communication means are diversified.

[0064] Furthermore, the cloud server displays the received intrusion warning results and images, monitors the occurrence of illegal intrusions in the defense area in real time, and issues alarm reminders. At the same time, the cloud server is also used to respond to the user's status query and parameter configuration request for the multi-sensor load unit 10 and the main control communication processing unit 20. The monitoring personnel can query the status and configure the parameters of any vibration photoelectric fusion type unattended alarm device for intelligent management and control. The work intensity of the management department is reduced by means of science and technology to ensure the safety of the perimeter.

[0065] The unmanned perimeter alarm device in this embodiment can be set to multiple, such as Figure 2 As shown, multiple unmanned perimeter alarm devices transmit data to the cloud server in a directionally manner through their respective IoT communication modules and communication antennas, and the cloud server uploads the data to the control center or mobile phone APP for data interaction.

[0066] The vibration photoelectric fusion unmanned perimeter alarm system proposed in the present invention is suitable for a variety of complex climate environments, adopts a variety of wireless communication modules to complete data interaction, and reports the warning status to the cloud server in real time, realizing an intelligent perimeter security system, using scientific and technological means to reduce the workload of management departments, improve the work efficiency of management departments, and ensure perimeter safety.

[0067] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0068] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A vibration photoelectric fusion type unmanned perimeter alarm system, characterized in that: The invention comprises at least one unmanned perimeter alarm device, wherein the unmanned perimeter alarm device comprises a multi-sensor load unit (10), a main control communication processing unit (20) and a signal acquisition unit (30), wherein the multi-sensor load unit (10) comprises a photoelectric control module (11), an image acquisition device (12), a communication antenna (13) and a GPS antenna (14), the main control communication processing unit (20) comprises a signal processing module (21), a power module (22), a sound alarm module (23), a flashing light alarm module (24), a GPS positioning module (25) and an Internet of Things communication module (27), and the signal acquisition unit (30) comprises a vibration sensor (31); The vibration sensor (31) senses vibration in real time. When the vibration sensor (31) detects a target, it outputs vibration data to the signal processing module (21). The photoelectric control module (11) is synchronously triggered and controls the image acquisition device (12) to start. The image acquisition device (12) transmits the acquired image data to the signal processing module (21). The signal processing module (21) uses a vibration photoelectric fusion strategy to determine the final intrusion warning result, and sends control commands to the sound alarm module (23) and the flashing light alarm module (24) according to the intrusion warning result, so that the sound alarm module (23) emits a warning signal. The alarm sounds, causing the flashing alarm module (24) to emit light. At the same time, the signal processing module (21) also uploads the intrusion warning results and the captured images to the cloud server via the Internet of Things communication module (27) and the communication antenna (13). The GPS positioning module (25) synchronously uploads the current location information to the cloud server via the GPS antenna (14). The vibration photoelectric fusion strategy refers to combining the dual-dimensional information of vibration and image using a multimodal information fusion method, and using a lightweight edge-end neural network to extract features with high discrimination, forming a high-dimensional space vector, and identifying and classifying the target.

2. The vibration photoelectric fusion type unattended perimeter alarm system according to claim 1 is characterized in that: The multimodal information fusion method comprises the following steps: The edge neural network and fine-grained target recognition technology are used to train feature vectors for the original vibration data and image data, and a lightweight artificial neural network is used to obtain the target classification results and probability distribution of the vibration data and the target classification results and probability distribution of the image data respectively; The weighted fusion method is used at the decision level to obtain the final target classification result and its probability. The formula of the weighted fusion method is as follows: （1） in, Indicates that the target is The probability weighted normalized value of the class result; is the weighted value of vibration data, using the normalized vibration energy ratio; The target for vibration data identification is The probability of the class, The target for image data recognition is Probability of class; The calculation formula is as follows: （2） in, is the number of points of all vibration data, Indicates The amplitude of the vibration data, is the number of points of valid vibration data, Indicates The amplitude of the effective vibration data, Represents the sum of the energy of the total vibration data, Represents the sum of the energy of valid vibration data.

