Intelligent supervision system, method and terminal based on dynamic virtual electronic fence

Abstract

The invention discloses an intelligent supervision system and method based on a dynamic virtual electronic fence, and belongs to the technical field of intelligent perception and active safety. According to the system, a self-organizing network is formed by a plurality of collaborative terminals, and the system can comprise a server; under the driving of a task mode, the system sets a virtual electronic fence and establishes a monitoring relationship with a target terminal to obtain information data; and based on the task mode, the rule set and the information data, the system adaptively selects a terminal direct connection or wide area network assisted relative spatial relationship measurement and calculation mode to determine state data, so that adaptive adjustment of the virtual electronic fence and situation assessment and hierarchical response of the supervision area are realized. High-precision cooperative sensing independent of V2X fixed facilities is realized, and a low-cost and high-reliability universal solution is provided for accurately preventing sudden traffic risks such as a ghost probe and the like, quickly positioning trapped persons on a disaster site, intelligently searching and the like.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent perception and active safety warning, in particular to an intelligent monitoring system, method and terminal based on a dynamic virtual electronic fence. BACKGROUND

[0002] In the field of road traffic safety, "ghost probe" accidents have become a typical traffic nuisance due to their high suddenness and danger. The essence of such accidents is the failure to perceive the real-time and accurate relative position and relative motion situation between the two parties.

[0003] The prior art solutions cannot fundamentally solve this problem. For example, the invention patent with the authorization announcement number CN114932902B provides a solution based on V2X and neighbor car sensor sharing. The effective implementation of this solution relies on a mature Internet of Vehicles ecosystem, available neighbor car sensors, and powerful cloud real-time processing capabilities. This dependence on complex external infrastructure and high-cost technology restricts the feasibility and timeliness of its large-scale popularization.

[0004] On the other hand, consumer-level terminals based on wide-area satellite positioning (e.g., GPS, Beidou) such as smartwatches and anti-lost devices, the technical core of which is to obtain the absolute geographic coordinates of the terminal. This approach has two principle defects that make it unsuitable for "ghost probe" warning:

[0005] Insufficient positioning accuracy and reliability: In complex environments such as urban canyons, satellite signals are easily blocked and interfered with, resulting in positioning errors of consumer-level devices up to several meters or even tens of meters, far from the sub-meter accuracy required for anti-collision warning.

[0006] Lack of key relative motion relationships: This technical approach cannot directly and continuously provide accurate relative distance, relative speed, and motion direction between the monitoring party and the monitored party, and these dynamic vector information is the core data necessary for predicting collision risks and achieving millisecond-level warning.

[0007] In fact, the above technical limitations are not limited to the "ghost probe" scenario. In disaster site personnel search and rescue, smart care, family or team travel management, and other wide-ranging applications that require high-precision relative perception, low-latency response, and do not rely on complex external infrastructure, the existing technology also faces significant challenges.

[0008] Therefore, there is an urgent need in the field for a general technical architecture that does not rely on specific external infrastructure and can autonomously achieve high-precision relative perception and real-time intelligent risk assessment. This architecture should make breakthroughs in technical feasibility, handling reliability, deployment flexibility, and economy to meet the urgent needs of various scenarios from traffic warning to personnel monitoring. SUMMARY

[0009] To address the problems of existing technologies, such as excessive reliance on complex external ecosystems like V2X, insufficient absolute positioning accuracy, and inability to intelligently adapt to diverse scenario requirements, this invention provides an intelligent monitoring system and method based on a dynamic virtual electronic fence. Its core objective is to achieve a general technical solution through a task-driven intelligent sensing network that does not rely on complex fixed facilities and can autonomously complete high-precision relative perception and intelligent situational awareness assessment. This allows for low-cost and highly reliable responses to sudden traffic risks such as "ghost pedestrians," personnel search and rescue, and regional security scenarios.

[0010] To achieve the above objectives, the core of the technical solution of this invention lies in constructing a dynamic network composed of collaborative terminals. In this network, terminal roles (monitoring execution terminals or target terminals) can dynamically operate as monitoring execution terminals or target terminals according to the task mode.

[0011] The technical solution of this invention revolves around the main theme of "task-driven" and executes the following core processes in sequence:

[0012] (a) Task triggering and virtual electronic fence setup and management

[0013] In response to static settings, external commands, or internal events, at least one monitoring execution terminal sets up and manages a dynamic virtual electronic fence based on a task mode and an associated rule set, forming a monitored area.

[0014] (b) Establishment of dynamic identification and monitoring relationship of target terminals

[0015] The monitoring execution terminal establishes a monitoring relationship with at least one target terminal within the monitored area, and obtains information data for situation assessment through the cooperation of the target terminal;

[0016] The collaborative terminal, as an independent functional unit, can also be deployed and operated independently in some embodiments. For example, a collaborative terminal configured as a monitoring and execution terminal (e.g., a security terminal installed in a restricted area to prevent intrusion or an anti-theft terminal installed in a valuable bag) may work independently most of the time, in a low-power standby state, continuously monitoring the environment or broadcasting its detectable signals.

[0017] When a monitoring task needs to be performed, the monitoring terminal can establish a monitoring relationship with at least one target terminal. Once this relationship is established, these collaborative terminals work together according to the collaborative workflow provided by this invention to achieve complete intelligent monitoring functions. This design allows the solution to be flexibly deployed as a single terminal product, and to realize the full capabilities of the system instantly through dynamic collaboration between terminals when needed.

[0018] In some embodiments, the information data may include various types such as status data, environmental data, attribute data, and perception data, but its core is to include at least the relative spatial relationship data between the monitoring execution terminal and the target terminal.

[0019] The relative spatial relationship data refers to the core physical quantities used to characterize the spatial situation between the two, such as one or more of the following: relative position, relative distance, relative velocity, relative azimuth angle, and relative motion trajectory.

[0020] When establishing a monitoring relationship, the monitoring execution terminal obtains and verifies the identifier of the target terminal through at least one of the following methods: automatic scanning via short-range wireless communication; parsing instructions issued by the server; receiving user input; or triggering collaborative terminal reporting by broadcasting virtual electronic fence parameters.

[0021] (c) Intelligent perception and adaptive state determination

[0022] Based on the task mode, rule set, and information data, the monitoring execution terminal adaptively selects a relative spatial relationship measurement and calculation mode to determine or correct the relative spatial relationship data and form updated information data.

[0023] As a preferred implementation, the selection can be guided by evaluating the degree of matching between current conditions and task accuracy. The relative spatial relationship measurement and calculation modes include at least a direct terminal-to-terminal measurement mode and a wide area network-assisted calculation mode.

[0024] The short-range wireless technology used in the direct connection measurement mode between terminals includes at least one of SparkLink and Ultra Wideband (UWB).

[0025] (d) Adaptive electronic fence and situation assessment of the monitored area

[0026] The monitoring execution terminal adaptively adjusts the reference point positions and geometric parameters of the virtual electronic fence based on updated information data, task modes, and rule sets. Simultaneously, it performs a situational assessment of the monitored area based on the updated information data, task modes, and rule sets. This situational assessment can generate quantitative situational values ​​characterizing the risk or status of the monitored area or target terminals, and when multiple target terminals exist, they can be sorted according to these quantitative situational values.

[0027] (e) Execution of hierarchical response operations

[0028] Based on the situation assessment results, and according to predefined response rules associated with the task mode, corresponding response operations are triggered. When the situation assessment includes a ranking of multiple target terminals, the priority level or type of the response operation can be determined accordingly.

[0029] In this system, when a collaborative terminal acts as the target terminal, it is configured to: receive and respond to signals or instructions from the monitoring execution terminal, and at least cooperate with the monitoring execution terminal to acquire the relative spatial relationship data. The "receive and respond" encompasses the entire process from responding to low-level signals such as ranging and direction finding to determine the relative spatial relationship, to responding to data request instructions to report various information data such as status and attributes, and finally to responding to application instructions such as alarms and control. Its core lies in achieving collaboration with the monitoring execution terminal.

[0030] Task Modes and Rule Sets

[0031] This invention abstracts diverse regulatory needs into computable models through predefined task modes. These task modes include, but are not limited to: restricted area protection mode, restraint monitoring mode, search and location mode, path guidance mode, state awareness mode, and group scheduling mode.

[0032] Each task mode is associated with a set of rules, which includes at least one of the following categories of rules:

[0033] Rules used to define the configuration and adjustment of virtual electronic fence parameters;

[0034] Rules used to define the logic for selecting the measurement and calculation mode of the relative spatial relationship;

[0035] Rules used to define the situation assessment methods for regulatory areas;

[0036] This set of mechanisms together enables the system to be adaptive and intelligent.

[0037] The aforementioned task modes and their associated rule sets constitute the core configuration framework shared by the system and method of this invention.

[0038] Response Operation

[0039] Based on the results of the situation assessment, and according to predefined response rules associated with the task mode, the level or type of the response operation to be performed is determined, which includes at least one of the following types:

[0040] Alarm operation: Generate and output alarm commands to trigger itself and / or the target terminal to perform local reminder or display operations;

[0041] Control operations: Generate and issue control commands to change the state or behavior of the target terminal or associated device;

[0042] Data reporting operation: Upload information data, including the target terminal identifier, to the server.

[0043] server

[0044] In some embodiments, the server is configured to operate as a monitoring execution terminal, independently or in collaboration with other cooperating terminals, performing one or more of the functions of a monitoring execution terminal as described above.

[0045] The server is also configured to: establish communication connections with collaborative terminals in the system; establish communication connections with associated external terminals or platforms; and provide at least one of the following services for monitoring execution terminals and / or target terminals: auxiliary management of virtual electronic fences, data storage and processing, and instruction issuance and forwarding.

[0046] Furthermore, the server analyzes and learns user information data, and integrates real-time data to conduct situational assessments or detect anomalies in the monitored area. In one embodiment, the server establishes an activity baseline by analyzing users' historical information data; when real-time data deviates significantly from this activity baseline, it is determined to be an anomaly.

[0047] Hardware implementation of collaborative terminals

[0048] The present invention also protects a collaborative terminal for the said intelligent monitoring system, comprising:

[0049] processor;

[0050] Memory, used to store computer programs;

[0051] Multi-mode communication unit, supporting wide area network communication and short-range wireless communication;

[0052] When the processor is configured to execute the program, it can dynamically switch between the roles of monitoring execution terminal and target terminal according to the task mode, and perform the following functions:

[0053] When switching to the monitoring execution terminal role, it is configured to implement the aforementioned intelligent supervision method;

[0054] When switching to the target terminal role, it is configured to: receive and respond to signals or instructions from other cooperating terminals that act as monitoring execution terminals, and at least cooperate with the monitoring execution terminal to obtain the relative spatial relationship data.

