A light linkage shopping guide method and system

Abstract

The present application relates to the technical field of lighting control, in particular to a light linkage shopping guide method and system, comprising: receiving a shopping request of a user terminal and determining a target shelf position; based on the target shelf position, real-time position and motion state of the user terminal, using an anti-occlusion indoor navigation algorithm supported by a Bluetooth beacon network integrated by intelligent lamps, generating a navigation path to the target shelf; mapping the navigation path to a lamp topology network, generating dynamic light guide instructions, controlling the intelligent lamps along the way and at the target shelf to light up in a preset mode, forming a light flow guide pointing to the target; when the user approaches the target, controlling the associated lighting device to switch to a light interaction mode highlighting the goods, and sending interaction information to the terminal. The present application solves the technical problems of disconnection between light guidance and real-time navigation of the user, poor positioning stability and insufficient near-field interaction experience in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of lighting control, and particularly to a lighting linkage shopping guide method and system. Background Art

[0002] With the development of intelligent lighting and Internet of Things technologies, retail venues have begun to attempt to use lighting for product guidance. Existing solutions mostly adopt preset scenarios or simple linkage control, and generally have the following problems: First, the lighting guidance is disconnected from the user's real-time position and travel path, and cannot provide dynamic and continuous guidance; Second, in a complex indoor environment, the positioning signal is easily blocked and interfered, resulting in inaccurate starting positions for guidance; Third, the guidance method is single, and it fails to provide progressive lighting interaction according to the proximity of the user to the product, and the shopping guide experience is not coherent. Therefore, there is an urgent need for a lighting linkage shopping guide method and system that can integrate real-time precise positioning and adaptive lighting control to achieve lighting linkage shopping guidance from path guidance to near-field interaction. Summary of the Invention

[0003] The main object of the present invention is to propose a lighting linkage shopping guide method and system, aiming to solve the technical problems in the prior art that the lighting guidance is disconnected from the user's real-time navigation, the positioning stability is poor, and the near-field interaction experience is insufficient.

[0004] To achieve the above object, in the first aspect of the present invention, a lighting linkage shopping guide method is proposed, including:

[0005] Step S100: Receive a shopping guide request containing target product information sent by a user terminal, and determine the corresponding target shelf position based on the target product information;

[0006] Step S200: Generate a navigation path from the real-time position to the target shelf position based on the target shelf position, the real-time position and the motion state of the user terminal according to a preset anti-occlusion indoor navigation algorithm; wherein, the anti-occlusion indoor navigation algorithm uses a Bluetooth beacon network integrated by intelligent lamps for positioning;

[0007] Step S300: Map the navigation path to the lamp topology network, generate a dynamic lighting guidance instruction, and control the intelligent lamps along the navigation path and at the target shelf position to be lit according to a preset guidance mode to form an optical flow guidance pointing to the target shelf position;

[0008] Step S400: In response to the distance between the real-time position of the user terminal and the target shelf position being less than a preset distance threshold, control the intelligent lighting device associated with the target product to switch to a lighting interaction mode for highlighting the target product, and send interaction information associated with the target product to the user terminal.

[0009] Preferably, step S200 includes:

[0010] Step S210: Collect inertial data from the user terminal's inertial measurement unit and radio frequency signal data broadcast by the Bluetooth beacon network integrated by the smart lighting fixture, and synchronize and preprocess the collected data to obtain time-synchronized fused data.

[0011] Step S220: Based on the inertial data in the fused data, identify the current motion state of the user terminal; combine the current motion state with the radio frequency signal data in the fused data, and calculate the adaptive ranging result according to the preset adaptive ranging model;

[0012] Step S230: Based on the radio frequency signal data and the current motion state, perform occlusion determination, and generate the corresponding fusion weight strategy of inertial data and radio frequency signal data in positioning according to the determination result;

[0013] Step S240: Based on the adaptive ranging results, fusion weight strategy, current motion state and fusion data, perform multi-feature fusion positioning calculation to solve the real-time position of the user terminal;

[0014] Step S250: Based on the calculated real-time location, current motion status, and target shelf location, and combined with indoor electronic map information, generate a navigation path.

[0015] Preferably, step S210 includes:

[0016] Step S211: Inertial data is collected at a first preset frequency through the inertial measurement unit of the user terminal; the inertial data includes acceleration, angular velocity and heading angle data;

[0017] Step S212: Collect radio frequency signal data at a second preset frequency through the Bluetooth module of the user terminal; the radio frequency signal data includes the Received Signal Strength Indicator (RSSI) and Time of Arrival (TOF) data from the Bluetooth beacon, as well as forwarded signal data containing the number of forwarding hops and the forwarding node identifier, which is forwarded through the Bluetooth Mesh network.

[0018] Step S213: Based on a unified time reference, align the timestamps of the inertial data and the radio frequency signal data;

[0019] Step S214: Remove outliers from the radio frequency signal data whose intensity exceeds the preset reasonable range; remove outliers from the inertial data whose intensity exceeds the physical motion range using the 3σ criterion; and calibrate the inertial data after removing outliers using adaptive complementary filtering.

[0020] Preferably, in step S220, the step of identifying the current motion state of the user terminal based on the inertial data in the fused data includes:

[0021] Step S221: Extract motion features from the fused data of inertial data, including acceleration magnitude. angular velocity magnitude and rate of change of heading angle The specific calculation expression is as follows:

[0022]

[0023]

[0024] In the formula, , , These are the acceleration components of the inertial measurement unit in the three coordinate axes; , , These are the angular velocity components of the inertial measurement unit along the three coordinate axes; For heading angle Rate of change over time;

[0025] Step S222: Set the acceleration magnitude angular velocity magnitude and rate of change of heading angle Input a preset Naive Bayes classifier to identify the current motion state of the user terminal; the current motion state is classified according to preset acceleration and angular velocity thresholds, including stationary, walking, running, going up and down stairs, and turning.

[0026] Preferably, in step S220, the preset adaptive ranging model expression is as follows:

[0027]

[0028] In the formula, This represents the estimated distance from the user terminal to the Bluetooth beacon. These are the model correction coefficients corresponding to the current motion state; This indicates the received signal strength from the Bluetooth beacon. The reference signal strength is at 1 meter. The environmental degradation index corresponding to the current motion state; Mesh forwarding hop count for radio frequency signal data The corresponding weighting coefficients; This refers to the arrival time of the Bluetooth beacon signal. The inertial correction weights corresponding to the current motion state; To be based on the acceleration modulus The fluctuation amount is calculated within a preset time window.

[0029] Preferably, in step S230, the step of determining occlusion based on radio frequency signal data and the current motion state includes:

[0030] Step S231: Based on the radio frequency signal data, calculate the signal strength change rate of each Bluetooth beacon signal; if the signal strength change rate is less than a preset threshold and the duration exceeds a preset duration, it is determined that the Bluetooth beacon has experienced a signal change.

[0031] Step S232: When a signal change is detected, check whether there is a valid path in the Bluetooth Mesh network that forwards the Bluetooth beacon signal through other nodes; if there is no valid path, and the user terminal is determined to be in motion based on the current motion state, mark the Bluetooth beacon as suspected obstruction;

[0032] Step S233: For Bluetooth beacons marked as suspected obstruction, verify them based on the user terminal's movement direction and indoor map information; if the angle between the movement direction and the direction of the line connecting to the Bluetooth beacon is less than a preset angle, and there is a known obstacle on the line, then mark the Bluetooth beacon as confirmed obstruction.

[0033] Preferably, step S230, which involves generating a corresponding fusion weighting strategy for inertial data and radio frequency signal data in positioning based on the determination result, includes:

[0034] Step S234: Determine the current obstruction level based on the number of Bluetooth beacons marked as confirmed obstructions;

[0035] Step S235: Determine the dynamic fusion weight of inertial data and radio frequency signal data based on the occlusion level and a preset compensation strategy; wherein, the higher the occlusion level, the greater the fusion weight assigned to the inertial data.

[0036] Step S236: When the number of confirmed obstructed beacons is reduced to below a preset recovery threshold, perform signal recovery calibration to improve the fusion weight of radio frequency signal data.

