WO2025190094A1 - 定位数据处理方法、装置、设备及存储介质 - Google Patents

定位数据处理方法、装置、设备及存储介质

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Publication number
WO2025190094A1
WO2025190094A1 PCT/CN2025/080118 CN2025080118W WO2025190094A1 WO 2025190094 A1 WO2025190094 A1 WO 2025190094A1 CN 2025080118 W CN2025080118 W CN 2025080118W WO 2025190094 A1 WO2025190094 A1 WO 2025190094A1
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WO
WIPO (PCT)
Prior art keywords
signal
noise ratio
observation data
test
positioning
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/CN2025/080118
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English (en)
French (fr)
Inventor
张强
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tencent Technology (Shenzhen) Co Ltd
Original Assignee
Tencent Technology (Shenzhen) Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tencent Technology (Shenzhen) Co Ltd filed Critical Tencent Technology (Shenzhen) Co Ltd
Priority to EP25769266.5A priority Critical patent/EP4793670A1/en
Publication of WO2025190094A1 publication Critical patent/WO2025190094A1/zh
Priority to US19/640,065 priority patent/US20260235772A1/en
Anticipated expiration legal-status Critical
Pending legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/35—Constructional details or hardware or software details of the signal processing chain
    • G01S19/37—Hardware or software details of the signal processing chain
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/24—Acquisition or tracking or demodulation of signals transmitted by the system
    • G01S19/25—Acquisition or tracking or demodulation of signals transmitted by the system involving aiding data received from a cooperating element, e.g. assisted GPS
    • G01S19/258—Acquisition or tracking or demodulation of signals transmitted by the system involving aiding data received from a cooperating element, e.g. assisted GPS relating to the satellite constellation, e.g. almanac, ephemeris data, lists of satellites in view
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/43—Determining position using carrier phase measurements, e.g. kinematic positioning; using long or short baseline interferometry
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/346—Noise values

Definitions

  • the present application relates to positioning technology and provides a positioning data processing method, device, equipment and storage medium.
  • 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.
  • the present invention provides a method for processing positioning data, including:
  • 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;
  • 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;
  • 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.
  • the present application also provides a positioning data processing device, including:
  • 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.
  • 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.
  • An embodiment of the present application further provides a computer-readable storage medium, which includes program code.
  • 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.
  • 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.
  • 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.
  • SNR signal-to-noise ratio
  • FIG1A is an optional schematic diagram of an application scenario in an embodiment of the present application.
  • FIG1B is a logic diagram of determining model parameters between the intelligent terminal and the server
  • FIG1C is a schematic flow chart of a positioning data processing method according to an embodiment of the present application.
  • FIG1D is a schematic diagram of the structure of a positioning data processing device provided in an embodiment of the present application.
  • FIG2A is a schematic diagram of a process for processing positioning data of a smart terminal according to an embodiment of the present application
  • FIG2B is a logic diagram of processing positioning data of a smart terminal according to an embodiment of the present application.
  • FIG2C is a schematic diagram of a process for generating an original index pair according to an embodiment of the present application.
  • 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.
  • FIG2E is a schematic diagram of a process for performing a single peak test according to an embodiment of the present application.
  • FIG2F is a schematic diagram of a normality test process according to an embodiment of the present application.
  • FIG2G is a schematic diagram of image distribution with different kurtosis values provided in an embodiment of the present application.
  • FIG2H is a schematic diagram of image distribution with different skewness values provided by an embodiment of the present application.
  • 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.
  • 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
  • FIG3B is a logical diagram of processing and applying positioning data in a map application according to an embodiment of the present application
  • 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
  • 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
  • FIG5 is a schematic structural diagram of a positioning data processing device provided in an embodiment of the present application.
  • FIG6 is a schematic diagram of a hardware structure of a computer device using an embodiment of the present application.
  • FIG7 is a schematic diagram of a hardware structure of another computer device to which an embodiment of the present application is applied.
  • AI 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • AI AI-generated content
  • GNSS Global Navigation Satellite System
  • 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.
  • GPS Global Positioning System
  • BDS BeiDou Navigation Satellite System
  • GLONASS GLONASS
  • Galileo satellite navigation system Galileo Satellite Navigation system
  • 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.
  • observation data is subject to interference from various error sources and random errors, reducing positioning accuracy.
  • these interfering factors can be effectively suppressed during data processing to improve positioning accuracy.
  • Common stochastic models include Gaussian and exponential models.
  • the ratio between signal strength and noise intensity in an electronic device or system is a key indicator of the quality of satellite signals received by a receiver.
  • SNR signal-to-noise ratio
  • 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.
  • 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.
  • a suitable model is usually selected from several preset random models for different types of smart terminals.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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 (CDN), and big data and artificial intelligence platforms. This application does not impose any restrictions on this.
  • 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.
  • 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.
  • a positioning function such as a map application, a social application that can share a location, etc.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the index may be a target index pair that has been merged.
  • 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.
  • the at least one model parameter can be provided for configuring the random model for positioning in the smart terminal of the preset type.
  • the server 130 can synchronize model parameters adapted for the smart terminal model to a parameter database.
  • 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.
  • the terminal can be classified according to the brand, that is, different types correspond to different terminal brands.
  • the terminal can be classified according to the model, that is, different types correspond to different models of terminals.
  • 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.
  • 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.
  • SNR signal-to-noise ratio
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the adjustment process of the random model can be simplified, processing resources can be saved, and processing speed can be accelerated.
  • Each embodiment further provides a positioning data processing device.
  • the device 100 may include:
  • 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;
  • the data processing module 102 is used to map the navigation and positioning information in each set of original observation data to an index;
  • 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;
  • 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.
  • the process of processing the location data of a single smart terminal is described using a single smart terminal as an example.
  • 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.
  • each original observation data includes: navigation positioning information and original signal-to-noise ratio generated when the smart terminal is positioning.
  • 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.
  • “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.
  • 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.
  • original index pairs i.e., composite indexes, used as indexes of original observation data
  • Data cleaning primarily involves removing invalid data, extracting valid data, and classifying data by model.
  • Step 1 Eliminate invalid 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.
  • 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.
  • Step 2 Extract valid data.
  • 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.
  • 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.
  • Each navigation and positioning system may be represented by a pre-set identifier.
  • 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.
  • the carrier frequency is the frequency of the electromagnetic waves used to transmit information in wireless communication systems.
  • 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.
  • 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.
  • 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.
  • Table 1 shows the conversion relationship between constellation type and navigation positioning system
  • Table 2 shows the conversion relationship between navigation positioning system, frequency, and frequency point.
  • the navigation positioning information (ConstellationType: 1, CarrierFrequencyHz: 1575420000Hz) is converted to the original index pair GPS L1.
  • the original index pair GPS L1 can also be converted to the index "G0.”
  • the original signal-to-noise ratios of different navigation satellites and epochs 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.
  • 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.
  • the above rounding operation can be omitted and the original signal-to-noise ratio can be used directly.
  • Step 3 Classify by model.
  • the original index pairs and frequency statistics tables of the same model are divided into one group to facilitate subsequent data analysis.
  • S203 For each target index pair, the following operations are performed: anomaly detection is performed on multiple signal-to-noise ratios associated with a target index pair that are greater than a first set threshold value to obtain each signal-to-noise ratio that passes the test (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, the statistical characteristics of the signal-to-noise ratio corresponding to the target index pair (also referred to as signal-to-noise ratio characteristics) are obtained; based on the signal-to-noise ratio characteristics, combined with a pre-constructed random model, the model parameters of the random model are fitted.
  • a target signal-to-noise ratio for ease of description, also referred to as a target signal-to-noise ratio
  • the statistical characteristics of the signal-to-noise ratio corresponding to the target index pair also referred to as signal-to-noise ratio characteristics
  • 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.
  • 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 > T 1 , i.e., 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.
  • 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.
