High-precision indoor and outdoor fusion positioning system and method

Abstract

The invention relates to a high-precision indoor and outdoor fusion positioning system and method. The system comprises a data processing center, an outdoor positioning module and an indoor positioning module, wherein the outdoor positioning module and the indoor positioning module are respectively in communication connection with the data processing center; the outdoor positioning module adopts a GNSS receiver and is used for receiving satellite signals and realizing outdoor accurate positioning; the indoor positioning module comprises a plurality of beacon nodes deployed indoors and used for transmitting signals, and a receiver on a mobile terminal and used for receiving the signals of the beacon nodes and realizing indoor positioning; and the data processing center is used for receiving and processing the positioning data sent by the outdoor positioning module and the indoor positioning module so as to realize automatic switching and fusion of indoor and outdoor positioning. According to the invention, continuous and high-precision positioning of the vehicle in indoor and outdoor environments can be realized.

Description

Technical Field

[0001] The present invention relates to the field of vehicle positioning technology, and in particular to a high-precision indoor and outdoor fusion positioning system and method. Background Art

[0002] In the integrated operation of indoor and outdoor, especially in the operation of airport aprons, the management and positioning of non-powered vehicles (such as luggage trailers, carts, etc.) is an important link. At present, in the process of apron operations, since the vehicles themselves do not have driving power and batteries, the equipment on the vehicles is required to operate for a long time with low power consumption and high precision.

[0003] In the existing technology, the indoor and outdoor positioning system of non-powered vehicles has certain limitations in positioning accuracy and stability, and it is difficult to meet the growing demand for high-precision positioning. With the rapid development of intelligent transportation systems and autonomous driving technologies, high-precision indoor and outdoor positioning of non-powered vehicles has become increasingly important. However, the traditional GPS positioning of non-powered vehicles has serious signal attenuation in indoor environments and airport aprons, especially in indoor or building-dense areas. The signal quality is poor, resulting in reduced positioning accuracy. Therefore, there is an urgent need for a solution that can integrate indoor and outdoor positioning information to achieve high-precision positioning. Summary of the invention

[0004] In view of the deficiencies in the prior art, the object of the present invention is to provide a high-precision indoor and outdoor fusion positioning system and method, which can achieve continuous and high-precision positioning of vehicles in indoor and outdoor environments.

[0005] The above-mentioned object of the present invention is achieved through the following technical solutions: A high-precision indoor and outdoor fusion positioning system, comprising a data processing center and an outdoor positioning module and an indoor positioning module respectively connected to the data processing center for communication; The outdoor positioning module adopts a GNSS receiver to receive satellite signals and achieve outdoor precise positioning; The indoor positioning module includes a plurality of beacon nodes deployed indoors for transmitting signals, and a receiver on the mobile terminal for receiving signals from the beacon nodes to achieve indoor positioning; The data processing center is used to receive and process the positioning data sent by the outdoor positioning module and the indoor positioning module to achieve automatic switching and integration of indoor and outdoor positioning.

[0006] As a further technical solution of the present invention: the data processing center includes a fusion algorithm module, which is used to receive positioning data from the outdoor positioning module and the indoor positioning module, and realize the fusion of indoor and outdoor positioning data through a preset fusion algorithm, thereby outputting a high-precision positioning result.

[0007] As a further technical solution of the present invention: the data processing center includes a switching judgment module, and the switching judgment module is used to automatically judge and smoothly switch the indoor and outdoor positioning modes according to the source and quality of the positioning data.

[0008] As a further technical solution of the present invention: the indoor positioning module also includes a positioning sensor arranged in an indoor environment and an algorithm arranged in the positioning sensor, and positioning is performed through the positioning sensor and the algorithm, and the algorithm includes but is not limited to an inertial navigation algorithm, a particle filtering algorithm, and a fingerprint matching algorithm.

[0009] As a further technical solution of the present invention: the data processing center also includes a data cleaning module and a data standardization module. The data cleaning module is used to remove outliers, noise and duplicate data to ensure the accuracy of input data. The data standardization module is used to convert positioning data from different sources into a unified format and standard to facilitate subsequent processing.

