A cross-domain collaborative security supervision method and system

Abstract

The application discloses a kind of cross-domain collaborative security supervision method and system, belong to wireless communication and public security supervision technical field.System includes the multiple short-range wireless communication nodes of personnel and / or operation equipment on deployment, each node automatically discovers and constructs the multi-hop self-organizing network of linear or mesh topology.Personnel node collects physiological state data and generates the physiological evaluation parameter of characterizing emergency degree;Each node will be collected data through multi-hop relay link hop by hop to target network node, and target network node will be gathered data transmission to remote platform through wide area link.The personnel node, equipment node of the application are organically integrated, and can be managed fixed node by hierarchical identification system, realizes the priority scheduling and collaborative rescue of physiological evaluation parameter driven, special relay intelligent replacement, target network node dynamic reselection and anti-damage adaptive reconstruction, suitable for power repair, fire rescue, earthquake rescue and other cross-domain operation scene.

Description

Technical Field

[0001] This invention belongs to the fields of wireless communication, intelligent sensing and public safety supervision technology, and specifically relates to a cross-domain collaborative safety supervision method and system that realizes self-organizing network communication of multiple types of terminals, data relay backhaul and collaborative perception of personnel status. Background Technology

[0002] Currently, in cross-domain operation scenarios such as power emergency repair, forest fire fighting, indoor security, urban emergency response, flood, earthquake and ice storm disaster relief, and collaborative handling in extreme environments, safety supervision and communication collaboration face the following core pain points:

[0003] 1. The dilemma of communication blind spots and information silos

[0004] In extreme disaster scenarios, commonly used communication networks often experience congestion or even paralysis. Even when using other communication methods such as walkie-talkies or satellite phones, the problem is difficult to completely solve. For example, walkie-talkies have short transmission distances and are difficult to transmit large amounts of data, while satellite phones are expensive and do not support multi-node networking. Data generated or collected by equipment and personnel in disaster areas and remote regions is difficult to transmit back in real time, making it difficult for on-site personnel, rescue teams, or command and decision-making departments to understand the situation on the ground. This often creates information silos, seriously affecting the efficiency and effectiveness of disaster relief.

[0005] 2. Insufficient cross-scenario and multi-type terminal collaboration capabilities Traditional security monitoring systems typically target single scenarios, or primarily focus on personnel or equipment, managing and monitoring these entities independently. It can be said that current technology lacks a system-level solution capable of unified identity management, cross-domain networking, and collaborative scheduling for personnel nodes, equipment nodes, and fixed nodes.

[0006] 3. Existing self-organizing network solutions are not suitable for the combined requirements of sparse, high-reliability, and low-power consumption in the field.

[0007] While self-organizing network solutions based on Wi-Fi or traditional mesh exist, they are typically designed for high-density geographically distributed nodes and therefore consume significant power. In long-distance, sparsely populated deployments in the field, the network topology becomes extremely unstable, making it difficult to achieve long-distance, highly reliable multi-hop relay and data fusion under low-power constraints. Furthermore, communication nodes lack intelligent backup capabilities. When using wide-area network (WAN) communication technologies such as satellite or 4G / 5G, insufficient bandwidth and high latency in data transmission fail to meet the real-time requirements of disaster sites. Summary of the Invention

[0008] This invention provides a cross-domain collaborative security supervision method and system, which focuses on organically integrating people, machines, and things through a unified cross-domain self-organizing network architecture to achieve data collaboration, task collaboration, and damage-resistant adaptive reconfiguration among heterogeneous nodes.

[0009] This invention provides a cross-domain collaborative security monitoring system, comprising:

[0010] Multiple short-range wireless communication nodes are deployed on personnel and / or work equipment within the monitored area. Each of the short-range wireless communication nodes is configured to automatically discover neighboring nodes and form a multi-hop self-organizing network within a cross-domain work area; the multi-hop self-organizing network supports linear topology and / or mesh topology.

[0011] The short-range wireless communication nodes deployed on the personnel are called personnel nodes. These personnel nodes are also used to collect physiological state data of the personnel and generate physiological assessment parameters characterizing the urgency level of the personnel based on the physiological state data. Each node in the multi-hop self-organizing network transmits the collected data hop-by-hop back to the target network node through a multi-hop relay link. The target network node is any node in the multi-hop self-organizing network that connects to a wide-area communication link, and is used to transmit the data aggregated by the multi-hop self-organizing network to a remote platform.

[0012] Preferably, the short-range wireless communication technology is starburst technology.

[0013] Furthermore, the personnel node is also used to: automatically increase the data reporting priority of the node and trigger a collaborative rescue response when the physiological assessment parameters exceed a preset threshold.

[0014] Furthermore, the personnel nodes and the equipment nodes deployed on the operating equipment establish data collaboration within the same multi-hop self-organizing network, realizing situational sharing and hybrid cluster collaboration between personnel and equipment.

[0015] Furthermore, the physiological state data includes at least one of heart rate data, skin conductance data, body temperature data, and exercise data.

[0016] Furthermore, the cross-domain operation area includes at least one of the following scenarios: outdoor, indoor, underground, transportation routes, and power lines.

[0017] Furthermore, the short-range wireless communication node has a hierarchical terminal identifier, which is divided into personnel node ID, equipment node ID and fixed node ID based on the physical form and functional attributes of the node; the system schedules the node to undertake sensing, relay or execution tasks in a differentiated manner according to the hierarchical classification of the terminal identifier.

[0018] Furthermore, the system is also used to generate and present at least one of the following types of regulatory information: (1) Personnel status regulatory information: including personnel physiological assessment parameter curves, location trajectories, urgency levels and loss of contact warnings; (2) Equipment status regulatory information: including the location, power, sensor data and task execution status of the operating equipment; (3) Environmental risk regulatory information: including thermal distribution maps, gas concentration distribution maps or meteorological condition maps of the operating area; (4) Task and resource scheduling regulatory information: including task progress, spatial distribution of personnel and equipment, relay network topology and link quality.

[0019] Another aspect of the present invention provides a cross-domain collaborative safety supervision method, comprising the following steps: within a cross-domain operation area, multiple short-range wireless communication nodes deployed on personnel and / or work equipment automatically discover neighboring nodes and form a multi-hop self-organizing network; personnel nodes collect physiological state data of personnel and generate physiological assessment parameters characterizing the urgency level of personnel based on the physiological state data; each node transmits the collected data hop-by-hop back to the target network node through the multi-hop relay link in the multi-hop self-organizing network; the target network node transmits the data aggregated by the multi-hop self-organizing network to a remote platform through a wide-area communication link.

[0020] Preferably, the multi-hop self-organizing network is deployed in a linear or quasi-linear topology along the geographical strip structure of the cross-domain operation area, and each node relays data back to the target network node along the deployment direction through a hop-by-hop relay method.

[0021] Furthermore, the multi-hop self-organizing network composed of the multiple short-range wireless communication nodes is configured with at least one dedicated relay node. The dedicated relay node only performs the data relay forwarding function and does not collect or actively report its own data.

[0022] Furthermore, the dedicated relay node includes a mobile relay device that, in response to a scheduling command, is dynamically deployed to a designated location to supplement or extend the coverage of the multi-hop self-organizing network.

[0023] Furthermore, the geographical strip structure includes at least one of highways, railways, power transmission lines, oil pipelines, and riverbanks.

