A team-level on-site security guard system

Abstract

The application discloses a kind of team level residential security warning system, it is related to security protection and low-altitude defense technical field.System includes hardware perception layer, data transmission layer, software processing layer and alarm display layer.Hardware perception layer integrates fixed image acquisition module, laser radar module, sound acquisition module as undetachable integrated multi-modal perception unit, three kinds of sensors complete inner parameter, pose and clock synchronization pre-calibration before factory, and multi-sensor space-time registration is not needed in field.Software processing layer uses multi-source data fusion and three-dimensional positioning parallel processing architecture, one way realizes multi-modal feature fusion and target identification, another way directly calculates target three-dimensional coordinates based on original measurement data, and according to target identification result and position information triggers hierarchical alarm.The application is suitable for low-altitude security warning of field training camp, temporary duty point, with the characteristics of lightweight, rapid deployment, free field calibration and low false alarm.

Description

Technical Field

[0001] This invention relates to the field of safety protection and low-altitude defense technology, specifically to a low-altitude safety warning system for field training camps, temporary duty points, and other team-level residences. Background Technology

[0002] With the increasing prevalence of small drones and low-altitude, small, and slow-moving aircraft, the low-altitude threats faced by squad-level outposts are becoming increasingly severe. Existing prevention and control methods mainly fall into two categories: first, manual visual observation, which suffers from limited sensing range, susceptibility to fatigue, and the inability to achieve 24 / 7 monitoring; second, professional-grade radar or electro-optical air defense systems, while offering high accuracy, suffer from high cost, bulky size, and complex deployment processes, making them unsuitable for the lightweight and rapid deployment needs of squad-level units. Furthermore, existing multimodal sensing systems generally require on-site multi-sensor spatiotemporal registration and time preprocessing, resulting in low deployment efficiency, high debugging difficulty, and significant data alignment errors and false alarm rates in complex field environments. Therefore, there is an urgent need for a low-altitude surveillance solution for squad-level outposts that can balance low cost, high accuracy, rapid deployment, and the elimination of on-site calibration requirements. Summary of the Invention

[0003] The purpose of this invention is to provide a team-level site security alert system that enables low-cost, lightweight, on-site calibration-free, and rapid deployment automatic detection and hierarchical alarm of low-altitude threats. This system solves the technical problems of low efficiency, high cost of professional systems, difficult deployment, and cumbersome on-site registration of multiple sensors in existing team-level site low-altitude threat prevention and control methods, thus meeting the needs of team-level site low-altitude security alerts. Technical solution

[0004] The module numbers of this invention and Figure 1 The modules are consistent, with the hardware perception layer being module 1, the data transmission layer being module 2, the software processing layer being module 3, and the alarm display layer being module 4. The sub-modules under the hardware perception layer are numbered 101-106, the sub-modules under the data transmission layer are numbered 201-202, the sub-modules under the software processing layer are numbered 301-305, and the sub-modules under the alarm display layer are numbered 401-402.

[0005] This invention provides a team-level site security monitoring system, employing a four-layer modular architecture: hardware perception layer, data transmission layer, software processing layer, and alarm display layer. Figure 1 As shown.

[0006] (a) Hardware Perception Layer (Module 1) The hardware perception layer (module 1) is the front-end multimodal perception unit of the system, including a support carrier, an image acquisition module (101), a lidar module (102), a sound acquisition module (103), an embedded main control unit (104), a power supply module (105), and a hardware clock unit (106).

[0007] The support carrier adopts a retractable aluminum alloy bracket, and a ring mounting plate with a diameter of 500mm is fixed on the top of the bracket, which serves as a unified mounting carrier for the three types of sensing sensors, ensuring that the relative positions of the sensors are fixed and the structure is stable.

[0008] The image acquisition module (101) uses a low-light CMOS device, including at least 3 low-light CMOS camera modules, which are evenly distributed along the circumference of the ring mounting plate. The horizontal field of view of adjacent modules overlaps by 10% to 15%, achieving 360° horizontal coverage without blind spots.

[0009] The lidar module (102) adopts a TOF system lidar and is fixedly installed at the center of the ring mounting plate.

