Bluetooth + UWB + RTK fusion positioning method and system

Abstract

The invention discloses a Bluetooth + UWB + RTK fusion positioning method and system, and relates to the technical field of multi-source fusion positioning and intelligent security management and control, and the method comprises the steps: collecting feature data of an environment where a positioning object is located, and obtaining environment feature data; performing preprocessing to obtain preprocessed environment feature data; judging a space scene where the positioning object is located to obtain a space scene type; setting fusion strategy parameters; bluetooth ranging data, UWB positioning data, RTK positioning data and inertial navigation data are respectively collected according to the fusion strategy parameters; carrying out fusion calculation to obtain real-time three-dimensional high-precision positioning data; real-time three-dimensional high-precision positioning data is utilized to monitor the position and track of the power grid worker in real time, and occurrence of emergencies is detected. According to the method, the positioning precision, the track continuity and the anti-interference capability in a complex scene are improved, the abnormal condition detection efficiency and the alarm response speed of power grid workers are remarkably improved, and technical guarantee is provided for power grid safety production and personnel protection.

Description

Technical Field

[0001] The present invention relates to the field of multi-source fusion positioning and intelligent safety management and control technology, and specifically to a Bluetooth+UWB+RTK fusion positioning method and system. Background Art

[0002] In recent years, with the rapid development of the Internet of Things (IoT), wireless communications, and high-precision positioning technologies, positioning methods based on multi-source fusion have become a research hotspot in fields such as smart power, the Industrial Internet, and emergency management. Technologies such as Bluetooth, ultra-wideband (UWB), and real-time kinematic (RTK) within the Global Navigation Satellite System (GNSS) have been widely used to locate people and objects in both indoor and outdoor environments. Each of these technologies offers advantages in specific scenarios: Bluetooth is suitable for low-power, large-scale deployment, UWB provides centimeter-level high-precision positioning, and RTK can achieve sub-meter to centimeter-level positioning over a wide area. However, in practical applications, a single technology often fails to meet the stringent requirements for positioning accuracy, continuity, and real-time performance in large-scale, complex scenarios. To enhance the intelligence and versatility of positioning systems, academia and industry continue to promote the fusion and dynamic optimization of multi-source, heterogeneous positioning data.

[0003] However, the existing positioning technology system still has many shortcomings. First, existing multi-mode fusion methods are often based on static weights or simple scene discrimination, making it difficult to achieve intelligent and adaptive switching of positioning modes in changing environments such as power grid plants and industrial sites, resulting in unstable positioning continuity and accuracy. Second, existing technologies are mostly limited to planar (two-dimensional) positioning. For complex three-dimensional spaces such as tall factories and multi-story buildings, they lack the ability to compensate for high-precision three-dimensional fusion based on multi-mode features, which can easily lead to problems such as floor misjudgment and altitude drift. In addition, traditional fusion algorithms are slow to respond to environmental changes, signal obstruction, and abnormal movement, which can easily lead to safety hazards such as false alarms and missed positioning. In scenarios such as power grids that require high reliability and real-time warnings, fusion methods that rely solely on a single positioning source or lack dynamic adaptive mechanisms cannot achieve accurate control and efficient response to worker locations, trajectories, and emergencies.

[0004] Therefore, there is an urgent need for a method that can fully perceive the environmental status, intelligently identify spatial scenes, and dynamically optimize the collaborative participation weights and algorithm strategies of various positioning technologies for different scenarios, thereby realizing high-precision and intelligent personnel safety positioning control methods in all spaces and all time periods. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problems solved by the present invention are: the existing multi-source fusion positioning methods are difficult to achieve intelligent scene recognition and dynamic weight adjustment in complex environments, lack a high-precision fusion compensation mechanism for three-dimensional space, and have insufficient positioning continuity and safety warning response capabilities, as well as how to achieve high-precision intelligent positioning of personnel across the entire area and real-time safety response to emergencies in complex scenarios such as power grids.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides a Bluetooth+UWB+RTK fusion positioning method, comprising collecting characteristic data of the environment in which the positioning object is located to obtain environmental characteristic data; Preprocessing the environmental characteristic data to obtain preprocessed environmental characteristic data; Based on the pre-processed environmental feature data, the spatial scene where the positioning object is located is determined to obtain the spatial scene type; Set the fusion strategy parameters of Bluetooth, UWB, RTK and inertial navigation according to the space scene type; According to the fusion strategy parameters, Bluetooth ranging data, UWB positioning data, RTK positioning data and inertial navigation data are collected respectively; Fuse Bluetooth ranging data, UWB positioning data, RTK positioning data and inertial navigation data to obtain real-time three-dimensional high-precision positioning data; Utilize real-time three-dimensional high-precision positioning data to monitor the location and trajectory of power grid workers in real time, and implement alarm response when emergencies are detected.

[0008] As a preferred solution of the Bluetooth + UWB + RTK fusion positioning method described in the present invention, the collection of characteristic data of the environment in which the positioning object is located includes collecting acceleration data, angular velocity data, air pressure data, Bluetooth signal strength data, UWB signal quality data, GNSS satellite signal strength data and lighting data of the current position of the positioning object, and combining them to obtain environmental characteristic data.

[0009] As a preferred solution of the Bluetooth + UWB + RTK fusion positioning method described in the present invention, the preprocessing of the environmental feature data includes denoising, normalization, time alignment and missing value completion of the environmental feature data to obtain preprocessed environmental feature data.

[0010] As a preferred solution of the Bluetooth + UWB + RTK fusion positioning method described in the present invention, the method of determining the spatial scene of the positioning object based on the pre-processed environmental feature data includes inputting the pre-processed environmental feature data into a preset scene discrimination model, determining whether the positioning object is currently in an ordinary indoor area, a high-precision indoor area, an outdoor open area, and a floor change area, and outputting the corresponding spatial scene type.

[0011] As a preferred solution of the Bluetooth + UWB + RTK fusion positioning method described in the present invention, the preset scene discrimination model includes using the labeled historical pre-processed environmental feature data and the actual spatial scene type, training through a machine learning algorithm, and establishing a classification model that can output the corresponding spatial scene type based on the input pre-processed environmental feature data.