[0037] Preferably, step S240 includes:

[0038] Step S241: Based on the adaptive ranging results, fused data, current motion state, and pre-built fingerprint database, construct a feature vector containing multiple positioning-related features;

[0039] Step S242: Using an improved entropy weighting method that incorporates motion state information, dynamic weights are assigned to each feature in the feature vector. The formula is expressed as follows:

[0040]

[0041] In the formula, This represents the weight of the j-th feature in the feature vector under the current motion state s; The dynamic adaptation coefficient of the j-th feature corresponding to the current motion state s; Let be the information entropy of the j-th feature; j is the index of the feature vector; N is the dimension of the feature vector;

[0042] Step S243: Input the weighted feature vector into the particle filter algorithm to calculate the real-time position of the user terminal.

[0043] Preferably, step S400 includes:

[0044] Step S410: When the distance between the real-time location of the user terminal and the location of the target shelf is less than the first preset distance threshold, control at least one adjustable intelligent spotlight to turn towards the target shelf area, so as to illuminate the area where the target shelf is located by a first lighting parameter that is different from the ambient lighting.

[0045] Step S420: When the distance between the real-time location of the user terminal and the location of the target shelf is less than the second preset distance threshold, control the lighting unit deployed on the target shelf corresponding to the target product to work with the second lighting parameter, which is different from the first lighting parameter, to form a visual indication of the target product.

[0046] Step S430: Send interactive information containing detailed information about the target product and light guidance prompts to the user terminal.

[0047] A second aspect of this invention provides a lighting-linked shopping guide system, comprising:

[0048] The shopping guide request processing module is used to receive shopping guide requests containing target product information sent by user terminals, and determine the corresponding target shelf location based on the target product information;

[0049] The anti-occlusion navigation path generation module generates a navigation path from the real-time location to the target shelf location based on the target shelf location, the real-time location and movement status of the user terminal, and according to a preset anti-occlusion indoor navigation algorithm.

[0050] The dynamic lighting guidance control module is used to map the navigation path to the lighting topology network, generate dynamic lighting guidance instructions, and control the smart lights along the navigation path and at the target shelf location to light up according to the preset guidance mode, so as to form a light flow guidance pointing to the target shelf location.

[0051] The near-field lighting interaction module is used to control the smart lighting device associated with the target product to switch to a lighting interaction mode to highlight the target product when the distance between the real-time location of the user terminal and the target shelf location is less than a preset distance threshold, and to send interactive information associated with the target product to the user terminal.

[0052] The lighting-linked shopping guide method and system provided by this invention integrates Bluetooth beacon functionality into existing smart lighting fixtures, achieving a three-in-one integration of lighting, communication, and positioning infrastructure. This improves the economy and convenience of system deployment and avoids secondary construction and modification. By employing a multi-source fusion indoor navigation algorithm with anti-obstruction capabilities, it effectively addresses signal interference and blockage in complex dynamic environments, improving the stability of the positioning process and the reliability of navigation guidance. By converting digital navigation paths into dynamic optical flow guidance in real time, it provides intuitive, natural, and non-complex visual direction indications, improving the ease of use and smoothness of user pathfinding. By automatically triggering differentiated lighting interactions and information pushes based on distance thresholds, it achieves seamless integration from macro-path navigation to micro-product positioning, improving the completeness of shopping guide services and product conversion efficiency.

[0053] Furthermore, this invention improves positioning stability and accuracy through multi-source data fusion and state-adaptive ranging; enhances anti-interference capabilities through intelligent occlusion detection and weight adjustment; improves navigation accuracy through multi-feature fusion and precise path planning; enhances the integrity of positioning information sources by synchronously acquiring inertial and Bluetooth multi-hop signal data; improves the accuracy of data fusion by aligning multi-source data timestamps; improves raw data quality and positioning accuracy through anomaly removal and adaptive filtering calibration; enhances navigation adaptability to different user behaviors by extracting multi-dimensional motion features and combining them with a classifier to achieve precise motion state recognition; improves the accuracy and robustness of distance estimation in complex dynamic environments by employing a motion state-adaptive ranging model that fuses multi-source signals and inertial compensation; and improves occlusion recognition by monitoring signal mutations and network forwarding status. Timeliness and reliability; improving the accuracy of occlusion detection by combining motion direction and map for geometric verification; enhancing the system's adaptability and robustness to different occlusion levels by adaptively adjusting fusion weights according to occlusion level; improving the accuracy and stability of positioning recovery from occlusion by prioritizing RF weight calibration during signal recovery; enhancing the comprehensiveness and discriminative power of positioning information by constructing multi-dimensional fusion feature vectors; improving the rationality of feature utilization and scene adaptability by combining adaptive entropy weighting with motion state for weight allocation; improving the accuracy and stability of position estimation through particle filter algorithm; improving shopping guide efficiency and product search accuracy by achieving a precise visual transition from area to product through graded lighting guidance; and improving user experience and conversion rate by synchronously pushing product information to achieve a closed-loop fusion of physical guidance and digital information.

[0054] In summary, the lighting-linked shopping guide method and system proposed in this invention solves the technical problems of the disconnect between lighting guidance and real-time user navigation, poor positioning stability, and insufficient near-field interactive experience in the prior art. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0056] Figure 1 A flowchart of a lighting-linked shopping guide method provided in an embodiment of the present invention;

[0057] Figure 2 A flowchart of an anti-obstruction indoor navigation and positioning method provided in an embodiment of the present invention;

[0058] Figure 3 A flowchart illustrating a method for fusing inertial data and radio frequency signal data according to an embodiment of the present invention;

[0059] Figure 4 A flowchart of a motion state recognition method provided in an embodiment of the present invention;

[0060] Figure 5 A flowchart of an occlusion determination method based on signal and motion state provided in an embodiment of the present invention;

[0061] Figure 6 A flowchart of a dynamic fusion weight generation method provided in an embodiment of the present invention;

[0062] Figure 7 This is a flowchart of a multi-feature fusion localization calculation method provided in an embodiment of the present invention;

[0063] Figure 8 A flowchart of a graded lighting guidance and interaction method provided in an embodiment of the present invention;

[0064] Figure 9 This is a schematic diagram of a lighting-linked shopping guide system provided in an embodiment of the present invention;

[0065] Figure 10 This is a schematic diagram of a computer device for light-linked shopping guide provided in an embodiment of the present invention.

[0066] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0067] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0068] It should be noted that if the embodiments of the present invention involve directional indicators, such as up, down, left, right, front, back, etc., the directional indicators are only used to explain the relative positional relationship and movement of the components in a specific posture. If the specific posture changes, the directional indicators will also change accordingly.

[0069] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the meaning of "and / or" throughout the text includes three parallel solutions; for example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0070] The main objective of this invention is to propose a lighting-linked shopping guide method and system, which aims to solve the technical problems in the prior art such as the disconnect between lighting guidance and real-time user navigation, poor positioning stability, and insufficient near-field interactive experience.

[0071] To achieve the above objectives, such as Figures 1 to 8 As shown, the first aspect of this invention proposes a lighting-linked shopping guide method, comprising:

[0072] Step S100: Receive a shopping guide request containing target product information sent by the user terminal, and determine the corresponding target shelf location based on the target product information;

[0073] Step S200: Based on the target shelf location, the real-time location and motion status of the user terminal, a navigation path from the real-time location to the target shelf location is generated according to a preset anti-obstruction indoor navigation algorithm; wherein, the anti-obstruction indoor navigation algorithm uses a Bluetooth beacon network integrated by smart lighting fixtures for positioning;

[0074] Step S300: Map the navigation path to the lighting topology network, generate dynamic lighting guidance instructions, and control the smart lights along the navigation path and at the target shelf location to light up according to the preset guidance mode, so as to form a light flow guidance pointing to the target shelf location;

[0075] Step S400: In response to the fact that the distance between the real-time location of the user terminal and the location of the target shelf is less than a preset distance threshold, control the smart lighting device associated with the target product to switch to the light interaction mode for highlighting the target product, and send the interactive information associated with the target product to the user terminal.