  • the abnormality test mainly includes two parts, one is a unimodal test, and the other is a normality test.
  • 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.
  • the second threshold value may be in the range of 5 to 10 dBHz to ensure the effectiveness of the unimodal test.
  • 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.
  • 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.
  • Formula 1 shows the calculation formula of the initial kurtosis K and the initial skewness S.
  • 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
  • x1 is the i-th signal-to-noise ratio
  • 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.
  • 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.
  • 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.
  • 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.
  • S2032′ Normalize the initial kurtosis and initial skewness data respectively to obtain corresponding reference kurtosis and reference skewness.
  • a chi-square test (with a significance level ⁇ of 0.05 to 0.2) is used to test the reference kurtosis Z k and reference skewness Z s .
  • the signal-to-noise ratio set is considered to have passed the normality test.
  • the significance level ⁇ can be appropriately relaxed.
  • 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.
  • 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.
  • 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.
  • Formula 3 shows the calculation formula for the outlier test. A value that does not meet the signal-to-noise ratio of the following formula is considered an outlier: Where Q1 is the 25% quantile, Q3 is the 75% quantile, and the interquartile range IQR is the difference between Q3 and Q1 ( Q3 - Q1 ). Q 1 -3I QR ⁇ R ⁇ Q 3 +3I QR Formula 3;
  • the signal-to-noise ratio feature of the target signal-to-noise ratio set is calculated.
  • 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.
  • n SNR data points in the target SNR set Based on the frequency of occurrence of each target SNR in multiple original observations, we can determine the total number of n SNR data points in the target SNR set. We then arrange the target SNRs in ascending order. When n is an odd number, the (n+1)/2th data point in the set is the median SNR, R m . When n is an even number, the n/2th data point in the set is the median SNR, R m .
  • 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.
  • Step 1 Subtract the average signal-to-noise ratio of the target signal-to-noise ratio set from each target signal-to-noise ratio;
  • Step 2 Square each value obtained in step 1 and add up the squared values.
  • Step 3 Divide the result of step 2 by (n-1);
  • Step 4 Perform a square root operation on the value obtained in step 3 to obtain the standard deviation signal-to-noise ratio R s of the target signal-to-noise ratio set.
  • the maximum signal-to-noise ratio RM and the average signal-to-noise ratio of the target signal-to-noise ratio set are The median signal-to-noise ratio R m and the standard deviation signal-to-noise ratio R s are used as the signal-to-noise ratio features of a target index pair associated with the target signal-to-noise ratio set.
  • the SNR distribution and the calculation results of the related eigenvalues 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 Q 1 and the upper edge representing Q 3 .
  • the triangle represents the average SNR, and the gray line represents the median SNR.
  • the technical solution proposed in this application configures a unified random model for different types of smart terminals, reducing the difficulty of adaptation.
  • the technical solution proposed in this application can be applied to random models that include signal-to-noise ratio parameters.
  • ⁇ 2 is the observation noise calculated by the random model
  • E is the elevation angle
  • a and b are elevation-related parameters
  • c, d, and g are adjustable signal-to-noise ratio model parameters.
  • the same model parameters c and d can be set for the same aircraft model.
  • the model parameters c and d can be obtained by fitting based on only one target SNR and the corresponding SNR characteristics.
  • a possible model parameter fitting process is as follows.
  • the inter-frequency difference reduction parameter g of the random model is determined.
  • 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.
  • the SNR parameter difference can be taken as the difference between the median SNRs or the maximum SNRs between the frequency points.
  • 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.
  • the altitude angle related part g(E) is set to a constant during fitting, and the above formula will be converted into
  • 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.
  • model parameters c and d 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the smart terminal 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the smart terminal 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.
  • module 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.
  • a processor or multiple processors or memories
  • each module or unit can be part of an overall module or unit that includes the function of the module or unit.
  • the present application embodiment also provides a positioning data processing device.
  • the positioning data processing device 500 may include:
  • 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;
  • 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;
  • the parameter fitting module 503 is used to perform the following operations for each target index pair:
  • the model parameters of the random model are fitted.
  • 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 .
  • the parameter fitting module 503 is used to:
  • 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.
  • the parameter fitting module 503 is used to:
  • 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.
  • the parameter fitting module 503 is used to:
  • 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;
  • a normality test is performed on the reference kurtosis and reference skewness to obtain the corresponding normality test results.
  • the parameter fitting module 503 is used to:
  • 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;
  • the standard deviation SNR of the target SNR set is determined
  • 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.
  • the data processing module 502 is used to:
  • the constellation type in a navigation and positioning information is mapped to the corresponding navigation and positioning system
  • the frequency in a navigation and positioning information is mapped to the corresponding frequency point
  • the mapped navigation positioning system and frequency point are associated as an original index pair.
  • the parameter fitting module 503 is used to:
  • the frequency difference reduction parameters of the random model are determined
  • the pre-built random model is converted into a weighted model with a linear relationship
  • 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.
  • 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:
  • the first set threshold is adjusted upward
  • 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.
  • each module or unit
  • the functions of each module can be implemented in the same or multiple software or hardware.
  • the computer device may be a server, such as server 130 shown in FIG1A .
  • 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.
  • 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.
  • 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.
  • RAM random-access memory
  • 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.
  • Processor 602 may include one or more central processing units (CPUs) or digital processing units. Processor 602 is configured to implement the aforementioned positioning data processing method by invoking a computer program stored in memory 601.
  • CPUs central processing units
  • Processor 602 is configured to implement the aforementioned positioning data processing method by invoking a computer program stored in memory 601.
  • the communication module 603 is used to communicate with terminal devices and other servers.
  • connection medium between the memory 601, communication module 603, and processor 602 is not limited in the embodiments of the present application.
  • 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.
  • Figure 6 depicts only one bold line, but this does not indicate that there is only one bus or only one type of bus.
  • 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.
  • the computer device may be another computer device, such as the terminal device 110 shown in FIG1A .
  • 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.
  • 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.
  • 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.
  • 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 .
  • at least one sensor 750 such as an acceleration sensor 751 , a distance sensor 752 , a fingerprint sensor 753 , and a temperature sensor 754 .
  • the audio circuit 760 , the speaker 761 , and the microphone 762 may provide an audio interface between the object and the terminal device 110 .
  • 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.
  • processor 780 may include one or more processing units; processor 780 may also integrate an application processor and a baseband processor.
  • 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.
  • 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.
  • the computer device may execute the steps shown in Figure 2A.
  • 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.
  • 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.
  • a computer-usable storage media including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.
  • 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.
  • 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.
  • 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.

