Positioning method, device, electronic device and readable storage medium

Abstract

The embodiments of the present disclosure provide a positioning method, apparatus, electronic device, and readable storage medium, which are applied to a terminal device, the method comprising: obtaining sensor data and an initial position of the terminal device; extracting a zero-speed detection feature of the sensor data; determining the operating state of the terminal device based on the zero-speed detection feature; if it is determined that the terminal device is in a stationary state, performing convergence processing on the initial position to obtain the target position of the terminal device. The embodiments of the present disclosure determine the operating state of the terminal device based on the zero-speed detection feature, which can improve the accuracy of determining the operating state of the terminal device; and the embodiments of the present disclosure can avoid positioning jitter caused by problems such as cold start of GNSS and poor quality of positioning device chips in a stationary state, and can provide stable positioning information for the terminal device, reduce positioning offset, and improve positioning accuracy.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer processing technology, and more particularly, to a positioning method, apparatus, electronic device, and readable storage medium. Background Art

[0002] Location Based Services (LBS) are basic services that use various types of positioning technologies to obtain the current location of a positioning device and provide information resources to the positioning device through the mobile Internet, such as food delivery services, taxi services, and shared bicycle services. Ideally, when the positioning device is stationary, the positioning result should be stable at a single point, and when the positioning device is in motion, stable and continuous positioning results should be provided to the positioning device. However, problems such as cold start of the GNSS (Global Navigation Satellite System) and poor quality of the positioning device chip will cause the positioning result to be prone to jitter when the positioning device is stationary, resulting in poor positioning accuracy. Summary of the Invention

[0003] The embodiments of the present disclosure provide a positioning method, apparatus, electronic device, and readable storage medium, which can solve the problem that when a terminal device is in a stationary state, the positioning results are prone to jitter and the positioning accuracy is poor due to factors such as cold start of GNSS and poor quality of the positioning device chip.

[0004] According to a first aspect of an embodiment of the present disclosure, a positioning method is provided, which is applied to a terminal device, and the method includes:

[0005] Obtaining sensor data and an initial location of the terminal device;

[0006] extracting a zero-speed detection feature from the sensor data;

[0007] determining the operating state of the terminal device according to the zero-speed detection feature;

[0008] If it is determined that the terminal device is in a stationary state, a convergence process is performed on the initial position to obtain a target position of the terminal device.

[0009] According to a second aspect of an embodiment of the present disclosure, a positioning apparatus is provided, applied to a terminal device, the apparatus comprising:

[0010] A data acquisition module, configured to acquire sensor data and an initial position of the terminal device;

[0011] A feature extraction module, configured to extract zero-speed detection features from the sensor data;

[0012] a state determination module, configured to determine an operating state of the terminal device according to the zero-speed detection feature;

[0013] A convergence processing module is used to perform convergence processing on the initial position to obtain the target position of the terminal device if it is determined that the terminal device is in a stationary state.

[0014] According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device, including:

[0015] A processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the aforementioned positioning method is implemented when the processor executes the program.

[0016] According to a fourth aspect of an embodiment of the present disclosure, a readable storage medium is provided. When instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the aforementioned positioning method.

[0017] Embodiments of the present disclosure provide a positioning method, apparatus, electronic device, and readable storage medium, which are applied to a terminal device. The method includes: obtaining sensor data and an initial position of the terminal device; extracting a zero-speed detection feature from the sensor data; determining the operating state of the terminal device based on the zero-speed detection feature; and if it is determined that the terminal device is in a stationary state, performing convergence processing on the initial position to obtain a target position of the terminal device.

[0018] The embodiments of the present disclosure can obtain sensor data and an initial position of a terminal device, extract a zero-speed detection feature from the sensor data, determine the operating status of the terminal device based on the zero-speed detection feature, and improve the accuracy of determining the operating status of the terminal device; and, when determining that the terminal device is in a stationary state, the embodiments of the present disclosure perform convergence processing on the initial position of the terminal device to obtain the target position of the terminal device, which can avoid positioning jitter caused by problems such as cold start of GNSS and poor quality of positioning device chips in a stationary state, and can provide stable positioning information for the terminal device, reduce positioning offset, and improve positioning accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the description of the embodiments of the present disclosure. Obviously, the drawings described below are only some embodiments of the embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] Figure 1A flowchart showing the steps of a positioning method in an embodiment of the present disclosure is shown;

[0021] Figure 2 A structural block diagram of a positioning device in an embodiment of the present disclosure is shown;

[0022] Figure 3 A structural diagram of an electronic device in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, but not all of them. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the embodiments of the present disclosure.