[0024] With a good wide area network (WAN), the system can further leverage the long transmission distance of WAN to improve its functionality and robustness. Sensors deployed on equipment (drones, robots / robot dogs) collect at least one type of environmental data, such as audio and video, temperature, wind direction and force, air composition and concentration, and blast shock waves. Search and rescue robots and drones can also be equipped with acoustic sensors to detect abnormal environmental signals such as blast shock waves and structural micro-vibrations before secondary collapses.

[0025] The cross-domain collaborative safety supervision system provided by this invention achieves unified perception and collaborative management of at least one of the following types of supervised objects in cross-domain operation scenarios through the self-organizing network collaboration of multiple types of terminals: (1) Personnel status supervision: including personnel's physiological status, location trajectory, parameters representing the degree of urgency and loss of contact warning; (2) Equipment status supervision: including the location, power, sensor data and task execution status of operation equipment such as drones and robots; (3) Environmental risk supervision: including the fusion perception and visualization of environmental conditions such as heat distribution, gas concentration and meteorological conditions in the operation area; (4) Task and resource scheduling supervision: including the progress monitoring of inspection or emergency repair tasks, dynamic planning of relay node deployment locations, collaborative task allocation of personnel and equipment and instruction issuance.

[0026] In this invention, the term "cross-domain" should be interpreted broadly. It not only refers to operational scenarios spanning different physical environments (such as indoors and outdoors, underground and above ground, urban and rural areas), but more importantly, it refers to the collaborative integration of at least one of the following heterogeneous elements:

[0027] (1) Cross-domain collaboration of heterogeneous nodes: refers to the system simultaneously managing and scheduling nodes with different physical forms, mobility and functional attributes, including but not limited to personnel nodes (such as people wearing wearable devices), equipment nodes (such as drones, robots, vehicle terminals) and fixed nodes (such as fixed sensing devices, beacon nodes).

[0028] (2) Cross-domain fusion of heterogeneous data: refers to the unified processing and correlation analysis of data from different sources and with different modalities, including but not limited to physiological data of personnel, location and motion data of nodes, environmental sensor data and task context data;

[0029] (3) Cross-domain adaptation of heterogeneous tasks: refers to the system's ability to adaptively adjust the network topology, perception strategy and response mechanism according to different regulatory task modes (such as restricted area protection, search and location, emergency rescue and daily inspection).

[0030] Therefore, as long as the system involves the collaboration, integration or adaptation of any of the above heterogeneous elements, regardless of whether its physical environment is singular, it falls under the "cross-domain" category described in this invention.

[0031] storage media

[0032] The present invention also protects a storage medium on which a computer program is stored, which, when executed, implements the cross-domain collaborative security monitoring method. Beneficial effects

[0033] Compared with the prior art, the present invention has the following significant advantages:

[0034] (1) Network coverage: Existing technologies form "information islands" in areas without wide area networks, and data cannot be transmitted back; the present invention extends the coverage to the entire working area through multi-hop self-organizing networks, eliminating communication blind spots.

[0035] (2) Terminal collaboration: In the existing technology, people, machines and objects are managed independently and cannot be uniformly scheduled; the present invention establishes a unified hierarchical ID system to realize intelligent collaboration of cross-domain hybrid clusters.

[0036] (3) Network resilience: Existing technologies are prone to link interruption due to single point of failure; the present invention supports dynamic routing self-healing and achieves intelligent replacement through dedicated relay nodes to ensure uninterrupted communication. As an example, in the preferred embodiment using Star Flash technology, the multi-hop self-organizing network can support data relay transmission of no less than 8 hops, and the number of concurrent access nodes in a single network can reach hundreds; when a link break is detected, the scheduling response time of the dedicated relay node can usually be controlled within a few seconds (e.g., 1-3 seconds), and path reconstruction can be completed in the sub-second level, thereby quickly restoring network connectivity after node damage or terrain changes.

[0037] (4) Power consumption and access volume: Traditional Mesh solutions have high power consumption and limited access volume; this invention preferably adopts star flash technology, which supports dozens or even hundreds of concurrent nodes under low power consumption and has a long battery life.

[0038] (5) System intelligence: Existing technology is a pure communication channel without data fusion; the present invention realizes the fusion of communication and perception, and the data is prioritized during transmission. The command end obtains the global situation map and can issue control commands to form a closed loop.

[0039] (6) Task coordination and resource scheduling: In the existing technology, each node operates independently and lacks unified scheduling; the present invention integrates people, machines and materials into the supervision framework to realize the automatic function handover in the event of normal task allocation and sudden damage.

[0040] (7) The system-level fusion of multi-source heterogeneous sensing data has been realized: This invention organically integrates heterogeneous data sources such as personnel physiological state perception (e.g., emergency level index generated based on physiological data), coordination degree analysis of physiological data and behavioral data, equipment status monitoring, and environmental risk perception. It achieves fusion processing and intelligent scheduling through a unified cross-domain collaborative architecture, overcoming the defects of isolated operation of each subsystem in the prior art.

[0041] It is understood that the specific internal algorithm implementations of the functional modules in this invention can all adopt technical solutions known in the art. The core of this invention lies in the system-level integration of these functional modules through a unified cross-domain self-organizing network architecture, and in realizing collaborative work and data flow between modules, rather than being limited to a specific algorithm. Attached Figure Description

[0042] Figure 1 System overall architecture and topology diagram

[0043] Figure 2 Flowchart of Intelligent Replacement and Dynamic Reselection for Dedicated Relays

[0044] Figure 3 Personnel physiological assessment parameter-driven priority scheduling flowchart Detailed Implementation

[0045] The present invention will be further described in detail below with reference to specific embodiments.

[0046] Example 1: Power Line Inspection and Management

[0047] Application Scenario: A power transmission line in a mountainous area experienced a fault due to freezing rain. Repair personnel needed to venture into uninhabited areas without wide area network coverage to conduct inspections and repairs. Simultaneously, drones were dispatched to survey the power line.

[0048] System deployment (see) Figure 1 (In the diagram, the thick black curve represents the direction of the power line, the thin arrows indicate the data flow within the short-range wireless network, and the thick arrows indicate the data flow transmitted to the remote monitoring platform.)

[0049] Repair personnel wear smartwatches (personnel nodes) with integrated StarFlash communication modules.

[0050] The drone / robot dog is equipped with a StarFlash communication module and a camera (device node).

[0051] On-site command center (such as) Figure 1 The emergency command vehicle in this embodiment, which is the target network node (which can be simplified to a computer or a data receiving and processing terminal), is deployed in a place with satellite signal or public network signal to serve as the target network node or data aggregation center.

[0052] Workflow:

[0053] After repair personnel and drones / robots enter the uninhabited area, each node automatically discovers and forms a linear topology multi-hop self-organizing network (i.e., Figure 1 (Short-range wireless network). Personnel nodes operate dispersedly along the line, while drones / robot dogs patrol the line or its airspace, maintaining connections between nodes via a short-range wireless network (e.g., Starlink).

[0054] Personnel nodes collect real-time physiological data such as heart rate and body temperature from repair personnel, generating physiological assessment parameters. For example, as a simple implementation, the system can pre-establish a resting heart rate baseline for each person (such as the average heart rate during non-exercise periods over the past 24 hours). When the real-time heart rate consistently exceeds 30% of this baseline and remains elevated for more than 10 seconds, it is considered an abnormal physiological assessment parameter, and the physiological assessment parameter value is increased accordingly (see [link to documentation]). Figure 3 When the physiological assessment parameters exceed a preset threshold, the system automatically prioritizes the data reporting of that node and triggers a coordinated rescue response. As an example of this coordinated rescue response, the command center can initiate a video or audio call request to the personnel node to confirm the situation on-site; alternatively, if the parameters indicate suspected disability or injury, it can issue a check order to other nearby personnel nodes, or directly dispatch a drone / robot dog carrying a first-aid kit to the location. By associating abnormal physiological assessment parameters with specific response actions, the system achieves seamless integration from status awareness to rescue operations.