[0055] The collaborative terminal also includes at least one of the following:

[0056] Alert device: configured to issue an alert, including at least one of vibration, sound and light, or text signals, in response to a response operation;

[0057] Display device: configured to integrate at least one of the status data of the collaborative terminal and information related to response operations with electronic map data to generate and display a visualization interface;

[0058] Alarm device: configured to send an alarm signal in response to a user's alarm operation.

[0059] storage media

[0060] The present invention also protects a storage medium on which a computer program is stored, which, when executed by a collaborative terminal, implements the aforementioned intelligent monitoring method.

[0061] Through the above workflow, this invention constructs a general intelligent regulatory solution with forward-looking regulatory logic, intelligent perception mode, and adaptive regulatory strategy.

[0062] The beneficial effects of this invention are:

[0063] (a) A decentralized, dynamically self-organizing collaborative sensing architecture was constructed: Through a network driven by tasks and supporting dynamic configuration of terminal roles, intelligent sensing was achieved without relying on fixed infrastructure such as V2X roadside facilities. This architecture allows regulatory areas to be flexibly and quickly established and removed as needed, providing a highly flexible and low-deployment-cost general solution for scenarios such as traffic early warning, regional security, and personnel search.

[0064] (b) An adaptive balance between sensing accuracy and system performance is achieved: By introducing an evaluation mechanism for the matching degree between measurement conditions and task accuracy, and intelligent decision-making based on task modes and rule sets, the system can dynamically select and schedule the most suitable measurement mode according to the real-time scenario. Intelligent switching and combination between modes such as direct connection measurement between terminals and wide area network-assisted computing not only significantly improves the sensing accuracy in key scenarios, but also avoids the continuous operation of high-power modules, optimizing the overall power consumption and cost of the system.

[0065] (c) Breaking through the limitations of line-of-sight perception, the system achieves advanced early warning of potential risks: By utilizing the signal propagation characteristics of high-precision short-range wireless technologies (e.g., satellite flash, UWB) under non-line-of-sight conditions, the "absolute blind spot" in traditional perception is transformed into a "calcifiable risk area." The system can perceive targets and calculate their relative motion trends in advance, even in the presence of visual obstructions. This provides valuable early warning time for dealing with sudden risks such as "ghost peeking out," realizing a shift from passive response to proactive prevention.

[0066] (d) Improved the intelligence of system decision-making and the efficiency of resource scheduling: Through quantitative calculation of the regulatory situation and multi-objective ranking, the system can accurately identify the highest risk target and prioritize the allocation of early warning and computing resources, thereby significantly improving the system's response efficiency and reliability in complex multi-objective scenarios.

[0067] (e) Through the structured design of the rule set, the system achieves intelligent configurability and interpretability: This invention explicitly defines the decision logic as three types of rules associated with the task mode (parameter configuration and adjustment rules, measurement mode selection rules, and situation quantification assessment rules), making the system's adaptive behavior no longer a "black box." On the one hand, this design greatly improves the system's maintainability and adaptability to different scenarios (achieved by configuring different rule sets); on the other hand, it also enhances the transparency and interpretability of the decision-making process, laying the foundation for the reliable application of the system in security-critical scenarios.

[0068] (f) A highly reliable and low-cost proactive safety closed loop is formed: This solution is based on widely deployable collaborative terminals (e.g., vehicle terminals, smartphones, smartwatches, etc.), and defines their roles and rules through software, making them act as "sentinels" for each other, thus building a plug-and-play, flexibly expandable collaborative protection network to achieve "beyond line of sight" perception and early warning. The entire solution makes full use of the computing and communication capabilities of existing or incremental devices, providing an effective technical path for long-standing safety pain points such as child safety monitoring and traffic early warning with extremely high cost-effectiveness. Attached Figure Description

[0069] Figure 1 Example of virtual electronic fence networking and management with server participation

[0070] Figure 2 Examples of preventing "ghost pedestrian" traffic accidents Detailed Implementation

[0071] This invention provides an intelligent monitoring system and method based on a dynamic virtual electronic fence. The system consists of two or more collaborative terminals; these terminals can network together and exchange data. Depending on the task mode, the collaborative terminals can dynamically operate as monitoring execution terminals or target terminals, thereby forming a flexible and reconfigurable monitoring network.

[0072] The core feature of collaborative terminals lies in their "dynamic role switching" capability, which means that the same terminal can flexibly assume the role of monitoring or target in different tasks or relationships.

[0073] The monitoring execution terminal is configured to perform the following core operations: setting up and managing a virtual electronic fence to form a monitored area based on a predefined task mode and associated rule set; establishing a monitoring relationship with at least one target terminal within the monitored area to obtain its information data, including at least relative spatial relationship data; adaptively selecting a relative spatial relationship measurement and calculation mode based on the task mode, rule set, and information data to determine or correct status data related to the target terminal, forming updated information data; subsequently, adaptively adjusting the virtual electronic fence parameters based on the updated information data, performing a situation assessment of the monitored area, and triggering response operations based on the assessment results; the situation assessment includes generating a situation quantification value to characterize risk or status.

[0074] The target terminal is configured to receive and respond to signals or instructions from the monitoring execution terminal, and at least cooperate with the monitoring execution terminal to acquire the relative spatial relationship data.

[0075] Explanation of the target terminal response mode

[0076] In this invention, the target terminal achieves coordination by receiving and responding to signals or instructions from the monitoring and execution terminal according to task requirements. Its response modes mainly include, but are not limited to, the following two types:

[0077] Perception-coordinated response: To cooperate in determining the relative spatial relationship, the target terminal receives and responds to low-level wireless signals (e.g., UWB pulses, star flash signals) initiated by the monitoring execution terminal, such as ranging and direction finding. For example, in the vehicle collision avoidance of Embodiment 4 and the "ghost peek" warning of Embodiment 5, pedestrian or vehicle terminals cooperate in this way to complete high-precision ranging.

[0078] Data and Instruction Response: The target terminal receives and responds to specific instructions from the monitoring execution terminal. This includes responding to data reporting instructions to provide information such as its own status and attributes (e.g., in Example 2, the child terminal reports its age attribute); and responding to application instructions to perform operations such as reminders and controls (e.g., in Example 1, the target terminal receives an alarm instruction and triggers a local reminder). These two response modes together achieve the core function of the target terminal "at least cooperating with the monitoring execution terminal to obtain the relative spatial relationship data," and on this basis, support richer collaborative monitoring interactions.

[0079] As a preferred extended or enhanced implementation, the system may also include a server. See also... Figure 1(Example of virtual electronic fence networking and management with server participation). In this diagram, the server acts as a network node to provide services to the monitoring execution terminal and target terminal in virtual electronic fence 1 (e.g., storing data, providing computing power, and assisting in management); at the same time, it can also directly participate in the creation and management of virtual electronic fence 2 as a monitoring execution terminal (e.g., monitoring the spatial relationship between target terminal 3 or target terminal 4 and virtual electronic fence 2, performing situational assessment of the monitored area and triggering a response).

[0080] To fully elucidate the technical basis of this invention, the measurement and calculation modes of relative spatial relationships throughout all embodiments are described below. These modes are the core technical means for achieving dynamic perception and adaptive adjustment.

[0081] Wide Area Network-Assisted Computing Mode: This mode transmits raw sensing data acquired by the terminal (e.g., satellite navigation observations, inertial measurement unit data, cellular signal characteristics, etc.) to the peer or network-side computing node (e.g., a server) via a wide area network (e.g., 4G / 5G / 6G cellular networks, broadband networks, satellite internet). The relative position, velocity, and other state data are then calculated through data fusion. Technologies that can be used in this mode include, but are not limited to: real-time dynamic differential positioning, precise point positioning, fingerprint matching based on cellular networks, or time-of-arrival positioning.

[0082] Direct-connection measurement mode between terminals: This mode is based on a two-way direct communication link established between terminals. It calculates high-precision relative distance, azimuth, and velocity by measuring physical layer parameters such as time of flight, time difference of arrival, and angle of arrival. The short-range wireless technologies used include, but are not limited to, SparkLink and Ultra-Wideband (UWB).

[0083] The system adaptively selects or switches between at least two modes based on task requirements, rules, and real-time conditions (e.g., distance between terminals, signal quality, accuracy requirements) to achieve an optimal balance between coverage, positioning accuracy, and device power consumption. This system also possesses the capability to be compatible with future new communication or sensing technologies.

[0084] The collaborative terminal described in this invention includes, but is not limited to, smartphones, smartwatches, in-vehicle devices, and dedicated tags. Its essential hardware architecture mainly includes: a processor; a memory for storing computer programs and data; and a multi-mode communication unit for supporting wide area networks and short-range wireless communication. The processor executes the computer program to achieve the functions described in the various embodiments of this invention. Unless otherwise stated, the terms "terminal," "monitoring execution terminal," and "target terminal" mentioned in the following embodiments refer to logical entities based on the aforementioned hardware architecture that can dynamically switch roles according to software configuration.

[0085] The information data described in this invention includes status data, environmental data, and attribute data. It may also include sensing data from the terminal itself or associated external sensors used to assist in evaluation or mode selection, such as vital signs, equipment operating parameters, and ambient sounds.

[0086] It should be noted that the technical solution of this invention is specifically explained below through descriptions of various task modes and their application scenarios. This descriptive approach is intended to clearly illustrate the principles and application flexibility of this invention, rather than to limit the scope of protection of this invention. The scope of protection of this invention is defined by the appended claims.

[0087] Example 1: Configuration, dynamic adjustment, and situational assessment of a virtual electronic fence based on rule sets.

[0088] This embodiment illustrates, through multiple application scenarios, how to configure virtual electronic fences according to different task modes and associated rule sets, and how to adaptively adjust them. It also demonstrates the application of a model for assessing the situation in a monitored area.

[0089] It should be specifically noted that any specific parameters, calculation formulas, thresholds, example data, and calculation processes listed in this embodiment and subsequent scenario embodiments are merely examples to illustrate the principles of the technical solution of the present invention, and are not intended to limit the only implementation of the present invention. Under the concept of the present invention, those skilled in the art can flexibly adjust, replace, or combine the rule set, mathematical model, coefficient weights, threshold conditions, etc., according to actual application scenarios. These variations and improvements based on the same inventive concept should all fall within the protection scope of the present invention.

