Dynamic area perception and adaptive control method and system based on Wi-Fi FTM distance measurement

Abstract

The invention discloses a dynamic area perception and self-adaptive control method and system based on Wi-Fi FTM ranging, and relates to the technical field of indoor positioning and intelligent control, and the method comprises the steps that an intelligent terminal carries out ranging with a plurality of wireless access points APs through a Wi-Fi FTM protocol, and obtains ranging results of the intelligent terminal and the plurality of wireless access points APs; obtaining two-dimensional and / or three-dimensional position coordinates of the intelligent terminal and a plurality of wireless access points (APs); matching with a preset virtual area, and judging whether the image is in the preset virtual area or not; when the intelligent terminal is currently located in the preset virtual area, and the current position state of the intelligent terminal and the motion state data, the physiological index data and the environment data detected by the intelligent terminal meet preset trigger rules, corresponding control actions are controlled to be executed, and multi-terminal linkage is intelligently controlled through the Internet of Things. According to the invention, the position data and the user state can be fused, and the corresponding control operation can be intelligently triggered.

Description

Technical Field

[0001] This invention relates to the field of indoor positioning and intelligent control technology, and in particular to a dynamic area perception and adaptive control method, system, intelligent terminal and storage medium based on Wi-Fi FTM ranging. Background Technology

[0002] With the development of smart homes, wearable devices, and IoT technologies, the demand for high-precision indoor location sensing is increasing. Current mainstream indoor positioning technologies have many limitations: GPS signals attenuate severely indoors and become unusable; RSSI-based Wi-Fi positioning is affected by multipath effects, resulting in an accuracy of only 5-15 meters; BLE positioning requires additional beacon deployment and lacks stability; while UWB offers high accuracy, it suffers from high cost and power consumption. Wi-Fi FTM technology, based on the IEEE 802.11mc standard, can achieve ranging accuracy of 1-2 meters by measuring the signal round-trip time and can directly utilize existing Wi-Fi infrastructure.

[0003] However, existing Wi-Fi FTM-based applications have significant shortcomings: First, the systems generally employ a fixed coordinate point positioning mode, making it impossible to flexibly define and dynamically adjust virtual area boundaries, thus failing to adapt to the changing needs of area division in real-world scenarios. Second, existing solutions only focus on location information itself, failing to integrate and analyze location data with user movement status, physiological indicators, and environmental data. Furthermore, the systems lack deep integration mechanisms with smart home devices, making it impossible to achieve automated control based on user location and status. These issues make it difficult for existing technologies to meet the demands for precise area perception and intelligent linkage in scenarios such as smart homes and health monitoring.

[0004] Therefore, existing technologies still need improvement and development. Summary of the Invention

[0005] The technical problem this invention aims to solve is to address the aforementioned deficiencies of existing technologies by providing a dynamic area perception and adaptive control method, system, smart terminal, and storage medium based on Wi-Fi FTM ranging. This invention, based on Wi-Fi FTM ranging, enables flexible definition, updating, or learning of virtual areas for ranging positions; it also combines user status information—location data and user status—to intelligently trigger corresponding control operations, greatly facilitating user operation; and it has the advantages of dynamically adjusting virtual area boundaries, achieving multi-dimensional data fusion judgment, and intelligent linkage.

[0006] This application provides a dynamic area sensing and adaptive control method based on Wi-Fi FTM ranging, and the technical solution is as follows: A smart terminal equipped with a Wi-Fi FTM ranging module can measure distances with multiple wireless access points (APs) that support FTM via the Wi-Fi FTM protocol to obtain the ranging results between the smart terminal and the multiple wireless access points (APs). The ranging results between the smart terminal and multiple wireless access points (APs) are obtained through a positioning fusion algorithm to calculate and fuse the positions, thereby obtaining the two-dimensional and / or three-dimensional position coordinates of the smart terminal and the multiple wireless access points (APs). The obtained two-dimensional and / or three-dimensional position coordinates of the smart terminal and multiple wireless access points (APs) are matched with a preset virtual area to determine whether the smart terminal is currently within the preset virtual area. When the smart terminal is currently within a preset virtual area, the smart terminal is controlled to collect motion status data, physiological indicator data, and environmental data in parallel and synchronize them in time. The current location status of the smart terminal is compared and analyzed with the motion status data, physiological index data and environmental data detected by the smart terminal to determine whether the preset triggering rules are met. If the preset triggering rules are met, the corresponding control action is executed, and multiple terminals are linked through IoT intelligent control.

[0007] The dynamic area perception and adaptive control method based on Wi-Fi FTM ranging, wherein the intelligent terminal equipped with a Wi-Fi FTM ranging module performs ranging with multiple FTM-supporting wireless access points (APs) via the Wi-Fi FTM protocol to obtain the ranging results between the intelligent terminal and the multiple wireless access points (APs) includes the following steps before the step: A Wi-Fi FTM ranging module is pre-installed on the smart terminal to calculate the round-trip delay between the smart terminal and each wireless access point (AP) and convert it into a one-way distance by initiating a request and receiving a response.

[0008] The aforementioned dynamic area perception and adaptive control method based on Wi-Fi FTM ranging, wherein the step of calculating and fusing the ranging results between the smart terminal and multiple wireless access points (APs) through a positioning fusion algorithm to obtain the two-dimensional and / or three-dimensional position coordinates of the smart terminal and the multiple wireless access points (APs) includes: The ranging results obtained from the smart terminal and multiple wireless access points (APs) are used to perform position calculation and fusion through a positioning fusion algorithm. Kalman filtering or weighted average algorithm is used to fuse the ranging results from multiple wireless access points (APs) to eliminate multipath interference and obtain the two-dimensional and / or three-dimensional position coordinates of the smart terminal and multiple wireless access points (APs).

[0009] The dynamic area perception and adaptive control method based on Wi-Fi FTM ranging, wherein the step of matching the obtained two-dimensional and / or three-dimensional position coordinates of the smart terminal and multiple wireless access points (APs) with a preset virtual area to determine whether the smart terminal is currently within the area further includes: Multiple corresponding virtual regions are generated in advance based on the historical activity trajectory of the smart terminal or through manual configuration; Among the multiple corresponding virtual areas, the boundary shape of some virtual areas is dynamically variable, and the settings can automatically adapt to environmental changes based on the addition or removal of wireless access points.

[0010] The dynamic area perception and adaptive control method based on Wi-Fi FTM ranging, wherein the step of matching the obtained two-dimensional and / or three-dimensional position coordinates of the smart terminal and multiple wireless access points (APs) with a preset virtual area to determine whether the smart terminal is currently within the preset virtual area includes: The method involves matching the obtained two-dimensional and / or three-dimensional position coordinates of the smart terminal and multiple wireless access points (APs) with a preset virtual area to determine whether the smart terminal is currently within the preset virtual area. If the boundary shape of the preset virtual area is dynamically variable, the boundary of the area is adaptively adjusted according to the historical trajectory and the scene, and then it is determined whether the smart terminal is currently within the preset virtual area.

[0011] The aforementioned dynamic area perception and adaptive control method based on Wi-Fi FTM ranging, wherein the step of controlling the intelligent terminal to collect motion state data, physiological indicator data, and environmental data in parallel and perform time synchronization when the intelligent terminal is currently within a preset virtual area includes: When the smart terminal is currently in a preset virtual area, the control smart terminal collects and acquires accelerometer data, gyroscope data, heart rate sensor data, and temperature sensor data in real time through the Internet of Things, so as to collect the user's motion status data, physiological index data, and environmental data in real time and synchronize them in time.

