Navigation positioning security management system based on indoor intelligent hardware ad hoc network

Abstract

The invention provides a navigation positioning security management system based on an indoor intelligent hardware ad hoc network, and the system comprises a high-precision UWB positioning module, a cloud fusion management and control platform, an indoor environment sensing suite, an intelligent image collection and analysis assembly, and a portable interaction terminal. The high-precision UWB positioning module is based on hardware of a building and realizes an ad-hoc network through communication modules such as Bluetooth MESH and CAT1 so as to form a gridding ultra-precise UWB positioning array, and the UWB positioning array is composed of a plurality of miniaturized and low-power UWB positioning base stations. Positioning labels carried by personnel and valuable assets are accurately tracked, the real-time three-dimensional coordinate positioning precision is stabilized within 10 cm, and packet loss and positioning drift do not occur even if the personnel rapidly move.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent indoor space management, and in particular to a navigation, positioning and security management system based on an indoor intelligent hardware ad hoc network. Background Art

[0002] With the acceleration of urbanization and the rapid development of the Internet of Things and smart building technologies, large indoor spaces such as shopping malls, complex hospital buildings, high-rise office buildings, and underground tunnels are becoming increasingly common. Traditional wayfinding methods that rely on signage are inefficient, and indoor positioning, navigation, and security are becoming increasingly important in smart buildings. Existing indoor navigation technologies, such as those based on conventional wireless signals like Wi-Fi and Bluetooth, are subject to interference from factors like indoor multipath, complex electromagnetic environments, and signal obstruction, resulting in significant positioning errors and failing to meet people's needs for fast and accurate destinations. Indoor positioning and navigation systems typically rely on complex sensors, networks, and expensive hardware, or rely on external signals like GPS, which can be weak or unavailable indoors, resulting in reduced navigation accuracy. Furthermore, indoor security systems often operate independently, resulting in high deployment costs and difficult maintenance. They lack effective integration and intelligent management, making them difficult to adapt to the needs of diverse buildings and application scenarios. Furthermore, existing systems often lack flexibility and scalability. At the same time, the indoor security situation is severe, the indoor population is dense, the layout is complex, and the dense flow of people makes illegal intrusions, thefts, sudden accidents and other security problems frequent, and there are many safety hazards. Existing security monitoring mostly relies on manual monitoring of the monitoring screen, which makes it difficult to detect dangerous situations in time, such as the elderly suddenly falling to the ground due to illness, criminals sneaking in secretly, and smoke spreading in the early stage of fire. These anomalies often miss the best time to deal with them, posing a huge safety hazard.

[0003] Therefore, it is necessary to develop a highly integrated and intelligent indoor navigation and security system to improve the accuracy and safety of indoor navigation while realizing asset management and fire safety management. Summary of the Invention

[0004] In order to solve the above problems, the present invention proposes a navigation, positioning and security management system based on an indoor intelligent hardware self-organizing network to more accurately solve the above problems.

[0005] The present invention is achieved through the following technical solutions: The present invention proposes a navigation, positioning and security management system based on an indoor intelligent hardware self-organizing network, including a high-precision UWB positioning module, a cloud-based fusion management and control platform, an indoor environment perception kit, an intelligent image acquisition and analysis component, and a portable interactive terminal. The high-precision UWB positioning module is based on the building's own hardware and realizes self-organizing networking through communication modules such as Bluetooth MESH and CAT1 to form a grid and is supplemented by an ultra-precise UWB positioning array. The UWB positioning array is composed of multiple miniaturized, low-power UWB positioning base stations. The UWB positioning base stations send an initialization completion signal to the cloud-based fusion management and control platform through a wired or wireless network, and realize positioning based on the flight time and arrival time difference algorithm, and transmit their own detailed parameters such as the hardware serial number, initial coordinate position, and transmission power setting. The high-precision UWB positioning module sets multiple intelligent hardware nodes, each node includes at least one communication module and one UWB positioning base station, and at least one positioning gateway for collecting and processing the positioning data sent by the node.

[0006] The intelligent image acquisition and analysis components include smart cameras and edge computing devices deployed in key areas. The smart cameras are preferably high-resolution, wide dynamic range cameras, and are paired with a deep learning-based multimodal behavior analysis engine built into the edge computing devices. They utilize artificial intelligence image recognition algorithms to perform real-time recognition of facial features and body movements of people, as well as rapid identification of common dangerous items. The indoor environment sensing kit includes temperature and humidity sensors, harmful gas sensors, and smoke alarms that integrate nano-scale sensitive materials. These sensors are connected to a convergence node via a wireless ad hoc network to monitor environmental parameters and trigger abnormal warnings. The portable interactive terminal includes a wearable portable device (such as a lightweight bracelet or badge style), the wearable portable device has a built-in UWB tag and a multi-sensor fusion module that cooperates with the UWB positioning module to achieve positioning compensation and realize autonomous inertial navigation-assisted positioning; The cloud-based integrated management and control platform is built based on containerization and microservice architecture. It is connected to high-precision UWB positioning modules, intelligent image acquisition and analysis components, indoor environment perception kits and portable interactive terminals through high-speed fiber optic networks. It has the ability to access, store and analyze massive amounts of data in real time, integrate positioning, vision and environmental data, generate personalized navigation paths and security situation assessment reports through intelligent algorithms, and push information in both directions to managers and users' mobile terminals.

