Positioning data processing method and apparatus, and device and storage medium

Abstract

A positioning data processing method and apparatus, and a device and a storage medium. The method comprises: acquiring a plurality of sets of raw observation data from a preset type of smart terminal (S11); mapping to one index navigation positioning information in each set of raw observation data (S12); for each index, performing anomaly detection to remove abnormal data from at least two sets of raw observation data, which are selected from the raw observation data associated with the index and have a signal-to-noise ratio greater than a first preset threshold, so as to obtain an observation data subset, and on the basis of the number of times when each signal-to-noise ratio in the observation data subset appears therein, obtaining statistical features of signal-to-noise ratios in the observation data subset (S13); and using the observation data subset and the statistical features to adjust a pre-constructed stochastic model, and determining the value of at least one model parameter of the stochastic model (S14), wherein the value of the at least one model parameter is provided and used for configuring a stochastic model in the preset type of smart terminal, which stochastic model is used for positioning.

Description

Positioning data processing method, device, equipment and storage medium

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on March 14, 2024, with application number 202410295630.X and invention name “Positioning data processing method, device, equipment and storage medium”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to positioning technology and provides a positioning data processing method, device, equipment and storage medium.

[0003] Background of the Invention

[0004] With the growing demand for location-based services and the rapid development of low-cost navigation chips, high-precision positioning technology for smart terminals has attracted considerable attention. Related technologies typically evaluate the weighting parameters of smart terminals, selecting either an altitude-based weighting mode or a raw standard deviation-based weighting mode based on the type of smart terminal. A random model is then selected based on the navigation chip onboard the smart terminal. A carrier phase differential (RTK) positioning model is then constructed based on the selected random model and the repaired carrier phase observations. This allows the smart terminal to provide high-precision positioning services based on this constructed RTK positioning model. Summary of the Invention

[0005] The embodiments of the present application provide a positioning data processing method, apparatus, device, and storage medium to solve the problem of how to determine model parameters adapted to the terminal type.

[0006] The present invention provides a method for processing positioning data, including:

[0007] Acquire multiple sets of raw observation data from a preset type of smart terminal, wherein each set of raw observation data includes: navigation positioning information and a signal-to-noise ratio generated when positioning the smart terminal, the navigation positioning information being information about resources used for positioning;

[0008] Map the navigation positioning information in each set of original observation data to an index;

[0009] For each index, outlier data are eliminated from at least two groups of original observation data associated with the index and having a signal-to-noise ratio greater than a first set threshold value through an outlier test to obtain an observation data subset, and statistical characteristics of the signal-to-noise ratio in the observation data subset are obtained based on the number of times each signal-to-noise ratio in the observation data subset appears in the observation data subset;

[0010] By adjusting a pre-built random model using the observation data subset and the statistical features, a value of at least one model parameter of the random model is determined, and the value of the at least one model parameter is provided for configuring the random model for positioning in the smart terminal of the preset type.

[0011] The present application also provides a positioning data processing device, including:

[0012] A data acquisition module is used to obtain multiple sets of raw observation data from a preset type of smart terminal, wherein each set of raw observation data includes: navigation positioning information and signal-to-noise ratio generated when positioning the smart terminal, and the navigation positioning information is information about the resources used for positioning;

[0013] A data processing module is used to map the navigation and positioning information in each set of raw observation data to an index;

[0014] a feature extraction module configured to, for each index, eliminate abnormal data from at least two groups of original observation data associated with the index and having a signal-to-noise ratio greater than a first set threshold value through an anomaly test to obtain an observation data subset, and obtain a statistical feature of the signal-to-noise ratio in the observation data subset based on the number of times each signal-to-noise ratio in the observation data subset appears in the observation data subset;

[0015] A parameter fitting module is used to adjust a pre-built random model by using the observation data subset and the statistical features to determine the value of at least one model parameter of the random model, and the value of the at least one model parameter is provided for configuring the random model for positioning in the smart terminal of the preset type.

[0016] An embodiment of the present application further provides a computer device, including a processor and a memory, wherein the memory stores program code, and when the program code is executed by the processor, the processor executes the positioning data processing method of each embodiment.

[0017] An embodiment of the present application further provides a computer-readable storage medium, which includes program code. When the program product is run on a computer device, the program code is used to enable the computer device to execute the positioning data processing method of each embodiment.

[0018] The embodiments of the present application further provide a computer program product, including computer instructions, which are used by a processor to execute the positioning data processing methods of various embodiments.

[0019] The technical solution of the embodiments of the present application extracts statistical characteristics of the signal-to-noise ratio (SNR) of the same type of smart terminal from the raw observation data generated during positioning. These statistical characteristics are used to adjust a preset random model, enabling the random model to better adapt to the positioning characteristics and actual observation environment of this type of terminal, and obtaining model parameters that are more suitable for this type of smart terminal. When this type of smart terminal uses the random model configured with the corresponding model parameters for positioning, the resulting positioning results are more accurate.

[0020] BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0022] FIG1A is an optional schematic diagram of an application scenario in an embodiment of the present application;

[0023] FIG1B is a logic diagram of determining model parameters between the intelligent terminal and the server;

[0024] FIG1C is a schematic flow chart of a positioning data processing method according to an embodiment of the present application;

[0025] FIG1D is a schematic diagram of the structure of a positioning data processing device provided in an embodiment of the present application;

[0026] FIG2A is a schematic diagram of a process for processing positioning data of a smart terminal according to an embodiment of the present application;

[0027] FIG2B is a logic diagram of processing positioning data of a smart terminal according to an embodiment of the present application;

[0028] FIG2C is a schematic diagram of a process for generating an original index pair according to an embodiment of the present application;

[0029] FIG2D is a logic diagram of the occurrence frequency of an original signal-to-noise ratio in the updated frequency statistics table provided by an embodiment of the present application;

[0030] FIG2E is a schematic diagram of a process for performing a single peak test according to an embodiment of the present application;

[0031] FIG2F is a schematic diagram of a normality test process according to an embodiment of the present application;

[0032] FIG2G is a schematic diagram of image distribution with different kurtosis values ​​provided in an embodiment of the present application;

[0033] FIG2H is a schematic diagram of image distribution with different skewness values ​​provided by an embodiment of the present application;

[0034] FIG2I is a schematic diagram of the calculation results of the signal-to-noise ratio distribution and related eigenvalues ​​provided in an embodiment of the present application;

[0035] FIG3A is a schematic diagram of a process for processing and applying positioning data in a map application according to an embodiment of the present application;

[0036] FIG3B is a logical diagram of processing and applying positioning data in a map application according to an embodiment of the present application;

[0037] FIG4A is a schematic diagram of a process for processing and applying location data in a social application with a location function according to an embodiment of the present application;

[0038] FIG4B is a logical diagram of processing and applying location data in a social application with location functionality according to an embodiment of the present application;

[0039] FIG5 is a schematic structural diagram of a positioning data processing device provided in an embodiment of the present application;

[0040] FIG6 is a schematic diagram of a hardware structure of a computer device using an embodiment of the present application;

[0041] FIG7 is a schematic diagram of a hardware structure of another computer device to which an embodiment of the present application is applied.

[0042] Modes for Carrying Out the Invention

[0043] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of the technical solutions of this application, but not all of them. Based on the embodiments described in this application document, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the technical solutions of this application.

[0044] 1. Artificial Intelligence (AI):

[0045] Artificial intelligence (AI) refers to the theories, methods, techniques, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, to perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results. In other words, AI is a comprehensive technology within computer science that seeks to understand the essence of intelligence and produce new intelligent machines that can respond in a manner similar to human intelligence. AI also involves studying the design principles and implementation methods of various intelligent machines, enabling them to possess the capabilities of perception, reasoning, and decision-making.

[0046] Artificial intelligence (AI) technology is a comprehensive discipline encompassing a wide range of fields, encompassing both hardware and software technologies. Foundational AI technologies generally include sensors, specialized AI chips, cloud computing, distributed storage, big data processing, pre-trained models, operating / interaction systems, and mechatronics. Pre-trained models, also known as large models or basic models, can, after fine-tuning, be widely applied to downstream tasks across various AI disciplines. AI software technologies primarily encompass computer vision, speech processing, natural language processing, and machine learning / deep learning.

[0047] With the research and advancement of artificial intelligence technology, artificial intelligence has been studied and applied in many fields, such as common smart homes, smart customer service, virtual assistants, smart speakers, smart marketing, unmanned driving, autonomous driving, robots, smart medical care, etc. I believe that with the development of technology, artificial intelligence will be applied in more fields and play an increasingly important role.