[0024] Example 1

[0025] Reference Figure 1 , which shows a flowchart of the steps of the positioning method in an embodiment of the present disclosure, as follows:

[0026] Step 101: Acquire sensor data and an initial position of the terminal device.

[0027] Step 102: Extract zero-speed detection features of the sensor data.

[0028] Step 103: Determine the operating status of the terminal device according to the zero-speed detection feature.

[0029] Step 104: If it is determined that the terminal device is in a stationary state, a convergence process is performed on the initial position to obtain a target position of the terminal device.

[0030] The positioning method provided in the embodiments of the present disclosure is applied to terminal devices, which may include but are not limited to smartphones, tablet computers, e-book readers, MP3 (Moving Picture Experts Group Audio Layer III) players, MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers, car computers, desktop computers, set-top boxes, smart TVs, wearable devices, etc.

[0031] The sensor data is data collected by the terminal device's built-in sensors at a preset sampling frequency. For example, the terminal device's built-in sensors may include an accelerometer, a gyroscope, and a magnetometer, and the sensor data may include accelerometer data, gyroscope data, and magnetometer data. It should be noted that the sampling frequency of the terminal device's built-in sensors is generally 50 Hz.

[0032] The zero-speed detection feature refers to a characteristic value used to characterize the operating state of the terminal device, obtained by analyzing sensor data collected by the terminal device's sensors over a certain period of time. Optionally, the zero-speed detection feature includes at least one of a generalized likelihood ratio detection value, an accelerometer measurement variance detection value, an accelerometer measurement amplitude detection value, an angular velocity measurement energy detection value, and a magnetometer measurement variance detection value.

[0033] As an example, sensor data can be collected based on a preset sliding window, and the collected sensor data can be analyzed to obtain the zero-speed detection feature of the terminal device. For example, assuming that the size of the preset sliding window is W, the measurement noise variance of the accelerometer is The measurement noise variance of the gyroscope is The measurement noise variance of the magnetometer is Then, the generalized likelihood ratio test value T GLRT It can be expressed as:

[0034]

[0035] Accelerometer measurement variance detection value T AMV It can be expressed as:

[0036]

[0037] Accelerometer measurement amplitude detection value T AMAG It can be expressed as:

[0038]

[0039] Angular velocity measurement energy detection value T ARE It can be expressed as:

[0040]

[0041] The magnetometer measurement variance detection value can be expressed as:

[0042]

[0043] Among them, n is the starting position of the sliding window, k is the ranking value in the sliding window at the current moment, is the data collected by the accelerometer at the current moment, is the data collected by the accelerometer at the starting position of the sliding window, is the data collected by the gyroscope at the starting position of the sliding window, is the data collected by the gyroscope at the current moment, is the data collected by the magnetometer at the current moment, and g is the acceleration due to gravity.

[0044] After extracting the zero-speed detection feature, the current operating state of the terminal device can be determined based on the zero-speed detection feature. Specifically, in an embodiment of the present disclosure, the motion state of the terminal device can be determined by comparing the extracted zero-speed detection feature with a preset threshold according to a pre-set determination strategy. Alternatively, a machine learning model for identifying the operating state of the terminal device can be pre-trained, and the extracted zero-speed detection feature can be input into the trained machine learning model for processing to determine the operating state of the terminal device.

[0045] It should be noted that the positioning method provided by the present disclosure is primarily intended to address issues such as jitter and poor positioning accuracy in positioning results caused by GNSS cold starts and poor positioning device chip quality when the terminal device is stationary. Therefore, in an embodiment of the present disclosure, if it is determined that the terminal device is stationary, convergence processing is performed on the initial position of the terminal device to obtain the target position of the terminal device, providing the terminal device with stable positioning information, reducing positioning offset, and improving positioning accuracy.

[0046] The initial location of the terminal device can be obtained using GPS (Global Positioning System) data. Specifically, the initial location of the terminal device is determined based on the GPS data of the terminal device. Of course, the initial location of the terminal device can also be obtained using other methods, which are not specifically limited in the embodiments of the present disclosure.

[0047] In an optional embodiment of the present disclosure, the determining of the operating status of the terminal device according to the zero-speed detection feature in step 103 includes:

[0048] Step S11: inputting the zero-speed detection feature into a pre-trained state detection model for processing to obtain a state label of the terminal device;

[0049] Step S12: If the status tag of the terminal device is a target tag, it is determined that the terminal device is in a stationary state.