[0007] Furthermore, the UWB positioning base station has a built-in environmental monitoring sensor. When it detects that the electromagnetic interference exceeds the threshold or the temperature and humidity are beyond the working range, it automatically switches the frequency band or reduces the transmission power, and sends an early warning to the cloud-based integrated management and control platform. The portable interactive terminal carrying the UWB tag wakes up through the low-power monitoring mode, interacts with the base station when the signal strength reaches the threshold, and triggers a replacement reminder when the tag battery is lower than the preset value.

[0008] Furthermore, the edge computing device has a built-in image recognition model based on deep learning, which tracks human joints through the OpenPose algorithm, determines abnormal behavior in combination with motion trajectory analysis, and detects dangerous objects through the YOLOv5 model; after identifying the anomaly, the edge device will transmit the alarm information containing key frames, location coordinates and timestamps to the cloud-based fusion management and control platform at millisecond speeds, and receive the incremental training model issued by the cloud-based fusion management and control platform to improve the recognition accuracy.

[0009] Furthermore, the aggregation node performs CRC check on the sensor data. When it detects that the environmental parameters exceed the safety threshold, it triggers a local sound and light alarm and uploads the abnormal data to the cloud-based integrated management and control platform; the cloud-based integrated management and control platform controls the ventilation system and fire sprinkler device based on the abnormal data, and dynamically adjusts the sampling frequency and alarm threshold of the sensor.

[0010] Furthermore, the multi-sensor fusion module built into the wearable portable device includes sensors such as acceleration, gyroscope, and geomagnetism, which triggers path replanning when the user deviates from the navigation route by more than 5 meters; The portable interactive terminal establishes a dedicated line connection with the security center through the mobile network module, and the transmission of emergency assistance signals is not affected by the indoor network environment.

[0011] Furthermore, the wearable portable device is equipped with a high-brightness OLED touch screen and a one-touch SOS emergency button.

[0012] Furthermore, the cloud-based integrated management and control platform adopts a modular design, supports rapid upgrades and functional expansion, and implements data encryption transmission and local storage mechanisms to ensure user privacy and data security.

[0013] Furthermore, the cloud-based integrated management and control platform uses distributed message queues to buffer massive amounts of data, and plans real-time routes through an improved Dijkstra algorithm, updating the navigation route every 5 seconds. When security or environmental anomalies are received, the cloud-based integrated management and control platform pushes the suspect target tracking image and escape route prediction to the security terminal, and controls the activation of sound and light alarms and fire-fighting equipment.

[0014] Furthermore, the system also includes: inter-module collaborative operation logic, including the terminal automatically connecting to the UWB base station and Wi-Fi hotspot when the user enters the room, the cloud-based integrated management and control platform dynamically adjusts the security strategy based on real-time positioning data and image analysis results, and optimizes energy consumption through environmental perception data.

[0015] Beneficial effects of the present invention: 1. The high-precision UWB positioning module in this invention is based on the building's own hardware and uses Bluetooth, MESH, CAT1 and other communication modules to achieve self-organizing networking, thereby forming a grid and supplemented by an ultra-precise UWB positioning array. The UWB positioning array is composed of multiple miniaturized, low-power UWB positioning base stations. According to the preset indoor layout configuration file, it automatically adjusts the transmission power and frequency band, continuously transmits ultra-wideband pulse signals to the surrounding space, and builds a positioning signal field to ensure signal coverage without blind spots. It accurately tracks the positioning tags carried by people and valuable assets, and the real-time three-dimensional coordinate positioning accuracy is stable within 10 centimeters. Even in the scene of rapid movement of people, there will be no packet loss or positioning drift. 2. This invention allows users to input their target location on an interactive terminal. The cloud-based integrated management and control platform calls the indoor map database and real-time positioning data. The engine combines real-time positioning with AI algorithms and the Dijkstra algorithm improved by the ant colony optimization algorithm to plan the optimal path, avoiding obstacles and crowds, and provides step-by-step guidance in the form of 3D animation. 3. After the intelligent camera of the present invention captures abnormal behavior or objects, it performs preliminary analysis and screening on the edge computing device, and transmits key information including image fragments, image key frames, location coordinate information, timestamps, etc. to the cloud-based integrated management and control platform at millisecond speed to determine whether there are abnormal behaviors such as illegal intrusion and suspicious wandering. It can also quickly identify common dangerous objects, such as knives and flammable and explosive items. Once discovered, it will immediately issue an early warning and push detailed warning information to the mobile terminal of security personnel to assist in arrest. On the other hand, it will activate emergency broadcasts and sound and light alarms in the incident area and surrounding areas to evacuate people and warn surrounding people. 4. The present invention detects the external environment through an indoor environment sensing kit and accurately controls ventilation, fire protection, ventilation and smoke exhaust, sprinkler dust reduction and other systems based on sensor feedback, taking a multi-pronged approach to curb risks and minimize losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a structural diagram of the navigation, positioning and security management system in the present invention. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention. Example 1

