Fiber Optic Fence Intrusion Alarm System

Abstract

The present invention discloses an optical fiber fence intrusion alarm system, which relates to the field of security monitoring technology. The system includes the following components: an optical fiber fence subsystem, a biometric identification subsystem, a weak biological signal detection module, an intelligent analysis and decision-making platform, and a communication and linkage module. The present invention uses the sensitivity of optical fiber sensors to weak biological signals such as heartbeat and breathing through the weak biological signal detection module to assist in judging the physiological state and possible behavioral intentions of intruders, which not only provides security personnel with more comprehensive intruder information, but also enables the system to make more intelligent decisions based on this information. At the same time, the intelligent analysis and decision-making platform can automatically select and trigger the most suitable alarm response measures for the current situation by comprehensively analyzing the collected multi-source data, thereby greatly improving the response speed and efficiency of security prevention and enhancing the intelligence level of the system.

Description

Technical Field

[0001] The invention relates to the technical field of security monitoring, in particular to an optical fiber fence intrusion alarm system. Background Art

[0002] With the rapid development of science and technology, security monitoring technology has become an important means to maintain social security and order. In various important areas, such as military bases, government agencies, financial institutions and private residences, the demand for intrusion detection and alarm systems is becoming increasingly urgent. Traditional security monitoring methods mostly rely on video surveillance, infrared detection and other technologies, but these methods often have problems such as high false alarm rate and susceptibility to environmental interference. In recent years, fiber optic sensing technology and biometric technology, as emerging security monitoring technologies, have gradually attracted attention due to their unique advantages.

[0003] The traditional fiber optic fence system, as an intrusion detection method based on the fiber optic sensing principle, senses intrusion behavior by monitoring the vibration or breakage of the optical fiber. It has the advantages of fast response speed and good concealment. However, the system still has significant shortcomings in practical applications. First, it can only detect the occurrence of intrusion events, but cannot accurately identify the identity of the intruder, which limits its application in situations that require high security. Secondly, the fiber optic fence system lacks in-depth analysis of the intruder's behavioral intentions and cannot provide sufficient decision-making support for security personnel. In addition, environmental factors such as wind, rain, animal activities, etc. may also cause false alarms, increasing the system's maintenance cost and difficulty of use.

[0004] In view of the above problems, it is necessary to optimize the existing fiber optic fence intrusion alarm system. The fiber optic fence subsystem monitors abnormal signals in the area and triggers the biometric identification subsystem to authenticate the intruder. At the same time, the weak biological signal detection module is used to capture and analyze the intruder's physiological information to assist in judging his behavioral intentions. Therefore, it is of great significance to develop a fiber optic fence intrusion alarm system that can comprehensively realize the above characteristics. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the prior art and provide a fiber optic fence intrusion alarm system, which can monitor abnormal vibrations or pressure changes in the area through the fiber optic fence subsystem. Once a potential intrusion behavior is detected, it immediately triggers the biometric identification subsystem to authenticate the intruder. At the same time, the weak biological signal detection module is used to capture and analyze the intruder's heartbeat, breathing and other physiological information to assist in judging his physiological state and possible behavioral intentions. Finally, the intelligent analysis and decision-making platform comprehensively processes the data of each subsystem, automatically selects and triggers the corresponding alarm response measures, thereby significantly improving the accuracy and efficiency of security prevention.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: an optical fiber fence intrusion alarm system, which includes the following components: an optical fiber fence subsystem, a biometric identification subsystem, a weak biological signal detection module, an intelligent analysis and decision-making platform, and a communication and linkage module;

[0007] The biometric identification subsystem is integrated into key monitoring points and mobile monitoring devices, including but not limited to a fingerprint recognition module and a face recognition camera. After the optical fiber fence subsystem detects an intrusion, it immediately identifies the identity of the intruder;

