Positioning model training method, positioning method and computer readable storage medium
A model training and model technology, applied in positioning, neural learning methods, biological neural network models, etc., can solve problems such as inaccurate positioning, single feature, and inability to reflect the spatial correlation of multiple features, so as to improve accuracy and extract space Correlation Effect
Active Publication Date: 2022-05-13
深圳依时货拉拉科技有限公司
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However, these methods either use a single feature, which makes them only suitable for small-scale indoor positioning, or have a strong dependence on the size of the network selection, and cannot reflect the spatial correlation of multiple features. It is easy to cause inaccurate positioning
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[0025] The following will clearly and completely describe the technical solutions in the embodiments of the application with reference to the drawings in the embodiments of the application. Apparently, the described embodiments are only some of the embodiments of the application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.
[0026] In this specification, adjectives such as first and second may only be used to distinguish one element or action from another without necessarily requiring or implying any actual such relationship or order. Reference to an element or component or step (etc.) should not be construed as being limited to only one of the element, component, or step, but may be one or more of the element, component, or step, etc., where the circumstances permit.
[0027] In this specification, for...
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The invention relates to the field of artificial intelligence, provides a positioning model training method, a positioning method and a computer readable storage medium, and aims to realize accurate positioning in various scenes. The method comprises the following steps: recalling wireless access equipment fingerprint data in a wireless access equipment fingerprint database according to related data of wireless access equipment in a positioning request sent by a terminal; according to the recalled fingerprint data of the wireless access device, obtaining a first feature map corresponding to the related data of the wireless access device; performing first convolutional neural network training by using the first feature map to obtain a trained first target positioning model, and predicting by using the model to obtain a recall center corresponding to related data; and intercepting a second feature map taking the recall center as the center from the first feature map, and training a second convolutional neural network by taking the second feature map as input information to obtain a trained second target positioning model. According to the technical scheme, accurate positioning in various scenes can be realized.
Description
technical field [0001] The invention relates to the field of artificial intelligence, in particular to a positioning model training method, a positioning method and a computer-readable storage medium. Background technique [0002] With the continuous development of location-based service technology, market services have higher and higher requirements for user positioning accuracy. However, in some scenarios (for example, inside or between buildings, underground parking lots, special weather, etc.), GPS / GNSS positioning signals are likely to be interfered, resulting in inaccurate or even impossible positioning. In order to solve the above problems, the existing technology proposes a method of using wireless access point (Access Point, AP) signal information collected by smart terminals such as smart phones and combining with machine learning models to estimate the actual location of the target. However, these methods either use a single feature, which makes them only suitabl...
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Login to View More IPC IPC(8): H04W64/00G06N3/04G06N3/08G01S5/02
CPCH04W64/006G06N3/08G01S5/02G06N3/045
Inventor 樊旭颖吴玉花李隽颖
Owner 深圳依时货拉拉科技有限公司