[0018] A navigation, positioning, and security management system based on an indoor intelligent hardware ad hoc network includes a high-precision UWB positioning module, a cloud-based integrated management and control platform, an indoor environment perception suite, an intelligent image acquisition and analysis component, and a portable interactive terminal. The high-precision UWB positioning module utilizes a building's existing hardware (including but not limited to lighting, emergency lights, emergency signs, doors and windows, locks, hinges, smoke detectors, sprinklers, and other indoor assets) and uses Bluetooth, MESH, and CAT1 communication modules to form an ad hoc grid. This is supplemented by an ultra-precise UWB positioning array, comprised of multiple miniaturized, low-power UWB positioning base stations. The high-precision UWB positioning module utilizes multiple intelligent hardware nodes, each of which includes at least one communication module and one UWB positioning base station, as well as at least one positioning gateway for collecting and processing positioning data sent by the node. The UWB positioning base station uses adaptive power regulation and dynamic time slot allocation technology. According to the preset indoor layout configuration file, it automatically adjusts the transmission power and frequency band, continuously transmits ultra-wideband pulse signals to the surrounding space, builds a positioning signal field, ensures signal coverage without blind spots, and accurately tracks the positioning tags carried by people and valuable assets. The real-time three-dimensional coordinate positioning accuracy is stable within 10 centimeters, and there is no packet loss or positioning drift even in scenarios with rapid movement of people.

[0019] The technical solution in the above embodiment of the present application is specifically implemented as follows: 1. Initialization and signal transmission: At system startup, after the UWB positioning base station completes a self-test, it automatically adjusts its transmit power (initial power set to 10 dBm to ensure initial coverage) and frequency band (preferably the 5 GHz band for minimal interference) based on the preset indoor layout configuration file. It continuously transmits ultra-wideband pulse signals into the surrounding space, establishing a positioning signal field. Simultaneously, the base station sends an initialization completion signal to the cloud-based integrated management and control platform via a wired or wireless network. Positioning is achieved using a time-of-flight and time-difference-of-arrival algorithm, along with detailed parameters (such as hardware serial number, initial coordinates, and transmit power setting) for unified management and monitoring by the cloud-based integrated management and control platform.

[0020] When a person or device carrying a UWB tag enters a room, the tag's built-in RF receiving module is in a low-power monitoring state. Once it detects that the base station signal strength reaches the wake-up threshold (-70 dBm), it immediately starts and enters the signal interaction process.

[0021] 2. Positioning calculation and dynamic adjustment: After receiving the tag's feedback signal, the UWB positioning base station uses high-precision clock synchronization technology (with nanosecond accuracy) to accurately measure the signal's time of flight (ToF) or time difference of arrival (TDoA). Combined with the known relative positions between the base stations (preliminary calibration with centimeter-level accuracy), it uses triangulation or multilateral positioning algorithms to calculate the tag's corresponding three-dimensional coordinate position in real time.

[0022] Every one second, the UWB positioning base station smooths the positioning data to remove outliers caused by signal jitter (using a mean filter algorithm with a window size of 5) to ensure data stability. If the positioning calculation results deviate by more than 0.5 meters for five consecutive times (e.g., due to rapid movement, signal obstruction, etc.), the base station automatically increases the signal transmission frequency (from 10 to 20 times per second) and sends a positioning accuracy warning to the cloud-based integrated management and control platform, requesting that the cloud-based integrated management and control platform coordinate with surrounding base stations for collaborative positioning.

[0023] 3. Environmental adaptation and troubleshooting: The base station's built-in environmental monitoring sensors (such as electromagnetic interference sensors and temperature and humidity sensors) collect real-time data about the surrounding environment. When electromagnetic interference intensity exceeds a threshold (30 dBμV / m), the base station automatically switches frequency bands (attempting to switch bands in sequence according to a preset frequency priority list) and reports the interference to the cloud-based integrated management and control platform. If the temperature and humidity exceed the normal operating range (temperature below 0°C or above 45°C, humidity below 20% or above 80%), the base station activates self-protection mode, reducing transmit power by 50% and sending a fault warning to the cloud-based integrated management and control platform, pending maintenance personnel's attention.