[0090] Scenario 1: Example of road risk warning under restricted area protection mode

[0091] The restricted area protection mode is designed to prevent targets from illegally entering or existing in a specific area. This mission mode includes, but is not limited to, vehicle collision avoidance in traffic scenarios, perimeter security of sensitive areas (e.g., important locations, airports, hazardous industrial areas), protection of critical infrastructure (e.g., substations, reservoir dams), security of temporary construction areas, and security management in commercial and civilian scenarios (e.g., warehouses, office areas, residences).

[0092] For example, the triggering conditions of the associated rule set can be based on parameters such as the relative spatial location of the target terminal and the monitored area, relative motion data, intrusion depth, or dwell time.

[0093] The following example uses a road collapse pit as an illustration:

[0094] (a) Initialize the configuration of the virtual electronic fence based on the rule set.

[0095] To prevent pedestrians or vehicles from falling into roadside sinkholes, at least one terminal is fixedly installed near the sinkhole as a monitoring and execution terminal. Simultaneously, the virtual electronic fence is initialized as a rectangular area with the center of the sinkhole as a reference point, covering the collapsed area and its upstream and downstream road sections as the monitoring zone.

[0096] (b) Dynamically adjust the virtual electronic fence based on the rule set and information data.

[0097] Because the physical installation location of the monitoring terminal does not coincide with the reference point of the virtual electronic fence, the system first obtains the absolute position data of itself and the target terminal through a wide area network-assisted calculation mode (such as GNSS positioning), and then calculates the relative spatial relationship data (e.g., relative position). Subsequently, the relative position data is unified into the virtual electronic fence coordinate system with the reference point as the origin through coordinate transformation, ensuring that the positions of all target terminals can be determined in a unified coordinate system.

[0098] For example, the geometric parameters of the virtual electronic fence are adjusted based on environmental data (e.g., weather conditions). For instance, in low-adhesion weather (rain, snow, ice), the length of the monitored area is extended from 50 meters to 80 meters (for example only) to compensate for the increased braking distance; in sunny weather, it reverts to 50 meters.

[0099] For example, the geometric parameters of the virtual electronic fence can be adjusted based on the attributes of the target terminal (e.g., the pedestrians or vehicles associated with the terminal). When the monitoring terminal is primarily responsible for monitoring pedestrians entering the monitored area, the coverage area can be reduced relative to the monitored vehicles, for example, shortening the length from 50 meters to 30 meters.

[0100] The system allows users (e.g., site managers) to manually set or fine-tune the parameters of the virtual electronic fence.

[0101] (c) Establishment of monitoring relationships

[0102] By monitoring the networking discovery function of the short-range wireless module (e.g., StarFlash, UWB, etc.) in the multi-mode communication unit configured in the execution terminal, the system automatically scans and acquires the identifiers of nearby pedestrian or vehicle terminals (as target terminals). After verification, a dynamic monitoring relationship is established, and information data of the target terminals within the monitored area is obtained. Alternatively, the execution terminal can broadcast parameters such as the range and reference points of its virtual electronic fence. Collaborating terminals listening to this broadcast and whose positions are within the range of these fence parameters can proactively report their own identifiers upon receiving the broadcast. The execution terminal receives and verifies the report before executing subsequent actions.

[0103] In some implementations, the "forming updated information data" may include the following logic: when a new measurement value is obtained through the current measurement cycle, the original data is replaced with the new value; when no new value is obtained due to signal obstruction, communication interruption, or other reasons, the valid data of the previous decision cycle is confirmed as the valid data of the current cycle, so as to maintain the continuous judgment capability of the system.

[0104] (d) Conduct situational assessment of the regulatory area based on rule sets and information data.

[0105] Based on the state data (e.g., relative spatial location data, relative motion data), environmental data (e.g., rainy weather, freezing weather), and attribute data (e.g., large trucks) contained in the information data, the system performs situation assessment based on a predefined set of rules and calculates a situation quantification value. The core logic of the rule set is: the closer the target terminal is to the reference point, the faster its relative speed towards the reference point, and the greater the mass of its associated vehicle, the larger the calculated situation quantification value, indicating a higher risk of the vehicle falling into the collapse pit.

[0106] To specifically implement the above logic, this embodiment provides a configurable mathematical model for situation quantification. The formula for calculating the situation quantification value R by weighted fusion of multi-factor data is as follows:

[0107] R = S × E × A

[0108] in:

[0109] iS (basic risk value): determined by relative distance (d), radial velocity (v_r), and orientation angle (θ), and calculated using the following formula:

[0110] S = α × exp(-d / D0) + β × (v_r / V0) × F_dir(θ)

[0111] d: Real-time Euclidean distance (meters) between the target terminal and the reference point of the collapse pit.

[0112] v_r: The radial component (m / s) of the target terminal velocity vector in the direction pointing towards the reference point, v_r = v ×cosθ, where v is the magnitude of the resultant velocity. When the target moves away from the reference point, cosθ is negative and v_r is 0.

[0113] θ: The angle between the instantaneous direction of motion of the target terminal and the line connecting the target to the reference point (0° ≤ θ ≤ 180°). θ=0° indicates motion towards the reference point, and θ=180° indicates motion away from the reference point.

[0114] F_dir(θ): Direction modulation factor, used to amplify the risk of facing motion. In this embodiment, F_dir(θ) = cos(θ / 2). When θ = 0°, F_dir = 1; when θ = 180°, F_dir = 0.

[0115] D0, V0: These are configurable feature distances and feature velocities used for normalization.

[0116] α, β: Configurable weighting coefficients that satisfy α+β=100, used to balance the effects of distance and velocity terms.

[0117] ii. E (Environmental Risk Coefficient): This is calculated by amplifying the risk based on environmental data, using the following formula:

[0118] E = 1.0 + Σ(environmental factor weights)

[0119] Example weighting configuration: Sunny / Dry road surface: +0.0; Rainy / Slippery road surface: +0.3; Snowy / Icy road surface: +0.5; Night / Foggy road surface: +0.2.

[0120] iii. A (Attribute Risk Coefficient): This amplifies the risk based on vehicle attribute data. The calculation formula is as follows:

[0121] A = 1.0 + min(1.0, (actual vehicle mass - reference mass) / reference mass)

[0122] A benchmark mass can be taken as that of a typical small car (e.g., 1.5 tons). For example, the A coefficient for a 20-ton truck is approximately 2.0.

[0123] To illustrate the calculation process of the above model, an example is given below:

[0124] Assume the system parameters are configured as follows: α=50, β=50, D0=50, V0=15, and the environment is sunny (E=1.0).

[0125] Two vehicles have entered the regulated area:

[0126] Vehicle A (truck): Distance d = 100 meters, speed v = 100 km / h (≈27.8 m / s), direction angle θ = 10°, mass 20 tons (A≈2.0).

[0127] Car B (sedan): distance d = 80 meters, speed v = 50 km / h (≈13.9 m / s), direction angle θ = 30°, mass 1.5 tons (A = 1.0).

[0128] First, calculate the baseline state risk S for each vehicle:

[0129] Vehicle A (Truck):

[0130] Radial velocity v_r = v × cosθ = 27.8 × cos10° ≈ 27.8 × 0.9848 ≈ 27.38m / s.

[0131] Distance term: 50 × exp(-100 / 50) = 50 × exp(-2) ≈ 50 × 0.1353 ≈ 6.77

[0132] Velocity term: 50 × (27.38 / 15) × cos(10° / 2) ≈ 50 × 1.825 × 0.9962 ≈ 90.91

[0133] S_A ≈ 6.77 + 90.91 = 97.68

[0134] Car B (Sedan):

[0135] Radial velocity v_r = 13.9 × cos30° ≈ 13.9 × 0.8660 ≈ 12.04 m / s.

[0136] Distance term: 50 × exp(-80 / 50) = 50 × exp(-1.6) ≈ 50 × 0.2019 ≈ 10.10

[0137] Velocity term: 50 × (12.04 / 15) × cos(30° / 2) ≈ 50 × 0.8027 × 0.9659 ≈ 38.77

[0138] S_B ≈ 10.10 + 38.77 = 48.87

[0139] Then, calculate the comprehensive situation quantification value R:

[0140] Car A: R_A = S_A × E × A = 97.68 × 1.0 × 2.0 ≈ 195.36

[0141] Car B: R_B = S_B × E × A = 48.87 × 1.0 × 1.0 ≈ 48.87

[0142] Conclusion: Although vehicle A is farther from the collapse pit, its extremely high speed and mass result in a significantly higher situational quantification value (R_A, 195.36) than vehicle B's (R_B, 48.87). This result verifies that the proposed model can effectively integrate distance, speed, direction, and attribute data for risk assessment. The system will rank targets based on this quantification value.

[0143] (e) Response actions triggered based on the situational quantitative assessment results of the regulated area

[0144] When the target's situation quantification value R exceeds a preset threshold, the system initiates a response, such as sending an alert signal to the target terminal (including but not limited to vibration, sound and light, and SMS), or issuing control commands that can trigger the vehicle's auxiliary braking system or limit power output. If the monitoring execution terminal is connected to the server, it can also upload on-site information data.

[0145] When multiple target terminals exist simultaneously, the system calculates and compares their respective situation quantification values ​​in real time, and prioritizes triggering response operations on the target with the highest risk level (such as vehicle A in the example above).

[0146] (f) Collaboration of multiple monitoring and execution terminals

[0147] Multiple monitoring and execution terminals on site can be networked and coordinate tasks. For example, different terminals can monitor different target terminals (such as vehicles and pedestrians) or be assigned different monitoring areas to achieve seamless coverage.

[0148] When multiple monitoring terminals simultaneously monitor the same target terminal, priority values ​​can be calculated based on parameters such as coverage area, remaining battery power, signal strength, and target movement direction, and the monitoring task can be taken over or transferred accordingly.

[0149] Scenario 2: Childcare under a restrictive and supervisory model

[0150] The aforementioned restraint and supervision mode aims to ensure that the target is always within a preset safety or authorized range. This task mode includes, but is not limited to, applications such as personnel safety monitoring (e.g., children, the elderly, patients with special diseases), valuable asset tracking (e.g., museum exhibits, core warehouse materials, important document boxes), and equipment safety area management (e.g., specialized tools at construction sites, and limiting the activity range of warehouse robots).

[0151] For example, the triggering conditions of the associated rule set can be based on parameters such as the relative spatial location of the target terminal and the regulatory area, relative motion data, departure time, and attribute data.

[0152] The following example uses a childcare application for illustration:

[0153] (a) Initialize the configuration of the virtual electronic fence based on the rule set.

