A positioning method and device for eyeglass positioning

Abstract

This invention discloses a positioning method and apparatus for eyeglasses positioning, belonging to the field of wearable device positioning technology. The method includes acquiring Bluetooth signal data features and ultra-wideband signal data features of the target eyeglasses; inputting the Bluetooth signal data features and ultra-wideband signal data features into a pre-constructed CNN model for feature fusion to obtain fused features, and processing the fused features to obtain location information; encrypting the location information using a pre-acquired asymmetric encryption algorithm to obtain encrypted location information; and sending the encrypted location information to a terminal for decryption to obtain the location information of the target eyeglasses. This invention improves positioning accuracy and achieves reliable positioning of the target eyeglasses by processing Bluetooth signal data features and ultra-wideband signal data features.

Description

Technical Field

[0001] This invention belongs to the field of wearable device positioning technology, specifically relating to a positioning method and device for eyeglass positioning. Background Technology

[0002] In today's society, eyeglasses have become an indispensable tool for many people, especially those with myopia, presbyopia, and other vision impairments. Losing eyeglasses often brings great inconvenience and financial loss. However, despite the extremely high frequency of daily use of eyeglasses, there is no effective solution on the market specifically designed to prevent eyeglasses from being lost. Most conventional item anti-loss devices fail to fully consider the unique shape and wearing needs of eyeglasses, and cannot seamlessly fit the usage scenarios of eyeglasses, leaving users helpless when their eyeglasses are lost.

[0003] Currently available smart anti-loss devices generally suffer from large size and poor portability. These products are usually designed as independent pendants or cards, making them difficult to securely attach to glasses. This not only affects the normal use of the glasses but also adds extra burden to carrying them. In addition, due to size and design limitations, they cannot balance functionality with comfort and aesthetics, causing users to feel uncomfortable or unwilling to use them for extended periods.

[0004] Existing positioning technologies include, but are not limited to: Bluetooth (Bluetooth Low Energy, BLE) technology, which is often used for short-range indoor positioning due to its low power consumption, high adoption rate, and moderate cost. However, Bluetooth technology is greatly affected by transmission distance and obstructions, limiting its positioning accuracy and generally suitable for a spatial range of about 10 meters. Ultra-wideband (UWB) technology provides higher positioning accuracy and is suitable for precise indoor positioning, unaffected by multipath effects, but it is relatively expensive in terms of power consumption and hardware costs, and has not yet been widely commercialized. RFID (Radio Frequency Identification) technology is mainly used for identification and tracking, requiring no direct line-of-sight transmission, making it suitable for inventory management and logistics tracking, but its positioning capability is relatively weak, providing only approximate area positioning, and radio frequency interference may affect accuracy. GPS (Global Positioning System) technology provides high-precision positioning services globally and is widely used in outdoor positioning scenarios, but due to poor indoor signal conditions, it cannot meet the precise positioning needs of small objects such as glasses indoors.

[0005] While various wireless positioning technologies have met the needs of item tracking to some extent, there is still a lack of an ideal solution for the specific application scenario of glasses that can satisfy both compactness and portability, as well as reliable positioning and efficient anti-loss functions. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a positioning method and device for eyeglass positioning. By processing the characteristics of Bluetooth signal data and ultra-wideband signal data, the positioning accuracy is improved, and reliable positioning of the target eyeglasses is achieved. Moreover, the entire device is compact and easy to carry.

[0007] This invention provides the following technical solution:

[0008] Firstly, a positioning method for eyeglass positioning is provided, comprising:

[0009] Acquire Bluetooth signal data characteristics of the target glasses and ultra-wideband signal data characteristics ;

[0010] The Bluetooth signal data features and ultra-wideband signal data characteristics The input is fed into a pre-built CNN model for feature fusion to obtain fused features, and the fused features are processed to obtain location information;

[0011] The location information is encrypted using a pre-obtained asymmetric encryption algorithm to obtain encrypted location information;

[0012] The encrypted location information is sent to the terminal for decryption to obtain the location information of the target glasses;

[0013] Among them, the Bluetooth signal data features and ultra-wideband signal data characteristics The input is fed into a pre-built CNN model for feature fusion to obtain fused features, and the fused features are processed to obtain location information, including:

[0014] The input layer of the CNN model receives Bluetooth signal data features. and ultra-wideband signal data characteristics ;

[0015] The preprocessing layer of the CNN model uses an adaptive filtering algorithm for filtering and noise reduction;

[0016] The processing layers of the CNN model respectively process the features of Bluetooth signal data. and ultra-wideband signal data characteristics Perform two convolutions to obtain the corresponding convolution output features;

[0017] The feature fusion layer of the CNN model fuses the convolutional output features of Bluetooth signal data features and ultra-wideband signal data features, and combines them with an attention mechanism to obtain merged features.

[0018] The output layer of the CNN model calculates the location information of the merged features to obtain the location information.

[0019] As a preferred embodiment of the present invention, the preprocessing layer of the CNN model employs an adaptive filtering algorithm for filtering and noise reduction, including:

[0020] The adaptive filtering algorithm is expressed as follows: ;

[0021] in, Represents the filter weights. Represents the gain vector. Indicates the desired signal. Indicates the output signal. Indicates the forgetting factor, n This represents the nth sampling point in the time series. The vector representing the input signal includes vectors of Bluetooth signal data features and vectors of UWB signal data features.

[0022] As a preferred embodiment of the present invention, the processing layers of the CNN model respectively process the features of Bluetooth signal data. and ultra-wideband signal data characteristics Perform two convolutions to obtain the corresponding convolution output features, including:

[0023] The processing layers of the CNN model include a first convolutional layer, a first activation layer, a first pooling layer, a second convolutional layer, a second activation layer, and a second pooling layer.

[0024] The first convolutional layer respectively analyzes the features of Bluetooth signal data. and ultra-wideband signal data characteristics Perform convolution operations to obtain the first local features. Represented as: ;

[0025] in, This represents the convolution operation. This represents the input signal data, which includes Bluetooth signal data characteristics. and ultra-wideband signal data characteristics , and These represent the convolution kernel and bias term of the first convolutional layer, respectively.

[0026] The first activation layer for the first local feature Perform a nonlinear transformation to obtain the first activation output result. Represented as: ;

[0027] in, express Activation function This indicates taking the larger value;

[0028] The first pooling layer outputs the first activation result. Perform max pooling to obtain the first max pooling output. Represented as: ;

[0029] in, This indicates max pooling;

[0030] The second convolutional layer outputs the result of the first max pooling. Perform convolution to obtain the second local features. Represented as: ;

[0031] in, and These represent the convolution kernel and bias term of the second convolutional layer, respectively.

[0032] The second activation layer for the second local features Perform a nonlinear transformation to obtain the second activation output result. Represented as: ;

[0033] The second pooling layer outputs the second activation result. Perform max pooling to obtain the second max pooling output. Represented as: ;

[0034] The convolutional output features of the Bluetooth signal data features are represented as follows: The convolutional output features of the ultra-wideband signal data features are represented as follows: .

[0035] As a preferred embodiment of the present invention, the feature fusion layer of the CNN model fuses local features of Bluetooth signal data features and ultra-wideband signal data features, and combines an attention mechanism to obtain merged features, including:

[0036] The feature fusion layer fuses the convolutional output features of Bluetooth signal data features and ultra-wideband signal data features to obtain fused features. Represented as: ;

[0037] in, and All represent weighting coefficients;

[0038] Simultaneously, the weights of the convolutional output features of the Bluetooth signal data features and the ultra-wideband signal data features are adjusted through an attention mechanism to obtain the attention output result. A Represented as: ;

[0039] in, express The activation function, where W represents the weight matrix;

[0040] Fusion features With attention output results A Merge to obtain merge characteristics Represented as: .

[0041] As a preferred embodiment of the present invention, the output layer of the CNN model calculates the location information of the merged features to obtain the location information, including:

[0042] Expand the merged features , to obtain the unfolded features F Represented as: ;

[0043] in, Indicates an expand operation;

[0044] The unfolded features are integrated through a fully connected layer. F To obtain integrated features And activate, as shown below: ;

[0045] ;

[0046] in, and These represent the weight matrix and bias term of the fully connected layer, respectively.