[0010] The sound acquisition module (103) uses a microphone array, including at least one set of four microphone arrays, which is installed on a ring mounting plate; when two or more sets are set, they are symmetrically arranged on both sides of the ring mounting plate, with a sampling rate of not less than 48kHz, which can extract the dynamic frequency characteristics and Doppler frequency shift characteristics of the target propeller.

[0011] The embedded main control unit (104) is used to receive, preprocess and perform edge computing of multimodal sensing data, and coordinate the synchronous operation of each hardware module; the power supply module (105) is used to provide power support for all units of the hardware sensing layer; the hardware clock unit (106) adopts a high-precision temperature-compensated crystal oscillator with an accuracy of ±1ppm to provide a unified hardware time reference for the multimodal sensing units and ensure microsecond-level time synchronization of multiple sensors.

[0012] The image acquisition module (101), lidar module (102), and sound acquisition module (103) are integrated and mounted on the aforementioned ring mounting plate. Their relative positions are fixed and non-removable, forming an integrated multimodal sensing unit. Before leaving the factory, the above three types of sensors complete the calibration of the camera's own parameters, the calibration of the multi-camera ring panoramic stitching, the calibration of the spatial position of the microphone array and lidar, the calibration of the spatial position of the lidar and camera, and the calibration of the hardware clock synchronization deviation of the three types of sensors. All calibration parameters are solidified and stored in the non-volatile memory of the software processing layer (module 3). The system, combined with the hardware clock unit (106), achieves microsecond-level time synchronization of multiple sensors. During on-site deployment, there is no need to carry out additional multi-sensor spatiotemporal registration, time preprocessing, and parameter debugging operations. It can enter a stable working state as soon as it is powered on.

[0013] The total weight of the system is no more than 15kg, the protection level is no less than IP54, all of which use mass-produced commercial components, and there is no dedicated customized hardware. It features lightweight design and is easy to equip in batches at the team level.

[0014] (ii) Data transmission layer (module 2) The data transmission layer (module 2) is the data interaction hub of the system. It is used to realize multimodal perception data transmission between the hardware perception layer (module 1) and the software processing layer (module 3), as well as bidirectional transmission of alarm commands, target information and system configuration data between the software processing layer (module 3) and the alarm display layer (module 4), ensuring low latency, high reliability and stability of system data interaction.

[0015] The data transmission layer adopts a layered transmission architecture, which is divided into an internal high-speed transmission unit (module 201) and an external wireless transmission unit (module 202): Internal high-speed transmission unit (module 201): used for local data interaction between the hardware perception layer (module 1) and the software processing layer (module 3), adopting USB 3.0 and MIPI high-speed interface protocol to realize real-time transmission of image, LiDAR point cloud and sound data; External wireless transmission unit (module 202): used for wireless data interaction between the software processing layer (module 3) and the remote handheld terminal display unit (402); local alarm unit (401) is directly connected to the software processing layer (module 3) via USB interface.

[0016] The data transmission layer has a built-in data priority scheduling mechanism that sets key information such as alarm commands and target 3D coordinates as the highest priority to ensure transmission; and sets non-key information such as historical data and system logs as low priority to transmit when network bandwidth is sufficient, ensuring that emergency alarm information is pushed without delay.

[0017] (III) Software Processing Layer (Module 3) The software processing layer (module 3) is the core computing unit of the system. It is deployed based on the Linux lightweight operating system on the embedded main control unit (104) and adopts an architecture of parallel execution of multi-source data fusion and three-dimensional positioning. It includes a data preprocessing module (301), a multi-source data fusion module (302), a target recognition module (303), a three-dimensional positioning module (304), and a threat judgment and alarm control module (305).

