A Bluetooth-based positioning method, device, and storage medium

Abstract

The present invention discloses a positioning method, device and storage medium based on Bluetooth. The method includes: receiving an original Bluetooth beacon signal, a Wi-Fi signal, and an acceleration signal; generating a first signal sequence representing the change trend of a first received signal strength indication value based on the original Bluetooth beacon signal; generating a second signal sequence representing the change trend of a second received signal strength indication value based on the Wi-Fi signal; generating a third signal sequence representing the jitter state based on the acceleration signal; selecting a filtering set according to the first signal sequence, the second signal sequence and the third signal sequence; calling a filtering algorithm to perform filtering processing on the original Bluetooth beacon signal to obtain candidate Bluetooth beacon signals; screening out multiple candidate Bluetooth beacon signals with the highest quality values according to the first received signal strength indication value as target Bluetooth beacon signals; and performing positioning based on the target Bluetooth beacon signals. This embodiment improves the filtering effect and the performance of the Bluetooth beacon signal.

Description

Technical Field

[0001] The present invention relates to the technical field of Bluetooth, and in particular to a Bluetooth-based positioning method, device, and storage medium. Background Art

[0002] Mobile terminals such as mobile phones and watches are widely popular in people's lives and work. In places such as shopping malls and underground streets, Bluetooth is usually used to provide LBS (Location Based Services) services for mobile terminals. For example, child guardianship, navigation, electronic fences, games, and so on.

[0003] Bluetooth-based positioning technology usually filters the Bluetooth beacon signals broadcast by base stations, and solves the RSSI (Received Signal Strength Indication) of the filtered Bluetooth beacon signals to obtain the coordinates of the mobile terminal.

[0004] Currently, filtering processing is usually improved and integrated on the basis of the original filtering algorithm for a specific environment. However, the mobile terminal constantly changes the environment, and the environments of places such as shopping malls and underground streets are relatively complex, with building blockages, a large number of Wi-Fi (Wireless Fidelity) signal interferences, and disturbances increased by running of users such as children. Using a fixed filtering algorithm has limitations and a low matching degree with the environmental state and its own state, resulting in low Bluetooth-based positioning accuracy. Summary of the Invention

[0005] In view of this, the present invention provides a Bluetooth-based positioning method, device, and storage medium to improve the Bluetooth-based positioning accuracy.

[0006] The first aspect of the present invention provides a Bluetooth-based positioning method, including:

[0007] Receiving an external original Bluetooth beacon signal, a wireless fidelity signal, and an internal acceleration signal respectively;

[0008] Generating a first signal sequence representing the change trend of the first received signal strength indication value according to the original Bluetooth beacon signal;

[0009] Generating a second signal sequence representing the change trend of the second received signal strength indication value according to the wireless fidelity signal;

[0010] Generating a third signal sequence representing the jitter state according to the acceleration signal;

[0011] Select a filtering set according to the first signal sequence, the second signal sequence, and the third signal sequence; one or more filtering algorithms are included in the filtering set;

[0012] Call one or more of the filtering algorithms in sequence to perform filtering processing on the original Bluetooth beacon signal to obtain candidate Bluetooth beacon signals;

[0013] Screen out multiple candidate Bluetooth beacon signals with the highest quality value in positioning according to the first received signal strength indication value as target Bluetooth beacon signals;

[0014] Perform positioning according to the target Bluetooth beacon signal to obtain target coordinate information.

[0015] The second aspect of the present invention provides a Bluetooth-based positioning device, including:

[0016] A signal receiving module for respectively receiving an external original Bluetooth beacon signal, a Wi-Fi signal, and an internal acceleration signal;

[0017] A first signal sequence generation module for generating a first signal sequence representing the change trend of the first received signal strength indication value according to the original Bluetooth beacon signal;

[0018] A second signal sequence generation module for generating a second signal sequence representing the change trend of the second received signal strength indication value according to the Wi-Fi signal;

[0019] A third signal sequence generation module for generating a third signal sequence representing the jitter state according to the acceleration signal;

[0020] A filtering set selection module for selecting a filtering set according to the first signal sequence, the second signal sequence, and the third signal sequence; one or more filtering algorithms are included in the filtering set;

[0021] A filtering processing module for sequentially calling one or more of the filtering algorithms to perform filtering processing on the original Bluetooth beacon signal to obtain candidate Bluetooth beacon signals;

[0022] A beacon signal screening module for screening out multiple candidate Bluetooth beacon signals with the highest quality value in positioning according to the first received signal strength indication value as target Bluetooth beacon signals;

[0023] A Bluetooth positioning module for performing positioning according to the target Bluetooth beacon signal to obtain target coordinate information.

[0024] The third aspect of the present invention provides an electronic device, and the electronic device includes:

[0025] At least one processor; and

[0026] a memory communicatively connected to the at least one processor; wherein

[0027] the memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute the Bluetooth-based positioning method as described in the first aspect above.

[0028] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, it implements the Bluetooth-based positioning method as described in the first aspect above.

[0029] A fifth aspect of the present invention provides a computer program product including a computer program, and when the computer program is executed by a processor, it implements the Bluetooth-based positioning method as described in the first aspect above.