[0048] 2. Machine Learning

[0049] Machine learning is a multidisciplinary field that encompasses probability theory, statistics, approximation theory, convex analysis, and algorithmic complexity theory. It specifically studies how computers can simulate or implement human learning behaviors to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their performance.

[0050] Machine learning is the core of artificial intelligence and the fundamental way to make computers intelligent. Its applications span all areas of AI, including deep learning, reinforcement learning, transfer learning, inductive learning, and self-learning. Pretrained models are the latest development in deep learning, integrating these technologies.

[0051] 3. Autonomous driving technology

[0052] Autonomous driving refers to the ability of a vehicle to drive itself without a driver. This typically involves technologies such as high-precision maps, environmental perception, computer vision, behavioral decision-making, path planning, and motion control. Autonomous driving encompasses multiple development paths, including single-vehicle intelligence, vehicle-road collaboration, and networked cloud control. Autonomous driving technology has broad application prospects, currently in logistics, public transportation, taxis, and smart transportation, and is expected to further develop in the future.

[0053] With the advancement of AI research and technology, AI is being studied and applied in a wide range of fields. Examples include smart homes, smart wearables, virtual assistants, smart speakers, smart marketing, driverless and autonomous driving, drones, digital twins, virtual humans, robots, AI-generated content (AIGC), conversational interaction, smart healthcare, smart customer service, and gaming AI. With technological advancements, AI will be applied in even more areas and play an increasingly important role.

[0054] 4. Global Navigation Satellite System (GNSS):

[0055] Space-based radio navigation and positioning systems, capable of providing all-weather three-dimensional coordinates, velocity, and time information anywhere on Earth's surface or in near-Earth space, include satellite navigation systems such as the Global Positioning System (GPS), the BeiDou Navigation Satellite System (BDS), GLONASS, and the Galileo satellite navigation system. Currently, satellite navigation systems are widely used in navigation, communications, surveying and mapping, timing, consumer entertainment, vehicle management, automotive navigation, and information services. The overall development trend is to provide high-precision services for real-time applications.

[0056] 5. Random Model:

[0057] During GNSS positioning, observation data is subject to interference from various error sources and random errors, reducing positioning accuracy. By analyzing observation data based on constructed stochastic models and understanding the error distribution characteristics of the observation data, these interfering factors can be effectively suppressed during data processing to improve positioning accuracy. Common stochastic models include Gaussian and exponential models.

[0058] 6. Signal-to-noise ratio (SNR or S / N):

[0059] The ratio between signal strength and noise intensity in an electronic device or system. In GNSS positioning, the signal-to-noise ratio (SNR) is a key indicator of the quality of satellite signals received by a receiver. A higher SNR indicates greater signal strength and lower noise intensity, making it easier for the receiver to detect satellite signals, thereby improving positioning accuracy. During GNSS data processing, observations can also be filtered based on the SNR and used as an independent variable in random models to enhance the reliability of positioning results.

[0060] In related technologies, a suitable model is usually selected from several preset random models for different types of smart terminals. However, the model parameters used in the preset random models cannot fully adapt to the positioning chips, antenna layouts and gain strategies of different types of smart terminals. As a result, when each smart terminal performs GNSS positioning based on the configured random model, it is easy for the standard model defined by the algorithm to be incompatible with the landing scenario, resulting in a loss of positioning accuracy.

[0061] In order to solve this problem, the present application proposes a positioning data processing method. The method includes: receiving multiple original observation data uploaded by the smart terminal, each original observation data includes: navigation positioning information and original signal-to-noise ratio generated when the smart terminal is positioned; mapping each navigation positioning information separately to obtain their respective original index pairs, and using the original index pairs with unique content as the target index pairs, and merging multiple original index pairs with the same content into one target index pair; for each target index pair, performing the following operations respectively: performing an abnormality test on multiple original signal-to-noise ratios associated with a target index pair that are greater than a first set threshold, obtaining each target signal-to-noise ratio that passes the test, and obtaining the signal-to-noise ratio characteristics of a target index pair based on the frequency of occurrence of each target signal-to-noise ratio in multiple original observation data, and fitting the model parameters of the random model based on the signal-to-noise ratio characteristics combined with a pre-built random model.

[0062] In the technical solution proposed in this application, the same random model is configured for smart terminals of different models, and then based on the original observation data generated during the positioning of the smart terminals of each model, model parameters that are more suitable for the positioning chip, antenna layout and gain strategy of the corresponding smart terminal are fitted. Therefore, when each smart terminal performs GNSS positioning based on the random model configured with the corresponding model parameters, it has higher adaptability to the landing scene, which is conducive to improving positioning accuracy.

[0063] The preferred embodiments of the present application are described below in conjunction with the drawings in the specification. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application and are not used to limit the present application. In addition, the embodiments and features in the embodiments of the present application can be combined with each other if there is no conflict.

[0064] The method provided in the embodiments of the present application can be applied to various scenarios such as cloud technology, artificial intelligence, smart transportation, and assisted driving. For smart terminals used in the corresponding scenarios, model parameters that are consistent with their own positioning chips, antenna layouts, and gain strategies are configured, and GNSS positioning is performed based on the random model configured with the corresponding model parameters, providing high-precision positioning services for the users of the smart terminals to meet their positioning needs.

[0065] FIG1A shows one application scenario, which includes two smart terminals 110 and a server 130 . The smart terminal 110 establishes a communication connection with the server 130 via a wired network or a wireless network.

[0066] Among them, the smart terminal 110 includes but is not limited to: mobile phones, computers (such as tablets, laptops, desktop computers, etc.), smart home appliances (such as smart speakers, smart refrigerators, etc.), smart voice interaction devices (such as smart watches, smart glasses, etc.), vehicle-mounted terminals, aircraft, etc.

[0067] The server 130 of the embodiment of the present application can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. This application does not impose any restrictions on this.

[0068] The processor for processing positioning data is deployed on the server 130, and the server with more powerful computing power performs the task to determine the model parameters adapted to the corresponding model of the smart terminal 110.

[0069] As shown in FIG1B , during the data collection phase, the smart terminal 110 can respond to a triggering operation of an application software with a positioning function (such as a map application, a social application that can share a location, etc.) by a user (e.g., a user of the smart terminal 110), and obtain multiple raw observation data generated during the previous positioning through the triggered application software, and upload each raw observation data to the observation value database for storage. In the case where the user does not trigger the application software with a positioning function, the smart terminal 110 can also automatically collect multiple raw observation data generated by itself during positioning through the application software with a positioning function, and upload the collected data to the observation value database for storage. In particular, for the case where the smart terminal 110 automatically collects data, a collection cycle can also be set for it so that the smart terminal 110 regularly collects the raw observation data generated within the cycle according to the set collection cycle, so as to avoid missing some data, affecting the selection of appropriate model parameters, and thus reducing the positioning accuracy. The observation value database can be set in a storage device built into the server 130, or in another device that the server 130 can access.

[0070] The server 130 calls a processor to obtain and process the original observation data from the observation database. The processing performed by the server 130 may be shown in FIG1C , including the following steps.

[0071] S11, obtaining multiple groups of original observation data from smart terminals of preset types.

[0072] Each set of raw observation data includes navigation positioning information and the signal-to-noise ratio (SNR) generated when positioning the smart terminal (the SNR in the raw observation data is also referred to as the raw SNR). Navigation positioning information is information about the resources used for positioning. The resources used for positioning may include the satellite system used for positioning and the carrier frequency of the satellite signal. The SNR is the signal-to-noise ratio of the satellite signal received during positioning.

[0073] S12, mapping the navigation positioning information in each set of original observation data to an index.

[0074] The index here can be a single index value or a composite index consisting of at least two indexes, such as an index pair consisting of two indexes. For a composite index, each data element of the navigation positioning information in a set of raw observation data can be mapped to an index, and the indexes of at least two data elements are combined to form a composite index for the set of raw observation data.

[0075] In some embodiments, the index may be a target index pair that has been merged. For example, when the navigation and positioning information in each set of original observation data includes two data elements, the navigation and positioning information in each set of original observation data may be mapped to obtain respective original index pairs. The original index pairs with unique content may be used as the target index pair, and multiple original index pairs with the same content may be merged into a single target index pair.

[0076] S13, for each index, eliminate abnormal data from the original observation data associated with the index and at least two groups of original observation data whose original signal-to-noise ratio is greater than a first set threshold through anomaly detection to obtain an observation data subset, and obtain statistical characteristics of the signal-to-noise ratio in the observation data subset based on the number of times each signal-to-noise ratio in the observation data subset appears in the observation data subset.

[0077] S14, adjusting a pre-built random model by using the observation data subset and the statistical features to determine at least one model parameter of the random model. The at least one model parameter can be provided for configuring the random model for positioning in the smart terminal of the preset type.

