57 results about "Perimeter Security" patented technology

The invention relates to the technical field of perimeter intrusion monitoring, in particular to a perimeter intrusion monitoring system based on cooperation of infrared thermal imaging and an intelligent algorithm. The perimeter intrusion monitoring system based on infrared thermal imaging and intelligent algorithm cooperation comprises a multi-source sensing layer, an edge computing node, a cloud analysis platform and a response execution layer. According to the perimeter intrusion monitoring system based on cooperation of infrared thermal imaging and an intelligent algorithm, environmental noise interference is effectively suppressed through a dynamic threshold compensation mechanism, and accurate classification of personnel, vehicles, unmanned aerial vehicles and animals is realized by combining multi-modal feature fusion of infrared thermal imaging, visible light vision and millimeter wave radar; according to the method, intention modes of passing, observation, invasion and the like can be identified, threat level dynamic adaptation is realized in combination with a hierarchical response system, the technical bottlenecks of poor environmental adaptability, high target misjudgment rate, behavior analysis deficiency and the like of a traditional system are solved, and the intelligent level and all-weather protection capability of perimeter security and protection are remarkably improved.

The invention discloses a regional security management system based on multi-source data fusion, and relates to the technical field of security management. The system comprises a security intrusion detection model, a perimeter security linkage module and a security disposal effect evaluation module. According to the method, after key safety intrusion area marking is carried out on the designated area, when the perimeter safety condition of the designated area is monitored in real time, multi-source data is input into the designated safety intrusion detection model for analysis, the intrusion detection result containing the existence of the intrusion object and the perimeter safety is output, and if the intrusion object exists, the intrusion detection result is output. If yes, a perimeter security alarm mechanism is triggered immediately to take security linkage measures, and after security disposal is finished, security disposal effect evaluation is carried out to determine whether intrusion detection qualification verification is carried out or not, so that the security management efficiency of the perimeter of the specified area is improved, and the security of the specified area is ensured. The problem that in the prior art, when safety intrusion detection is carried out on the area perimeter, linkage synchronism of alarm and emergency response is poor is solved.

A perimeter security intrusion event recognition method, relating to the technical field of perimeter security intrusion event monitoring. The method comprises the following steps: S1, collecting a background noise vibration signal and an intrusion event vibration signal, and acquiring a model training set, a model test set, and a model verification set; S2, constructing a perimeter security intrusion event recognition network; S3, training the perimeter security intrusion event recognition network to obtain a perimeter security intrusion event recognition network model; and S4, processing the collected intrusion event vibration signal by using step S1, inputting the processed intrusion event vibration signal into the perimeter security intrusion event recognition network model, and outputting predicted background noise and the classification accuracy of three intrusion events.

The invention discloses a 3D (three-dimensional) perimeter security system and an operation method thereof, the system comprises a server terminal, a front-end sensing system, a data transmission system and a central control system, the front-end sensing system is networked in multiple groups of partitions, and each group of partitions of the front-end sensing system is provided with a nine-axis sensor, an edge computing all-in-one machine and a switch; and lighting, wind and rain sensor, broadcasting, microwave radar and camera equipment. The data transmission system transmits data; the central control system comprises an application server and a data server. The system forms a first defense line for active detection of the microwave radar and the camera and a second defense line for vibration sensing of the nine-axis sensor, and a monitoring blind area is eliminated through three-dimensional data fusion. The operation method comprises four groups of subsystem modules. According to the method, 'sky-ground-fence 'dead-corner-free monitoring is realized, the false alarm rate is reduced by combining multi-modal data fusion with an intelligent algorithm, self-evolution is realized by edge-cloud federal learning, and threat disposal efficiency is improved by automatic linkage response.