[0010] A method for high-precision indoor and outdoor fusion positioning, comprising the following steps: Step 1: Obtain outdoor positioning data through the outdoor positioning module; Step 2: When entering an indoor environment, the switching judgment module is used to automatically switch to the indoor positioning module, and the indoor positioning data is obtained by receiving the signal of the beacon node; Step 3: The data processing center processes the received outdoor positioning data and indoor positioning data, and realizes the smooth transition and fusion of indoor and outdoor positioning through the fusion algorithm module.

[0011] As a further technical solution of the present invention: in the step 3, the smooth transition and integration of indoor and outdoor positioning are specifically achieved, including the following steps: the system uses the outdoor positioning module to perform precise positioning in an outdoor environment. When the vehicle enters indoors from outdoors, the system will automatically detect the signal change, and the outdoor positioning module will send the signal to the data processing center. The switching judgment module of the data processing center switches to the indoor positioning mode and sends a signal to the indoor positioning module. The indoor and outdoor modules are positioned through the beacon nodes deployed indoors and the receivers on the mobile terminal. These beacon nodes will emit signals, and the receivers will capture these signals to determine the position of the vehicle. When the vehicle moves indoors, the system will continue to receive and process these signals to maintain the accuracy of positioning.

[0012] As a further technical solution of the present invention: the fusion algorithm module realizes a smooth transition of indoor and outdoor positioning data by calculating weights. When the vehicle is outdoors, the weight of the GNSS data can be set to be higher; as the vehicle moves indoors, the data weight of the indoor positioning module gradually increases to ensure the continuity of positioning.

[0013] As a further technical solution of the present invention: realizing the smooth transition of indoor and outdoor positioning data by calculating weights specifically comprises the following steps: a. Dynamic weight adjustment: Dynamically adjust the weight of indoor and outdoor positioning data according to the distance between the user's current location and the indoor and outdoor boundaries. When the user is close to the indoor and outdoor boundaries, the weight of indoor positioning data can be gradually increased, while the weight of outdoor positioning data can be reduced; b. Signal quality assessment: Evaluate the signal quality of outdoor GNSS signals and indoor beacon nodes, and adjust the weights according to the signal quality. If the GNSS signal is blocked or interfered with, reduce its weight and increase the data weight of the indoor positioning module. c. Historical data fusion: Use the user's past location data and movement trajectory to predict the current location, and use this predicted location as a reference factor for weight adjustment. If the predicted location is closer to indoors, increase the weight of indoor positioning data; d. Machine learning optimization: Use machine learning algorithms to fuse indoor and outdoor positioning data. Through training models, learn how to dynamically adjust the weights of indoor and outdoor positioning data based on multiple factors such as the current environment, signal quality, and user behavior to achieve a smoother transition. e. Fusion algorithm selection: Select appropriate data fusion algorithms according to specific circumstances, such as Kalman filtering and particle filtering. These algorithms can dynamically adjust weights according to the reliability and accuracy of the data to achieve optimal fusion of indoor and outdoor positioning data; f. User interface feedback: Design a user interface that displays the changes in positioning data and the weight adjustment process during the positioning transition, so that users can understand the current positioning status more intuitively, thereby enhancing users' trust in the positioning system.

[0014] In summary, the present invention includes at least one of the following beneficial technical effects: 1. The present invention discloses a high-precision indoor and outdoor fusion positioning system and method, which realizes the continuous and smooth switching of indoor and outdoor high-precision positioning, enhances the stability and reliability of the positioning system, and is of great significance to improving the safety of intelligent transportation systems and autonomous driving technologies. At the same time, it can significantly improve the positioning accuracy and stability, help improve the efficiency of airport operations, and reduce the risk of safety accidents.

[0015] 2. The present invention realizes high precision and continuity of indoor and outdoor positioning, realizes automatic switching and fusion of indoor and outdoor positioning, improves the stability and reliability of positioning, has strong practicality and wide applicability, and can meet the positioning needs in various scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is the overall structural block diagram of the present invention.

[0017] Figure 2 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application; obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments, and all other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application without making creative work are within the scope of protection of the present application.