[0024] The UWB positioning base station has a built-in local cache module. If the communication between the base station and the cloud-based integrated management and control platform is interrupted for more than 5 seconds, it will immediately enter local redundancy mode and store the positioning data of the last 10 seconds in the local cache (cache capacity 100 KB). After the communication is restored, it will be uploaded to the cloud-based integrated management and control platform in batches to ensure that the data is not lost. Example 2

[0025] The intelligent image acquisition and analysis component includes smart cameras and edge computing devices deployed in key areas. The smart cameras are preferably high-resolution, wide dynamic range cameras, coupled with the edge computing device's built-in deep learning-based multimodal behavior analysis engine. Using AI image recognition algorithms, they identify facial features and body movements in real time to determine abnormal behavior such as trespassing or suspicious wandering. They can also quickly identify common dangerous objects, such as knives and flammable and explosive items, and issue an immediate warning upon detection. Furthermore, they can instantly identify suspicious actions, such as theft (by modeling body movements and object handling postures), and signs of physical discomfort (such as changes in posture and convulsions during fainting) even in complex lighting and obstruction conditions. Furthermore, they have the ability to relay tracking across cameras, ensuring that abnormal behavior is fully documented.

[0026] The technical solution in the above embodiment of the present application is specifically implemented as follows: 1. Image acquisition startup and preprocessing: After powering on, the smart camera automatically adjusts the lens focal length, aperture size (for example, in a dimly lit corridor, the aperture is automatically adjusted to F2.8), and shooting angle based on the preset monitoring area profile (including the coordinates of key monitoring points, field of view, and monitoring time period) to ensure optimal image acquisition. Simultaneously, it initiates image preprocessing, utilizing the built-in dedicated image processor chip to perform real-time noise reduction (using a wavelet noise reduction algorithm with a decomposition level of 3) and enhancement (histogram equalization to enhance contrast) on the captured images, improving image clarity and recognition.

[0027] The camera captures images at a preset frame rate (initial 25 frames per second) and resolution (initial 1080p) and multicasts the image data to the edge computing device via a low-latency streaming protocol such as RTSP. During transmission, forward error correction (FEC) technology (with 20% redundancy) is used to ensure image data integrity and reduce packet loss.

[0028] 2. Behavior and object recognition analysis: After receiving image data, the edge computing device immediately feeds it into a built-in AI image recognition model (trained and optimized using deep learning frameworks such as TensorFlow or PyTorch). The model then performs real-time classification and identification of human behavior (running, falling, fighting, etc.) and dangerous objects (knives, flammable and explosive containers, etc.).

[0029] For human behavior recognition, the model tracks key human joints (using the OpenPose algorithm) and analyzes motion trajectories, combining preset behavioral threshold parameters (such as running speed and posture change angles) to determine whether abnormal behavior exists. For hazardous object recognition, an object detection model (such as YOLOv5) is used to extract and match features of objects in the image. When the confidence level exceeds a set threshold (0.8 for knives and 0.85 for flammable and explosive containers), it is determined to be a hazardous object.

[0030] 3. Abnormal feedback and model optimization: Once the edge computing device identifies an abnormal situation, it immediately transmits detailed information including the abnormal image key frame (5 seconds of video clips before and after, using H.264 encoding format), the location information (accurate coordinates are obtained by linking with the UWB positioning system, with an accuracy of 0.1 meter), timestamp, abnormality type description, etc. to the cloud-based integrated management and control platform via a dedicated line (fiber-based VPN channel) at millisecond speed.

[0031] After receiving the information, the cloud-based integrated management and control platform will compare and analyze the abnormal situation with historical data to verify and confirm the authenticity of the alarm; on the other hand, it will add the newly collected abnormal image data to the training sample set, regularly (every 24 hours) perform incremental training and optimization on the AI image recognition model, update the model parameters, and send the optimized model to the edge computing device to improve recognition accuracy. Example 3

[0032] The indoor environmental sensing suite includes sensors for temperature and humidity, harmful gases (such as formaldehyde and carbon monoxide), fine particles (PM2.5 and smoke particles), hazardous gas sensors, and smoke alarms, all integrated with nanoscale sensitive materials. Leveraging low-power wireless communication technology for networking, and utilizing microelectromechanical systems (MEMS) technology for miniaturization and low-power integration, the suite features distributed deployment and self-organizing network communication. This suite continuously monitors indoor environmental quality in real time. If environmental parameters deviate from safety thresholds, the suite immediately activates the appropriate alarm mechanism, prompting personnel to evacuate and coordinating emergency response measures such as ventilation and fire sprinkler activation. This suite provides real-time quantitative perception of environmental risks, triggering multi-level warnings the moment thresholds are exceeded.

[0033] The technical solution in the above embodiment of the present application is specifically implemented as follows: 1. Data collection and local processing: After power is applied to various environmental sensing devices, such as temperature and humidity sensors, hazardous gas sensors, and smoke alarms, they simultaneously start collecting data at a preset sampling frequency (initially every 5 seconds). Temperature and humidity sensors utilize thermistors and capacitive humidity sensors to accurately measure ambient temperature and humidity. Hazardous gas sensors detect the concentration of harmful gases like carbon monoxide and formaldehyde through chemical adsorption and electrochemical reactions. Smoke alarms monitor smoke concentrations based on optical scattering or ion sensing.