[0078] For example, the server 130 can synchronize model parameters adapted for the smart terminal model to a parameter database. During the positioning calculation phase, the smart terminal 110, in response to a positioning operation triggered by the user on the application software, sends a parameter acquisition request to the parameter database, obtaining the model parameters adapted for the smart terminal model from the parameter database. Positioning is then performed using a random model adapted for the corresponding model parameters. A map of the smart terminal's location is displayed on the application software's positioning interface 120, and the current location of the smart terminal is marked on the map using pins, annotation points, and other display formats.

[0079] In each embodiment, at least two types of smart terminals can be preset, and the method of each embodiment can be performed for each type. Various reasonable classification methods can be adopted according to actual needs. For example, the terminal can be classified according to the brand, that is, different types correspond to different terminal brands. For another example, the terminal can be classified according to the model, that is, different types correspond to different models of terminals. For another example, the positioning module parameters (or a combination of parameters) in the smart terminal can be classified, that is, different types of terminals use different positioning module parameters (or a combination of parameters). The parameters of the positioning module can be selected from, for example, the model or type of the navigation chip, the antenna layout method, the gain strategy, etc.

[0080] Thus, the technical solutions of each embodiment extract statistical characteristics of the signal-to-noise ratio (SNR) of the same type of smart terminal from the raw observation data generated during positioning. These statistical characteristics are used to adjust the preset stochastic model, enabling the stochastic model to better adapt to the positioning characteristics and actual observation environment of this type of terminal, and obtaining model parameters that are more suitable for this type of smart terminal. When this type of smart terminal uses the stochastic model configured with the corresponding model parameters for positioning, the resulting positioning results are more accurate.

[0081] In various embodiments, the abnormality test of S13 may include: using at least two signal-to-noise ratio subsets to perform a unimodal test on the signal-to-noise ratio in the at least two groups of original observation data, and, based on the initial kurtosis and initial skewness of the signal-to-noise ratio in the at least two groups of original observation data, performing a normality test on the signal-to-noise ratio in the at least two groups of original observation data. The at least two signal-to-noise ratio subsets are obtained by grouping the signal-to-noise ratio in the at least two groups of original observation data according to numerical values. When the signal-to-noise ratio in the at least two groups of original observation data passes the unimodal test and the normality test, the signal-to-noise ratio in the at least two groups of original observation data is tested for outliers, and the original observation data whose signal-to-noise ratio is not within the set outlier range is deleted to obtain the observation data subset.

[0082] In this way, by performing unimodal test, normality test and outlier test on the original observation data, the data reliability of the observation data subset used to adjust the random model can be ensured, so that the adjusted random model can produce more accurate positioning results.

[0083] In each embodiment, the above-mentioned unimodal test may include: clustering the signal-to-noise ratios in the at least two groups of original observation data to obtain the above-mentioned at least two signal-to-noise ratio subsets; respectively determining the signal-to-noise ratio difference between the cluster centers of each two signal-to-noise ratio subsets in the two signal-to-noise ratio subsets; when at least one signal-to-noise ratio difference is not less than a second set threshold, determining that the unimodal test has failed; when each signal-to-noise ratio difference is less than the second set threshold, determining that the unimodal test has passed.

[0084] In each embodiment, the above-mentioned normality test may include: determining the initial kurtosis and initial skewness of the signal-to-noise ratio in the at least two groups of original observation data, wherein the initial kurtosis is used to reflect the sharpness of the normal distribution image of the signal-to-noise ratio, and the initial skewness is used to reflect the symmetry of the normal distribution image of the signal-to-noise ratio; normalizing the initial kurtosis and the initial skewness respectively to obtain reference kurtosis and reference skewness; and obtaining the normality test results of the signal-to-noise ratio in the above-mentioned at least two groups of original observation data by performing a normality test on the reference kurtosis and reference skewness.

[0085] In each embodiment, when the signal-to-noise ratio in the above-mentioned at least two groups of original observation data fails to pass at least one of the unimodal test and the normality test, the following operations are performed until the first set threshold reaches a preset maximum threshold, or the signal-to-noise ratio in the at least two groups of original observation data passes the unimodal test and the normality test: the first set threshold is increased by a set threshold increase amount; based on the above-mentioned at least two signal-to-noise ratio subsets, a unimodal test is performed on the signal-to-noise ratio in the at least two groups of original observation data; and, based on the initial kurtosis and initial skewness of the signal-to-noise ratio in the at least two groups of original observation data, a normality test is performed on the signal-to-noise ratio in the at least two groups of original observation data.

[0086] In various embodiments, the method of obtaining the statistical characteristics of the signal-to-noise ratio in the above-mentioned observation data subset in S13 may include: obtaining at least one of the maximum signal-to-noise ratio, mean value, median and standard deviation of the signal-to-noise ratio in the above-mentioned observation data subset as the statistical characteristics.

[0087] In each embodiment, S14 may include: using the signal-to-noise ratio and statistical characteristics in the observation data subset to determine the value of the frequency difference parameter of the random model; converting the pre-constructed random model into a weight model in which the output value and the signal-to-noise ratio are linearly related, and the weight model includes the at least one model parameter; using the signal-to-noise ratio and statistical characteristics in the observation data subset to fit the weight model to obtain an estimated value of at least one model parameter.

[0088] In this way, by converting the pre-built random model into a linear weight model, the adjustment process of the random model can be simplified, processing resources can be saved, and processing speed can be accelerated.

[0089] Each embodiment further provides a positioning data processing device. As shown in FIG1D , the device 100 may include:

[0090] The data acquisition module 101 is configured to acquire multiple sets of raw observation data from a preset type of smart terminal, wherein each set of raw observation data includes: navigation positioning information and a signal-to-noise ratio generated when positioning the smart terminal, wherein the navigation positioning information is information about the resources used for positioning;

[0091] The data processing module 102 is used to map the navigation and positioning information in each set of original observation data to an index;

[0092] The feature extraction module 104 is configured to, for each index, eliminate abnormal data from at least two groups of original observation data associated with the index and having a signal-to-noise ratio greater than a first set threshold value through an anomaly test to obtain an observation data subset, and obtain a statistical feature of the signal-to-noise ratio in the observation data subset based on the number of times each signal-to-noise ratio in the observation data subset appears in the observation data subset;

[0093] The parameter fitting module 103 is used to adjust the pre-built random model by using the above-mentioned observation data subset and the above-mentioned statistical characteristics to determine at least one model parameter of the random model, and the at least one model parameter is provided for configuring the random model for positioning in the above-mentioned preset type of smart terminal.

[0094] The specific functions of the above modules can be found in the description of the corresponding steps of the methods of each embodiment, and will not be repeated here.

[0095] The following describes the solutions of various embodiments through specific examples. The various details involved in these examples are only to help understand the above solutions, and the implementation of the solutions of various embodiments does not rely on any details in these examples.

[0096] For ease of description, the process of processing the location data of a single smart terminal is described using a single smart terminal as an example. In various embodiments, the location data of multiple smart terminals can be processed in a similar manner. Referring to the schematic diagrams shown in Figures 2A and 2B , the process of processing the location data of a single smart terminal is as follows.

[0097] S201: Receive multiple original observation data uploaded by a smart terminal, each original observation data includes: navigation positioning information and original signal-to-noise ratio generated when the smart terminal is positioning.

[0098] During the data collection phase, the smart terminal uses the map application to reflow multiple raw observation data generated during previous positioning and upload each raw observation data to the observation database for storage. Here, "reflow" refers to obtaining the raw observation data generated during previous positioning from the smart terminal's positioning module through the map application. The server then calls the processor to read multiple raw observation data from the observation database. It also supports reading data collected within a set period, such as reading multiple raw observation data collected by the smart terminal most recently, or reading data collected by the smart terminal within the past month.

[0099] S202: Map each navigation positioning information separately to obtain their respective original index pairs (i.e., composite indexes, used as indexes of original observation data), and use the original index pairs with unique content as target index pairs, and merge multiple original index pairs with the same content into one target index pair.

[0100] Because the amount of raw observation data returned is enormous, data cleaning is necessary to reduce the processing pressure on the server. Data cleaning primarily involves removing invalid data, extracting valid data, and classifying data by model.

[0101] Step 1: Eliminate invalid data.

[0102] When a smart terminal performs GNSS positioning, it generates multiple raw observation data. Each raw observation data consists of a GNSS clock (GNSSClock) field and a GNSS measurement (GNSSMeasurement) field. The GNSSClock field indicates the time the observation data was generated, and the GNSSMeasurement field indicates the n raw observation values ​​generated when the smart terminal performs multi-frequency observations of navigation satellites at a specific epoch (i.e., the observation time). Each raw observation value includes navigation positioning information and the corresponding raw signal-to-noise ratio.