The invention discloses a lightweight optical fiber vibration intrusion event identification method and system for perimeter security and protection, and belongs to the technical field of optical fiber sensing technology and mode identification, and the method comprises the steps: collecting an original vibration signal of a perimeter monitoring region through a distributed optical fiber vibration sensing system; performing wavelet threshold de-noising preprocessing on the original vibration signal to obtain a de-noised signal; performing feature extraction on the denoised signal, and constructing a high-dimensional feature vector; performing dimension reduction processing on the high-dimensional feature vector by using a linear discriminant analysis method to obtain a low-dimensional classification feature vector; and inputting the low-dimensional classification feature vector into a pre-trained lightweight convolutional neural network model for classification and identification, and outputting a corresponding intrusion event category. According to the method, through cooperation of front-end LDA dimension reduction and a rear-end lightweight network, the model parameter quantity and calculation overhead are greatly reduced while high recognition precision is guaranteed, efficient real-time deployment on edge equipment is achieved, and the method is suitable for intrusion detection in perimeter security and protection.

The invention belongs to the technical field of distributed optical fiber sound wave sensing and artificial intelligence, and discloses a CNN-GRU-based DAS perimeter security real-time intrusion identification method, which comprises the following steps: extracting an original time sequence signal acquired by a DAS perimeter security system; performing S transformation on the original time sequence signal to obtain a two-dimensional time-frequency spectrum; 3, the two-dimensional time-frequency spectrum obtained through S transformation is input into a trained CNN-GRU network, the prediction probability is output through the CNN-GRU network, and the CNN-GRU network comprises a CNN network, an expansion layer, a first-layer GRU unit, a second-layer GRU unit, a full connection layer and a Softmax layer which are connected in sequence; and according to the prediction probability output by the CNN-GRU network, whether intrusion occurs and the intrusion type are judged. According to the method, efficient identification and classification of DAS intrusion signals can be realized, and the front-end processing complexity is remarkably reduced.

The embodiment of the invention provides a perimeter security system detection method and device, electronic equipment and a storage medium, and relates to the technical field of security. The method comprises the following steps: acquiring a first video obtained by shooting a perimeter detection area by a perimeter security system; superposing the target animation on the first video to obtain a second video, the target animation being an animation matted from a third video and moving the first sample object towards a preset direction, and the third video being shot by other devices except the perimeter security system; obtaining a judgment result whether the behavior of the first sample object in the second video can trigger the perimeter security system to give an alarm; inputting the second video into a perimeter security system for perimeter detection, and observing whether the perimeter detection triggers the perimeter security system to give an alarm or not to obtain an observation result; and if the judgment result is inconsistent with the observation result, determining that the perimeter security system is abnormal. According to the application, the reliability of the perimeter security and protection system is improved, and the operation and maintenance cost is reduced at the same time.

The invention relates to the technical field of rail transit perimeter safety protection, and discloses a rail transit station perimeter intrusion logic early warning system, which comprises a detection point location network, a topology modeling module and a logic arbitration module, the topology modeling module constructs a grid topology model according to the physical coordinates of the point locations, and sets the detection point locations in the non-sensitive area as silent reference nodes; the logic arbitration module obtains real-time trigger data of a detection point location, calculates a cross-correlation characteristic value of a to-be-identified point location and a silent reference node, deducts a common-mode component in a characteristic signal flow when environment interference is identified, extracts a transient envelope fingerprint, matches a displacement continuity evolution rule, and outputs an alarm instruction. According to the method, coherent disturbance caused by train passing or electromagnetic discharge is stripped from an original signal by establishing an environment background interference reference, systematic false alarm is effectively inhibited, and accurate identification of an intrusion track in a complex random noise environment is realized.

The invention relates to the field of intelligent fences, and particularly discloses an intelligent fence system based on LORA networking, which constructs a low-cost and wide-area ad hoc network composed of a plurality of highly integrated intelligent fence devices. Each fence device serves as an independent terminal, and a control panel, a battery module, a Hall sensor and a fence telescopic belt are integrated in a cylindrical shell of each fence device. The key point is that an LORA communication module is integrated on the control panel instead of a traditional WiFi or 4G module. Therefore, numerous fence devices can automatically form a private long-distance wireless network, the fence devices can be accessed to the Internet only through one LORA gateway, and low-cost and high-efficiency perimeter security and protection are realized.