[0030] In this embodiment, an external original Bluetooth beacon signal, a Wi-Fi signal, and an internal acceleration signal are received respectively; a first signal sequence representing the change trend of the first received signal strength indication value is generated based on the original Bluetooth beacon signal; a second signal sequence representing the change trend of the second received signal strength indication value is generated based on the Wi-Fi signal; a third signal sequence representing the jitter state is generated based on the acceleration signal; a filtering set is selected according to the first signal sequence, the second signal sequence, and the third signal sequence; the filtering set contains one or more filtering algorithms; one or more filtering algorithms are sequentially called to perform filtering processing on the original Bluetooth beacon signal to obtain candidate Bluetooth beacon signals; multiple candidate Bluetooth beacon signals with the highest quality value in positioning are screened out according to the first received signal strength indication value as target Bluetooth beacon signals; positioning is performed based on the target Bluetooth beacon signals to obtain target coordinate information. In this embodiment, the environmental state and the self-state are comprehensively evaluated from the RSSI of the Bluetooth beacon signal, the RSSI of the Wi-Fi signal, and the acceleration signal, and a suitable filtering algorithm is selected to perform filtering processing on the Bluetooth beacon signal, with high flexibility, effectively improving the matching degree with the environmental state and the self-state, improving the filtering effect, improving the performance of the Bluetooth beacon signal, and moreover, evaluating the quality of positioning based on the RSSI of the Bluetooth beacon signal, screening out Bluetooth beacon signals with higher quality for positioning, and effectively improving the accuracy of Bluetooth-based positioning.

[0031] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0033] Figure 1 It is a flowchart of a positioning method based on Bluetooth provided in the first embodiment of the present invention.

[0034] Figure 2 It is a schematic structural diagram of a filtering and classification network provided in the first embodiment of the present invention.

[0035] Figure 3 It is a schematic structural diagram of a positioning device based on Bluetooth provided in the second embodiment of the present invention.

[0036] Figure 4 It is a schematic structural diagram of an electronic device provided in the third embodiment of the present invention. Detailed implementation manners

[0037] In order to enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0038] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above accompanying drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can cover sequences other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0039] Embodiment 1

[0040] See Figure 1, which shows a flowchart of a Bluetooth-based positioning method provided in the first embodiment of the present invention. This method can be executed by a Bluetooth-based positioning device, which can be implemented in the form of hardware and / or software. The Bluetooth-based positioning device can be configured in an electronic device, which can be a server or a mobile terminal. As Figure 1 shown, the method includes:

[0041] Step 101, respectively receive an external original Bluetooth beacon signal, a Wi-Fi signal, and an internal acceleration signal.

[0042] As Figure 2 shown, components such as a Bluetooth module, a Wi-Fi module, and an acceleration sensor are set in the mobile terminal. During the process of starting LBS in the mobile terminal, the Bluetooth module can be called to receive the external original Bluetooth beacon signal broadcast by the base station, the Wi-Fi module can be called to receive the Wi-Fi signal broadcast by the external AP (Wireless Access Point), and the acceleration signal generated by the internal acceleration sensor can be read.

[0043] For mobile terminals with low computing resources such as watches, this embodiment can be applied to a server to receive the original Bluetooth beacon signal, Wi-Fi signal, and acceleration signal uploaded by mobile terminals such as watches, perform operations in the server, provide positioning services for mobile terminals such as watches, and send the target coordinate information to mobile terminals such as watches.

[0044] For user-end devices with high computing resources such as mobile phones, this embodiment can be applied to mobile terminals such as mobile phones to perform operations in the mobile terminals such as mobile phones and provide positioning services for the mobile terminals such as mobile phones.

[0045] Step 102, generate a first signal sequence representing the change trend of the first received signal strength indication value based on the original Bluetooth beacon signal.

[0046] If there are factors such as building occlusion, Wi-Fi signal interference, and the user moving while carrying the mobile terminal, the influence of these factors will be comprehensively reflected in the first received signal strength indication value (RSSI) of the original Bluetooth beacon signal. Then, as Figure 2 shown, statistical analysis can be performed on the first received signal strength indication value (RSSI) of the original Bluetooth beacon signal to generate a first signal sequence Sequence_1 representing the change trend of the first received signal strength indication value (RSSI).

[0047] In a specific implementation, a window can be added to the first received signal strength indication value of each original Bluetooth beacon signal, and the average value of the first received signal strength indication values (RSSI) within the window of the same original Bluetooth beacon signal is calculated to obtain the Bluetooth received signal strength indication value.

[0048] Generally, Bluetooth beacon signals with higher RSSI are selected for positioning processing. Therefore, multiple Bluetooth beacon signals with the highest Bluetooth received signal strength indication values can be screened out for statistical analysis.

[0049] On the one hand, for multiple Bluetooth beacon signals with the highest Bluetooth received signal strength indication values, the average value of the first received signal strength indication values at the same moment can be calculated to obtain the first co-location received signal strength indication value.

[0050] On the other hand, for multiple Bluetooth beacon signals with the highest Bluetooth received signal strength indication values, the minimum and maximum values of the first received signal strength indication values at the same moment are respectively statistically analyzed, so as to form the first co-location fluctuation amplitude by combining the minimum and maximum values of the first received signal strength indication values at the same moment.

[0051] The first co-location received signal strength indication values at each moment and the first co-location fluctuation amplitudes at each moment are spliced to form a first signal sequence representing the change trend of the first received signal strength indication value.

[0052] Step 103: Generate a second signal sequence representing the change trend of the second received signal strength indication value based on the Wi-Fi signal.

[0053] In places such as shopping malls and underground streets, merchants will use a large number of routers, mobile phones, etc. as APs to access other smart devices. At this time, a large amount of Wi-Fi signals are generated. Wi-Fi and Bluetooth may both use the 2.4GHz frequency band at the same time. The channels of Wi-Fi overlap with the frequencies of Bluetooth, making the Wi-Fi signal one of the main factors interfering with Bluetooth-based positioning processing. Especially when the Wi-Fi signal is strong and continuously transmitted, it may mask the Bluetooth signal, resulting in the loss of Bluetooth data packets.

[0054] Then, as Figure 2 shown, statistical analysis can be performed on the second received signal strength indication value (RSSI) of the Wi-Fi signal to generate a second signal sequence Sequence_2 representing the change trend of the second received signal strength indication value (RSSI).

[0055] In a specific implementation, since the Wi-Fi signals broadcast by each AP may interfere with the Bluetooth signal, the number of AP-broadcast Wi-Fi signals can be statistically analyzed at each moment.