[0012] The aforementioned dynamic area perception and adaptive control method based on Wi-Fi FTM ranging, wherein the steps of comprehensively comparing and analyzing the current position state of the smart terminal with the motion state data, physiological index data, and environmental data detected by the smart terminal to determine whether a preset triggering rule is met, and controlling the execution of corresponding control actions when the preset triggering rule is met, and then controlling the linkage of multiple terminals through IoT intelligent control, include: A pre-set composite triggering condition is established by comprehensively comparing and analyzing the location status of the smart terminal with the motion status data, physiological index data, and environmental data detected by the smart terminal. This is the preset triggering rule. The current location status of the smart terminal is compared and analyzed in conjunction with the motion status data, physiological index data and environmental data detected by the smart terminal. Determine whether the preset trigger rules are met. If the preset trigger rules are met, control the execution of the corresponding control action and link multiple terminals through IoT intelligent control. When it is detected that an elderly person wearing a smart terminal bracelet enters a preset virtual area (bathroom) and the bracelet's heart rate is higher than a preset value, control the trigger of an alarm. When it is detected that a smart terminal user enters a preset virtual area (meeting room) with a smart terminal, control the automatic turning on of lights, air conditioning, and the meeting room screen projection. If the preset triggering rules are not met, the control system enters a low-power mode, retaining only the event wake-up mechanism.

[0013] A dynamic area sensing and adaptive control system based on Wi-Fi FTM ranging, wherein the system includes: The Wi-Fi FTM ranging module is used to control a smart terminal equipped with a Wi-Fi FTM ranging module to perform ranging with multiple wireless access points (APs) that support FTM via the Wi-Fi FTM protocol, and obtain the ranging results between the smart terminal and the multiple wireless access points (APs). The multi-point ranging fusion module is used to perform position calculation and fusion on the ranging results obtained between the smart terminal and multiple wireless access points (APs) through a positioning fusion algorithm to obtain the two-dimensional and / or three-dimensional position coordinates of the smart terminal and the multiple wireless access points (APs). The virtual area management module is used to match the obtained two-dimensional and / or three-dimensional position coordinates of the smart terminal and multiple wireless access points (APs) with a preset virtual area to determine whether the smart terminal is currently within the preset virtual area. The sensor status acquisition module is used to control the smart terminal to collect motion status data, physiological index data and environmental data in parallel when the smart terminal is currently in a preset virtual area, and to perform time synchronization. The condition triggering and adaptive control module is used to comprehensively compare and analyze the current position status of the smart terminal with the motion state data, physiological index data and environmental data detected by the smart terminal to determine whether the preset triggering rules are met. When the preset triggering rules are met, the corresponding control action is executed, and multiple terminals are linked through IoT intelligent control. The low-power standby control module is used to control the system to enter a low-power mode when the preset triggering rules are not met, while retaining only the event wake-up mechanism.

[0014] An intelligent terminal, which includes a memory, and one or more programs. One or more programs are stored in the memory and are configured to be executed by one or more processors. The one or more programs include those for executing any of the methods described above.

[0015] A computer-readable storage medium, wherein when the instructions in the storage medium are executed by a processor of an intelligent terminal, the intelligent terminal is enabled to execute any of the methods described above.

[0016] As can be seen from the above, a dynamic area perception and adaptive control method, system, intelligent terminal and computer-readable storage medium based on Wi-Fi FTM ranging provided by the present application. First, the present invention uses Wi-Fi ranging technology to achieve high-precision area detection rather than being limited to coordinate positioning; secondly, combined with the status information of device sensors, it realizes intelligent and conditional action triggering; third, on the premise of controllable power consumption and privacy protection, it realizes multi-terminal linkage control. The present invention realizes dynamic area perception and intelligent control by integrating multi-dimensional data, and has the advantages of dynamically adjusting the virtual area boundary, realizing multi-dimensional data fusion judgment and intelligent linkage. Brief Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a schematic flowchart of the dynamic area perception and adaptive control method based on Wi-Fi FTM ranging provided in Embodiment 1 of the present invention.

[0019] Figure 2 It is a block diagram for illustrating the dynamic area perception and adaptive control method based on Wi-Fi FTM ranging provided in Embodiment 2 of the present invention with an office scenario as an example

[0020] Figure 3 It is a principle block diagram of the dynamic area perception and adaptive control system based on Wi-Fi FTM ranging provided in the embodiments of the present invention.

[0021] Figure 4 It is an internal structure principle block diagram of the intelligent terminal provided in the embodiments of the present invention. Detailed Description of the Embodiments

[0022] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of this application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0023] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0024] In existing technologies, indoor positioning technology has evolved from relying on satellite signals to utilizing wireless communication infrastructure. Signal strength-based positioning methods are susceptible to environmental interference and struggle to meet high-precision requirements; while ultra-wideband (UWB) technology offers higher accuracy, its hardware costs limit its widespread application. Current solutions primarily focus on acquiring location coordinates, lacking the ability to define dynamic virtual areas and failing to combine user physiological states with environmental data for intelligent control. For example, in elderly care scenarios, when an elderly person enters high-risk areas such as a bathroom, existing systems cannot trigger warnings based on abnormal heart rate data, making it difficult to effectively prevent safety hazards.

[0025] To address the aforementioned issues, the inventors of this application discovered that existing positioning technologies can only provide static coordinate information, failing to adapt to the dynamic changes in area boundaries in real-world scenarios. Analysis revealed that fusing high-precision ranging with multi-source sensor data can enhance the comprehensiveness of environmental perception. Further consideration was given to how to correlate location information with user status, designing composite triggering rules to achieve precise control. Ultimately, this resulted in a technical approach that achieves adaptive linkage through multi-dimensional data fusion and dynamic area matching.

[0026] Therefore, the present application provides a dynamic area perception and adaptive control method based on Wi-Fi FTM ranging. An intelligent terminal equipped with a Wi-Fi FTM ranging module obtains distance data by ranging with multiple APs through a protocol, calculates the terminal coordinates using a positioning fusion algorithm, matches the coordinates with a preset virtual area to determine the position status, synchronously collects motion, physiological, and environmental data when within the area, and triggers a control instruction to link Internet of Things devices through comprehensive comparison and analysis. The technical solution realizes high-precision area detection through Wi-Fi ranging technology rather than being limited to coordinate positioning; combines the status information of device sensors to achieve intelligent and conditional action triggering; and realizes multi-terminal linkage control on the premise of controllable power consumption and privacy protection. The specific embodiments are as follows: As Figure 1 shown, the present application proposes a dynamic area perception and adaptive control method based on Wi-Fi FTM ranging, including the following steps: Step S100: An intelligent terminal equipped with a Wi-Fi FTM ranging module ranges with multiple FTM-enabled wireless access points (APs) through the Wi-Fi FTM protocol to obtain the ranging results of the intelligent terminal and the multiple wireless access points APs; In the embodiment of the present invention, a Wi-Fi FTM (Fine Timing Measurement) ranging module is configured and enabled on the intelligent terminal, and ranges with several FTM-enabled wireless access points (APs) by means of the FTM protocol. Through the FTM process (usually including sending time synchronization probes, receiving responses, round-trip time calculation, etc.), the distance information between the intelligent terminal and each AP is obtained.

[0027] In this step, a set of ranging values of the intelligent terminal and the wireless access point AP are represented in meters or RTT (round-trip time), and can be accompanied by metadata such as ranging error, signal strength, and ranging validity flag.

[0028] In this step, FTM can provide stable and repeatable distance information, which is an important input for indoor high-precision positioning. Compared with pure signal strength positioning, the distance information is less sensitive to multipath and occlusion, can improve the positioning stability, and is beneficial to subsequent multi-point positioning fusion.

[0029] Step S200: Perform position calculation and fusion on the obtained ranging results of the intelligent terminal and the multiple wireless access points APs through a positioning fusion algorithm to obtain the two-dimensional and / or three-dimensional position coordinates of the intelligent terminal and the multiple wireless access points APs; In this step, distance information between several smart terminals and each wireless access point (AP) is input into the positioning fusion algorithm. The process includes: geometric solution based on triangulation / polygonal positioning; state estimation and noise fusion using Kalman filtering, Extended Kalman Filter (EKF), or Unscented Kalman Filter (UKF); and handling nonlinear and non-Gaussian noise scenarios using probabilistic positioning and particle filtering.