[0018] The data preprocessing module (301) sequentially performs Gaussian noise filtering, lens distortion correction, and low-light adaptive enhancement processing on each frame of image data to output a standardized color image; it performs statistical filtering on each frame of lidar point cloud data to remove abnormal ranging values ​​that exceed the detection range or have a dispersion greater than the threshold, and extracts effective point cloud data; it uses beamforming spatial filtering and generalized cross-correlation phase difference analysis technology with a multi-microphone array to suppress environmental interference noise such as wind and insect chirping, and extracts the characteristic acoustic signal of the target propeller; based on the unified time reference provided by the hardware clock unit (106), it performs microsecond-level timestamp alignment and synchronization on the above three types of preprocessed data, and the synchronized data is input into the multi-source data fusion module (302) and the three-dimensional positioning module (304) respectively.

[0019] Multi-source data fusion module (302): An end-to-end deep learning network model consisting of a feature extraction layer, a feature fusion layer, and an output layer, which realizes feature-level fusion of multi-modal data. 1) Feature extraction layer: The MobileNetV2 lightweight convolutional network is used to extract visual features such as target contours and textures from the image; the PointNet lightweight point cloud network is used to extract geometric features such as target spatial coordinates, size, and distance from the LiDAR point cloud; Mel spectrogram transformation is performed on the acoustic data, and a one-dimensional convolutional network is used to extract the propeller dynamic frequency features and Doppler frequency shift features of the target. 2) Feature Fusion Layer: Visual features, geometric features, and acoustic features are mapped to a unified feature space of the same dimension through linear projection; a multimodal cross-attention mechanism and an environment adaptive evaluation algorithm are introduced to extract image illumination, LiDAR point cloud density, and environmental sound signal-to-noise ratio as prior conditions in real time, dynamically calculate and adaptively adjust the fusion weights of the three types of features; the association matching of the three types of features is realized through multimodal feature cross-validation, the false detection results of visual and point cloud features are assisted by acoustic features, and the physical target corresponding to the sound source is located by visual and point cloud features.

[0020] 3) Output layer: Outputs a unified multimodal feature vector after fusion. This feature vector contains three core types of information: visual contour, point cloud spatial parameters, and acoustic frequency features, providing a unified feature input for the target recognition module (303).

[0021] Target recognition module (303): Taking the multimodal feature vector output by the multi-source data fusion module (302) as input, the YOLOv5n lightweight deep learning target detection algorithm is adopted to perform special optimization for common low-altitude threats in the team's station, so as to achieve accurate target classification and discrimination; the propeller Doppler frequency shift feature in the fused feature vector is called to perform confidence weighting and misjudgment penalty on the preliminary classification results.

[0022] The 3D positioning module (304) is executed independently of the multi-source data fusion process. It does not rely on fusion features and directly uses three types of raw measurement data that have been processed and time-stamped by the data preprocessing module (301) to achieve target 3D coordinate calculation and continuous trajectory tracking. A Local Tangent Plane (LTP) coordinate system is established with the system deployment point as the origin as a unified spatial reference. The distance information of the target is extracted from the effective point cloud of the lidar. Based on the imaging position of the target in the image and combined with the pre-calibrated camera intrinsic parameters, the horizontal and vertical angles of the target relative to the equipment are calculated. The horizontal orientation information of the sound source is obtained from the acoustic data. The above distance, angle, and orientation information are uniformly mapped to the LTP coordinate system through the pre-calibrated inter-sensor rotation and translation matrix to complete the spatial registration and output the target's three-dimensional coordinates and motion trajectory.

[0023] Threat assessment and alarm control module (305): Taking the target category output by the target recognition module (303) and the target's three-dimensional coordinates and motion trajectory output by the three-dimensional positioning module (304) as core inputs, and combining them with preset threat assessment rules, it completes the target threat level classification, abnormal behavior identification and alarm control, realizing closed-loop management of "identification-location-threat assessment-alarm". 1) Preset low-altitude safety control thresholds and threat assessment standards for the base area, and clearly define the categories of threatening and non-threatening targets; 2) Combine the target's three-dimensional coordinates to determine whether it has entered the preset control area, and analyze the target's continuous multi-frame motion trajectory to identify abnormal behavior; 3) Based on the threat level and anomaly determination results, automatically trigger the corresponding level of graded alarm.