[0154] The monitoring terminal (e.g., the mother's mobile phone) initializes the virtual electronic fence with its own location as the reference point and a preset distance (e.g., a circular area with a radius of 50 meters) as the initial monitoring range. The user (guardian) can manually adjust the safety boundary according to the actual situation (e.g., the presence of hazards in the home).

[0155] (b) Dynamically adjust the virtual electronic fence based on rule sets and information data.

[0156] The system dynamically optimizes the virtual electronic fence based on preset adjustment strategies in the rule set and combined with real-time information data:

[0157] Adjustments based on attribute data: For example, if the target associated object is a "five-year-old child", the monitoring radius will be adjusted from 50 meters to 30 meters based on the child's activity level.

[0158] Adjustments based on environmental data: For example, if hazards such as "a road in front of the house" or "a ditch to the west" are identified, the boundary on the side closer to the road will be moved inward by 5 meters, and the boundary on the side closer to the ditch will be moved inward by 2 meters.

[0159] Adjustments based on time-based strategies: For example, expanding or narrowing the coverage area based on time periods such as daytime and nighttime.

[0160] After the above adjustments, the shape of the virtual electronic fence can evolve from the initial circle to a more realistic irregular polygon.

[0161] (c) Establishment of monitoring relationships

[0162] The monitoring execution terminal (mother's mobile phone) obtains the identifier of the target terminal (children's watch) by input or selection. Then, it uses a short-range wireless module (such as StarFlash, UWB, etc.) to scan and discover the target terminal. After verifying the identifier, a monitoring relationship is established, and its status data is continuously acquired.

[0163] (d) Conduct situational assessment of the regulatory area based on rule sets and information data.

[0164] In this embodiment, a safety deviation model is used to calculate the situation quantification value (denoted as D). This model quantifies the degree to which a child is out of a safe situation; a higher D value indicates a greater risk. The calculation formula is as follows:

[0165] D = F_dist(d, d_th) × (1 + γ × T) × (1 + ΣEnv_risk)

[0166] in:

[0167] i.F_dist(d, d_th) (Distance Risk Function): Measures the immediate risk of the current location.

[0168] d represents the real-time distance between the child and their guardian (reference point).

[0169] d_safe is the absolute safety radius (e.g., 5 meters). When d ≤ d_safe, F_dist = 0.

[0170] d_th is the dynamic warning threshold radius, which is determined based on attribute data (such as age) (for example, 30 meters for a 5-year-old child).

[0171] The function is defined as:

[0172] When d_safe < d ≤ d_th, F_dist = ((d - d_safe) / (d_th - d_safe))^2

[0173] When d > d_th, F_dist = 1 + ((d - d_th) / d_th)

[0174] ii. (1 + γ × T) (Time accumulation coefficient): Punishment for leaving the safe zone for an extended period of time.

[0175] T represents the duration (in minutes) during which the child remains in the state d > d_safe.

[0176] γ is the time weighting coefficient (e.g., γ = 0.1 / minute).

[0177] iii. (1 + ΣEnv_risk) (Environmental risk coefficient): Superimposed geographical risks in a specific direction.

[0178] Env_risk is a preset risk value for different hazards (e.g., the side closer to the road: +0.5, the side closer to the ditch: +0.3).

[0179] Calculation example: A 5-year-old child (d_th=30 meters, d_safe=5 meters) is playing outdoors.

[0180] Scenario 1: Walk to a distance of 25 meters from the guardian (T≈0).

[0181] F_dist = ((25-5) / (30-5))^2 = 0.64

[0182] D = 0.64 × (1+0) × (1+0.5) = 0.96

[0183] Scenario 2: After 5 minutes, walk to 35 meters (T=5, which is beyond d_th).

[0184] F_dist = 1 + ((35-30) / 30) ≈ 1.17

[0185] D = 1.17 × (1+0.1× 5) × (1+0.5) ≈ 2.63

[0186] As the distance and time increase, the situation quantification value D rises from 0.96 to 2.63. The system can set tiered thresholds (e.g., D>1.0 triggers a primary alert, D>2.0 triggers a strong alarm).

[0187] (e) Response operation triggering based on rule set

[0188] Once the situation quantification value D exceeds the set threshold, the system immediately triggers a response operation, such as simultaneously sending tiered alarms to both the monitoring execution terminal (mother's mobile phone) and the target terminal (child's watch) (the intensity can be distinguished by vibration frequency and sound level). At the same time, the system can report the child's activity data for server learning and analysis, providing a foundation for building an individual behavior baseline and achieving intelligent prediction (e.g., as described in Scenario 5), thus upgrading from simple location alarms to intelligent prediction based on behavioral habits.

[0189] Scenario 3: Intelligent Search and Rescue in Search and Location Mode

[0190] The search and location mode is designed to proactively discover, locate, and determine the position of lost or untraceable targets. This task mode includes, but is not limited to, applications in emergency rescue (e.g., locating injured persons in fires, earthquakes, floods, and mine accidents), public safety (e.g., searching for missing persons), and asset management and logistics (e.g., locating items in warehouses). The following explanation uses fire rescue as an example:

[0191] (a) Virtual electronic fence initialization configuration

[0192] The terminals held by rescue personnel serve as monitoring and execution terminals, initializing a virtual electronic fence covering the fire area, with the center of the fire as the reference point for the search zone.

[0193] (b) Dynamically adjust the virtual electronic fence based on information data.

[0194] The system dynamically adjusts the shape and extent of the virtual electronic fence (e.g., expanding to new hazard areas or dividing it into multiple responsibility zones) based on real-time fire scene information data (such as temperature distribution, smoke diffusion maps, and structural hazard zone markings). On-site commanders can also manually intervene and adjust the fence.

[0195] (c) Establishment of monitoring relationships

[0196] The monitoring terminal uses short-range wireless technology (such as star flash, UWB, etc.) to establish a monitoring relationship with the target terminal in the fire scene and obtain its information data.

[0197] (d) Conduct situational assessment of the regulatory area based on rule sets and information data.

[0198] In this embodiment, the rule set supports a hierarchical evaluation strategy, which can activate different models based on the rescue stage and data completeness.

[0199] Strategy 1 (Rapid Preliminary Ranking): Employs an emergency situation quantification model (Model M1) for rapid assessment when data is limited. Formula: P_urgent = (1 / (d + 10)) × 100 + λ × (100 - Vital_Score)

[0200] Where d: the distance (in meters) between the trapped person and the nearest fire or entrance.

[0201] Vital_Score: Vital signs score (0-100, based on heart rate, blood oxygen, etc.).

[0202] λ: Vital sign weight (e.g., λ=2.0).

[0203] The higher the situation quantification value P_urgent, the higher the priority in the ranking.

[0204] Strategy Two (Refined Comprehensive Assessment): Employing a comprehensive situational quantification model (Model M2) for refined assessment when data is complete. Formula: R_total = w1 × F(d) + w2 × G(Vital) + w3 × H(Temp) + w4 × I(Smoke) + w5 × J(Structure)

[0205] R_total is the calculated comprehensive situation quantification value.

[0206] F(d) = 100 × exp(-d / L): Distance decay function (L is the characteristic distance, such as 50 meters).

[0207] G(Vital) = 100 - Vital_Score: Vital signs risk function.

[0208] H(Temp)=min(100, (Temp-T_safe) × k_t): Temperature risk function (T_safe is the safe temperature).

[0209] I(Smoke): Smoke concentration risk function (similar to temperature).

[0210] J(Structure): Structural stability risk (0-100).

[0211] w1~w5: The weights of each factor, defined by the rule set and summed to 1 (e.g., {0.3, 0.4, 0.15, 0.1, 0.05}).

[0212] Examples of calculation and decision-making:

[0213] Two trapped persons were found: Target A (d = 20 m, Vital_Score = 30, Temp = 80°C), Target B (d = 35 m, Vital_Score = 90, Temp = 50°C).

[0214] Use Model M1 for quick sorting:

[0215] P_urgent_A = (1 / (20 + 10)) × 100 + 2 × (100 - 30) ≈ 3.3 + 140 = 143.3

[0216] P_urgent_B = (1 / (35 + 10)) × 100 + 2 × (100 - 90) ≈ 2.2 + 20 = 22.2

[0217] Conclusion: A >> B, A should be explored first.

[0218] Use Model M2 for comprehensive evaluation (assuming only three factors d, Vital, Temp, with weights 0.4, 0.4, 0.2):

[0219] R_total_A = 0.4 × F(20) + 0.4 × G(30) + 0.2 × H(80) = 0.4 × 67 + 0.4 × 70 + 0.2 × 100 = 74.8

[0220] R_total_B = 0.​​​​​​​​​​​The system visualizes the sorted rescue list and real-time situation on the command terminal (e.g., by overlaying an electronic map). A rule set defines a tiered response mechanism: when the target's situational quantification value exceeds different thresholds, the system automatically triggers corresponding response actions, including but not limited to highlighting the target on the interface, issuing tiered audible and visual alarms, and automatically issuing rescue instructions containing the target's location and optimal path to the most suitable rescue team. This forms a closed loop of perception-decision-execution, dynamically optimizing rescue resources.

[0225] Scenario 4: Navigation Correction in Path Guidance Mode

[0226] The path guidance mode is designed to guide or regulate the movement of a target along a preset path or to precisely arrive at a designated location. This task mode includes, but is not limited to, applications in logistics and security (e.g., escorting valuables, protecting important personnel), intelligent transportation (e.g., vehicle platooning, autonomous parking guidance), public services (e.g., park visitor guidance, finding seats in large venues), and personal services (e.g., precise appointment navigation, monitoring children's safe activity paths).

[0227] To overcome the accuracy limitations of wide-area satellite navigation in local areas, the following example illustrates a typical application: "navigation in the last fifty meters." When user A navigates based on an address shared by friend B, in the final tens of meters before reaching the destination, due to satellite positioning errors of several to tens of meters, the navigation system may continuously indicate deviation or even navigate in the wrong direction, making it difficult for A to accurately reach B's actual location. At this point, this invention provides precise guidance for user A in the "last leg" by switching to high-precision direct ranging.

[0228] (a) Initialization of virtual electronic fence and establishment of monitoring relationship

[0229] User A uses their terminal as the monitoring execution terminal and establishes a monitoring relationship with the target terminal (User B's terminal). Based on the rule set of the path guidance mode, the system automatically initializes a dynamic virtual electronic fence with the target terminal as the reference point. Based on the task mode, rule set, and initial information data, the system adaptively selects a relative spatial relationship measurement and calculation mode to determine or correct their relative spatial positions. When the two terminals enter the effective range of short-range wireless communication, the high-precision guidance phase is activated.