[0019] In the description of the present application, it should be noted that the terms "upper", "lower", "inner", "outer", "top / bottom" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as limiting the present application. In addition, the terms "first" and "second" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0020] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "provided with", "set / connected", "connected", etc. should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0021] Embodiment 1: Reference Figure 1 , a high-precision indoor and outdoor fusion positioning system disclosed in the present invention, comprising a data processing center and an outdoor positioning module and an indoor positioning module respectively connected to the data processing center for communication; The outdoor positioning module uses a GNSS receiver to receive satellite signals to achieve precise outdoor positioning; the indoor positioning module includes multiple beacon nodes deployed indoors to transmit signals, and a receiver on the mobile terminal to receive signals from the beacon nodes to achieve indoor positioning; the data processing center is used to receive and process the positioning data sent by the outdoor positioning module and the indoor positioning module to achieve automatic switching and fusion of indoor and outdoor positioning.

[0022] The data processing center specifically includes: Signal strength and stability detection unit: Switching mechanism module, the system detects the strength and stability of outdoor GNSS signals and indoor beacon node signals in real time. When the user enters indoors from outdoors, the GNSS signal gradually weakens, while the indoor beacon node signal gradually strengthens. The system automatically switches to indoor positioning mode based on this change. Fusion strategy module: In the signal overlapping area, the system combines the strength and stability of the two signals and achieves smooth transition and fusion of positioning data through algorithm weighting.

[0023] Pattern recognition unit based on machine learning: Data training module, using historical positioning data, trains a machine learning model to enable it to recognize the user's indoor and outdoor movement patterns. Switching and fusion module: Based on the results predicted by the model, the system automatically determines whether the user is currently in an indoor or outdoor environment, and switches the positioning mode accordingly. At the interface between indoor and outdoor, the model can provide fused positioning results to ensure the continuity of positioning.

[0024] The collaborative working unit of the beacon node and the GNSS receiver: the collaborative positioning module, through the collaborative positioning algorithm, enables the indoor beacon node and the outdoor GNSS receiver to share and exchange positioning information. Switching logic module: When the system detects that the GNSS signal is lost or the beacon node signal is significantly enhanced, it automatically triggers the switching of indoor and outdoor positioning modes. At the same time, the collaborative algorithm ensures the smoothness and accuracy of the positioning data during the switching process.

[0025] Intelligent switching unit based on map and scene perception: Map data integration module: Integrate indoor and outdoor map data to build a unified positioning scene model. Scene perception module: Use sensor data and map information to perceive the specific scene the user is currently in (such as entering a building, leaving a building, etc.). Intelligent switching and fusion module: According to the results of scene perception, the system automatically switches the indoor and outdoor positioning mode, and combines the two positioning data when necessary to provide a more accurate and stable fusion positioning result.

[0026] In summary, through technical means such as signal strength and stability detection, pattern recognition based on machine learning, collaborative work of beacon nodes and GNSS receivers, and intelligent switching based on map and scene perception, the automatic switching and fusion functions of indoor and outdoor positioning in the high-precision indoor and outdoor fusion positioning system can be effectively realized.

[0027] The data processing center includes a fusion algorithm module, which is used to receive the positioning data of the outdoor positioning module and the indoor positioning module, and realize the fusion of indoor and outdoor positioning data through a preset fusion algorithm, so as to output a high-precision positioning result.

[0028] Among them, the preset fusion algorithm is one of the weighted average fusion method, the Kalman filter fusion method, the fusion algorithm based on machine learning, the deep learning fusion network and the fuzzy logic fusion method.

[0029] The principle of weighted average fusion method: assign different weights to the outdoor positioning module and indoor positioning module according to their signal strength, stability or positioning accuracy. Implementation: fuse the positioning data of the two modules by weighted average. For example, if the outdoor GNSS signal is strong and stable, it can be given a higher weight; when entering the room, the signal of the beacon node gradually increases, and its weight also increases accordingly.

[0030] Principle of Kalman filter fusion method: Kalman filter is an efficient recursive filter that can estimate the state of a dynamic system from a series of incomplete and noisy measurements. Implementation: Take outdoor and indoor positioning data as observations and fuse them through the Kalman filter algorithm to obtain smoother and more accurate positioning results.

[0031] The principle of the fusion algorithm based on machine learning: Use historical positioning data to train the machine learning model so that it can learn and predict the most likely positioning results. Implementation: Use outdoor and indoor positioning data as input features, perform fusion prediction through the trained model, and output high-precision positioning results. This method can automatically adapt to different environments and scenarios.