[0024] (iv) Alarm Display Layer (Module 4) The alarm display layer (module 4) is the human-computer interaction interface of the system. It is connected to the software processing layer (module 3) to receive alarm commands and output alarm information, realizing local real-time alarm and remote collaborative handling. It includes a local alarm unit (401) and a remote handheld terminal display unit (402).

[0025] The local alarm unit (401) is directly connected to the software processing layer (module 3) via a USB interface, including an LED display module and an audible and visual alarm module: the LED display module is used to display the system operating status, alarm level, target category, distance and location information in real time; the audible and visual alarm module outputs differentiated prompt signals according to different alarm levels, with a level 1 alarm triggering an audible and visual alarm and a level 2 alarm triggering a light prompt.

[0026] The remote handheld terminal display unit (402) is wirelessly connected to the software processing layer (module 3) via wireless communication. It is equipped with supporting interactive software that supports real-time alarm information push, viewing of target three-dimensional coordinates and motion trajectory, system parameter configuration, and querying and reviewing historical alarm data. On-duty personnel can remotely adjust alarm sensitivity and modify threat judgment thresholds through the handheld terminal to achieve remote control in unattended scenarios.

[0027] Performance testing This system underwent performance verification based on an embedded hardware platform and typical field application scenarios. Test conditions covered normal daytime lighting, low nighttime lighting, and complex field environments. Test results showed that the system can achieve effective low-altitude surveillance coverage, stably identify and locate typical low-altitude targets in three dimensions, and its positioning accuracy and alarm response speed meet the requirements for rapid surveillance and timely handling at the squad level. The system can operate stably under different lighting and environmental noise conditions day and night, effectively suppressing false alarms. Overall reliability and environmental adaptability are good, and it can meet the low-altitude safety surveillance needs in scenarios such as field training and temporary duty without mains power and mobile deployment.

[0028] Beneficial effects 1) This system is built using mass-produced commercial components, without any dedicated custom hardware. The overall hardware cost is controllable, which greatly reduces the equipment procurement and maintenance pressure of team-level units and solves the problem of high cost and difficulty in large-scale promotion of traditional low-altitude defense systems.

[0029] 2) The system is small in size and light in weight, and adopts a retractable support structure. It can be quickly deployed and withdrawn by a single person without the need for professional tools and complicated operations. It can quickly adapt to mobile scenarios such as field training and temporary duty points, overcoming the shortcomings of traditional professional equipment that are bulky and have complicated deployment processes.

[0030] 3) The system achieves multimodal information complementarity by working in concert with three types of sensors: image, lidar, and sound. This effectively overcomes the shortcomings of single sensors being susceptible to light, weather, and obstruction, enabling stable 24-hour operation. It provides reliable detection and stable positioning of low-altitude, small, and slow-moving targets, thus improving the robustness of surveillance in complex environments.

[0031] 4) The system adopts a sensor pre-calibration design at the factory and requires no on-site registration. It automatically enters the working mode after power-on, without the need for complicated manual configuration and professional debugging. The system responds quickly and adopts a hierarchical alarm mechanism, which makes it easy for team members to quickly perceive threats and deal with them in a timely manner. It effectively makes up for the shortcomings of low efficiency, easy fatigue and inability to be on duty around the clock by manual lookout.

[0032] 5) The system adopts a four-layer modular design consisting of a hardware perception layer, a data transmission layer, a software processing layer, and an alarm display layer. The structure is clear and the interfaces are standardized, which facilitates subsequent function expansion, algorithm upgrades and hardware iterations, and has strong applicability and maintainability.

[0033] 6) This invention adopts a parallel and separate architecture design for positioning and recognition. The three-dimensional positioning module directly uses the pre-processed raw measurement data without relying on high-dimensional fusion features. This not only reduces the power consumption of the software operation, but also avoids information loss during the fusion process, and significantly improves the positioning accuracy and system response speed. Attached Figure Description

[0034] Figure 1 This is a block diagram of the overall architecture of the team-level site security and early warning system of the present invention; Figure 2 This is a top view of the integrated multimodal sensing unit of the present invention; Figure 3 This is a three-dimensional outline of the team-level site security and early warning system of the present invention; Detailed Implementation