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Abstract

一种定位数据处理方法、装置、设备及存储介质。该方法包括：获取来自预设类型的智能终端的多组原始观测数据（S11）；将每组原始观测数据中的导航定位信息映射到一个索引（S12）；针对每个索引，通过异常检验从与索引关联的原始观测数据中、信噪比大于第一设定阈值的至少两组原始观测数据中剔除异常数据，得到观测数据子集，并基于观测数据子集中的每个信噪比在观测数据子集中出现的次数，得到观测数据子集中信噪比的统计特征（S13）；通过使用观测数据子集及统计特征调整预先构建的随机模型，确定随机模型的至少一个模型参数的值（S14），至少一个模型参数的值被提供用于配置预设类型的智能终端中用于定位的随机模型。

Description

定位数据处理方法、装置、设备及存储介质
本申请要求于2024年03月14日提交中国专利局、申请号为202410295630.X、发明名称为“定位数据处理方法、装置、设备及存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及定位技术，提供了一种定位数据处理方法、装置、设备及存储介质。
发明背景
随着大众位置服务需求的增长以及低成本导航芯片的快速发展，高精度智能终端定位技术备受关注。相关技术通常会按照智能终端类型，选择基于高度角加权模式或者基于原始标准差加权模式，对智能终端的加权参数进行评估，并结合智能终端搭载的导航芯片为其选取随机模型；再基于所选随机模型与修复后的载波相位观测值，构建载波相位差分技术(Real-time kinematic，RTK)定位模型，以使智能终端基于构建的RTK定位模型提供高精度定位服务。
发明内容
本申请实施例提供了一种定位数据处理方法、装置、设备及存储介质，以解决如何确定与终端类型相适配的模型参数的问题。
本申请实施例提供了一种定位数据处理方法，包括：
获取来自预设类型的智能终端的多组原始观测数据，其中，每组原始观测数据包括：对智能终端进行定位时生成的导航定位信息与信噪比，导航定位信息是关于定位所使用的资源的信息；
将每组原始观测数据中的导航定位信息映射到一个索引；
针对每个索引，通过异常检验从与所述索引关联的原始观测数据中、信噪比大于第一设定阈值的至少两组原始观测数据中剔除异常数据，得到观测数据子集，并基于所述观测数据子集中的每个信噪比在所述观测数据子集中出现的次数，得到所述观测数据子集中信噪比的统计特征；
通过使用所述观测数据子集及所述统计特征调整预先构建的随机模型，确定所述随机模型的至少一个模型参数的值，所述至少一个模型参数的值被提供用于配置所述预设类型的智能终端中用于定位的随机模型。
本申请实施例还提供了一种定位数据处理装置，包括：
数据采集模块，用于获取来自预设类型的智能终端的多组原始观测数据，其中，每组原始观测数据包括：对智能终端进行定位时生成的导航定位信息与信噪比，导航定位信息是关于定位所使用的资源的信息；
数据处理模块，用于将每组原始观测数据中的导航定位信息映射到一个索引；
特征提取模块，用于针对每个索引，通过异常检验从与所述索引关联的原始观测数据中、信噪比大于第一设定阈值的至少两组原始观测数据中剔除异常数据，得到观测数据子集，并基于所述观测数据子集中的每个信噪比在所述观测数据子集中出现的次数，得到所述观测数据子集中信噪比的统计特征；
参数拟合模块，用于通过使用所述观测数据子集及所述统计特征调整预先构建的随机模型，确定所述随机模型的至少一个模型参数的值，所述至少一个模型参数的值被提供用于配置所述预设类型的智能终端中用于定位的随机模型。
本申请实施例还提供了一种计算机设备，包括处理器和存储器，其中，所述存储器存储有程序代码，当所述程序代码被所述处理器执行时，使得所述处理器执行各实施例的定位数据处理方法。
本申请实施例还提供了一种计算机可读存储介质，其包括程序代码，当程序产品在计算机设备上运行时，所述程序代码用于使所述计算机设备执行各实施例的定位数据处理方法。
本申请实施例还提供了一种计算机程序产品，包括计算机指令，计算机指令被处理器执行各实施例的定位数据处理方法。
本申请实施例的技术方案从同一类型的智能终端定位时产生的原始观测数据中提取出该类型终端定位时的信噪比的统计特征，用这些统计特征来调整预设的随机模型，能够使随机模型更好地适应这种类型的终端的定位特性和实际观测环境，并得到与该类型的智能终端更适配的模型参数。当该类型的智能终端使用配置了相应模型参数的随机模型进行定位时，得到的定位结果会更准确。
附图简要说明
此处所说明的附图用来提供对本申请的进一步理解，构成本申请的一部分，本申请的示意性实施例及其说明用于解释本申请，并不构成对本申请的不当限定。在附图中：
图1A为为本申请实施例中一种应用场景的一个可选示意图；
图1B为在智能终端与服务器之间确定模型参数的逻辑示意图；
图1C为本申请实施例的定位数据处理方法的流程示意图；
图1D为本申请实施例提供的定位数据处理装置的结构示意图；
图2A为本申请实施例提供的处理一个智能终端的定位数据的流程示意图；
图2B为本申请实施例提供的处理一个智能终端的定位数据的逻辑示意图；
图2C为本申请实施例提供的生成原始索引对的流程示意图；
图2D为本申请实施例提供的更新频数统计表中一个原始信噪比的出现频次的逻辑示意图；
图2E为本申请实施例提供的进行单峰检验的流程示意图；
图2F为本申请实施例提供的进行正态检验的流程示意图；
图2G为本申请实施例提供的不同峰度值的图像分布示意图；
图2H为本申请实施例提供的不同偏度值的图像分布示意图；
图2I为本申请实施例提供的信噪比分布情况与相关特征值的计算结果的示意图；
图3A为本申请实施例提供的在地图应用中处理与应用定位数据的流程示意图；
图3B为本申请实施例提供的在地图应用中处理与应用定位数据的逻辑示意图；
图4A为本申请实施例提供的在具备定位功能的社交应用中处理与应用定位数据的流程示意图；
图4B为本申请实施例提供的在具备定位功能的社交应用中处理与应用定位数据的逻辑示意图；
图5为本申请实施例提供的一种定位数据处理装置的结构示意图；
图6为应用本申请实施例的一种计算机设备的一个硬件组成结构示意图；
图7为应用本申请实施例的另一种计算机设备的一个硬件组成结构示意图。
实施本发明的方式
为使本申请实施例的目的、技术方案和优点更加清楚，下面将结合本申请实施例中的附图，对本申请的技术方案进行清楚、完整地描述，显然，所描述的实施例是本申请技术方案的一部分实施例，而不是全部的实施例。基于本申请文件中记载的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本申请技术方案保护的范围。
1、人工智能(Artificial Intelligence，AI)：
人工智能是利用数字计算机或者数字计算机控制的机器模拟、延伸和扩展人的智能，感知环境、获取知识并使用知识获得最佳结果的理论、方法、技术及应用系统。换句话说，人工智能是计算机科学的一个综合技术，它企图了解智能的实质，并生产出一种新的能以人类智能相似的方式做出反应的智能机器。人工智能也就是研究各种智能机器的设计原理与实现方法，使机器具有感知、推理与决策的功能。
人工智能技术是一门综合学科，涉及领域广泛，既有硬件层面的技术也有软件层面的技术。人工智能基础技术一般包括如传感器、专用人工智能芯片、云计算、分布式存储、大数据处理技术、预训练模型技术、操作/交互系统、机电一体化。其中，预训练模型又称大模型、基础模型，经过微调后，可以广泛应用于人工智能各大方向的下游任务中。人工智能软件技术主要包括计算机视觉技术、语音处理技术、自然语言处理技术以及机器学习/深度学习等几大方向。