[0012] As a preferred solution of the Bluetooth + UWB + RTK fusion positioning method described in the present invention, the setting of the fusion strategy parameters of Bluetooth, UWB, RTK and inertial navigation includes dynamically allocating the weight parameters of the Bluetooth, UWB, RTK and inertial navigation positioning data in the fusion calculation according to the spatial scene type obtained by discrimination, determining the data source type and priority involved in the fusion calculation, and setting the collection frequency and activation status of each data source.

[0013] As a preferred solution of the Bluetooth + UWB + RTK fusion positioning method described in the present invention, the separate collection of Bluetooth ranging data, UWB positioning data, RTK positioning data and inertial navigation data includes: obtaining Bluetooth ranging data by measuring the Bluetooth signal strength and calculating, obtaining UWB positioning data by two-way time-of-flight measurement of UWB signals, obtaining RTK positioning data by analyzing the satellite positioning differential signal by RTK method, and collecting inertial navigation data by measuring the acceleration and angular velocity of the positioning object.

[0014] As a preferred solution of the Bluetooth+UWB+RTK fusion positioning method of the present invention, wherein: the fusion calculation of Bluetooth ranging data, UWB positioning data, RTK positioning data and inertial navigation data includes calculating the initial three-dimensional position information of the positioning object using Bluetooth ranging data, UWB positioning data and RTK positioning data based on the fusion strategy parameters; Using inertial navigation data to calculate the real-time motion trend of the positioning object, and dynamically correcting the initial three-dimensional position information; The corrected initial three-dimensional position information is fused with the motion trend calculated by inertial navigation data through adaptive weighting to obtain real-time updated three-dimensional high-precision positioning data.

[0015] As a preferred embodiment of the Bluetooth+UWB+RTK fusion positioning method of the present invention, the real-time monitoring of the position and trajectory of power grid workers includes continuously comparing the current position of power grid workers with preset safety zone boundaries, historical trajectories, and operational specifications based on real-time three-dimensional high-precision positioning data to determine whether there are any abnormal conditions such as crossing the boundary, prolonged stationary state, gathering of people, abnormal movement trajectory, and entering a dangerous area. When any abnormal situation is detected, an alarm response is automatically made and pushed to the monitoring terminal.

[0016] In a second aspect, an embodiment of the present invention provides a Bluetooth+UWB+RTK fusion positioning system, including: Environmental feature data acquisition module: collects feature data of the environment where the positioning object is located to obtain environmental feature data; Preprocessing module: preprocesses environmental feature data to obtain preprocessed environmental feature data; Spatial scene discrimination module: discriminates the spatial scene where the positioning object is located based on the pre-processed environmental feature data and obtains the spatial scene type; Fusion strategy parameter setting module: Set the fusion strategy parameters of Bluetooth, UWB, RTK and inertial navigation according to the space scene type; Multi-source data acquisition module: collects Bluetooth ranging data, UWB positioning data, RTK positioning data and inertial navigation data according to the fusion strategy parameters; Multi-source fusion calculation module: fuses Bluetooth ranging data, UWB positioning data, RTK positioning data and inertial navigation data to obtain real-time three-dimensional high-precision positioning data; Trajectory monitoring and alarm module: Uses real-time three-dimensional high-precision positioning data to monitor the location and trajectory of power grid workers in real time, and implements alarm response when emergencies are detected.

[0017] Beneficial effects of the present invention: The present invention integrates multiple positioning data sources such as Bluetooth, UWB, RTK and inertial navigation, and combines intelligent identification of environmental characteristics and dynamic weight adaptive strategies to achieve high-precision three-dimensional positioning in all spaces and all scenarios under complex power grid environments. Compared with the existing technology, the present invention can not only significantly improve the continuity and accuracy of positioning across regions and floors indoors and outdoors, but also intelligently switch the optimal positioning strategy according to actual scene changes, effectively reducing errors caused by signal obstruction, multipath interference, etc. Through real-time monitoring and trajectory analysis, emergencies can be quickly discovered and responded to, which improves the operational safety and emergency response capabilities of power grid workers. The method of the present invention has the advantages of flexible deployment, strong scalability, high adaptability, stable positioning accuracy, etc., and can be widely used in the fields of smart power, industrial safety and intelligent positioning management of large-scale complex scenes. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive work, among which: Figure 1 This is an overall flow chart of a Bluetooth+UWB+RTK fusion positioning method provided by the first embodiment of the present invention. DETAILED DESCRIPTION

[0019] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0020] Example 1, reference Figure 1 , as an embodiment of the present invention, provides a Bluetooth+UWB+RTK fusion positioning method, including: S1: Collect characteristic data of the environment where the positioning object is located to obtain environmental characteristic data.

[0021] The acceleration data, angular velocity data, air pressure data, Bluetooth signal strength data, UWB signal quality data, GNSS satellite signal strength data and illumination data of the current position of the positioning object are collected and combined to obtain environmental feature data.

[0022] In step S1, the process involves multiple types of raw environmental perception data. Acceleration data and angular velocity data refer to the linear acceleration and rotational angular velocity of the positioning object at each moment in three-dimensional space, respectively. These two types of data can be obtained in real time using the accelerometer and gyroscope in the inertial measurement unit (IMU), effectively reflecting the instantaneous motion state and dynamic change trends of the positioning object. Barometric pressure data is used to sense changes in the object's altitude, enabling accurate identification of scenes such as upper and lower floors, stairways, and elevators. It is one of the core auxiliary information for achieving three-dimensional positioning. Bluetooth signal strength data reflects the changes in the distance between the positioning object and surrounding Bluetooth beacons, providing effective support for coarse-grained or auxiliary positioning. UWB signal quality data analyzes the reception strength and arrival time of ultra-wideband signals to evaluate the wireless environment's connectivity and multipath effects. This helps distinguish between obstructed and highly visible scenes, improving the adaptability and anti-interference capabilities of positioning fusion. "GNSS satellite signal strength data" reflects an object's ability to receive Beidou / GPS and other satellite signals outdoors, helping to determine whether the object is in an open environment or obstructed by structures, providing a basis for dynamically switching positioning modes. "Lighting data" can be collected through photosensors to help determine whether the object is in a bright or dark environment, or whether it has entered an enclosed or semi-enclosed space.