[0050] The state detection model is a machine learning model with data classification capabilities, which can classify the input zero-speed detection features and determine the state label of the terminal device corresponding to the zero-speed detection features. A machine learning model is a model that has certain capabilities after learning from samples, and can specifically be a neural network model, such as a CNN (Convolutional Neural Networks) model, an RNN (Recurrent Neural Networks) model, etc. Of course, other types of machine learning models can also be used.

[0051] For example, the machine learning model can be iteratively trained based on training samples. In each round of training, the loss value of the machine learning model is calculated based on the true labels corresponding to the training samples and the output results of the machine learning model. The model parameters of the machine learning model are adjusted according to the loss value until the loss value meets the preset convergence condition to obtain the state detection model required by the embodiment of the present disclosure. Then, the operating state of the terminal device is determined based on the state detection model to improve data processing efficiency and accuracy. The target label is a label indicating that the terminal device is in a stationary state.

[0052] In addition, the operating state of the terminal device may also be determined based on a pre-set determination strategy. In an optional embodiment of the present disclosure, the step 103 of determining the operating state of the terminal device according to the zero-speed detection feature includes:

[0053] Step S21: If at least one zero-speed detection feature has a value less than its corresponding preset threshold, it is determined that the terminal device is in a stationary state;

[0054] Step S22: If the at least one zero-speed detection feature is greater than or equal to its corresponding preset threshold, it is determined that the terminal device is in motion.

[0055] When determining the operating status of the terminal device based on a pre-set judgment strategy, as long as at least one of the five zero-speed detection features listed above, namely, the generalized likelihood ratio detection value, the accelerometer measurement variance detection value, the accelerometer measurement amplitude detection value, the angular velocity measurement energy detection value, and the magnetometer measurement variance detection value, is less than its corresponding preset threshold, it can be determined that the terminal device is in a stationary state. If at least one of the zero-speed detection features is greater than or equal to its corresponding preset threshold, it can be determined that the terminal device is in a moving state. For example, if the generalized likelihood ratio detection value of the terminal device is less than a first threshold, it is determined that the terminal device is in a stationary state; if the generalized likelihood ratio detection value of the terminal device is greater than or equal to the first threshold, it is determined that the terminal device is in a moving state. Alternatively, if the accelerometer measurement variance detection value of the terminal device is less than a second threshold, it is determined that the terminal device is in a stationary state; if the accelerometer measurement variance detection value of the terminal device is greater than or equal to the second threshold, it is determined that the terminal device is in a moving state. Alternatively, if the generalized likelihood ratio detection value of the terminal device is less than the first threshold and the angular velocity measurement energy detection value is less than the third threshold, it is determined that the terminal device is in a stationary state; if the generalized likelihood ratio detection value of the terminal device is greater than or equal to the first threshold and the angular velocity measurement energy detection value is greater than or equal to the third threshold, it is determined that the terminal device is in a moving state, and so on.

[0056] In an optional embodiment of the present disclosure, the step 104 of performing convergence processing on the initial position to obtain the target position of the terminal device includes:

[0057] Step S31: performing region segmentation processing on a preset rectangular region to obtain at least two rectangular sub-regions, wherein the preset rectangular region is centered at the initial position;

[0058] Step S32: Calculate a first distribution probability of the terminal device in the at least two rectangular sub-areas;

[0059] Step S33: Accumulate the first distribution probabilities of the terminal devices in the at least two rectangular sub-areas within a preset time to obtain a second distribution probability of the terminal devices in the at least two rectangular sub-areas;

[0060] Step S34: performing attenuation processing on the second distribution probability of the terminal device in the at least two rectangular sub-areas to obtain a third distribution probability of the terminal device in the at least two rectangular sub-areas;

[0061] Step S35: Determine the target location of the terminal device according to the third distribution probability of the terminal device in the at least two rectangular sub-areas.

[0062] The preset rectangular area is centered on the initial location of the terminal device and is used to define the distribution range of the current positioning point of the terminal device. The size of the preset rectangular area can be set according to actual needs. For example, the length of the preset rectangular area can be set to 500 meters and the width can be set to 200 meters, etc. This disclosure does not impose specific limitations on this.

