97 results about "Perimeter Security" patented technology

The invention discloses an invading object detection method and device applied to a perimeter security system, and relates to the technical field of invading object detection.According to the invading object detection method and device, a background model is dynamically updated by combining adaptive background modeling with time sequence analysis, background stability is kept in dynamic environments such as illumination variation and leaf shaking, and the invading object detection efficiency is improved. Different types of background changes are distinguished through a Gaussian mixture model, and the coexistence condition of long-term static background and short-time dynamic change can be adapted; in the target detection stage, a front-and-back frame difference method and a background subtraction method are combined, a moving target is extracted by utilizing complementary characteristics of the two methods, in the target classification and behavior analysis stage, spatial morphological characteristics of the target are extracted, the motion trend of the target is analyzed and predicted in combination with a time sequence, and track prediction is performed by adopting Kalman filtering. Even if the target is shielded for a short time or moves slowly, the tracking stability can still be kept.

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 embodiment of the invention relates to the technical field of information, in particular to an intelligent perimeter security and protection method and system based on multi-data fusion. The method comprises the following steps: acquiring multi-source sensor data; identifying the type of an intrusion object passing through the perimeter through the video data, synchronously recording detection values of other sensors when the intrusion object passes through the perimeter, and identifying an event or a behavior according to the detection values; constructing a multi-modal feature data set according to the event and the behavior; based on the multi-modal feature data set, generating a reference baseline for perimeter intrusion identification; comparing subsequently acquired multi-source sensor data of the same invasion object type with the reference baseline for multiple times, and correcting the reference baseline according to a comparison result; and recognizing perimeter intrusion based on the corrected reference baseline to obtain an intrusion object type, and generating an intrusion detection result according to the intrusion object type.

The invention discloses a high-precision perimeter security and protection method and system for intelligent diagnosis, which can accurately mark a physical boundary and perform reasonable partitioning by accurately acquiring a security and protection perimeter contour diagram, thereby facilitating more effective deployment of intrusion detection equipment, improving the overall security of a target area, and improving the security and protection efficiency of the target area. Real intrusion and false alarm are distinguished by adopting a predefined rule to set a threshold value and training a dichotomy model, the false alarm rate can be remarkably reduced through manual recheck of suspected intrusion events, it is ensured that safety response is more accurate and effective, low-level, middle-level and high-level alarm mechanisms are introduced, and the safety and reliability of the system are improved. The abnormal condition can be properly responded according to the risk level, so that the processing efficiency is improved, and corresponding emergency plans can be adopted for threats of different levels.

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.

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 provides a multi-modal fusion oil and gas pipeline perimeter security and protection method and system and a storage medium, relates to the field of oil and gas pipeline security and protection, and aims to solve the problem that radar and image / video multi-modal fusion-based oil and gas pipeline perimeter security and protection methods are lacked in the prior art. Comprising the following steps: step S100, acquiring point cloud data of an oil and gas pipeline perimeter environment through a radar, determining a suspicious target, and enabling a camera to track the target and acquire image data; s200, performing feature extraction and fusion on the point cloud data to obtain multi-modal fusion data; step S300, constructing a target detection model based on YOLO, and introducing an MaSA module for introducing explicit space prior in the visual backbone network, so that the model focuses on target features; the RCS-OSA module is used for multi-scale feature extraction and aggregation; an MLIA module is also introduced and is used for enabling the module to have a second-order information interaction capability and calculating the local importance of pixels; and step S400, detecting an intrusion target in the multi-modal fusion data based on a target detection model of YOLO.

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 discloses a perimeter security system and method based on deep learning, and relates to the technical field of security, a multi-modal fusion technology is introduced in a target detection stage, and sufficient key point information can be obtained even if a target is only partially exposed through combined extraction of optical, thermal and infrared features; in the target recognition stage, the target form is speculated by adopting a target complementation technology and combining with a generative adversarial network, and the shielded part is complemented through a semantic segmentation method, so that the system completes recognition without depending on a complete target contour, and the classification stability is improved; in the target tracking stage, LSTM trajectory prediction and reinforcement learning are combined, so that the system can speculate the next motion position of the target based on a historical trajectory, and the target cannot be lost due to transient shielding; meanwhile, under the condition of long-time shielding, the system adopts a Markov decision process to optimize the probability of occurrence of the target, and the recognition rate when the target appears again is improved.

The invention relates to the technical field of DAS data enhancement processing, in particular to a DAS data enhancement method based on an improved DRAGAN, and the method comprises the following steps: S1, constructing a DAS perimeter security system, and collecting and preprocessing data; s2, a DRAGAN data enhancement model is improved, an EnhancedMCAM module is introduced into a generator, a residual block and a CBAM module are introduced into a discriminator, and an MCDRAGAN data enhancement model is formed; s3, training a data enhancement model, and adopting an adaptive learning rate adjustment strategy; s4, inputting the preprocessed data into an MCDRAGAN data enhancement model to obtain a generated sample; s5, evaluating the MCDRAGAN data enhancement model by adopting an AlexNet classification model, and verifying the validity of the data enhancement model; according to the method, the improved MCDRAGAN data enhancement model is used to generate the new data sample, the distribution characteristics of the data are well simulated, the diversity of the data set is expanded, and better identification accuracy can be obtained.