[0055] The drone / robot dog will capture images of line faults and sense environmental data (such as wind speed and direction, temperature).

[0056] These data are transmitted back to the field command center hop-by-hop via multi-hop relay links from personnel nodes or equipment nodes (e.g., ...). Figure 1 The robotic dog in the system acts as a relay between two short-range wireless networks, and instructions from the on-site command center are also transmitted to each node through these links (see data flow diagram). Figure 1 (The thin arrow line in the middle).

[0057] The on-site command center transmits the aggregated global situational awareness data (personnel status, fault location, on-site images, environmental data) to a remote monitoring platform or command center via a wide area network (e.g., satellite network, 4G / 5G public network) (see data flow diagram). Figure 1 (The thick arrow in the image). The remote monitoring platform and the on-site command center can also send work instructions to each node via wide area network and short-range wireless network, such as ordering drones to fly to the maintenance site to drop tools, materials, and food, and the robotic dogs can carry the wounded and provide lighting.

[0058] When a personnel node loses contact with the short-range wireless network due to terrain obstruction or excessive distance traveled, the system dispatches a drone to the personnel's location to re-establish the link. The drone acts as a temporary relay node, enabling the lost personnel node to resume communication with the network (see [link]). Figure 1 Example personnel nodes can be connected via drone relay and short-range wireless network.

[0059] When the on-site command center loses contact with the remote monitoring platform, other nodes equipped with wide area network communication modules (e.g., satellite communication modules, 4G / 5G communication modules, Starlink communication modules) can take over the task of exchanging data with the remote monitoring platform (see [link]). Figure 1 Drones replace the on-site command center in uploading on-site data and transmitting instructions.

[0060] Supplementary regulatory content:

[0061] (1) Personnel status monitoring: The command terminal can view the physiological assessment parameter curves, precise location and movement trajectory of each emergency repair personnel in real time. When a person's physiological assessment parameters exceed the threshold, the person's icon on the command terminal map will automatically turn red and flash, and the abnormality type will be displayed. The commander can initiate voice confirmation or dispatch nearby personnel to check with one click.

[0062] (2) Equipment status monitoring: The command terminal can view the real-time location, flight altitude, remaining battery power, and camera footage of each drone. Line faults discovered by drone inspections are automatically marked on the map and fault reports are generated.

[0063] (3) Task and resource scheduling and monitoring: Based on the emergency repair task list, the system displays the task completion progress, the spatial distribution map of personnel and equipment, the deployment location of relay nodes, and the link quality in real time. When additional relays are needed, the system automatically calculates the optimal relay deployment location and recommends available drones.

[0064] Examples of generating physiological assessment parameters:

[0065] Method 1: Based on a dynamically adjustable rule engine

[0066] In a preferred embodiment of the present invention, the dynamic fusion model is implemented using a configurable rule engine. This rule engine maintains a set of preset decision rules, each rule consisting of a condition and a weight adjustment value.

[0067] The conditions are based on real-time acquired heart rate data and auxiliary data. For example:

[0068] Condition 1: If the heart rate remains above 30% of the resting baseline for 10 seconds, the baseline physiological assessment parameter value is increased by 0.2.

[0069] Condition 2: If the exercise state is 'resting' and the heart rate exceeds 50% of the resting baseline, then the baseline physiological assessment parameter value is increased by 0.3;

[0070] Condition 3: If a 'coordination alarm' is received from a related external entity (such as a smoke alarm), the basic physiological assessment parameter value is increased by 0.4;

[0071] Condition 4: If location data indicates that the user is in a preset low-risk area (such as at home), the basic physiological assessment parameter value is -0.1. The rule engine polls these rules in real time, accumulating the adjustment values ​​generated when each rule is met to a baseline value, thereby generating dynamically changing physiological assessment parameters. Both rules and weights can be dynamically updated via cloud distribution to adapt to the personalized needs of different users or different scenarios.

[0072] Method 2: Machine learning model based on random forest algorithm

[0073] In another embodiment of the present invention, the dynamic fusion model may employ a pre-trained random forest classifier. This classifier takes heart rate variability (HRV), mean acceleration, and environmental audio feature vectors as input features and directly outputs a risk probability value between 0 and 1, which can be used as the physiological assessment parameter.

[0074] The model's training data is constructed from a massive amount of historical alarm records (including successful rescue cases and false alarm cases). Through continuous iterative updates, the accuracy of physiological assessment parameters in representing the true level of urgency can be continuously improved.

[0075] It is understood that the above heart rate rules are only examples. In other implementations, any one or more combinations of skin conductance, body temperature, blood pressure, and blood oxygen saturation can be used for fusion evaluation. Alternatively, alarms based on a single indicator with a fixed threshold (e.g., triggering an alarm when the heart rate exceeds the resting baseline by 30%), alarm signals triggered manually by the user, or abnormal behavior signals identified by external devices can be used to replace or supplement these methods.

[0076] It should be noted that the multiple methods of generating physiological assessment parameters in this embodiment are only for illustrating the principle and are not intended to limit the invention. Those skilled in the art can also use other methods (such as mathematical logic operations) to achieve the same result.

[0077] Example 2: Fire Scene Rescue Management

[0078] Application Scenario: A fire breaks out in a high-rise building, and the wide area network signal is severely attenuated inside the building. Firefighters enter the building to conduct search and rescue operations, and firefighting robots are deployed to detect high-temperature areas.

[0079] System Deployment:

[0080] Firefighters wear helmets equipped with integrated star-flash communication modules (personnel nodes).

[0081] The firefighting robot is equipped with a StarFlash communication module and a thermal imager (equipment node).

[0082] The fire command vehicle outside the building serves as a target network node.

[0083] Workflow:

[0084] After firefighters and robots entered the building, the nodes automatically formed a multi-hop self-organizing network with a mesh topology. Due to the complex internal structure of the building, some nodes maintained connections with the command vehicle through multi-hop relays.

[0085] Personnel nodes monitor firefighters' heart rate, body temperature, and activity levels in real time, generating physiological assessment parameters. When a firefighter experiences heat stress or remains still for an extended period, the system automatically elevates their alarm priority.

[0086] The robot transmits thermal imaging data back to the command vehicle via a self-organizing network, allowing the commander to monitor the distribution of fire sources and the location of personnel inside the building in real time.

[0087] When a team member's descent into the basement causes a communication loss, the system identifies this as a sudden loss of contact and automatically dispatches a robot to the team member's last known location to act as a relay node to restore communication. Simultaneously, it sends coordinated rescue instructions to other team members (see [link]). Figure 2 ).

[0088] Supplementary regulatory content:

[0089] (1) Personnel status monitoring: The command terminal can view the physiological assessment parameters, floor and precise location of each firefighter, and remaining breathing time in real time. When a firefighter shows abnormality, the system will automatically mark the firefighter as "high risk" and trigger coordinated rescue.