[0076] For details, see Figure 1 In one specific embodiment of the present invention, firstly, in an intelligent lighting system distributed throughout shopping malls or shops, each key location's intelligent light fixture integrates a low-power Bluetooth beacon module and a wireless networking module, enabling it to provide basic lighting functions while also serving as a node in a wireless positioning network, continuously broadcasting radio frequency signals containing its own identity and coordinates. These lights are powered by wires and interconnected using a wireless mesh or existing control bus to form a physical network that combines lighting, communication, and positioning functions. Its topology and the physical coordinates of each light fixture are recorded in the intelligent lighting system. Secondly, users need to possess an intelligent terminal, such as a smartphone or tablet, equipped with an inertial measurement unit, Bluetooth module, and network communication capabilities. Finally, servers responsible for business logic, data fusion, positioning calculation, and command issuance are deployed on the cloud or local edge.

[0077] When customers enter a shopping mall and have shopping needs, they can initiate a shopping guide request through an application on their smartphone, such as the mall's mini-program / official account / app. This request can include target product information in various ways, such as manually entering the product name, scanning the QR code on the product poster, or directly inputting voice. After the shopping guide request is sent to the back-end system, it is parsed and, based on the product information contained therein, is queried and matched in the mall's or store's digital product inventory database to quickly determine the specific target shelf location where the product is currently displayed. The specific target shelf location is represented in three-dimensional coordinates and linked to an electronic map. Subsequently, the user's smartphone continuously collects raw inertial data output by its own inertial measurement unit, including triaxial acceleration and angular velocity. Simultaneously, the phone's Bluetooth module scans and receives radio frequency signals broadcast from Bluetooth beacons integrated into the smart lights in the surrounding environment, acquiring parameters such as signal strength, and uploads this data to the processing unit (cloud or edge). Based on a preset anti-obstruction indoor navigation algorithm, this multi-source data is processed. Specifically, the inertial data and Bluetooth signal data are first synchronized in time and filtered to form a fused data frame. Then, the algorithm analyzes the characteristics of the inertial data to identify the user's motion state. Combining the current motion state with the Bluetooth signal data, the algorithm estimates the distance between the user and each light beacon using a preset calculation model and assesses the obstruction level of each light beacon. When pedestrians, shelves, or other objects in the indoor environment obstruct the direct wireless link between a user and a lighting beacon, causing abnormal attenuation of the Bluetooth signal, the algorithm can comprehensively determine the location by analyzing signal abrupt changes, combining the user's movement status, and utilizing the relay signals from the mesh network between lighting fixtures. It dynamically adjusts the weight ratio of inertial data and Bluetooth signal data in the final positioning calculation, thus maintaining the continuity and stability of the positioning function even in scenarios with partial signal obstruction, outputting relatively accurate real-time user location coordinates and movement trends. After obtaining the user's real-time location and movement status, the system, combined with the known location of the target shelf, can automatically plan an optimal navigation path from the user's current location to the target shelf on an electronic map.

[0078] Next, the calculated optimal navigation path is mapped and matched with the physical topology network of the aforementioned smart lighting fixtures. Based on the path's direction, a series of dynamic lighting commands controlled according to specific timing and logic are automatically generated and sent to specific lighting fixtures along the path via the control network. For example, the lights along the path in front of the user can be controlled to illuminate sequentially at higher brightness, forming a forward-extending light strip; or the lights can be controlled to gradually brighten and dim in a wave-like manner, creating a flowing light effect. The lighting updates in real time as the user moves, always indicating the next direction of travel in front of the user. At the same time, the basic lighting of the target shelf area is enhanced or adjusted to a more striking tone in advance, serving as a distant visual target so that the user can identify it from a distance.

[0079] When the system continuously monitors and detects that the actual distance between the user's terminal and the target shelf location has shortened to a preset proximity threshold, it determines that the user has arrived in the vicinity of the shelf. The system automatically switches the lighting guidance mode from path guidance to precise product positioning and information interaction mode. It controls specific lighting devices installed on the target shelf and associated with the target product, such as miniature LED indicators below the product or adjustable spotlights above it, to execute preset lighting interaction actions. The indicator lights flash with specific colors and rhythms to mark the precise display location of the target product. Alternatively, intelligent spotlights can adjust their beam angle, color temperature, and brightness to precisely project a focused spot of light onto the target product, making it stand out on the shelf. Simultaneously, the system pushes detailed information related to the target product to the user's smartphone, such as price, promotions, user reviews, or usage videos, completing a closed-loop experience from physical space guidance to digital information access.

[0080] Understandably, this embodiment integrates Bluetooth beacon functionality into existing smart lighting fixtures, achieving a three-in-one integration of lighting, communication, and positioning infrastructure. This improves the economy and convenience of system deployment and avoids secondary construction and modification. By employing a multi-source fusion indoor navigation algorithm with anti-obstruction capabilities, it effectively addresses signal interference and blockage in complex dynamic environments, improving the stability of the positioning process and the reliability of navigation guidance. By converting digital navigation paths into dynamic optical flow guidance in real time, it provides intuitive, natural, and non-complex visual direction indications, enhancing the ease of use and smoothness of user pathfinding. By automatically triggering differentiated lighting interactions and information pushes based on distance thresholds, it achieves seamless integration from macro-level path navigation to micro-level product positioning, improving the completeness of shopping guide services and product conversion efficiency.

[0081] Based on the above technical solutions, those skilled in the art can make corresponding equivalent improvements according to the specific characteristics of the application scenario or system requirements. For example, the Bluetooth beacon integrated in the smart lighting fixture can be replaced with a Zigbee communication module that also supports Mesh networking to adapt to different IoT ecosystems; or the role of the user terminal in collecting inertial data and radio frequency signal data can be replaced by the smart lighting fixture for signal detection and preliminary processing to share the terminal's computing pressure.

[0082] Preferably, step S200 includes:

[0083] Step S210: Collect inertial data from the user terminal's inertial measurement unit and radio frequency signal data broadcast by the Bluetooth beacon network integrated by the smart lighting fixture, and synchronize and preprocess the collected data to obtain time-synchronized fused data.

[0084] Step S220: Based on the inertial data in the fused data, identify the current motion state of the user terminal; combine the current motion state with the radio frequency signal data in the fused data, and calculate the adaptive ranging result according to the preset adaptive ranging model;

[0085] Step S230: Based on the radio frequency signal data and the current motion state, perform occlusion determination, and generate the corresponding fusion weight strategy of inertial data and radio frequency signal data in positioning according to the determination result;

[0086] Step S240: Based on the adaptive ranging results, fusion weight strategy, current motion state and fusion data, perform multi-feature fusion positioning calculation to solve the real-time position of the user terminal;

[0087] Step S250: Based on the calculated real-time location, current motion status, and target shelf location, and combined with indoor electronic map information, generate a navigation path.

[0088] For details, see Figure 2In one specific embodiment of the present invention, the inertial measurement unit of the user terminal collects raw inertial data including triaxial acceleration and triaxial angular velocity at a preset frequency, and continuously scans and receives radio frequency signals broadcast by the Bluetooth beacon network integrated by the smart lamp through the Bluetooth module. The radio frequency signals include beacon identification and received signal strength. The collected data are timestamped and uploaded to the positioning calculation unit. The positioning calculation unit first performs time synchronization and alignment of the inertial data stream and the radio frequency signal data stream to ensure the time consistency of the data. Then, the two types of data are preprocessed separately. The inertial data is filtered to smooth noise, and the radio frequency signal data is filtered out according to the preset effective strength range. Finally, a set of time-synchronized and quality-controllable fused data is output. Then, the positioning computing unit analyzes the inertial data portion of the preprocessed fused data. By extracting its statistical features and inputting them into a pre-trained classification model, it identifies in real time whether the user terminal is currently stationary, walking, or running. Simultaneously, it combines the identified current motion state with a pre-set adaptive ranging model, using the current motion state as an input parameter to dynamically adjust the correction of the radio frequency signal path loss model. It also integrates other information such as the time of arrival (TOF) to comprehensively calculate a more accurate set of distance estimates from the user terminal to surrounding beacons—the adaptive ranging result. Next, the positioning computing unit continuously monitors changes in radio frequency signal data. When an abnormally rapid attenuation of the signal strength from a specific beacon is detected, meeting pre-set conditions, an occlusion determination process is initiated. This process comprehensively evaluates whether the signal attenuation is accompanied by an interruption of the Mesh network relay path, and cross-validates this with the previously identified user motion state. If occlusion is determined, a data fusion weighting strategy is dynamically generated based on the number of obstructed beacons or other evaluation indicators. This strategy clearly defines the respective weight ratios of inertial data and remaining reliable radio frequency signal data in subsequent positioning calculations under the current environment. Next, the adaptive ranging results, fusion weight strategy, current motion state, and other effective features extracted from the fused data are integrated. Particle filtering and other state estimation algorithms are used, guided by the aforementioned fusion weight strategy, to iteratively estimate the user terminal's position and velocity state, ultimately outputting the calculated real-time position coordinates of the user terminal. Finally, based on the real-time position coordinates, current motion state, and known target shelf coordinates, combined with an indoor electronic map storing information on passageways and obstacles, a passable optimal navigation path is calculated and generated, starting from the user's real-time location and ending at the target shelf location, considering the user's motion trend and map constraints.