[0008] The weak biological signal detection module uses an optical fiber sensor to collect weak biological signals generated by the intruder, including heartbeat and respiration, and processes the collected weak signals through signal enhancement technology and filtering algorithms. Biometric parameters of the heartbeat frequency and respiration frequency are extracted from the processed signals through algorithms. The extraction algorithm formula for the heartbeat frequency is: where Δt heart_i is the time interval corresponding to two adjacent heartbeat peaks within the selected scale range, and n is the logarithm of the counted heartbeat peaks. The extraction algorithm formula for the respiration frequency is: where is the angular frequency corresponding to the peak frequency in the spectrum. According to the extracted biometric parameters, combined with a preset physiological state and behavior intention model, the current state of the intruder is analyzed and judged. Specifically, let the heartbeat frequency be F h , the respiration frequency be F r , the average heartbeat frequency in the preset normal state be F h0 , the average respiration frequency in the normal state be F r0 , the heartbeat frequency weight coefficient be α, the respiration frequency weight coefficient be β, and a comprehensive deviation index D is defined: The state of the intruder is judged according to the comprehensive deviation index D. Thresholds T1 and T2 are set, and T1 < T2. For D ≤ T1, it is judged as a relatively calm state. For T1 < D ≤ T2, it is judged as a slightly tense state. For D > T2, it is judged as a highly tense or potentially threatening state, and the analysis result is fed back to the intelligent analysis and decision-making platform;

[0009] The intelligent analysis and decision-making platform receives data from the optical fiber fence and the biometric identification subsystem, conducts comprehensive analysis, and judges whether the identity of the intruder is legal and their behavior intention;

[0010] The communication and linkage module realizes the linkage response between subsystems under the control of the intelligent analysis and decision-making platform.

[0011] Furthermore, the fiber optic fence subsystem deploys a fiber optic sensor network around the area to be protected according to a predetermined layout plan, monitors changes in vibration and pressure parameters in the area in real time, and pre-processes the original signal collected by the fiber optic sensor through a dedicated signal processing unit to extract intrusion signal characteristics, and compares the processed signal with a preset intrusion threshold. If the threshold is exceeded, it is determined to be a potential intrusion behavior and a preliminary intrusion alarm signal is generated. When the intrusion behavior is confirmed, the fiber optic fence subsystem immediately sends an alarm message to the intelligent analysis and decision-making platform through the communication module, and is ready to receive further instructions.

[0012] Furthermore, after the optical fiber fence subsystem triggers an alarm, the fingerprint recognition module and facial recognition camera in the biometric recognition subsystem are quickly started and enter a working state, and the facial features and fingerprint information of the intruder are captured by a high-definition camera, and the collected biometric data are pre-processed to extract feature parameters, and the extracted feature parameters are compared with the pre-stored authorized personnel database, and the recognition algorithm is used to determine whether the identity of the intruder is legal, and the identity comparison result is sent to the intelligent analysis and decision-making platform for subsequent decision-making processing.

[0013] Furthermore, the biometric recognition subsystem preprocesses the collected biological data to extract characteristic parameters. Specifically, the collected biological data is assumed to be a matrix X, in which each row represents a sample data, and each column represents a characteristic dimension. For a total of n samples, each sample has m characteristic dimensions, that is, X is an n×m matrix. The mean of each characteristic dimension is calculated to obtain a mean vector μ, where x i is the i-th row of matrix X, representing a sample data. Subtract the mean vector from each sample data to obtain the decentralized matrix X ′ , that is, X ′ =X-μ, calculate the decentralized matrix X ′ The covariance matrix S

[0014] Where X ′T Yes X ′ The transposed matrix of the covariance matrix S is decomposed into eigenvalues ​​to obtain eigenvalues ​​λ1,λ2,…,λ m and the corresponding eigenvectors v1,v2,…,v m , sort the eigenvectors in descending order of eigenvalues, and select the eigenvectors corresponding to the first k eigenvalues ​​to form the projection matrix P, where P = (v1, v2, ..., v k ), project the original data into the principal component space and obtain the characteristic parameter matrix Y, that is, Y = X ′ P.