随着人工智能技术研究和进步，人工智能在多个领域展开研究和应用，例如常见的智能家居、智能客服、虚拟助理、智能音箱、智能营销、无人驾驶、自动驾驶、机器人、智能医疗等，相信随着技术的发展，人工智能将在更多的领域得到应用，并发挥越来越重要的价值。
2、机器学习：
机器学习是一门多领域交叉学科，涉及概率论、统计学、逼近论、凸分析、算法复杂度理论等多门学科。专门研究计算机怎样模拟或实现人类的学习行为，以获取新的知识或技能，重新组织已有的知识结构使之不断改善自身的性能。
机器学习是人工智能的核心，是使计算机具有智能的根本途径，其应用遍布人工智能的各个领域，包括深度学习、强化学习、迁移学习、归纳学习、式教学习等技术。预训练模型是深度学习的最新发展成果，融合了以上技术。
3、自动驾驶技术：
指车辆在无驾驶员操作的情况下，实现自行驾驶。通常包括高精地图、环境感知、计算机视觉、行为决策、路径规划、运动控制等技术。自动驾驶包括单车智能、车路协同、联网云控等多种发展路径。自动驾驶技术有着广泛的应用前景，目前的领域为物流、公共交通、出租车、智慧交通领域外，未来将得到进一步发展。
随着人工智能技术的研究和进步，人工智能技术在多个领域展开研究和应用。例如，常见的智能家居、智能穿戴设备、虚拟助理、智能音箱、智能营销、无人驾驶、自动驾驶、无人机、数字孪生、虚拟人、机器人、人工智能生成内容(Artificial intelligence generates content，AIGC)、对话式交互、智能医疗、智能客服、游戏AI等，相信随着技术的发展，人工智能技术将在更多领域得到应用，并发挥越来越重要的价值。
4、全球导航卫星系统(Global Navigation Satellite System，GNSS)：
能在地球表面或者近地空间的任何地点，为使用对象提供全天候的三维坐标、速度以及时间信息的空基无线电导航定位系统，包括全球定位系统(Global Positioning System，GPS)、北斗卫星导航系统(BeiDou Navigation Satellite System，BDS)、格洛纳斯(GLONASS)、伽利略定位系统(Galileo satellite navigation system)等卫星导航系统。目前，卫星导航系统广泛应用于航海、通信、测绘、授时、消费娱乐、车辆管理、汽车导航与信息服务等方面，总的发展趋势是为实时应用提供高精度服务。
5、随机模型：
在GNSS定位的过程中，观测数据会受到多种误差源与随机误差等因素的干扰，降低定位精度。基于构建的随机模型分析观测数据，了解观测数据的误差分布特性，在数据处理过程中，对这些干扰因素进行有效抑制，以达到提高定位精度的目的。其中，常见的随机模型有高斯模型、指数模型等。
6、信噪比(SIGNAL-NOISE RATIO，SNR或者S/N)：
指一个电子设备或者电子系统中信号强度与噪声强度之间的比例。在GNSS定位的过程中，信噪比是衡量接收机接收到的卫星信号质量的重要指标之一。较高的信噪比意味着信号强度较大，而噪声强度较低，那么，接收机能更容易地检测卫星信号，从而达到提高定位精度的目的。在GNSS进行数据处理的过程中，也可以基于信噪比筛选观测数据，作为随机模型的自变量，从而提高定位结果的可靠性。
相关技术中，通常为不同机型的智能终端从几个预设的随机模型中选择一个合适的模型，而预设随机模型所使用的模型参数，不能完全适配不同机型智能终端的定位芯片、天线布设和增益策略，导致各智能终端基于配置的随机模型进行GNSS定位时，容易出现算法定义的标准模型与落地场景不适配的情况，产生定位精度损失。
为了解决该问题，本申请提出了一种定位数据处理方法。该方法包括：接收智能终端上传的多个原始观测数据，每个原始观测数据包括：智能终端定位时生成的导航定位信息与原始信噪比；分别对各导航定位信息进行映射处理，获得各自的原始索引对，并将内容唯一的原始索引对作为目标索引对，以及将内容相同的多个原始索引对合并为一个目标索引对；针对各目标索引对，分别执行以下操作：对一个目标索引对关联的大于第一设定阈值的多个原始信噪比进行异常性检验，得到通过检验的各目标信噪比，并基于各目标信噪比各自在多个原始观测数据中的出现频次，得到一个目标索引对的信噪比特征，基于信噪比特征结合预先构建的随机模型，拟合得到随机模型的模型参数。
在本申请提出的技术方案中，为不同机型的智能终端配置了同一个随机模型，再分别基于各机型的智能终端定位时产生的原始观测数据，拟合得到与相应机型智能终端的定位芯片、天线布设和增益策略更适配的模型参数，进而各智能终端基于配置相应模型参数的随机模型进行GNSS定位时，与落地场景之间的适配性更高，有利于提高定位精度。
以下结合说明书附图对本申请的优选实施例进行说明，应当理解，此处所描述的优选实施例仅用于说明和解释本申请，并不用于限定本申请，并且在不冲突的情况下，本申请中的实施例及实施例中的特征可以相互组合。
本申请实施例提供的方法，可应用于云技术、人工智能、智慧交通、辅助驾驶等各种场景中，为在相应场景中使用的智能终端，配置符合自身定位芯片、天线布设和增益策略的模型参数，再基于配置相应模型参数的随机模型进行GNSS定位，为智能终端的使用对象提供高精度定位服务，以满足其定位需求。
图1A示出了其中一种应用场景，在本场景中包括两个智能终端110与一台服务器130，智能终端110通过有线网络或者无线网络，与服务器130建立通信连接。
其中，智能终端110包括但不限于：手机、电脑(如平板电脑、笔记本电脑、台式计算机等)、智能家电(如智能音箱、智能冰箱等)、智能语音交互设备(如智能手表、智能眼镜等)、车载终端、飞行器等。
本申请实施例的服务器130可以是独立的物理服务器，也可以是多个物理服务器构成的服务器集群或分布式系统，还可以是提供云服务、云数据库、云计算、云函数、云存储、网络服务、云通信、中间件服务、域名服务、安全服务、内容分发网络(Content Delivery Network，CDN)、以及大数据和人工智能平台等基础云计算服务的云服务器，本申请在此不做限制。
将用于处理定位数据的处理器部署在服务器130上，由算力更为强大的服务器执行该任务，确定适配相应机型智能终端110的模型参数。
如图1B所示，在数据收集阶段，智能终端110可响应于使用对象(例如，智能终端110的用户)对具备定位功能的应用软件(如地图应用、可共享位置的社交应用等)的触发操作，通过所触发的应用软件，得到之前定位时生成的多个，并将各原始观测数据上传至观测值数据库进行存储。在使用对象未触发具备定位功能的应用软件的情况下，智能终端110也可以通过具备定位功能的应用软件，自动采集自身在定位时生成的多个原始观测数据，并将采集到的数据上传至观测值数据库中进行存储。其中，针对智能终端110自动采集数据的情况，还可以为其设定采集周期，以使智能终端110按照设置好的采集周期，规律性地采集周期内生成的原始观测数据，以免遗漏部分数据，影响选取合适的模型参数，进而降低定位精度。观测值数据库可以设置在服务器130内置的存储设备中，也可以设置在服务器130能够访问的另一设备中。
服务器130调用处理器，从观测值数据库中获取原始观测数据并进行处理。服务器130执行的处理过程可以如图1C所示，包括以下步骤。
S11，获取来自预设类型的智能终端的多组原始观测数据。
其中，每组原始观测数据包括对智能终端进行定位时生成的导航定位信息与信噪比(也将原始观测数据中的信噪比称为原始信噪比)。导航定位信息是关于定位所使用的资源的信息。定位所使用的资源可以包括定位所使用的卫星系统，卫星信号的载波频率等。信噪比是定位时接收到的卫星信号的信噪比。