[0023] The above seven types of data are combined to form a multimodal environmental feature dataset, enabling comprehensive perception of the spatial environment. Compared to relying solely on a single sensor source (such as inertial or wireless signals), this solution leverages multi-dimensional collaborative perception to not only effectively identify the complex spatial scenarios (e.g., indoor / outdoor, different floors, and obstructed / unobstructed) within which the positioning object resides, but also capture dynamic environmental changes in real time (e.g., entering special areas such as elevators, corridors, and tunnels). This comprehensive environmental perception mechanism lays the foundation for subsequent intelligent spatial scene recognition, adaptive adjustment of positioning source weights, and the reliability of multi-source fusion algorithms. Specifically, relying solely on IMU inertial information is prone to cumulative drift errors, while relying solely on Bluetooth or UWB signals is severely affected by obstructions. Fusion of acceleration, angular velocity, and air pressure with multiple wireless signal quality and light level data can dynamically enhance the positioning system's environmental adaptability and fault robustness in real-world engineering scenarios, effectively ensuring the sustainability and consistency of high-precision positioning results in large-scale, highly variable environments.

[0024] S2: Preprocess the environmental characteristic data to obtain preprocessed environmental characteristic data.

[0025] The environmental feature data are subjected to denoising, normalization, time alignment and missing value filling processing to obtain preprocessed environmental feature data.

[0026] In step S2, "preprocessing the environmental characteristic data to obtain preprocessed environmental characteristic data" includes performing a series of signal processing operations on the collected acceleration data, angular velocity data, air pressure data, Bluetooth signal strength data, UWB signal quality data, GNSS satellite signal strength data and illumination data, specifically including denoising, normalization, time alignment and missing value completion.

[0027] Denoising refers to filtering out high-frequency noise and outliers introduced into raw environmental feature data due to electromagnetic interference, device jitter, and occasional failures. Methods such as sliding average, low-pass filtering, and Kalman filtering can be used to effectively improve data stability and reliability. Normalization involves mapping data from different sources and dimensions to the same numerical range (e.g., 0–1). This eliminates weight bias in subsequent discrimination and fusion algorithms, ensuring comparability and synergy between features like acceleration and signal strength within the same fusion framework. Time alignment involves aligning multi-source data with different acquisition timestamps or inconsistent sampling frequencies to ensure temporal consistency and complete scene characterization for subsequent multimodal analysis. Missing value completion corrects for data gaps caused by short-term signal loss or sensor anomalies during actual acquisition. Linear interpolation, nearest neighbor interpolation, or model prediction are used to ensure complete feature data within each time window, enhancing algorithm robustness.

[0028] This preprocessing step plays a key role in the actual system deployment. On the one hand, through denoising and missing value completion, the real pain points of easy signal distortion and interruption in complex power / industrial environments are effectively overcome, ensuring the basic data quality of subsequent spatial scene discrimination and multi-source fusion; on the other hand, normalization and time alignment ensure that the environmental characteristics from different sensor sources are comparable and spatiotemporally coordinated, greatly reducing the risk of fusion errors caused by differences in sensor accuracy or acquisition delays. Compared with the common direct input of raw data or single preprocessing methods in the prior art, the multi-link and multi-method comprehensive preprocessing of the present invention greatly improves the reliability and utilization efficiency of environmental feature data, and provides a solid foundation for the subsequent intelligent discrimination of complex scenes and adaptive adjustment of fusion strategies, thereby supporting the entire system to achieve the goal of high-precision personnel positioning and safety management in all scenarios.

[0029] S3: Based on the pre-processed environmental feature data, the spatial scene where the positioning object is located is determined to obtain the spatial scene type.

[0030] The pre-processed environmental feature data is input into the preset scene discrimination model to determine whether the positioning object is currently in an ordinary indoor area, a high-precision indoor area, an outdoor open area, or a floor change area, and the corresponding spatial scene type is output.

[0031] In this step, the so-called "spatial scene type" refers to the subdivision of the current environment of the positioning object, for example: ordinary indoor areas (such as general working areas in large factories), high-precision indoor areas (such as areas where precise operations or high-risk precision equipment are required), open outdoor areas (such as factory roads and open-air equipment areas), and floor change areas (such as vertical movement scenes such as stairs, elevators, slopes, and intersections).

[0032] The preprocessed environmental feature data obtained in step S2 is fed into a pre-set scene discrimination model. The model then automatically categorizes the environment based on the spatiotemporal distribution and statistical properties of various features, such as acceleration, air pressure, and signal strength. For example, sudden changes in air pressure and vertical acceleration often indicate floor transitions. A strong GNSS signal and a weak UWB / Bluetooth signal indicate an outdoor location, while a weak signal indicates an indoor location. This automatic discrimination enables instant identification of environmental types in complex scenarios, avoiding the challenges of traditional methods such as manually set thresholds and scene switching delays, and providing scientific foundational data for downstream adaptive fusion strategies.

[0033] The preset scene discrimination model includes using the labeled historical pre-processed environmental feature data and the actual spatial scene type, training through a machine learning algorithm, and establishing a classification model that can output the corresponding spatial scene type based on the input pre-processed environmental feature data.

[0034] Furthermore, the "Spatial Scene Type Classification Model" uses a two-stage judgment process of "rule screening + supervised learning" to identify spatial scene types. Using a sliding time window of two seconds in length and a step size of 0.5 seconds, preprocessed multi-source features are extracted as model inputs. These features include: the mean and variance of the three-axis acceleration and angular velocity within the time window, the mean of the vertical acceleration, and the discrimination score for stationary or zero speed; the mean air pressure, the pressure difference within three seconds, and the rate of change of air pressure; the stability of the ultra-wideband arrival time estimate, the confidence level of the line of sight derived from the head-path to multipath energy ratio, and the variance of the ranging residual; the number of visible satellites, the median carrier-to-noise ratio, the horizontal and positional dilutions of precision, the availability of differential corrections, and the stability of the baseline solution for satellite positioning; the number of visible Bluetooth beacons, the mean and variance of their signal strength, and the neighboring beacon density index; and the mean and fluctuation amplitude of illumination. The first-level decision uses rapid screening based on rules. For example, if the number of visible satellites is at least eight and the horizontal precision dilution is no greater than 1.5, the candidate is prioritized as an outdoor candidate. A three-second pressure difference is no less than 0.600 Pa and the vertical acceleration variance increases significantly, the candidate is marked as a floor change candidate. The second-level decision uses a supervised classifier trained on labeled data (e.g., a random forest with approximately 200 trees, a maximum depth of six, and balanced class weights). This outputs probability distributions for four categories: outdoor open, indoor standard, indoor high-precision, and floor change. A hysteresis condition is set, requiring the switch to occur only when three consecutive time windows are consistent, to prevent category jitter.