[0230] (b) Precise guidance

[0231] The monitoring terminal connects directly to the target terminal via short-range wireless technology (e.g., stroboscopic telescope, UWB), continuously measuring the relative distance and direction between them. This high-precision positional information is then fused with an electronic map to generate and display an intuitive guidance interface, similar to a radar scan (e.g., displaying "Target is 5 meters to your left and in front"). Thanks to the high precision of stroboscopic telescope (sub-meter level) or UWB (centimeter level) technology, user A can be guided to successfully reach the target.

[0232] (c) Quantitative assessment and response to the situation in the regulatory area

[0233] The system performs a comprehensive situation assessment based on multi-source data such as lateral offset distance, heading angle deviation, and relative distance to the target terminal, calculates the main situation quantification value, and provides users with real-time quantitative feedback on the correctness of the path.

[0234] To achieve closed-loop verification and fault tolerance, the system can also calculate auxiliary situation quantification values ​​in parallel. For example, a "target location credibility" assessment is introduced, which is generated by calculating the "target location difference" between the precise location obtained by the target terminal from high-precision direct connection measurement and its declared wide-area positioning coordinates. When this difference exceeds a reasonable threshold defined by the rule set, it indicates that the initial target information may be incorrect or the target has moved, and the system will trigger a response operation such as "Please reconfirm the target address".

[0235] The aforementioned situation assessment and response logic can be flexibly configured using a rule set. In some embodiments, the rule set can be configured as a multi-level rule-based decision engine: the first-level rule determines whether to trigger a basic correction prompt based on the absolute value of the lateral offset distance; the second-level rule combines the offset distance and heading angle deviation to determine the urgency of the yaw and escalate the alarm; the third-level rule can incorporate historical correction data to dynamically adjust the response threshold. For target position confidence assessment, the rule engine can also define corresponding threshold rules to trigger a verification operation when the difference exceeds the limit and record the abnormal event for optimizing subsequent decisions.

[0236] Scenario 5: Monitoring of Behavioral Anomalies in a State-Aware Mode

[0237] The described state-aware mode elevates the monitoring from "spatial surveillance" to "behavior and state surveillance," thereby realizing the core value of the invention at a deeper level. This mode aims to establish a normal behavior baseline by performing long-term learning and analysis on the target terminal's behavioral trajectory, temporal patterns, and other state data (e.g., vital signs, device status) within a virtual electronic fence. It then identifies abnormal patterns deviating from the baseline and triggers intelligent responses.

[0238] This task mode includes, but is not limited to, applications in areas such as smart elderly care and medical monitoring, livestock management, and predictive maintenance of industrial equipment.

[0239] The following two examples illustrate this:

[0240] Application Case 1: Health Care for Elderly People Living Alone

[0241] (a) Establishment of virtual electronic fence and monitoring relationship

[0242] The children's devices act as monitoring execution terminals, establishing a monitoring relationship with the elderly person's device (the target device). The server, as the core analysis unit of the system, connects to this network. The system initializes a macro-geographic area (e.g., the residential community) covering the elderly person's daily activities as a virtual electronic fence, using this as the boundary to collect and establish an effective sample set for behavioral data analysis.

[0243] (b) Behavioral baseline modeling and data analysis

[0244] The server continuously analyzes multi-dimensional information data reported by the target terminal in response to commands: status data (e.g., location, movement speed), environmental data (e.g., time, weather), attribute data (e.g., user age, medical history), and sensory data (e.g., vital signs from wearable devices). Machine learning algorithms are used to characterize and dynamically update the personalized lifestyle baselines of the elderly, such as their usual activity routes, times of day, and pace of movement.

[0245] (c) Status anomaly identification and response operation triggering

[0246] The system compares current activity data with a baseline of lifestyle habits in real time and calculates a real-time situational quantification value based on a rule set. In some implementations, the machine learning algorithm may employ an ensemble learning model (such as a random forest) or a time-series prediction model to establish a multi-dimensional behavioral baseline by analyzing historical data. The anomaly identification process can generate a situational quantification value based on the Mahalanobis distance or similarity score between real-time feature vectors and the behavioral baseline. When multiple anomaly indicators occur concurrently, the system can improve the risk confidence level based on the rule set.

[0247] The typical exception patterns defined in the rule set include:

[0248] An unusually small range of activity: Not leaving home for several consecutive days (may indicate physical discomfort or risk of falling).

[0249] Significant deviation from movement trajectory: irregular changes in daily routes or prolonged periods of staying in one place (may indicate cognitive impairment).

[0250] Severely disrupted behavioral rhythms: prolonged periods of time spent outdoors at unaccustomed times, or persistently significantly lower than baseline activity levels.

[0251] When the quantified status value exceeds the threshold, the system sends a tiered alert to the children's terminals (e.g., "Potential health risk detected, attention recommended"), thus shifting from passive response to proactive care.

[0252] Application Case 2: Livestock Health Supervision in Animal Husbandry

[0253] (a) Establishment of virtual electronic fence and monitoring relationship

[0254] The ranch manager's terminal acts as the monitoring execution terminal, establishing a group monitoring relationship with the smart collars worn by the livestock (as the target terminal). The monitoring execution terminal (including the server) initializes the entire ranch as a virtual electronic fence, providing a unified spatial context for behavioral analysis.

[0255] (b) Health baseline modeling and data analysis

[0256] The server aggregates real-time and historical data from all livestock: status data (e.g., location, activity level), environmental data (e.g., barn temperature, humidity), attribute data (e.g., livestock breed, age), and sensory data (e.g., data from body temperature sensors and stomach acid pH sensors). Algorithms establish dynamic health behavior baselines for individuals or groups, quantifying key behavioral indicators such as total activity level, rumination rest time, and frequency of visits to watering points and feeding areas under normal conditions.

[0257] (c) Status anomaly identification and response operation triggering

[0258] The system calculates an individual's disease status quantification value by comparing real-time data with a health baseline both horizontally (group-wide) and vertically (individual history). Based on the rule set, abnormal indicators include:

[0259] Individual activity levels are significantly lower than their baseline and group levels (signs of hoof disease or infection).

[0260] An abnormally long resting time: lying down for extended periods without entering the rumination cycle (indicating digestive problems or prenatal signs).

[0261] Isolated trajectory in a group: persistently detached from the group and wandering alone (a strong characteristic of illness or injury).

[0262] Abnormal frequency of visits to key areas: failure to visit watering points or feed troughs on time (associated with loss of appetite).

[0263] When the quantified status value exceeds the threshold, the system sends a precise alert to the administrator (e.g., "Cow No. 1013 is highly likely to have health problems, and it is recommended to check it"), thus shifting from extensive management to precise prevention.

[0264] The process described above, which establishes a behavioral or health baseline by analyzing historical information data, is the specific implementation of "server analysis and learning of user information data." When real-time data deviates significantly from the baseline, the system completes "anomaly detection."

[0265] Scenario 6: Spatial resource optimization under group scheduling mode

[0266] The aforementioned group scheduling mode aims to collaboratively optimize the spatial distribution, formation, and task allocation of multiple terminals. This task mode includes, but is not limited to, applications such as robot swarm scheduling (e.g., warehousing and logistics, workshop handling), drone formation management, vehicle collaboration in intelligent transportation, and crowd control in public places.

[0267] Application Case 1: Warehouse Robot Scheduling

[0268] (a) Initialization of virtual electronic fence and establishment of monitoring relationship

[0269] One or more terminals are fixedly installed in the warehouse as monitoring and execution terminals, establishing a monitoring relationship with all warehouse robots (as target terminals). The system initializes the entire work area as a virtual electronic fence, serving as a logical global scheduling domain.

[0270] (b) Adaptive scheduling and load balancing

[0271] The system monitors robot density and task load in each sub-region within the scheduling domain in real time. Based on the rule set, it generates a regional situation quantification value by calculating the deviation between actual and expected resources in each sub-region.

[0272] When the situation quantization value of a certain area exceeds the reasonable range, indicating that the robot distribution in that area is uneven or the load is too high, the system triggers one or more of the following scheduling operations:

[0273] Dynamic weight adjustment: Temporarily change the regional attraction weights in the path planning algorithm to guide the robot to sparse areas.

[0274] Direct command issuance: Send explicit diffusion or transfer commands to idle robots in high-density areas.

[0275] Through the above mechanism, the entire robot swarm is continuously driven toward dynamic equilibrium, thereby optimizing overall operational efficiency.

[0276] The scheduling strategy can be configured into different optimization algorithm frameworks by the rule set. For example, in robot scheduling scenarios, a distributed decision-making model based on the potential field method can be used, where each robot autonomously adjusts its movement direction based on local density information; or a centralized Hungarian algorithm can be used for optimal task allocation. The system can dynamically select the appropriate algorithm framework from the rule set based on real-time computing resources, communication latency, and problem size.

[0277] Application Case 2: Security Collaborative Scheduling in Core Product Processing Workshop

[0278] (a) Initialization of virtual electronic fence and establishment of monitoring relationship

[0279] The system schedules one or more terminals fixedly installed around the workshop as monitoring execution terminals, and assigns each terminal a fixed monitoring sub-area aligned with the physical boundary. By overlaying the monitored areas, the system initializes the entire core product processing workshop as a macroscopic virtual electronic fence, i.e., a security dispatch domain.

[0280] (b) Dynamic allocation and resource scheduling of monitoring tasks

[0281] When a target (e.g., a person or vehicle) enters the scheduling domain, the system dynamically executes the following scheduling strategies based on a rule set (which comprehensively considers factors such as proximity, load balancing, and terminal capabilities):

[0282] Task allocation: One or more most suitable monitoring execution terminals are designated to continuously track the target. The task allocation decision can be based on a multi-objective optimization model, comprehensively considering multiple optimization objectives such as shortest distance, load balancing, and terminal capability matching. A rule set configures dynamic weights for each objective, enabling adaptive scheduling under different scenarios.

[0283] Resource reallocation: When a coverage blind spot occurs in a certain area due to the departure of a terminal, the surrounding terminals are instructed to temporarily expand the monitoring range.

[0284] Mode switching and capability expansion: When fixed terminal resources are insufficient (e.g., at night), the server can dynamically designate mobile terminals carried by employees as temporary monitoring execution terminals according to the rule set to fill monitoring blind spots.

[0285] (c) Direct supervision mode of the server

[0286] The server can directly intervene in supervision as a monitoring execution terminal. It initializes a virtual electronic fence and requests data (including identifiers and location data) from terminals entering the fence via a wide area network. Based on this data, the server can independently perform situational awareness assessments and trigger response actions when thresholds are exceeded.