[0090] (2) Environmental risk monitoring: The system integrates thermal imaging data collected by multiple robots and gas sensor data carried by team members to generate a fire thermal distribution map and a toxic gas concentration distribution map at the command end, assisting the commander in planning attack and evacuation routes.

[0091] (3) Task and resource scheduling and supervision: The system displays the progress of search and rescue tasks, the spatial distribution of personnel and robots, and the relay link topology in real time, and automatically identifies the breakpoint and schedules replacement when communication is interrupted.

[0092] Example 3: Emergency Communication in Indoor and Underground Spaces

[0093] Application Scenario: A gas leak occurs in an underground utility tunnel, requiring repair personnel to enter for inspection and repair. The tunnel has no wide area network signal and is a long, narrow strip.

[0094] System Deployment:

[0095] Repair personnel wear StarScan communication terminals (personnel nodes).

[0096] Fixed beacon nodes (fixed nodes) are deployed at regular intervals (e.g., 50 meters) at the entrance of the utility tunnel or inside the tunnel. These fixed beacon nodes have pre-stored precise absolute geographic coordinates, which serve as location references and gateways.

[0097] Repair personnel carry portable repeaters, which can be deployed as needed while on the move.

[0098] Workflow:

[0099] After the repair personnel enter the utility tunnel, each node automatically forms a multi-hop self-organizing network with a linear topology along the direction of the utility tunnel.

[0100] When personnel venture deeper into the utility tunnel, causing communication signals with the entrance beacon to weaken, the system determines a gradual loss of connection based on the signal change rate and prompts personnel to deploy a portable repeater. The repeater joins the network as a dedicated relay node, extending communication coverage.

[0101] Personnel nodes continuously collect physiological data and generate physiological assessment parameters, and use ranging modules (such as starbursts) to acquire spatial location data (including relative and absolute spatial locations). The physiological assessment parameters and spatial location data are transmitted hop-by-hop back to the entry beacon via repeaters, and then transmitted to the ground command center via wide area links.

[0102] When personnel encounter an emergency that causes a sudden change in physiological assessment parameters, the system automatically sets the data of that node to the highest priority and sends an emergency alarm to the command center.

[0103] Supplementary regulatory content:

[0104] (1) Personnel status monitoring: The command terminal can view the physiological assessment parameters, precise location, and entry time of each emergency repair personnel in the utility tunnel in real time, and automatically issue an alarm when there is an abnormality or failure to return within the time limit.

[0105] (2) Environmental risk monitoring: The gas sensor data carried by personnel is transmitted back through a multi-hop network. The command terminal generates a gas concentration distribution curve in the pipe gallery and automatically sends an evacuation order to the personnel in the area when the concentration exceeds the limit.

[0106] (3) Communication network monitoring: The command terminal displays the topology diagram of the multi-hop self-organizing network in real time, including the connection relationship of each node, the link signal strength, the remaining power of the repeater, and prompts for replacement or replenishment when the power is insufficient.

[0107] (4) Positioning correction: The positioning data of personnel nodes (such as relative position based on inertial navigation or gait estimation) can be fused and corrected with the ranging information of the nearest fixed beacon node to eliminate accumulated errors and obtain a more accurate absolute position estimate.

[0108] In addition to acquiring spatial location data through star-flash ranging, any technology capable of obtaining the relative distance or orientation between nodes can be used, such as Wi-Fi RTT, Bluetooth 5.1 AoA / AoD, Zigbee RTLS, LoRa TDOA, ultrasonic ranging, infrared ranging, LiDAR point cloud matching, visual SLAM, and satellite positioning data. Optionally, fixed nodes with pre-set precise satellite coordinates can be used to correct the spatial location data.

[0109] Example 4: In-depth assessment of personnel status based on the degree of coordination between physiological and behavioral data

[0110] This embodiment demonstrates how the system can collaboratively analyze physiological and behavioral data collected from personnel nodes by introducing a coordination degree assessment mechanism, thereby identifying hidden risks that are difficult to detect with traditional single-indicator alarms.

[0111] In high-voltage operations such as power grid repair and fire rescue, personnel may exhibit abnormalities such as elevated heart rate and cessation of movement. These could be due to injury or disability, extreme fear, physical exhaustion, or even malicious intent. Relying solely on a single physiological indicator for alarms cannot differentiate between these situations, easily leading to misjudgments and resource misallocation.

[0112] In addition to generating physiological assessment parameters, the personnel nodes in this system can optionally be configured with a coordination degree assessment module. This module acquires personnel's physiological and behavioral data, and generates a quantitative index characterizing the personnel's "physical-mental consistency" by calculating the degree of coordination between the two types of data over time.

[0113] The specific implementation of the coordination degree assessment can employ multi-modal data fusion methods known in the art (such as baseline deviation analysis, cross-modal dynamic time warping, etc.) as an optional quantification method. The system can calculate the Pearson correlation coefficient between the heart rate sequence and the exercise intensity sequence; under normal circumstances, the two should show a high positive correlation. If, within a preset time window (e.g., 30 seconds), the heart rate significantly increases while the exercise intensity remains stable, resulting in a correlation coefficient lower than a preset threshold (e.g., 0.3), it is determined to be a physiological and behavioral incoordination.

[0114] It should be noted that the threshold determination based on Pearson correlation coefficient described above is only an example. In other implementations, dynamic time warping (DTW) distance, cross-correlation function, or end-to-end anomaly detection model based on neural network can also be used to quantify the degree of coordination between physiological data and behavioral data. Those skilled in the art can choose specific statistical indicators or algorithms according to actual needs.

[0115] For example, the system can monitor the synchronous changes between heart rate and exercise intensity sequences: under normal circumstances, an increase in heart rate should be accompanied by a synchronous increase in exercise intensity; if a significant increase in heart rate is detected while the exercise intensity remains stable, it is determined to be a physiological and behavioral incoordination, and the quantitative indicators are adjusted accordingly.

[0116] Example 5: Safety Supervision of Crowds at Large Events

[0117] Application Scenario: A marathon is being held in a city, with tens of thousands of participants and spectators gathering at the start, along the route, and at the finish line. While the overall WAN signal is good, congestion occurs in extremely densely populated areas due to high concurrency. Furthermore, relying solely on the WAN cannot achieve high-precision location sensing and differentiated guidance for individual participants.

[0118] System Deployment:

[0119] Participants and staff wear smartwatches or number-book-style terminals (personnel nodes) with integrated StarFlash communication modules.

[0120] Fixed beacon nodes (fixed nodes) are deployed at medical points and supply stations along the route.

[0121] The event command center is connected to each fixed beacon node via a wide area network.

[0122] Workflow:

[0123] During the competition, the WAN signal was generally good, but in densely populated areas such as the starting arch, WAN congestion caused delays and insufficient accuracy in location updates. Fixed beacon nodes deployed in this area automatically activated, forming a local multi-hop self-organizing network with surrounding personnel nodes. They exchanged relative position information via star links, achieving sub-meter positioning accuracy. The system also calculated the physiological evaluation parameters of each node in real time.

[0124] When the population density in a certain area exceeds the safety threshold and multiple individuals simultaneously exhibit abnormal physiological assessment parameters, a stampede risk is identified, and a differentiated response is automatically triggered: evacuation instructions are sent to personnel in the core area, reinforcement orders are sent to nearby security personnel, and warnings are sent to those attempting to enter. Simultaneously, high-precision data is uploaded for evidence storage.