[0035] Furthermore, the corresponding relationship between features and scenarios is as follows: Outdoor open conditions typically manifest as at least eight visible satellites, a horizontal dilution of precision no greater than 1.5, a median carrier-to-noise ratio no less than 35dBHz, a small number of visible Bluetooth beacons, and a lower-than-typical UWB line-of-sight confidence level than indoor conditions. Indoor normal conditions typically manifest as at least five visible Bluetooth beacons, UWB availability, a medium line-of-sight confidence level, and unstable satellite positioning (e.g., no more than four visible satellites or a horizontal dilution of precision greater than three). Indoor high-precision conditions typically manifest as an UWB line-of-sight confidence level no less than 0.7, a low variance in ranging residuals, at least eight visible Bluetooth beacons, and virtually unavailable satellite positioning. Floor change conditions typically manifest as a pressure difference no less than 0.600 Pa within three seconds, an increase in vertical acceleration variance by approximately 50% compared to the previous time window, and a monotonic change in altitude or a brief decrease in horizontal velocity. These rules serve as the basis for first-level rapid screening and also provide interpretable prior constraints for supervised learning models.

[0036] It should also be noted that the "pre-set scene discrimination model" refers to a training set using a large amount of historically collected, labeled data (i.e., each set of feature data corresponds to its actual scene type). This model is trained using a machine learning algorithm, ultimately forming an automatic classifier whose input is multidimensional environmental features and whose output is the spatial scene type. Specific implementation methods include decision trees, support vector machines (SVMs), random forests, and neural networks. Decision trees enable interpretable rules based on feature segmentation, while neural networks are suitable for nonlinear feature extraction in complex environments with large sample sizes. The training process repeatedly adjusts model parameters to optimize classification accuracy, ultimately resulting in a discriminant model with strong generalization capabilities and good adaptability to new environments. Unlike traditional rule-based judgment or single-feature thresholding methods, this approach can globally learn the discrimination logic for complex scenes from multimodal features, effectively avoiding misjudgments when a single feature is unstable or abnormal, significantly improving the accuracy of the system's environmental adaptation and spatial type discrimination.

[0037] S4: Set the fusion strategy parameters of Bluetooth, UWB, RTK and inertial navigation according to the space scene type; According to the identified spatial scene type, the weight parameters of Bluetooth, UWB, RTK and inertial navigation positioning data in the fusion calculation are dynamically allocated, the data source types and their priorities involved in the fusion calculation are determined, and the collection frequency and activation status of each data source are set.

[0038] In step S4, "based on the type of space scene, set the fusion strategy parameters of Bluetooth, UWB, RTK and inertial navigation", which involves multiple key technical links such as the allocation of fusion weight parameters of multi-source positioning data, determination of data source participation type, collection frequency setting and activation status switching.

[0039] The "fusion weight parameter" refers to the numerical weights assigned to the four types of positioning data—Bluetooth, UWB, RTK, and inertial navigation—in subsequent fusion calculations based on the currently identified spatial scenario type (e.g., ordinary indoors, high-precision indoors, open outdoors, floor-to-floor). For example, in open outdoor areas, due to strong GNSS / RTK signals, the weight of RTK data is significantly increased, and UWB and Bluetooth data are automatically reduced or even ignored. In high-precision indoor areas, the weight of UWB data is increased, and inertial navigation and Bluetooth assistance are appropriately introduced. For floor-to-floor or signal-blocking scenarios, the participation of inertial navigation and barometric pressure data is strengthened to compensate for areas where traditional wireless positioning is prone to failure.

[0040] Specifically, the weight parameters for each positioning data type in the fusion calculation are dynamically assigned. Scenario-based weights are set based on the determined scenario type (Bluetooth, UWB, RTKD, and inertial navigation), and are corrected online based on source quality. Example scenario-based weights are as follows: in open outdoor environments, RTKD accounts for approximately 55%, inertial for approximately 25%, UWB for approximately 15%, and Bluetooth for approximately 5%; in ordinary indoor environments, UWB accounts for approximately 45%, Bluetooth for approximately 25%, inertial for approximately 25%, and RTKD for approximately 5%; in high-precision indoor environments, UWB accounts for approximately 60%, inertial for approximately 30%, and Bluetooth for approximately 10%; in floor-to-floor environments, inertial for approximately 45%, UWB for approximately 30%, Bluetooth for approximately 15%, and RTKD for approximately 10%. Source quality scores are derived as follows: Real-time dynamic differencing (RTD) is based on a combination of the median carrier-to-noise ratio (CNR), horizontal or positional precision dilution (DOP), and the number of visible satellites; ultra-wideband (UWB) is based on a combination of line-of-sight confidence and ranging residual variance; Bluetooth is based on a combination of the number of visible beacons and signal strength variance; and inertial is based on a combination of short-window stability and the inverse of bias estimation. Temporary weights are normalized by multiplying the scene-based weights by the quality score for each source and are limited to a minimum of 5% and a maximum of 75% for a single source. Exponential smoothing is used for temporal analysis, with an update period of 0.5 seconds to suppress transient fluctuations.