[0047] The output layer obtains the result through activation. The location information P is calculated and represented as: ;

[0048] ;in, and These represent the weight matrix and bias term of the output layer, respectively.

[0049] As a preferred embodiment of the present invention, the step of encrypting the location information using a pre-acquired asymmetric encryption algorithm to obtain encrypted location information includes:

[0050] The location information is encrypted using a public key with an asymmetric encryption algorithm, as follows: ;

[0051] Where C represents encrypted location information, This represents the public-key encryption function, and P represents the location information.

[0052] As a preferred embodiment of the present invention, the encrypted location information is sent to a terminal for decryption to obtain the location information of the target glasses, including:

[0053] The terminal uses its private key to decrypt the encrypted location information, which is represented as: ;

[0054] in, This represents the private key decryption function, and M represents the location information of the target glasses after decryption.

[0055] Secondly, a positioning device for eyeglass positioning is provided, comprising:

[0056] Silicone outer body, used to cover the temples of the target glasses;

[0057] The main control module is located on one side of the silicone outer body and connected to the terminal, and is used to execute the positioning method for eyeglass positioning described in the first aspect;

[0058] A signal module, located on one side of the silicone outer body, connects the main control module and the terminal, and is used to send signal data to the main control module and upload location information to the terminal;

[0059] An alarm module is located on one side of the silicone outer body and connected to the main control module. It is used to receive commands from the main control module and emit a sound.

[0060] A power module, located on the other side of the silicone outer body, is used to provide electrical energy.

[0061] As a preferred embodiment of the present invention, the signal module includes a Bluetooth signal module and an ultra-wideband signal module. The Bluetooth signal module is used to connect to the terminal and send Bluetooth signal data features to the main control module; the ultra-wideband signal module sends ultra-wideband signal data features to the main control module.

[0062] Compared with the prior art, the beneficial effects of the present invention are:

[0063] 1. The positioning method for eyeglasses provided by this invention reduces power consumption and improves positioning accuracy by processing Bluetooth signal data features and ultra-wideband signal data features, thereby achieving reliable positioning of the target eyeglasses and effectively preventing eyeglasses from being lost.

[0064] 2. The positioning device for eyeglasses provided by the present invention has a compact structure, small size, is easy to carry, and can be worn on eyeglasses for a long time. Attached Figure Description

[0065] Figure 1 This is a flowchart of a positioning method for eyeglass positioning in an embodiment of the present invention;

[0066] Figure 2 This is a schematic diagram of the CNN model in an embodiment of the present invention;

[0067] Figure 3 This is a schematic diagram of the positioning device for eyeglass positioning in an embodiment of the present invention.

[0068] The components in the diagram are labeled as follows: 1. Silicone outer shell; 2. Main control module; 3. Signal module; 4. Alarm module; 5. Power supply module. Detailed Implementation

[0069] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0070] Example 1

[0071] This embodiment provides a positioning method for eyeglass positioning. For example... Figure 1 As shown, the specific steps include the following:

[0072] Step 1: Obtain Bluetooth signal data characteristics of the target glasses and ultra-wideband signal data characteristics .

[0073] Step 2: Collect the Bluetooth signal data characteristics. and ultra-wideband signal data characteristics The input is fed into a pre-built CNN (Convolutional Neural Network) model for feature fusion to obtain fused features, and the fused features are then processed to obtain location information. For example... Figure 2 As shown, it specifically includes:

[0074] 2.1 Features of Bluetooth signal data received by the input layer of the CNN model and ultra-wideband signal data characteristics .

[0075] 2.2 The preprocessing layer of the CNN model uses an adaptive filtering algorithm for filtering and noise reduction.

[0076] Specifically, an adaptive filtering algorithm is used to replace the traditional Kalman filter in order to better cope with signal changes in dynamic environments, reduce the impact of environmental noise, and obtain more stable distance estimates.

[0077] The adaptive filtering algorithm is expressed as follows: ;

[0078] in, Represents the filter weights. Represents the gain vector. Indicates the desired signal. Indicates the output signal. Indicates the forgetting factor, n This represents the nth sampling point in the time series. The vector representing the input signal includes vectors of Bluetooth signal data features and vectors of ultra-wideband signal data features. In this embodiment, the initial settings are as follows: .