[0034] Each sensor performs preliminary local processing on the raw data it collects, including data calibration (correcting deviations in measured values based on a built-in calibration table) and data format conversion (converting it to JSON format to facilitate subsequent transmission and parsing). The data is then sent to the local aggregation node via low-power ZigBee wireless ad hoc network technology (channels 11-26, data transmission rate 250 kbps).

[0035] 2. Abnormal judgment and warning upload: After receiving sensor data, the sink node first performs a data integrity check (using a 16-bit CRC algorithm) to ensure data accuracy. It then determines whether there are any environmental anomalies based on pre-set safety threshold parameters (e.g., temperature exceeding 40°C, hazardous gas concentration exceeding safety standards, etc.).

[0036] If an anomaly is detected, the convergence node immediately triggers a local audible and visual alarm (at a frequency of 1 Hz and a brightness of 100 cd, if the device is equipped with this function), alerting nearby personnel to safety. Simultaneously, the abnormal environmental data and corresponding sensor location information (with an accuracy of 0.5 meters) are quickly uploaded to the cloud-based integrated management and control platform via Wi-Fi or a wired network (such as Ethernet).

[0037] 3. Control response and dynamic optimization: Upon receiving environmental anomaly information, the cloud-based integrated management and control platform, combined with multiple sources of information, including indoor space layout and occupant distribution, comprehensively determines whether a comprehensive emergency response is necessary. If necessary, dedicated interfaces with control systems for large-scale equipment, such as ventilation and fire protection systems, directly issue commands for starting, stopping, and adjusting operating parameters (e.g., increasing ventilation volume by 30% for a large-scale ventilation system or triggering a sprinkler system with a pressure of 0.5 MPa).

[0038] The cloud-based integrated management and control platform analyzes environmental trends based on long-term accumulated environmental data (for example, using time series analysis to predict temperature trends within the next two hours) and dynamically optimizes indoor environmental control strategies. It also adaptively adjusts sensor configuration parameters (such as sampling frequency and alarm thresholds) (for example, increasing the temperature sampling frequency to every three seconds during hot weather) and distributes these new parameters to each sensor through the aggregation node. Example 4

[0039] Portable interactive terminals include wearable devices (such as lightweight wristbands or badges). These devices incorporate a multi-sensor fusion module (integrating accelerometers, gyroscopes, and geomagnetic sensors) to achieve autonomous inertial navigation-assisted positioning. This module, in conjunction with a UWB positioning module, provides positioning compensation and autonomous inertial navigation-assisted positioning. The wearable device receives positioning information from the positioning gateway and displays indoor maps and navigation routes on a high-brightness OLED touchscreen. It also features a one-touch SOS button that connects directly to the security control center with a 3-second press, ensuring timely assistance. This allows users to easily request help in emergencies, with a direct signal connection to the security control center.

[0040] In addition, it can provide precise positioning management of specific production equipment in large factories, and real-time monitoring and rapid rescue route planning for special patients in hospitals.

[0041] The technical solution in the above embodiment of the present application is specifically implemented as follows: 1. Power-on self-test and connection establishment: After powering on a portable interactive terminal (wristband, smart badge, etc.), it first enters a self-test routine. This routine tests key components, including the built-in accelerometer, gyroscope, Wi-Fi module, Bluetooth module, and battery. The accelerometer and gyroscope use built-in self-test algorithms to verify that their measurement range (±2 g and ±200° / s) and accuracy (0.1°) meet standards. The Wi-Fi module scans for available networks and checks the connection success rate (a success rate of at least 80% must be achieved after five attempts). The Bluetooth module pairs with a nearby test device and checks the transmission rate (minimum 1 Mbps) and connection stability (no more than one disconnection per hour). The battery uses a charge detection circuit to measure the charge level (with an accuracy of ±5%). If the charge level falls below the low-battery threshold (10%), a low-battery indicator appears on the display.

[0042] After the self-test is complete, the terminal automatically connects to public Wi-Fi hotspots indoors using the Wi-Fi module. If the connection fails, it switches to Bluetooth mode to establish a close-range connection with a nearby UWB positioning base station, assisting in receiving more accurate positioning signals and ensuring a reliable connection between the terminal and the system. Simultaneously, it sends device status information, including self-test results, device model, and user ID, to the cloud-based integrated management and control platform.

[0043] 2. Navigation and security information interaction: Upon receiving navigation route information from the cloud-based integrated management and control platform, the terminal utilizes its built-in graphics processing chip to display the route on its display (128×64 resolution, 118 ppi) using intuitive 3D animation or simple 2D arrow indicators. Furthermore, a built-in vibration motor (150 Hz frequency, 0.5g intensity) provides vibration reminders at key points, such as turns, stairs, and so on, to assist users in following the route.

[0044] Security alerts, such as "Unusual traffic ahead, please detour," are displayed in bold red text on the display screen, accompanied by a warning tone (800 Hz, 60 dB), reminding users to be vigilant. Once the user enters their destination on the terminal (e.g., using the virtual keyboard on the touchscreen display or voice input with over 90% voice recognition accuracy), the terminal immediately sends the destination request via Wi-Fi to the cloud-based integrated management and control platform.