[0287] Description of the server

[0288] In this invention, the server is an optional enhancement component that can carry out functions such as virtual electronic fence management, calculation, and scheduling logic. Its physical form can be a centralized or distributed server, a cloud instance, or an edge computing node.

[0289] As a typical and efficient application model, the server can be flexibly deployed into autonomous units at different levels according to actual management needs, such as building dedicated security protection nodes for individual schools, enterprises, or traffic police and emergency departments. These units can operate independently or be linked in a hierarchical manner to form a wide-area collaborative network covering streets, towns, and even cities. This "point-to-surface" architecture provides a solid technical foundation for achieving large-scale, low-cost, and high-efficiency coverage and promotion of the platform.

[0290] Explanation of the mechanism for determining the monitoring execution terminal:

[0291] As shown in the various scenarios, the mechanism for determining the monitoring execution terminal is flexible and diverse, reflecting the decentralized and self-organizing characteristics of the system. Specific methods include:

[0292] Static configuration: Suitable for terminals that need to maintain a fixed role for a long time (e.g., the terminal installed in the collapse pit in Scenario 1).

[0293] Custom settings: Applicable to temporary monitoring relationships created by users (e.g., mother monitoring and caring for child in Scenario 2).

[0294] Server dynamic assignment: Applicable to dynamic allocation by the server to optimize global monitoring efficiency (e.g., specifying collaborative terminals in Scenario 6).

[0295] This diversified mechanism ensures that the system can be flexibly adapted to scenarios ranging from simple to complex.

[0296] It should be emphasized that the specific mathematical models, rule engines, machine learning algorithms, and optimization methods described in the above embodiments are all exemplary illustrations to demonstrate the feasibility of the technical solutions of the present invention. Under the concept of the present invention, those skilled in the art can select, combine, or replace them with other equivalent computing models, algorithm frameworks, or implementation methods according to actual application needs. These variations and improvements based on the same inventive concept should all fall within the protection scope of the present invention.

[0297] Example 2: Adaptive Adjustment of Virtual Electronic Fence Based on Rule Set and Vehicle Information Data

[0298] This embodiment further develops the restricted area protection mode in Embodiment 1, focusing on vehicle safety and aiming to fully demonstrate the closed-loop technical process from adaptive adjustment of the virtual electronic fence to intelligent selection of the measurement mode. The following will elaborate on how the virtual electronic fence makes fine adjustments based on dynamically changing information data, naturally connecting to the adaptive selection mechanism of the measurement mode described in subsequent embodiments. As a preferred implementation method applied to vehicle safety scenarios, the predefined task mode belongs to the restricted area protection mode.

[0299] (a) Example of virtual electronic fence configuration and adaptive adjustment when the vehicle is stationary

[0300] The location where the collaborative terminal is deployed on the vehicle is defined as the reference point of the virtual electronic fence. When the vehicle is stationary, according to the rule set, the virtual electronic fence is initialized as a circular area with the reference point as the center and a preset safety distance (e.g., radius R = 2 meters) as the range. This area is used to monitor people, vehicles, or obstacles that are too close in the surrounding area while the vehicle is parked, thereby achieving parking safety protection and collision warning.

[0301] (b) Example of virtual electronic fence configuration and adaptive adjustment when the vehicle is in motion

[0302] When a vehicle enters a moving state, the system dynamically adjusts the virtual electronic fence based on the rule set and information data:

[0303] i. Adaptive adjustment of shape and reference point: The rule set predefines the mapping relationship between fence shape and vehicle motion state:

[0304] Forward driving mode: The reference point of the virtual electronic fence is automatically moved forward to the center of the vehicle's front bumper, and its shape is adjusted from a circle to a fan shape that opens forward towards the front of the vehicle (e.g., with a center angle of 60 degrees) to focus on monitoring potential forward collision risks.

[0305] Reversing mode: The reference point and main monitoring area are automatically adjusted to the rear of the vehicle, and their shape is adjusted to a fan shape that opens towards the rear of the vehicle to achieve reversing safety warning.

[0306] ii. Dynamic adjustment of boundary range: The rule set establishes a positive correlation between the boundary (e.g., leading edge distance) of the virtual electronic fence and real-time vehicle speed and environmental condition data (e.g., rain, snow, fog, road surface slippage). That is, the faster the vehicle speed and the more severe the environment, the larger the boundary range of the fence, so as to dynamically match the increasing safety braking distance requirements.

[0307] Example 3: Decision Logic for Adaptive Selection of Measurement and Calculation Modes

[0308] This embodiment aims to detail how the system intelligently selects the relative spatial relationship measurement and calculation mode based on task requirements and current conditions. The core of this adaptive selection process lies in evaluating the compatibility between currently available measurement conditions (e.g., distance between terminals, signal quality, available technology, or hardware configuration) and the accuracy, timeliness, and other objectives required by the task mode. As an example, the system may follow these principles: when high accuracy is required and the terminals are within direct connection range, the direct connection measurement mode between terminals is prioritized; when accuracy requirements are moderate or direct connection is unavailable, the wide area network-assisted calculation mode is selected. Those skilled in the art will understand that this evaluation and selection process can be implemented through predefined rules, lookup tables, or more complex decision models. Examples are as follows:

[0309] (a) When the task mode and rule set require high-precision perception (e.g., vehicle collision avoidance, finding the injured at a fire scene, location monitoring of valuables):

[0310] If the current primary data source is the direct connection measurement mode between terminals (which provides high accuracy), then the current mode is deemed appropriate.

[0311] If the current primary data source is the WAN-assisted computing mode (which has relatively low precision), the evaluation system tends to trigger an attempt to switch to a higher precision mode.

[0312] (b) When the task mode has relatively relaxed accuracy requirements (e.g., large-scale personnel search, guarding of restricted areas, other macroscopic behavioral perception):

[0313] If the current WAN-assisted computing mode is appropriate (which usually meets the accuracy requirements and consumes fewer resources), then the current mode is deemed suitable.

[0314] If the current direct connection measurement mode between terminals continues to be used (which may cause unnecessary resource consumption), the evaluation system tends to trigger an attempt to switch to a more economical mode.

[0315] Based on the above evaluation results, the system decision-making process is as follows:

[0316] If the current measurement mode is deemed appropriate, the system will maintain the current measurement mode.

[0317] If a switch is required based on the assessment, the system will trigger a mode adjustment mechanism. Whether the switch can be successfully completed depends on a comprehensive assessment of whether the implementation conditions are met, such as whether the relative distance between the terminals has entered the short-range communication range and whether the terminal hardware supports the target mode.

[0318] Those skilled in the art should understand that the above assessment of the conformity or suitability between the currently available measurement conditions and the objectives such as accuracy and timeliness required by the task mode can be divided into discrete levels of "high, medium, and low", or it can be continuously characterized by numerical values ​​such as percentages.

[0319] This evaluation process can be implemented through predefined lookup tables, logical judgment rules, or more complex evaluation models (such as decision models built based on machine learning algorithms). All such evaluation and decision-making methods for achieving adaptive mode selection fall within the scope of this invention.

[0320] Example 4: Dynamic Application Example of Adaptive Selection of Measurement and Calculation Modes

[0321] This embodiment details how the system adaptively selects the relative spatial relationship measurement and calculation mode based on real-time changing task conditions and environmental information. The following example, vehicle collision avoidance, demonstrates how the system intelligently adjusts and optimizes the measurement mode by assessing the match between current conditions and task requirements.

[0322] Scenario Setting: Taking two vehicles traveling towards each other on a road and using an onboard cooperative terminal for collision avoidance as an example, both vehicles are equipped with cooperative terminals that are statically configured as monitoring and execution terminals. Based on a rule set, the two can establish a monitoring relationship, treating each other as target terminals and providing or obtaining information data from the other.

[0323] (a) Data accuracy requirements

[0324] Vehicle collision avoidance is a specific application of the restricted area protection mode. To prevent two vehicles from colliding, for example, the rule set defines the relative distance between the two vehicles with high precision (e.g., centimeter-level).

[0325] (b) Adaptively select or adjust the relative spatial relationship measurement and calculation mode according to real-time conditions.

[0326] Phase 1: When the distance is relatively far, the suitability is low and conditions are limited.

[0327] When the relative distance between two vehicles is large (e.g., greater than 300 meters), the relative distance can only be obtained through wide area network-assisted calculation mode. Due to the large error of satellite positioning, the accuracy is far lower than the high accuracy (e.g., centimeter level) required by the rule set. The system judges based on the task mode, rule set, and current measurement and calculation conditions, determines that the current matching degree is low, and triggers the need to adjust to a better mode.

[0328] However, the adjustment is limited by the relative distance between terminals. Since the system is not within the effective range of the short-range wireless network, it cannot perform mode switching and can only maintain the wide area network assisted computing mode.

[0329] Phase Two: As the distance between the two vehicles decreases, the measurement mode can be further adjusted.

[0330] When the two vehicles enter the short-range wireless communication range (e.g., less than 80 meters), the system successfully switches to the direct-connection measurement mode between terminals. In this mode, at least one of the high-precision short-range wireless technologies, such as StarFlash and UWB, can be selected. As a further example of optimization, the system can select or combine multiple supported technologies according to distance and accuracy requirements, for example, enabling the more accurate UWB at closer ranges.

[0331] At this point, the data accuracy perfectly matches the task requirements, and the system enters a stable monitoring state, requiring no further adjustments.

[0332] This embodiment clearly demonstrates that different task modes have different requirements for data accuracy, and the adaptive mechanism of this invention can intelligently match these requirements. For example, applications such as vehicle collision avoidance and fire rescue require decimeter-level or centimeter-level high accuracy; while in scenarios such as finding missing persons and livestock management, sub-meter-level or even meter-level accuracy is sufficient. This invention achieves precise adaptation to diverse scenarios through real-time evaluation and dynamic scheduling of different modes.

[0333] (c) Initial and adjacent scene processing:

[0334] It should be noted that if the initial relative positions of the two terminals are already within the effective coverage area of ​​the short-range wireless network at the initial moment of establishing the monitoring relationship, the system will directly select and activate the direct connection measurement mode between the terminals according to the rule set and task requirements, so as to obtain high-precision relative spatial positions with the highest efficiency.

[0335] (d) Mechanism for determining or correcting information data:

[0336] To obtain more accurate status data from information data, this invention supports the use of data fusion algorithms to perform fusion calculations on positioning data from multiple sources and adaptively determine or correct them.