[0125] Advantages of short-range networking:

[0126] In this embodiment, the advantages of short-range networking over wide area networks are specifically manifested in the following ways:

[0127] (1) Low latency: Wide area network is congested in densely populated areas, and the location update delay can be several seconds; StarShine local networking latency is ≤20ms, which can sense changes in the distance between individuals in real time.

[0128] (2) High precision: Wide area network positioning accuracy is only tens of meters; star flash relative positioning accuracy reaches sub-meter level, which can accurately identify crowd density.

[0129] (3) High concurrency: Insufficient WAN bandwidth; StarShine single network supports 4096 concurrent devices, and local networking is not affected by WAN congestion.

[0130] (4) Differentiated instructions: Wide area network can only broadcast unified notifications; StarNet can send differentiated guidance instructions based on the precise location of an individual.

[0131] (5) Data storage: After local fusion processing, only the desensitized results are uploaded, saving traffic and computing power.

[0132] Example 6: Supervision of Collaborative Operations in High-Risk Positions

[0133] Application Scenario: During maintenance at a chemical plant, multiple workers entered the reactor area to perform equipment repairs. While the WAN signal was good within the plant area, significant metal obstructions in the repair area severely weakened the signal. Furthermore, the work involved high-risk operations such as confined space entry and hot work.

[0134] System Deployment:

[0135] Workers wear safety helmets or smartwatches (personnel nodes) with integrated StarFlash communication modules.

[0136] Deploy fixed beacon nodes (fixed nodes) at the entrance of a confined space.

[0137] The inspection robot is equipped with a StarFlash communication module and a gas sensor (equipment node).

[0138] The factory safety monitoring center is connected to fixed beacon nodes via a wide area network.

[0139] Workflow:

[0140] After personnel entered the reactor area, the wide area network signal was severely attenuated or even interrupted due to metal obstructions. Each node automatically discovered and formed a multi-hop self-organizing network to maintain connectivity.

[0141] Personnel nodes continuously collect physiological data and generate physiological assessment parameters, and automatically switch to "high-risk monitoring" mode after entering a confined space.

[0142] The robot nodes collect and transmit gas concentration and temperature data in real time, and the command center generates a situation map.

[0143] When the system detects that personnel physiological assessment parameters exceed limits, gas concentration exceeds limits, personnel cross boundaries, or communication is interrupted, it automatically triggers the corresponding coordinated response.

[0144] In this embodiment, short-range networking and wide area network work together to complement each other:

[0145] (1) Open areas of the factory: The wide area network signal is good and can transmit global data; short-range networking is optional for low-power daily communication. The collaborative effect is: the wide area network undertakes coarse-grained supervision, and the short-range networking is activated as needed.

[0146] (2) Metal-blocked areas: Wide area network signals are severely attenuated or interrupted; short-range networking maintains the connection through multi-hop relays. The synergistic effect is: short-range networking fills the blind spots of the wide area network and ensures uninterrupted communication.

[0147] (3) Within a confined space: the wide area network has no signal at all; short-range networking extends coverage through dedicated relays. The synergistic effect is that short-range networking is the only means of communication.

[0148] (4) Emergency alarm: Wide area network may experience delays due to congestion or blind spots; short-range networking enables local priority scheduling and provides a response within seconds. The collaborative effect is: short-range networking ensures real-time performance, while wide area network is responsible for remote reporting.

[0149] Example 7: Damage-resistant communication and multi-level command chain reconstruction in earthquake rubble rescue scenarios

[0150] Application Scenario: A strong earthquake strikes a region, paralyzing all wide area network base stations and turning the area into a communication island. Rescue teams enter the disaster area equipped with robots, drones, and wearable devices. The disaster area faces secondary disasters such as aftershocks, explosions, and landslides, and the on-site command tents are also at risk of destruction.

[0151] System Deployment:

[0152] Rescue personnel wear smartwatches or helmets with integrated StarScan communication modules (personnel nodes).

[0153] The search and rescue robot is equipped with a StarFlash communication module, a life detection radar, and a camera (equipment node).

[0154] The reconnaissance drone is equipped with a star-flash communication module, an optical camera, and a thermal imager (equipment node).

[0155] An emergency communication vehicle, equipped with a satellite communication terminal and a StarScan gateway, serves as the initial target network node.

[0156] A long-endurance unmanned aerial vehicle (UAV) equipped with a microsatellite communication terminal serves as a backup target network node.

[0157] Portable command terminals are installed inside the on-site command tent.

[0158] Wide-area communication links are not limited to satellites. In areas with coverage, connections can also be established through 4G / 5G cellular networks, broadband leased lines, microwave relays, shortwave radios, tropospheric scattering communications, low-Earth orbit satellite constellations (such as Starlink), or even other self-organizing network gateways.

[0159] Workflow:

[0160] Phase 1: After entering the ruins, each node automatically forms a multi-hop self-organizing network with a mesh topology, and transmits the global situation back to the provincial command center via satellite link from the communication vehicle.

[0161] Phase Two: A sudden explosion damages search and rescue robots and reconnaissance drones, causing rescue personnel to collapse. The system identifies the sudden loss of contact and issues an alarm. The high physiological assessment parameters of the collapsed personnel automatically trigger the highest priority rescue; simultaneously, a backup drone is automatically dispatched as a dedicated relay to restore the connection.

[0162] Phase Three: A strong aftershock triggered a landslide, with boulders directly hitting and destroying the emergency communication vehicle, and the satellite link was interrupted. Based on node priority rules, the system automatically reselected a high-altitude long-endurance UAV as the new target network node, seamlessly switching the data stream to the provincial command center, and simultaneously transferring on-site command authority. The provincial command center's understanding of the situation was completely identical to that before the command vehicle was destroyed.

[0163] Phase 4: The system continuously monitors the network status and issues an early warning and automatically takes over when the backup relay drone's battery is low.

[0164] In this embodiment, the key mechanism of the present invention is mainly embodied in:

[0165] (1) Abrupt loss identification: By the signal strength change rate, the instantaneous damage caused by explosion / impact (abrupt loss) and the signal attenuation caused by terrain blockage (gradual loss) are distinguished, and different response strategies are triggered.

[0166] (2) Automatic rescue triggering of physiological assessment parameters: After a person falls to the ground due to impact, the high physiological assessment parameters generated by the fusion of multimodal data such as heart rate and posture will automatically trigger rescue without the need for manual alarm.

[0167] (3) Dedicated relay intelligent replacement: After detecting a link break, the drone is automatically dispatched to the broken area to act as a dedicated relay and restore network connectivity.

[0168] (4) Dynamic reselection of target network nodes: After the communication vehicle is damaged, a UAV with satellite communication capability is automatically assigned to take over the gateway role based on the capability tag and priority, and the connection with the rear command link is rebuilt.

[0169] (5) Adaptive reconstruction of multi-level command chain: After the physical damage of the on-site decision-making center, the system automatically migrates the communication and command capabilities to the backup node and re-establishes the connection with the higher-level command center through the backup node.

[0170] (6) Wide Area Access Capability Virtualization: Satellite communication capability is no longer fixed to a specific node, but is a resource pool in the network that can be dynamically scheduled and replaced. Any node with this capability can be assigned by the system as a new wide area gateway.

[0171] Example 8: Heterogeneous Cluster Collaborative Search and Rescue and Dynamic Resource Scheduling in Earthquake-Stricken Areas

[0172] This embodiment demonstrates the complete process of unified supervision and dynamic task scheduling of heterogeneous nodes such as personnel, multi-functional robots / robot dogs, drones, and vehicles in an earthquake ruins scenario.