[0056] For each Wi-Fi signal, calculate the average value of the second received signal strength indication (RSSI) at the same moment to obtain the second co-located received signal strength indication value.

[0057] For each Wi-Fi signal, respectively, count the minimum value and the maximum value of the second received signal strength indication at the same moment, so as to form the second co-located fluctuation amplitude by the minimum value and the maximum value of the second received signal strength indication at the same moment.

[0058] Combine the second co-located received signal strength indication values at each quantity and each moment with the second co-located fluctuation amplitudes at each moment to form a second signal sequence representing the change trend of the second received signal strength indication value.

[0059] Step 104: Generate a third signal sequence representing the jitter state based on the acceleration signal.

[0060] When the user moves with a mobile terminal such as a watch, if the mobile terminal such as a watch shakes greatly, it may cause large changes in the distance and angle between the mobile terminal such as a watch and the base station in a short time, resulting in fluctuations in the RSSI of the Bluetooth beacon signal, making the jitter one of the main factors interfering with the Bluetooth-based positioning process.

[0061] Then, as Figure 2 shown, the acceleration signal can be statistically analyzed to generate a third signal sequence Sequence_3 representing the jitter state.

[0062] In a specific implementation, the acceleration sensor can collect the acceleration signal in the X direction, the acceleration signal in the Y direction, and the acceleration signal in the Z direction at the same moment. When moving, the jitter of the mobile terminal such as a watch mainly occurs in the Z direction. Therefore, the acceleration signal in the Z direction is amplified. For example, the acceleration signal in the Z direction is multiplied by a preset amplification factor, where the amplification factor is greater than 1.

[0063] At the same moment, based on the sum value of the acceleration signal in the X direction and the acceleration signal in the Y direction, add the amplified acceleration signal in the Z direction to obtain the jitter value.

[0064] Combine the jitter values at each moment to form a third signal sequence representing the jitter state.

[0065] Step 105: Select a filtering set based on the first signal sequence, the second signal sequence, and the third signal sequence.

[0066] In this embodiment, experiments on Bluetooth positioning of a mobile terminal can be carried out using different filtering algorithms in different environments. A better filtering algorithm is selected to construct a filtering library based on the performance indicators of the experiment (such as the accuracy of positioning). Among them, there are multiple filtering sets in the filtering library, and each filtering set contains one or more filtering algorithms.

[0067] The filtering algorithms in the same filtering set are used simultaneously for one Bluetooth positioning. When the filtering set contains at least two filtering algorithms, the filtering set uses methods such as setting the sorting of the filtering algorithms as the order of the filtering algorithms to limit the order of the filtering algorithms.

[0068] Exemplarily, the filtering algorithms include the following types:

[0069] 1. Kalman Filter and Extended Kalman Filter (EKF)

[0070] The Kalman filter estimates the optimal state by combining the state equation and the observation equation with the prediction and measurement values. The single Kalman filter is suitable for processing linear systems and Gaussian noise and is often used for dynamic positioning.

[0071] The Extended Kalman Filter is an extension of the Kalman Filter. It linearizes and processes nonlinear problems. The single Extended Kalman Filter is suitable for nonlinear systems, such as positioning in complex environments and positioning when the user carrying the mobile terminal moves significantly.

[0072] 2. Gaussian Filter

[0073] The Gaussian filter selects weights for linear smoothing according to the shape of the Gaussian function and is a filter based on the Gaussian normal distribution.

[0074] 3. Particle Filter

[0075] The Particle Filter represents the state distribution through a large number of particles and is suitable for nonlinear and non-Gaussian noise.

[0076] The single Particle Filter is suitable for positioning in complex environments.

[0077] 4. Low-pass Filter

[0078] The Low-pass Filter filters out high-frequency noise and retains low-frequency signals. The single Low-pass Filter is suitable for removing high-frequency noise in Bluetooth beacon signals.

[0079] 5. Moving Average Filter

[0080] Moving average filtering takes the average value of the signal over a period of time to smooth the data. A single moving average filtering is suitable for removing short-term fluctuations in Bluetooth beacon signals and retaining long-term trends.

[0081] When the noise of the Bluetooth beacon signal is large and the system changes dynamically, a filtering set of Kalman filtering and low-pass filtering can be used for filtering.

[0082] When performing high-precision positioning in a complex environment, a filtering set of extended Kalman filtering and particle filtering can be used for filtering.

[0083] When the fluctuations of the Bluetooth beacon signal are large and rely on smoothing processing, a filtering set of moving average filtering and Kalman filtering can be used for filtering.

[0084] When the complexity of the indoor environment is high and the base stations are sparse, a filtering set of particle filtering, Kalman filtering and low-pass filtering can be used for filtering.

[0085] Of course, the above filtering sets are only examples. When implementing this embodiment, other filtering sets can be set according to actual situations, and this embodiment does not limit this. In addition, in addition to the above filtering sets, those skilled in the art can also adopt other filtering sets according to actual needs, and this embodiment does not limit this either.

[0086] In this embodiment, the state of the environment where the mobile terminal is located can be evaluated by synthesizing the first signal sequence, the second signal sequence and the third signal sequence, so as to select a filtering set suitable for the current environmental state from the filtering library.

[0087] In specific implementation, when using different filtering algorithms to perform Bluetooth positioning experiments on the mobile terminal in different environments, the experimental data (that is, the first signal sequence, the second signal sequence and the third signal sequence, and the selected filtering set) can be used to train a multi-class deep learning model as a filtering classification network.

[0088] As Figure 2 shown, the type of the filtering classification network is a convolutional neural network (Convolutional Neural Networks, CNN), which includes a first convolutional layer Conv_1, a second convolutional layer Conv_2, a third convolutional layer Conv_3, a fourth convolutional layer Conv_4, a depthwise separable convolutional layer (Depthwise Conv, DConv) and a fully connected layer (Fully Connected Layer, FC).