[0045] 3. Emergency assistance and feedback response: The terminal is equipped with a one-touch emergency call button. When the user presses and holds the button for more than 3 seconds, a dedicated line connection is established with the security control center through the built-in mobile network module (such as the 4G / 5G module. 4G supports LTE-FDD and LTE-TDD, and the 5G frequency band is the same as mentioned above, supporting VoLTE and VoNR high-definition voice calls). This ensures that the help signal is delivered quickly and directly without being affected by the indoor network environment.

[0046] After the security control center confirms receipt of the distress signal and initiates the response process, the terminal receives feedback from the security center, such as "Rescue personnel have departed and are expected to arrive in 3 minutes. Please remain calm." This informs the user of the rescue progress and allows them to wait with peace of mind. Simultaneously, based on the user's movement (using accelerometers and gyroscopes to sense motion and combine positioning information), the terminal provides real-time feedback to the cloud-based integrated management and control platform on whether the user is following the route. If the user deviates by a certain distance (e.g., 5 meters), the cloud-based integrated management and control platform replans the route and sends a new notification. Example 5

[0047] The cloud-based integrated management and control platform is built on a containerized, microservices-based architecture. Connected via a high-speed fiber optic network to high-precision UWB positioning modules, intelligent image acquisition and analysis components, indoor environmental sensing kits, and portable interactive terminals, it boasts real-time access, storage, and analysis capabilities for massive amounts of data. It integrates positioning, visual, and environmental data, generating personalized navigation routes and security situation assessment reports through intelligent algorithms, and pushes these reports to both administrators and users on their mobile devices. Built on a cloud computing architecture, it boasts powerful data storage, processing, and analysis capabilities. It aggregates data from various hardware components in real time and applies intelligent algorithms to deeply mine this data. This not only generates personalized, dynamically optimized navigation routes for users, but also accurately assesses the overall security situation, identifying potential risks and allocating resources accordingly. The platform's modular design supports rapid upgrades and functional expansion, ensuring compatibility and scalability with advanced positioning technologies. For its intelligent image acquisition and analysis components, it accommodates future emerging image recognition algorithms and multimodal fusion technologies. Data encryption and local storage ensure user privacy and data security.

[0048] The technical solution in the above embodiment of the present application is specifically implemented as follows: 1. Data aggregation and real-time processing: The cloud-based integrated management and control platform, serving as the core hub of the entire system, establishes bidirectional data transmission channels with various hardware modules via a high-speed fiber optic network (with transmission rates exceeding 10 Gbps). This platform receives real-time and stable data from the high-precision UWB positioning module, intelligent image acquisition and analysis components, the indoor environment perception suite's aggregation nodes, and user interaction terminals. Distributed message queues (such as Apache Kafka) are used to buffer and distribute massive amounts of data, ensuring timely and orderly data processing.

[0049] For positioning data, the cloud-based integrated management and control platform integrates with the indoor map database in real time to perform coordinate conversion, route planning, and other operations. Leveraging multi-threaded concurrent processing technology, it can process thousands of positioning data points per second, ensuring real-time updates of navigation routes. For image data, it collaborates with edge computing devices to further verify anomalies. Once a warning is confirmed, the corresponding security plan is immediately activated. For environmental data, comprehensive environmental risks are assessed and indoor environmental management strategies are adjusted in a timely manner.

[0050] 2. Linked decision-making and instruction issuance: When a user enters a destination request through an interactive terminal, the cloud-based integrated management and control platform quickly accesses the indoor map database and uses an improved Dijkstra algorithm or other efficient path planning algorithms, combined with real-time positioning data, to plan the optimal route within one second and push this information to the user's interactive terminal. Simultaneously, the cloud-based integrated management and control platform updates and optimizes the navigation path at regular intervals (e.g., every five seconds) based on the latest positioning data and real-time indoor conditions (such as congestion and temporary traffic control information), ensuring that users always follow the optimal route.

[0051] If the intelligent image acquisition and analysis component or the indoor environment perception suite reports an abnormality, the cloud-based integrated control platform pushes detailed alerts to security personnel's mobile devices, including real-time tracking of the suspect (using a Kalman filter-based target tracking algorithm with a tracking accuracy of 0.2 meters) and predictions of possible escape routes (using an AI algorithm combined with indoor map layout to predict the three most likely escape routes), to assist in the capture. Furthermore, an audible and visual alarm is issued in the incident area and surrounding areas to evacuate people. Furthermore, based on the alert level, the platform directly controls the start / stop and operating parameter adjustment of control systems for large equipment such as ventilation and fire protection, achieving comprehensive indoor environmental control and emergency response.