[0337] As a preferred implementation, the system can fuse high-precision direct-connection measurement results between terminals with absolute position data obtained through wide-area network-assisted calculations. For example, short-range ranging data such as stroboscopic and UWB measurements can be used as observations to filter the position data calculated by satellite positioning (e.g., GPS). By employing algorithms such as Kalman filtering and particle filtering for recursive calculations, the system can effectively smooth noise and errors from a single data source. As the relative movement between terminals progresses, the position estimates will gradually converge, thereby obtaining a stable and significantly more accurate corrected position. This mechanism provides a solid data foundation for highly reliable early warning.

[0338] (e) Mechanism advantages: Through the adaptive selection and data fusion mechanism demonstrated in this embodiment, the system intelligently achieves a seamless connection between the robustness of global coverage and the high precision of local areas, thereby achieving a dynamic balance between accuracy, power consumption and connection reliability in different operating scenarios.

[0339] Example 5: Collaborative Early Warning Process for "Ghost Pedestrian" Risk

[0340] This embodiment, as the focus, comprehensively demonstrates, in conjunction with the aforementioned embodiments, how the present invention solves the typical dangerous scenario of "ghost peeking out" (see reference). Figure 2 Its technical solution is built upon the aforementioned system of adaptive selection of measurement modes.

[0341] Scene: Pedestrians suddenly cross the street from under the cover of a bus.

[0342] (a) Background and early perception (utilizing signal diffraction and penetration properties)

[0343] The vehicle is equipped with an onboard collaborative terminal as a monitoring and execution terminal. This monitoring and execution terminal is configured with a collision-avoiding fan-shaped virtual electronic fence based on the task mode and the associated rule set. As the vehicle continues to move forward, the onboard terminal continuously manages the corresponding dynamic virtual electronic fence (according to Embodiment 2, the shape or coverage of the virtual electronic fence is dynamically adjusted according to factors such as vehicle speed and weather).

[0344] A car was driving on a city road, and the driver's view was severely obstructed by a bus parked on the side of the road. A pedestrian (related to...) Figure 2 The target terminal 2 shown is preparing to cross the front blind spot of the bus. Despite visual obstruction, short-range wireless signals have a certain diffraction and penetration capability, enabling the onboard monitoring terminal to detect and identify the target terminal within the visual blind spot. Based on the task and real-time conditions, the system automatically activates and adopts a direct-connection measurement mode between terminals (e.g., star flash, UWB) for high-precision sensing.

[0345] (b) Identify or correct information and data and conduct situational assessments of the monitored areas.

[0346] The system acquires high-precision spatial location data through a direct connection measurement mode between terminals, using this data as key status data. It then integrates this data with other information (e.g., environmental and attribute data) to conduct situational assessments of the monitored area based on a rule set. During the situational assessment, the system can combine environmental data (e.g., road surface slippage), attribute data (e.g., pedestrian age and health status), and baseline data established based on status perception patterns and historical behavior (e.g., children's hyperactivity characteristics) to generate a quantitative situational value reflecting real-time risk.

[0347] For example, if data shows that the user (e.g., a child) has a history of being active and often running on the roadside, the system can use this baseline data to introduce a higher risk adjustment coefficient when calculating the situation quantification value.

[0348] It is worth emphasizing that the “baseline data” here is established and updated through long-term learning and analysis of the state awareness mode as described in Scenario 5 of Example 1. This reflects the sharing and collaboration of underlying data and capabilities between different task modes.

[0349] (c) Response Operation

[0350] When the situation quantification value exceeds a set threshold, the system triggers a corresponding response: for example, issuing an audible and visual alarm to the driver stating "Danger in the right blind spot!", while simultaneously sending vibration and voice prompts to the pedestrian terminal. The system can also upload relevant data to update the behavior baseline.

[0351] For other pedestrians located directly in front of the vehicle, the system uses the same technical process for perception, assessment, and warning.

[0352] (d) Supplementary scenario: Addressing the risk of static / low-speed blind spots

[0353] The early warning mechanism of this invention can also effectively prevent low-speed accidents caused by temporary obstruction of vision. For example, in a residential area, drivers of slow-moving vehicles may fail to notice children directly in front or to the side due to blind spots created by oncoming headlights, bright lights from billboards, or the vehicle's structure. In this case, the vehicle terminal, acting as a monitoring execution terminal, continuously scans to detect the device worn by the child (e.g., Figure 2 The target terminal 3 shown enters a high-precision ranging state. Once the system determines that there is a risk of being run over based on the relative position and motion state, it will immediately trigger the highest level of audible and visual alarm, and even assist in braking, thereby completely avoiding the accident.

[0354] Similarly, this invention can also be used to prevent "door-opening kills" (a dangerous traffic behavior where vehicle occupants open their doors without checking behind them, leading to collisions with pedestrians or other vehicles. The characteristic of this behavior is that the door is suddenly opened after the vehicle has stopped, causing oncoming vehicles to be unable to avoid it, potentially resulting in injury or death). The principle is that when the vehicle stops, the monitoring terminal continuously monitors the virtual electronic fence, detecting approaching target terminals. Upon receiving a door-opening signal, the system immediately calculates the relevant monitoring quantification values. If it determines there is a risk of a "door-opening kill," it immediately issues an alarm or locks the door.

[0355] (e) Final Results and Technical Summary

[0356] The driver was able to brake in advance, and the pedestrian was alerted to stop temporarily, successfully avoiding a potential "ghost pedestrian" accident. This embodiment fully demonstrates that, through the complete technical chain of "perception-assessment-response" described above, the present invention transforms the "unknowable blind spot" that relies on vision in traditional traffic safety into a "controllable risk domain" that can be accurately measured, assessed in real time, and intelligently warned, providing a reliable technical solution for addressing typical sudden risks in urban traffic.

[0357] Example 6: Multi-terminal role switching and collaborative networking application

[0358] This embodiment aims to illustrate in detail the flexibility of the collaborative terminal in the system, and the method of obtaining identifiers through server commands. The following is an example of a group tour scenario.

[0359] (a) Multi-layered virtual electronic fences and dynamic role assignment

[0360] Main Fence (Team Level): The tour guide's terminal acts as the monitoring and execution terminal, initializing a virtual electronic fence covering the entire team's activity area (this application corresponds to the group scheduling mode). When a group member (whose terminal acts as the target terminal) moves away from the team and their relative position crosses the fence boundary, the system triggers an alarm according to the rule set, simultaneously sending a warning to both the tour guide's and the group member's terminals.

[0361] Sub-fence (group level): Member B can establish a new monitoring relationship with several friends. In this relationship, Member B's terminal acts as the monitoring execution terminal, initializing a dedicated sub-virtual electronic fence. Accordingly, Member B's terminal achieves dynamic role switching and concurrent execution: being monitored as a target terminal in the main fence, and performing monitoring as a monitoring execution terminal in the sub-fence.

[0362] (b) Identifier Acquisition and Collaborative Networking

[0363] When establishing the above monitoring relationship, the monitoring execution terminal obtains the identifier of the target terminal in the following way:

[0364] For familiar individuals (e.g., B, C, D), users can manually select from their friend list or enter their identifier for confirmation.

[0365] For unfamiliar individuals (e.g., E and F), the tour guide sends a group request to the server via their terminal. The server then issues a command to terminals E and F, which includes the tour guide's terminal identifier and connection request. After terminals E and F parse the command and authorize, their identifiers are obtained, thus establishing the monitoring relationship.

[0366] This mechanism enables secure and convenient collaborative networking in contactless scenarios.

[0367] (c) Adaptive selection of measurement mode

[0368] Based on the initial relative position and following the task mode, rule set, and real-time conditions, the system adaptively selects the measurement mode for the terminals within the team:

[0369] For group members in close proximity, the system automatically activates the direct connection measurement mode between terminals (e.g., Star Flash, UWB) for accurate distance measurement.

[0370] For members who are temporarily away, a wide area network-assisted computing mode is used for preliminary location and tracking.

[0371] (d) Collaborative Search Extended Applications

[0372] When the team receives an instruction from the server to assist in locating a missing person (user G), the guide terminal parses the instruction to obtain user G's terminal identifier and adds it to the permission list. Once any member of the team's terminal detects this identifier during a scan, it can immediately establish a monitoring relationship, use a high-precision measurement mode to locate target G, and report its relative spatial position to the server.

[0373] This application is also applicable to family travel management, demonstrating how the system can dynamically expand the monitoring network and monitoring functions through commands, quickly transforming collaborative terminals into monitoring execution terminals to perform new monitoring tasks, and greatly improving emergency response capabilities in public safety scenarios.

[0374] Example 7: Intelligent Search for Lost Children

[0375] This embodiment demonstrates the application of intelligent search for lost children under restraint supervision mode, especially highlighting the central scheduling role of the server and the collaborative capabilities of multiple terminals.

[0376] (a) Cloud-based intelligent security monitoring

[0377] The monitoring user (e.g., a mother) initializes a virtual electronic fence based on the after-school activity patterns of the monitored object (e.g., a child) and sets up the server and its own terminal to jointly manage the fence in order to achieve safe supervision of the child's journey home from school.

[0378] Based on the server's ability to learn from historical data using state-aware patterns (e.g., as described in Scenario 5 of Example 1), it establishes a personalized spatiotemporal behavior model (e.g., habitual path and time pattern) for the child's school commute.

[0379] If the server detects a significant deviation from the established model in real-time comparison, it immediately sends an alert to the mother's device. After confirming the anomaly on her device, the mother can formally report the issue to the server via the alarm device.

[0380] (b) Response Operations: Intelligent Scheduling and Collaborative Search of Servers

[0381] Upon receiving the alarm, the server immediately initiates the collaborative search process:

[0382] Initial search command issuance: The server broadcasts a search command to collaborating terminals within a specific geographical area (e.g., a 500-meter radius) centered on the last known location of the target terminal (child). This command encapsulates the identifier and relevant coordinates of the target terminal.

[0383] Terminal response and adaptive connection attempt: After receiving and parsing the instructions, the cooperating terminals within range adaptively attempt to establish a monitoring relationship with the target terminal based on the system's assessment of task requirements and current connection conditions.

[0384] Prioritize scanning and connection via short-range wireless communication technologies (e.g., Starflash, UWB).

[0385] If a short-range connection fails, it will automatically switch to wide area network (WAN) technology to attempt a connection.

[0386] The terminal reports data such as connection status and acquired relative pose to the server.

[0387] Intelligent optimization and dynamic networking: The server calculates priorities and selects the best option based on the data reported by each terminal and according to a preset set of rules. Calculation factors include, but are not limited to: relative distance, battery level, and signal strength.

[0388] Based on this calculation, the server dynamically designates one or more optimal terminals from the response terminals as monitoring execution terminals, instructing them to establish or maintain a monitoring relationship with the target terminal in order to continuously acquire and upload real-time data from the target terminal.