[0030] The output then shows the location information of the smart terminal in two-dimensional space (x, y) and / or three-dimensional space (x, y, z), as well as the corresponding confidence interval or error estimate.

[0031] This step improves positioning accuracy and robustness because single distance measurement is susceptible to errors and environmental factors, while the integration of multi-point ranging in this invention significantly improves positioning accuracy. Furthermore, this invention can achieve positioning in any dimension, as it supports two-dimensional or three-dimensional positioning, adapting to different scenarios (such as multi-story buildings and three-dimensional spaces).

[0032] Step S300: Match the obtained two-dimensional and / or three-dimensional position coordinates of the smart terminal and multiple wireless access points (APs) with a preset virtual area to determine whether the smart terminal is currently within the preset virtual area; In this embodiment, the positioning result is compared with one or more preset virtual areas. The virtual area can be a polygon, circle, rectangle, or a more complex polygonal area, representing, for example, an office area, a secure area, or a restricted area. A Boolean value is used to determine whether the current terminal falls within a preset virtual area, along with additional information such as the possible area identifier and distance from the boundary.

[0033] This invention features a context-aware triggering mechanism, as the next action is only initiated when a specific area is located, reducing the probability of false triggers. Furthermore, multiple areas can be defined, along with their permissions, monitoring items, and trigger conditions, facilitating reuse in different scenarios and providing flexible rule definition.

[0034] Step S400: When the smart terminal is currently in a preset virtual area, control the smart terminal to collect motion state data, physiological index data and environmental data in parallel, and perform time synchronization. In this embodiment, after determining the region that meets S300, parallel data acquisition is initiated: motion state data (such as acceleration, velocity, and posture), physiological indicator data (such as heart rate, skin temperature, and blood oxygen, depending on the device), and environmental data (such as temperature, humidity, odor, noise, light intensity, and air quality). Simultaneously, time synchronization is performed to ensure alignment of multi-source data on the timeline. The output is a timestamp-aligned multimodal data set, facilitating subsequent analysis.

[0035] In this embodiment, multimodal data can provide a more comprehensive scene description, which is beneficial to high-precision behavior recognition, state assessment, and event detection. And the unified timestamp makes cross-sensor correlation analysis, event triggering, and causal inference feasible. Moreover, in subsequent triggering rules, more intelligent decisions can be made based on the comprehensive state of multi-source data.

[0036] Step S500: Comprehensively compare and analyze the current position state of the intelligent terminal with the motion state data, physiological index data, and environmental data detected by the intelligent terminal to determine whether the preset triggering rule is satisfied. When the preset triggering rule is satisfied, control the execution of corresponding control actions and realize multi-terminal linkage through IoT intelligent control.

[0037] In this step of the embodiment, the positioning information and multimodal sensing data are input into the triggering rule engine for comprehensive comparison and analysis. If the triggering condition is established, control the execution of predefined control actions (such as adjusting the device state, issuing an alarm, starting an emergency process, etc.); specifically, the linkage control of multiple terminals can be realized through the Internet of Things (IoT), such as collaborative start / stop, synchronous actions, resource scheduling, etc. The output results of this step are: triggering events, execution instructions, and status feedback of linkage execution.

[0038] In the embodiment of the present invention, actions are only executed when exact conditions are met, which improves the responsiveness, accuracy, and controllability of the system. And the present invention can achieve collaborative work through multi-terminal linkage, such as achieving unified and coordinated control effects in scenarios such as security, safety prevention, and health monitoring.

[0039] The embodiment of this application combines wireless positioning (FTM), multi-source data perception, and intelligent trigger control to realize a closed-loop system for high-precision indoor positioning, environmental perception, and intelligent linkage control.

[0040] Typical application scenarios include: Indoor safety and security prevention: Trigger security linkages (alarm, access control, camera linkage) when positioning a person in a specific area.

[0041] Medical health and elderly care: Monitor physiological and environmental data in the concerned area and trigger health management or emergency response.

[0042] Smart exhibition hall / office scenario: Realize device collaboration, resource scheduling, and interactive experience based on location and environmental data.

[0043] In a further embodiment of the present invention, specifically, the Wi-Fi FTM ranging module refers to a hardware unit supporting the IEEE 802.11mc protocol, which determines the distance between the terminal and the access point (AP) by calculating the signal round-trip time. The positioning fusion algorithm includes data processing methods such as Kalman filtering to eliminate multipath interference and improve positioning accuracy. The preset virtual area refers to an electronic fence defined by coordinate ranges, the boundaries of which can be dynamically adjusted according to the AP distribution. Motion state data includes displacement information collected by accelerometers and gyroscopes, and physiological indicator data includes vital sign parameters obtained by heart rate sensors. Time synchronization refers to adding a unified timestamp to multi-source data to ensure timely analysis, and triggering rules refer to a preset set of logical judgment conditions.

[0044] Specifically, the smart terminal periodically interacts with access points (APs) deployed in the environment through FTM ranging, acquiring distance data from at least three APs. After processing ranging errors using Kalman filtering, the terminal coordinates are calculated using trilateration. The system compares the real-time coordinates with a pre-generated polygonal electronic fence to determine their spatial relationship. When the coordinates fall within the fence's range, multi-sensor data acquisition is initiated. The acquired acceleration data is used to identify user fall states, heart rate data is used to monitor physiological abnormalities, and temperature and humidity data are used to assess environmental risks. When the coordinates are in the bathroom area and the heart rate consistently exceeds a threshold, the system sends an alarm signal to the nursing platform and activates emergency lighting. When a user is detected entering the conference room area, the projection equipment is automatically activated and the air conditioning is adjusted to a preset mode.

[0045] Compared to existing technologies, traditional solutions rely on fixed coordinate points for area determination, which cannot adapt to layout changes caused by furniture movement or the addition or removal of access points (APs). This application solves the problem of misjudgment caused by changes in the physical environment by dynamically adjusting the boundaries of virtual areas. While existing technologies process location information and sensor data separately, this application achieves multi-dimensional data correlation analysis through timestamp synchronization, making the triggering conditions more reliable. Compared to basic linkages that only support device on / off switching, this application can execute a tiered response strategy based on user status.

[0046] Through the above technical solutions, this application achieves sub-meter level accuracy in dynamic area perception, reducing the false alarm rate to 30% of traditional solutions in elderly care monitoring scenarios. By integrating the dual judgment of abnormal heart rate and area location, it effectively identifies high-risk events such as bathroom slips. In smart office scenarios, the response time of conference room equipment linkage is shortened to within 2 seconds, and it can automatically switch energy-saving modes based on the movement status of personnel. The system can automatically update the area model according to changes in AP deployment, reducing manual maintenance costs by more than 60%.

[0047] This application further proposes to pre-set a Wi-Fi FTM ranging module in the smart terminal, which is used to calculate the round-trip delay between the smart terminal and each wireless access point (AP) and convert it into a one-way distance by initiating a request and receiving a response. The Wi-Fi FTM ranging module refers to a hardware or software functional unit based on the IEEE 802.11mc standard. Specifically, it can be implemented using a terminal device with an integrated Wi-Fi chip, and is used to perform the interaction and calculation of the ranging protocol.

[0048] Initiating a request and receiving a response refers to the ranging module actively sending a ranging request frame to the wireless access point and receiving the corresponding ranging response frame. This can be achieved through timers and frame transmission and reception mechanisms in the protocol stack, and is used to establish a bidirectional communication link.

[0049] Round-trip delay refers to the time difference between when the signal is sent from the ranging module to the time it receives the response. Specifically, the sending and receiving times can be recorded by hardware timestamps to eliminate the impact of clock deviation on ranging accuracy.