S12，将每组原始观测数据中的导航定位信息映射到一个索引。
这里的索引可以是一个索引值，也可以是至少两个索引组成的复合索引，例如由两个索引组成的索引对。对于复合索引，可以将一组原始观测数据中导航定位信息的每个数据元素映射到一个索引，将至少两个数据元素的索引组合起来构成该组原始观测数据复合索引。
一些实施例中，索引可以是经过合并处理的目标索引对。例如，当每组原始观测数据中的导航定位信息包括两个数据元素时，可以分别对各组原始观测数据中的导航定位信息进行映射处理，获得各自的原始索引对，并将内容唯一的原始索引对作为目标索引对，以及将内容相同的多个原始索引对合并为一个目标索引对。
S13，针对每个索引，通过异常检验(anomaly detection)从与该索引关联的原始观测数据中、原始信噪比大于第一设定阈值的至少两组原始观测数据中剔除异常数据，得到观测数据子集，并基于观测数据子集中每个信噪比在该观测数据子集中出现的次数，得到该观测数据子集中信噪比的统计特征。
S14，通过使用上述观测数据子集及上述统计特征调整预先构建的随机模型，确定该随机模型的至少一个模型参数。该至少一个模型参数可以被提供用于配置所述预设类型的智能终端中用于定位的随机模型。
例如，服务器130可以将适配该机型智能终端的模型参数，同步到参数数据库中。在定位解算阶段，智能终端110响应于使用对象针对应用软件触发的定位操作，向参数数据库发送参数获取请求，在参数数据库中获取适配本机型智能终端的模型参数。再使用适配相应模型参数的随机模型进行定位解算，在应用软件的定位界面120中展示智能终端所处位置的地图，并以图钉、标注点等展示形式，在地图上标注智能地图的当前位置。
各实施例中，可以预设智能终端的至少两种类型，并针对每种类型执行各实施例的方法。可以根据实际需要采用各种合理的分类方式。例如，可以按照终端的品牌分类，即，不同的类型对应不同的终端品牌。又例如，可以按照终端的型号进行分类，即，不同的类型对应不同型号的终端。再例如，可以按照智能终端中的定位模块参数(或参数的组合)进行分类，即，不同的类型的终端采用不同的定位模块的参数(或参数的组合)。定位模块的参数可以选自，例如，导航芯片的型号或类型、天线布设方式、增益策略，等。
这样，各实施例的技术方案从同一类型的智能终端定位时产生的原始观测数据中提取出该类型终端定位时的信噪比的统计特征，用这些统计特征来调整预设的随机模型，能够使随机模型更好地适应这种类型的终端的定位特性和实际观测环境，并得到与该类型的智能终端更适配的模型参数。当该类型的智能终端使用配置了相应模型参数的随机模型进行定位时，得到的定位结果会更准确。
各实施例中，S13的异常检验可以包括：利用至少两个信噪比子集，对上述至少两组原始观测数据中的信噪比进行单峰检验，以及，基于所述至少两组原始观测数据中的信噪比的初始峰度与初始偏度，对所述至少两组原始观测数据中的信噪比进行正态检验。其中，所述至少两个信噪比子集是对所述至少两组原始观测数据中的信噪比按照数值进行分组而得到的。在该至少两组原始观测数据中的信噪比通过单峰检验与正态检验时，对所述至少两组原始观测数据中的信噪比进行离群值检验，并删除信噪比未位于设定离群值范围的原始观测数据，得到所述观测数据子集。
这样，通过对原始观测数据进行单峰检验、正态检验和离群值检验，可以确保用于调整随机模型的观测数据子集的数据可靠性，从而使调整后的随机模型能够产生更准确的定位结果。
各实施例中，上述单峰检验可以包括：对该至少两组原始观测数据中的信噪比进行聚类，得到上述至少两个信噪比子集；分别确定该两个信噪比子集中每两个信噪比子集的聚类中心之间的信噪比差值；当至少一个信噪比差值不小于第二设定阈值时，确定未通过单峰检验；当各信噪比差值均小于第二设定阈值时，确定通过单峰检验。
各实施例中，上述正态检验可以包括：确定该至少两组原始观测数据中的信噪比的初始峰度与初始偏度，其中，初始峰度用于反映信噪比的正态分布图像的尖锐程度，初始偏度用于反映信噪比的正态分布图像的对称程度；分别对初始峰度与初始偏度进行归一化，获得参考峰度与参考偏度；通过对参考峰度与参考偏度进行正态检验，得到上述至少两组原始观测数据中的信噪比的正态检验结果。
各实施例中，在上述至少两组原始观测数据中的信噪比未通过单峰检验与正态检验中的至少一个时，执行以下操作，直至第一设定阈值达到预设的最大阈值，或者至少两组原始观测数据中的信噪比通过单峰检验与正态检验时为止：使所述第一设定阈值增加设定的阈值增长量；基于上述至少两个信噪比子集，对至少两组原始观测数据中的信噪比进行单峰检验，以及，基于至少两组原始观测数据中的信噪比的初始峰度与初始偏度，对至少两组原始观测数据中的信噪比进行正态检验。
各实施例中，S13中得到上述观测数据子集中信噪比的统计特征的方式可以包括：获得上述观测数据子集中信噪比的最大信噪比、平均值、中位数和标准差中的至少一个，作为所述统计特征。
各实施例中，S14可以包括：利用观测数据子集中的信噪比以及统计特征，确定随机模型的频点间差异参数的值；将预先构建的随机模型转换为输出值与信噪比为线性关系的权重模型，该权重模型包括所述至少一个模型参数；利用观测数据子集中的信噪比以及统计特征，对权重模型进行拟合，得到至少一个模型参数的估计值。
这样，通过将预先构建的随机模型转换为线性的权重模型，可以简化随机模型的调整过程，节约处理资源，加快处理速度。
各实施例还提供了一种定位数据处理装置。如图1D所示，该装置100可以包括：
数据采集模块101，用于获取来自预设类型的智能终端的多组原始观测数据，其中，每组原始观测数据包括：对智能终端进行定位时生成的导航定位信息与信噪比，导航定位信息是关于定位所使用的资源的信息；
数据处理模块102，用于将每组原始观测数据中的导航定位信息映射到一个索引；
特征提取模块104，用于针对每个索引，通过异常检验从与该索引关联的原始观测数据中、信噪比大于第一设定阈值的至少两组原始观测数据中剔除异常数据，得到观测数据子集，并基于观测数据子集中的每个信噪比在观测数据子集中出现的次数，得到该观测数据子集中信噪比的统计特征；
参数拟合模块103，用于通过使用上述观测数据子集及上述统计特征调整预先构建的随机模型，确定该随机模型的至少一个模型参数，该至少一个模型参数被提供用于配置上述预设类型的智能终端中用于定位的随机模型。
以上各模块的具体功能可以参考各实施例的方法的相应步骤的描述，这里不再赘述。
下面通过具体的例子来对各实施例的方案进行说明。这些例子涉及的各种细节只是为了帮助理解上述方案，各实施例的方案的实现并不依赖这些例子中的任何细节。
为了便于描述，以一个智能终端为例，介绍处理该终端定位数据的过程。各实施例中，多个智能终端的定位数据的集合可以以类似的方式处理。请参阅图2A～2B示出的示意图，处理一个智能终端的定位数据的过程如下。
S201：接收智能终端上传的多个原始观测数据，每个原始观测数据包括：智能终端定位时生成的导航定位信息与原始信噪比。
在数据收集阶段，智能终端通过地图应用，回流得到之前定位时生成的多个原始观测数据，并将各原始观测数据上传至观测值数据库进行存储。这里，“回流”是指通过地图应用，从智能终端的定位模块获取之前定位时生成的原始观测数据。服务器再调用处理器，从观测值数据库中读取多个原始观测数据。还支持读取设定周期内收集的数据，如读取智能终端最新一次收集的多个原始观测数据，又如读取智能终端在过去一个月内收集的数据。