[0041] "Data source types and priorities involved in fusion calculations" refers to the automatic determination of which data sources will be included in the fusion calculations and their processing order based on the real-time spatial scenario. For example, when degraded UWB signal quality or GNSS signal loss is detected, the system automatically prioritizes inertial navigation and Bluetooth to ensure positioning continuity and availability. Specifically, source selection gating and hysteresis strategies automatically determine which data sources to include or bypass. When fewer than five satellites are visible or the position dilution of precision is greater than four, real-time kinematic differentials are bypassed, serving only as constraints and not directly participating in the solution. They are then included in the main solution only when the number of visible satellites returns to at least eight and the median carrier-to-noise ratio is no less than 35 decibels per second. Ultra-wideband (UWB) line-of-sight confidence level is less than 0.35 or the ranging residual variance exceeds a certain threshold, which data source is temporarily excluded. Its weighting is gradually increased after three consecutive time windows have returned to 0.5 or above. Bluetooth is used only as auxiliary information when fewer than three beacons are visible or when signal strength fluctuations exceed a threshold. In floor-to-floor scenarios, the inertia and air pressure contributions are increased to a minimum of 40% combined, while the growth rate of other contributions is limited to avoid high jitter. Regardless of the scenario, the minimum contribution of inertia is maintained at a minimum of 10% to ensure trajectory continuity.

[0042] The "collection frequency" and "activation status" settings further demonstrate the system's dynamic trade-off between power consumption and accuracy. In normal operating areas, some data sources can reduce sampling frequency or enter dormancy to conserve energy. In high-risk or critical locations, the collection frequency and operating status of UWB / RTK and inertial navigation systems are automatically increased, enabling precise, on-demand control.

[0043] S5: According to the fusion strategy parameters, Bluetooth ranging data, UWB positioning data, RTK positioning data and inertial navigation data are collected respectively.

[0044] Bluetooth ranging data is obtained by measuring the Bluetooth signal strength and calculating it, UWB positioning data is obtained through two-way time-of-flight measurement of UWB signals, RTK positioning data is obtained by analyzing the satellite positioning differential signal through RTK, and inertial navigation data is collected by measuring the acceleration and angular velocity of the positioning object.

[0045] In step S5, "Bluetooth ranging data" is obtained by measuring the real-time signal strength (RSSI) between the positioning object and surrounding Bluetooth beacons, estimating the distance based on a propagation loss model, and ultimately obtaining the positioning object's distance to each beacon. This method enables low-power assisted positioning in environments with wide coverage and flexible deployment. It can serve as a backup data source in the event of UWB or RTK failure in large, general operating areas.

[0046] "UWB positioning data" leverages the high temporal resolution of UWB (ultra-wideband) communications, employing two-way Time of Flight (TOF) ranging to precisely measure the propagation delay between the target and multiple UWB base stations. Combined with the base station's spatial coordinates, this data is used to achieve centimeter-level, high-precision two- or three-dimensional positioning. UWB ranging offers exceptional resistance to multipath interference and performs exceptionally well in complex factory buildings and metal-intensive areas, making it a core data source for high-precision positioning in critical scenarios.

[0047] RTK positioning data is obtained by receiving satellite navigation signals and applying a real-time kinematic (RTK) algorithm to obtain high-precision three-dimensional coordinates. This data is suitable for outdoor open areas and cross-regional scenarios requiring absolute spatial coordinates. It provides precise positioning within sub-meter or even centimeter-level errors and serves as a benchmark for the global consistency of multi-source positioning systems.

[0048] Inertial navigation data measures the three-axis acceleration and angular velocity of the positioned object, providing real-time estimates of its motion state, changing trends, and short-term displacement compensation. Inertial data is particularly useful during periods of brief signal loss or sudden environmental changes, enabling seamless and continuous positioning trajectory compensation and effectively minimizing error accumulation caused by system interruptions.

[0049] Based on the fusion strategy parameters, the frequency, participation, and priority of each data type can be dynamically adjusted based on the spatial scenario and the system's real-time status. For example, during floor changes or signal obstruction, the system increases the frequency of inertial navigation data collection to ensure trajectory continuity. In areas with strong UWB and RTK signals, the workload of inertial and Bluetooth navigation systems is reduced, achieving optimal synergy between accuracy and energy consumption.

[0050] S6: Fuse the Bluetooth ranging data, UWB positioning data, RTK positioning data and inertial navigation data to obtain real-time three-dimensional high-precision positioning data.

[0051] Based on the fusion strategy parameters, the initial three-dimensional position information of the positioning object is calculated using the Bluetooth ranging data, the UWB positioning data and the RTK positioning data; Using inertial navigation data to calculate the real-time motion trend of the positioning object, and dynamically correcting the initial three-dimensional position information; The corrected initial three-dimensional position information is fused with the motion trend calculated by inertial navigation data through adaptive weighting to obtain real-time updated three-dimensional high-precision positioning data.

[0052] In step S6, "Bluetooth ranging data, UWB positioning data, RTK positioning data and inertial navigation data are integrated and calculated to obtain real-time three-dimensional high-precision positioning data." This process is the core technical link for the multimodal positioning system of the present invention to achieve high precision, continuity and anti-interference capability improvement.

[0053] Fusion strategy parameters are dynamically set based on intelligent spatial scene analysis in the preceding steps. They control the weighting and priority of various data sources in the fusion algorithm. For example, UWB is given a higher weight in indoor high-precision areas, while inertial navigation is prioritized in areas with floor transitions. This dynamic weighting adjustment significantly enhances the system's adaptability.

[0054] "Calculating the initial 3D position of the object using Bluetooth ranging data, UWB positioning data, and RTK positioning data" involves performing a multi-point positioning solution (such as least squares, Kalman filtering, and extended Kalman filtering) on ​​the distance and coordinate information collected by each data source to obtain preliminary 3D coordinates of the current position. The complementary spatial coverage and accuracy of different data sources in different scenarios ensure that the initial positioning is stable and globally consistent across the entire space.

[0055] "Using inertial navigation data to calculate the real-time motion trend of the positioned object and dynamically correct the initial 3D position information" refers to predicting and compensating the initial 3D coordinates based on short-term motion estimates derived from acceleration and angular velocity. This approach leverages the short-term, high-frequency advantage of inertial navigation to maintain trajectory continuity when wireless signals fade or are temporarily lost, avoiding positioning jumps or interruptions caused by anomalies in a single data source.