[0015] Furthermore, the biometric identification subsystem uses an identification algorithm to determine whether the identity of the intruder is legitimate, and the algorithm formula is: Among them, k is the adjustment parameter. , it is judged as a legal identity, where T is the preset similarity threshold.

[0016] Furthermore, the intelligent analysis and decision-making platform receives data and alarm information from the fiber optic fence subsystem, the biometric identification subsystem and the weak biological signal detection module, and performs a comprehensive analysis on the received data and information, including the identity recognition results of the intruder, the physiological state analysis and the severity of the intrusion behavior. According to the results of the comprehensive analysis, the intelligent analysis and decision-making platform formulates corresponding response measures, sends the decision results to the corresponding execution unit through the communication module, monitors the execution of the execution unit, records the entire event process and generates a report.

[0017] Furthermore, the intelligent analysis and decision-making platform receives data and alarm information from the optical fiber fence subsystem, the biometric identification subsystem and the weak biological signal detection module, and performs a comprehensive analysis on the received data and information. The algorithm formula for the analysis is: Among them, I fiber is the intrusion possibility indicator provided by the fiber optic fence subsystem, I bio is the identity legitimacy indicator provided by the biometric subsystem, T stress is the stress level indicator provided by the weak biological signal detection module, T stressmax is the preset maximum tension value, w1, w2, and w3 are the weight coefficients of the optical fiber fence subsystem, the biometric identification subsystem, and the weak biological signal detection module, respectively.

[0018] Furthermore, the intelligent analysis and decision-making platform formulates corresponding response measures based on the results of the comprehensive analysis, and the specific measures include:

[0019] (1) Based on the urgency and potential threat of the intrusion incident, the intelligent analysis and decision-making platform initiates a series of automatic defense measures, including activating automatic door locks, spraying non-lethal defense sprays, and releasing warning gas;

[0020] (2) Dynamically adjust the deployment of security resources based on the intruder's behavior patterns and risk assessment results;

[0021] Record and store all data related to intrusion incidents, including the identity information of the intruder, the intrusion process, and the implementation of countermeasures.

[0022] Compared with the existing technology, the fiber optic fence intrusion alarm system has the following beneficial effects:

[0023] 1. The present invention uses the sensitivity of optical fiber sensors to weak biological signals such as heartbeat and breathing through a weak biological signal detection module to assist in judging the physiological state and possible behavioral intentions of the intruder, which not only provides security personnel with more comprehensive intruder information, but also enables the system to make more intelligent decisions based on this information. At the same time, the intelligent analysis and decision-making platform can automatically select and trigger the most suitable alarm response measures for the current situation through comprehensive analysis of the collected multi-source data, thereby greatly improving the response speed and efficiency of security precautions and enhancing the intelligence level of the system.

[0024] 2. The present invention integrates biometric technology and fiber optic sensing technology to achieve accurate identification of the identity of intruders, overcomes the limitation of traditional fiber optic fence systems that can only detect intrusion behaviors but cannot identify the identities of intruders, and effectively reduces the false alarm rate caused by environmental factors. At the same time, the high accuracy and stability of biometric technology ensure that intruders can be accurately identified even at night or in complex environments, further improving the overall security and reliability of the system.

[0025] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] 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 or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 A flowchart of the fiber optic fence intrusion alarm system;

[0028] Figure 2 Schematic diagram of the fiber optic fence intrusion alarm system. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] Embodiment 1

[0031] This embodiment describes in detail the specific application of the fiber optic fence intrusion alarm system in a scientific research laboratory environment. By integrating the fiber optic fence subsystem, the biometric recognition subsystem, the weak biological signal detection module, the intelligent analysis and decision-making platform, and the communication and linkage module, it protects the safety of scientific research results and experimental equipment.