The application discloses a multi-dimensional perimeter security invasion event recognition method and recognition system, comprising: acquiring a phase-modulated interference signal by using a double Mach-Zehnder interferometer to construct a one-dimensional time sequence signal of an invasion event; converting the one-dimensional time sequence signal into a GPS signal and a two-dimensional time-frequency signal; a UAV shoots a video according to the GPS signal to form a three-dimensional video signal; an industrial computer performs synchronous recognition of the invasion event on the two-dimensional time-frequency signal and the three-dimensional video signal in a multi-dimensional feature extraction deep learning network which has been trained, multiplies the confidence of each type of event obtained and normalizes the confidence, and outputs an event with the maximum confidence as a final recognized event. The application not only extracts features of an invasion event by using an optical signal, but also extracts features by using a video signal shot by a UAV, multi-dimensionally describes the invasion event, widens the number of detectable invasion events, increases the pattern recognition accuracy, and increases the reliability of the system.

The application discloses a perimeter security disturbance recognition algorithm based on GAF-ConvNeXt-TF, baseline removal and denoising pretreatment are respectively performed on one-dimensional time sequence signals generated by various disturbance behaviors; the pretreated one-dimensional time sequence signals are converted into two-dimensional time sequence diagrams, and a data total set is made according to the types of the disturbances; a ConvNeXt model is established, the training set is imported into the ConvNeXt model for training, so that a training weight file is acquired; and the test set is used to test the accuracy rate of model classification recognition. The application can enable the model to better learn the information of the time dimension; by using transfer learning, loading the pre-training weight can greatly reduce the training parameters, accelerate the model training convergence speed, and improve the accuracy of the disturbance recognition result, and provide an effective algorithm for the disturbance recognition field of the distributed optical fiber sensing system in perimeter security.

1. The name of the design product is: graphical user interface for perimeter security management of electronic equipment. 2. The use of the design product is: for display and human-computer interaction. 3. The design points of the design are: the graphical user interface content in the screen. 4. The picture or photo that best indicates the design points is: the front view. 5. The interface use is: the graphical user interface is used to show the interaction interface of air field security scene information, alarm statistical information, security equipment state information and other functions. The front view is the initial interface in the open state; click the "perimeter intrusion prevention" control of the front view to enter the interface change state diagram 1; in the interface change state diagram 1, move the mouse to the right side corresponding to the "alarm information" column content and stay, and the content of the interface change state diagram 2 will pop up.

The invention provides a wooden ancient building multi-defense-area intrusion and fire danger integrated monitoring system, and belongs to the technical field of distributed optical fibers. The system comprises a broadband light source, laser emitted by the broadband light source is divided into two paths after passing through a first 1 * 2 coupler, the two paths of laser are input into two composite structure light paths respectively, each composite structure comprises an annular Sagnac interference structure and an MZ type interference structure which are parallel to each other, the annular Sagnac interference structures are used for monitoring the temperature in an arranged defense area, and the MZ type interference structures are used for monitoring the temperature in the defense area. Intrusion vibration events around the ancient building are monitored through an MZ type interference structure; according to the system, a Sagnac structure and an MZ structure are multiplexed in parallel, the vernier effect is ingeniously utilized, function integration on a single sensing optical fiber is achieved, and material consumption and installation complexity are remarkably reduced; besides, the panda type polarization maintaining optical fiber is integrated in the annular Sagnac structure, and the MZI made of the tapering single-mode optical fiber is combined, so that the sensitivity of temperature sensing and perimeter security protection is greatly improved, and the high-standard requirement of security protection of ancient buildings is met.

1. The name of the design product: electronic fence pulse host. 2. The use of the design product: for perimeter security. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

The invention provides an alarm method based on multi-technology fusion and a perimeter security system, which can reduce missing alarm and false alarm caused by animal activity and environmental interference, improve alarm accuracy and enhance accuracy and reliability of the system. The method comprises the steps that when a first detection result of core detection equipment is received for the first time, an event attribute set containing state information of rechecking equipment and the type of the first detection result is generated, and the initial type of an event corresponding to the event attribute set is set as early warning. And if the event attribute set is not updated, triggering a corresponding alarm process according to the initial type of the event. And when the event attribute set is updated, if the event attribute set has the type of the second detection result of the rechecking equipment, updating the type of the event according to the type of the first detection result and the type of the second detection result. And if the type of the second detection result reported by the rechecking device does not exist in the event attribute set, updating the type of the event based on the running state of the rechecking device and the type of the first detection result.