[0089] Understandably, this embodiment synchronously collects and preprocesses terminal inertial data and lighting beacon radio frequency signals to form time-aligned fused data, providing a reliable data foundation for subsequent high-precision positioning; it improves the accuracy of distance estimation under different movement modes by identifying the user's motion state in real time based on inertial data and dynamically adapting the ranging model to this state; it improves the robustness and adaptability of the positioning system in complex occlusion environments by establishing occlusion judgment logic for fused signal mutations, network states, and user motion, and generating dynamic weight strategies accordingly; it improves the accuracy and stability of the final position calculation by integrating multiple features for weighted fusion positioning calculation; and it improves the rationality and practicality of the navigation path by combining real-time position, motion state, and electronic map path planning.

[0090] Preferably, step S210 includes:

[0091] Step S211: Inertial data is collected at a first preset frequency through the inertial measurement unit of the user terminal; the inertial data includes acceleration, angular velocity and heading angle data;

[0092] Step S212: Collect radio frequency signal data at a second preset frequency through the Bluetooth module of the user terminal; the radio frequency signal data includes the Received Signal Strength Indicator (RSSI) and Time of Arrival (TOF) data from the Bluetooth beacon, as well as forwarded signal data containing the number of forwarding hops and the forwarding node identifier, which is forwarded through the Bluetooth Mesh network.

[0093] Step S213: Based on a unified time reference, align the timestamps of the inertial data and the radio frequency signal data;

[0094] Step S214: Remove outliers from the radio frequency signal data whose intensity exceeds the preset reasonable range; remove outliers from the inertial data whose intensity exceeds the physical motion range using the 3σ criterion; and calibrate the inertial data after removing outliers using adaptive complementary filtering.

[0095] For details, see Figure 3In a specific embodiment of the present invention, step S211 is executed by the user terminal. The terminal operating system calls the application programming interface (API) of the inertial measurement unit (IPU) (such as Core Motion in iOS or SensorManager in Android) to periodically read and encapsulate the raw sensor data output by the IPU at a first preset frequency (e.g., 100 Hz). This data typically includes the three-axis linear acceleration components, three-axis angular velocity components, and heading angle data provided by the sensor fusion algorithm in the user terminal's device coordinate system. After the data is read, it is temporarily stored in the terminal memory, awaiting subsequent processing. Step S212 is executed in parallel with step S211. The Bluetooth module of the user terminal is configured to be in scanning mode and listens to the surrounding environment at a second preset frequency (e.g., 50 Hz). When a signal packet broadcast by a Bluetooth beacon integrated by a smart light is detected, the Bluetooth module parses it. The collected radio frequency signal data mainly includes three parts: first, the direct signal from the beacon, such as the Received Signal Strength Indicator (RSSI) and the precisely measured Time of Arrival (TOF); second, the forwarded signal from the Bluetooth Mesh network, such as the number of hops. The forwarding node ID and signal attenuation coefficient are used. Step S213 is executed after the data is uploaded to the positioning calculation unit. For each group of received inertial data packets and radio frequency signal data packets, based on the terminal local timestamp they carry, clock offset compensation and interpolation algorithms are used to map them to a unified timeline to ensure that different source data describing the state at the same moment are time-aligned, and the error is controlled within the millisecond level. Step S214 enhances the quality of the time-aligned data. For radio frequency signal data, the system sets reasonable upper and lower limits for signal strength based on prior knowledge. RSSI or TOF values ​​below the lower limit (e.g., the signal is too weak) or above the upper limit (e.g., the signal is subject to sudden interference) are judged as outliers and removed. For inertial data, a reasonable acceleration and angular velocity range for human motion is first set based on a large amount of experimental data. The 3σ criterion is applied to each frame of data to identify and remove data points that significantly exceed the physical motion range. Then, an adaptive complementary filtering algorithm is applied to the inertial data stream after removing outliers for calibration (in this embodiment, the filtering coefficient is...). Dynamically adjusts with the state of motion, when at rest When walking When running The system dynamically integrates the advantages and disadvantages of accelerometers and gyroscopes to suppress the zero-bias drift of gyroscopes and the high-frequency noise of accelerometers, outputting smoother and more accurate attitude and motion information, providing a high-quality inertial data source for subsequent processing.

[0096] Understandably, this embodiment improves the dimensionality and completeness of the positioning information source by acquiring complete inertial data containing angular velocity and heading angle, as well as rich radio frequency data containing direct and relay signals, in parallel at a specific frequency; it improves the time consistency of multi-source data during fusion by strictly aligning heterogeneous data streams with timestamps based on a unified high-precision time reference, laying the foundation for subsequent accurate correlation analysis; it improves the reliability and robustness of input data and reduces the impact of noise and interference on the system by removing outliers in radio frequency signals within preset intervals and removing outliers in inertial data based on the 3σ criterion of physical range; and it effectively suppresses inherent sensor errors, especially gyroscope drift, and improves the medium- and long-term accuracy and availability of inertial data by calibrating the inertial data using adaptive complementary filtering.

[0097] Based on the above technical solutions, those skilled in the art can make corresponding equivalent improvements according to the specific characteristics of the application scenario or system requirements. For example, in inertial data acquisition, the nine-axis sensor data containing heading angle can be replaced with six-axis sensor data containing only acceleration and angular velocity to reduce hardware dependence and power consumption; or in radio frequency signal acquisition, the scheme of simultaneously acquiring RSSI and TOF can be replaced with a scheme that relies only on RSSI or multi-carrier phase information to adapt to different chipset capabilities; or in the outlier removal stage, the 3σ criterion can be replaced with a method based on moving median and absolute deviation to deal with non-Gaussian noise.

[0098] Preferably, in step S220, the step of identifying the current motion state of the user terminal based on the inertial data in the fused data includes:

[0099] Step S221: Extract motion features from the fused data of inertial data, including acceleration magnitude. angular velocity magnitude and rate of change of heading angle The specific calculation expression is as follows:

[0100]

[0101]

[0102] In the formula, , , These are the acceleration components of the inertial measurement unit in the three coordinate axes; , , These are the angular velocity components of the inertial measurement unit along the three coordinate axes; For heading angle Rate of change over time;

[0103] Step S222: Set the acceleration magnitude angular velocity magnitude and rate of change of heading angle Input a preset Naive Bayes classifier to identify the current motion state of the user terminal; the current motion state is classified according to preset acceleration and angular velocity thresholds, including stationary, walking, running, going up and down stairs, and turning.

[0104] For details, see Figure 4 In a specific embodiment of the present invention, the acceleration magnitude is first obtained by performing a square root operation on the sum of the squares of the three-axis acceleration components. This reflects the magnitude of the resultant acceleration experienced by the equipment; secondly, the angular velocity magnitude is obtained by taking the square root of the sum of the squares of the three-axis angular velocity components. This reflects the degree of drastic rotation of the entire equipment; finally, the rate of change of the heading angle is obtained by performing a difference calculation on the heading angle at consecutive time points. This reflects how quickly the equipment can turn horizontally. Then, the acceleration modulus... angular velocity magnitude and rate of change of heading angle Feature vectors, as input data, are submitted in real time to a pre-trained Naive Bayes classifier model. The labeling of the training data is based on a preset multi-dimensional threshold rule; for example, when the acceleration magnitude value is continuously below a low threshold, it is marked as a stationary state (e.g., ...). When the acceleration magnitude is within a typical walking range and the angular velocity magnitude is below a threshold, it is marked as a walking state (e.g., , When both the acceleration magnitude and angular velocity magnitude exceed a higher threshold, it is marked as running (e.g., , When the vertical acceleration exhibits periodic fluctuations within a specific frequency range, it is marked as a stair-climbing state (e.g., climbing stairs). Fluctuation frequency 2-4Hz); when the rate of change of heading angle exceeds a set threshold, it is marked as a turning state (e.g., The Naive Bayes classifier model learns the probabilistic relationship between these rules and feature data. When running online, it can calculate the posterior probability of each preset motion state category based on the input feature value and Bayes' theorem, and output the category with the highest probability as the recognition result of the current motion state of the user terminal, including stationary, walking, running, going up and down stairs, and turning.