[0089] Among them, the first convolutional layer Conv_1, the second convolutional layer Conv_2, the third convolutional layer Conv_3, and the fourth convolutional layer Conv_4 are all convolutional layers. Their main operation is to perform a sliding convolutional operation on the input data through a convolutional kernel (also known as a filter). The convolutional kernel is a two-dimensional matrix. During the sliding process, the convolutional kernel multiplies and sums the corresponding regions of the input data element by element to obtain an output value. This process is repeated continuously, and finally, a convolutional feature map is generated to extract the local features of the input data.

[0090] The depthwise separable convolutional layer DConv decomposes the standard convolutional operation into depthwise convolution and pointwise convolution. Among them, depthwise convolution applies a convolutional kernel to each channel of the input separately for convolutional operation to generate a feature map with the same number of channels as the input. Depthwise convolution is mainly used to extract the spatial features of the input data. Pointwise convolution uses a 1×1 convolutional kernel to perform convolutional operation on the output of depthwise convolution, combines the features of different channels, and generates the final feature map. Pointwise convolution is mainly used to adjust the number of channels.

[0091] The computational amount and the number of parameters of the depthwise separable convolutional layer DConv are significantly reduced, making the filtering classification network more lightweight and suitable for running on resource-constrained mobile terminals.

[0092] Furthermore, the first convolutional layer Conv_1, the second convolutional layer Conv_2, the third convolutional layer Conv_3, and the depthwise separable convolutional layer DConv can all be configured with activation functions such as ReLU (Rectified Linear Unit) to implement the activation operation.

[0093] When selecting the filtering set, the filtering classification network can be determined and loaded for operation.

[0094] Use methods such as Reshape (matrix transformation) to convert the first signal sequence Sequence_1 into the first feature map FeatureMap_1, and input the first feature map FeatureMap_1 into the first convolutional layer Conv_1 to perform convolutional operation and activation operation in sequence to obtain Bluetooth beacon features.

[0095] Use methods such as Reshape (matrix transformation) to convert the second signal sequence Sequence_2 into the second feature map FeatureMap_2, and input the second feature map FeatureMap_2 into the second convolutional layer Conv_2 to perform convolutional operation and activation operation in sequence to obtain Wi-Fi features.

[0096] Convert the third signal sequence Sequence_3 into the third feature map FeatureMap_3 by means of Reshape (matrix transformation), etc., and input the third feature map FeatureMap_3 into the third convolutional layer Conv_3 to perform convolutional operation and activation operation in sequence to obtain the jitter feature.

[0097] The first signal sequence Sequence_1, the second signal sequence Sequence_2, and the third signal sequence Sequence_3 are all one-dimensional sequence data, which reflects the linear order relationship between data. For the possible local patterns or structures in the data (such as the fluctuations of Bluetooth beacon signals, Wi-Fi signals, etc.), there are difficulties in expression. Converting the one-dimensional sequence data into two-dimensional feature maps (i.e., the first feature map FeatureMap_1, the second feature map FeatureMap_2, and the third feature map FeatureMap_3) can reorganize the data, not only retaining the time sequence information, but also making the originally hidden local structures more obvious. Moreover, the two-dimensional feature maps can be adapted to CNN, giving play to the ability advantage of CNN in extracting local features of the feature maps.

[0098] Use functions such as Concat (concatenation) to fuse the Bluetooth beacon feature, Wi-Fi feature, and jitter feature into the original multimodal feature.

[0099] Input the original multimodal feature into the depthwise separable convolutional layer DConv to perform convolutional operation and activation operation in sequence to obtain the candidate multimodal feature.

[0100] Input the candidate multimodal feature into the fourth convolutional layer Conv_4 to perform convolutional operation to obtain the target multimodal feature.

[0101] Input the target multimodal feature into the fully connected layer FC to map it into the classification feature, and use activation functions such as Sigmoid (S-shaped function) to perform activation operation on the classification feature to obtain the probabilities of each filtering set, and output the filtering set with the highest probability as the selected filtering set.

[0102] Step 106: Call one or more filtering algorithms in sequence to filter the original Bluetooth beacon signal to obtain the candidate Bluetooth beacon signal.

[0103] When selecting the filtering set, one or more filtering algorithms can be called in sequence according to the indication of the filtering set to filter the original Bluetooth beacon signal (especially RSSI) to obtain the candidate Bluetooth beacon signal.

[0104] Step 107: Select multiple candidate Bluetooth beacon signals with the highest quality value in positioning according to the first received signal strength indication value as the target Bluetooth beacon signal.

[0105] In practical applications, the quality of each candidate Bluetooth beacon signal in positioning can be evaluated according to the change trend of the first received signal strength indication value to obtain a quality value, so as to select multiple candidate Bluetooth beacon signals with the highest quality values, denoted as target Bluetooth beacon signals, thereby improving the accuracy of Bluetooth positioning.

[0106] In an embodiment of the present invention, step 107 may include the following steps:

[0107] Step 1071: Determine a beacon detection network.

[0108] In this embodiment, a recurrent neural network (RNN) can be trained as a beacon detection network in an offline environment, so that the beacon detection network has the ability to detect the quality value of Bluetooth beacon signals in positioning.

[0109] Among them, the structure of the beacon detection network is not limited to a manually designed recurrent neural network. For example, LSTM (Long Short-Term Memory), GRU (Gate Recurrent Unit), etc., or a recurrent neural network optimized by a model quantization method, a recurrent neural network searched for the characteristics of Bluetooth beacon signals through NAS (Neural Architecture Search), etc. This embodiment does not limit this.

[0110] When detecting the quality value of candidate Bluetooth beacon signals, the beacon detection network can be loaded and run.