[0063] In an embodiment of the present disclosure, in order to further narrow the distribution range of the positioning points of the terminal device and improve the positioning accuracy, the preset rectangular area can be first segmented to divide the preset rectangular area into at least two rectangular sub-areas. In practical applications, a spatial index can be established for the preset rectangular area to divide the preset rectangular area into multiple rectangular sub-areas, each of which corresponds to a spatial index value. For example, a GeoHash algorithm can be used to establish a spatial index for the preset rectangular area, or a Google S2 algorithm can be used to establish a spatial index for the preset rectangular area, and so on.

[0064] Then, the first distribution probability of the terminal device on each rectangular sub-area is calculated. The first probability distribution is the real-time distribution probability of the positioning point of the terminal device. Since the terminal device is in a stationary state, the positioning point of the terminal device is relatively fixed within a certain period of time, or the moving distance of the positioning point is small. Therefore, the first distribution probability of the terminal device on each rectangular sub-area within the preset time can be accumulated to obtain the second distribution probability of the terminal device on each rectangular sub-area. For example, it is assumed that within the preset time, two sets of first distribution probabilities of the terminal device on each rectangular sub-area are calculated, wherein the first distribution probability of the terminal device on the rectangular sub-area G1 is 0.0625 and 0.0625, the first distribution probability on the rectangular sub-area G2 is 0.0625 and 0.125, and the first distribution probability on the rectangular sub-area G3 is 0.0625 and 0.5. The first distribution probability of the terminal device in each rectangular sub-area within the preset time is accumulated to obtain the second distribution probability of the terminal device in each rectangular sub-area, wherein the second distribution probability of the terminal device in the rectangular sub-area G1 is 0.125, the second distribution probability in the rectangular sub-area G2 is 0.1875, and the second distribution probability in the rectangular sub-area G3 is 0.5625.

[0065] The first distribution probability of the terminal device is accumulated. If the first distribution probability of a certain rectangular sub-area at a certain moment is large, the second distribution probability of the rectangular sub-area will also be large. If the terminal device changes from a stationary state to a moving state after a preset time, the positioning point determined according to the second distribution probability will not match the actual position of the terminal device. Therefore, in order to avoid inaccurate positioning results due to changes in the operating state of the terminal device after a preset time, the embodiment of the present disclosure performs attenuation processing on the second distribution probability of the terminal device in each rectangular sub-area to obtain the third distribution probability of the terminal device in each rectangular sub-area, and determines the target position of the terminal device based on the third distribution probability of the terminal device in each rectangular sub-area. Specifically, the maximum value in the third distribution probability can be determined, and the position of the rectangular sub-area corresponding to the maximum value is determined as the target position of the terminal device.

[0066] In an optional embodiment of the present disclosure, the calculating of the first distribution probability of the terminal device in the at least two rectangular sub-areas in step S32 includes:

[0067] Sub-step S321, determining a two-dimensional Gaussian distribution function of the terminal device according to the initial position and a preset accuracy;

[0068] Sub-step S322: Calculate the integral of the two-dimensional Gaussian distribution function over the at least two rectangular sub-areas to obtain a first distribution probability of the terminal device over the at least two rectangular sub-areas.

[0069] It should be noted that the preset accuracy is used to limit the accuracy of the terminal device, that is, the drift of the positioning point. The preset accuracy can be determined according to the product model of the terminal device. Different models of terminal devices have different performance and positioning accuracy. Assuming that the preset accuracy corresponding to longitude and latitude are the same, both are σ, the initial position of the terminal device is Then the two-dimensional Gaussian distribution function of the terminal device can be expressed as:

[0070]

[0071] By calculating the integral of the two-dimensional Gaussian distribution function of the terminal device in each rectangular sub-area, the first distribution probability of the terminal device in each rectangular sub-area can be obtained.

[0072] Of course, other methods may also be used to calculate the first distribution probability of the terminal device in each rectangular sub-area, and the embodiments of the present disclosure do not specifically limit this.

[0073] In an optional embodiment of the present disclosure, the attenuation processing of the second distribution probability of the terminal device in the at least two rectangular sub-areas in step S34 to obtain the third distribution probability of the terminal device in the at least two rectangular sub-areas includes:

[0074] Sub-step S341, calculating the product of the preset time and the first attenuation value to obtain a time attenuation value;

[0075] Sub-step S342: Calculate the difference between the second distribution probability of the terminal device in the at least two rectangular sub-areas and the time attenuation value to obtain a third distribution probability of the terminal device in the at least two rectangular sub-areas.