[0050] The one-way distance refers to the unidirectional propagation distance calculated based on the round-trip delay and the speed of light. Specifically, it can be achieved by dividing the round-trip delay by two and then multiplying it by the speed of light, in order to avoid errors caused by signal path asymmetry. Specifically, the ranging module completes its initial configuration before the positioning process begins, establishing connections with multiple wireless access points via a Wi-Fi interface. When ranging is required, the ranging module sends a request frame carrying a transmission timestamp to the target access point. The access point receives the frame, records the reception timestamp, and generates a response frame carrying the same timestamp, returning it to the target access point. The ranging module calculates the precise round-trip time based on its locally recorded transmission timestamp and the reception timestamp in the response frame, combined with transmission delay compensation for the response frame. This delay is divided by two to obtain the one-way propagation time, which is then multiplied by the speed of light to obtain the straight-line distance between the ranging module and the access point. Compared to existing technologies, which typically rely on fixed, dedicated ranging equipment or require additional hardware modules, this application achieves ranging functionality by reusing the existing Wi-Fi communication module of a smart terminal, eliminating the need to modify existing wireless access points or add external equipment. Existing methods for estimating distance using signal strength are susceptible to environmental interference, while this application's round-trip delay calculation based on timestamps effectively reduces errors caused by multipath effects. Through the above technical solution, this application solves the problems of existing ranging methods relying on dedicated equipment and having poor anti-interference capabilities, providing high-precision raw ranging data for subsequent positioning fusion. By reusing existing terminal hardware resources, the system deployment cost and complexity are reduced, while the timestamp-based delay calculation mechanism lays a reliable data foundation for dynamic area perception.

[0051] This application further proposes to calculate and fuse the ranging results of the smart terminal and multiple wireless access points (APs) through a positioning fusion algorithm, and to use Kalman filtering or weighted averaging algorithm to fuse the ranging results of multiple wireless access points (APs) to eliminate multipath interference and obtain the two-dimensional and / or three-dimensional position coordinates of the smart terminal and multiple wireless access points (APs).

[0052] Positioning fusion algorithms are computational methods that improve positioning accuracy by combining information from multiple data sources. Specifically, they can be implemented using Kalman filtering or weighted averaging algorithms. Kalman filtering dynamically adjusts the weights of ranging results from each wireless access point, correcting position estimation errors in real time and eliminating multipath interference caused by environmental reflections. Weighted averaging algorithms assign weights based on the signal quality or historical reliability of each wireless access point, weighting the ranging results to reduce the impact of abnormal ranging values ​​on the overall positioning result.

[0053] Specifically, after the smart terminal completes ranging with three or more wireless access points, the system inputs the ranging results into the positioning fusion algorithm. If Kalman filtering is selected, the position coordinates are iteratively predicted and updated through state equations and observation equations, dynamically adjusting the contribution of each ranging value in the fusion process and suppressing ranging fluctuations caused by multipath effects. If a weighted average algorithm is used, a weight coefficient is assigned to each ranging result based on the signal strength indication value of the wireless access points or the historical ranging error statistics. For example, access points with higher signal strength are given higher weights, and the fused position coordinates are finally obtained through weighted summation. Both algorithms can effectively eliminate multipath interference caused by wall reflections and personnel movement, making the positioning results closer to the true location.

[0054] Compared to existing technologies, traditional RSSI-based positioning methods rely on only a single signal strength parameter and cannot distinguish between direct and reflected signals, resulting in significant positioning errors. This application, however, by fusing ranging data from multiple wireless access points and incorporating a dynamic weighting mechanism, effectively identifies and suppresses the impact of multipath interference on ranging results. Compared to a single positioning algorithm, this method significantly improves positioning stability in complex indoor environments.

[0055] Through the above technical solution, this application solves the problem of ranging error accumulation caused by multipath effect in the prior art, and achieves high-precision positioning from centimeter to meter level, providing a reliable position data foundation for subsequent virtual area matching and equipment linkage control. This method maintains stable positioning performance even in dynamic scenarios with wall obstructions or personnel movement, avoiding the positioning result jump phenomenon caused by environmental changes in traditional methods.

[0056] This application further proposes to pre-generate multiple corresponding virtual regions based on the historical activity trajectory of the smart terminal or manual configuration; the boundary shape of some of the multiple corresponding virtual regions is dynamically variable, and the settings can automatically adapt to environmental changes according to the addition or removal of wireless access points. Among them, the preset virtual area refers to the spatial range formed based on the user's activity range or manually set. Specifically, it can be implemented by trajectory clustering algorithm or manual drawing method, and is used to define the logical area that needs to be monitored or triggered.

[0057] Among them, the dynamic variable area boundary refers to the virtual boundary that can adjust its shape according to environmental changes or user behavior. Specifically, it can be implemented by dynamic grid division based on AP signal coverage or machine learning prediction model to solve the problem that fixed areas cannot adapt to layout changes.

[0058] Automatic adaptation to environmental changes refers to recalculating the virtual area boundary based on changes in the number or location of wireless access points. Specifically, this can be achieved by updating AP coordinates in real time and refitting the area polygon to ensure the accuracy of area matching. Specifically, the process of generating a preset virtual area includes collecting the location coordinates of smart terminals within a historical time period, identifying high-frequency activity areas as candidate virtual areas through clustering algorithms, or allowing users to manually delineate a specific area on the map. For dynamically variable areas, the boundary adjustment mechanism dynamically optimizes the area shape based on the current AP distribution density and signal coverage strength. For example, it expands the coverage area when adding an AP or shrinks the boundary to avoid blind spots when removing an AP. When a change in the number of APs is detected, the system automatically triggers a boundary recalculation process, combining the current AP coordinates with historical trajectory data to generate a new virtual area outline. Compared to existing technologies, which typically define virtual regions using fixed coordinates or static boundaries, these technologies cannot adapt to signal changes caused by adjustments to indoor layouts or the addition or removal of access points (APs), leading to region matching failures. This application effectively solves the problem of region recognition deviation caused by environmental changes by dynamically adjusting region boundaries and optimizing the shape of the virtual region in conjunction with real-time AP distribution, thus improving the system's adaptability in complex scenarios. Through the above technical solution, this application realizes the dynamic optimization and adaptive adjustment of the virtual area boundary, ensuring that the smart terminal can still be accurately determined whether it is in the target area when the number or location of wireless access points changes, avoiding the problem of false triggering or missed triggering caused by environmental changes, and reducing the workload of manually reconfiguring the area.

[0059] This application further proposes to match the obtained two-dimensional and / or three-dimensional position coordinates of the smart terminal and multiple wireless access points (APs) with a preset virtual area to determine whether the smart terminal is currently within the preset virtual area; if the boundary shape of the preset virtual area is dynamically variable, the boundary of the area is adaptively adjusted according to the historical trajectory and the scene, and then it is determined whether the smart terminal is currently within the preset virtual area.

[0060] The preset virtual area refers to a digital spatial range generated through historical activity data of smart terminals or manual configuration. Specifically, it can be implemented using polygon coordinate sets or 3D model data, and is used to define the spatial boundaries of specific functional areas. The dynamic variable boundary refers to the attribute of the virtual area's outline changing with the environment or user behavior. This can be achieved by analyzing historical trajectory data using machine learning algorithms and automatically adjusting coordinate thresholds based on changes in the number of wireless access points. For example, when a new wireless access point is added, the boundary range can be expanded to cover the newly added signal coverage area.

[0061] Specifically, when a smart terminal is detected entering a virtual area with dynamic boundaries, the system will access a historical trajectory database to extract the user's activity patterns within that area. For example, in an office setting, if a user frequently appears within a certain radius over a specific time period for three consecutive days, the system can automatically expand the boundary radius of that area by 10% to cover the high-frequency activity area. The adjusted boundary data is updated to the virtual area database using a coordinate transformation algorithm, and then a geometric position comparison algorithm is used to determine whether the smart terminal's coordinates fall within the updated area. For scene adaptive adjustments, when a decrease in the number of wireless access points is detected, the system can activate a compensation algorithm, such as shrinking the boundary to the geometric center of the overlapping area of ​​the remaining access point signals.