[0105] Understandably, this embodiment improves the comprehensiveness and discriminativeness of describing complex motion patterns by extracting a comprehensive feature vector containing acceleration magnitude, angular velocity magnitude, and heading angle change rate from calibrated inertial data; it improves the real-time performance and computational efficiency of motion state recognition by using a Naive Bayes classifier based on a probabilistic model to perform real-time state discrimination of the features; and it improves the accuracy, interpretability, and consistency of system behavior by defining clear motion state categories based on preset multi-dimensional physical thresholds and providing clear training basis for the classifier.

[0106] Preferably, in step S220, the preset adaptive ranging model expression is as follows:

[0107]

[0108] In the formula, This represents the estimated distance from the user terminal to the Bluetooth beacon. These are the model correction coefficients corresponding to the current motion state; This indicates the received signal strength from the Bluetooth beacon. The reference signal strength is at 1 meter. The environmental degradation index corresponding to the current motion state; Mesh forwarding hop count for radio frequency signal data The corresponding weighting coefficients; This refers to the arrival time of the Bluetooth beacon signal. The inertial correction weights corresponding to the current motion state; To be based on the acceleration modulus The fluctuation amount is calculated within a preset time window.

[0109] Specifically, in one embodiment of the present invention, the corresponding model correction coefficient is first retrieved from a predefined mapping table based on the motion state s (stationary / walking / running / going up and down stairs / turning). (As an example, in this embodiment, the stationary state is...) ,walk ,run Going up and down stairs Turn ), environmental degradation index (As an example, in this embodiment, the stationary state is...) ,walk ,run Going up and down stairs Turn and inertia correction weights (As an example, in this embodiment, the stationary state is...) ,walk ,run Going up and down stairs Turn These parameter values ​​were calibrated based on extensive experimental data under different motion modes to correct for deviations in signal propagation and device motion characteristics under these motion conditions. Subsequently, the Received Signal Strength Indicator (RSSI) and Time of Arrival (ToF) of the target Bluetooth beacon were extracted from the radio frequency signal data, and the number of hops the signal made in the Bluetooth Mesh network was obtained. Its corresponding hop count weight coefficient Also derived from a preset mapping (as an example, in this embodiment, 1 hop). 2 jumps 3 jumps 4 jumps This is used to quantify the signal attenuation caused by multi-hop forwarding; simultaneously, it calculates the fluctuation of recent acceleration magnitude from inertial data. This serves as a measure of instantaneous motion disturbance; finally, all the above parameters are compared with the known reference signal strength at 1 meter. These are substituted into the model expression for calculation. The first term of this adaptive ranging model is RSSI ranging based on the logarithmic path loss model, but its path loss exponent n and correction coefficient α are dynamically adjusted with the motion state s to adapt to the differences in multipath effects and channel variations under different motion speeds; the second term introduces ToF ranging information and... The first term is weighted to supplement and correct RSSI ranging; the third term introduces the contribution of inertial data, through... Weighted acceleration fluctuation Δa ( , The acceleration is the average of 50 frames to compensate for instantaneous errors in radio frequency ranging caused by sudden acceleration, deceleration, or vibration of the user. This adaptive ranging model integrates multi-source information and adaptively adjusts according to the motion state, ultimately outputting a more robust and accurate adaptive ranging result d for subsequent fusion positioning.

[0110] Understandably, this embodiment improves the adaptability and accuracy of the ranging model under different movement modes by dynamically adjusting the scaling factor and environmental attenuation index of the path loss model using the current motion state as a key parameter; it improves the robustness and reliability of the overall ranging results when a single signal fades or is interfered with by fusing three types of heterogeneous information: received signal strength indication, signal arrival time, and inertial acceleration fluctuation. Furthermore, it improves the quality assessment and effective utilization efficiency of radio frequency signals that have undergone multiple relays by introducing a weighting coefficient associated with the number of Bluetooth Mesh forwarding hops to adjust the confidence level of the arrival time information.

[0111] Preferably, in step S230, the step of determining occlusion based on radio frequency signal data and the current motion state includes:

[0112] Step S231: Based on the radio frequency signal data, calculate the signal strength change rate of each Bluetooth beacon signal; if the signal strength change rate is less than a preset threshold and the duration exceeds a preset duration, it is determined that the Bluetooth beacon has experienced a signal change.

[0113] Step S232: When a signal change is detected, check whether there is a valid path in the Bluetooth Mesh network that forwards the Bluetooth beacon signal through other nodes; if there is no valid path, and the user terminal is determined to be in motion based on the current motion state, mark the Bluetooth beacon as suspected obstruction;

[0114] Step S233: For Bluetooth beacons marked as suspected obstruction, verify them based on the user terminal's movement direction and indoor map information; if the angle between the movement direction and the direction of the line connecting to the Bluetooth beacon is less than a preset angle, and there is a known obstacle on the line, then mark the Bluetooth beacon as confirmed obstruction.

[0115] For details, see Figure 5 In a specific embodiment of the present invention, step S231 first calculates the first derivative of the received signal strength indication, i.e., the rate of change of signal strength, over a very short period of time for each monitored Bluetooth beacon based on its radio frequency signal data. The formula is as follows:

[0116]

[0117] In the formula, Indicates the rate of change of the received signal strength indication; This represents the instantaneous value of the received signal strength indication from a Bluetooth beacon measured at the current sampling time t; This represents the instantaneous value of the received signal strength indication from a Bluetooth beacon measured at the previous sampling time t-1.

[0118] When the system detects that the rate of change from a beacon is consistently below a preset negative threshold (e.g., -5dB / 0.01s), and the cumulative duration of this abnormally low rate of change exceeds another preset duration (e.g., 0.5s), it determines that the beacon has experienced a signal mutation event, which usually means that the signal link has been initially affected by sudden interference or physical obstruction. Step S232 further analyzes this determination. When a beacon is marked as having experienced a signal mutation, the system immediately queries the real-time topology and routing status of the Bluetooth Mesh network to check if there are other network nodes that can forward the signal of the abnormal beacon, thereby forming a backup effective communication path. If the query results indicate that there is no such effective alternative forwarding path within the network, and combined with the user terminal's current motion state information identified in step S220, it confirms that the user is not in a stationary state (e.g., ...). If a beacon is temporarily marked as potentially obstructed, it usually means that the signal interruption is more likely due to a physical blockage of the direct path between the user and the beacon, rather than normal attenuation caused by the user moving away from the beacon. Step S233 performs final confirmation. For each beacon marked as potentially obstructed, the system calls the stored indoor electronic map data and performs geometric relationship verification in combination with the real-time estimated direction of the user terminal's movement. It calculates the angle between the current direction vector of the user terminal's movement and the direction vector from the user's position to the beacon's position. If the angle is less than a preset small angle threshold, it indicates that the user is moving towards the beacon, and the electronic map information shows that there is a known fixed obstacle, such as a shelf or wall, on the line. In this case, it is finally confirmed that the Bluetooth beacon is indeed physically obstructed.

[0119] Understandably, this embodiment identifies signal abrupt events by monitoring the rate of change of signal strength and setting duration conditions, thereby improving the initial sensitivity and accuracy of sensing sudden occlusion or interference. After determining the signal abrupt change, it further verifies the Mesh network forwarding path and combines it with the user's movement status to effectively distinguish whether the signal attenuation is due to physical occlusion or the user's natural movement away, thus improving the reliability of the initial attribution of the occlusion cause. Finally, by combining the user's movement direction with a high-precision indoor map for geometric verification, it confirms the relationship between the movement direction and the obstacle position, ultimately confirming the suspected occlusion as a real occlusion, greatly reducing the false judgment rate and improving the accuracy and reliability of the entire occlusion determination process.