[0052] 3. System optimization and continuous improvement: The cloud-based integrated management and control platform continuously optimizes its algorithms and models based on various feedback data from system operations (such as user satisfaction surveys and security incident handling effectiveness evaluations). It utilizes machine learning algorithms to mine and analyze historical navigation data, continuously improving the efficiency and accuracy of its path planning algorithms. Deep learning of large amounts of abnormal image data enhances the accuracy and generalization capabilities of its AI image recognition models. Long-term trend analysis of environmental data optimizes indoor environmental control strategies and reduces energy consumption.

[0053] Regular (weekly) health checks are conducted on the entire system, including online status monitoring of hardware modules, network connection quality assessment, and software system performance testing. If any problems are found, relevant maintenance personnel will be notified in a timely manner to address them, ensuring high system reliability and stability.

[0054] Through the close cooperation and linkage mechanism of the above modules, the indoor intelligent hardware navigation and positioning security system can provide users with navigation services efficiently and accurately, and ensure the safety of indoor places in all aspects.

[0055] Collaborative operation logic: Connect as soon as you enter: The moment a user steps into a room, the wearable terminal or the interactive terminal they carry automatically scans for nearby UWB positioning base stations, connects to the nearest Wi-Fi hotspot, Bluetooth, or CAT1 signal, and quickly pairs with the user. Simultaneously, the user's basic information is uploaded to the cloud-based integrated management and control platform for identity authentication. Once authenticated, identity authentication and system registration are completed, the device self-checks and transmits status information back to the cloud. A welcome interface, an overview map of the venue, and guidance information about important facilities are then displayed on the terminal, enabling intelligent services.

[0056] Dynamic navigation: Users enter their target location on the interactive terminal, and the cloud-based integrated management and control platform calls the indoor map database and real-time positioning data. The engine combines real-time positioning with AI algorithms and the Dijkstra algorithm improved by the ant colony optimization algorithm to plan the optimal path. It plans the optimal path to avoid obstacles and crowds, and provides turn-by-turn guidance in the form of 3D animation. A clear and easy-to-understand graphical interface guides users forward step by step, taking into account various behavioral changes during use, such as users running fast and frequently changing directions. The path is updated every certain time interval (such as 5 seconds) based on the latest positioning feedback to ensure that the route is always accurate and effective. In the event of sudden congestion or temporary control along the way, the route is recalculated in seconds and updated based on changes in crowd density perceived by the positioning base station and the on-site images captured by the smart camera.

[0057] Security linkage: After the smart camera captures abnormal behavior or objects, it conducts preliminary analysis and screening on the edge computing device, and transmits key information including image fragments, image key frames, location coordinate information, timestamps, etc. to the cloud-based integrated management and control platform at millisecond speed. After further verification and confirmation by the cloud-based integrated management and control platform, it is pushed to the cloud for review within 1 second. After it is confirmed as an alarm, on the one hand, detailed alarm information is pushed to the mobile terminal of the security personnel, including video stream, location, suspect characteristics, real-time tracking of suspect targets, possible escape route prediction, etc., to assist in the arrest; on the other hand, emergency broadcasts are initiated to the incident area and surrounding areas, sound and light alarms are issued, crowds are evacuated, and surrounding personnel are warned. At the same time, environmental control perception kits such as ventilation and smoke exhaust and sprinkler dust reduction are triggered. Based on sensor feedback and environmental feedback, ventilation, fire protection and other systems are precisely controlled to curb risks and minimize losses in a multi-pronged manner.

[0058] Of course, the present invention may have many other implementations. Based on this implementation, other implementations obtained by ordinary technicians in this field without any creative work are all within the scope of protection of the present invention.

Claims

1. A navigation, positioning and security management system based on an indoor intelligent hardware ad hoc network, comprising a high-precision UWB positioning module, a cloud-based integrated management and control platform, an indoor environment perception kit, an intelligent image acquisition and analysis component, and a portable interactive terminal, characterized in that: The high-precision UWB positioning module is based on the building's own hardware and uses Bluetooth MESH, CAT1 and other communication modules to achieve self-organizing networking to form a grid and supplemented by an ultra-precise UWB positioning array. The UWB positioning array is composed of multiple miniaturized, low-power UWB positioning base stations. The UWB positioning base stations send initialization completion signals to the cloud-based integrated management and control platform via wired or wireless networks, and achieve positioning based on flight time and arrival time difference algorithms, and transmit their own detailed parameters such as hardware serial number, initial coordinate position, and transmission power setting. The high-precision UWB positioning module is equipped with multiple intelligent hardware nodes, each of which includes at least one communication module and one UWB positioning base station, and at least one positioning gateway for collecting and processing positioning data sent by the node; The intelligent image acquisition and analysis components include smart cameras and edge computing devices deployed in key areas. The smart cameras are preferably high-resolution, wide dynamic range cameras, and are paired with a deep learning-based multimodal behavior analysis engine built into the edge computing devices. They utilize artificial intelligence image recognition algorithms to perform real-time recognition of facial features and body movements of people, as well as rapid identification of common dangerous items. The indoor environment sensing kit includes temperature and humidity sensors, harmful gas sensors, and smoke alarms that integrate nano-scale sensitive materials. These sensors are connected to a convergence node via a wireless ad hoc network to monitor environmental parameters and trigger abnormal warnings. The portable interactive terminal includes a wearable portable device (such as a lightweight bracelet or badge style), the wearable portable device has a built-in UWB tag and a multi-sensor fusion module that cooperates with the UWB positioning module to achieve positioning compensation and realize autonomous inertial navigation-assisted positioning; The cloud-based integrated management and control platform is built based on containerization and microservice architecture. It is connected to high-precision UWB positioning modules, intelligent image acquisition and analysis components, indoor environment perception kits and portable interactive terminals through high-speed fiber optic networks. It has the ability to access, store and analyze massive amounts of data in real time, integrate positioning, vision and environmental data, generate personalized navigation paths and security situation assessment reports through intelligent algorithms, and push information in both directions to managers and users' mobile terminals.