[0032] First, a fiber optic sensor network is laid out around the laboratory building according to a carefully designed layout plan, which not only covers all potential intrusion paths in the laboratory, but also extends coverage to surrounding channels that may be used for intrusion, such as ventilation ducts, underground pipelines and other areas. The signal processing unit monitors the vibration and pressure changes in the area in real time, and uses advanced adaptive filtering technology to amplify and filter the original signals collected by the fiber optic sensor to better adapt to signal interference in different environments and extract more accurate intrusion signal characteristics. When presetting the intrusion threshold, a dynamic threshold adjustment mechanism is set up considering the influence of different time periods and environmental factors. For example, at night or when the surrounding environment is relatively quiet, the threshold is lowered to increase sensitivity; when there is more external interference during the day, the threshold is appropriately increased to reduce false alarms. When the processed signal exceeds the threshold, it is determined to be a potential intrusion behavior and a preliminary intrusion alarm signal is generated. Once the intrusion behavior is confirmed, the alarm information is immediately sent to the intelligent analysis and decision-making platform through the high-speed communication module, and the local sound and light warning device is activated at the same time, which has a preliminary deterrent effect on the intruder.

[0033] Secondly, facial recognition cameras and fingerprint recognition modules are installed at the key entrances and important monitoring points of the laboratory, and iris recognition equipment is added to improve the accuracy and security of identity recognition. When the fiber optic fence subsystem triggers an alarm, the biometric subsystem is quickly started. The high-definition camera not only captures the facial features of the intruder, but also uses intelligent image analysis technology to determine whether the intruder's behavior is abnormal. The fingerprint recognition module uses non-contact technology to improve collection efficiency and hygiene. At the same time, the iris recognition equipment uses infrared light sources and can accurately identify under different lighting conditions. The collected biological data is pre-processed, and feature parameters are extracted according to the principal component analysis method. The features are further optimized in combination with deep learning algorithms. The processed feature parameters are compared with the pre-stored authorized personnel database, and advanced recognition algorithms are used to determine whether the intruder's identity is legal. If the similarity is greater than the preset similarity threshold, it is judged as a legal identity, otherwise it is an illegal identity. The comparison results are sent to the intelligent analysis and decision-making platform and stored locally for backup for subsequent investigation.

[0034] Then, using optical fibers, the sensor collects weak biological signals such as the heartbeat and breathing of potentially intruding personnel. The sensor adopts highly sensitive fiber grating technology, which can detect weak signals more accurately. The weak signals collected are processed through signal enhancement technology and filtering algorithms. According to the extraction algorithms for heartbeat frequency and breathing frequency, the biometric parameters of heartbeat frequency and breathing frequency are calculated respectively. At the same time, artificial intelligence algorithms are introduced to analyze the biological signals to identify different emotional states, such as tension, fear, anger, etc., providing more basis for judging the behavior intention of the intruder. Specifically, let the heartbeat frequency be F h , and the breathing frequency be F r . The average heartbeat frequency under the preset normal state is F h0 , and the average breathing frequency under the normal state is F r0 . The weight coefficient of the heartbeat frequency is α, and the weight coefficient of the breathing frequency is β. Define a comprehensive deviation index D: Judge the state of the intruder according to the comprehensive deviation index D. Set thresholds T1 and T2, and T1 < T2. For D ≤ T1, it is judged as a relatively calm state. For T1 < D ≤ T2, it is judged as a slightly tense state. For D > T2, it is judged as a highly tense or potentially threatening state. The analysis results are not only fed back to the intelligent analysis and decision-making platform but also sent to the handheld devices of on-site security personnel through wireless communication so that they can timely understand the state of the intruder.

[0035] Next, the intelligent analysis and decision-making platform receives the data and alarm information from the optical fiber fence subsystem, the biometric recognition subsystem, and the weak biological signal detection module. Using big data analysis technology, it mines the historical intrusion event data to optimize the comprehensive analysis algorithm. According to the comprehensive analysis algorithm formula conduct a comprehensive analysis, where I f iber is the intrusion possibility index provided by the optical fiber fence subsystem, I bio is the identity legitimacy index provided by the biometric recognition subsystem, T stress is the tension degree index provided by the weak biological signal detection module, and w1, w2, and w3 are the weight coefficients of each subsystem respectively, T stressmaπThe maximum tension value is preset. According to different security level requirements and actual conditions, the weight coefficient is dynamically adjusted to achieve more accurate analysis and decision-making. According to the comprehensive analysis results, corresponding response measures are formulated. For example, if it is judged to be an illegal intrusion and the situation is urgent, the sound and light alarm is activated, and a remote notification is sent to the security personnel. According to the intruder's behavior pattern and risk assessment results, the deployment of security resources is dynamically adjusted. At the same time, all data related to the intrusion incident is recorded and stored, including the intruder's identity information, the intrusion process, and the implementation of response measures. Detailed reports are generated for subsequent analysis and improvement. In an emergency, real-time linkage with the police command center can be achieved to improve the emergency response speed.