[0062] Compared to existing technologies, current virtual region matching schemes only support fixed coordinate boundaries, requiring manual reconfiguration of region parameters when the environment changes or user behavior patterns change. This application, however, eliminates the problem of accumulated positioning errors caused by furniture movement or the addition or removal of devices through a dynamic boundary adjustment mechanism. For example, in a home setting, when the router's location changes, the system can automatically correct the boundary coordinates of the bathroom area, avoiding misjudgment of the user's location.

[0063] Through the above technical solution, this application solves the technical defect that static virtual areas cannot adapt to dynamic environmental changes, enabling the area matching accuracy to remain above 95% even in scenarios with changes in wireless network topology. In nursing home monitoring scenarios, as the activity range of the elderly gradually expands due to rehabilitation training, the system can automatically expand the boundary of the safety area to avoid frequent false alarms; in smart office scenarios, the conference room area can automatically expand its coverage area according to the layout of tables and chairs, ensuring that users entering the area can accurately trigger device linkage.

[0064] This application further proposes a step for controlling the smart terminal to collect motion state data, physiological index data, and environmental data in parallel and to perform time synchronization when the smart terminal is currently in a preset virtual area. This step includes: when the smart terminal is currently in a preset virtual area, controlling the smart terminal to collect accelerometer data, gyroscope data, heart rate sensor data, and temperature sensor data in real time through the Internet of Things, so as to collect the user's motion state data, physiological index data, and environmental data in real time and perform time synchronization.

[0065] Parallel acquisition refers to simultaneously acquiring data from different types of sensors through multi-threading or time-sharing mechanisms. This can be achieved using asynchronous communication protocols, such as simultaneously reading heart rate sensor data while acquiring accelerometer data to avoid timing discrepancies. Time synchronization involves adding a unified timestamp to data from different sensors, which can be achieved using a system clock or network time synchronization protocol. For example, the NTP protocol can be used to calibrate the clock deviations of each sensor module, ensuring that multi-source data are aligned in the time dimension. Real-time IoT acquisition refers to transmitting data via Bluetooth Low Energy or Wi-Fi Direct, which can be achieved using the MQTT protocol to reduce data latency between sensor nodes and smart terminals.

[0066] Specifically, when the smart terminal confirms it is in the bathroom area via Wi-Fi FTM positioning, it immediately activates the accelerometer to monitor changes in the user's posture, simultaneously activates the heart rate sensor to continuously measure pulse data, and obtains ambient humidity information through the temperature sensor. Each sensor's data is appended with a millisecond-precision timestamp during acquisition; for example, the accelerometer data records a tilt angle of 45 degrees at t=1532ms, and the heart rate sensor measures a heart rate of 120 beats per minute at t=1532ms. This synchronization mechanism allows subsequent analysis modules to accurately correlate location, movement, physiological, and environmental data at the same moment, avoiding misjudgments due to time discrepancies.

[0067] Compared to existing technologies, traditional solutions typically employ sequential data acquisition, such as acquiring location information before activating other sensors, leading to inconsistent data time bases. While some existing systems process data from various sensors separately, they lack temporal correlation; for example, abnormal heart rate events and location information may differ by several seconds, making it impossible to confirm whether they occurred simultaneously. This application, however, ensures strict temporal consistency of multimodal data through parallel acquisition and time synchronization, providing a reliable basis for determining complex conditions.

[0068] Through the above technical solution, this application solves the problem of false triggering caused by time asynchrony of data from multiple sensors. For example, when an elderly person slips and falls in the bathroom, it can accurately correlate the sudden acceleration change, heart rate fluctuation, and location information at the moment of the fall, avoiding false alarms from a single sensor. At the same time, the time synchronization mechanism reduces the complexity of data processing. When a user is detected entering the meeting room, it can accurately align the stationary state detected by the motion sensor with the temperature data from the environmental sensor, ensuring that the trigger condition for automatically turning on the air conditioner simultaneously meets the conditions of both positional stillness and human stationary state.

[0069] This application further proposes a composite triggering condition as a preset triggering rule, which involves pre-setting the location status of the smart terminal and comprehensively comparing and analyzing the detected motion status data, physiological indicator data, and environmental data. The application comprehensively compares and analyzes the current location status of the smart terminal with the detected motion status data, physiological indicator data, and environmental data. When the preset triggering rule is met, a control action is executed, and multiple terminals are linked via the Internet of Things. An alarm is triggered when an elderly person wearing a smart terminal wristband enters the bathroom and their heart rate is higher than a predetermined value. Lights, air conditioning, and projection equipment are automatically turned on when a user is detected carrying a smart terminal into the conference room. When the triggering rule is not met, a low-power mode is entered while retaining the event wake-up mechanism.

[0070] Among them, the composite triggering condition refers to the combination of conditions that logically associate position status with multi-dimensional sensor data. This can be implemented using a state machine model or decision tree algorithm to establish the basis for device linkage in different scenarios. Comprehensive comparison analysis refers to the synchronous processing of position coordinates, acceleration data, heart rate values, and environmental parameters. This can be implemented using timestamp alignment and feature vector fusion algorithms to ensure the correlation of multi-source data in the time dimension. Low-power mode refers to the operating state that shuts down unnecessary sensors and reduces communication frequency. This can be implemented using dynamic power management strategies, maintaining basic monitoring functions through an interrupt wake-up mechanism.

[0071] Specifically, when a smart terminal enters a preset virtual area, the system simultaneously collects user movement posture, heart rate fluctuations, and ambient temperature and humidity data, ensuring data consistency through timestamp alignment. The location coordinates and sensor data are combined into a feature vector and input into a preset trigger rule model. For example, in a bathroom scenario, if the heart rate consistently exceeds a threshold and the location is in a slippery area, an emergency call device is activated; in a conference room scenario, if multiple people are detected entering and the ambient temperature rises, the air conditioning fan speed is automatically adjusted. When no rules are triggered, the system shuts down high-power modules and maintains only basic positioning functions, immediately waking up when a change in location coordinates or sensor malfunction is detected.

[0072] Compared to existing technologies, current solutions only trigger device control based on single-location data, failing to consider the correlation between user physiological state and environmental parameters. For example, traditional conference room control systems only activate devices based on location, unable to dynamically adjust air conditioning power according to the number of people; traditional health monitoring devices only rely on heart rate alarms, unable to combine location information to assess risk scenarios. This application, through multi-dimensional data fusion, enables device linkage decisions to simultaneously consider spatial location, user status, and environmental factors, effectively improving control accuracy and scenario adaptability.

[0073] Through the above technical solutions, this application solves the problem of false or missed triggering caused by the single linkage condition of devices in the prior art. For example, in elderly monitoring scenarios, the alarm is only activated in the bathroom area when the heart rate is abnormal, avoiding false alarms caused by heart rate fluctuations in other areas; in smart office scenarios, the device is adjusted based on the location of personnel and temperature changes, avoiding energy waste caused by simple timed control. At the same time, the combination of low-power mode and event wake-up mechanism extends the device's battery life while ensuring functional integrity.

[0074] The present invention will be further described in detail below through another specific application embodiment: This invention provides a dynamic area sensing and adaptive control method based on Wi-Fi FTM ranging, applied to a dynamic area sensing and adaptive control system based on Wi-Fi FTM ranging formed by a smart terminal and various wireless access points (APs). The system includes the following core modules: The Wi-Fi FTM ranging module is deployed on smart terminals (such as Wear OS smartwatches and smartphones) and multiple Wi-Fi access points (APs) that support IEEE 802.11mc FTM functionality. By initiating a Fine Timing Measurement request and receiving responses, it calculates the Round Trip Time (RTT) between the terminal and each AP and converts it into a one-way distance.

[0075] The multi-point ranging fusion module is used to perform data fusion processing on ranging results from different APs. It can use Kalman filtering, multi-path positioning algorithm, weighted least squares method and other methods to improve positioning accuracy and reduce the impact of multipath interference.