2. A navigation, positioning and security management system based on indoor intelligent hardware ad hoc network according to claim 1, characterized in that: The UWB positioning base station has a built-in environmental monitoring sensor. When it detects that the electromagnetic interference exceeds the threshold or the temperature and humidity are outside the working range, it automatically switches the frequency band or reduces the transmission power, and sends an early warning to the cloud-based integrated management and control platform. The portable interactive terminal carrying the UWB tag wakes up in low-power monitoring mode, interacts with the base station when the signal strength reaches the threshold, and triggers a replacement reminder when the tag battery is lower than the preset value.

3. The navigation, positioning and security management system based on indoor intelligent hardware ad hoc network according to claim 1 is characterized in that: The edge computing device has a built-in image recognition model based on deep learning, which tracks human joints through the OpenPose algorithm, determines abnormal behavior through motion trajectory analysis, and detects dangerous objects through the YOLOv5 model. After identifying the anomaly, the edge device will transmit the alarm information containing key frames, location coordinates and timestamps to the cloud-based fusion management and control platform at millisecond speeds, and receive the incremental training model issued by the cloud-based fusion management and control platform to improve recognition accuracy.

4. The navigation, positioning and security management system based on indoor intelligent hardware ad hoc network according to claim 1 is characterized in that: The aggregation node performs CRC check on the sensor data. When it detects that the environmental parameters exceed the safety threshold, it triggers the local sound and light alarm and uploads the abnormal data to the cloud-based fusion management and control platform. The cloud-based fusion management and control platform controls the ventilation system and fire sprinkler device based on the abnormal data, and dynamically adjusts the sampling frequency and alarm threshold of the sensor.

5. The navigation, positioning and security management system based on indoor intelligent hardware ad hoc network according to claim 1 is characterized in that: The wearable portable device has a built-in multi-sensor fusion module including acceleration, gyroscope, geomagnetic and other sensors. When the user deviates from the navigation route by more than 5 meters, the path replanning is triggered. The portable interactive terminal establishes a dedicated line connection with the security center through the mobile network module, and the transmission of emergency assistance signals is not affected by the indoor network environment.

6. The navigation, positioning and security management system based on indoor intelligent hardware ad hoc network according to claim 5 is characterized in that: The wearable portable device is equipped with a high-brightness OLED touch screen and a one-touch SOS emergency button. The wearable portable device receives positioning information sent by the positioning gateway and displays indoor maps and navigation paths through the high-brightness OLED touch screen.

7. The navigation, positioning and security management system based on indoor intelligent hardware ad hoc network according to claim 1 is characterized in that: The cloud-based integrated management and control platform adopts a modular design, supports rapid upgrades and function expansion, and implements data encryption transmission and local storage mechanisms to ensure user privacy and data security.

8. The navigation, positioning and security management system based on indoor intelligent hardware ad hoc network according to claim 1 is characterized in that: The cloud-based integrated management and control platform uses distributed message queues to buffer massive amounts of data, and plans real-time routes through an improved Dijkstra algorithm, updating the navigation route every 5 seconds. When a security or environmental anomaly is received, the cloud-based integrated management and control platform pushes the suspect target tracking image and escape route prediction to the security terminal, and controls the activation of sound and light alarms and fire-fighting equipment.

9. The navigation, positioning and security management system based on indoor intelligent hardware ad hoc network according to any one of claims 1 to 8, characterized in that: The system also includes: inter-module collaborative operation logic, including the terminal automatically connecting to the UWB base station and Wi-Fi hotspot when the user enters the room, the cloud-based integrated management and control platform dynamically adjusts security strategies based on real-time positioning data and image analysis results, and optimizes energy consumption through environmental perception data.

10. The navigation, positioning and security management system based on indoor intelligent hardware ad hoc network according to any one of claims 1 to 9, characterized in that: The system includes but is not limited to indoor building hardware, including but not limited to lighting, emergency lights, emergency signs, doors and windows, locks, hinges, smoke detectors, sprinklers, and indoor assets and other hardware.

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