[0111] In an embodiment of the present invention, step 1071 may further include the following steps:

[0112] Step 10711: Sample any two frames of sample Bluetooth beacon signals as a sample set.

[0113] In this embodiment, if there are multiple base stations broadcasting test Bluetooth beacon signals in the same scenario, appropriate filtering sets can be used to filter the test Bluetooth beacon signals to obtain sample Bluetooth beacon signals. At this time, the Bluetooth device can use any sample Bluetooth beacon signal for positioning. Then, any two frames of sample Bluetooth beacon signals can be sampled from multiple sample Bluetooth beacon signals as a sample set.

[0114] Step 10712: Label quality tags for the sample set according to the accuracy of the sample Bluetooth beacon signals for positioning.

[0115] In this embodiment, the accuracy of the sample Bluetooth beacon signal for positioning can be evaluated in this scenario, and the sample set can be sorted according to the accuracy of the sample Bluetooth beacon signal for positioning, so as to label quality tags, where the quality tag indicates that the accuracy of one frame of the sample Bluetooth beacon signal for positioning is better than that of another frame of the sample Bluetooth beacon signal for positioning.

[0116] In a specific implementation, in the same scenario, two frames of sample Bluetooth beacon signals in the sample set are respectively combined with n - 1 frames of reference Bluetooth beacon signals to form two test beacon sets; wherein, the actual coordinate information of the Bluetooth device is configured in this scenario.

[0117] Position the Bluetooth device according to each test beacon set to obtain test coordinate information.

[0118] Calculate the difference between each actual coordinate information and the test coordinate information to obtain a single-point positioning offset.

[0119] Repeat the above tests multiple times in the same way to accumulate multiple single-point positioning offsets.

[0120] For each frame of the sample Bluetooth beacon signal, calculate the average value of the single-point positioning offsets to obtain an overall positioning offset.

[0121] Compare the overall positioning offsets of two frames of the sample Bluetooth beacon signals in the sample set, and label the quality tag for the sample set as that the accuracy of the sample Bluetooth beacon signal with a smaller overall positioning offset for positioning is better than that of the sample Bluetooth beacon signal with a larger overall positioning offset for positioning.

[0122] Step 10713: Input the third received signal strength indication values of two frames of the sample Bluetooth beacon signals in the sample set into the beacon detection network to detect the quality values of the sample Bluetooth beacon signals in positioning.

[0123] Input the third received signal strength indication values (sequences) of two frames of the sample Bluetooth beacon signals in the sample set into the beacon detection network respectively, and the beacon detection network detects the quality values of the two frames of the sample Bluetooth beacon signals in positioning respectively.

[0124] Step 10714: Generate a pairwise loss value according to the quality value and the quality tag.

[0125] For the same sample set, the quality value and the quality tag can be substituted into a preset pairwise ranking loss function for calculation to obtain a pairwise loss value.

[0126] Among them, the pairing loss function includes Hinge Loss, Contrastive Loss, RankNet Loss, and so on.

[0127] Step 10715: Update the beacon detection network according to the pairing loss value.

[0128] In this embodiment, the pairing loss value can be substituted into optimization algorithms such as SGD (stochastic gradient descent) and Adam (Adaptive momentum) to perform backpropagation on the beacon detection network and update the parameters in the beacon detection network until the performance of the beacon detection network meets the requirements.

[0129] Step 1072: Input the first received signal strength indication value of the candidate Bluetooth beacon signal into the beacon detection network to detect the quality value of the candidate Bluetooth beacon signal in terms of positioning.

[0130] In this embodiment, the (sequence) of the first received signal strength indication values of each candidate Bluetooth beacon signal is input into the beacon detection network, and the beacon detection network detects the quality value of the candidate Bluetooth beacon signal in terms of positioning.

[0131] Step 1073: Screen out the m candidate Bluetooth beacon signals with the highest quality values to obtain the target beacon set.

[0132] In this embodiment, the quality values of each candidate Bluetooth beacon signal are compared, and the m candidate Bluetooth beacon signals with the highest quality values are screened out and recorded as the target beacon set.

[0133] Step 1074: In the target beacon set, query the accuracy of using any n candidate Bluetooth beacon signals.

[0134] In this embodiment, methods such as testing and user-reported error correction can be used to record the accuracy when positioning with different combinations of n base stations. Then, in the target beacon set, the accuracy of using any n candidate Bluetooth beacon signals broadcast by the corresponding base stations in combined positioning can be queried.

[0135] Among them, both m and n are positive integers, and m is greater than n. For example, m is 6 and n is 3, and so on.

[0136] For the case of testing, if the positioning deviation is within a preset threshold (such as 1 meter), it can be regarded as correct positioning; if the positioning deviation is outside the preset threshold (such as 1 meter), it can be regarded as incorrect positioning, and the positioning accuracy is calculated accordingly.

[0137] For the situation where the user reports an error correction, if the user does not report a positioning error, it can be regarded as correct positioning. If the user reports a positioning error, it can be regarded as incorrect positioning, and the positioning accuracy is calculated accordingly.

[0138] Step 1075: Screen out the n candidate Bluetooth beacon signals with the highest accuracy to obtain the target Bluetooth beacon signal.

[0139] Compare the accuracies corresponding to the n candidate Bluetooth beacon signals, screen out the n candidate Bluetooth beacon signals with the highest accuracy, and obtain the target Bluetooth beacon signal.

[0140] In this embodiment, the real-time positioning quality value is combined with the historical positioning accuracy, and the target Bluetooth beacon signal is selected from the aspects of real-time performance and stability, so as to reduce the fluctuation of the target Bluetooth beacon signal as much as possible, improve the performance of the target Bluetooth beacon signal, and thus improve the accuracy of Bluetooth-based positioning.

[0141] Step 108: Perform positioning based on the target Bluetooth beacon signal to obtain the target coordinate information.