[0103] Raw observation data with incomplete field parameters is considered invalid and discarded. Since valid observation values ​​are only considered reasonable when the raw signal-to-noise ratio is within the set range, this helps improve the compatibility between model parameters and the specific smart device. Therefore, after discarding invalid data, the raw signal-to-noise ratio is filtered based on whether it falls within the set range. Only GNSS data with a raw signal-to-noise ratio within the set range is retained. The range can be set as needed, for example, to greater than 1dBHz, or 1 to 63dBHz, etc.

[0104] Step 2: Extract valid data.

[0105] Each navigation and positioning message includes the Constellation Type field and the Carrier Frequency field. Because these two fields are long and do not explicitly record the relationship between the navigation and positioning information and the navigation and positioning system, this hinders the subsequent configuration of model parameters for smart terminals. Therefore, as shown in Figure 2C, the following operations are performed for each navigation and positioning message to obtain the corresponding raw index pair.

[0106] S2021: Based on the conversion relationship between the constellation type and the navigation and positioning system, map the constellation type in the navigation and positioning information to the corresponding navigation and positioning system.

[0107] Each navigation and positioning system may be represented by a pre-set identifier. In various embodiments, the identifier of the navigation and positioning system may include at least one letter, at least one number, at least one symbol, or any combination thereof.

[0108] The carrier frequency is the frequency of the electromagnetic waves used to transmit information in wireless communication systems. In digital communications, the carrier frequency is typically a fixed value. It can also be understood as a fixed-frequency electromagnetic wave that carries the modulated signal. The frequency point is the number assigned to a fixed frequency. Therefore, in radio communications, there is a one-to-one correspondence between frequency points and frequencies; each frequency point corresponds to a specific frequency.

[0109] S2022: Based on the conversion relationship between the navigation and positioning system, frequency and frequency point, map the frequency in the navigation and positioning information to the corresponding frequency point, and associate the mapped navigation and positioning system and frequency point into an original index pair.

[0110] After determining the navigation and positioning system used by the smart terminal, the frequencies in the navigation and positioning information are mapped to corresponding frequencies based on the conversion relationship between navigation and positioning systems, frequencies, and frequency points. The mapped navigation and positioning system and frequency point are then associated with an original index pair. The original index pair is used as the index for the corresponding navigation and positioning information.

[0111] For example, Table 1 shows the conversion relationship between constellation type and navigation and positioning system, and Table 2 shows the conversion relationship between navigation and positioning system, frequency, and frequency point. Based on the conversion relationships listed in Tables 1 and 2, the navigation and positioning information (ConstellationType: 1, CarrierFrequencyHz: 1575420000Hz) is converted to the original index pair GPS L1. To facilitate subsequent statistics and plotting, the original index pair GPS L1 can also be converted to the index "G0".

[0112] Table 1

[0113] Table 2

[0114] In order to reduce the storage pressure and data analysis pressure on the server, the original signal-to-noise ratios of different navigation satellites and epochs (indicating time) can be converted into integers to obtain updated signal-to-noise ratios (as the signal-to-noise ratios used in subsequent processing), and then each signal-to-noise ratio is traversed in turn until all traversals are completed to obtain the frequency statistics table shown in Table 3. As shown in Figure 2D, each time a traversal is performed, if the current traversal signal-to-noise ratio has been recorded in the frequency statistics table, the frequency of occurrence of the signal-to-noise ratio in the table is updated; if the current traversal signal-to-noise ratio is not recorded in the frequency statistics table, it is written into the table and the frequency of occurrence of the signal-to-noise ratio in the table is updated. In other embodiments, the above rounding operation can be omitted and the original signal-to-noise ratio can be used directly.

[0115] Table 3

[0116] Step 3: Classify by model.

[0117] According to the model category of the smart terminal, the original index pairs and frequency statistics tables of the same model are divided into one group to facilitate subsequent data analysis.

[0118] S203: For each target index pair, the following operations are performed: an anomaly detection is performed on multiple signal-to-noise ratios associated with a target index pair that are greater than a first set threshold, and each signal-to-noise ratio that passes the test is obtained (for ease of description, also referred to as a target signal-to-noise ratio), and based on the frequency of occurrence of each target signal-to-noise ratio in multiple original observation data, a statistical feature of the signal-to-noise ratio corresponding to the target index pair (also referred to as a signal-to-noise ratio feature) is obtained; based on the signal-to-noise ratio feature, in combination with a pre-constructed random model, the model parameters of the random model are fitted.

[0119] Due to the differences in antenna layout, gain strategy, cutoff signal-to-noise ratio, and the severity of the actual observation environment of different models of smart terminals, the signal-to-noise ratio collected by the return flow may show multiple peaks and severe skewness. Therefore, it is necessary to first screen out the target signal-to-noise ratio with higher reliability.

[0120] First, based on a first set threshold T1, a preliminary screening is performed on multiple signal-to-noise ratios R associated with a target index pair to select signal-to-noise ratios that satisfy R>T1, that is, multiple signal-to-noise ratios that are greater than the first set threshold. The first set threshold can be determined within a range of 10 to 20 dBHz based on the model of the smart terminal.

[0121] Then, based on a target index pair, a plurality of signal-to-noise ratio subsets are obtained by dividing a plurality of signal-to-noise ratios associated with the plurality of signal-to-noise ratios that are greater than a first set threshold, a unimodal test is performed on the signal-to-noise ratio set composed of the plurality of signal-to-noise ratios, and, based on the initial kurtosis and initial skewness of the signal-to-noise ratio set, a normality test is performed on the signal-to-noise ratio set.

[0122] In some embodiments, the abnormality test mainly includes two parts, one is a unimodal test, and the other is a normality test.

[0123] (1) Referring to the flow chart shown in FIG2E , the process of performing a single peak test is as follows.

[0124] S2031: Clustering multiple signal-to-noise ratios associated with a target index pair that are greater than a first set threshold to obtain multiple signal-to-noise ratio subsets, then determining the signal-to-noise ratio difference between each signal-to-noise ratio subset and the cluster center of other signal-to-noise ratio subsets except itself, and comparing each signal-to-noise ratio difference with a second set threshold to obtain a corresponding comparison result.

[0125] S2032: When at least one comparison result shows that the signal-to-noise ratio difference is not less than the second set threshold, determine that the single-peak test result is that the signal-to-noise ratio set fails the single-peak test; when all comparison results show that the signal-to-noise ratio difference is less than the second set threshold, determine that the signal-to-noise ratio set passes the single-peak test.

[0126] In one embodiment, the second threshold value may be in the range of 5 to 10 dBHz to ensure the effectiveness of the unimodal test. For example, a k-means algorithm is used to classify multiple signal-to-noise ratios greater than 10 dBHz into two clusters. After the algorithm converges, the difference in signal-to-noise ratio between the two cluster centers is tested to see if it is less than 7 dBHz. If so, the unimodal test result is determined to indicate that the signal-to-noise ratio set passes the unimodal test; otherwise, the unimodal test result is determined to indicate that the signal-to-noise ratio set fails the unimodal test.

[0127] (2) Referring to the flowchart shown in FIG2F , the process of performing the normality test is as follows:

[0128] S2031′: Based on multiple signal-to-noise ratios in the signal-to-noise ratio set, determine the initial kurtosis and initial skewness of the signal-to-noise ratio set; wherein the initial kurtosis is used to reflect the sharpness of the normal distribution image of the signal-to-noise ratio set, and the initial skewness is used to reflect the symmetry of the normal distribution image of the signal-to-noise ratio set.

[0129] Formula 1 shows the calculation formula of the initial kurtosis K and the initial skewness S. In the formula, m2 is the second-order sample central moment, m3 is the third-order sample central moment, m4 is the fourth-order sample central moment, h is the total number of signal-to-noise ratios in the signal-to-noise ratio set, and x is the total number of signal-to-noise ratios in the signal-to-noise ratio set. i is the i-th signal-to-noise ratio, is the mean value of the signal-to-noise ratio set.

[0130] Kurtosis, also known as kurtosis coefficient, is an indicator of the height of the peak of a probability density distribution curve at its mean. Intuitively, kurtosis reflects the sharpness of the peak and can be used to indicate the degree of deviation in the data. It is often used as an indicator of normality.