[0173] Application Scenario: Continuing from Example 7. Multiple heterogeneous nodes are deployed on-site. The system needs to simultaneously complete multiple tasks such as personnel search and rescue, casualty transfer, material distribution, and communication support, and automatically adjust task allocation when secondary disasters cause node damage.

[0174] System Deployment:

[0175] Personnel Node: Rescue personnel wear smartwatches or helmets

[0176] Search and rescue robot dog: equipped with a star flash module, life detection radar, and camera.

[0177] Reconnaissance drone: equipped with a star flash module, optical camera and thermal imager

[0178] Material transport robot dog: equipped with a star-flash module and cargo compartment

[0179] Robotic dog for transporting wounded soldiers: equipped with a star-flash module and stretcher

[0180] Medical transport drone: equipped with a star-flash module and medical cargo box

[0181] Logistics vehicles: serving as nodes for material dispatch

[0182] Emergency communication vehicle: Initial target network node

[0183] Backup search and rescue robot dogs, backup supply transport robot dogs, and backup reconnaissance drones are on standby.

[0184] Workflow:

[0185] Phase 1 (Routine Collaboration): The system generates a global situation map and automatically allocates search and rescue tasks, dispatches supplies, schedules the transfer of the wounded, and deploys communication relays.

[0186] Phase Two (Sudden Damage and Functional Succession): A strong aftershock caused damage to the casualty transport robot, a cargo hold malfunction in the supply transport robot, and a reconnaissance drone to crash. The system immediately identified the damage, pushed casualty information to the nearest rescue personnel, and dispatched backup casualty transport robots and medicine transport drones to provide support; unfinished delivery tasks were reassigned to idle supply transport robots; backup reconnaissance drones were launched to take over reconnaissance missions and, if necessary, fill the communication relay role.

[0187] Phase 3 (Continuous Optimization): The system continuously monitors the status of all nodes. When the number of backup nodes falls below a threshold, it requests resource replenishment from the provincial command center via satellite link. Complete mission logs are available for post-event review.

[0188] Core value: The system is not only a communication channel, but also an intelligent scheduling and task collaboration platform for heterogeneous clusters, realizing efficient resource allocation in normal times and automatic functional takeover in case of sudden damage, making the rescue system an adaptive, damage-resistant, and dynamically reconfigurable organic whole.

[0189] Example 9: Unified Bearer, Dynamic Framing, and Secure Transmission of Multi-mode Data and Instructions

[0190] This embodiment demonstrates how the system can collaboratively carry multiple data streams such as status data, environmental data, and command data under a unified cross-domain self-organizing network architecture, as well as the mechanism to ensure data security and resist interception in harsh environments.

[0191] (a) Data types and flow

[0192] The data transmitted in the system mainly includes the following three categories:

[0193] (1) Status data: reported periodically or event-driven by each node, including but not limited to:

[0194] Physiological assessment parameters of personnel nodes, including heart rate, body temperature, movement speed, direction of movement, and location coordinates;

[0195] Remaining battery power, current task status, and sensor health status of device nodes;

[0196] Link quality, signal strength, and topology role of network nodes.

[0197] (2) Environmental data: collected by device nodes or fixed nodes, including but not limited to:

[0198] Temperature, humidity, air pressure, wind speed and direction;

[0199] Concentration of toxic and harmful gases;

[0200] Infrared thermal imaging, visible light video streaming;

[0201] Ice thickness on power lines (measured visually or by mechanical sensors), and line sag / drop angle.

[0202] (3) Command data: generated by the command terminal or system autonomous decision-making and sent to field nodes, including but not limited to:

[0203] Send instructions to designated rescue personnel to adjust their route, evacuate, or perform specific rescue actions.

[0204] Send instructions to a designated drone to fly to a certain coordinate, take video or infrared thermal images in a specific direction, and measure the thickness of ice accretion and sag of the line;

[0205] Send instructions to the designated robot dog to carry the wounded to the medical vehicle or to deploy a relay beacon.

[0206] (ii) Dynamic framing and encryption mechanisms for data frames

[0207] To ensure data security and anti-interception capabilities during wireless transmission, the system can employ a dynamically variable data frame format. The core idea is that the position, length, and encryption method of each functional field in the data frame are not fixed, but are dynamically negotiated and determined by the sender and receiver based on a session key.

[0208] One possible implementation is as follows:

[0209] (1) Session key negotiation: When two nodes establish a communication relationship for the first time or periodically update their keys, the sender generates a random key and sends it to the receiver through a secure channel (such as encryption based on a pre-shared key or digital certificate). This random key is used to determine the frame structure parameters for this session or within a preset time period.

[0210] (2) Dynamic determination of frame structure parameters: The sender determines at least one of the following based on the random key using a preset mapping algorithm (such as hash operation or table lookup):

[0211] The order in which the data fields are arranged within the frame (e.g., status data first, instruction data second, or vice versa).

[0212] The starting offset and length of each data field;

[0213] Encryption algorithms for data fields (such as AES-256-GCM, SM4) and methods for deriving encryption keys;

[0214] The location and calculation method of the verification field (such as CRC32, HMAC).

[0215] (3) Assembly and transmission of data frames: The sender fills in each data field to be sent in sequence according to the dynamically determined frame structure, encrypts sensitive fields, calculates and adds verification fields, and then sends the data.

[0216] (4) Parsing by the receiver: The receiver uses the same random key and mapping algorithm to independently calculate the frame structure parameters for this time, and then parses, decrypts and verifies the integrity of the received data frame.

[0217] (III) Safety Effect

[0218] Through this mechanism, even if an attacker intercepts the wireless signal, they cannot obtain the random key for the current session, nor can they determine the boundaries and meanings of the fields in the data frame, making proper decryption impossible. The use of different keys for each session or time period further increases the difficulty of decryption.

[0219] It should be noted that the aforementioned dynamic framing and encryption mechanisms are only optional security enhancement schemes. In actual deployment, depending on the terminal's computing power, power consumption constraints, and security level requirements, a simplified version (such as a fixed frame format with periodic key updates), standard security protocols (such as IPsec, DTLS), or a complete reliance on physical layer security mechanisms can be selected. Regardless of the specific implementation used, as long as its core lies in the unified bearing and secure transmission of multi-mode data and instructions in a cross-domain self-organizing network architecture, it should be considered an optional technical feature of this invention.

[0220] (iv) Typical Scenario Examples

[0221] In the earthquake rescue scenario (continuing from Example 8):

[0222] When a rescuer's physiological assessment parameters suddenly rise, the system automatically generates an alarm command, which is sent to the nearest rescuer node via a multi-hop link. The command reads, "Please go to coordinates XX to confirm the status of person A."

[0223] Meanwhile, the system dispatched a drone to the area, instructing it to take infrared thermal images to confirm whether there were any other trapped personnel, and transmitted the real-time video stream back to the command center.

[0224] In a power line inspection scenario, the command terminal issues instructions to the drone: "Fly along the XX line, measure the ice thickness and line sag between towers #123 and #124, and transmit the measurement data back every 5 minutes."

[0225] The aforementioned instruction data, along with the status and environmental data reported by each node, are transmitted together in the same multi-hop ad hoc network. The system dynamically adjusts the transmission queue according to data priority (e.g., alarm instructions take precedence over regular status reports), and the aforementioned encryption mechanism ensures that instructions cannot be forged or tampered with.