S202：分别对各导航定位信息进行映射处理，获得各自的原始索引对(即复合索引，用作原始观测数据的索引)，并将内容唯一的原始索引对作为目标索引对，以及将内容相同的多个原始索引对合并为一个目标索引对。
由于回流得到的原始观测数据量十分庞大，为了降低海量数据对服务端造成的处理压力，需要对收集的数据进行数据清洗。数据清洗主要包括剔除无效数据、提取有效数据、按机型分类等多个步骤。
步骤1：剔除无效数据。
在智能终端进行GNSS定位时生成多个原始观测数据，一个原始观测数据包括一个GNSS时钟(GNSSClock)字段和一个GNSS测量(GNSSMeasurement)字段。GNSSClock字段表示观测数据的生成时间，GNSSMeasurement字段表示智能终端在某一历元(即观测时刻)对导航卫星进行多频观测时，产生的n条原始观测值，每条原始观测值包括导航定位信息与对应的原始信噪比。
将字段参数不完整的原始观测数据视为无效数据，剔除读取到的无效数据。由于原始信噪比在设定取值范围内时，才可认定为合理的有效观测值，有利于提高模型参数与该机型智能终端之间的适配度。因此，在剔除无效数据之后，再以原始信噪比是否落入设定取值范围内为筛选条件，仅保留各原始观测数据中原始信噪比在设定取值范围内的GNSS数据。取值范围可根据需要设置为，例如，大于1dBHz，或者1～63dBHz，等。
步骤2：提取有效数据。
每个导航定位信息包括：星座类型(ConstellationType)字段和载波频率(CarrierFrequencyHz)字段。由于这两个字段的字段参数较长，且未显式记录导航定位信息与导航定位系统之间的关系，不利于后续为智能终端配置模型参数。因此，如图2C所示，针对各导航定位信息，分别执行以下操作，获得相应的原始索引对。
S2021：基于星座类型与导航定位系统之间的转换关系，将一个导航定位信息中的星座类型映射为相应的导航定位系统。
可以利用预先设定的标识来表示各个导航定位系统。各实施例中，导航定位系统的标识可以包括至少一个字母、至少一个数字、至少一个符号，或其任意组合。
载波频率是无线通信系统中用于传输信息的电磁波的频率。在数字通信中，载波频率通常是一个固定值，也可以将载波频率理解为一种固定频率的电磁波，用于携带调制信号。频点则是给固定频率设置的编号，因此，在无线电通信中，频点与频率之间是一一对应的，一个频点对应着一个特定的频率。
S2022：基于导航定位系统、频率与频点之间的转换关系，将该导航定位信息中的频率映射为相应的频点，并将映射得到的导航定位系统与频点，关联为一个原始索引对。
在确定智能终端所使用导航定位系统的基础上，基于导航定位系统、频率与频点之间的转换关系，将该导航定位信息中的频率映射为相应的频点，并将映射得到的导航定位系统与频点，关联到一个原始索引对。原始索引对用作对应的导航定位信息的索引。
例如，表1示出了星座类型与导航定位系统之间的转换关系，表2示出了导航定位系统、频率与频点之间的转换关系，基于表1、表2中列出的转换关系，将导航定位信息(ConstellationType：1，CarrierFrequencyHz：1575420000Hz)转换为原始索引对GPS L1。为了方便之后进行统计与绘图，还可以将原始索引对GPS L1转换为索引“G0”。
表1
表2
为了降低服务端的存储压力与数据分析压力，可以将不同导航卫星和历元(表示时间)的各原始信噪比转换为整数，得到更新后的信噪比(作为后续处理中使用的信噪比)，再依次遍历各信噪比，直到全部遍历结束，得到如表3所示的频数统计表。其中，如图2D所示，每遍历一次，若当前遍历的信噪比已记录在频数统计表中，则更新表格中该信噪比的出现频次；若当前遍历的信噪比未记录在频数统计表中，则将其写入表格中，并更新表格中该信噪比的出现频次。其它实施例中，也可以不做上述取整操作，而是直接使用原始的信噪比。
表3
步骤3：按机型分类。
按照智能终端的机型类别，将同一机型的各原始索引对和各频数统计表，划分到一组中，便于进行之后的数据分析工作。
S203：针对各目标索引对，分别执行以下操作：对一个目标索引对关联的大于第一设定阈值的多个信噪比进行异常性检验(anomaly detection)，得到通过检验的各信噪比(为便于描述，也称为目标信噪比)，并基于各目标信噪比各自在多个原始观测数据中的出现频次，得到该目标索引对对应的信噪比的统计特征(也简称为信噪比特征)；基于信噪比特征，结合预先构建的随机模型，拟合得到随机模型的模型参数。
由于不同机型的智能终端的天线布设、增益策略、截止信噪比和实际观测时的环境恶劣程度不同，回流收集到的信噪比可能呈现多峰值和严重偏态的情况，因此，需要先筛选出可靠性较高的目标信噪比。
首先，根据第一设定阈值T1，对一个目标索引对关联的多个信噪比R进行初步筛选，筛选出满足R>T1的信噪比，即大于第一设定阈值的多个信噪比。其中，可根据智能终端的机型，在10～20dBHz的取值范围中确定第一设定阈值。
然后，基于一个目标索引对关联的大于第一设定阈值的多个信噪比划分得到的多个信噪比子集，对由多个信噪比组成的信噪比集进行单峰检验，以及，基于信噪比集的初始峰度与初始偏度，对信噪比集进行正态检验。
一些实施例中，异常性检验主要包括两部分，一个是单峰检验，另一个是正态检验。
(1)、参阅图2E示出的流程示意图，进行单峰检验的过程如下。
S2031：对一个目标索引对关联的大于第一设定阈值的多个信噪比进行聚类，得到多个信噪比子集，再分别确定各信噪比子集与除自身以外的其他信噪比子集的聚类中心之间的信噪比差值，并将各信噪比差值与第二设定阈值进行比较，得到相应的比较结果。
S2032：当至少一个比较结果为信噪比差值不小于第二设定阈值时，确定单峰检验结果为信噪比集未通过单峰检验；当各比较结果均为信噪比差值小于第二设定阈值时，确定信噪比集通过单峰检验。
在其中一种实施方式中，第二设定阈值的取值范围可为5～10dBHz，以保证单峰检验的有效性。例如，采用k-means算法将大于10dBHz的多个信噪比分为两类，待算法收敛后，检验两个聚类中心之间的信噪比差值是否小于7dBHz，若是，确定单峰检验结果为信噪比集通过单峰检验；否则，确定单峰检验结果为信噪比集未通过单峰检验。
(2)、参阅图2F示出的流程示意图，进行正态检验的过程如下：
S2031＇：基于信噪比集中的多个信噪比，确定信噪比集的初始峰度与初始偏度；其中，初始峰度用于反映信噪比集的正态分布图像的尖锐程度，初始偏度用于反映信噪比集的正态分布图像的对称程度。
公式1示出了初始峰度K与初始偏度S的计算公式。其中，公式中的m2是二阶样本中心矩，m3是三阶样本中心矩，m4是四阶样本中心矩，h是信噪比集中的信噪比总数，xi是第i个信噪比，是信噪比集的平均值。
峰度又称峰态系数，是表征概率密度分布曲线在平均值处峰值高低的指标。直观地说，峰度反映了峰部的尖度，可用来表示数据的偏离程度，通常是作为一种判断正态性的指标。
该指标的计算是相对于正态分布而言的，峰度包括正态分布(峰度值＝3)、厚尾(峰度值>3)和瘦尾(峰度值<3)。在统计学中，峰度值<3意味着数据中有更多大于或小于平均值的极端差值，在图像上表现为数据分布比正态分布更尖峰、尾部更薄；峰度值>3意味着数据中的极端差值较少，分布的中心区域相对分散，数据点更倾向于分布在两端，在图像上表现为数据分布比正态分布更平坦、尾部更厚。峰度值＝3意味着数据分布与正态分布相似，没有特别突出的尖峰特征或者平坦特征。
图2G中展示了不同峰度值的图像分布，黑色实线是正态分布曲线，峰部形状较尖，比正态分布曲线更为陡峭的黑色虚线是瘦尾，而峰部形状较钝，比正态分布曲线更为平缓的灰色实线是厚尾。