[0131] This metric is calculated relative to a normal distribution. Kurtosis includes normal distribution (kurtosis value = 3), thick tail (kurtosis value > 3), and thin tail (kurtosis value < 3). In statistics, a kurtosis value < 3 means that the data contains more extreme differences above or below the mean, which is reflected in the data distribution as a sharper peak and thinner tail than a normal distribution. A kurtosis value > 3 means that the data contains fewer extreme differences, the central area of ​​the distribution is relatively dispersed, and the data points tend to be distributed at the ends. This is reflected in the data distribution as a flatter and thicker tail than a normal distribution. A kurtosis value of 3 means that the data distribution is similar to a normal distribution, without any particularly prominent peaks or flat features.

[0132] Figure 2G shows the image distribution of different kurtosis values. The black solid line is the normal distribution curve. The black dotted line with a sharper peak shape and steeper than the normal distribution curve is a thin tail, while the gray solid line with a blunter peak shape and flatter than the normal distribution curve is a thick tail.

[0133] Skewness, also known as the coefficient of skewness, is a measure of the direction and degree of skewness of the statistical data distribution and a numerical characteristic of the degree of asymmetry of the statistical data distribution. As shown in Figure 2H(1), for a normal distribution, its skewness is 0, and the lengths of the tails on both sides are symmetrical. As shown in Figure 2H(2), if the skewness of the distribution is less than 0, it means that the distribution has a negative deviation (i.e., left skewness). At this time, the data to the left of the mean is less than the data to the right of the mean, which is intuitively manifested as the left tail being longer than the right tail. As shown in Figure 2H(3), if the skewness of the distribution is greater than 0 (i.e., right skewness), the data to the left of the mean is more than the data to the left of the mean, which is intuitively manifested as the right tail being longer than the left tail. As shown in Figure 2H(3), if the skewness of the distribution is equal to 0, it means that the distribution is relatively symmetrical and presents a normal distribution.

[0134] S2032′: Normalize the initial kurtosis and initial skewness data respectively to obtain corresponding reference kurtosis and reference skewness.

[0135] Formula 2 shows the reference kurtosis Z k With the reference skewness Z s The calculation formula of Z k =K / σ(K) Z s =S / σ(S) Formula 2;

[0136] S2033': Perform a normality test on the reference kurtosis and reference skewness to obtain the corresponding normality test results.

[0137] In one embodiment, the chi-square test (the significance level α can be 0.05-0.2) is used to test the reference kurtosis Z k and reference skewness Z s When both the reference kurtosis and the reference skewness pass the test, the signal-to-noise ratio set can be considered to have passed the normality test. When performing the normality test, only severely skewed distributions need to be excluded. Therefore, the value of the significance level α can be appropriately relaxed.

[0138] When the signal-to-noise ratio set fails at least one of the unimodal test and the normality test, the following operations are performed until the first set threshold is increased to the maximum threshold specified by the value range, or the signal-to-noise ratio set passes the abnormality test: the first set threshold is increased according to the set threshold increment; a unimodal test is then performed on the signal-to-noise ratio set composed of multiple signal-to-noise ratios, based on a target index, to obtain multiple signal-to-noise ratio subsets obtained by dividing the associated multiple signal-to-noise ratios greater than the adjusted first set threshold; and a normality test is performed on the signal-to-noise ratio set based on the initial kurtosis and initial skewness of the signal-to-noise ratio set.

[0139] However, if the signal-to-noise ratio set still fails to pass the abnormality test after the first set threshold is raised multiple times, it means that the collected original observation data has a large degree of randomness, and it is necessary to wait for more data to be obtained before analysis.

[0140] When the signal-to-noise ratio set passes the unimodal test and the normality test, the signal-to-noise ratio set is further tested for outliers, and the signal-to-noise ratios that are not within the set outlier range are deleted to obtain the corresponding target signal-to-noise ratio.

[0141] Formula 3 shows the calculation formula of the outlier test. The signal-to-noise ratio that does not meet the following formula is considered an outlier. Where Q1 is the 25% quantile, Q3 is the 75% quantile, and the interquartile range I QR Is the difference between Q3 and Q1 (Q3-Q1). Q1-3I QR <R<Q3+3I QR Formula 3;

[0142] Based on each target signal-to-noise ratio that satisfies Formula 3, the signal-to-noise ratio feature of the target signal-to-noise ratio set is calculated.

[0143] For example, the average signal-to-noise ratio and the median signal-to-noise ratio of a target signal-to-noise ratio set composed of the target signal-to-noise ratios may be determined based on the occurrence frequency of each target signal-to-noise ratio in a plurality of original observation data.

[0144] Based on the frequency of occurrence of each target signal-to-noise ratio in multiple original observation data, it can be determined that there are n signal-to-noise ratio data in the target signal-to-noise ratio set. Arrange the target signal-to-noise ratios in ascending order. When n is an odd number, the (n+1) / 2th data in the set is the median signal-to-noise ratio R m ; When n is an even number, the n / 2th data in the set is the median signal-to-noise ratio R m .

[0145] Based on the frequency of occurrence of each target signal-noise ratio in multiple original observation data, it can be determined that there are n signal-noise ratio data in the target signal-noise ratio set, and the ratio between the sum of the target signal-noise ratios and the total number of signal-noise ratio data is used as the average signal-noise ratio.

[0146] The standard deviation signal-to-noise ratio of the target signal-to-noise ratio set may also be determined based on the average signal-to-noise ratio and each target signal-to-noise ratio and the corresponding occurrence frequency.

[0147] Based on the frequency of occurrence of each target signal-to-noise ratio in multiple original observation data, determine that there are n signal-to-noise ratio data in the target signal-to-noise ratio set. Then perform the following steps in sequence to obtain the standard deviation signal-to-noise ratio R of the target signal-to-noise ratio set. s :

[0148] Step 1: Subtract the average signal-to-noise ratio of the target signal-to-noise ratio set from each target signal-to-noise ratio;

[0149] Step 2: Square each value obtained in step 1 and add up the squared values.

[0150] Step 3: Divide the result of step 2 by (n-1);

[0151] Step 4: Perform square root operation on the value obtained in step 3 to obtain the standard deviation signal-to-noise ratio R of the target signal-to-noise ratio set s .

[0152] The maximum signal-to-noise ratio R of the target signal-to-noise ratio set M , average signal-to-noise ratio Median signal-to-noise ratio R m and standard deviation signal-to-noise ratio R s , as the signal-to-noise ratio feature of a target index pair associated with the target signal-to-noise ratio set.

[0153] For example, for the target SNR set associated with this model of smart terminal, the SNR distribution and related eigenvalue calculation results are generated as shown in Figure 2I. The horizontal axis of the figure represents the target index pair, and the vertical axis represents the target SNR. The chart is a box plot, with the lower edge of the rectangle representing Q1 and the upper edge representing Q3. The triangle represents the average SNR, and the gray line represents the median SNR.

[0154] In the technical solution proposed in this application, a uniform random model is configured for different models of smart terminals, which reduces the difficulty of adaptation. The technical solution proposed in this application can be applied to a random model containing a signal-to-noise ratio parameter. In the random model shown in Formula 4, σ 2 Observation noise calculated by the random model, E is the elevation angle, a and b are elevation angle related parameters, and c, d, and g are adjustable signal-to-noise ratio model parameters.

[0155] However, to simplify the configuration process of the stochastic model, the same model parameters c and d can be set for the same aircraft model. In this way, when a single aircraft model has multiple target SNRs that meet the requirements, there is no need to calculate multiple sets of model parameters c and d. Instead, the model parameters c and d can be obtained by fitting based on only one target SNR and the corresponding SNR characteristics.

[0156] In various embodiments, a possible model parameter fitting process is as follows.

[0157] Based on the signal-to-noise ratio characteristics and the reference signal-to-noise ratio, the inter-frequency difference reduction parameter g of the random model is determined.

[0158] For example, the SNR parameter difference between the GNSS frequency point in a target index pair and the reference frequency point can be used as the difference reduction parameter g of the random model. To simplify the calculation, the SNR parameter difference can be taken as the difference between the median SNRs or the maximum SNRs between the frequency points.

[0159] Afterwards, the pre-built random model is converted into a weighted model with a linear relationship based on the inter-frequency difference reduction parameters and the altitude angle parameters.

[0160] In the technical solution proposed in this application, a uniform random model σ is configured for smart terminals of different models. 2 =g(E)H(R). For simplicity, the altitude angle related part g(E) is set to a constant during fitting, and the above formula will be converted to

[0161] right Taking the logarithm, we get Then take the logarithm of the exponential term log10 and convert it into a linear function

[0162] Finally, the weight model and the random model are fitted using the linear least squares method or other fitting algorithms to obtain the signal-to-noise ratio parameter c and the standardized signal-to-noise ratio parameter d of the random model.