[0036] Finally, the communication and linkage module establishes stable and reliable communication links between subsystems and between subsystems and external security systems, and adopts redundant design and encryption technology to ensure the security and reliability of real-time data transmission and information sharing. When the fiber optic fence subsystem triggers an alarm, the biometric identification subsystem is automatically started for identity recognition. At the same time, the intelligent analysis and decision-making platform initiates automatic defense measures according to the situation, such as activating the automatic door lock of the laboratory to prevent intruders from further entering important areas; when necessary, spray non-lethal defense spray and release warning gas to stop the intruder's actions and alert surrounding personnel. In addition, it can also link with surrounding surveillance cameras to achieve all-round monitoring coverage. At the same time, the status of the communication link is monitored in real time, and intelligent fault diagnosis technology is used to promptly discover and solve problems in data transmission. When a communication link or subsystem fails, the backup communication channel and emergency handling procedures are automatically started to ensure the normal operation of the system. At the same time, the system is regularly maintained and upgraded to adapt to changing security needs.

[0037] In summary, the fiber optic fence subsystem of this embodiment realizes all-round intrusion monitoring around the laboratory through careful layout and advanced signal processing technology. The dynamic threshold adjustment mechanism effectively reduces false alarms and improves the accuracy of alarms. The biometric recognition subsystem integrates multiple devices and uses deep learning algorithms to pre-process biological data to accurately identify identities. The weak biological signal detection module extracts biological characteristic parameters and analyzes emotional states. The intelligent analysis and decision-making platform integrates the data of each subsystem to formulate response measures. The communication and linkage module establishes a reliable communication link to achieve linkage to provide the laboratory with more comprehensive, accurate and efficient security protection, and to safeguard the safety of scientific research results and experimental equipment.

[0038] Embodiment 2

[0039] This embodiment describes in detail the specific application of the fiber optic fence intrusion alarm system in the security protection of a bank vault, and provides security protection for the bank vault by integrating a fiber optic fence subsystem, a biometric identification subsystem, a weak biological signal detection module, an intelligent analysis and decision-making platform, and a communication and linkage module.

[0040] First, a fiber optic sensor network is carefully laid out on all possible intrusion paths such as the walls, passages, entrances and exits, and ventilation ducts around the vault. These fiber optic sensors are made of highly sensitive materials and advanced manufacturing processes, and can detect extremely small vibrations and pressure changes. The signal processing unit is equipped with a high-performance processor and professional signal analysis software to continuously monitor the vibration and pressure changes in the area, and perform multi-stage preprocessing on the collected raw signals, including noise reduction, filtering, and feature extraction steps, to accurately identify potential intrusion signals. When an abnormal signal is detected, it is immediately determined as a potential intrusion behavior and an alarm signal is triggered. At the same time, through linkage with the vault's access control system, lighting system, and ventilation system, once an alarm is triggered, all entrances and exits are automatically locked, unnecessary lighting is turned off, and the ventilation system is adjusted to prevent intruders from escaping or using environmental factors to cause damage. In order to improve the reliability of the system, the fiber optic fence subsystem is also equipped with a redundant backup mechanism. When the main sensor fails, the backup sensor can automatically take over the monitoring task to ensure uninterrupted security protection.