偏度又称偏态系数，是统计数据分布偏斜方向和程度的度量，是统计数据分布非对称程度的数字特征。如图2H(1)所示，对于正态分布，其偏度为0，两侧尾部长度对称。如图2H(2)所示，若分布的偏度小于0，说明该分布具有负偏离(即左偏态)，此时位于均值左边的数据少于位于均值右边的数据，直观表现为左边的尾部长于右边的尾部。如图2H(3)所示，若分布的偏度大于0(即右偏态)，此时位于均值左边的数据多于位于均值左边的数据，直观表现为右边的尾部长于左边的尾部。如图2H(3)所示，若分布的偏度等于0，说明该分布比较对称，呈现正态分布。
S2032＇：分别对初始峰度与初始偏度进行数据归一化，获得相应的参考峰度与参考偏度。
公式2示出了参考峰度Zk与参考偏度Zs的计算公式，
Zk＝K/σ(K)
Zs＝S/σ(S)                     公式2；
S2033＇：对参考峰度与参考偏度进行正态检验，得到相应的正态检验结果。
在其中一种实施方式中，采用卡方检验法(显著性水平α可为0.05～0.2)检验参考峰度Zk和参考偏度Zs，当参考峰度与参考偏度均通过检验时，可认为信噪比集通过正态检验。在进行正态检验时，只排除过于严重的偏态分布即可，因此，可适当放宽显著性水平α的取值。
在信噪比集未通过单峰检验与正态检验中的至少一个时，执行以下操作，直至第一设定阈值已上调至取值范围规定的最大阈值，或者信噪比集通过异常性检验时为止：按照设定阈值增长量，上调第一设定阈值；再基于一个目标索引对关联的大于已调整的第一设定阈值的多个信噪比划分得到的多个信噪比子集，对由多个信噪比组成的信噪比集进行单峰检验，以及，基于信噪比集的初始峰度与初始偏度，对信噪比集进行正态检验。
但是，若多次上调第一设定阈值后，信噪比集仍无法通过异常性检验，说明收集的原始观测数据存在较大随机性，需等待回流得到更多数据后再进行分析。
在信噪比集通过单峰检验与正态检验时，对信噪比集进一步进行离群值检验，并删除未位于设定离群值范围的信噪比，得到相应的目标信噪比。
公式3示出了离群值检验的计算公式，不满足下式的信噪比，被认定为离群值。其中，Q1是25％分位数，Q3是75％分位数，四分位距IQR是Q3与Q1之间的差值(Q3-Q1)。
Q1-3IQR<R<Q3+3IQR            公式3；
基于满足公式3的各目标信噪比，计算所属目标信噪比集的信噪比特征。
例如，可以基于各目标信噪比各自在多个原始观测数据中的出现频次，确定由各目标信噪比组成的目标信噪比集的平均信噪比与中位数信噪比。
可以基于各目标信噪比各自在多个原始观测数据中的出现频次，确定目标信噪比集中共有n个信噪比数据。按照从小到大的顺序排列各目标信噪比，当n为奇数时，集合中第(n+1)/2个数据即为中位数信噪比Rm；当n为偶数时，集合中第n/2个数据即为中位数信噪比Rm。
可以基于各目标信噪比各自在多个原始观测数据中的出现频次，确定目标信噪比集中共有n个信噪比数据，将目标信噪比总和与信噪比数据总数之间的比值，作为平均信噪比
还可以基于平均信噪比与各目标信噪比及相应的出现频次，确定目标信噪比集的标准差信噪比。
基于各目标信噪比各自在多个原始观测数据中的出现频次，确定目标信噪比集中共有n个信噪比数据。再依次执行以下步骤，得到目标信噪比集的标准差信噪比Rs：
步骤1：每个目标信噪比减去目标信噪比集的平均信噪比；
步骤2：分别对步骤1所得的各个数值进行平方运算，再将各平方值相加；
步骤3：把步骤2的结果除以(n-1)；
步骤4：对步骤3所得数值进行平方根运算，得到目标信噪比集的标准差信噪比Rs。
将目标信噪比集的最大信噪比RM、平均信噪比中位数信噪比Rm及标准差信噪比Rs，作为目标信噪比集关联的一个目标索引对的信噪比特征。
例如，针对该机型智能终端关联的目标信噪比集，生成如图2I所示的信噪比分布情况与相关特征值的计算结果。其中，图中的横轴为目标索引对，纵轴目标信噪比，图表为箱型图，长方形下沿为Q1，上沿为Q3，三角形表示平均信噪比，灰色线段表示中位数信噪比。
在本申请提出的技术方案中，为不同机型的智能终端配置了形式统一的随机模型，降低了适配难度。本申请提出的技术方案可应用于包含信噪比参数的随机模型，在公式4所示的随机模型中，σ2随机模型计算的观测噪声，E是高度角，a、b是高度角相关参数，c、d、g是可调整的信噪比模型参数。
不过，为了简化随机模型的配置流程，可以为同一机型设定相同的模型参数c、d。这样，当一个机型存在多个满足要求的目标信噪比时，无需计算多组模型参数c、d，只基于其中一个目标信噪比及相应的信噪比特征，拟合得到模型参数c、d即可。
各实施例中，一种可能的模型参数拟合过程如下。
基于信噪比特征与参考信噪比，确定随机模型的频点间差异归算参数g。
例如，可以将一个目标索引对中GNSS频点与参考频点之间的信噪比参数差值，作为随机模型的差异归算参数g。为了简化计算，信噪比参数差值可取为频点间的中位数信噪比之差或者最大值之差。
之后，基于频点间差异归算参数与高度角参数，将预先构建的随机模型转换为具有线性关系的权重模型。
在本申请提出的技术方案中，为不同机型的智能终端配置了形式统一的随机模型σ2＝g(E)H(R)。为了简化起见，拟合时把高度角相关部分g(E)设置为某个常数，上述公式将转化为
对取对数，得到再对指数项log10取对数，将其转化为线性函数
最后，采用线性最小二乘法或其他拟合算法，对权重模型与随机模型进行拟合，得到随机模型的信噪比参数c与标准化信噪比参数d。
在拟合结束之后，采用公式5检验模型参数c、d的有效性。若检验通过，则将其作为相应机型智能终端的模型参数c、d、g同步到参数数据库中。在定位解算阶段，智能终端响应于使用对象针对应用软件触发的定位操作，向参数数据库发送参数获取请求，在参数数据库中获取适配本机型智能终端的模型参数。再使用适配相应模型参数的随机模型进行定位解算，在应用软件的定位界面中展示智能终端所处位置的地图。
本申请实施例提供的模型参数确定方法，可应用于包含信噪比参数的随机模型中，分别基于各机型的智能终端定位时产生的原始观测数据，拟合得到与相应机型智能终端的定位芯片、天线布设和增益策略更适配的模型参数，进而各智能终端基于配置相应模型参数的随机模型进行GNSS定位时，与落地场景之间的适配性更高，有利于提高定位精度。
如图3A～3B所示，将本申请实施例提供的方法应用于地图应用中，处理与应用定位数据的过程如下。
S301：智能终端通过地图应用，回流得到之前定位时生成的多个原始观测数据，并将各原始观测数据上传至观测值数据库中。
S302：服务器从观测值数据库中读取多个原始观测数据，分别对各原始观测数据中的导航定位信息进行映射处理，获得各自的原始索引对，并将内容唯一的原始索引对作为目标索引对，以及将内容相同的多个原始索引对合并为一个目标索引对。
S303：针对各目标索引对，分别执行以下操作：对一个目标索引对关联的大于第一设定阈值的多个信噪比进行异常性检验，得到通过检验的各目标信噪比，并基于各目标信噪比各自在多个原始观测数据中的出现频次，得到一个目标索引对的信噪比特征，基于信噪比特征结合预先构建的随机模型，拟合得到随机模型的模型参数。
S304：检验模型参数c、d的有效性，当检验通过时，将其作为相应机型智能终端的模型参数c、d、g同步到参数数据库中。
S305：智能终端响应于使用对象针对地图应用触发的定位操作，向参数数据库发送参数获取请求，在参数数据库中获取适配本机型智能终端的模型参数，再使用适配相应模型参数的随机模型进行定位解算，在应用软件的定位界面中展示智能终端所处位置的地图。
如图4A～4B所示，将本申请实施例提供的方法应用于具备定位功能的社交应用中，处理与应用定位数据的过程如下。