[0079] 2.3 The processing layers of the CNN model respectively process the features of Bluetooth signal data. and ultra-wideband signal data characteristics Perform two convolutions to obtain the corresponding convolution output features.

[0080] The processing layers of the CNN model include a first convolutional layer, a first activation layer, a first pooling layer, a second convolutional layer, a second activation layer, and a second pooling layer.

[0081] Specifically, the first convolutional layer respectively processes the Bluetooth signal data features and ultra-wideband signal data characteristics Perform convolution operations to obtain the first local features. Represented as: ;

[0082] in, This represents the convolution operation. This represents the input signal data, which includes Bluetooth signal data characteristics. and ultra-wideband signal data characteristics , and These represent the convolution kernel and bias term of the first convolutional layer, respectively.

[0083] The first activation layer for the first local feature Perform a nonlinear transformation to obtain the first activation output result. Represented as: ;

[0084] in, express Activation function This indicates taking the larger value.

[0085] The first pooling layer outputs the first activation result. Perform max pooling to obtain the first max pooling output. Represented as: ;

[0086] in, This indicates max pooling.

[0087] The second convolutional layer outputs the result of the first max pooling. Perform convolution to obtain the second local features. Represented as: ;

[0088] in, and These represent the convolution kernel and bias term of the second convolutional layer, respectively.

[0089] The second activation layer for the second local features Perform a nonlinear transformation to obtain the second activation output result. Represented as: ;

[0090] The second pooling layer outputs the second activation result. Perform max pooling to obtain the second max pooling output. Represented as: .

[0091] After processing by the pooling layer, the size of the feature map is reduced while retaining the most important features, thereby simplifying the calculation of subsequent feature fusion and attention mechanisms. This ensures that the model has extracted the most significant features when performing feature fusion and attention mechanism processing, improving the efficiency and accuracy of the model.

[0092] After the above process, the Bluetooth signal data characteristics and ultra-wideband signal data characteristics Each obtains convolutional output features The convolutional output features of the Bluetooth signal data features are represented as follows: The convolutional output features of the ultra-wideband signal data features are represented as follows: .

[0093] 2.4 The feature fusion layer of the CNN model fuses the convolutional output features of Bluetooth signal data features and ultra-wideband signal data features, and combines them with an attention mechanism to obtain merged features.

[0094] Specifically, the feature fusion layer fuses the convolutional output features of Bluetooth signal data features and ultra-wideband signal data features to obtain fused features. Represented as: ;

[0095] in, and All of these represent weighting coefficients.

[0096] Simultaneously, the weights of the convolutional output features of the Bluetooth signal data features and the ultra-wideband signal data features are adjusted through an attention mechanism to obtain the attention output result. A Represented as: ;

[0097] in, express The activation function, where W represents the weight matrix.

[0098] Fusion features With attention output results A Merge to obtain merge characteristics Represented as: .

[0099] 2.5 The output layer of the CNN model calculates the location information of the merged features to obtain the location information.

[0100] Specifically, expand the merged features , to obtain the unfolded features F Represented as: ;

[0101] in, This indicates an expand operation.

[0102] The unfolded features are integrated through a fully connected layer. F To obtain integrated features And activate, as shown below: ;

[0103] ;

[0104] in, and These represent the weight matrix and bias term of the fully connected layer, respectively.

[0105] The output layer obtains the result through activation. The location information P is calculated and represented as: ;

[0106] ;in, and These represent the weight matrix and bias term of the output layer, respectively.

[0107] Step 3: Encrypt the location information using a pre-obtained asymmetric encryption algorithm to obtain encrypted location information.

[0108] In this embodiment, an asymmetric encryption algorithm is used to generate a public-private key pair. The private key is stored on the tracking server, and the public key is deployed on the positioning device. The positioning device broadcasts the public key and basic information as a Bluetooth signal every 5 seconds, with a signal coverage range of 10 meters. Nearby terminals, upon receiving the Bluetooth signal, record the positioning device's public key and their geographical location information at the time of reception.

[0109] Specifically, the location information is encrypted using a public key with an asymmetric encryption algorithm, as follows: ;

[0110] Where C represents encrypted location information, This represents the public-key encryption function, and P represents the location information.