[0076] It should be noted that, by attenuating the second distribution probability of the terminal device in each rectangular sub-area based on the time attenuation value, the influence of the first distribution probability in the previous period on the final positioning result can be reduced, which is conducive to improving the positioning accuracy. Among them, the first attenuation value can be set according to actual needs. For example, the first attenuation value can be set to 0.05, the preset time is 2s, and the time attenuation value is 0.1. The second distribution probability of the terminal device in the rectangular sub-area G1 is 0.125, the second distribution probability in the rectangular sub-area G2 is 0.1875, and the second distribution probability in the rectangular sub-area G3 is 0.5625. Then, after attenuation processing, the third distribution probability of the terminal device in the rectangular sub-area G1 is 0.025, the third distribution probability in the rectangular sub-area G2 is 0.0875, and the third distribution probability in the rectangular sub-area G3 is 0.4625.

[0077] In an optional embodiment of the present disclosure, calculating the difference between the second distribution probability of the terminal device in the at least two rectangular sub-areas and the time attenuation value in sub-step S342 to obtain a third distribution probability of the terminal device in the at least two rectangular sub-areas includes:

[0078] Step A11: If the running state of the terminal device is updated from the static state to the moving state after the preset time, performing time decay processing on the second distribution probability according to the time decay value to obtain a decayed second distribution probability;

[0079] Step A12: Perform motion attenuation processing on the attenuated second distribution probability according to the second attenuation value to obtain a third distribution probability of the terminal device in the at least two rectangular sub-areas.

[0080] If after a preset time, the operating state of the terminal device changes from a stationary state to a moving state, in order to further improve the positioning accuracy, in addition to performing time-attenuation processing on the second distribution probability of the terminal device in each rectangular sub-area, the embodiment of the present disclosure also performs motion-attenuation processing on the second distribution probability after time attenuation to reduce the impact of the state change of the terminal device on the positioning result.

[0081] The specific process of performing time decay processing on the second distribution probability according to the time decay value may refer to the aforementioned steps S321 to S322, which will not be further elaborated in the embodiments of the present disclosure.

[0082] The second attenuation value can also be set according to actual needs. For example, assuming the second attenuation value is 0.0625, after time attenuation processing, the second distribution probability of the terminal device on the rectangular sub-area G1 is 0.025, the second distribution probability on the rectangular sub-area G2 is 0.0875, and the second distribution probability on the rectangular sub-area G3 is 0.4625. Then, after motion attenuation processing, the third distribution probability of the terminal device on the rectangular sub-area G1 is -0.0375, the third distribution probability on the rectangular sub-area G2 is 0.0250, and the third distribution probability on the rectangular sub-area G3 is 0.4. Based on the third distribution probability of the terminal device on each rectangular sub-area, the target position of the terminal device can be determined.

[0083] In summary, the embodiments of the present disclosure provide a positioning method, which obtains sensor data and an initial position of a terminal device, extracts a zero-speed detection feature from the sensor data, and then determines the operating state of the terminal device based on the zero-speed detection feature. When it is determined that the terminal device is in a stationary state, the initial position of the terminal device is converged to obtain the target position of the terminal device. The embodiments of the present disclosure can avoid positioning jitter caused by problems such as cold start of GNSS and poor quality of positioning device chips in a stationary state, and can provide stable positioning information for the terminal device, reduce positioning offset, and improve positioning accuracy.

[0084] Example 2

[0085] Reference Figure 2 , which shows a structural diagram of a positioning device in an embodiment of the present disclosure, applied to a terminal device, specifically as follows:

[0086] A data acquisition module 201 is used to acquire sensor data and an initial position of the terminal device;

[0087] A feature extraction module 202 is used to extract zero-speed detection features of the sensor data;

[0088] A state determination module 203 is configured to determine an operating state of the terminal device according to the zero-speed detection feature;

[0089] The convergence processing module 204 is configured to perform convergence processing on the initial position to obtain a target position of the terminal device if it is determined that the terminal device is in a stationary state.

[0090] Optionally, the convergence processing module includes:

[0091] A region segmentation processing submodule is used to perform region segmentation processing on a preset rectangular region to obtain at least two rectangular subregions, wherein the preset rectangular region is centered on the initial position;

[0092] A first distribution probability calculation submodule, configured to calculate a first distribution probability of the terminal device in the at least two rectangular sub-areas;

[0093] a second distribution probability calculation submodule, configured to accumulate the first distribution probability of the terminal device in the at least two rectangular sub-areas within a preset time to obtain a second distribution probability of the terminal device in the at least two rectangular sub-areas;

[0094] an attenuation processing submodule, configured to perform attenuation processing on the second distribution probability of the terminal device in the at least two rectangular sub-areas to obtain a third distribution probability of the terminal device in the at least two rectangular sub-areas;

[0095] The target position determination submodule is configured to determine the target position of the terminal device according to the third distribution probability of the terminal device in the at least two rectangular sub-areas.