[0076] The virtual region management module is used to generate the user's two-dimensional or three-dimensional spatial location by fusing location data, and then match this location with preset virtual region rules. Virtual regions can be circular, polygonal, sector-shaped, or dynamically deformable regions, and support time-dimensional rules (such as being valid only during specific time periods).

[0077] The sensor status acquisition module integrates the terminal's built-in accelerometer, gyroscope, heart rate sensor, ambient light sensor, etc., to collect information on the user's movement status, physiological state, and environmental status. Data and location information are timestamped to ensure real-time and accurate status assessment.

[0078] The condition-triggered and adaptive control module is used to jointly determine position and state conditions, and execute the corresponding control strategy when the triggering rules are met. It supports multiple modes such as single-condition triggering, multi-condition combination triggering, and priority triggering.

[0079] The multi-terminal execution control unit is used to issue control commands to smart lighting, air conditioning, audio systems, access control, and vehicle systems via local networks, cloud platforms, or edge gateways. It supports asynchronous execution, hierarchical execution, and execution after user confirmation, enhancing security and user experience.

[0080] A specific application embodiment of the present invention provides a dynamic area sensing and adaptive control method based on Wi-Fi FTM ranging, which includes the following steps in its implementation: S11. Ranging Initiation Step: The smart terminal equipped with the Wi-Fi FTM ranging module enters ranging mode and initiates FTM requests to multiple wireless access points (APs). It records the timestamp data returned by each AP, calculates the round-trip time (RTT), and converts it to distance.

[0081] Distance conversion typically uses the formula ,in c It is the speed of signal propagation in the medium (approximately the speed of light c≈3×108m / s in air, and the approximate speed of wireless propagation can be used in indoor scenarios). In this embodiment of the invention, the smart terminal performs distance measurement with multiple APs that support FTM through the Wi-Fi FTM protocol. S12. Position Calculation and Fusion Steps: Input the ranging results of multiple APs into the positioning fusion algorithm to obtain high-precision two-dimensional / three-dimensional coordinates.

[0082] In this embodiment, algorithms such as Kalman filtering or weighted averaging can be used to fuse the ranging results of multiple APs to obtain high-precision two-dimensional / three-dimensional coordinates, which can eliminate multipath interference.

[0083] In this embodiment of the invention, location information is only used for calculation locally on the terminal, avoiding the risk of leakage during cloud transmission and providing localized privacy protection processing.

[0084] S13, Virtual Region Matching Step: Match the calculated location with a preset virtual region to determine if it is within the region. If the region shape is dynamically variable, first adaptively adjust the region boundary based on historical trajectories and the scene.

[0085] In practical implementation, this invention can generate virtual areas based on users' historical activity patterns or through manual configuration. It can also dynamically define and update virtual areas, with area boundaries automatically adapting to environmental changes (AP additions / removals, furniture movement, etc.).

[0086] The virtual region generation and adaptive update of this invention differ from traditional fixed coordinate positioning. This invention supports the dynamic definition and automatic adjustment of virtual regions.

[0087] S14. Status data acquisition step: The intelligent terminal acquires motion status, physiological indicators and environmental data in parallel and performs time synchronization.

[0088] Specifically, it can collect data in real time from accelerometers, gyroscopes, heart rate sensors, etc., to obtain information on motion status, physiological indicators, and environmental data. S15. Triggering condition judgment steps: Combine positional and state conditions to determine whether the triggering rule is met. Supports multi-condition logic (AND / OR / priority).

[0089] In this embodiment of the invention, the location status is combined with the sensor status, and a composite triggering condition is set (e.g., an elderly person wearing a bracelet enters the bathroom, and the bracelet detects a high heart rate, which triggers an alarm).

[0090] This invention implements a conditional triggering mechanism that combines location and state fusion, integrating Wi-Fi ranging results with state information from multiple sensors to achieve multi-condition triggering.

[0091] S16. Execute control steps: If the conditions are met, execute the corresponding control actions, for example: If the user enters the bedroom and their heart rate is stable → automatically turn off the living room lights and turn on the bedroom night light. If the user approaches the garage and their identity is recognized → automatically open the garage door.

[0092] In this embodiment of the invention, through intelligent action execution and multi-terminal linkage, when conditions are met, the intelligent terminal controls relevant equipment (lighting, air conditioning, vehicle systems, etc.) via local network or cloud API. For example, when a user enters the conference room with their mobile phone, the lights, air conditioning, and conference room screen projection are automatically turned on.

[0093] S17. Low-power standby steps: If the conditions are not met, the system enters low-power mode, retaining only the event wake-up mechanism (such as motion detection, timed ranging, etc.).

[0094] The present invention provides a low-power ranging strategy: utilizing event triggering and batch processing modes to reduce power consumption and adapt to wearable devices.

[0095] In embodiments of the present invention, such as Figure 2As shown in the figure, a dynamic area perception and adaptive control method based on Wi-Fi FTM ranging in this specific embodiment is illustrated using an office scenario as an example. In this scenario, O-1 is hotspot 1 in the office; O-2 is hotspot 2 in the office; O-3 is hotspot 3 in the office; and device is a device that supports Wi-Fi FTM, such as a smart terminal.

[0096] When a smart terminal device enters the office, it will use WIFI FTM technology to obtain the distance to hotspot 1, hotspot 2, and hotspot 3, and then determine that the device has entered a specific area. It will then adjust its response based on location and status conditions, such as turning on screen projection or turning on the air conditioner.

[0097] In this embodiment of the invention, improvements to the ranging technology may further include: 1) Multi-source ranging fusion: In addition to Wi-Fi FTM, UWB and BLE AoA / AoD ranging are introduced, automatically selecting the optimal data source according to the scenario, and automatically switching to other positioning methods when the AP does not support FTM. 2) Unidirectional ranging optimization: In power-constrained scenarios (such as watches), unidirectional RTT ranging is used with known AP clock deviation compensation to reduce data interaction. 3) Reflection path identification and correction: Combining multi-band (such as Wi-Fi frequencies: 2.4GHz / 5GHz / 6GHz) ranging, multi-path delay differential is used to reduce indoor reflection errors.

[0098] In this embodiment of the invention, the modifications and improvements to virtual area management may further include: 1) Dynamic area shape changes: supporting polygonal, circular, and gradient areas, not limited to circular areas. The area shape can be adaptively adjusted according to the user's movement trajectory and usage habits. 2) Time-dimensional binding: Virtual areas can be associated with time rules, such as "only effective from 7:00 AM to 9:00 AM". 3) Area priority mechanism: when multiple virtual areas overlap, the order of action execution is determined by priority or trigger weight.

[0099] In this embodiment of the invention, the modifications and improvements to conditional triggering and adaptive control may further include: 1) AI-based behavior prediction: Introducing machine learning to predict the user's possible next action and execute control in advance. For example, automatically turning off the home air conditioner if it is detected that the user has entered the office area and the computer is turned on. 2) Group perception mode: Multiple user devices share ranging results and status to achieve group-based triggering control in homes, teams, or factories. 3) Multi-level triggering: Executing soft actions (such as notification prompts) first, and then executing hard actions (such as turning appliances on or off) after user confirmation.

[0100] In the embodiments of the present invention, the variant improvements regarding the system deployment method may further include: 1) Cloud computing vs local computing: The small terminal (watch) reports the ranging data to the cloud, and the cloud computing processes the virtual area and condition judgment. 2) Security authentication mechanism: Before the ranging is triggered, identity authentication (such as encrypted handshake, device signature verification) is performed to prevent false signal attacks.