[0142] In practical applications, the mobile terminal can be positioned based on the target Bluetooth beacon signal (especially RSSI) to obtain the target coordinate information.

[0143] Exemplarily, the relationship model can be obtained by modeling RSSI and distance. Substitute the first received signal strength indication value of each frame of the target Bluetooth beacon signal into the relationship model to obtain the distance between the mobile terminal and the base station.

[0144] Among them, the relationship model is as follows:

[0145] P(d)= P(d 0 )-10nlog 10 (d / d 0 );

[0146] Among them, P(d) is the RSSI when the distance from the base station is d, P(d 0 ) is the RSSI when the distance from the base station is d 0 , and n is the path loss exponent.

[0147] Using algorithms such as trilateration, multilateration, or weighted centroid positioning method, calculate the target coordinate information of the mobile terminal according to the distances between the mobile terminal and each base station.

[0148] In this embodiment, an external original Bluetooth beacon signal, a Wi-Fi signal, and an internal acceleration signal are received respectively; a first signal sequence representing the change trend of the first received signal strength indication value is generated based on the original Bluetooth beacon signal; a second signal sequence representing the change trend of the second received signal strength indication value is generated based on the Wi-Fi signal; a third signal sequence representing the jitter state is generated based on the acceleration signal; a filtering set is selected according to the first signal sequence, the second signal sequence, and the third signal sequence; the filtering set contains one or more filtering algorithms; one or more filtering algorithms are called in sequence to perform filtering processing on the original Bluetooth beacon signal to obtain candidate Bluetooth beacon signals; multiple candidate Bluetooth beacon signals with the highest quality value in positioning are selected according to the first received signal strength indication value as target Bluetooth beacon signals; positioning is performed based on the target Bluetooth beacon signals to obtain target coordinate information. In this embodiment, the environmental state and the self-state are comprehensively evaluated from the RSSI of the Bluetooth beacon signal, the RSSI of the Wi-Fi signal, and the acceleration signal, and a suitable filtering algorithm is selected to perform filtering processing on the Bluetooth beacon signal, which has high flexibility, effectively improves the matching degree with the environmental state and the self-state, improves the filtering effect, improves the performance of the Bluetooth beacon signal, and moreover, the quality of positioning is evaluated based on the RSSI of the Bluetooth beacon signal, and Bluetooth beacon signals with higher quality can be selected for positioning, which can effectively improve the accuracy of Bluetooth-based positioning.

[0149] Embodiment 2

[0150] See Figure 3 , which shows a schematic structural diagram of a Bluetooth-based positioning device provided in Embodiment 2 of the present invention. As Figure 3 shown, the device includes:

[0151] A signal receiving module 301, configured to receive an external original Bluetooth beacon signal, a Wi-Fi signal, and an internal acceleration signal respectively;

[0152] A first signal sequence generating module 302, configured to generate a first signal sequence representing the change trend of the first received signal strength indication value based on the original Bluetooth beacon signal;

[0153] A second signal sequence generating module 303, configured to generate a second signal sequence representing the change trend of the second received signal strength indication value based on the Wi-Fi signal;

[0154] A third signal sequence generating module 304, configured to generate a third signal sequence representing the jitter state based on the acceleration signal;

[0155] A filtering set selection module 305, configured to select a filtering set according to the first signal sequence, the second signal sequence, and the third signal sequence; the filtering set contains one or more filtering algorithms;

[0156] A filtering processing module 306 is used to sequentially call one or more filtering algorithms to filter the original Bluetooth beacon signal to obtain a candidate Bluetooth beacon signal;

[0157] A beacon signal screening module 307, configured to screen out a plurality of candidate Bluetooth beacon signals with the highest positioning quality values ​​according to the first received signal strength indicator value as target Bluetooth beacon signals;

[0158] The Bluetooth positioning module 308 is used to perform positioning according to the target Bluetooth beacon signal to obtain target coordinate information.

[0159] In one embodiment of the present invention, the first signal sequence generating module 302 is further configured to:

[0160] Calculating an average value of the first received signal strength indicator value of the same original Bluetooth beacon signal to obtain a Bluetooth received signal strength indicator value;

[0161] For the plurality of Bluetooth beacon signals having the highest Bluetooth received signal strength indicator values, calculating an average value of the first received signal strength indicator values ​​at the same time to obtain a first co-located received signal strength indicator value;

[0162] For the plurality of Bluetooth beacon signals with the highest Bluetooth received signal strength indicator values, respectively counting minimum and maximum values ​​of the first received signal strength indicator values ​​at the same time to form a first co-location fluctuation amplitude;

[0163] The first co-located received signal strength indicator value at each moment and the first co-located fluctuation amplitude at each moment are combined into a first signal sequence representing a variation trend of the first received signal strength indicator value.

[0164] In one embodiment of the present invention, the second signal sequence generating module 303 is further configured to:

[0165] Counting the number of the wireless fidelity signals;

[0166] For each of the wireless fidelity signals, calculating an average of the second received signal strength indication values ​​at the same time to obtain a second co-located received signal strength indication value;

[0167] For each of the wireless fidelity signals, respectively, calculating minimum and maximum values ​​of the second received signal strength indicator values ​​at the same time to form a second co-location fluctuation amplitude;

[0168] Form a second signal sequence that characterizes the change trend of the second received signal strength indication value by combining each of the above-mentioned quantities and the second co-location received signal strength indication values at each moment with the second co-location fluctuation amplitude at each moment.

[0169] In an embodiment of the present invention, the third signal sequence generation module 304 is further configured to:

[0170] Amplify the acceleration signal in the Z direction;

[0171] At the same moment, based on the sum value of the acceleration signal in the X direction and the acceleration signal in the Y direction, add the amplified acceleration signal in the Z direction to obtain a jitter value;

[0172] Form a third signal sequence that characterizes the jitter state by the jitter values at each moment.