[0096] Optionally, the attenuation processing submodule includes:

[0097] a time decay value calculation unit, configured to calculate the product of the preset time and the first decay value to obtain a time decay value;

[0098] The attenuation processing unit is used to calculate the difference between the second distribution probability of the terminal device in the at least two rectangular sub-areas and the time attenuation value to obtain the third distribution probability of the terminal device in the at least two rectangular sub-areas.

[0099] Optionally, the attenuation processing unit includes:

[0100] a time decay processing subunit, configured to, if the running state of the terminal device is updated from a stationary state to a moving state after the preset time, perform time decay processing on the second distribution probability according to the time decay value to obtain a decayed second distribution probability;

[0101] The motion attenuation processing subunit is configured to perform motion attenuation processing on the attenuated second distribution probability according to a second attenuation value to obtain a third distribution probability of the terminal device in the at least two rectangular sub-areas.

[0102] Optionally, the first distribution probability calculation submodule includes:

[0103] a Gaussian distribution function determining unit, configured to determine a two-dimensional Gaussian distribution function of the terminal device according to the initial position and a preset accuracy;

[0104] The first distribution probability calculation unit is used to calculate the integral of the two-dimensional Gaussian distribution function on the at least two rectangular sub-areas to obtain the first distribution probability of the terminal device on the at least two rectangular sub-areas.

[0105] Optionally, the state determination module includes:

[0106] A model processing submodule, configured to input the zero-speed detection feature into a pre-trained state detection model for processing to obtain a state label of the terminal device;

[0107] The first state determination submodule is configured to determine that the terminal device is in a stationary state if the state tag of the terminal device is a target tag.

[0108] Optionally, the sensor data includes accelerometer data, gyroscope data and magnetometer data, and the zero-speed detection feature includes at least one of a generalized likelihood ratio detection value, an accelerometer measurement variance detection value, an accelerometer measurement amplitude detection value, an angular velocity measurement energy detection value and a magnetometer measurement variance detection value.

[0109] Optionally, the state determination module includes:

[0110] A second state determination submodule is configured to determine that the terminal device is in a stationary state if at least one zero-speed detection feature has a value less than a corresponding preset threshold;

[0111] The third state determination submodule is configured to determine that the terminal device is in motion if the at least one zero-speed detection feature is greater than or equal to its corresponding preset threshold.

[0112] In summary, the embodiments of the present disclosure provide a positioning device that obtains sensor data and an initial position of a terminal device, extracts a zero-speed detection feature from the sensor data, and then determines the operating state of the terminal device based on the zero-speed detection feature. When it is determined that the terminal device is in a stationary state, the initial position of the terminal device is converged to obtain the target position of the terminal device. The embodiments of the present disclosure can avoid positioning jitter caused by problems such as cold start of GNSS and poor quality of positioning device chips in a stationary state, and can provide stable positioning information for the terminal device, reduce positioning offset, and improve positioning accuracy.

[0113] The second embodiment is a device embodiment corresponding to the first embodiment. For detailed description, please refer to the first embodiment and will not be repeated here.

[0114] The embodiment of the present disclosure also provides an electronic device, referring to Figure 3 , including: a processor 301, a memory 302, and a computer program 3021 stored in the memory 302 and capable of running on the processor, and when the processor 301 executes the program, the positioning method of the aforementioned embodiment is implemented.

[0115] An embodiment of the present disclosure further provides a readable storage medium. When instructions in the storage medium are executed by a processor of an electronic device, the electronic device can execute the positioning method of the aforementioned embodiment.

[0116] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0117] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems may also be used together with the teachings herein. Based on the above description, it is apparent that the structure required for constructing such systems is suitable. In addition, the embodiments of the present disclosure are not directed to any specific programming language. It should be understood that various programming languages ​​may be utilized to implement the contents of the embodiments of the present disclosure described herein, and the above description of specific languages ​​is intended to disclose the best mode of implementation of the embodiments of the present disclosure.