[0111] Step 4: Send the encrypted location information to the terminal for decryption to obtain the location information of the target glasses.

[0112] Specifically, the terminal uses a private key to decrypt the encrypted location information, as shown below: ;

[0113] in, This represents the private key decryption function, and M represents the location information of the target glasses after decryption.

[0114] Example 2

[0115] This embodiment, based on Embodiment 1, provides a positioning device for eyeglass positioning. For example... Figure 3 As shown, it includes:

[0116] Silicone outer body 1, used to cover the temples of the target glasses.

[0117] Furthermore, fluorescent material has been added to the silicone outer shell, which can be used as a non-slip cover for glasses and also provide visual cues in dark environments.

[0118] The main control module 2 is located on one side of the silicone outer body 1 and connected to the terminal, and is used to execute the positioning method for eyeglass positioning described in Embodiment 1.

[0119] The signal module 3 is located on one side of the silicone outer body 1 and connects the main control module 2 and the terminal. It is used to send signal data to the main control module 2 and upload location information to the terminal.

[0120] Furthermore, the signal module 3 includes a Bluetooth signal module and an ultra-wideband signal module. The Bluetooth signal module is used to connect to the terminal and send Bluetooth signal data characteristics to the main control module 2; the ultra-wideband signal module sends ultra-wideband signal data characteristics to the main control module 2.

[0121] Alarm module 4 is located on one side of the silicone outer body 1 and is connected to the main control module 2. It is used to receive instructions from the main control module 2 and emit a sound.

[0122] Furthermore, the alarm module 4 integrates a buzzer sound alarm element, which can emit a sound when it receives a command from the main control module 2 to help the user quickly locate the position of the glasses.

[0123] The power module 5 is located on the other side of the silicone outer body 1 and is used to provide electrical energy.

[0124] Furthermore, power module 5 is powered by solar energy.

[0125] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are used only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0126] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0127] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A positioning method for eyeglass positioning, characterized in that, include: Acquire Bluetooth signal data characteristics of the target glasses and ultra-wideband signal data characteristics ; The Bluetooth signal data features and ultra-wideband signal data characteristics The input is fed into a pre-built CNN model for feature fusion to obtain fused features, and the fused features are processed to obtain location information; The location information is encrypted using a pre-obtained asymmetric encryption algorithm to obtain encrypted location information; The encrypted location information is sent to the terminal for decryption to obtain the location information of the target glasses; Among them, the Bluetooth signal data features and ultra-wideband signal data characteristics The input is fed into a pre-built CNN model for feature fusion to obtain fused features, and the fused features are processed to obtain location information, including: The input layer of the CNN model receives Bluetooth signal data features. and ultra-wideband signal data characteristics ; The preprocessing layer of the CNN model uses an adaptive filtering algorithm for filtering and noise reduction; The processing layers of the CNN model respectively process the features of Bluetooth signal data. and ultra-wideband signal data characteristics Perform two convolutions to obtain the corresponding convolution output features; The feature fusion layer of the CNN model fuses the convolutional output features of Bluetooth signal data features and ultra-wideband signal data features, and combines them with an attention mechanism to obtain merged features. The output layer of the CNN model calculates the location information of the merged features to obtain the location information.

2. The positioning method for eyeglass positioning according to claim 1, characterized in that: The preprocessing layer of the CNN model employs an adaptive filtering algorithm for filtering and noise reduction, including: The adaptive filtering algorithm is expressed as follows: ; in, Represents the filter weights. Represents the gain vector. Indicates the desired signal. Indicates the output signal. Indicates the forgetting factor, n This represents the nth sampling point in the time series. The vector representing the input signal includes vectors of Bluetooth signal data features and vectors of UWB signal data features.