[0035] Example 1 In this embodiment, the squad-level base is a field training camp without a stable mains power supply, and this system is deployed for low-altitude safety surveillance. 1) Hardware configuration Embedded main control unit (104): The RDX X5 embedded main control board is selected, which meets the requirements of CPU ≥ 8 cores, main frequency ≥ 1.5GHz, supports multiple MIPI and USB 3.0 interfaces, integrates WiFi 6 transmission module and embedded low power neural network inference accelerator, power consumption ≤ 30W, and is suitable for low power operation in the field. Image acquisition module (101): 4 commercial off-the-shelf (COTS) camera modules with specific focal lengths and photosensitive areas, equipped with adjustable sunshades to effectively reduce glare from strong sunlight in the field, with an observation distance of ≥200 meters and support for continuous video acquisition; LiDAR Module (102): One civilian TOF LiDAR with a detection range of up to 350 meters, a wide field of view of 360°×90°, a ranging accuracy of ±1 meter within 200 meters, supports ≥32 line scanning capability, a frame rate of ≤20Hz, and can work stably without being affected by outdoor lighting or day and night environments. Sound acquisition module (103): 2 sets of four-microphone arrays (a total of 8 microphones), sampling rate ≥48kHz, which can effectively distinguish the sound of drone propellers from outdoor environmental noise and achieve accurate sound source location; Power supply module (105): It adopts a 12V / 20Ah lithium battery to meet the system power supply requirements in the field without mains power and ensure the continuous and stable operation of the system; Hardware clock unit (106): It adopts a high-precision temperature-compensated crystal oscillator with an accuracy of ±1ppm to provide a unified hardware time reference for the three types of sensors, ensuring microsecond-level time synchronization of multiple sensors. Its synchronization deviation parameters have been calibrated before leaving the factory.

[0036] Protection and Deployment Module: IP54 waterproof and dustproof shell, which can withstand complex environments such as wind, sand and light rain in the wild; the telescopic aluminum alloy bracket has a total weight of ≤7kg, which can effectively ensure wind resistance and stability in the wild. When unfolded, the height is 1.5-2 meters, which can be fixed to the ground and facilitates quick deployment and dismantling by a single person.

[0037] The image acquisition module (101), lidar module (102), and sound acquisition module (103) mentioned above are all integrated on a 500mm diameter ring mounting plate before leaving the factory. Their relative positions are fixed and cannot be disassembled. They also complete five integrated pre-calibrations (camera self-parameter calibration, multi-camera ring panoramic stitching calibration, spatial position correspondence calibration of microphone array and lidar, relative position and angle calibration of lidar and camera, and hardware clock synchronization deviation calibration of three types of sensors). The calibration parameters are solidified and stored in the non-volatile memory of the software processing layer. No sensor assembly and debugging operations are required on site.

[0038] The specific pre-calibration method is as follows. All calibration operations are completed before leaving the factory to ensure calibration accuracy and consistency: Calibration equipment preparation: A 12×9 checkerboard calibration board (grid spacing 20mm) is used for calibration of camera parameters and multi-camera splicing; a circular planar calibration target (diameter 300mm, reflectivity ≥90%) is used for relative pose calibration of LiDAR and camera; a standard sound source (frequency range 20Hz-20kHz, sound pressure level 94dB) is used for spatial position calibration of microphone array and LiDAR; a high-precision pulse signal generator (accuracy ±10ns) is used for hardware clock synchronization deviation calibration of the three types of sensors.