[0118] In the description provided herein, numerous specific details are described. However, it is understood that embodiments of the present disclosure may be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0119] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present disclosure, various features of the embodiments of the present disclosure are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed approach should not be interpreted as reflecting an intention that the claimed embodiments of the present disclosure require more features than are expressly recited in each claim. Rather, as reflected in the claims below, inventive aspects lie in less than all of the features of the individual embodiments disclosed above. Accordingly, the claims that follow the detailed description are hereby expressly incorporated into the detailed description, with each claim itself serving as a separate embodiment of the present disclosure.

[0120] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition may be divided into multiple submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed herein may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0121] The various component embodiments of the embodiments of the present disclosure may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or digital signal processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components of the file processing device according to the embodiments of the present disclosure. The embodiments of the present disclosure may also be implemented as a device or apparatus program for executing part or all of the methods described herein. Such a program implementing the embodiments of the present disclosure may be stored on a computer-readable medium, or may be in the form of one or more signals. Such a signal may be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0122] It should be noted that the above embodiments illustrate rather than limit the embodiments of the present disclosure, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbols placed between brackets should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The embodiments of the present disclosure may be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

[0123] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0124] The above description is merely a preferred embodiment of the embodiments of the present disclosure and is not intended to limit the embodiments of the present disclosure. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the embodiments of the present disclosure shall be included in the protection scope of the embodiments of the present disclosure.

[0125] The above description is merely a specific implementation of the embodiments of the present disclosure, but the scope of protection of the embodiments of the present disclosure is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the embodiments of the present disclosure should be included in the scope of protection of the embodiments of the present disclosure. Therefore, the scope of protection of the embodiments of the present disclosure should be based on the scope of protection of the claims.

Claims

1. A positioning method, characterized in that: Applied to a terminal device, the method includes: Obtaining sensor data and an initial location of the terminal device; extracting a zero-speed detection feature from the sensor data; determining the operating state of the terminal device according to the zero-speed detection feature; If it is determined that the terminal device is in a stationary state, then Performing region segmentation processing on a preset rectangular region to obtain at least two rectangular sub-regions, wherein the preset rectangular region is centered at the initial position; Calculating a first distribution probability of the terminal device in the at least two rectangular sub-areas; Accumulating the first distribution probability of the terminal device in the at least two rectangular sub-areas within a preset time to obtain a second distribution probability of the terminal device in the at least two rectangular sub-areas; performing attenuation processing on the second distribution probability of the terminal device in the at least two rectangular sub-areas to obtain a third distribution probability of the terminal device in the at least two rectangular sub-areas; The target position of the terminal device is determined according to the third distribution probability of the terminal device in the at least two rectangular sub-areas.

2. The method according to claim 1, characterized in that The attenuating the second distribution probability of the terminal device in the at least two rectangular sub-areas to obtain a third distribution probability of the terminal device in the at least two rectangular sub-areas includes: Calculating the product of the preset time and the first attenuation value to obtain a time attenuation value; A difference between the second distribution probability of the terminal device in the at least two rectangular sub-areas and the time attenuation value is calculated to obtain a third distribution probability of the terminal device in the at least two rectangular sub-areas.

3. The method according to claim 2, characterized in that The calculating a difference between the second distribution probability of the terminal device in the at least two rectangular sub-areas and the time attenuation value to obtain a third distribution probability of the terminal device in the at least two rectangular sub-areas includes: If the running state of the terminal device is updated from the static state to the moving state after the preset time, the second distribution probability is subjected to time decay processing according to the time decay value to obtain the attenuated second distribution probability; The attenuated second distribution probability is subjected to motion attenuation processing according to the second attenuation value to obtain a third distribution probability of the terminal device in the at least two rectangular sub-areas.

4. The method according to claim 1, wherein The calculating a first distribution probability of the terminal device in the at least two rectangular sub-areas includes: Determine a two-dimensional Gaussian distribution function of the terminal device according to the initial position and a preset accuracy; The integral of the two-dimensional Gaussian distribution function over the at least two rectangular sub-areas is calculated to obtain a first distribution probability of the terminal device over the at least two rectangular sub-areas.

5. The method according to claim 1, wherein The determining the operating state of the terminal device according to the zero-speed detection feature includes: Inputting the zero-speed detection feature into a pre-trained state detection model for processing to obtain a state label of the terminal device; If the status tag of the terminal device is a target tag, it is determined that the terminal device is in a stationary state.