This invention provides a system and method integrating perimeter security and underground monitoring, relating to the fields of engineering safety monitoring and intelligent security technology. The system includes: a data acquisition module, a time synchronization module, a data processing module, and an alarm module. The data acquisition module includes multiple sensors deployed at different locations within the monitoring area, which collect vibration data at their respective locations. The time synchronization module synchronizes the vibration data transmitted by each sensor. The data processing module detects perimeter intrusion events and calculates relative changes in seismic velocity based on the vibration data. The data processing module also sends a corresponding alarm command to the alarm module when a perimeter intrusion event is detected or when the relative change in seismic velocity meets preset conditions. The alarm module executes corresponding alarm actions based on the alarm command. This invention can simultaneously and continuously monitor surface activity and underground mechanical conditions with reliable monitoring performance.

The application utilizes piezoelectric energy storage sensors to perceive the stress distribution change of the surrounding environment of key places, integrates the electrical signals generated by piezoelectric effect, and starts the perimeter security system when the threshold is exceeded, and realizes perimeter security recognition through pattern recognition and other means. The passive sensor mechanism proposed in the application can effectively reduce the energy consumption required for the perimeter security system to maintain the on state when there is no intrusion event. The electrical signals generated by the piezoelectric energy storage sensor can be used as input signals to start the system, and also have signal acquisition function. With the energy storage system, it is expected to provide energy supplement for the perimeter security system. The application adopts a variety of passive stress sensors, and uses heterogeneous sensor data mutual verification to enrich the feature details of intrusion events, and greatly improves the intrusion event discrimination ability. At the same time, through the operation mode of "no abnormal standby, abnormal start" and the charging mode of "signal processing, energy storage", the running time of the passive perimeter security system is greatly improved, and a more feasible technical solution is provided for the super-long time continuous monitoring of the safety-related places.

The invention relates to an intelligent system for railway locomotive head perimeter panoramic safety monitoring, which comprises a plurality of fisheye camera modules arranged at the front end, the rear end and the side edge of a locomotive head and used for collecting real-time images of the bottom environment of the locomotive head; the image fusion module is used for fusing the real-time images obtained by the fisheye camera modules according to the positions of the fisheye camera modules to obtain a fused to-be-detected image; the intelligent detection module is used for receiving the fused to-be-detected image, performing target identification on a preset target in the to-be-detected image based on a pre-deployed artificial intelligence model, and performing position relation judgment on the detected target in a three-dimensional space of the locomotive head; and the early warning module is used for giving corresponding alarms according to different results of position relation judgment.

This invention relates to a vision-based method and system for identifying and classifying the boundary crossing behavior of intelligent agents in oilfield electronic fences. Belonging to the fields of oilfield perimeter security and computer vision technology, it addresses the problems of existing electronic fences, such as inability to identify target types, inability to quantify boundary crossing risks, single warning levels, and low system integration. The method involves target detection and continuous tracking of oilfield perimeter image data to obtain target type and movement trajectory. Boundary crossing verification is performed on the target's center position based on a polygonal electronic fence, and the boundary crossing distance from the target to the fence boundary is calculated. This distance is then fused with target type risk weights to obtain an adjusted boundary crossing distance. Classified warnings are implemented based on preset thresholds. Simultaneously, the warning information is visualized and logged, achieving accurate identification and differentiated warnings for boundary crossing behavior. This invention is applicable to perimeter security monitoring and intelligent risk warning in complex environments such as oilfield mining areas, well sites, and oil and gas storage areas.

The application discloses a method for reducing the false alarm rate of biological induction perimeter alarm, and particularly relates to the field of infrared induction technology; background temperature data of a monitoring area is acquired, a background thermal field benchmark model is constructed, and a background benchmark temperature value is calculated; current background temperature is acquired according to a preset time interval, and a dynamic background temperature difference drift rate is calculated; heat source change information is extracted from a thermal imaging image sequence, the variance of the heat source change in a time window is calculated, and the stability of the static heat source signal is obtained; the above two features are normalized, and a feature parameter set is constructed; the set is input into a fuzzy logic reasoning model, a PIR sensor signal credibility parameter is output according to a preset fuzzy rule; according to the credibility result, an alarm judgment path is dynamically selected, intelligent fusion and judgment of the PIR signal and the video recognition result are realized; the method can effectively identify the PIR false triggering risk in a complex thermal environment, reduce the false alarm rate, and improve the reliability and adaptability of the perimeter security system.