3. A vibration photoelectric fusion type unmanned perimeter alarm system according to claim 1 or 2, characterized in that: When the signal processing module (21) performs target classification and recognition on the original vibration data, it first uses a modal decomposition method to perform noise reduction processing on the vibration data collected by the vibration sensor (31). The noise reduction processing process includes the following steps: The vibration signal is transformed into Decompose into The formula is as follows: （3） For the decomposition modes, which are divided into information-dominated mode, noise-dominated mode and pure noise mode, where the information-dominated mode is the first mode to the modes, the noise dominant mode is from The dividing point between the noise-dominated mode and the pure noise mode Determined by the following formula: （4） in, is the original vibration signal and the Mode The Euclidean distance between ; is the original vibration signal and the Mode The Euclidean distance between is the scaling factor; The subsequent modes are considered to be pure noise modes. After retaining the information-dominant modes and discarding the pure noise modes, the noise-dominant modes are processed using wavelet threshold denoising to obtain all the modes after denoising. , ,……, ; Using the noise-reduced mode , ,……, Constructing the denoised signal , the formula is as follows: （5） in, is the number of remaining modes after denoising.

4. The vibration photoelectric fusion type unmanned perimeter alarm system according to claim 3 is characterized in that: The denoised signal The ground multiple motion target recognition model is input for target recognition, and the target classification result and probability distribution of the vibration data are obtained, wherein the ground multiple motion target recognition model is obtained by combining a lightweight human-vehicle classification model and a multiple vehicle classification model, and the accuracy of the ground multiple motion target recognition model is verified using multiple motion target sample data and verification data, and the misclassified samples are analyzed, and the ground multiple motion target recognition model is improved in combination with a machine learning algorithm, and the model parameters are adjusted to seek the optimal solution of the ground multiple motion target recognition model.

5. A vibration photoelectric fusion type unmanned perimeter alarm system according to claim 1 or 2, characterized in that: The cloud server displays the received intrusion warning results and images, and is also used to respond to user status inquiries and parameter configuration requests for the multi-sensor load unit (10) and the main control communication processing unit (20).

6. A vibration photoelectric fusion type unmanned perimeter alarm system according to claim 1 or 2, characterized in that: The main control communication processing unit (20) further comprises a control button module (26) connected to the signal processing module (21), and the control button module (26) is used to select a working mode of the alarm system.

7. The vibration photoelectric fusion type unmanned perimeter alarm system according to claim 6 is characterized in that: The working mode includes a warning mode, and the warning mode is: a) the flashing light of the flashing light alarm module (24) remains in a slow flashing state, and after the intrusion warning result is determined, the flashing frequency is increased from far to near according to the target distance, and the flashing light alarm module (24) is turned off after the intrusion warning is lifted; b) After determining the intrusion warning result, the sound alarm module (23) emits an alarm sound and speeds up the alarm sound from far to near according to the target distance.

8. The vibration photoelectric fusion type unmanned perimeter alarm system according to claim 6 is characterized in that: The working mode includes a sentinel mode, and the sentinel mode is: The flashing light alarm module (24) is turned off by default. After the intrusion warning result is determined, the flashing light of the flashing light alarm module (24) starts flashing and the flashing frequency increases from far to near according to the target distance. The sound alarm module (23) emits an alarm sound. After the intrusion warning is lifted, the flashing light alarm module (24) is turned off again.

9. The vibration photoelectric fusion type unmanned perimeter alarm system according to claim 6 is characterized in that: The working mode includes a duty mode, and the duty mode is: The flashing light alarm module (24) is turned off by default and will not be turned on even after the intrusion warning result is confirmed.

10. A vibration photoelectric fusion type unmanned perimeter alarm system according to claim 1 or 2, characterized in that: The Internet of Things communication module (27) adopts any one of a 2G module, a 4G module, a NB-IoT module, a LoRa module, a Wi-Fi module and a BlueTooth module, or a combination of any of the above.

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