6. The method according to any one of claims 1 to 5, characterized in that: The sensor data includes accelerometer data, gyroscope data and magnetometer data, and the zero-speed detection feature includes at least one of a generalized likelihood ratio detection value, an accelerometer measurement variance detection value, an accelerometer measurement amplitude detection value, an angular velocity measurement energy detection value and a magnetometer measurement variance detection value.

7. The method according to claim 6, characterized in that The determining the operating state of the terminal device according to the zero-speed detection feature includes: If there is at least one zero-speed detection feature that is less than its corresponding preset threshold, it is determined that the terminal device is in a stationary state; if the at least one zero-speed detection feature is greater than or equal to its corresponding preset threshold, it is determined that the terminal device is in a moving state.

8. A positioning device, characterized in that: Applied to a terminal device, the device includes: A data acquisition module, configured to acquire sensor data and an initial position of the terminal device; A feature extraction module, configured to extract zero-speed detection features from the sensor data; a state determination module, configured to determine an operating state of the terminal device according to the zero-speed detection feature; A convergence processing module is configured to perform convergence processing on the initial position to obtain a target position of the terminal device if it is determined that the terminal device is in a stationary state; the convergence processing module includes: A region segmentation processing submodule is used to perform region segmentation processing on a preset rectangular region to obtain at least two rectangular subregions, wherein the preset rectangular region is centered on the initial position; A first distribution probability calculation submodule, configured to calculate a first distribution probability of the terminal device in the at least two rectangular sub-areas; a second distribution probability calculation submodule, configured to accumulate the first distribution probability of the terminal device in the at least two rectangular sub-areas within a preset time to obtain a second distribution probability of the terminal device in the at least two rectangular sub-areas; an attenuation processing submodule, configured to perform attenuation processing on the second distribution probability of the terminal device in the at least two rectangular sub-areas to obtain a third distribution probability of the terminal device in the at least two rectangular sub-areas; The target position determination submodule is configured to determine the target position of the terminal device according to the third distribution probability of the terminal device in the at least two rectangular sub-areas.

9. The device according to claim 8, characterized in that The attenuation processing submodule includes: a time decay value calculation unit, configured to calculate the product of the preset time and the first decay value to obtain a time decay value; The attenuation processing unit is used to calculate the difference between the second distribution probability of the terminal device in the at least two rectangular sub-areas and the time attenuation value to obtain the third distribution probability of the terminal device in the at least two rectangular sub-areas.

10. The device according to claim 9, characterized in that The attenuation processing unit includes: a time decay processing subunit, configured to, if the running state of the terminal device is updated from a stationary state to a moving state after the preset time, perform time decay processing on the second distribution probability according to the time decay value to obtain a decayed second distribution probability; The motion attenuation processing subunit is configured to perform motion attenuation processing on the attenuated second distribution probability according to a second attenuation value to obtain a third distribution probability of the terminal device in the at least two rectangular sub-areas.

11. The device according to claim 8, characterized in that The first distribution probability calculation submodule includes: a Gaussian distribution function determining unit, configured to determine a two-dimensional Gaussian distribution function of the terminal device according to the initial position and a preset accuracy; The first distribution probability calculation unit is used to calculate the integral of the two-dimensional Gaussian distribution function on the at least two rectangular sub-areas to obtain the first distribution probability of the terminal device on the at least two rectangular sub-areas.

12. The device according to claim 8, characterized in that The state determination module includes: A model processing submodule, configured to input the zero-speed detection feature into a pre-trained state detection model for processing to obtain a state label of the terminal device; The first state determination submodule is configured to determine that the terminal device is in a stationary state if the state tag of the terminal device is a target tag.

13. The device according to any one of claims 8 to 12, characterized in that The sensor data includes accelerometer data, gyroscope data and magnetometer data, and the zero-speed detection feature includes at least one of a generalized likelihood ratio detection value, an accelerometer measurement variance detection value, an accelerometer measurement amplitude detection value, an angular velocity measurement energy detection value and a magnetometer measurement variance detection value.

14. The device according to claim 13, characterized in that The state determination module includes: A second state determination submodule is configured to determine that the terminal device is in a stationary state if at least one zero-speed detection feature has a value less than a corresponding preset threshold; The third state determination submodule is configured to determine that the terminal device is in motion if the at least one zero-speed detection feature is greater than or equal to its corresponding preset threshold.

15. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the positioning method according to any one of claims 1 to 7 when executing the program.

16. A readable storage medium, characterized in that When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the positioning method according to any one of method claims 1 to 7.

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