[0056] Adaptive weighted fusion refers to a system that uses real-time fusion strategies to perform weighted averaging or probabilistic fusion of initial positioning coordinates and inertial navigation motion trend results using fusion algorithms such as weighted Kalman filtering and Bayesian inference. This method leverages the strengths of each data source, eliminates errors caused by anomalies or drift, and ultimately outputs real-time, error-minimized, high-precision three-dimensional positioning results. The weights here are not only based on environmental discrimination but also dynamically perceive the actual error level and confidence level of each source, achieving accurate and robust positioning in all scenarios.

[0057] S7: Utilizes real-time, three-dimensional, high-precision positioning data to monitor the location and trajectory of power grid personnel in real time, and implements alarm responses when emergencies are detected.

[0058] Based on real-time 3D high-precision positioning data, the current location of power grid workers is continuously compared with the preset safe zone boundaries, historical trajectories, and operational specifications to determine whether there are any abnormal situations such as crossing the boundary, prolonged inactivity, gathering of people, abnormal movement trajectories, and entering dangerous areas. When any abnormal situation is detected, an alarm response is automatically made and pushed to the monitoring terminal.

[0059] In step S7, "real-time three-dimensional high-precision positioning data is used to monitor the location and trajectory of power grid personnel in real time, and an alarm response is implemented when an emergency is detected." This link is the core implementation step of the present invention for intelligent management of security scenarios.

[0060] "Real-time three-dimensional high-precision positioning data" refers to the current three-dimensional coordinate points and time-series trajectories of power grid workers, which are dynamically updated through the aforementioned multi-source fusion algorithm. It contains absolute spatial position, altitude information, and movement direction, providing high-precision, all-time positioning support for safety management.

[0061] The system continuously compares the current location with pre-set safe zone boundaries, historical trajectories, and operational specifications. This means the system has built-in spatial boundaries (such as work zones, prohibited areas, and danger zones), historical trajectory data (used to determine normal or abnormal movement paths), and operational specifications (such as single-person operation requirements and maximum dwell time). For each set of real-time positioning data, the system automatically performs rule comparison and spatiotemporal analysis.

[0062] "Determine whether there are abnormal situations such as crossing the boundary, prolonged inactivity, gathering of people, abnormal movement trajectory, and entering dangerous areas", specifically including: Out of Boundary: The positioning point crosses the boundary of the work permit area, indicating a violation of regulations or mistaken entry into a dangerous area; Long-term inactivity: If the 3D coordinates of the same person remain unchanged for a long period of time, an accident or illegal stay may occur; Gathering of people: Multiple staff members gather in the same space within a short period of time, violating on-site control regulations or causing an emergency situation; Abnormal motion trajectory: such as abnormal motion speed, sudden breakpoints in the trajectory, large Z-axis jumps, etc., may be caused by falls, slips, or equipment abnormalities; Entering hazardous areas: Positioning points to enter hazardous areas such as high voltage, flammable, and confined spaces.

[0063] "Automatic alarm response and push notification to monitoring terminals" means that once the system detects any of the above abnormal events, it will automatically trigger an alarm mechanism. This can include multi-channel simultaneous warnings such as audio and visual alarms, SMS / APP push notifications, and pop-up notifications on the monitoring screen. The event location, time, and personnel information will be pushed to the monitoring center and relevant responsible personnel in real time, enabling rapid intervention and disposal.

[0064] Example 2 is the second embodiment of the present invention, which is different from the previous embodiment in that: If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art or the portion of the current technical solution, can be embodied in the form of a software product. The current computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0065] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0066] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, and then editing, interpreting, or processing in another suitable manner as necessary, and then storing it in a computer memory.

[0067] Example 3 is an embodiment of the present invention, which provides a Bluetooth + UWB + RTK fusion positioning system, including an environmental feature data acquisition module, a preprocessing module, a spatial scene discrimination module, a fusion strategy parameter setting module, a multi-source data acquisition module, a multi-source fusion calculation module and a trajectory monitoring and alarm module.

[0068] Environmental feature data acquisition module: collects feature data of the environment where the positioning object is located to obtain environmental feature data; Preprocessing module: preprocesses environmental feature data to obtain preprocessed environmental feature data; Spatial scene discrimination module: discriminates the spatial scene where the positioning object is located based on the pre-processed environmental feature data and obtains the spatial scene type; Fusion strategy parameter setting module: Set the fusion strategy parameters of Bluetooth, UWB, RTK and inertial navigation according to the space scene type; Multi-source data acquisition module: collects Bluetooth ranging data, UWB positioning data, RTK positioning data and inertial navigation data according to the fusion strategy parameters; Multi-source fusion calculation module: fuses Bluetooth ranging data, UWB positioning data, RTK positioning data and inertial navigation data to obtain real-time three-dimensional high-precision positioning data; Trajectory monitoring and alarm module: Uses real-time three-dimensional high-precision positioning data to monitor the location and trajectory of power grid workers in real time, and implements alarm response when emergencies are detected.

[0069] Example 4 is an embodiment of the present invention, which provides a Bluetooth+UWB+RTK fusion positioning method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation / comparative experiments.

[0070] This experiment used a power facility as a typical test scenario. Positioning testing was conducted in various areas of the facility (including indoor standard areas, indoor high-precision areas, outdoor open areas, and floor transition areas). Six field workers, designated as test subjects A through F, underwent positioning testing. Prior to the test, a positioning system was established by deploying Bluetooth beacons, UWB base stations, and RTK ground differential base stations. Each test subject was equipped with a fusion positioning terminal to ensure the test environment resembled actual power grid operations.

[0071] During the experiment, each subject carried a fusion positioning terminal and moved around in different areas. First, the positioning terminal collected real-time environmental characteristic data, including acceleration, angular velocity, air pressure, Bluetooth signal strength, UWB signal quality, GNSS satellite signal strength, and illumination data. This data was synchronously collected and stored on a local server. The server then preprocessed the collected environmental characteristic data, including denoising, normalization, time alignment, and missing value imputation to ensure data integrity and accuracy. This preprocessed environmental characteristic data was then input into a pre-trained scene discrimination model to quickly and accurately determine the subject's current spatial context. Strategy parameters for the fusion positioning data of Bluetooth, UWB, RTK, and inertial navigation were automatically set based on the scenario type, and Bluetooth ranging data, UWB positioning data, RTK positioning data, and inertial navigation data were collected separately. This data was fed into the fusion positioning calculation module in real time to obtain a highly accurate, updated 3D position. Finally, the positioning system's backend monitoring platform monitored the location and movement trajectory of each test subject in real time. Upon detecting an abnormal event, such as crossing a boundary, being stationary for too long, or entering a dangerous area, the system immediately triggered an alarm mechanism and quickly pushed the alarm information to the on-site monitoring terminal, enabling a rapid response. The entire test process was carried out strictly in accordance with the pre-set experimental plan to objectively record the actual results of the method of this invention. For specific reference data, please refer to Table 1.