[0039] Step-by-step calibration process: Camera self-parameter calibration: Using Zhang's calibration method, the checkerboard calibration board is placed at different angles and distances (0.5m-5m) of the camera, and no less than 20 clear calibration images are collected. The intrinsic parameters (focal length, principal point coordinates, distortion coefficient) and extrinsic parameters of the camera are calculated by the algorithm to complete the single camera parameter calibration and ensure the accuracy of image acquisition. Multi-camera ring panoramic stitching calibration: Place the checkerboard calibration plate at different positions around the ring mounting plate, simultaneously acquire calibration images of 4 camera modules, calculate the extrinsic parameters (rotation matrix, translation vector) of adjacent camera modules, correct the field-of-view overlap deviation of adjacent modules, and complete 360° panoramic stitching calibration to ensure no blind spots and no misalignment; LiDAR and camera relative pose calibration: Using the hand-eye calibration method, a circular plane calibration target is placed 10m-30m in front of the sensor. Simultaneously, image data of the calibration target and point cloud data of the LiDAR are collected. The rotation and translation matrix between the LiDAR and the camera is calculated by the algorithm to complete the relative pose calibration of the two and ensure the spatial alignment of the visual data and the point cloud data. Spatial position calibration of microphone array and lidar: Place standard sound sources at different azimuth angles (0°-360°) and different distances (5m-30m), simultaneously collect acoustic signals of the sound sources and lidar ranging data, establish the mapping relationship between the sound source azimuth and spatial coordinates, complete the spatial position calibration of the two, and ensure the consistency between sound source localization and lidar detection. Hardware clock synchronization deviation calibration for three types of sensors: A high-precision pulse signal generator simultaneously sends synchronization pulse signals to the image acquisition module, lidar module, and sound acquisition module. The response time difference of each sensor receiving the pulse signal is measured, the clock synchronization deviation is calculated and compensated, and the compensation parameters are stored to ensure microsecond-level time synchronization of multiple sensors.

[0040] Calibration accuracy requirements: Based on the commercial sensors and calibration equipment selected in this embodiment, the following accuracies can be stably achieved after calibration: camera intrinsic parameter calibration error ≤ 0.5 pixels, multi-camera stitching deviation ≤ 1 pixel, relative pose calibration error between lidar and camera ≤ 0.1°, spatial mapping error between microphone array and lidar ≤ 1°, and clock synchronization deviation ≤ 1μs, ensuring that the calibration effect meets the system's working requirements.

[0041] 2) Software Deployment The software layer is deployed on a lightweight Linux operating system mounted on the RDX X5 main control board, strictly following the core logic of "data preprocessing - multi-source data fusion - target recognition - 3D localization - threat judgment and alarm". The algorithm parameters are optimized for field training scenarios. The specific deployment is as follows: Data preprocessing module (301) deployment: Deploy image noise filtering and distortion correction algorithms to optimize image preprocessing parameters for complex lighting scenarios such as strong light, low light, and shadow in the wild; deploy lidar point cloud outlier removal algorithm to improve the effectiveness of ranging data under complex terrain in the wild; and equip multi-microphone array spatial filtering and phase difference analysis algorithms to enhance the extraction of acoustic features of UAV propellers and suppress environmental interference such as wind noise and insect chirping in the wild.

[0042] Multi-source data fusion module (302) deployment: Deploy a deep learning network model including a feature extraction layer, a feature fusion layer, and an output layer. The feature extraction layer is equipped with a lightweight two-dimensional convolutional backbone network, a three-dimensional point cloud feature extraction network, and a spectrogram conversion and frequency domain analysis algorithm to extract high-dimensional visual semantics, spatial geometry, and acoustic frequency shift features. The feature fusion layer introduces a multimodal cross-attention mechanism and an environment adaptive evaluation algorithm. The system extracts the average brightness and contrast of the image in real time, the effective point cloud density of the lidar, and the environmental background signal-to-noise ratio extracted by the microphone array. The above real-time environmental parameters are used as prior conditions to dynamically calculate the confidence scores of visual, point cloud, and acoustic features in the current working scene. Subsequently, the attention mechanism network is used to assign dynamic weights to the three types of feature vectors (the weight allocation is based on the data quality of each modality, and the higher the data quality, the higher the weight ratio) and perform feature-level association and splicing to achieve adaptive fusion based on data quality. The output layer optimizes the output format of the multimodal feature vectors to ensure that the target recognition module (303) receives a coherent and unified feature input.