[0226] (v) The value of this embodiment

[0227] This embodiment demonstrates that the system described in this invention is not only a "sensor network" for data acquisition and transmission, but also a collaborative command platform that supports bidirectional, multi-mode, secure, and controllable communication. The system integrates three types of data streams—state perception, environmental monitoring, and command and control—into a cross-domain self-organizing network architecture, and ensures the confidentiality and integrity of data transmission through optional security mechanisms, providing a complete closed-loop solution for emergency rescue and operational supervision in harsh environments.

[0228] This invention also protects a computer-readable storage medium storing a computer program that, when executed, implements the cross-domain collaborative security monitoring method described above. The physical form of the storage medium can be any carrier capable of storing and running programs, such as a memory built into a terminal or server, a mobile storage device, or network storage space.

[0229] Supplementary Explanation

[0230] In some alternative implementations, to accommodate different cost or power consumption requirements, the system may omit the generation and evaluation function of the "physiological evaluation parameters" and instead be simplified into a pure data acquisition and feedback communication system. In this case, the personnel node only serves as a relay node for location and sensor data, and the overall collaborative response function of the system will be weakened accordingly, but its core architecture of networking and communication remains unchanged.

[0231] In some alternative implementations, the "physiological assessment parameters" can be replaced by a single physiological indicator alarm mechanism based on a fixed threshold, or by an alarm signal triggered manually by personnel. These simplifications and replacements do not depart from the protection scope of the cross-domain self-organizing network collaborative architecture constructed by this invention.

[0232] Explanation of alternative technologies

[0233] It is understood that although the above embodiments use Starflash technology as a preferred solution for short-range wireless communication, the core of this invention lies in constructing a self-organizing and collaborative monitoring architecture for multiple types of terminals, the implementation of which does not depend on a specific underlying communication protocol. It will be obvious to those skilled in the art that Wi-Fi Mesh, Zigbee, LoRa, UWB, Bluetooth 5.0 and above Mesh networks, and even other communication technologies with self-organizing and low-power characteristics that may emerge in the future, can all serve as the carrier technology for the "short-range wireless communication node" and be applied to the system architecture of this invention to achieve the same or similar technical effects. All such alternatives should be considered as part of the technical content disclosed in this invention.

[0234] It should be noted that the present invention preferably uses Starflash technology as a short-range wireless communication solution based on its comprehensive advantages in harsh environments: Starflash technology has sub-microsecond time synchronization accuracy, supporting high-precision relative positioning and millisecond-level collaborative response; its air interface latency is as low as 20 microseconds, far superior to Wi-Fi and Bluetooth; a single access point can support concurrent connections of hundreds of nodes, meeting the needs of large-scale node deployment; at the same time, Starflash technology has significantly better battery life in low-power mode than traditional Mesh solutions, making it particularly suitable for sparse, power-constrained operational scenarios in the field. These technical characteristics enable Starflash technology to be optimally matched with the physiological evaluation parameter-driven priority scheduling, dedicated relay intelligent replacement, and dynamic reselection of target network nodes mechanisms in this invention. However, the core architecture of this invention does not rely on Starflash technology; other communication technologies with similar low latency, high concurrency, and low power consumption characteristics can be used as alternatives.

[0235] Further explanation regarding alternative technologies and variant solutions

[0236] In addition to the short-range wireless communication technology alternatives described above, those skilled in the art will understand that many of the technical features involved in this invention can be implemented using other equivalent means. The following lists some non-exhaustive alternatives, and all such alternatives should be considered as part of the technical content disclosed in this invention:

[0237] (1) Alternative network topologies: Multi-hop self-organizing networks are not limited to linear and mesh topologies, but can also be star, tree, hybrid, or any topology that enables data relay between nodes.

[0238] (2) Alternatives for selecting target network nodes: When the on-site decision-making center is destroyed, the new target network node is not limited to being elected through preset priority rules, but can also be elected by random election, election based on consensus algorithms (such as Raft, Paxos), election based on auction mechanism, or directly designated by the remote platform.

[0239] (3) Alternative solutions for node damage identification: not limited to the determination by signal change rate, but also by combining the timeout of the heartbeat message sent by the node itself, abnormal sensor data, environmental audio characteristics and other multi-source information for comprehensive determination.

[0240] (4) Alternative solutions for relay replacement: Dedicated relay nodes are not limited to drones, but can also be mobile robots, vehicle-mounted relay stations, drop-able beacons, tethered balloons, or even portable repeaters carried by personnel. Replacement triggering conditions are not limited to link breakage, but can also include link quality below a threshold, insufficient node power, changes in task priority, etc.

[0241] (5) Alternative solutions for data fusion and decision-making: Crisis level identification is not limited to Bayesian networks, but can also use any supervised or unsupervised learning model such as rule engines, fuzzy logic, support vector machines, random forests, gradient boosting trees, and deep neural networks.

[0242] (6) Explanation of numerical examples: The specific numerical values, physiological assessment parameters, percentages exceeding the resting baseline, and relay placement distances, such as every 5 minutes, 0.2%, 30%, 50 meters, etc., appearing in this specification are merely exemplary descriptions to help understand the technical solution of this invention and are not intended to limit the scope of protection of this invention. Those skilled in the art can flexibly select or adjust the specific values ​​of these parameters according to the actual needs of the scenario.

[0243] The core inventive point of this invention lies in the organic integration of personnel, equipment, and fixed nodes through a unified cross-domain self-organizing network architecture, achieving data collaboration, task collaboration, and damage-resistant adaptive reconfiguration among heterogeneous nodes, rather than being limited to any specific implementation method mentioned above. Any equivalent substitutions or simple modifications made under the guidance of the above architectural concept fall within the protection scope of this invention.

[0244] Explanation of system configuration flexibility and minimum system requirements

[0245] It should be clarified that the node types, command nodes, and functional modules described in the above embodiments of the present invention are not all components necessary to achieve the core objective of the present invention. The core of the present invention lies in constructing a self-organizing collaborative supervision architecture for multiple types of terminals, rather than relying on the existence of any specific component.

[0246] Specifically:

[0247] (1) Node type can be missing: The system can deploy only personnel nodes, only equipment nodes, or only fixed nodes, or any combination of two or three of the above types. Regardless of which type of node is missing, as long as there are at least two short-range wireless communication nodes in the system that can automatically discover and form a multi-hop self-organizing network, the cross-domain collaborative supervision architecture described in this invention can operate.

[0248] (2) Decentralization of the command node: The on-site decision-making center is not a central node at the network protocol level. The multi-hop self-organizing network of this invention is decentralized at the communication protocol level—any node can assume the roles of relay forwarding, data aggregation, or master node, and the master node is dynamically elected among nodes through priority values. The existence of the on-site decision-making center is only due to the fact that the node is equipped with special wide-area access equipment such as satellite communication terminals, which physically enables it to upload data to a remote platform. When the node is damaged or does not exist, the system can still complete self-organizing network communication, data relay, and local collaborative response within a local area; if any node in the network has wide-area access capabilities, that node can be dynamically assigned as a new target network node to take over the data transmission function.

[0249] (3) The non-essentiality of wide area access capability: Wide area access devices such as satellite communication terminals are not required for every node. Due to considerations of cost, power consumption, or deployment scenarios, only some nodes (or even zero nodes) in the network may have wide area access capability (e.g., satellite). When no node has wide area access capability, the system can still complete data acquisition, relay, local storage, and inter-node collaboration within the local self-organizing network range, and upload data after the network recovers or the system moves to an area with wide area coverage. In other words, wide area access capability exists as an enhanced function rather than an essential function in this invention.