The application belongs to the technical field of airport perimeter security monitoring, and discloses an airport perimeter hierarchical early warning linkage method and system based on fuzzy logic reasoning. The method generates infrared and visible light panoramic images through panoramic scanning of the airport perimeter, detects and identifies the intrusion targets in the perimeter defense area in the infrared and visible light panoramic images through a multi-modal target detection algorithm based on deep learning, divides the intrusion target warning levels through a fuzzy logic algorithm, judges the priority of the intrusion target warning levels, uses an OPENCV algorithm library to mark the trajectories of multiple coordinate points closest to the intrusion target in the perimeter panoramic base map, displays the motion trajectory of the current intrusion target, and judges the intention of the intrusion target. The application can adapt to different airport environments through the self-optimization capability of the rule base, reduce the artificial patrol load while ensuring high reliability, and enhance the intelligent level and safety and control efficiency of the perimeter security system.

Testing methods and systems provide a Diagnostic Testing Service as well as an attack simulation service, for a perimeter security system, in a non-disruptive manner, so that the production targets can be tested while normally functioning, and do not have to be brought down for testing. The testing methods can be implemented, such that they are vendor and device type neutral, considering the overall strategy utilized to prevent perimeter security system attacks.

The invention relates to the technical field of radars, in particular to a radar and multi-camera combined perimeter security monitoring device and method, and the device comprises a radar module which is used for detecting a perimeter monitoring region and obtaining the target point cloud data of a target object; the visual camera module comprises a plurality of long-focus cameras which are connected in parallel, and the plurality of long-focus cameras are configured in a manner that field angles of the plurality of long-focus cameras are jointly spliced so as to cover a preset azimuth range of the perimeter monitoring area and output video data in the preset azimuth range; and the processing module is used for receiving the target point cloud data of the radar module and the video data of the visual camera module, and performing coordinate conversion and data fusion processing on the target point cloud data and the video data so as to realize real-time monitoring and identification of the target. According to the scheme, the time delay of the cameras can be reduced through splicing of multiple groups of long-focus cameras and combination of the radar, so that the monitoring efficiency of the perimeter area is improved.

The invention discloses a method for reducing the false alarm rate of biological induction perimeter alarm, and particularly relates to the technical field of infrared induction. Acquiring background temperature data of a monitoring area, constructing a background thermal field reference model and calculating a background reference temperature value; obtaining a current background temperature according to a preset time interval, and calculating a dynamic background temperature difference drift rate; heat source change information is extracted through the thermal imaging image sequence, the change variance of a heat source in a time window is calculated, and the static heat source signal stability is obtained; performing normalization processing on the two features, and constructing a feature parameter set; inputting the set into a fuzzy logic reasoning model, and outputting PIR sensor signal credibility parameters according to a preset fuzzy rule; according to a credibility result, an alarm determination path is dynamically selected, and intelligent fusion and determination of a PIR signal and a video identification result are realized; according to the method, the PIR false triggering risk in the complex thermal environment can be effectively identified, the false alarm rate is reduced, and the reliability and adaptability of a perimeter security system are improved.

The invention relates to a perimeter security monitoring method and system based on a multi-core optical fiber, and belongs to the technical field of perimeter security, and the method comprises the steps: obtaining the optical fiber sensing information of the multi-core optical fiber laid at the perimeter of a target area; when preliminarily determining that the perimeter is in a security intrusion state based on the optical fiber sensing information, judging whether the local time delay cross correlation coefficient is greater than a first preset threshold value or not and whether the global time delay cross correlation coefficient is smaller than a second preset threshold value or not; wherein the first preset threshold value is greater than the second preset threshold value; and when the local time delay cross correlation coefficient is greater than a first preset threshold value and the global time delay cross correlation coefficient is smaller than a second preset threshold value, generating security intrusion alarm information. According to the invention, long-distance real-time monitoring of the perimeter can be realized, and misjudgment caused by natural interference can be reduced in the monitoring process.