[0163] After fitting, the validity of model parameters c and d is verified using Equation 5. If the verification passes, these parameters are synchronized to the parameter database as the model parameters c, d, and g for the corresponding smart terminal model. During the positioning solution phase, the smart terminal, in response to a positioning operation triggered by the user on the application software, sends a parameter acquisition request to the parameter database and retrieves the model parameters adapted for this smart terminal model. Positioning is then performed using a random model adapted to the corresponding model parameters, and a map of the smart terminal's location is displayed in the application software's positioning interface.

[0164] The model parameter determination method provided in the embodiment of the present application can be applied to a random model including a signal-to-noise ratio parameter. Based on the original observation data generated when the smart terminal of each model is positioned, model parameters that are more adapted to the positioning chip, antenna layout and gain strategy of the smart terminal of the corresponding model are fitted. Therefore, when each smart terminal performs GNSS positioning based on the random model configured with the corresponding model parameters, the adaptability between the smart terminal and the landing scene is higher, which is conducive to improving the positioning accuracy.

[0165] As shown in FIG. 3A and FIG. 3B , the method provided in the embodiment of the present application is applied to a map application, and the process of processing and applying positioning data is as follows.

[0166] S301: The smart terminal uses a map application to retrieve a plurality of original observation data generated during the previous positioning, and uploads each original observation data to an observation value database.

[0167] S302: The server reads multiple original observation data from the observation value database, maps the navigation positioning information in each original observation data respectively, obtains their respective original index pairs, and uses the original index pairs with unique content as the target index pairs, and merges multiple original index pairs with the same content into one target index pair.

[0168] S303: For each target index pair, the following operations are performed: multiple signal-to-noise ratios associated with a target index pair that are greater than a first set threshold are tested for abnormality to obtain each target signal-to-noise ratio that passes the test, and based on the frequency of occurrence of each target signal-to-noise ratio in multiple original observation data, a signal-to-noise ratio feature of a target index pair is obtained; based on the signal-to-noise ratio feature combined with a pre-constructed random model, model parameters of the random model are fitted.

[0169] S304: Check the validity of the model parameters c and d. If the verification is successful, synchronize them into the parameter database as the model parameters c, d, and g of the corresponding model smart terminal.

[0170] S305: In response to the positioning operation triggered by the user object for the map application, the smart terminal sends a parameter acquisition request to the parameter database, obtains the model parameters adapted to the local smart terminal in the parameter database, and then uses the random model adapted to the corresponding model parameters to perform positioning solution, and displays the map of the location of the smart terminal in the positioning interface of the application software.

[0171] As shown in FIG. 4A and FIG. 4B , the method provided in the embodiment of the present application is applied to a social application with a positioning function, and the process of processing and applying positioning data is as follows.

[0172] S401: The smart terminal uses a map application to retrieve a plurality of original observation data generated during the previous positioning, and uploads each original observation data to an observation value database.

[0173] S402: The server reads multiple original observation data from the observation value database, maps the navigation positioning information in each original observation data respectively, obtains their respective original index pairs, and uses the original index pairs with unique content as the target index pairs, and merges multiple original index pairs with the same content into one target index pair.

[0174] S403: For each target index pair, the following operations are performed: multiple signal-to-noise ratios associated with a target index pair that are greater than a first set threshold are tested for abnormality to obtain each target signal-to-noise ratio that passes the test, and based on the frequency of occurrence of each target signal-to-noise ratio in multiple original observation data, a signal-to-noise ratio feature of a target index pair is obtained; based on the signal-to-noise ratio feature combined with a pre-constructed random model, model parameters of the random model are fitted.

[0175] S404: Check the validity of the model parameters c and d. If the verification is successful, synchronize them into the parameter database as the model parameters c, d, and g of the corresponding model smart terminal.

[0176] S405: In response to the location sharing operation triggered by the user object for the social application, the smart terminal sends a parameter acquisition request to the parameter database, obtains the model parameters adapted to the local smart terminal in the parameter database, and then uses the random model adapted to the corresponding model parameters to perform positioning solution, and displays a map thumbnail of the location of the terminal in the chat interface of the social software.

[0177] In addition, it should be noted that in the specific implementation of this application, it involves collecting relevant object data such as the original observation data generated when the smart terminal is positioned. When the above embodiments of this application are applied to specific products or technologies, it is necessary to obtain the object's permission or consent, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0178] In the embodiments of the present application, the term "module" or "unit" refers to a computer program or a part of a computer program that has a predetermined function and works together with other related parts to achieve a predetermined goal, and can be implemented in whole or in part by using software, hardware (such as processing circuits or memories) or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be part of an overall module or unit that includes the function of the module or unit.

[0179] Based on the same inventive concept as the above method embodiment, the present application embodiment also provides a positioning data processing device. As shown in FIG5 , the positioning data processing device 500 may include:

[0180] The data acquisition module 501 is used to receive a plurality of original observation data uploaded by the smart terminal, each of which includes: navigation positioning information and signal-to-noise ratio generated when the smart terminal is positioning;

[0181] The data processing module 502 is used to map each navigation positioning information to obtain the corresponding original index pair, and use the original index pair with unique content as the target index pair, and merge multiple original index pairs with the same content into one target index pair;

[0182] The parameter fitting module 503 is used to perform the following operations for each target index pair:

[0183] Performing an abnormality test on multiple signal-to-noise ratios associated with a target index pair that are greater than a first set threshold, obtaining each target signal-to-noise ratio that passes the test, and obtaining a signal-to-noise ratio feature of the target index pair based on the frequency of occurrence of each target signal-to-noise ratio in multiple original observation data;

[0184] Based on the signal-to-noise ratio characteristics and combined with the pre-built random model, the model parameters of the random model are fitted.

[0185] In the embodiment of FIG. 5 , the parameter fitting module 503 may be viewed as a module obtained by combining the feature extraction module 104 and the parameter fitting module 103 in the embodiment of FIG. 1D .

[0186] In various embodiments, the parameter fitting module 503 is used to:

[0187] performing a unimodal test on a signal-to-noise ratio set consisting of the multiple signal-to-noise ratios, based on a target index pair and divided into multiple signal-to-noise ratio subsets that are greater than a first set threshold, and performing a normality test on the signal-to-noise ratio set based on initial kurtosis and initial skewness of the signal-to-noise ratio set;

[0188] When the signal-to-noise ratio set passes the unimodal test and the normality test, the signal-to-noise ratio set is further tested for outliers, and the signal-to-noise ratios that are not within the set outlier range are deleted to obtain the corresponding target signal-to-noise ratio.

[0189] In various embodiments, the parameter fitting module 503 is used to:

[0190] Clustering multiple signal-to-noise ratios associated with a target index pair that are greater than a first set threshold to obtain multiple signal-to-noise ratio subsets;

[0191] Determine the signal-to-noise ratio difference between each signal-to-noise ratio subset and the cluster center of other signal-to-noise ratio subsets except itself, and compare each signal-to-noise ratio difference with a second set threshold to obtain a corresponding comparison result;

[0192] When at least one comparison result shows that the signal-to-noise ratio difference is not less than the second set threshold, the single-peak test result is determined to be that the signal-to-noise ratio set fails the single-peak test; when all comparison results show that the signal-to-noise ratio difference is less than the second set threshold, it is determined that the signal-to-noise ratio set passes the single-peak test.

[0193] In various embodiments, the parameter fitting module 503 is used to:

[0194] Based on multiple signal-to-noise ratios in the signal-to-noise ratio set, determining the initial kurtosis and initial skewness of the signal-to-noise ratio set; wherein the initial kurtosis is used to reflect the sharpness of the normal distribution image of the signal-to-noise ratio set, and the initial skewness is used to reflect the symmetry of the normal distribution image of the signal-to-noise ratio set;

[0195] Normalize the initial kurtosis and initial skewness to obtain the corresponding reference kurtosis and reference skewness;

[0196] A normality test is performed on the reference kurtosis and reference skewness to obtain the corresponding normality test results.

[0197] In various embodiments, the parameter fitting module 503 is used to:

[0198] Based on the occurrence frequency of each target signal-noise ratio in multiple original observation data, the average signal-noise ratio and the median signal-noise ratio of the target signal-noise ratio set composed of each target signal-noise ratio are determined;

[0199] Based on the average SNR and each target SNR and their corresponding occurrence frequencies, the standard deviation SNR of the target SNR set is determined;

[0200] The maximum signal-to-noise ratio, average signal-to-noise ratio, median signal-to-noise ratio and standard deviation signal-to-noise ratio of the target signal-to-noise ratio set are used as the signal-to-noise ratio features of a target index pair associated with the target signal-to-noise ratio set.