[0120] Based on the above technical solutions, those skilled in the art can make corresponding equivalent improvements according to the specific characteristics of the application scenario or system requirements. For example, the method of monitoring the rate of change based on the first derivative can be replaced with a method based on the second derivative of signal strength or a more complex time series anomaly detection algorithm to capture more subtle signal change patterns; or the method of checking the static preset Mesh topology can be replaced with an active detection method that dynamically detects network connectivity or signal reachability to adapt to the dynamically changing network environment.

[0121] Preferably, step S230, which involves generating a corresponding fusion weighting strategy for inertial data and radio frequency signal data in positioning based on the determination result, includes:

[0122] Step S234: Determine the current obstruction level based on the number of Bluetooth beacons marked as confirmed obstructions;

[0123] Step S235: Determine the dynamic fusion weight of inertial data and radio frequency signal data based on the occlusion level and a preset compensation strategy; wherein, the higher the occlusion level, the greater the fusion weight assigned to the inertial data.

[0124] Step S236: When the number of confirmed obstructed beacons is reduced to below a preset recovery threshold, perform signal recovery calibration to improve the fusion weight of radio frequency signal data.

[0125] For details, see Figure 6 In a specific embodiment of the present invention, step S234 first quantifies the severity of signal obstruction in the current environment into a discrete obstruction level based on the specific number of Bluetooth beacons marked as confirmed obstructions. For example, obstruction of only a single beacon corresponds to a mild obstruction level, obstruction of two beacons may correspond to a moderate obstruction level, and obstruction of three or more beacons corresponds to a severe obstruction level. Step S235 queries or calculates a preset compensation strategy based on the determined obstruction level. The higher the obstruction level, the fewer reliable stable radio frequency signal sources there are. The system then allocates a higher fusion weight to the inertial data from the user terminal's inertial measurement unit, while reducing the fusion weight of the radio frequency signal data. This achieves a smooth transition and compensation from radio frequency signal-dominated to inertial data-dominated. For example, the fusion weight of inertial data is 0.3 for mild obstruction, 0.5 for moderate obstruction, and 0.7 for severe obstruction. Step S236 continuously monitors and confirms the number of blocked beacons. When the number decreases and falls below a preset recovery threshold, it indicates that the blockage has been lifted, and a signal recovery calibration process is executed. The calibration formula is:

[0126]

[0127] In the formula, This represents the final estimated user location output after signal recovery and calibration. This represents a position estimate calculated based on radio frequency signals; This represents the position estimate calculated based on data from the inertial measurement unit.

[0128] Understandably, this embodiment improves the consistency and automation of the system's environmental impact assessment by determining the occlusion level based on the number of confirmed occluded beacons; it enhances the adaptability and survivability of the positioning algorithm under different interference scenarios by establishing a preset mapping relationship between the occlusion level and the weights of inertial / RF data fusion; and it can quickly correct the accumulated errors of inertial inference by actively performing calibration and prioritizing the increase of RF signal weights when signal recovery is detected, thereby improving the convergence speed and overall stability of the positioning system when recovering from an occluded environment by utilizing the reusable high-precision reference signal.

[0129] Based on the above technical solutions, those skilled in the art can make corresponding equivalent improvements according to the specific characteristics of the application scenario or system requirements. For example, the method of determining the level based on the number of blocked beacons can be replaced by continuous level evaluation based on the average signal attenuation of all beacons or the channel quality index, so as to provide more refined environmental perception.

[0130] Preferably, step S240 includes:

[0131] Step S241: Based on the adaptive ranging results, fused data, current motion state, and pre-built fingerprint database, construct a feature vector containing multiple positioning-related features;

[0132] Step S242: Using an improved entropy weighting method that incorporates motion state information, dynamic weights are assigned to each feature in the feature vector. The formula is expressed as follows:

[0133]

[0134] In the formula, This represents the weight of the j-th feature in the feature vector under the current motion state s; The dynamic adaptation coefficient of the j-th feature corresponding to the current motion state s; Let be the information entropy of the j-th feature; j is the index of the feature vector; N is the dimension of the feature vector;

[0135] Step S243: Input the weighted feature vector into the particle filter algorithm to calculate the real-time position of the user terminal.

[0136] For details, see Figure 7 In a specific embodiment of the present invention, step S241 first receives the distance estimate d from the adaptive ranging model, fused data (including calibrated inertial data, radio frequency signal data and their Mesh forwarding information) from the preprocessing module, the current motion state label s, the pre-built IMU-signal linkage fingerprint database, and environmental feature data from the terminal's visual sensor. Based on these inputs, the module constructs a five-dimensional feature vector F=[f1,f2,f3,f4,f5] in real time. The first feature f1 is the value of the adaptive ranging result d after maximum and minimum value normalization, with its value range standardized to [0,1], reflecting the geometric distance relationship between the terminal and each reference beacon; the second feature f2 is the reliability of the Mesh forwarding signal, calculated as follows: ,in This represents the number of hops the radio frequency signal has been relayed in a Bluetooth Mesh network, reflecting the signal attenuation and uncertainty caused by multiple relays. The larger the value, the lower the reliability of f2; the third dimension feature f3 is the consistency of IMU motion trajectory, which is calculated as follows: The first dimension, f3, represents the curvature variance of the motion trajectory calculated based on recent inertial data. This feature reflects the smoothness of the inertial trajectory; the smaller the curvature variance v (the smoother the trajectory), the higher the consistency f3. The fourth dimension, f4, is the IMU-signal linkage matching degree. It is obtained by querying a pre-built IMU-signal linkage fingerprint database and calculating the Pearson correlation coefficient between the current inertial data features (such as signal features under a specific motion mode) and the historical patterns stored in the fingerprint database. This feature characterizes the similarity between the current observation and the historical experience pattern. The fifth dimension, f5, is the scene feature matching degree. It is based on the corner features extracted from the images captured in real time by the user terminal's visual sensor (such as a camera) and matched with the pre-stored scene database. Its calculation method is f5 = m / 30, where m is the number of successfully matched corners. This feature uses visual information to assist in the verification of positioning. Next, step S242 uses an improved entropy weighting method that combines motion state information to dynamically assign weights to the above five-dimensional features according to the formula. The calculation yields a weighted feature vector. Finally, step S243 uses the weighted feature vector as the key input to the observation model, feeding it into a particle filter algorithm. The particle filter maintains hundreds of particles representing possible position and velocity states. In each filtering cycle, the algorithm predicts particle state transitions based on inertial data, then calculates the observation likelihood value for each particle using the weighted feature vector, and updates the particle weights accordingly. As a possible implementation example, the number of particles is set to 500 when constructing the particle filter model, and the state vector is... (Position + Velocity) is the weighted result of the observation equation fused with the five-dimensional feature vector. Resampling employs a systematic resampling method, with an effective particle count threshold set to 200. When the effective particle count falls below this threshold during iteration, a resampling operation is triggered to suppress particle degradation and maintain filter stability and estimation accuracy. Through resampling and state estimation, the particle set eventually converges, and its weighted mean is output as the real-time position coordinates of the user terminal.

[0137] Understandably, this embodiment constructs a multi-dimensional feature vector that integrates ranging, signal, inertial, and fingerprint matching information to provide rich and complementary observation information for positioning, thereby improving the dimensionality and reliability of positioning clues. By employing an improved entropy weighting method that combines dynamic adaptation coefficients with the current motion state to allocate feature weights, the weight allocation strategy can adapt to changes in user behavior patterns, improving the intelligence of feature utilization and the scene adaptability of the positioning model. By inputting the dynamically weighted feature observation information into a particle filter framework for probabilistic state estimation, the nonlinearity and non-Gaussian noise problems in the positioning system are effectively handled, improving the accuracy, smoothness, and overall robustness of the final position calculation.

[0138] Preferably, step S400 includes:

[0139] Step S410: When the distance between the real-time location of the user terminal and the location of the target shelf is less than the first preset distance threshold, control at least one adjustable intelligent spotlight to turn towards the target shelf area, so as to illuminate the area where the target shelf is located by a first lighting parameter that is different from the ambient lighting.