[0041] Secondly, facial recognition cameras, fingerprint recognition modules, iris recognition equipment and palm print recognition devices are installed at the entrance of the vault and key monitoring points. These biometric devices use high-resolution image acquisition technology and advanced biometric feature extraction algorithms to quickly and accurately identify the identity of the person entering. When the fiber optic fence subsystem triggers an alarm, the biometric subsystem starts quickly. The high-definition camera can not only capture the facial features of the person entering, but also use intelligent image analysis technology to determine whether the person's expression and behavior are abnormal. The fingerprint recognition module and iris recognition device perform identity authentication at the same time to ensure that only authorized personnel can enter the vault. The palm print recognition device is used as an auxiliary means to further improve the accuracy of identity recognition. For intrusion by unauthorized personnel, the biometric subsystem can quickly identify and send information to the intelligent analysis and decision-making platform, and at the same time activate the local sound and light warning device to deter the intruder. The biometric subsystem is also connected to the bank's employee database and visitor management system in real time to timely update the authorized personnel information and strictly authenticate the visitors.

[0042] Then, the weak biological signal detection module uses optical fiber sensors and professional biological signal acquisition equipment to detect weak biological signals such as heartbeat, breathing, body temperature, etc. of possible intruders. These devices use advanced sensor technology and signal processing algorithms, and can accurately detect biological signals without contacting the intruder. Through advanced signal enhancement technology and filtering algorithms, the collected weak signals are processed to extract biological characteristic parameters such as heart rate, breathing rate and body temperature changes. Based on these parameters, combined with the preset physiological state and behavioral intention model, the intruder's tension, health status and potential threats are judged. For example, if an abnormal increase in heart rate and breathing rate and abnormal changes in body temperature are detected, it may indicate that the intruder is in a state of tension, illness or bad intentions. The system will immediately issue a higher level alarm. At the same time, the weak biological signal detection module can also be linked with the environmental monitoring system in the vault to adjust environmental parameters such as temperature, humidity and air quality according to changes in biological signals to increase the intruder's discomfort and prevent him from taking further action.

[0043] Next, the intelligent analysis and decision-making platform receives data and alarm information from the fiber optic fence subsystem, biometric identification subsystem and weak biological signal detection module, and uses big data analysis technology and artificial intelligence algorithms to deeply mine and analyze historical intrusion event data to optimize the comprehensive analysis algorithm. According to the comprehensive analysis algorithm formula, combined with the intrusion possibility index provided by the fiber optic fence subsystem, the identity legitimacy index provided by the biometric identification subsystem, and the tension and health status index provided by the weak biological signal detection module, a comprehensive and accurate comprehensive analysis is performed. Among them, the weight coefficient of each indicator is dynamically adjusted according to the actual situation to adapt to different security level requirements and risk conditions. According to the comprehensive analysis results, corresponding response measures are formulated. For example, if it is judged to be an illegal intrusion and the situation is urgent, the sound and light alarm is activated, the security personnel are notified, the police are contacted, and the defense system in the vault is automatically activated, such as releasing smoke, activating electronic barriers, etc. At the same time, all activities in the vault are recorded in real time, including video surveillance, biometric identification data and weak biological signal changes, etc., and detailed reports are generated for subsequent review and analysis. In an emergency, it can also be linked with financial regulatory agencies and other relevant departments in real time to improve emergency response speed and collaborative processing capabilities.

[0044] Finally, the communication and linkage module establishes a highly stable and reliable communication link between each subsystem and with the external security system, and adopts encryption technology and redundant communication channels to ensure the security and reliability of real-time data transmission and information sharing. When the fiber optic fence subsystem triggers an alarm, it automatically starts the biometric identification subsystem and the weak biological signal detection module to achieve rapid response and linkage. At the same time, it maintains real-time communication with the bank's security monitoring center, the police alarm system, the fire department and the medical emergency system to ensure that support can be obtained quickly in an emergency. The status of the communication link is monitored in real time, and intelligent fault diagnosis technology is used to promptly discover and solve problems in data transmission. When a communication link or subsystem fails, the backup communication channel and emergency handling procedure are automatically started to ensure the normal operation of the system. At the same time, the communication and linkage module can also be integrated with other business systems of the bank, such as the financial management system, the risk management system, etc., to achieve information sharing and collaborative processing, and improve the overall security management level of the bank.