The invention discloses a tower perimeter security and protection method and system based on multi-view fusion, relates to the field of high-voltage power transmission infrastructure security and protection, and aims to solve the problems of limited monitoring range, low recognition precision, rigid alarm and difficulty in deployment, operation and maintenance of an existing tower security and protection system. The method comprises the steps of video acquisition, preliminary screening of motion, target detection and tracking, dynamic defense area decision making and the like, is suitable for a remote scene without commercial power, and can realize accurate and intelligent security and protection of the periphery of a tower. The system comprises a multi-view perception layer, an edge calculation and communication layer, a cloud intelligent analysis platform and an intelligent power supply layer, wherein the multi-view perception layer realizes 360-degree dead-angle-free coverage through a high-definition intelligent camera; the edge calculation unit executes motion detection, improved YOLOv8 target detection and single-camera tracking; the cloud platform realizes cross-camera target association through a graph neural network, and dynamically adjusts the defense area level; the intelligent power supply layer ensures long-term field operation.

The present application belongs to the field of distributed optical fiber signal vibration event classification, and discloses a perimeter security area abnormal event identification method based on optical fiber signals. The phase-sensitive optical time domain reflectometer is used to collect optical fiber vibration event data sets and perform preprocessing to form a training set. The 1DCNN model is improved. The training sample is input into the improved 1DCNN deep learning model for training to obtain the trained improved 1DCNN model. And the abnormal event identification is performed. The present application improves the identification accuracy of perimeter security area abnormal events, solves the problems of traditional feature extraction methods of optical fiber signals, such as the need for artificial feature generation, serious dependence on expert knowledge, weak generalization ability, and large calculation amount in the identification process of existing models using optical fiber signals.

The invention relates to the technical field of perimeter security monitoring, and discloses a perimeter intrusion monitoring method based on an optical fiber sensing technology, and the method comprises the steps: deploying a main sensing optical cable according to a perimeter contour to form a first monitoring surface and a second monitoring surface which is not coplanar with the first monitoring surface, and enabling a vibration optical fiber host to emit a laser signal along the sensing optical cable, the sensing optical cable converts a vibration signal into scattered return light and transmits the scattered return light back to the vibration optical fiber host, the vibration optical fiber host sequentially performs photoelectric conversion processing and acquisition analysis on the returned scattered return light to finally obtain a suspected intrusion signal, and a suspected intrusion grading signal set in a specified time period is obtained in an equal period. Based on core data of a suspected intrusion grading signal set, an AI algorithm can be used for generating an intrusion fitting track after suspected intrusion point location grouping, the intrusion fitting track is judged, effective intrusion behaviors are judged and positioned, and an intrusion grading alarm result is obtained.

The utility model discloses a vehicle depot perimeter security alarm structure easy to disassemble and assemble, a plurality of positioning holes are formed in a fixing unit, and a stand column is arranged at the upper end of the fixing unit; the positioning assembly comprises a positioning cylinder, the positioning cylinder is sleeved with a first limiting ring, the upper end of the positioning cylinder is connected with the lower end of the support, a plurality of penetrating holes are formed in the lower half section of the positioning cylinder, the diameters of the penetrating holes are gradually decreased from outside to inside, positioning balls are arranged in the penetrating holes, the positioning cylinder is sleeved with a sliding sleeve, and a second limiting ring is arranged at the upper end of the sliding sleeve. The separating assembly comprises a mounting cylinder used in cooperation with the positioning cylinder, a circle of limiting grooves used in cooperation with the positioning beads are formed in the upper half section of the mounting cylinder, and a camera is arranged at the lower end of the mounting cylinder. The tool has the advantages that the operation is simple, convenient and quick, the technical requirements of operators are reduced, the working time of the operators at high altitude is shortened, and the safety risks of fatigue caused by long-time working at high altitude, injury to people due to falling of tools and the like are reduced.