[0140] Step S420: When the distance between the real-time location of the user terminal and the location of the target shelf is less than the second preset distance threshold, control the lighting unit deployed on the target shelf corresponding to the target product to work with the second lighting parameter, which is different from the first lighting parameter, to form a visual indication of the target product.

[0141] Step S430: Send interactive information containing detailed information about the target product and light guidance prompts to the user terminal.

[0142] For details, see Figure 8 In a specific embodiment of the present invention, step S410 first determines whether the distance between the user terminal and the target shelf location is less than a first preset distance threshold. The first preset distance threshold is usually set within the range where the user has entered the store or nearby area where the target shelf is located. When the condition is met, at least one smart spotlight with electric pan-tilt head and dimming capability is controlled to make its beam accurately turn and cover the entire area where the target shelf is located. The spotlight is also controlled to work with a first lighting parameter that is different from the basic lighting of the surrounding environment, such as using significantly higher brightness, a specific color temperature or slight dynamic effects, so that the target shelf area is visually highlighted from the background, providing the user with clear macroscopic location guidance. Step S420 is executed as the user continues to approach the target shelf. When the distance between the user terminal and the target shelf is further reduced to a smaller second preset distance threshold, it is determined that the user has arrived in front of the shelf. The dedicated lighting unit deployed on the target shelf and precisely corresponding to the target product is activated. The dedicated lighting unit can be a miniature LED indicator integrated under the shelf panel or a narrow beam spotlight for a single product display position. It works with the second lighting parameter, which is different from the first lighting parameter in step S410. For example, it can flash with a bright color (such as red) or illuminate the product with a focused, high color rendering beam. Through differentiated lighting changes, a visual transition from "area guidance" to "product labeling" is formed, directly guiding the user's attention to the target product itself. Step S430 pushes a set of structured interactive information to the user terminal through the application programming interface, including text or multimedia content such as detailed specifications, price, and promotional activities of the target product, and may also include a brief description of the current lighting guidance mode, such as prompting "The product you are looking for has been highlighted by the light", thereby establishing a closed-loop connection between physical space guidance and digital information space, assisting the user in completing the final confirmation and decision.

[0143] Understandably, this embodiment sets a first distance threshold and triggers intelligent spotlights to provide differentiated lighting to the target area, providing clear and unambiguous macroscopic visual anchor points when the user is close enough to the target shelf area, thus improving the efficiency and accuracy of remote positioning. By setting a closer second distance threshold and controlling the product-level lighting units to operate with significantly different parameters, a smooth visual transition from area guidance to precise product labeling is achieved, directly focusing the user's gaze on the target product, reducing the final search time in front of the shelf, and improving shopping guide efficiency and user experience. By synchronously pushing interactive content rich in product information to the user terminal, physical lighting guidance and digital product information are seamlessly integrated, providing users with comprehensive decision support and improving service integrity and conversion potential.

[0144] Based on the above technical solution, those skilled in the art can make corresponding equivalent improvements according to the specific characteristics of the application scenario or system requirements. For example, in the triggering logic, the judgment method based on the absolute distance threshold can be replaced with a judgment method triggered after the user terminal camera recognizes the visual features of a specific shelf.

[0145] See Figure 9 As shown, a second aspect of the present invention provides a lighting-linked shopping guide system, comprising:

[0146] The shopping guide request processing module is used to receive shopping guide requests containing target product information sent by user terminals, and determine the corresponding target shelf location based on the target product information;

[0147] The anti-occlusion navigation path generation module generates a navigation path from the real-time location to the target shelf location based on the target shelf location, the real-time location and movement status of the user terminal, and according to a preset anti-occlusion indoor navigation algorithm.

[0148] The dynamic lighting guidance control module is used to map the navigation path to the lighting topology network, generate dynamic lighting guidance instructions, and control the smart lights along the navigation path and at the target shelf location to light up according to the preset guidance mode, so as to form a light flow guidance pointing to the target shelf location.

[0149] The near-field lighting interaction module is used to control the smart lighting device associated with the target product to switch to a lighting interaction mode to highlight the target product when the distance between the real-time location of the user terminal and the target shelf location is less than a preset distance threshold, and to send interactive information associated with the target product to the user terminal.

[0150] A third aspect of the present invention also provides a storage medium storing a lighting-linked shopping guide processing program, wherein when the lighting-linked shopping guide program is executed by a processor, it implements the steps of the lighting-linked shopping guide method as described in any of the above embodiments.

[0151] See Figure 10As shown, a fourth aspect of the present invention provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the lighting-linked shopping guide method as described in any embodiment of the first aspect.

[0152] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the lighting-linked shopping guide system, connecting various parts of the entire lighting-linked shopping guide system through various interfaces and lines to process the various operable devices.

[0153] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the lighting-linked shopping guide system by running or executing the computer programs and / or modules stored in the memory and by calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0154] Compared with the prior art, the beneficial effects of the present invention include at least the following:

[0155] The lighting-linked shopping guide method and system provided by this invention integrates Bluetooth beacon functionality into existing smart lighting fixtures, achieving a three-in-one integration of lighting, communication, and positioning infrastructure. This improves the economy and convenience of system deployment and avoids secondary construction and modification. By employing a multi-source fusion indoor navigation algorithm with anti-obstruction capabilities, it effectively addresses signal interference and blockage in complex dynamic environments, improving the stability of the positioning process and the reliability of navigation guidance. By converting digital navigation paths into dynamic optical flow guidance in real time, it provides intuitive, natural, and non-complex visual direction indications, improving the ease of use and smoothness of user pathfinding. By automatically triggering differentiated lighting interactions and information pushes based on distance thresholds, it achieves seamless integration from macro-path navigation to micro-product positioning, improving the completeness of shopping guide services and product conversion efficiency.

[0156] Furthermore, this invention improves positioning stability and accuracy through multi-source data fusion and state-adaptive ranging; enhances anti-interference capabilities through intelligent occlusion detection and weight adjustment; improves navigation accuracy through multi-feature fusion and precise path planning; enhances the integrity of positioning information sources by synchronously acquiring inertial and Bluetooth multi-hop signal data; improves the accuracy of data fusion by aligning multi-source data timestamps; improves raw data quality and positioning accuracy through anomaly removal and adaptive filtering calibration; enhances navigation adaptability to different user behaviors by extracting multi-dimensional motion features and combining them with a classifier to achieve precise motion state recognition; improves the accuracy and robustness of distance estimation in complex dynamic environments by employing a motion state-adaptive ranging model that fuses multi-source signals and inertial compensation; and improves occlusion recognition by monitoring signal mutations and network forwarding status. Timeliness and reliability; improving the accuracy of occlusion detection by combining motion direction and map for geometric verification; enhancing the system's adaptability and robustness to different occlusion levels by adaptively adjusting fusion weights according to occlusion level; improving the accuracy and stability of positioning recovery from occlusion by prioritizing RF weight calibration during signal recovery; enhancing the comprehensiveness and discriminative power of positioning information by constructing multi-dimensional fusion feature vectors; improving the rationality of feature utilization and scene adaptability by combining adaptive entropy weighting with motion state for weight allocation; improving the accuracy and stability of position estimation through particle filter algorithm; improving shopping guide efficiency and product search accuracy by achieving a precise visual transition from area to product through graded lighting guidance; and improving user experience and conversion rate by synchronously pushing product information to achieve a closed-loop fusion of physical guidance and digital information.

[0157] In summary, the lighting-linked shopping guide method and system proposed in this invention solves the technical problems of the disconnect between lighting guidance and real-time user navigation, poor positioning stability, and insufficient near-field interactive experience in the prior art.

[0158] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention. Equivalent structural transformations made using the description and drawings of the present invention, or direct / indirect applications in other related technical fields, are all included within the scope of patent protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0159] It should be noted that, in this invention, an embodiment implemented on the side of a light-linked shopping guide system can be referenced to an embodiment implemented on the side of a light-linked shopping guide method, and will not be described in detail in this invention.