[0043] The target recognition module (303) is deployed as follows: It is equipped with a lightweight deep learning target detection algorithm, based on samples of common low-altitude targets (small drones, suspicious aircraft, kites, birds) in the field training camp. The sample set includes target images, point clouds and acoustic data under different lighting, distances and backgrounds to ensure the generalization ability of the model, complete the fine-tuning of the model weights, and optimize the target detection sensitivity at a distance of 200 meters and under complex backgrounds. The acoustic feature correction algorithm is deployed to call the propeller Doppler frequency shift feature in the fused feature vector to eliminate misjudgments caused by single-mode interference and improve the target recognition accuracy in complex field environments.

[0044] 3D positioning module (304) deployment: With the system deployment point as the origin, a local tangent plane (LTP) coordinate system is established, and a spatial registration algorithm for lidar ranging data, image angle data, and sound source azimuth data is deployed. The registration process combines the pre-calibrated inter-sensor rotation and translation matrix to uniformly map lidar ranging, image angle, and sound source azimuth data to the LTP coordinate system, completing the spatial alignment of multi-source data and ensuring the accuracy of target 3D coordinate calculation and trajectory tracking under unobstructed and complex terrain conditions in the field. It is executed independently of the multi-source data fusion process, does not rely on fusion features, and directly calls the pre-processed original measurement data, reducing software power consumption and adapting to lithium battery power supply requirements.

[0045] Threat assessment and alarm control module (305) deployment: preset low-altitude safety control threshold for field training camps, clearly define small drones and suspicious aircraft as key control targets, and kites and birds as non-threat targets; set target intrusion distance thresholds (100 to 200 meters as the warning range, less than 100 meters as the danger range), identify abnormal movement behaviors such as uniform approach and sudden turning; deploy hierarchical alarm algorithm, first-level alarm (danger range) triggers local sound and light warning and real-time push to handheld terminal, second-level alarm (warning range) only triggers handheld terminal prompt, adapting to the rapid response needs of field teams.

[0046] Alarm Display Layer (Module 4) Deployment: The local alarm unit (401) is placed outside the system protective shell and deployed together with the integrated multimodal sensing unit at the high ground of the guard post; the matching interactive software is installed in the handheld terminal (402) of the camp duty personnel, which supports WiFi 6 wireless transmission and realizes the functions of alarm information push, target information viewing, system parameter configuration and historical data review.

[0047] 3) Deployment and debugging Deployment point selection: The central high ground of the camp was selected as the system deployment point. This point has a wide field of view and no obvious obstructions, which can ensure 360° horizontal coverage without blind spots and is suitable for the needs of field surveillance.

[0048] Hardware Setup: A single-person extendable aluminum alloy stand is used, secured to the ground with a weighted base to ensure stability. The integrated multimodal sensing unit, pre-calibrated and fully integrated with sensors at the factory, is installed on top of the stand. A protective housing integrating the embedded main control board and lithium battery is fixed to the lower part of the stand, completing the standardized data and power line connections between the integrated sensing unit, the main control board, and the lithium battery, ensuring good contact and adequate protection. In this embodiment, the RDX X5 model is used as an example of the embedded main control board; other ARM architecture main control boards that meet performance requirements can also be used.

[0049] System startup and operation: Connect the 12V / 20Ah lithium battery power supply to start the system. The main control board automatically loads the factory pre-calibrated parameters and various software modules. Combined with the hardware clock unit, it completes microsecond-level time synchronization of multiple sensors. No professional debugging such as sensor installation, position adjustment, calibration, spatiotemporal registration, etc. are required on site. The system can enter a stable working state within 2 minutes.

[0050] 4) Running effect Battery life: It is powered by a 12V / 20Ah lithium battery, which can work continuously and stably for 8 hours on a single charge. It can also work continuously for 24 hours by quickly replacing the lithium battery, meeting the all-weather duty needs in the field where there is no mains power.

[0051] Warning effect: The system can effectively detect small drones that intrude at low altitudes within a 100-meter range. It can accurately classify and identify targets and perform three-dimensional positioning. The alarm response time is no more than 1 second, the positioning error is controlled within 2 meters, and the corresponding alarm signal is triggered in a timely manner. Alarm prompts are achieved through local sound and light warnings and push notifications from handheld terminals to ensure the safety of the warning area.