[0072] Table 1 Data reference table

[0073] It can be seen from the above test data table that the fusion positioning method of the present invention performs significantly better than the single technology positioning method in various indicators. First, in terms of positioning accuracy, the positioning error of Bluetooth single technology is generally more than 2 meters, which cannot meet the needs of precise positioning; the positioning errors of UWB and RTK single technologies are between 0.33 meters and 0.45 meters respectively. Although they have high accuracy, there are certain risks of stability and accuracy fluctuations in complex environments or dynamic scenes. After the fusion positioning method proposed by the present invention, the positioning errors of each test object were significantly reduced, and the positioning error after fusion was stably maintained between 0.15 meters and 0.20 meters, showing a clear accuracy advantage. Especially in the case of test object D, the fusion positioning error was 0.15 meters, which is more than double the positioning accuracy of single UWB and RTK methods.

[0074] In addition, from the perspective of abnormal event detection and alarm response speed indicators, the method of the present invention also performs outstandingly. The fusion positioning technology can not only quickly detect the abnormal position behavior of the test object, but also the detection time is generally within the range of 13.5 seconds to 16.7 seconds, and the alarm response time is stable between 2.1 seconds and 2.6 seconds, showing efficient real-time warning and rapid response capabilities. Compared with the alarm response delay or false alarm and missed alarm problems commonly found in the prior art, the present invention can effectively improve the safety management efficiency and emergency response speed of power grid workers in actual production operations, and has obvious on-site practical value and significant technical advantages.

[0075] Based on the above analysis, the innovation and novelty of the present invention lies in the realization of the intelligent dynamic fusion of multiple positioning technologies, which significantly improves the system's positioning accuracy, continuity, anti-interference ability and abnormal event response speed, and effectively solves the problems of poor adaptability to complex environments and insufficient security capabilities in existing technologies.

[0076] To further demonstrate the mechanism and technical effects of key technical measures in real working conditions, four representative working conditions were selected for chain recording (input factors → scenario discrimination → weight adjustment and source inclusion / bypass → positioning error and continuity → comparative proportion); Outdoor open working conditions (verifying the effect of "scene discrimination + dynamic weight"): walking continuously on the road outside the factory, the number of visible satellites is 11, the horizontal precision factor is 1.2, and the median carrier-to-noise ratio is 36; the number of visible Bluetooth beacons is 2, and the ultra-wideband line-of-sight confidence is low. The probability of the two-level discrimination output "outdoor open" is 0.91, and it stabilizes to this scene after triggering the hysteresis condition. Based on the quality score, the system stably increases the fusion weight of real-time dynamic difference to about 0.62, inertia to about 0.23, ultra-wideband to about 0.10, and Bluetooth to about 0.05. The planar error of the fusion result is 0.18 m, which is about 42% lower than the fixed weight comparison ratio (0.31 m); on the same trajectory, the number of scene jitters is reduced from 5 times in the comparison ratio to 0 times; Indoor high-precision working conditions (verifying the effect of "indoor UWB dominance + Z-axis drift suppression"): Circumventing in the high-precision equipment area, the ultra-wideband line-of-sight confidence is 0.78, the ranging residual variance is low, the number of Bluetooth visible beacons is 9, and the satellite is unavailable. The probability of the two-level discrimination output "indoor high precision" is 0.88, and it remains stable after confirmation by three-window hysteresis. The system increases the ultra-wideband fusion weight to about 0.66, inertia to about 0.27, and Bluetooth to about 0.07, and implements time smoothing for altitude measurement. The three-dimensional fusion error is 0.14m. Compared with the comparison ratio using only ultra-wideband, the Z-axis drift peak is reduced by about 55%, below 0.25m; the comparison ratio has instantaneous altitude jitter at the edge of turns and occlusions, with a peak of 0.41m; Floor change conditions (verifying the effectiveness of "gating / bypass + minimum participation + continuous transition"): After ascending the stairs, entering the elevator, and then descending, the air pressure difference within three seconds was 0.7 hPa, and the vertical acceleration variance increased by approximately 60% compared to the previous time window. The two-level discrimination output had a probability of 0.86 for "floor change" and remained stable; the system forced the combined participation of inertia and air pressure to be no less than 40%, while limiting the sudden increase rate of weights from other sources to prevent altitude jitter; the satellite signal weakened for a time, and the real-time dynamic difference was automatically bypassed, providing only constraint information; after the elevator exited the station, the signal was restored and automatically incorporated into the main solution according to the three-window slow rise strategy. Positioning was continuous and uninterrupted within the transition period of 10 seconds, with a final altitude error of 0.28 m; in the comparison (without gating and hysteresis), coordinate jumps occurred in the elevator section, with a maximum of 0.63 m, and a second jump occurred during recovery. Common, short-term indoor non-line-of-sight (NLOS) occlusion conditions (verifying the effectiveness of "quality-driven weight reduction and jump suppression"): Within a large metal equipment channel, the UWB line-of-sight confidence dropped from 0.65 to 0.28 for 2 seconds, with 6 Bluetooth beacons visible. The system automatically reduced the UWB weight to approximately 0.12 within two time windows, while increasing the combined Bluetooth and inertial weights to approximately 0.63, maintaining minimum inertial participation to ensure trajectory continuity. After the occlusion was removed, the system gradually restored UWB dominance according to a three-window ramp-up strategy. The maximum position jump in this section was 0.42 m, compared to 0.95 m for the fixed-weight scheme. The time required to return to steady state was three time windows longer than that of the original solution. End-to-end performance of the alarm chain (verifying the closed loop of "continuity → detection → response"): In a mixed task combining the four aforementioned operating conditions, emergency detection time remained within the 13.5–16.7 second range, and alarm response time remained within the 2.1–2.6 second range. In three safety drills, the false alarm rate due to jitter switching and jumps decreased from 6.8% in the control group to 2.3%, shortening the average response time by approximately 27%. These results demonstrate that two-level scene discrimination, quality-driven dynamic weighting, hysteresis and time smoothing, data source gating / bypassing, and a minimum participation mechanism synergize under critical operating conditions such as cross-scene, transition, and occlusion, significantly improving positioning accuracy, continuity, and robustness without sacrificing response speed, while also reducing the risk of false alarms.