[0160] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A lighting-linked shopping guide method, characterized in that, include: Step S100: Receive a shopping guide request containing target product information sent by the user terminal, and determine the corresponding target shelf location based on the target product information; Step S200: Based on the target shelf location, the real-time location and motion state of the user terminal, a navigation path from the real-time location to the target shelf location is generated according to a preset anti-obstruction indoor navigation algorithm; wherein, the anti-obstruction indoor navigation algorithm uses a Bluetooth beacon network integrated by smart lighting for positioning; Step S300: Map the navigation path to the lighting topology network, generate dynamic lighting guidance instructions, and control the smart lights along the navigation path and at the target shelf location to light up according to a preset guidance mode, so as to form a light flow guidance pointing to the target shelf location; Step S400: In response to the fact that the distance between the real-time location of the user terminal and the location of the target shelf is less than a preset distance threshold, control the smart lighting device associated with the target product to switch to the light interaction mode for highlighting the target product, and send the interactive information associated with the target product to the user terminal; Step S200 includes: Step S210: Collect inertial data from the user terminal inertial measurement unit and radio frequency signal data broadcast by the Bluetooth beacon network integrated by the smart lamp, and synchronize and preprocess the collected data to obtain time-synchronized fused data. Step S220: Based on the inertial data in the fused data, identify the current motion state of the user terminal; combine the current motion state with the radio frequency signal data in the fused data, and calculate the adaptive ranging result according to the preset adaptive ranging model; Step S230: Based on the radio frequency signal data and the current motion state, perform occlusion determination, and generate a corresponding fusion weight strategy for inertial data and radio frequency signal data in positioning according to the determination result; Step S240: Based on the adaptive ranging result, the fusion weight strategy, the current motion state, and the fusion data, perform multi-feature fusion positioning calculation to determine the real-time location of the user terminal; Step S250: Based on the calculated real-time location, current motion state, and target shelf location, and combined with indoor electronic map information, generate the navigation path; In step S230, the step of determining occlusion based on the radio frequency signal data and the current motion state includes: Step S231: Based on the radio frequency signal data, calculate the signal strength change rate of each Bluetooth beacon signal; if the signal strength change rate is less than a preset threshold and the duration exceeds a preset duration, then determine that the Bluetooth beacon has experienced a signal change. Step S232: When a signal change is detected, check whether there is a valid path in the Bluetooth Mesh network that forwards the Bluetooth beacon signal via other nodes; if there is no valid path, and the user terminal is determined to be in motion based on the current motion state, then mark the Bluetooth beacon as suspected obstruction; Step S233: For Bluetooth beacons marked as suspected obstruction, verification is performed based on the user terminal's movement direction and indoor map information; if the angle between the movement direction and the direction of the line connecting to the Bluetooth beacon is less than a preset angle, and there is a known obstacle on the line, then the Bluetooth beacon is marked as confirmed obstruction.

2. The lighting-linked shopping guide method as described in claim 1, characterized in that, Step S210 includes: Step S211: The inertial measurement unit of the user terminal collects inertial data at a first preset frequency; the inertial data includes acceleration, angular velocity and heading angle data; Step S212: Collect radio frequency signal data at a second preset frequency through the Bluetooth module of the user terminal; the radio frequency signal data includes Received Signal Strength Indicator (RSSI) and Time of Arrival (TOF) data from Bluetooth beacons, as well as forwarded signal data containing the number of forwarding hops and the forwarding node identifier, which is forwarded through the Bluetooth Mesh network. Step S213: Based on a unified time reference, align the inertial data and the radio frequency signal data with timestamps; Step S214: Remove outliers from the radio frequency signal data whose intensity exceeds a preset reasonable range; remove outliers from the inertial data whose intensity exceeds the physical motion range using the 3σ criterion; and calibrate the inertial data after removing outliers using adaptive complementary filtering.

3. The lighting-linked shopping guide method as described in claim 1, characterized in that, In step S220, the step of identifying the current motion state of the user terminal based on the inertial data in the fused data includes: Step S221: Extract motion features from the fused data of inertial data, the motion features including acceleration magnitude. angular velocity magnitude and rate of change of heading angle The specific calculation expression is as follows: In the formula, , , These are the acceleration components of the inertial measurement unit in the three coordinate axes; , , These are the angular velocity components of the inertial measurement unit along the three coordinate axes; For heading angle Rate of change over time; Step S222: Calculate the acceleration modulus value angular velocity magnitude and rate of change of heading angle Input a preset Naive Bayes classifier to identify the current motion state of the user terminal; the current motion state is classified according to preset acceleration and angular velocity thresholds, including stationary, walking, running, going up and down stairs, and turning.

4. The lighting-linked shopping guide method as described in claim 3, characterized in that, In step S220, the preset adaptive ranging model expression is as follows: In the formula, This represents the estimated distance from the user terminal to the Bluetooth beacon. These are the model correction coefficients corresponding to the current motion state; This indicates the received signal strength from the Bluetooth beacon. The reference signal strength is at 1 meter. The environmental attenuation index corresponding to the current motion state; Mesh forwarding hop count of the radio frequency signal data The corresponding weighting coefficients; This refers to the arrival time of the Bluetooth beacon signal. The inertial correction weight corresponding to the current motion state; To be based on the acceleration modulus The fluctuation amount is calculated within a preset time window.

5. The lighting-linked shopping guide method as described in claim 1, characterized in that, In step S230, the step of generating a corresponding fusion weight strategy for inertial data and radio frequency signal data in positioning based on the determination result includes: Step S234: Determine the current obstruction level based on the number of Bluetooth beacons marked as confirmed obstructions; Step S235: Based on the occlusion level, determine the dynamic fusion weight of the inertial data and the radio frequency signal data according to a preset compensation strategy; wherein, the higher the occlusion level, the greater the fusion weight assigned to the inertial data; Step S236: When the number of confirmed blocked Bluetooth beacons is detected to decrease to below a preset recovery threshold, perform signal recovery calibration to improve the fusion weight of the radio frequency signal data.

6. The lighting-linked shopping guide method as described in claim 1, characterized in that, Step S240 includes: Step S241: Based on the adaptive ranging result, the fused data, the current motion state, and the pre-built fingerprint database, construct a feature vector containing multiple positioning-related features; Step S242: Using an improved entropy weighting method that incorporates motion state information, dynamic weights are assigned to each feature in the feature vector. The formula is expressed as follows: In the formula, This represents the weight of the j-th feature in the feature vector under the current motion state s; The dynamic adaptation coefficient of the j-th feature corresponding to the current motion state s; Let be the information entropy of the j-th feature; j be the index of the feature vector; N be the dimension of the feature vector; Step S243: Input the weighted feature vector into the particle filter algorithm to calculate the real-time position of the user terminal.

7. The lighting-linked shopping guide method as described in claim 1, characterized in that, Step S400 includes: Step S410: When the distance between the real-time location of the user terminal and the location of the target shelf is less than a first preset distance threshold, control at least one adjustable intelligent spotlight to turn towards the target shelf area, so as to illuminate the area where the target shelf is located with a first lighting parameter that is different from the ambient lighting. Step S420: When the distance between the real-time location of the user terminal and the location of the target shelf is less than the second preset distance threshold, control the lighting unit deployed on the target shelf corresponding to the target product to work with the second lighting parameter, which is different from the first lighting parameter, to form a visual indication of the target product; Step S430: Send interactive information containing detailed information about the target product and light guidance prompts to the user terminal.

8. A lighting-linked shopping guide system, used to execute the lighting-linked shopping guide method as described in any one of claims 1 to 7, characterized in that, include: The shopping guide request processing module is used to receive a shopping guide request containing target product information sent by the user terminal, and determine the corresponding target shelf location based on the target product information; The anti-obstruction navigation path generation module generates a navigation path from the real-time location to the target shelf location based on the target shelf location, the real-time location and movement state of the user terminal, and according to a preset anti-obstruction indoor navigation algorithm. The dynamic lighting guidance control module is used to map the navigation path to the lighting topology network, generate dynamic lighting guidance instructions, and control the smart lights along the navigation path and at the target shelf location to light up according to a preset guidance mode, so as to form a light flow guidance pointing to the target shelf location. The near-field lighting interaction module is used to control the smart lighting device associated with the target product to switch to a lighting interaction mode to highlight the target product when the distance between the real-time position of the user terminal and the target shelf position is less than a preset distance threshold, and to send interactive information associated with the target product to the user terminal.

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