[0032] Principle of deep learning fusion network: Deep learning network has powerful feature extraction and representation learning capabilities, and can be used for the fusion of indoor and outdoor positioning data. Implementation: Design a deep learning network, such as convolutional neural network (CNN) or recurrent neural network (RNN), take outdoor and indoor positioning data as input, and output high-precision fusion positioning results through automatic learning and optimization of the network.

[0033] Principle of fuzzy logic fusion method: Fuzzy logic can handle uncertainty and ambiguity, and is suitable for indoor and outdoor positioning data fusion when the signal is unstable or there is noise. Implementation: Define fuzzy sets and fuzzy rules, perform fuzzy reasoning and decision-making based on factors such as signal quality and positioning accuracy of outdoor and indoor positioning data, and obtain the fused positioning results.

[0034] In summary, through technical means such as weighted average fusion method, Kalman filter fusion method, fusion algorithm based on machine learning, deep learning fusion network or fuzzy logic fusion method, the fusion of indoor and outdoor positioning data can be effectively realized, thereby outputting high-precision positioning results.

[0035] The data processing center includes a switching judgment module, which is used to automatically judge and smoothly switch the indoor and outdoor positioning modes according to the source and quality of the positioning data. Specifically, the switching judgment module includes the following steps: Step s1: Dynamic switching based on data quality assessment: the quality of outdoor GNSS signals and indoor beacon node signals is evaluated in real time through a quality assessment algorithm, including indicators such as signal strength, stability, and error range; and based on the quality assessment results, the switching judgment module can intelligently select the optimal positioning data source and smoothly transition between indoor and outdoor positioning modes. For example, when the outdoor signal quality decreases and the indoor signal quality increases, the system gradually switches from the outdoor positioning mode to the indoor positioning mode.

[0036] Step s2, multi-mode fusion and gradual transition, in the indoor and outdoor interface area, a multi-mode fusion strategy is adopted, and outdoor GNSS signals and indoor beacon node signals are used for positioning at the same time to improve positioning accuracy and stability.

[0037] Step s3: Intelligent switching based on deep learning. A smart switching model is built using deep learning technology to learn the switching mode by training a large amount of historical positioning data. The model can intelligently judge and automatically switch to the most appropriate positioning mode based on the quality and characteristics of the currently received positioning data. At the same time, the model also has predictive capabilities and can predict possible switching needs in advance.

[0038] Step s4. Adaptive weight adjustment and fusion, adaptive weight adjustment, dynamically adjust the weights of indoor and outdoor positioning signals in the positioning results according to their real-time quality. For example, when the outdoor signal quality is good, it is given a higher weight; conversely, the weight of the indoor signal is increased. Through weighted fusion or other advanced fusion algorithms, the indoor and outdoor positioning data are effectively combined to generate smooth and accurate positioning results. This method can automatically adjust when the indoor and outdoor signal quality changes to achieve smooth switching.

[0039] In summary, through technical solutions such as dynamic switching based on data quality assessment, multi-mode fusion and gradual transition, intelligent switching based on deep learning, and adaptive weight adjustment and fusion, it is possible to automatically switch and smoothly switch indoor and outdoor positioning modes according to the source and quality of positioning data.

[0040] The indoor positioning module also includes a positioning sensor set in the indoor environment and an algorithm set in the positioning sensor. Positioning is performed through the positioning sensor and the algorithm. The algorithm includes but is not limited to an inertial navigation algorithm, a particle filter algorithm, and a fingerprint matching algorithm.

[0041] The data processing center also includes a data cleaning module and a data standardization module. The data cleaning module is used to remove outliers, noise and duplicate data to ensure the accuracy of input data. The data standardization module is used to convert positioning data from different sources into a unified format and standard for subsequent processing.

[0042] Embodiment 2: Reference Figure 2 , a method for high-precision indoor and outdoor fusion positioning disclosed in the present invention comprises the following steps: Step 1: Obtain outdoor positioning data through the outdoor positioning module; Step 2: When entering an indoor environment, the switching judgment module is used to automatically switch to the indoor positioning module, and the indoor positioning data is obtained by receiving the signal of the beacon node; Step 3: The data processing center processes the received outdoor positioning data and indoor positioning data, and realizes the smooth transition and fusion of indoor and outdoor positioning through the fusion algorithm module.