[0173] In an embodiment of the present invention, the filtering set selection module 305 is further configured to:

[0174] Determine a filtering classification network; the filtering classification network includes a first convolutional layer, a second convolutional layer, a third convolutional layer, a fourth convolutional layer, a depthwise separable convolutional layer, and a fully connected layer;

[0175] Convert the first signal sequence into a first feature map, input the first feature map into the first convolutional layer to sequentially perform a convolution operation and an activation operation to obtain a Bluetooth beacon feature;

[0176] Convert the second signal sequence into a second feature map, input the second feature map into the second convolutional layer to sequentially perform a convolution operation and an activation operation to obtain a Wi-Fi feature;

[0177] Convert the third signal sequence into a third feature map, input the third feature map into the third convolutional layer to sequentially perform a convolution operation and an activation operation to obtain a jitter feature;

[0178] Fuse the Bluetooth beacon feature, the Wi-Fi feature, and the jitter feature into an original multimodal feature;

[0179] Input the original multimodal feature into the depthwise separable convolutional layer to sequentially perform a convolution operation and an activation operation to obtain a candidate multimodal feature;

[0180] Input the candidate multimodal feature into the fourth convolutional layer to perform a convolution operation to obtain a target multimodal feature;

[0181] Input the target multimodal feature into the fully connected layer to map it into a classification feature;

[0182] Perform an activation operation on the classification features to obtain the probabilities of each filtering set;

[0183] Output the filtering set with the highest probability.

[0184] In an embodiment of the present invention, the beacon signal screening module 307 includes:

[0185] A beacon detection network determination module, configured to determine a beacon detection network;

[0186] A quality value detection module, configured to input the first received signal strength indication value of the candidate Bluetooth beacon signal into the beacon detection network to detect the quality value of the candidate Bluetooth beacon signal in positioning;

[0187] A target beacon set screening module, configured to screen out the m candidate Bluetooth beacon signals with the highest quality value to obtain a target beacon set;

[0188] An accuracy query module, configured to query the accuracy of using any n candidate Bluetooth beacon signals in the target beacon set; both m and n are positive integers, and m is greater than n;

[0189] A target Bluetooth beacon signal determination module, configured to screen out the n candidate Bluetooth beacon signals with the highest accuracy to obtain a target Bluetooth beacon signal.

[0190] In an embodiment of the present invention, the beacon detection network determination module includes:

[0191] A sample set sampling module, configured to sample any two frames of sample Bluetooth beacon signals as a sample set;

[0192] A quality label annotation module, configured to annotate the sample set with quality labels according to the accuracy of the sample Bluetooth beacon signal for positioning;

[0193] A forward processing module, configured to input the third received signal strength indication values of the two frames of sample Bluetooth beacon signals in the sample set into the beacon detection network to detect the quality values of the sample Bluetooth beacon signals in positioning;

[0194] A paired loss value calculation module, configured to generate a paired loss value according to the quality value and the quality label;

[0195] A beacon detection network update module, configured to update the beacon detection network according to the paired loss value.

[0196] In an embodiment of the present invention, the quality label annotation module is further configured to:

[0197] In the same scenario, two frames of the sample Bluetooth beacon signals are respectively combined with n - 1 frames of reference Bluetooth beacon signals to form a test beacon set; the scenario is configured with actual coordinate information;

[0198] Positioning is performed based on each of the test beacon sets to obtain test coordinate information;

[0199] Calculate the difference between each of the actual coordinate information and the test coordinate information to obtain a single - point positioning offset;

[0200] For each frame of the sample Bluetooth beacon signals, calculate the average value of the single - point positioning offset to obtain an overall positioning offset;

[0201] Label the sample set with a quality label that the positioning accuracy of the sample Bluetooth beacon signals with a smaller overall positioning offset is better than that of the sample Bluetooth beacon signals with a larger overall positioning offset.

[0202] The Bluetooth - based positioning device provided by the embodiments of the present invention can execute the Bluetooth - based positioning method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the Bluetooth - based positioning method.

[0203] Embodiment 3

[0204] See Figure 4 , which shows a schematic structural diagram of an electronic device provided by an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workstations, personal digital assistants, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0205] As Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. Among them, the memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0206] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0207] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include but are not limited to a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the Bluetooth-based positioning method.

[0208] In some embodiments, the Bluetooth-based positioning method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the Bluetooth-based positioning method described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the Bluetooth-based positioning method by any other appropriate means (e.g., by means of firmware).

[0209] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), systems-on-chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0210] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine, or entirely on the remote machine or server.

[0211] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0212] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0213] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0214] A computing system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The relationship between the client and the server is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0215] Example 4

[0216] An embodiment of the present invention also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the Bluetooth-based positioning method provided in any embodiment of the present invention.

[0217] In the process of implementing the computer program product, computer program code for performing the operations of the present invention can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network - including a local area network (LAN) or a wide area network (WAN) - or, alternatively, can be connected to an external computer (e.g., by using an Internet service provider to connect through the Internet).

[0218] It should be understood that the various forms of the flow shown above can be used, steps can be reordered, added, or deleted. For example, the steps described in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitations are imposed herein.