[0201] In various embodiments, the data processing module 502 is used to:

[0202] For each navigation location information, perform the following operations:

[0203] Based on the conversion relationship between constellation type and navigation and positioning system, the constellation type in a navigation and positioning information is mapped to the corresponding navigation and positioning system;

[0204] Based on the conversion relationship between navigation and positioning systems, frequencies and frequency points, the frequency in a navigation and positioning information is mapped to the corresponding frequency point;

[0205] The mapped navigation positioning system and frequency point are associated as an original index pair.

[0206] In various embodiments, the parameter fitting module 503 is used to:

[0207] Based on the signal-to-noise ratio characteristics and the reference signal-to-noise ratio, the frequency difference reduction parameters of the random model are determined;

[0208] Based on the inter-frequency difference reduction parameters and the altitude angle parameters, the pre-built random model is converted into a weighted model with a linear relationship;

[0209] The weight model and the random model are fitted using a fitting algorithm to obtain the signal-to-noise ratio parameters and the standardized signal-to-noise ratio parameters of the random model.

[0210] In each embodiment, when the signal-to-noise ratio set fails to pass at least one of the unimodal test and the normality test, the parameter fitting module 503 performs the following operations until the first set threshold is increased to the maximum threshold specified in the value range, or the signal-to-noise ratio set passes the abnormality test:

[0211] According to the increase in the set threshold, the first set threshold is adjusted upward;

[0212] A plurality of signal-to-noise ratio subsets are obtained by dividing a plurality of signal-to-noise ratios associated with a target index pair and being greater than an adjusted first set threshold, a unimodal test is performed on the signal-to-noise ratio set consisting of the plurality of signal-to-noise ratios, and a normality test is performed on the signal-to-noise ratio set based on the initial kurtosis and initial skewness of the signal-to-noise ratio set.

[0213] For the convenience of description, the above parts are divided into modules (or units) according to their functions and described separately. Of course, when implementing this application, the functions of each module (or unit) can be implemented in the same or multiple software or hardware.

[0214] After introducing the positioning data processing method and apparatus according to an exemplary embodiment of the present application, a computer device according to another exemplary embodiment of the present application is introduced next.

[0215] Those skilled in the art will appreciate that various aspects of the present application can be implemented as systems, methods, or program products. Therefore, various aspects of the present application can be specifically implemented in the following forms: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation that combines hardware and software aspects, which may be collectively referred to herein as a "circuit," "module," or "system."

[0216] Based on the same inventive concept as the above-mentioned method embodiment, embodiments of the present application also provide a computer device. In one embodiment, the computer device may be a server, such as server 130 shown in FIG1A . In this embodiment, the structure of computer device 600 is shown in FIG6 , and may include at least a memory 601, a communication module 603, and at least one processor 602.

[0217] Memory 601 is used to store computer programs executed by processor 602. Memory 601 may mainly include a program storage area and a data storage area. The program storage area may store an operating system and programs required for running instant messaging functions, while the data storage area may store various instant messaging messages and operating instruction sets.

[0218] Memory 601 may be a volatile memory, such as random-access memory (RAM); a non-volatile memory, such as read-only memory, flash memory, a hard disk drive (HDD), or a solid-state drive (SSD); or any other medium capable of carrying or storing a desired computer program in the form of instructions or data structures and accessible by a computer, but is not limited thereto. Memory 601 may be a combination of the above memories.

[0219] The processor 602 may include one or more central processing units (CPUs) or digital processing units, etc. The processor 602 is configured to implement the above positioning data processing method when calling the computer program stored in the memory 601 .

[0220] The communication module 603 is used to communicate with terminal devices and other servers.

[0221] The specific connection medium between the memory 601, communication module 603, and processor 602 is not limited in the embodiments of the present application. In Figure 6, the memory 601 and processor 602 are connected via bus 604. Bus 604 is depicted as a bold line in Figure 6. The connection methods between other components are merely schematic and are not intended to be limiting. Bus 604 can be divided into an address bus, a data bus, a control bus, etc. For ease of description, Figure 6 depicts only one bold line, but this does not indicate that there is only one bus or only one type of bus.

[0222] The memory 601 stores a computer storage medium, which stores computer executable instructions for implementing the positioning data processing method of the embodiment of the present application. The processor 602 is used to execute the positioning data processing method, as shown in FIG2A.

[0223] In another embodiment, the computer device may be another computer device, such as the terminal device 110 shown in FIG1A . In this embodiment, the structure of the computer device may be as shown in FIG7 , including: a communication component 710, a memory 720, a display unit 730, a camera 740, a sensor 750, an audio circuit 760, a Bluetooth module 770, a processor 780, and other components.

[0224] The memory 720 can be used to store software programs and data. The processor 780 executes various functions and data processing of the terminal device 110 by running the software programs or data stored in the memory 720. The memory 720 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. The memory 720 stores the operating system that enables the terminal device 110 to run. In the present application, the memory 720 can store the operating system and various application programs, and may also store the computer program that executes the positioning data processing method according to the embodiments of the present application.

[0225] The display unit 730 may include a display screen 732 disposed on the front of the terminal device 110. The display unit 730 may also include a touch screen 731 disposed on the front of the terminal device 110.

[0226] The terminal device may further include at least one sensor 750 , such as an acceleration sensor 751 , a distance sensor 752 , a fingerprint sensor 753 , and a temperature sensor 754 .

[0227] The audio circuit 760 , the speaker 761 , and the microphone 762 may provide an audio interface between the object and the terminal device 110 .

[0228] Processor 780 is the control center of the terminal device. It connects various components of the entire terminal using various interfaces and lines. It executes software programs stored in memory 720 and accesses data stored in memory 720 to perform various functions of the terminal device and process data. In some embodiments, processor 780 may include one or more processing units; processor 780 may also integrate an application processor and a baseband processor.

[0229] In some possible implementations, various aspects of the positioning data processing method provided in the present application may also be implemented in the form of a program product, which includes a computer program. When the program product is run on a computer device, the computer program is used to enable the computer device to execute the steps of the positioning data processing method according to various exemplary embodiments of the present application described above in this specification. For example, the computer device may execute the steps shown in Figure 2A.

[0230] The program product can use any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0231] It should be noted that although several units or subunits of the device are mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, depending on the embodiment of the application, the features and functions of two or more units described above can be embodied in a single unit. Conversely, the features and functions of a single unit described above can be further divided and embodied by multiple units.

[0232] Furthermore, although the operations of the method of the present application are described in a particular order in the accompanying drawings, this does not require or imply that the operations must be performed in this particular order, or that all illustrated operations must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0233] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain a computer-usable computer program.

[0234] The present application is described with reference to the flow chart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present application. It should be understood that each flow process and / or box in the flow chart and / or block diagram and the combination of the flow process and / or box in the flow chart and / or block diagram can be realized by computer program commands. These computer program commands can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processing machine or other programmable data processing device to produce a machine, so that the command executed by the processor of the computer or other programmable data processing device produces a device for realizing the function specified in one flow chart flow or multiple flows and / or one box or multiple boxes of the block diagram.

[0235] These computer program commands may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the commands stored in the computer-readable memory produce a manufactured product including a command device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0236] These computer program commands can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the commands executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0237] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0238] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0239] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A positioning data processing method, executed by a server, comprising: Acquire multiple sets of raw observation data from a preset type of smart terminal, wherein each set of raw observation data includes: navigation positioning information and a signal-to-noise ratio generated when positioning the smart terminal, the navigation positioning information being information about resources used for positioning; Map the navigation positioning information in each set of original observation data to an index; For each index, outlier data are eliminated from at least two groups of original observation data associated with the index and having a signal-to-noise ratio greater than a first set threshold value through an outlier test to obtain an observation data subset, and statistical characteristics of the signal-to-noise ratio in the observation data subset are obtained based on the number of times each signal-to-noise ratio in the observation data subset appears in the observation data subset; By adjusting a pre-built random model using the observation data subset and the statistical features, a value of at least one model parameter of the random model is determined, and the value of the at least one model parameter is provided for configuring the random model for positioning in the smart terminal of the preset type.

2. The method according to claim 1, wherein The abnormality test removes abnormal data from at least two groups of original observation data associated with the index and having a signal-to-noise ratio greater than a first set threshold to obtain an observation data subset, including: performing a unimodal test on the signal-to-noise ratios in the at least two groups of original observation data using at least two signal-to-noise ratio subsets, and performing a normality test on the signal-to-noise ratios in the at least two groups of original observation data based on initial kurtosis and initial skewness of the signal-to-noise ratios in the at least two groups of original observation data, wherein the at least two signal-to-noise ratio subsets are obtained by grouping the signal-to-noise ratios in the at least two groups of original observation data according to numerical values; When the signal-to-noise ratio in the at least two groups of original observation data passes the unimodal test and the normal test, an outlier test is performed on the signal-to-noise ratio in the at least two groups of original observation data, and the original observation data whose signal-to-noise ratio is not within the set outlier range is deleted to obtain the observation data subset.