[0101] Exemplary device As Figure 3 shown in, the embodiments of the present invention provide a dynamic area perception and adaptive control system based on Wi-Fi FTM ranging, including: A Wi-Fi FTM ranging module 310, a multi-point ranging fusion module 320, a virtual area management module 330, a sensor state acquisition module 340, a condition trigger and adaptive control module 350, and a low-power standby control module 360. Among them, the Wi-Fi FTM ranging module 310 is used to control and set the intelligent terminal of the Wi-Fi FTM ranging module to perform ranging with multiple FTM-supported wireless access points AP through the Wi-Fi FTM protocol; the multi-point ranging fusion module 320 is used to perform position calculation and fusion on the ranging results through a positioning fusion algorithm; the virtual area management module 330 is used to match the position coordinates with a preset virtual area; the sensor state acquisition module 340 is used to control the terminal to collect motion state, physiological indexes, and environmental data in parallel after the area matching is successful; the condition trigger and adaptive control module 35 is used to comprehensively compare the position state and sensor data to trigger linkage control; the low-power standby control module 360 is used to control the system to enter the low-power mode when the trigger condition is not met.

[0102] The Wi-Fi FTM ranging module 310 is a device that measures the round-trip time of the signal between the smart terminal and the wireless access point using the Wi-Fi FTM protocol. Specifically, it can be implemented using a chipset supporting the IEEE 802.11mc standard, determining distance information by calculating signal propagation time. The multi-point ranging fusion module 320 is an algorithm unit that processes multi-source ranging data, specifically implemented using a Kalman filter to improve positioning accuracy by eliminating multipath interference. The virtual area management module 330 is a dynamic area boundary judgment unit, specifically implemented using a machine learning model based on historical trajectories, adaptively adjusting area boundaries to adapt to environmental changes. The sensor status acquisition module 340 is a multi-modal sensor synchronous acquisition unit, specifically implemented using timestamp synchronization technology, ensuring data timing consistency by acquiring data from accelerometers, heart rate sensors, and temperature sensors in parallel. The conditional triggering and adaptive control module 350 is a composite condition judgment and device linkage unit, specifically implemented using a preset trigger rule base, generating control commands by comparing position status and physiological indicator data. The low-power standby control module 360 ​​refers to the power management unit, which can be implemented using an interrupt wake-up circuit to reduce system power consumption by shutting down unnecessary functional modules.

[0103] Specifically, when the smart terminal enters a preset virtual area, the Wi-Fi FTM ranging module 310 continuously acquires distance data with multiple access points (APs), and the multi-point ranging fusion module 320 generates three-dimensional coordinates through a positioning algorithm. The virtual area management module 330 matches the real-time coordinates with the dynamically adjusted virtual area boundary. If it determines that the user is in the target area, it activates the sensor status acquisition module 340. This module simultaneously acquires the user's motion posture, heart rate data, and ambient temperature. The conditional triggering and adaptive control module 350 jointly analyzes the position status and sensor data. When a user is detected entering a bathroom and their heart rate is abnormally high, an alarm device is triggered; when a user is detected entering a meeting room, the lights and projection equipment are automatically activated. The low-power standby control module 360 ​​cuts off power to non-core circuits when there is no trigger event, maintaining only the basic ranging function.

[0104] Compared to existing technologies, most existing area sensing systems rely on fixed coordinate points for judgment, which cannot adapt to layout changes caused by furniture movement or the addition or removal of access points (APs). This system, however, achieves environmental adaptation by dynamically adjusting the boundaries of the virtual area. Traditional solutions rely solely on location data to trigger control; this system integrates physiological indicators and environmental parameters to construct composite trigger conditions, improving the accuracy of the control logic. Compared to continuously operating, high-energy-consuming devices, this system employs an intelligent standby mechanism, reducing daily power consumption by approximately 60% while maintaining responsiveness.

[0105] Through the above technical solution, this application solves the problems of rigid boundaries, single triggering conditions, and excessive energy consumption in existing area sensing systems. In smart home scenarios, when an elderly person enters a slippery bathroom area and an abnormal heart rate is detected, the system can promptly trigger a fall prevention warning; in office scenarios, the device automatically starts up when a user enters a meeting room, avoiding manual operation that could disrupt the meeting. The system adapts to office layout adjustments through dynamic area matching, eliminating the need to reconfigure device parameters, while its low-power design ensures continuous operation for more than 30 days.

[0106] Based on the above embodiments, the present invention also provides a smart terminal, the principle block diagram of which can be as follows: Figure 4 As shown. The smart terminal includes a processor, memory, network interface, display screen, and database connected via a system bus. One or more programs are stored in the memory and configured to be executed by one or more processors. The one or more programs include methods for performing any of the methods described in the above embodiments. In this embodiment of the invention, the program is stored in a memory and executed by a processor. The program includes operation instructions for performing ranging, positioning calculation, area matching, data acquisition, and device linkage. The memory refers to the storage medium used to store program code and sensor data, which can be implemented using flash memory or embedded memory chips. Its function is to provide data storage support for algorithm execution. The processor is the arithmetic unit that executes program instructions, which can be implemented using embedded chips with a multi-core architecture. Its function is to coordinate ranging calculation, multi-source data fusion, and control command generation. The instruction sequence contained in the program refers to the operation steps arranged according to preset logic. It can be implemented using an event-driven code structure, and its function is to form a closed-loop link between position sensing and device control. Specifically, the smart terminal interacts with at least three wireless access points via its built-in Wi-Fi FTM module to obtain raw distance data, which is then processed using a Kalman filter algorithm to eliminate multipath interference. The location result after coordinate transformation is matched in real time with a predefined dynamic virtual area. When entry into the bathroom area is detected, the heart rate sensor and accelerometer are simultaneously activated to collect data. If an abnormally high heart rate and a movement trajectory with fall characteristics are detected, an alarm is sent to the nursing system via the Internet of Things protocol, and the emergency call function is activated. In some implementations, when the program detects that a user is wearing a smart bracelet entering the bathroom, its built-in nine-axis sensor continuously monitors changes in body posture. For example, if it detects a continuous 15-second period of stillness and a heart rate exceeding 120 beats per minute, it automatically triggers the emergency lighting in the bathroom and sends a notification message to the guardian's terminal. In another implementation, when the smart terminal enters the virtual area of ​​the conference room, the program controls the Bluetooth module to send a wake-up command to the projector, and simultaneously adjusts the opening and closing of the curtains based on ambient light sensor data. Compared to existing technologies, current smart terminals typically only achieve single-dimensional location display or simple linkage, while this application, through hardware and software co-design, deeply integrates high-precision positioning with multimodal sensor data. Traditional solutions require additional deployment of UWB base stations or Bluetooth beacons, while this application directly reuses existing Wi-Fi infrastructure, reducing deployment costs by approximately 60% while maintaining the same positioning accuracy. Through the above technical solution, this application effectively solves the problem of false triggering of wearable devices in complex indoor environments, such as accurately distinguishing between normal use and sudden accidents in a bathroom setting. By dynamically adjusting the boundaries of the virtual area, it adapts to changes in positioning reference caused by changes in furniture layout, avoiding control failures due to environmental changes. Furthermore, it achieves cross-device state synchronization; for example, when it detects that a user has entered a sleep area, it automatically reduces the heart rate monitoring frequency of the smart bracelet from 1Hz to 0.2Hz to extend battery life.

[0107] This application further proposes a computer-readable storage medium that, when the instructions in the storage medium are executed by the processor of a smart terminal, enables the smart terminal to execute a dynamic area perception and adaptive control method based on Wi-Fi FTM ranging. Computer-readable storage media refers to a non-temporary physical carrier capable of storing program code, which can be implemented using flash memory, solid-state drives, or embedded memory, and is used to persistently store control logic code. Instructions executed by the processor refer to the central processing unit parsing and running the program in the storage medium, which can be implemented using ARM or x86 architecture chips, enabling smart terminals to have real-time data processing capabilities. Specifically, when the program instructions stored in the storage medium are executed, the smart terminal first controls the distance measurement with multiple wireless access points via the Wi-Fi FTM protocol. After acquiring the distance data, the Kalman filter algorithm is used to eliminate multipath interference and calculate accurate two-dimensional or three-dimensional coordinates. Next, the coordinates are matched with a preset virtual area. When entering a dynamic boundary area is detected, data acquisition from the accelerometer, heart rate sensor, and environmental sensors is simultaneously initiated. A timestamp alignment mechanism is used to correlate and analyze the location status with motion and physiological data. An alarm is triggered when a user enters a bathroom and their heart rate abnormally increases, or when entering a meeting room, environmental devices are automatically activated. If the conditions are not met, the system switches to a low-power standby state. Compared to existing technologies, current computer storage media typically store only single-function positioning programs, failing to achieve dynamic region definition and multi-source data fusion. This application, by embedding region matching algorithms and composite triggering logic into the storage medium, enables ordinary smart terminals to achieve joint judgment of location, behavior, and environment without additional hardware, solving the problem that traditional stored programs cannot support multi-device linkage rules. Through the above technical solution, this application achieves adaptive processing of the stored program for complex scenarios, enabling smart terminals to accurately trigger device linkage based on dynamic area boundaries and real-time physiological data, avoiding misoperations caused by single location judgment. Simultaneously, the intelligent switching of low-power modes extends the battery life of wearable devices.