[0250] (4) Customizability of functional modules: Functions such as personnel urgency assessment, physiological and behavioral coordination analysis, and adaptively adjustable spatial monitoring boundaries can all be enabled or disabled according to actual needs. In the minimum configuration, the system can only implement basic data acquisition and multi-hop backhaul, with personnel nodes serving only as relay nodes for location and sensor data. This simplification does not deviate from the protection scope of the cross-domain self-organizing network collaborative architecture constructed in this invention.

[0251] (5) Multifunctionality of fixed nodes: Fixed nodes (such as beacon devices installed on towers, poles, buildings, roadsides, or buried in the road surface) can be used as optional reinforcing components in this invention. They can perform one or more of the following functions:

[0252] Stable relays: Due to their fixed location and guaranteed power supply, they can provide more reliable long-term relay services than mobile nodes.

[0253] Position reference: Its pre-stored precise absolute coordinates (obtained through surveying) can serve as a reference for the relative positioning of dynamic nodes (personnel, robots), and can be used to correct the cumulative errors of relative positioning methods such as inertial navigation and visual odometry, thereby improving the global positioning accuracy;

[0254] Gateway function: Connecting to wide area communication links;

[0255] Environmental sensing: Integrates sensors for temperature, humidity, gas, vibration, etc., as fixed monitoring points.

[0256] The system may have no fixed nodes or one or more fixed nodes; the fixed nodes may perform only one or more of the functions described above. Any attempt to circumvent the scope of protection of this invention by changing the number or combination of functions of the fixed nodes shall not be considered as departing from the essential technical concept of this invention.

[0257] Furthermore, any attempt to circumvent the scope of protection of this invention by omitting a certain type of node, the command center, the wide area access equipment, or a certain functional module should not be considered as departing from the essential technical concept of this invention.

[0258] Supplementary explanation regarding node multi-mode communication capabilities and dynamic authorization of the central node

[0259] In some alternative embodiments of the present invention, the short-range wireless communication nodes (including personnel nodes, equipment nodes, and fixed nodes) can be further configured with various types of communication modules, such as 4G / 5G wide area network communication modules, satellite communication modules, Starlink / Bluetooth modules, low-Earth orbit satellite (such as Starlink) communication modules, etc. Through the integration of multi-mode communication capabilities, each node can not only form or join short-range wireless self-organizing network groups, but also directly access wide area networks or satellite networks for long-distance communication when needed.

[0260] Furthermore, the system can temporarily assign any node with wide-area communication capabilities as the target network node according to preset rules or a dynamic authorization mechanism. This target network node is responsible for aggregating data within the ad hoc network and interacting directly with the upper-level data center through its wide-area communication link. When the original target network node is damaged or disconnected from the network, the system can automatically or manually assign a new node to take over this function, ensuring that the upper-level data center can continue to receive field data and maintain a comprehensive understanding of the overall situation even in extremely harsh environments.

[0261] The aforementioned configuration of multi-mode communication capabilities and the dynamic authorization mechanism of the central node are optional enhancements, designed to further improve the robustness and deployment flexibility of the system. Those skilled in the art can selectively configure multi-mode communication modules for some or all nodes based on actual application scenarios and cost constraints, without departing from the core architecture of this invention.

[0262] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the design concept of the present invention should be included within the scope of protection of the present invention.

Claims

1. A cross-domain collaborative security monitoring system, characterized in that, include: Multiple short-range wireless communication nodes are deployed on personnel and / or work equipment within the monitored area; Each of the aforementioned short-range wireless communication nodes is configured to: automatically discover neighboring nodes and form a multi-hop self-organizing network within a cross-domain operation area; the multi-hop self-organizing network supports linear topology and / or mesh topology; Among them, the short-range wireless communication node deployed on the personnel is a personnel node, which is also used to collect the personnel's physiological state data and generate physiological assessment parameters characterizing the personnel's urgency based on the physiological state data. Each node in the multi-hop self-organizing network transmits the collected data hop-by-hop back to the target network node via a multi-hop relay link; the target network node is any node in the multi-hop self-organizing network that is connected to a wide area communication link, and is used to transmit the data aggregated by the multi-hop self-organizing network to a remote platform.

2. The system according to claim 1, characterized in that, The short-range wireless communication technology is star flash technology.

3. The system according to claim 1, characterized in that, The personnel node is also used to: automatically increase the data reporting priority of the node and trigger a collaborative rescue response when the physiological assessment parameters exceed a preset threshold.

4. The system according to claim 1, characterized in that, The personnel nodes and the equipment nodes deployed on the operating equipment establish data collaboration within the same multi-hop self-organizing network, realizing situational sharing and hybrid cluster collaboration between personnel and equipment.

5. The system according to claim 1, characterized in that, The physiological data includes at least one of heart rate data, skin conductance data, body temperature data, and exercise data.

6. The system according to claim 1, characterized in that, The cross-domain operation area includes at least one of the following scenarios: outdoor, indoor, underground, transportation routes, and power lines.

7. The system according to claim 1, characterized in that, The short-range wireless communication node has a hierarchical terminal identifier, which is divided into personnel node ID, equipment node ID and fixed node ID according to the physical form and functional attributes of the node. The system schedules the node to undertake sensing, relay or execution tasks in a differentiated manner according to the hierarchical classification of the terminal identifier.

8. The system according to claim 1, characterized in that, The system is also used to generate and present at least one of the following types of regulatory information: (1) Personnel status monitoring information: including personnel's physiological assessment parameter curves, location trajectory, urgency level and loss of contact warning prompts; (2) Equipment status monitoring information: including the location, power, sensor data and task execution status of the operating equipment; (3) Environmental risk monitoring information: including thermal distribution map, gas concentration distribution map or meteorological condition map of the work area; (4) Task and resource scheduling and monitoring information: including task progress, spatial distribution of personnel and equipment, relay network topology and link quality.

9. A cross-domain collaborative security supervision method, characterized in that, Includes the following steps: Within a cross-domain operation area, multiple short-range wireless communication nodes deployed on personnel and / or work equipment automatically discover neighboring nodes and form a multi-hop self-organizing network; Personnel nodes collect physiological state data of personnel and generate physiological assessment parameters characterizing the urgency level of personnel based on the physiological state data; Each node will transmit the collected data back to the target network node hop by hop through the multi-hop relay links in the multi-hop self-organizing network; The target network node transmits the data aggregated by the multi-hop self-organizing network to the remote platform via a wide area communication link.

10. The method according to claim 9, characterized in that, The multi-hop self-organizing network is deployed in a linear or quasi-linear topology along the geographical strip structure of the cross-domain operation area. Each node relays data back to the target network node along the deployment direction through a hop-by-hop relay method.

11. The method according to claim 10, characterized in that, The multi-hop self-organizing network composed of multiple short-range wireless communication nodes is configured with at least one dedicated relay node. The dedicated relay node only performs the function of relaying and forwarding data, and does not collect or actively report its own data.

12. The method according to claim 11, characterized in that, The dedicated relay node includes a mobile relay device that, in response to a scheduling command, is dynamically deployed to a designated location to supplement or extend the coverage of the multi-hop self-organizing network.

13. The method according to claim 10, characterized in that, The geographical strip structure includes at least one of highways, railways, power transmission lines, oil pipelines, and riverbanks.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the method as described in any one of claims 9 to 13.

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