The invention belongs to the technical field of airport boundary security and protection monitoring, and discloses an airport boundary grading early warning linkage method and system based on fuzzy logic reasoning. According to the method, an airport boundary is subjected to panoramic scanning to generate an infrared and visible light panoramic image, intrusion target detection and identification are performed on a boundary defense area in the infrared and visible light panoramic image through a multi-mode target detection algorithm based on deep learning, intrusion target early warning grade division is performed through a fuzzy logic algorithm, and an intrusion target early warning result is obtained. And carrying out early warning level priority judgment on the intrusion target, carrying out track labeling on a plurality of coordinate points closest to the intrusion target in the surrounding panoramic base map by using an OPENCV algorithm library, displaying the motion track of the current intrusion target, and judging the intention of the intrusion target. According to the invention, different airport environments are adapted through the rule base self-optimization capability, the manual patrol load is reduced while the high reliability is ensured, and the intelligent level and the safety prevention and control efficiency of the enclosure security and protection system are enhanced.

Multiple embodiments of this specification relate to the field of information technology, and specifically to a multi-data fusion intelligent perimeter security method and system. The method includes: acquiring multi-source sensor data; identifying the type of intrusion object crossing the perimeter through video data, and synchronously recording the detection values ​​of other sensors when the intrusion object crosses the perimeter, and identifying events or behaviors based on the detection values; constructing a multimodal feature data set based on the events and behaviors; generating a reference baseline for perimeter intrusion identification based on the multimodal feature data set; comparing subsequently collected multi-source sensor data of the same intrusion object type with the reference baseline multiple times, and revising the reference baseline based on the comparison results; identifying perimeter intrusion based on the revised reference baseline, obtaining the intrusion object type, and generating an intrusion detection result based on the intrusion object type.

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.

The present invention relates to the technical field of DAS data enhancement processing, and specifically designs a DAS data enhancement method based on an improved DRAGAN, comprising the following steps: S1, constructing a DAS perimeter security system, collecting and preprocessing data; S2, improving the DRAGAN data enhancement model, introducing an EnhancedMCAM module into a generator, and introducing a residual block and a CBAM module into a discriminator to form an MCDRAGAN data enhancement model; S3, training the data enhancement model, and adopting an adaptive learning rate adjustment strategy; S4, inputting the preprocessed data into the MCDRAGAN data enhancement model to obtain generated samples; S5, using an AlexNet classification model to evaluate the MCDRAGAN data enhancement model and verifying the effectiveness of the data enhancement model. The present invention generates new data samples by using the improved MCDRAGAN data enhancement model, which well simulates the distribution characteristics of the data, expands the diversity of the data set, and can obtain a better recognition accuracy.

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 belongs to the technical field of intelligent sensing, and relates to a micro-motion detection method for a perimeter security ground moving target, which comprises the following steps of: acquiring seismic motion data of the moving target; preprocessing the data, and constructing a data set: constructing a spatial-temporal feature joint extraction model, capturing spatial-temporal features of a short-term model by using a CNN, and identifying different features in the data, thereby completing intelligent identification of a suspicious target; physical quantities related to time sequence changes are continuously captured through the LSTM; a time mode attention mechanism is introduced into the spatial-temporal feature joint extraction model, and feature weights are dynamically adjusted, so that the model pays more attention to key time and key signals, and irrelevant noise is suppressed; and training and testing the obtained model, identifying shallow surface abnormal vibration data, and performing automatic extraction and classification. The detection method can reach 97% of identification accuracy in five types of ground moving target classification tasks, and the processing speed is improved by 36% compared with that of a traditional model.

A security fence includes a plurality of vertical fence posts spaced from one another along a fence line, each vertical fence post having an upper end and a lower end. A wire receiving slot is formed in each vertical fence post. Each wire receiving slot having an open, upper end and a closed, lower end. At least one reinforcing plate is secured to each vertical fence post for covering a gap in the vertical fence post defined by the wire receiving slot. The at least one reinforcing plate is configured to enhance the structural integrity of the vertical fence post in the vicinity of the wire receiving slot and retain the one or more conductive wires or cables within the wire receiving slot.

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.

The invention provides a perimeter false alarm filtering control method and system, and relates to the technical field of perimeter security, after original sampling data corresponding to a plurality of continuous sampling points of an area perimeter are acquired, two times of judgment are carried out to screen out sampling data with an instantaneous impact characteristic and a tension continuous jump characteristic, and the sampling data are subjected to false alarm filtering control according to the instantaneous impact characteristic and the tension continuous jump characteristic. After screening is completed, an actual tension attenuation data sequence in a preset time period is collected, the actual tension attenuation data sequence and a theoretical tension attenuation data sequence are subjected to coincidence comparison, and corresponding operation is executed according to a coincidence comparison result. Therefore, the method has the advantages that tension change and intrusion behaviors caused by the thermodynamic effect can be effectively distinguished, and false alarms caused by non-invasive factors such as environment temperature difference are remarkably reduced.

1. Name of the Design Product: Anti-intrusion Integrated Machine (Intelligent Wall Quick Anti-intrusion System). 2. Use of the Design Product: A device for perimeter security detection and alarm, real-time monitoring and video playback, data collection, security deployment and linkage alarm. 3. Design Key Points of the Design Product: Lies in the shape. 4. Picture or Photo that Best Illustrates the Design Key Points: 3D View 1 of Design 1. 5. The top surface design content of Design 2 is reflected in the 3D view, so the top view of Design 2 is omitted; the bottom surface of Design 2 is not visible or not easily visible during use, so the bottom view of Design 2 is omitted. 6. Design 1 is designated as the basic design.