[0043] In step 3, the smooth transition and integration of indoor and outdoor positioning are specifically achieved by the following steps: the system uses the outdoor positioning module to perform precise positioning in an outdoor environment. When the vehicle enters indoors from outdoors, the system automatically detects signal changes, and the outdoor positioning module sends the signal to the data processing center. The switching judgment module of the data processing center switches to the indoor positioning mode and sends a signal to the indoor positioning module. The indoor and outdoor modules are positioned through beacon nodes deployed indoors and receivers on mobile terminals. These beacon nodes will emit signals, and the receivers will capture these signals to determine the location of the vehicle. When the vehicle moves indoors, the system will continue to receive and process these signals to maintain the accuracy of positioning.

[0044] The fusion algorithm module achieves a smooth transition between indoor and outdoor positioning data by calculating weights. When the vehicle is outdoors, the weight of the GNSS data can be set higher; as the vehicle moves indoors, the data weight of the indoor positioning module gradually increases to ensure the continuity of positioning.

[0045] The following steps are specifically included to achieve smooth transition of indoor and outdoor positioning data by calculating weights: a. Dynamic weight adjustment: Dynamically adjust the weight of indoor and outdoor positioning data according to the distance between the user's current location and the indoor and outdoor boundaries. When the user is close to the indoor and outdoor boundaries, the weight of indoor positioning data can be gradually increased, while the weight of outdoor positioning data can be reduced; b. Signal quality assessment: Evaluate the signal quality of outdoor GNSS signals and indoor beacon nodes, and adjust the weights according to the signal quality. If the GNSS signal is blocked or interfered with, reduce its weight and increase the data weight of the indoor positioning module. c. Historical data fusion: Use the user's past location data and movement trajectory to predict the current location, and use this predicted location as a reference factor for weight adjustment. If the predicted location is closer to indoors, increase the weight of indoor positioning data; d. Machine learning optimization: Use machine learning algorithms to fuse indoor and outdoor positioning data. Through training models, learn how to dynamically adjust the weights of indoor and outdoor positioning data based on multiple factors such as the current environment, signal quality, and user behavior to achieve a smoother transition. e. Fusion algorithm selection: Select appropriate data fusion algorithms according to specific circumstances, such as Kalman filtering and particle filtering. These algorithms can dynamically adjust weights according to the reliability and accuracy of the data to achieve optimal fusion of indoor and outdoor positioning data; f. User interface feedback: Design a user interface that displays the changes in positioning data and the weight adjustment process during the positioning transition, so that users can understand the current positioning status more intuitively, thereby enhancing users' trust in the positioning system.

[0046] The implementation principle of the present invention is as follows: The present invention discloses a high-precision indoor and outdoor fusion positioning system and method, which realizes the continuous and smooth switching of indoor and outdoor high-precision positioning, enhances the stability and reliability of the positioning system, and is of great significance to improving the safety of intelligent transportation systems and autonomous driving technologies. At the same time, it can significantly improve the positioning accuracy and stability, help improve the efficiency of airport operations, and reduce the risk of safety accidents.

[0047] The embodiments of this specific implementation method are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A high-precision indoor and outdoor fusion positioning system, characterized in that: It includes a data processing center and an outdoor positioning module and an indoor positioning module which are respectively connected to the data processing center for communication; The outdoor positioning module adopts a GNSS receiver to receive satellite signals and achieve outdoor precise positioning; The indoor positioning module includes a plurality of beacon nodes deployed indoors for transmitting signals, and a receiver on the mobile terminal for receiving signals from the beacon nodes to achieve indoor positioning; The data processing center is used to receive and process the positioning data sent by the outdoor positioning module and the indoor positioning module to achieve automatic switching and integration of indoor and outdoor positioning.

2. A high-precision indoor and outdoor fusion positioning system according to claim 1, characterized in that: The data processing center includes a fusion algorithm module, which is used to receive positioning data from the outdoor positioning module and the indoor positioning module, and realize the fusion of indoor and outdoor positioning data through a preset fusion algorithm, so as to output a high-precision positioning result.