3. The method according to claim 2, wherein: The performing a unimodal test on the signal-to-noise ratio in the at least two groups of original observation data comprises: Clustering the signal-to-noise ratios in the at least two groups of original observation data to obtain the at least two signal-to-noise ratio subsets; respectively determining a signal-to-noise ratio difference between cluster centers of each two signal-to-noise ratio subsets in the at least two signal-to-noise ratio subsets; When at least one signal-to-noise ratio difference is not less than the second set threshold, it is determined that the single-peak test has not been passed; when all signal-to-noise ratio differences are less than the second set threshold, it is determined that the single-peak test has been passed.

4. The method according to claim 2, wherein: The performing a normality test on the signal-to-noise ratio in the at least two groups of original observation data comprises: Determining the initial kurtosis and initial skewness of the signal-to-noise ratio in the at least two sets of original observation data; wherein the initial kurtosis is used to reflect the sharpness of the normal distribution image of the signal-to-noise ratio, and the initial skewness is used to reflect the symmetry of the normal distribution image of the signal-to-noise ratio; Normalizing the initial kurtosis and the initial skewness respectively to obtain a reference kurtosis and a reference skewness; By performing a normality test on the reference kurtosis and the reference skewness, a normality test result of the signal-to-noise ratio in the at least two groups of original observation data is obtained.

5. The method according to claim 1, wherein The obtaining of the statistical characteristics of the signal-to-noise ratio in the observation data subset based on the number of times each signal-to-noise ratio in the observation data subset appears in the observation data subset includes: At least one of the maximum signal-to-noise ratio, the mean value, the median value, and the standard deviation of the signal-to-noise ratio in the observation data subset is obtained as the statistical feature.

6. The method according to any one of claims 1 to 5, wherein: Mapping the navigation positioning information in each set of original observation data to an index includes: Mapping the constellation type in the navigation and positioning information to a corresponding navigation and positioning system based on a conversion relationship between the constellation type and the navigation and positioning system; Based on the conversion relationship between the navigation and positioning system, frequency and frequency point, the frequency in the navigation and positioning information is mapped to the corresponding frequency point; An index pair obtained by associating the mapped navigation and positioning system with the frequency point is used as the index.

7. The method according to any one of claims 1 to 5, wherein: The step of adjusting a pre-built stochastic model by using the observed data subset and the statistical feature to determine a value of at least one model parameter of the stochastic model comprises: Determining a value of an inter-frequency difference parameter of the random model using a signal-to-noise ratio in the observed data subset and the statistical characteristics; Converting a pre-built random model into a weight model in which an output value and a signal-to-noise ratio are in a linear relationship, wherein the weight model includes the at least one model parameter; The weight model is fitted using the signal-to-noise ratio in the observation data subset and the statistical characteristics to obtain a value of the at least one model parameter.

8. The method of claim 2, wherein: When the signal-to-noise ratio in the at least two sets of original observation data fails to pass at least one of the unimodal test and the normality test, performing the following operations until the first set threshold reaches a preset maximum threshold, or the signal-to-noise ratio in the at least two sets of original observation data passes the unimodal test and the normality test: increasing the first set threshold by a set threshold increase amount; Based on the at least two signal-to-noise ratio subsets, a unimodal test is performed on the signal-to-noise ratios in the at least two groups of original observation data, and based on the initial kurtosis and initial skewness of the signal-to-noise ratios in the at least two groups of original observation data, a normality test is performed on the signal-to-noise ratios in the at least two groups of original observation data.

9. A positioning data processing device, comprising: A data acquisition module is used to obtain multiple sets of raw observation data from a preset type of smart terminal, wherein each set of raw observation data includes: navigation positioning information and signal-to-noise ratio generated when positioning the smart terminal, and the navigation positioning information is information about the resources used for positioning; A data processing module is used to map the navigation and positioning information in each set of raw observation data to an index; a feature extraction module configured to, for each index, eliminate abnormal data from at least two groups of original observation data associated with the index and having a signal-to-noise ratio greater than a first set threshold value through an anomaly test to obtain an observation data subset, and obtain a statistical feature of the signal-to-noise ratio in the observation data subset based on the number of times each signal-to-noise ratio in the observation data subset appears in the observation data subset; A parameter fitting module is used to adjust a pre-built random model by using the observation data subset and the statistical features to determine the value of at least one model parameter of the random model, and the value of the at least one model parameter is provided for configuring the random model for positioning in the smart terminal of the preset type.

10. The device according to claim 9, wherein The feature extraction module is used to: performing a unimodal test on the signal-to-noise ratios in the at least two groups of original observation data using at least two signal-to-noise ratio subsets, and performing a normality test on the signal-to-noise ratios in the at least two groups of original observation data based on initial kurtosis and initial skewness of the signal-to-noise ratios in the at least two groups of original observation data, wherein the at least two signal-to-noise ratio subsets are obtained by grouping the signal-to-noise ratios in the at least two groups of original observation data according to numerical values; When the signal-to-noise ratio in the at least two groups of original observation data passes the unimodal test and the normal test, an outlier test is performed on the signal-to-noise ratio in the at least two groups of original observation data, and the original observation data whose signal-to-noise ratio is not within the set outlier range is deleted to obtain the observation data subset.

11. The device according to claim 9, wherein The feature extraction module is used to: At least one of a maximum signal-to-noise ratio, an average value, a median, and a standard deviation of the signal-to-noise ratio in the observation data subset is obtained as the statistical feature.

12. The device according to any one of claims 9 to 11, wherein: The data processing module is used for: Mapping the constellation type in the navigation and positioning information to a corresponding navigation and positioning system based on a conversion relationship between the constellation type and the navigation and positioning system; Based on the conversion relationship between the navigation and positioning system, frequency and frequency point, the frequency in the navigation and positioning information is mapped to the corresponding frequency point; An index pair formed by associating the navigation and positioning system with the frequency point is used as the index.

13. The device according to any one of claims 9 to 11, wherein: The parameter fitting module is used to: Determining a value of an inter-frequency difference parameter of the random model using a signal-to-noise ratio in the observed data subset and the statistical characteristics; Converting a pre-built random model into a weight model in which an output value and a signal-to-noise ratio are in a linear relationship, wherein the weight model includes the at least one model parameter; The weight model is fitted using the signal-to-noise ratio in the observation data subset and the statistical characteristics to obtain a value of the at least one model parameter.

14. The apparatus of claim 10, wherein: The feature extraction module is used to: Clustering the signal-to-noise ratios in the at least two groups of original observation data to obtain the at least two signal-to-noise ratio subsets; respectively determining a signal-to-noise ratio difference between cluster centers of each two signal-to-noise ratio subsets in the at least two signal-to-noise ratio subsets; When at least one signal-to-noise ratio difference is not less than the second set threshold, it is determined that the single-peak test has not been passed; when all signal-to-noise ratio differences are less than the second set threshold, it is determined that the single-peak test has been passed.

15. The apparatus of claim 10, wherein: The feature extraction module is used to: Determining the initial kurtosis and initial skewness of the signal-to-noise ratio in the at least two sets of original observation data; wherein the initial kurtosis is used to reflect the sharpness of the normal distribution image of the signal-to-noise ratio, and the initial skewness is used to reflect the symmetry of the normal distribution image of the signal-to-noise ratio; Normalizing the initial kurtosis and the initial skewness respectively to obtain a reference kurtosis and a reference skewness; By performing a normality test on the reference kurtosis and the reference skewness, a normality test result of the signal-to-noise ratio in the at least two groups of original observation data is obtained.

16. The apparatus of claim 10, wherein: When the signal-to-noise ratio in the at least two sets of original observation data fails to pass at least one of the unimodal test and the normality test, performing the following operations until the first set threshold reaches a preset maximum threshold, or the signal-to-noise ratio in the at least two sets of original observation data passes the unimodal test and the normality test: increasing the first set threshold by a set threshold increase amount; Based on the at least two signal-to-noise ratio subsets, a unimodal test is performed on the signal-to-noise ratios in the at least two groups of original observation data, and based on the initial kurtosis and initial skewness of the signal-to-noise ratios in the at least two groups of original observation data, a normality test is performed on the signal-to-noise ratios in the at least two groups of original observation data.

17. A computer device comprising a processor and a memory, wherein: The memory stores program codes, and when the program codes are executed by the processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 8.

18. A computer-readable storage medium comprising program code, wherein when the program code is run on a computer device, the program code is configured to cause the computer device to execute the steps of the method according to any one of claims 1 to 8.

19. A computer program product comprising computer instructions, wherein when the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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