[0219] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A positioning method based on Bluetooth, characterized in that: include: Respectively receive external original Bluetooth beacon signals, wireless fidelity signals, and internal acceleration signals; Generating a first signal sequence representing a change trend of a first received signal strength indicator value according to the original Bluetooth beacon signal; generating a second signal sequence representing a change trend of a second received signal strength indicator value according to the wireless fidelity signal; generating a third signal sequence representing a shaking state according to the acceleration signal; Selecting a filter set according to the first signal sequence, the second signal sequence and the third signal sequence; the filter set contains one or more filter algorithms; Calling one or more of the filtering algorithms in sequence to filter the original Bluetooth beacon signal to obtain a candidate Bluetooth beacon signal; Filtering, according to the first received signal strength indicator value, a plurality of the candidate Bluetooth beacon signals with the highest positioning quality values ​​as target Bluetooth beacon signals; Positioning the target based on the target Bluetooth beacon signal to obtain target coordinate information; The step of selecting, according to the first received signal strength indicator value, a plurality of candidate Bluetooth beacon signals having the highest positioning quality values ​​as target Bluetooth beacon signals includes: Determine the beacon detection network; Inputting the first received signal strength indicator value of the candidate Bluetooth beacon signal into the beacon detection network to detect the quality value of the candidate Bluetooth beacon signal in positioning; Filter out the m candidate Bluetooth beacon signals with the highest quality values ​​to obtain a target beacon set; In the target beacon set, query the accuracy of using any n candidate Bluetooth beacon signals; m and n are both positive integers, and m is greater than n; The n candidate Bluetooth beacon signals with the highest accuracy are screened out to obtain a target Bluetooth beacon signal.

2. The method according to claim 1, characterized in that The step of generating a first signal sequence representing a change trend of a first received signal strength indicator value according to the original Bluetooth beacon signal comprises: Calculating an average value of the first received signal strength indicator value of the same original Bluetooth beacon signal to obtain a Bluetooth received signal strength indicator value; For the plurality of Bluetooth beacon signals having the highest Bluetooth received signal strength indicator values, calculating an average value of the first received signal strength indicator values ​​at the same time to obtain a first co-located received signal strength indicator value; For the plurality of Bluetooth beacon signals with the highest Bluetooth received signal strength indicator values, respectively counting minimum and maximum values ​​of the first received signal strength indicator values ​​at the same time to form a first co-location fluctuation amplitude; The first co-located received signal strength indicator value at each moment and the first co-located fluctuation amplitude at each moment are combined into a first signal sequence representing a variation trend of the first received signal strength indicator value.

3. The method according to claim 2, characterized in that The generating, according to the wireless fidelity signal, a second signal sequence representing a change trend of a second received signal strength indicator value comprises: Counting the number of the wireless fidelity signals; For each of the wireless fidelity signals, calculating an average of the second received signal strength indication values ​​at the same time to obtain a second co-located received signal strength indication value; For each of the wireless fidelity signals, respectively, calculating minimum and maximum values ​​of the second received signal strength indicator values ​​at the same time to form a second co-location fluctuation amplitude; Each of the numbers, the second co-located received signal strength indicator value at each moment, and the second co-located fluctuation amplitude at each moment are combined into a second signal sequence representing a change trend of the second received signal strength indicator value.

4. The method according to claim 3, characterized in that The step of generating a third signal sequence representing a shaking state according to the acceleration signal comprises: amplifying the acceleration signal in the Z direction; At the same time, the acceleration signal amplified in the Z direction is added to the sum of the acceleration signal in the X direction and the acceleration signal in the Y direction to obtain a jitter value; The jitter values ​​at various moments are combined into a third signal sequence representing the jitter state.

5. The method according to claim 4, characterized in that The selecting a filter set according to the first signal sequence, the second signal sequence, and the third signal sequence comprises: Determine a filtering classification network; the filtering classification network includes a first convolutional layer, a second convolutional layer, a third convolutional layer, a fourth convolutional layer, a depth-separable convolutional layer and a fully connected layer; Converting the first signal sequence into a first feature map, inputting the first feature map into the first convolution layer to sequentially perform convolution operations and activation operations to obtain Bluetooth beacon features; Converting the second signal sequence into a second feature map, inputting the second feature map into the second convolutional layer to sequentially perform convolution operations and activation operations to obtain wireless fidelity features; Converting the third signal sequence into a third feature map, inputting the third feature map into the third convolutional layer to sequentially perform convolution operations and activation operations to obtain a jitter feature; fusing the Bluetooth beacon feature, the wireless fidelity feature, and the jitter feature into an original multimodal feature; Inputting the original multimodal features into the depthwise separable convolutional layer to sequentially perform convolution operations and activation operations to obtain candidate multimodal features; Inputting the candidate multimodal features into the fourth convolutional layer to perform a convolution operation to obtain a target multimodal feature; Inputting the target multimodal features into the fully connected layer and mapping them into classification features; Performing an activation operation on the classification features to obtain the probability of each filter set; Output the filter set with the highest probability.

6. The method according to claim 1, characterized in that The determining of the beacon detection network comprises: Sample any two frames of sample Bluetooth beacon signals as a sample set; Marking the sample set with a quality label according to the positioning accuracy of the sample Bluetooth beacon signal; Inputting the third received signal strength indicator values ​​of the two frames of the sample Bluetooth beacon signals in the sample set into the beacon detection network respectively to detect the quality values ​​of the sample Bluetooth beacon signals in positioning; generating a pairing loss value according to the quality value and the quality label; The beacon detection network is updated according to the pairing loss value.

7. The method according to claim 6, characterized in that The step of labeling the sample set with a quality tag according to the positioning accuracy of the sample Bluetooth beacon signal includes: In the same scene, the two frames of sample Bluetooth beacon signals and n-1 frames of reference Bluetooth beacon signals are respectively combined into a test beacon set; the scene is configured with actual coordinate information; Performing positioning according to each of the test beacon sets to obtain test coordinate information; Calculate the difference between each of the actual coordinate information and the test coordinate information to obtain a single point positioning offset; For each frame of the sample Bluetooth beacon signal, an average value of the single point positioning offset is calculated to obtain an overall positioning offset; The sample set is annotated with a quality label that the positioning accuracy of the sample Bluetooth beacon signal with a smaller overall positioning offset is better than the positioning accuracy of the sample Bluetooth beacon signal with a larger overall positioning offset.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the Bluetooth-based positioning method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the Bluetooth-based positioning method according to any one of claims 1 to 7 is implemented.

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