3. The positioning method for eyeglass positioning according to claim 1, characterized in that: The processing layers of the CNN model respectively process the features of Bluetooth signal data. and ultra-wideband signal data characteristics Perform two convolutions to obtain the corresponding convolution output features, including: The processing layers of the CNN model include a first convolutional layer, a first activation layer, a first pooling layer, a second convolutional layer, a second activation layer, and a second pooling layer. The first convolutional layer respectively analyzes the features of Bluetooth signal data. and ultra-wideband signal data characteristics Perform convolution operations to obtain the first local features. Represented as: ; in, This represents the convolution operation. This represents the input signal data, which includes Bluetooth signal data characteristics. and ultra-wideband signal data characteristics , and These represent the convolution kernel and bias term of the first convolutional layer, respectively. The first activation layer for the first local feature Perform a nonlinear transformation to obtain the first activation output result. Represented as: ; in, express Activation function This indicates taking the larger value; The first pooling layer outputs the first activation result. Perform max pooling to obtain the first max pooling output. Represented as: ; in, Indicates max pooling; The second convolutional layer outputs the result of the first max pooling. Perform convolution to obtain the second local features. Represented as: ; in, and These represent the convolution kernel and bias term of the second convolutional layer, respectively. The second activation layer for the second local features Perform a nonlinear transformation to obtain the second activation output result. Represented as: ; The second pooling layer outputs the second activation result. Perform max pooling to obtain the second max pooling output. Represented as: ; The convolutional output features of the Bluetooth signal data features are represented as follows: The convolutional output features of the ultra-wideband signal data features are represented as follows: .

4. The positioning method for eyeglass positioning according to claim 3, characterized in that: The feature fusion layer of the CNN model fuses local features of Bluetooth signal data and ultra-wideband signal data, and combines them with an attention mechanism to obtain merged features, including: The feature fusion layer fuses the convolutional output features of Bluetooth signal data features and ultra-wideband signal data features to obtain fused features. Represented as: ; in, and All represent weighting coefficients; Simultaneously, the weights of the convolutional output features of the Bluetooth signal data features and the ultra-wideband signal data features are adjusted through an attention mechanism to obtain the attention output result. A Represented as: ; in, express The activation function, where W represents the weight matrix; Fusion features With attention output results A Merge to obtain merge characteristics Represented as: .

5. The positioning method for eyeglass positioning according to claim 4, characterized in that: The output layer of the CNN model calculates the location information of the merged features to obtain the location information, including: Expand the merged features , to obtain the unfolded features F Represented as: ; in, Indicates an expand operation; The unfolded features are integrated through a fully connected layer. F To obtain integrated features And activate, represented as: ; ； in, and These represent the weight matrix and bias term of the fully connected layer, respectively. The output layer obtains the result through activation. The location information P is calculated and represented as: ; ;in, and These represent the weight matrix and bias term of the output layer, respectively.

6. The positioning method for eyeglass positioning according to claim 1, characterized in that: The step of encrypting the location information using a pre-acquired asymmetric encryption algorithm to obtain encrypted location information includes: The location information is encrypted using a public key with an asymmetric encryption algorithm, as follows: ; Where C represents encrypted location information, This represents the public-key encryption function, and P represents the location information.

7. The positioning method for eyeglasses positioning according to claim 6, characterized in that: The encrypted location information is sent to the terminal for decryption to obtain the location information of the target glasses, including: The terminal uses its private key to decrypt the encrypted location information, which is represented as: ; in, This represents the private key decryption function, and M represents the location information of the target glasses after decryption.

8. A positioning device for eyeglass positioning, characterized in that, include: Silicone outer body (1), used to cover the temples of the target glasses; The main control module (2) is located on one side of the silicone outer body (1) and connected to the terminal, and is used to execute the positioning method for eyeglass positioning as described in any one of claims 1 to 7; The signal module (3) is located on one side of the silicone outer body (1), and is connected to the main control module (2) and the terminal. It is used to send signal data to the main control module (2) and upload the location information to the terminal. An alarm module (4) is located on one side of the silicone outer body (1) and connected to the main control module (2) for receiving instructions from the main control module (2) and emitting a sound. A power module (5) is located on the other side of the silicone outer body (1) and is used to provide electrical energy.

9. The positioning device for eyeglass positioning according to claim 8, characterized in that: The signal module (3) includes a Bluetooth signal module and an ultra-wideband signal module. The Bluetooth signal module is used to connect to the terminal and send Bluetooth signal data features to the main control module (2). The ultra-wideband signal module sends ultra-wideband signal data features to the main control module (2).

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