[0045] In summary, the fiber optic fence subsystem of this embodiment can accurately detect any tiny vibration and pressure changes around the safe through highly sensitive fiber optic sensors and advanced signal processing technology, thereby realizing comprehensive monitoring of potential intrusion behaviors. The biometric recognition subsystem combines multiple technologies such as facial recognition, fingerprint recognition, iris recognition, and palm print recognition to ensure that only authorized personnel can enter the safe. The weak biological signal detection module uses advanced sensors and signal processing algorithms to detect weak biological signals such as the heartbeat, breathing, and body temperature of the intruder. By analyzing these biometric parameters and combining them with the preset physiological state and behavioral intention model, it can accurately judge the intruder's tension and potential threats, providing an important basis for taking timely countermeasures.

[0046] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. Fiber optic fence intrusion alarm system, characterized by: The system includes the following components: optical fiber fence subsystem, biometric identification subsystem, weak biological signal detection module, intelligent analysis and decision-making platform and communication and linkage module; The optical fiber fence subsystem is arranged around the area to be protected, and uses optical fiber sensors to monitor vibration and pressure changes in the area. If an abnormal signal is detected, it is regarded as a potential intrusion behavior and an alarm signal is triggered; The biometric identification subsystem is integrated into key monitoring points and mobile monitoring devices, including but not limited to fingerprint identification modules and facial recognition cameras, and immediately identifies the intruder after the fiber optic fence subsystem detects the intrusion; The weak biological signal detection module uses an optical fiber sensor to collect weak biological signals including heartbeat and respiration generated by the intruder, and processes the collected weak signals through signal enhancement technology and filtering algorithms. The biological characteristic parameters of the heartbeat frequency and respiration frequency are extracted from the processed signals through algorithms. The extraction algorithm formula for the heartbeat frequency is: where Δt heart_i is the time interval corresponding to two adjacent heartbeat peaks within the selected scale range, and n is the logarithm of the counted heartbeat peaks. The extraction algorithm formula for the respiration frequency is: where is the angular frequency corresponding to the peak frequency in the spectrum. According to the extracted biological characteristic parameters and combined with the preset physiological state and behavior intention model, the current state of the intruder is analyzed and judged. Specifically, let the heartbeat frequency be F h , the respiration frequency be F r , the average heartbeat frequency under the preset normal state be F h0 , the average respiration frequency under the normal state be F r0 , the heartbeat frequency weight coefficient be α, the respiration frequency weight coefficient be β, and a comprehensive deviation index D is defined: The state of the intruder is judged according to the comprehensive deviation index D. Thresholds T1 and T2 are set, and T1 < T2. For D ≤ T1, it is judged as a relatively calm state. For T1 < D ≤ T2, it is judged as a slightly tense state. For D > T2, it is judged as a highly tense or potentially threatening state, and the analysis result is fed back to the intelligent analysis and decision-making platform; The intelligent analysis and decision-making platform receives data from the fiber optic fence and the biometric identification subsystem, performs comprehensive analysis, and determines whether the identity of the intruder is legal and the intention of his behavior; The communication and linkage module realizes linkage response among subsystems under the control of the intelligent analysis and decision-making platform.

2. The optical fiber fence intrusion alarm system according to claim 1, characterized in that: The fiber optic fence subsystem deploys a fiber optic sensor network around the area to be protected according to a predetermined layout plan, monitors the changes in vibration and pressure parameters in the area in real time, and pre-processes the original signal collected by the fiber optic sensor through a dedicated signal processing unit to extract the intrusion signal characteristics, and compares the processed signal with a preset intrusion threshold. If the threshold is exceeded, it is determined to be a potential intrusion behavior and a preliminary intrusion alarm signal is generated. When the intrusion behavior is confirmed, the fiber optic fence subsystem immediately sends an alarm message to the intelligent analysis and decision-making platform through the communication module and prepares to receive further instructions.