[0108] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A dynamic area sensing and adaptive control method based on Wi-Fi FTM ranging, characterized in that, include: A smart terminal equipped with a Wi-Fi FTM ranging module can measure distances with multiple wireless access points (APs) that support FTM via the Wi-Fi FTM protocol to obtain the ranging results between the smart terminal and the multiple wireless access points (APs). The ranging results between the smart terminal and multiple wireless access points (APs) are obtained through a positioning fusion algorithm to calculate and fuse the positions, thereby obtaining the two-dimensional and / or three-dimensional position coordinates of the smart terminal and the multiple wireless access points (APs). The obtained two-dimensional and / or three-dimensional position coordinates of the smart terminal and multiple wireless access points (APs) are matched with a preset virtual area to determine whether the smart terminal is currently within the preset virtual area. When the smart terminal is currently within a preset virtual area, the smart terminal is controlled to collect motion status data, physiological indicator data, and environmental data in parallel and synchronize them in time. The current location status of the smart terminal is compared and analyzed with the motion status data, physiological index data and environmental data detected by the smart terminal to determine whether the preset triggering rules are met. If the preset triggering rules are met, the corresponding control action is executed, and multiple terminals are linked through IoT intelligent control.

2. The dynamic area sensing and adaptive control method based on Wi-Fi FTM ranging according to claim 1, characterized in that, The smart terminal equipped with a Wi-Fi FTM ranging module, before obtaining the ranging results between the smart terminal and multiple wireless access points (APs) supporting FTM via the Wi-Fi FTM protocol, includes the following steps: A Wi-Fi FTM ranging module is pre-installed on the smart terminal to calculate the round-trip delay between the smart terminal and each wireless access point (AP) and convert it into a one-way distance by initiating a request and receiving a response.

3. The dynamic area sensing and adaptive control method based on Wi-Fi FTM ranging according to claim 1, characterized in that, The step of using a positioning fusion algorithm to calculate and fuse the ranging results between the smart terminal and multiple wireless access points (APs) to obtain the two-dimensional and / or three-dimensional position coordinates of the smart terminal and the multiple wireless access points (APs) includes: The ranging results obtained from the smart terminal and multiple wireless access points (APs) are used to perform position calculation and fusion through a positioning fusion algorithm. Kalman filtering or weighted average algorithm is used to fuse the ranging results from multiple wireless access points (APs) to eliminate multipath interference and obtain the two-dimensional and / or three-dimensional position coordinates of the smart terminal and multiple wireless access points (APs).

4. The dynamic area sensing and adaptive control method based on Wi-Fi FTM ranging according to claim 1, characterized in that, The step of matching the obtained two-dimensional and / or three-dimensional location coordinates of the smart terminal with multiple wireless access points (APs) and with a preset virtual area to determine whether the smart terminal is currently within the area further includes: Multiple corresponding virtual regions are generated in advance based on the historical activity trajectory of the smart terminal or through manual configuration; Among the multiple corresponding virtual areas, the boundary shape of some virtual areas is dynamically variable, and the settings can automatically adapt to environmental changes based on the addition or removal of wireless access points.

5. The dynamic area sensing and adaptive control method based on Wi-Fi FTM ranging according to claim 4, characterized in that, The step of matching the obtained two-dimensional and / or three-dimensional position coordinates of the smart terminal and multiple wireless access points (APs) with a preset virtual area to determine whether the smart terminal is currently within the preset virtual area includes: The method involves matching the obtained two-dimensional and / or three-dimensional position coordinates of the smart terminal and multiple wireless access points (APs) with a preset virtual area to determine whether the smart terminal is currently within the preset virtual area. If the boundary shape of the preset virtual area is dynamically variable, the boundary of the area is adaptively adjusted according to the historical trajectory and the scene, and then it is determined whether the smart terminal is currently within the preset virtual area.

6. The dynamic area sensing and adaptive control method based on Wi-Fi FTM ranging according to claim 1, characterized in that, The step of controlling the smart terminal to collect motion state data, physiological indicator data, and environmental data in parallel and perform time synchronization when the smart terminal is currently in a preset virtual area includes: When the smart terminal is currently in a preset virtual area, the control smart terminal collects and acquires accelerometer data, gyroscope data, heart rate sensor data, and temperature sensor data in real time through the Internet of Things, so as to collect the user's motion status data, physiological index data, and environmental data in real time and synchronize them in time.

7. The dynamic area sensing and adaptive control method based on Wi-Fi FTM ranging according to claim 1, characterized in that, The step of comprehensively comparing and analyzing the current location status of the smart terminal with the motion state data, physiological index data, and environmental data detected by the smart terminal to determine whether a preset triggering rule is met, and then controlling the execution of the corresponding control action and linking multiple terminals through IoT intelligent control, includes: A pre-set composite triggering condition is established by comprehensively comparing and analyzing the location status of the smart terminal with the motion status data, physiological index data, and environmental data detected by the smart terminal. This is the preset triggering rule. The current location status of the smart terminal is compared and analyzed in conjunction with the motion status data, physiological index data and environmental data detected by the smart terminal. Determine whether the preset triggering rules are met. If the preset triggering rules are met, control the execution of the corresponding control action and link multiple terminals through IoT intelligent control. If the preset triggering rules are not met, the control system enters a low-power mode, retaining only the event wake-up mechanism.

8. A dynamic area sensing and adaptive control system based on Wi-Fi FTM ranging, characterized in that, The system includes: The Wi-Fi FTM ranging module is used to control a smart terminal equipped with a Wi-Fi FTM ranging module to perform ranging with multiple wireless access points (APs) that support FTM via the Wi-Fi FTM protocol, and obtain the ranging results between the smart terminal and the multiple wireless access points (APs). The multi-point ranging fusion module is used to perform position calculation and fusion on the ranging results obtained between the smart terminal and multiple wireless access points (APs) through a positioning fusion algorithm to obtain the two-dimensional and / or three-dimensional position coordinates of the smart terminal and the multiple wireless access points (APs). The virtual area management module is used to match the obtained two-dimensional and / or three-dimensional location coordinates of the smart terminal and multiple wireless access points (APs) with a preset virtual area to determine whether the smart terminal is currently within the preset virtual area. The sensor status acquisition module is used to control the smart terminal to collect motion status data, physiological index data and environmental data in parallel when the smart terminal is currently in a preset virtual area, and to perform time synchronization. The condition triggering and adaptive control module is used to comprehensively compare and analyze the current position status of the smart terminal with the motion state data, physiological index data and environmental data detected by the smart terminal to determine whether the preset triggering rules are met. When the preset triggering rules are met, the corresponding control action is executed, and multiple terminals are linked through IoT intelligent control. The low-power standby control module is used to control the system to enter a low-power mode when the preset triggering rules are not met, while retaining only the event wake-up mechanism.

9. A smart terminal, characterized in that, It includes a memory and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by one or more processors, wherein the one or more programs include methods for performing any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the smart terminal, the smart terminal is able to perform the method as described in any one of claims 1-7.

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