[0052] System stability: The system operated continuously for 72 hours without equipment failure or data transmission interruption. Relying on multi-source data fusion algorithms, it can achieve stable operation day and night. Even in complex environments such as nighttime darkness, cloudy days, and light rain, it can still maintain reliable detection and identification accuracy. The number of false alarms and missed alarms is significantly lower than that of single sensor systems.

[0053] Ease of deployment: A single person can complete all hardware setup and system startup within 5 minutes, without the need for professional tools or debugging. Deployment efficiency is more than 90% higher than traditional multimodal detection systems, and it can quickly adapt to the mobile relocation needs of field training camps.

[0054] In summary, this system is suitable for the low-altitude safety and surveillance needs of field training camps. It has outstanding advantages such as low cost, lightweight design, no need for on-site calibration, and rapid deployment. It can effectively solve the practical problems of low-altitude threat prevention and control at field team-level camps and provide reliable technical support for camp safety.

[0055] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A team-level site security alert system, comprising a hardware sensing layer, a data transmission layer, a software processing layer, and an alarm display layer, wherein the hardware sensing layer is communicatively connected to the software processing layer via the data transmission layer, and the software processing layer is communicatively connected to the alarm display layer; characterized in that: The hardware sensing layer includes a support carrier, an image acquisition module, a lidar module, a sound acquisition module, and a hardware clock unit. The image acquisition module, lidar module, and sound acquisition module are integrated and installed on the top of the support carrier, with fixed relative poses and are non-removable, forming an integrated multimodal sensing unit. All three types of sensors undergo full-process pre-calibration of intrinsic parameters, relative poses, and time synchronization references before leaving the factory. The calibration parameters are stored in the non-volatile memory of the software processing layer. Combined with the hardware clock unit, multi-sensor time synchronization is achieved, eliminating the need for additional multi-sensor spatiotemporal registration during system deployment. The software processing layer is configured to execute multi-source data fusion and 3D positioning processes in parallel: after preprocessing and timestamp alignment of the three types of sensor data, one path achieves target recognition through multimodal feature fusion, while the other path directly uses the preprocessed raw measurement data to calculate the target's 3D coordinates. Hierarchical alarms are triggered based on the target recognition results and 3D coordinates.

2. The team-level site security and early warning system according to claim 1, characterized in that, The hardware sensing layer also includes an embedded main control unit and a power supply module; the supporting carrier is a retractable aluminum alloy bracket with a 500mm diameter annular mounting plate on top; the image acquisition module consists of at least three low-light CMOS camera modules, evenly distributed along the annular mounting plate, with adjacent modules overlapping horizontally by 10% to 15%, achieving 360° horizontal coverage without blind spots; the lidar module is a TOF lidar system, installed at the center of the annular mounting plate; the sound acquisition module consists of at least one four-microphone array with a sampling rate of not less than 48kHz; the hardware clock unit uses a temperature-compensated crystal oscillator with an accuracy of ±1ppm, providing a microsecond-level unified time reference for the three types of sensors; the total weight of the entire system is no more than 15kg, and the protection level is no less than IP54.

3. The team-level site security and early warning system according to claim 1, characterized in that, The data preprocessing of the software processing layer includes noise filtering, distortion correction, and low-light enhancement of images; removal of abnormal ranging values ​​from the LiDAR point cloud; and spatial filtering and phase difference analysis of the sound data. The multi-source data fusion employs a multimodal cross-attention mechanism, dynamically adjusting the fusion weights of the three types of features based on ambient lighting parameters and the sound signal-to-noise ratio. It uses LiDAR point cloud features as the query vector and image and acoustic features as key vectors for weighted feature fusion. The target recognition uses a lightweight deep learning model and leverages propeller Doppler frequency shift features to weight the recognition results with confidence and penalize false positives. The 3D positioning establishes a local tangent plane coordinate system with the system deployment point as the origin, and spatial registration of multi-source measurement data is completed based on a pre-calibrated inter-sensor rotation and translation matrix. The hierarchical alarm is divided into Level 1 and Level 2 alarms based on the target category, location, and motion trajectory.

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