3. The optical fiber fence intrusion alarm system according to claim 1, characterized in that: After the optical fiber fence subsystem triggers an alarm, the fingerprint recognition module and facial recognition camera in the biometric recognition subsystem quickly start up and enter the working state, and capture the facial features and fingerprint information of the intruder through the high-definition camera, and pre-process the collected biological data to extract feature parameters, and compare the extracted feature parameters with the pre-stored authorized personnel database, and use the recognition algorithm to determine whether the identity of the intruder is legal, and send the identity comparison result to the intelligent analysis and decision-making platform for subsequent decision-making processing.

4. The optical fiber fence intrusion alarm system according to claim 1, characterized in that: The biometric recognition subsystem preprocesses the collected biological data and extracts characteristic parameters. Specifically, the collected biological data is assumed to be a matrix X, in which each row represents a sample data and each column represents a characteristic dimension. For a total of n samples, each sample has m characteristic dimensions, that is, X is an n×m matrix. The mean of each characteristic dimension is calculated to obtain a mean vector μ, where x i is the i-th row of matrix X, representing a sample data. Subtract the mean vector from each sample data to obtain the decentralized matrix X ′ , that is, X ′ =X-μ, calculate the decentralized matrix X ′ The covariance matrix S , Where X ′T Yes X ′ The transposed matrix of the covariance matrix S is decomposed into eigenvalues ​​to obtain eigenvalues ​​λ1,λ2,…,λ m and the corresponding eigenvectors v1,v2,…,v m , sort the eigenvectors in descending order of eigenvalues, and select the eigenvectors corresponding to the first k eigenvalues ​​to form the projection matrix P, where P = (v1, v2, ..., v k ), project the original data into the principal component space and obtain the characteristic parameter matrix Y, that is, Y = X ′ P.

5. The optical fiber fence intrusion alarm system according to claim 1, characterized in that: The biometric identification subsystem uses an identification algorithm to determine whether the identity of the intruder is legal. The algorithm formula is: Among them, k is the adjustment parameter. , it is judged as a legal identity, where T is the preset similarity threshold.

6. The optical fiber fence intrusion alarm system according to claim 1, characterized in that: The intelligent analysis and decision-making platform receives data and alarm information from the fiber optic fence subsystem, the biometric identification subsystem and the weak biological signal detection module, and performs a comprehensive analysis on the received data and information, including the identity identification results of the intruder, the physiological state analysis and the severity of the intrusion behavior. According to the results of the comprehensive analysis, the intelligent analysis and decision-making platform formulates corresponding response measures, sends the decision results to the corresponding execution unit through the communication module, monitors the execution status of the execution unit, records the entire event process and generates a report.

7. The optical fiber fence intrusion alarm system according to claim 3, characterized in that: The intelligent analysis and decision-making platform receives data and alarm information from the optical fiber fence subsystem, the biometric identification subsystem and the weak biological signal detection module, and performs a comprehensive analysis on the received data and information. The algorithm formula for the analysis is: Among them, I fiber is the intrusion possibility indicator provided by the fiber optic fence subsystem, I bio is the identity legitimacy indicator provided by the biometric subsystem, T stress It is the stress level indicator provided by the weak biological signal detection module. is the preset maximum tension value, w1, w2, and w3 are the weight coefficients of the optical fiber fence subsystem, the biometric identification subsystem, and the weak biological signal detection module, respectively.

8. The optical fiber fence intrusion alarm system according to claim 4, characterized in that: The intelligent analysis and decision-making platform formulates corresponding response measures based on the results of the comprehensive analysis, and the specific measures include: (1) Based on the urgency and potential threat of the intrusion incident, the intelligent analysis and decision-making platform initiates a series of automatic defense measures, including activating automatic door locks, spraying non-lethal defense sprays, and releasing warning gas; (2) Dynamically adjust the deployment of security resources based on the intruder's behavior patterns and risk assessment results; (3) Record and store all data related to the intrusion incident, including the intruder’s identity information, the intrusion process, and the implementation of countermeasures.

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