[0077] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A Bluetooth+UWB+RTK fusion positioning method, characterized in that: include: Collect characteristic data of the environment in which the positioning object is located to obtain environmental characteristic data; Preprocessing the environmental characteristic data to obtain preprocessed environmental characteristic data; Based on the pre-processed environmental feature data, the spatial scene where the positioning object is located is determined to obtain the spatial scene type; Set the fusion strategy parameters of Bluetooth, UWB, RTK and inertial navigation according to the space scene type; According to the fusion strategy parameters, Bluetooth ranging data, UWB positioning data, RTK positioning data and inertial navigation data are collected respectively; Fuse Bluetooth ranging data, UWB positioning data, RTK positioning data and inertial navigation data to obtain real-time three-dimensional high-precision positioning data; Utilize real-time three-dimensional high-precision positioning data to monitor the location and trajectory of power grid workers in real time, and implement alarm response when emergencies are detected.

2. The Bluetooth+UWB+RTK fusion positioning method according to claim 1, characterized in that: The collecting of characteristic data of the environment in which the positioning object is located includes collecting acceleration data, angular velocity data, air pressure data, Bluetooth signal strength data, UWB signal quality data, GNSS satellite signal strength data and lighting data of the current position of the positioning object, and combining them to obtain environmental characteristic data.

3. The Bluetooth+UWB+RTK fusion positioning method according to claim 2, characterized in that: The preprocessing of the environmental feature data includes denoising, normalizing, time alignment and missing value filling of the environmental feature data to obtain preprocessed environmental feature data.

4. The Bluetooth+UWB+RTK fusion positioning method according to claim 3, wherein: The method of determining the spatial scene in which the positioning object is located based on the pre-processed environmental feature data includes inputting the pre-processed environmental feature data into a preset scene discrimination model, determining whether the positioning object is currently in an ordinary indoor area, a high-precision indoor area, an outdoor open area, and a floor change area, and outputting the corresponding spatial scene type.

5. The Bluetooth+UWB+RTK fusion positioning method according to claim 4, characterized in that: The preset scene discrimination model includes using the labeled historical pre-processed environmental feature data and the actual spatial scene type, training through a machine learning algorithm, and establishing a classification model that can output the corresponding spatial scene type based on the input pre-processed environmental feature data.

6. The Bluetooth+UWB+RTK fusion positioning method according to claim 5, characterized in that: The setting of the fusion strategy parameters for Bluetooth, UWB, RTK and inertial navigation includes dynamically allocating the weight parameters of the Bluetooth, UWB, RTK and inertial navigation positioning data in the fusion calculation according to the spatial scene type obtained by discrimination, determining the data source types and their priorities involved in the fusion calculation, and setting the collection frequency and activation status of each data source.

7. The Bluetooth+UWB+RTK fusion positioning method according to claim 6, characterized in that: The separate collection of Bluetooth ranging data, UWB positioning data, RTK positioning data and inertial navigation data includes: obtaining Bluetooth ranging data by measuring the Bluetooth signal strength and calculating it, obtaining UWB positioning data by two-way time flight measurement of UWB signals, obtaining RTK positioning data by analyzing the satellite positioning differential signal by RTK method, and collecting inertial navigation data by measuring the acceleration and angular velocity of the positioning object.

8. The Bluetooth+UWB+RTK fusion positioning method according to claim 7, characterized in that: The fusing and calculating the Bluetooth ranging data, the UWB positioning data, the RTK positioning data and the inertial navigation data includes calculating the initial three-dimensional position information of the positioning object using the Bluetooth ranging data, the UWB positioning data and the RTK positioning data based on the fusion strategy parameters; Using inertial navigation data to calculate the real-time motion trend of the positioning object, and dynamically correcting the initial three-dimensional position information; The corrected initial three-dimensional position information is fused with the motion trend calculated by inertial navigation data through adaptive weighting to obtain real-time updated three-dimensional high-precision positioning data.

9. The Bluetooth+UWB+RTK fusion positioning method according to claim 8, characterized in that: Real-time monitoring of the location and trajectory of power grid workers includes continuously comparing the current location of power grid workers with preset safety zone boundaries, historical trajectories, and operational specifications based on real-time three-dimensional high-precision positioning data to determine whether there are any abnormal situations such as crossing boundaries, prolonged inactivity, gathering of people, abnormal movement trajectories, and entering dangerous areas; When any abnormal situation is detected, an alarm response is automatically made and pushed to the monitoring terminal.

10. A Bluetooth+UWB+RTK fusion positioning system, used to implement the Bluetooth+UWB+RTK fusion positioning method according to any one of claims 1 to 9, characterized in that: include: Environmental feature data acquisition module: collects feature data of the environment where the positioning object is located to obtain environmental feature data; Preprocessing module: preprocesses environmental feature data to obtain preprocessed environmental feature data; Spatial scene discrimination module: discriminates the spatial scene where the positioning object is located based on the pre-processed environmental feature data and obtains the spatial scene type; Fusion strategy parameter setting module: Set the fusion strategy parameters of Bluetooth, UWB, RTK and inertial navigation according to the space scene type; Multi-source data acquisition module: collects Bluetooth ranging data, UWB positioning data, RTK positioning data and inertial navigation data according to the fusion strategy parameters; Multi-source fusion calculation module: fuses Bluetooth ranging data, UWB positioning data, RTK positioning data and inertial navigation data to obtain real-time three-dimensional high-precision positioning data; Trajectory monitoring and alarm module: Uses real-time three-dimensional high-precision positioning data to monitor the location and trajectory of power grid workers in real time, and implements alarm response when emergencies are detected.

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