3. The high-precision indoor and outdoor fusion positioning system according to claim 1, characterized in that: The data processing center includes a switching judgment module, which is used to automatically judge and smoothly switch the indoor and outdoor positioning modes according to the source and quality of the positioning data.

4. The high-precision indoor and outdoor fusion positioning system according to claim 1, characterized in that: The indoor positioning module also includes a positioning sensor set in an indoor environment and an algorithm set in the positioning sensor. Positioning is performed through the positioning sensor and the algorithm. The algorithm includes but is not limited to an inertial navigation algorithm, a particle filtering algorithm, and a fingerprint matching algorithm.

5. The high-precision indoor and outdoor fusion positioning system according to claim 1, characterized in that: The data processing center also includes a data cleaning module and a data standardization module. The data cleaning module is used to remove outliers, noise and duplicate data to ensure the accuracy of input data. The data standardization module is used to convert positioning data from different sources into a unified format and standard to facilitate subsequent processing.

6. A high-precision indoor and outdoor fusion positioning method, characterized in that: The following steps are involved: Step 1: Obtain outdoor positioning data through the outdoor positioning module; Step 2: When entering an indoor environment, the switching judgment module is used to automatically switch to the indoor positioning module, and the indoor positioning data is obtained by receiving the signal of the beacon node; Step 3: The data processing center processes the received outdoor positioning data and indoor positioning data, and realizes the smooth transition and fusion of indoor and outdoor positioning through the fusion algorithm module.

7. The method for high-precision indoor and outdoor fusion positioning according to claim 6, characterized in that: In step 3, the smooth transition and integration of indoor and outdoor positioning are specifically achieved by the following steps: the system uses the outdoor positioning module to perform precise positioning in an outdoor environment. When the vehicle enters indoors from outdoors, the system automatically detects signal changes, and the outdoor positioning module sends the signal to the data processing center. The switching judgment module of the data processing center switches to the indoor positioning mode and sends a signal to the indoor positioning module. The indoor and outdoor modules are positioned through beacon nodes deployed indoors and receivers on mobile terminals. These beacon nodes will emit signals, and the receivers will capture these signals to determine the location of the vehicle. When the vehicle moves indoors, the system will continue to receive and process these signals to maintain the accuracy of positioning.

8. The method for high-precision indoor and outdoor fusion positioning according to claim 6, characterized in that: The fusion algorithm module achieves a smooth transition of indoor and outdoor positioning data by calculating weights. When the vehicle is outdoors, the weight of the GNSS data can be set to be higher; as the vehicle moves indoors, the data weight of the indoor positioning module gradually increases to ensure the continuity of positioning.

9. The method for high-precision indoor and outdoor fusion positioning according to claim 7, characterized in that: The following steps are specifically included to achieve smooth transition of indoor and outdoor positioning data by calculating weights: a. Dynamic weight adjustment: Dynamically adjust the weight of indoor and outdoor positioning data according to the distance between the user's current location and the indoor and outdoor boundaries. When the user is close to the indoor and outdoor boundaries, the weight of indoor positioning data can be gradually increased, while the weight of outdoor positioning data can be reduced; b. Signal quality assessment: Evaluate the signal quality of outdoor GNSS signals and indoor beacon nodes, and adjust the weights according to the signal quality. If the GNSS signal is blocked or interfered with, reduce its weight and increase the data weight of the indoor positioning module. c. Historical data fusion: Use the user's past location data and movement trajectory to predict the current location, and use this predicted location as a reference factor for weight adjustment. If the predicted location is closer to indoors, increase the weight of indoor positioning data; d. Machine learning optimization: Use machine learning algorithms to fuse indoor and outdoor positioning data. Through training models, learn how to dynamically adjust the weights of indoor and outdoor positioning data based on multiple factors such as the current environment, signal quality, and user behavior to achieve a smoother transition. e. Fusion algorithm selection: Select appropriate data fusion algorithms according to specific circumstances, such as Kalman filtering and particle filtering. These algorithms can dynamically adjust weights according to the reliability and accuracy of the data to achieve optimal fusion of indoor and outdoor positioning data; f. User interface feedback: Design a user interface that displays the changes in positioning data and the weight adjustment process during the positioning transition, so that users can understand the current positioning status more intuitively, thereby enhancing users' trust in the positioning system.

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