[0389] This embodiment further demonstrates that the present invention forms an active protection system of "early warning, precise tracking during the event, and multi-party collaborative response" through the intelligent decision-making and scheduling of the server.

[0390] Example 8: Intelligent Fall Monitoring for the Elderly Based on Multimodal Perception

[0391] Objective: To demonstrate the system's ability to go beyond pure spatial monitoring in complex indoor environments by integrating multimodal sensor data to achieve precise status perception and risk assessment.

[0392] Scenarios and Implementation:

[0393] Deploying the system in an elderly person's home initializes the entire residence as a virtual electronic fence. The system comprehensively utilizes the following technologies:

[0394] Multi-source data fusion: The situation quantification value is calculated by weighted fusion based on spatial location, inertial sensor data as sensing data (e.g., impact acceleration), vital signs (e.g., heart rate), and environmental audio data.

[0395] Intelligent hierarchical response: Based on the rule set, a hierarchical response is triggered according to the situation quantification value: Low level → log recording; Medium level → send "abnormal activity" reminder to the guardian; High level → automatically dial emergency numbers and send location alarm.

[0396] Value Summary: This embodiment demonstrates that by introducing non-spatial sensor data as sensing data, the present invention achieves a leap from spatial monitoring of "where people are" to behavioral and health monitoring of "how people are in their condition".

[0397] Example 9: Collaborative Safety Management of Personnel and Equipment at Large Construction Sites

[0398] Objective: To demonstrate how the system can achieve real-time, dynamic collaborative monitoring and proactive safety isolation of people, machines, and the environment across different categories of entities in complex industrial environments.

[0399] Scenarios and Implementation:

[0400] On construction sites, excavators are used as monitoring and execution terminals. The core of the system executes the following processes:

[0401] Dynamic Hazard Area Modeling: Based on its own attribute data (e.g., joint angle, boom span), the excavator calculates and dynamically adjusts the virtual electronic fence around it in real time using a set of rules. This fence changes in real time with the machine's operating posture.

[0402] High-precision collaborative sensing and proactive response: When personnel enter the dynamic fence, the system automatically activates the direct-connection measurement mode between terminals (e.g., UWB) to obtain centimeter-level relative distances. Subsequently, based on the precise data, the system calculates the situation quantification value and triggers collaborative alarms (personnel terminals and operator terminals), and even automatically limits the speed of mechanical movement according to rules.

[0403] Value Summary: This embodiment demonstrates how the present invention upgrades security protection from static "delineating restricted areas" to dynamic, predictive "real-time isolation" and "proactive intervention" in the industrial internet scenario.

[0404] Summary

[0405] Those skilled in the art should understand that the above embodiments are merely illustrative examples to facilitate understanding of the present invention and are not intended to limit the scope of protection of the present invention. Although the application scenarios of task modes such as restricted area protection, restraint monitoring, search and location, path guidance, status awareness, and group scheduling vary, they are all implemented based on a unified core technical architecture of "collaborative networking, dynamic virtual electronic fences, task modes, and rule sets." Any modifications, equivalent substitutions, or improvements made to the methods or terminals under the guidance of the technical concept of this invention should be included within the scope of protection of this invention.

[0406] This invention also protects a computer-readable storage medium storing a computer program that, when executed by a collaborative terminal, enables the collaborative terminal to implement the intelligent 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.

[0407] Extended application scenario description

[0408] Those skilled in the art will understand that the core technical architecture of this invention possesses a high degree of versatility and scalability. In addition to the foregoing embodiments, its application scenarios can be extended to, but are not limited to, the following fields:

[0409] Industrial safety monitoring: Applying the restricted area protection mode to hazardous machinery areas, defining the permissions and safe distances of different personnel through rule sets to achieve graded proactive safety protection.

[0410] Smart city management: By combining the application of restricted area protection mode (defining no-parking zones) and restraint supervision mode (defining standardized parking areas), compliant parking management of shared mobile tools such as shared bicycles and electric scooters can be carried out.

[0411] In these extended scenarios, the system operates based on the core technical architecture defined in the foregoing claims and embodiments, achieving intelligent supervision through the synergistic effect of the task modes, rule sets, virtual electronic fences, and information data.

Claims

1. A dynamic virtual electronic fence based intelligent monitoring system, characterized in that, The system is composed of two or more cooperative terminals; the cooperative terminals can form a network and interact with each other, and can dynamically operate as a monitoring execution terminal or a target terminal according to a task mode; specifically, the system comprises: (a) at least one monitoring execution terminal; When the cooperative terminal operates as a monitoring execution terminal, it is configured to: set and manage a virtual electronic fence according to a predefined task mode and a rule set associated therewith, forming a supervision area; establish a monitoring relationship with at least one target terminal in the supervision area, and obtain information data for situation assessment through the cooperation of the target terminal, wherein the information data at least includes relative spatial relationship data between the monitoring execution terminal and the target terminal; based on the task mode, the rule set, and the obtained information data, adaptively select a relative spatial relationship measurement and calculation mode to determine or correct the relative spatial relationship data, forming updated information data; the relative spatial relationship measurement and calculation mode at least includes a direct connection measurement mode between terminals and a wide area network assisted calculation mode; based on the updated information data, the task mode, and the rule set, adaptively adjust the reference point position and / or geometric parameters of the virtual electronic fence; based on the updated information data, the task mode, and the rule set, perform situation assessment of the supervision area; according to the result of the situation assessment, trigger the execution of corresponding response operations; (b) target terminal; When the cooperative terminal operates as a target terminal, it is configured to: receive and respond to signals or instructions from the monitoring execution terminal, and at least cooperate with the monitoring execution terminal to obtain the relative spatial relationship data.

2. A dynamic virtual electronic fence-based intelligent supervision method, characterized in that, The following steps are performed by at least one cooperative terminal as a monitoring execution terminal: set and manage a virtual electronic fence according to a predefined task mode and a rule set associated therewith, forming a supervision area; establish a monitoring relationship with at least one target terminal in the supervision area, and obtain information data for situation assessment through the cooperation of the target terminal, wherein the information data at least includes relative spatial relationship data between the monitoring execution terminal and the target terminal; based on the task mode, the rule set, and the obtained information data, adaptively select a relative spatial relationship measurement and calculation mode to determine or correct the relative spatial relationship data, forming updated information data; the relative spatial relationship measurement and calculation mode at least includes a direct connection measurement mode between terminals and a wide area network assisted calculation mode; based on the updated information data, the task mode, and the rule set, adaptively adjust the reference point position and / or geometric parameters of the virtual electronic fence; based on the updated information data, the task mode, and the rule set, perform situation assessment of the supervision area; according to the result of the situation assessment, trigger the execution of corresponding response operations.

3. The system of claim 1, wherein, The system further comprises a server configured to: (a) establish a communication connection with the cooperative terminals of the system; and establish a communication connection with associated external terminals or platforms; (b) providing at least one of the following services to the monitoring execution terminal and / or the target terminal: assisting in managing the virtual electronic fence, data storage and processing, instruction issuing and forwarding; (c) being configured to be able to operate as a monitoring execution terminal, independently or in cooperation with the cooperative terminal, to perform the functions defined by the monitoring execution terminal as claimed in claim 1.

4. The system of claim 3, wherein, The server is configured to provide services including: Analyzing and learning user information data, fusing real-time data to assess the situation or find abnormalities in the monitored area.

5. The system of claim 1, wherein, The information data further includes at least one of the following: state data, environmental data, attribute data, and perception data.

6. The system of claim 1, wherein, The situation assessment of the monitored area includes generating a situation quantization value to represent the risk or state of the monitored area or target terminal; when there are multiple target terminals, the situation assessment further includes sorting the target terminals based on the situation quantization value.

7. The system of claim 1, wherein, When the monitoring execution terminal establishes a monitoring relationship with the target terminal, the monitoring execution terminal obtains the identifier of the target terminal and verifies it; the identifier can be obtained in at least one of the following ways: (a) automatically scanning surrounding cooperative terminals to obtain identifiers through the discovery function of short-range wireless communication technology; (b) receiving instructions issued by the server to obtain identifiers; (c) receiving user input identifiers; (d) triggering associated cooperative terminals to report their identifiers by broadcasting virtual electronic fence related parameters.

8. The system of claim 1, wherein, The direct measurement mode between terminals uses short-range wireless technology, which includes at least one of the following: SparkLink, Ultra-Wideband (UWB).

9. The system of claim 1 or the method of claim 2, wherein, The task mode includes at least one of the following: No-go zone protection mode, restraint supervision mode, search and positioning mode, path guidance mode, state perception mode, group scheduling mode.

10. The system of claim 1 or the method of claim 2, wherein, The rule set includes at least one of the following rules associated with the task mode: (a) rules for defining virtual electronic fence parameter configuration and adjustment; (b) rules for defining the logic of selecting the relative spatial relationship measurement and calculation mode; (c) rules for defining the situation assessment method of the monitored area.

11. The system of claim 1, wherein, Based on the results of the situation assessment, according to the pre-defined response rules associated with the task mode, determine the level or type of response operation to be performed; Wherein, the response operation includes at least one of the following types: (a) alarm operation: generate and output warning instructions to trigger self and / or the target terminal to perform local reminder or display operation; (b) control operation: generate and issue control instructions to change the state or behavior of the target terminal or associated equipment; (c) data reporting operation: upload the identifier of the target terminal and the information data to the server.

12. A coordination terminal for the intelligent supervisory system of claim 1, characterized by, It includes: Processor; Memory for storing computer programs; Multi-mode communication unit supporting wide area network communication and short-range wireless communication; Wherein, the processor is configured to execute the program, which can dynamically switch between the roles of the monitoring execution terminal and the target terminal according to the task mode, and implement the following functions: configured to implement the intelligent monitoring method as claimed in claim 2 when switching to the role of a monitoring execution terminal; configured to receive and respond to signals or instructions from other collaborative terminals which are also monitoring execution terminals, and to acquire the relative spatial relationship data in cooperation with at least one of the monitoring execution terminals when switching to the role of a target terminal.

13. The collaborative terminal of claim 12, wherein, Further comprising at least one of: (a) a reminding device configured to issue a reminder including at least one of vibration, sound and light, or a text signal in response to the responding operation; (b) a display device configured to fuse at least one of the status data of the collaborative terminals, information related to the responding operation, and electronic map data to generate a visual interface and display the same; (c) an alarm device configured to send an alarm signal to the outside in response to a user alarm operation.

14. A storage medium having a computer program stored thereon, which, when executed by a collaborative terminal, causes the collaborative terminal to implement the method of claim 2.

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