S401：智能终端通过地图应用，回流得到之前定位时生成的多个原始观测数据，并将各原始观测数据上传至观测值数据库中。
S402：服务器从观测值数据库中读取多个原始观测数据，分别对各原始观测数据中的导航定位信息进行映射处理，获得各自的原始索引对，并将内容唯一的原始索引对作为目标索引对，以及将内容相同的多个原始索引对合并为一个目标索引对。
S403：针对各目标索引对，分别执行以下操作：对一个目标索引对关联的大于第一设定阈值的多个信噪比进行异常性检验，得到通过检验的各目标信噪比，并基于各目标信噪比各自在多个原始观测数据中的出现频次，得到一个目标索引对的信噪比特征，基于信噪比特征结合预先构建的随机模型，拟合得到随机模型的模型参数。
S404：检验模型参数c、d的有效性，当检验通过时，将其作为相应机型智能终端的模型参数c、d、g同步到参数数据库中。
S405：智能终端响应于使用对象针对社交应用触发的位置共享操作，向参数数据库发送参数获取请求，在参数数据库中获取适配本机型智能终端的模型参数，再使用适配相应模型参数的随机模型进行定位解算，在社交软件的聊天界面中展示本终端所处位置的地图缩略图。
另外，需要注意的是，在本申请的具体实施方式中，涉及到收集智能终端定位时生成的原始观测数据等相关的对象数据，当本申请以上实施例运用到具体产品或技术中时，需要获得对象许可或者同意，且相关数据的收集、使用和处理需要遵守相关国家和地区的相关法律法规和标准。
本申请实施例中，术语“模块”或“单元”是指有预定功能的计算机程序或计算机程序的一部分，并与其他相关部分一起工作，以实现预定目标，并且可以通过使用软件、硬件(如处理电路或存储器)或其组合来全部或部分实现。同样的，一个处理器(或多个处理器或存储器)可以用来实现一个或多个模块或单元。此外，每个模块或单元都可以是包含该模块或单元功能的整体模块或单元的一部分。
与上述方法实施例基于同一发明构思，本申请实施例还提供了一种定位数据处理装置。如图5所示，定位数据处理装置500可以包括：
数据采集模块501，用于接收智能终端上传的多个原始观测数据，每个原始观测数据包括：智能终端定位时生成的导航定位信息与信噪比；
数据处理模块502，用于分别对各导航定位信息进行映射处理，获得各自对应的原始索引对，并将内容唯一的原始索引对作为目标索引对，以及将内容相同的多个原始索引对合并为一个目标索引对；
参数拟合模块503，用于针对各目标索引对，分别执行以下操作：
对一个目标索引对关联的大于第一设定阈值的多个信噪比进行异常性检验，得到通过检验的各目标信噪比，并基于各目标信噪比各自在多个原始观测数据中的出现频次，得到一个目标索引对的信噪比特征；
基于信噪比特征，结合预先构建的随机模型，拟合得到随机模型的模型参数。
图5的实施例中，参数拟合模块503可以看作是如图1D实施例中特征提取模块104和参数拟合模块103合并得到的模块。
各实施例中，参数拟合模块503用于：
基于一个目标索引对关联的大于第一设定阈值的多个信噪比划分得到的多个信噪比子集，对由多个信噪比组成的信噪比集进行单峰检验，以及，基于信噪比集的初始峰度与初始偏度，对信噪比集进行正态检验；
在信噪比集通过单峰检验与正态检验时，对信噪比集进一步进行离群值检验，并删除未位于设定离群值范围的信噪比，得到相应的目标信噪比。
各实施例中，参数拟合模块503用于：
对一个目标索引对关联的大于第一设定阈值的多个信噪比进行聚类，得到多个信噪比子集；
分别确定各信噪比子集与除自身以外的其他信噪比子集的聚类中心之间的信噪比差值，并将各信噪比差值与第二设定阈值进行比较，得到相应的比较结果；
当至少一个比较结果为信噪比差值不小于第二设定阈值时，确定单峰检验结果为信噪比集未通过单峰检验；当各比较结果均为信噪比差值小于第二设定阈值时，确定信噪比集通过单峰检验。
各实施例中，参数拟合模块503用于：
基于信噪比集中的多个信噪比，确定信噪比集的初始峰度与初始偏度；其中，初始峰度用于反映信噪比集的正态分布图像的尖锐程度，初始偏度用于反映信噪比集的正态分布图像的对称程度；
分别对初始峰度与初始偏度进行数据归一化，获得相应的参考峰度与参考偏度；
对参考峰度与参考偏度进行正态检验，得到相应的正态检验结果。
各实施例中，参数拟合模块503用于：
基于各目标信噪比各自在多个原始观测数据中的出现频次，确定由各目标信噪比组成的目标信噪比集的平均信噪比与中位数信噪比；
基于平均信噪比与各目标信噪比及相应的出现频次，确定目标信噪比集的标准差信噪比；
将目标信噪比集的最大信噪比、平均信噪比、中位数信噪比及标准差信噪比，作为目标信噪比集关联的一个目标索引对的信噪比特征。
各实施例中，数据处理模块502用于：
针对各导航定位信息，分别执行以下操作：
基于星座类型与导航定位系统之间的转换关系，将一个导航定位信息中的星座类型映射为相应的导航定位系统；
基于导航定位系统、频率与频点之间的转换关系，将一个导航定位信息中的频率映射为相应的频点；
将映射得到的导航定位系统与频点，关联为一个原始索引对。
各实施例中，参数拟合模块503用于：
基于信噪比特征与参考信噪比，确定随机模型的频点间差异归算参数；
基于频点间差异归算参数与高度角参数，将预先构建的随机模型转换为具有线性关系的权重模型；
采用拟合算法，对权重模型与随机模型进行拟合，得到随机模型的信噪比参数与标准化信噪比参数。
各实施例中，在信噪比集未通过单峰检验与正态检验中的至少一个时，参数拟合模块503执行以下操作，直至第一设定阈值已上调至取值范围规定的最大阈值，或者信噪比集通过异常性检验时为止：
按照设定阈值增长量，上调第一设定阈值；
基于一个目标索引对关联的大于已调整的第一设定阈值的多个信噪比划分得到的多个信噪比子集，对由多个信噪比组成的信噪比集进行单峰检验，以及，基于信噪比集的初始峰度与初始偏度，对信噪比集进行正态检验。
为了描述的方便，以上各部分按照功能划分为各模块(或单元)分别描述。当然，在实施本申请时可以把各模块(或单元)的功能在同一个或多个软件或硬件中实现。
在介绍了本申请示例性实施方式的定位数据处理方法和装置之后，接下来，介绍根据本申请的另一示例性实施方式的计算机设备。
所属技术领域的技术人员能够理解，本申请的各个方面可以实现为系统、方法或程序产品。因此，本申请的各个方面可以具体实现为以下形式，即：完全的硬件实施方式、完全的软件实施方式(包括固件、微代码等)，或硬件和软件方面结合的实施方式，这里可以统称为“电路”、“模块”或“系统”。
与上述方法实施例基于同一发明构思，本申请实施例中还提供了一种计算机设备。在一种实施例中，该计算机设备可以是服务器，如图1A所示的服务器130。在该实施例中，计算机设备600的结构如图6所示，可以至少包括存储器601、通讯模块603，以及至少一个处理器602。
存储器601，用于存储处理器602执行的计算机程序。存储器601可主要包括存储程序区和存储数据区，其中，存储程序区可存储操作系统，以及运行即时通讯功能所需的程序等；存储数据区可存储各种即时通讯信息和操作指令集等。
存储器601可以是易失性存储器(volatile memory)，例如随机存取存储器(random-access memory，RAM)；存储器601也可以是非易失性存储器(non-volatile memory)，例如只读存储器，快闪存储器(flash memory)，硬盘(hard disk drive，HDD)或固态硬盘(solid-state drive，SSD)；或者存储器601是能够用于携带或存储具有指令或数据结构形式的期望的计算机程序并能够由计算机存取的任何其他介质，但不限于此。存储器601可以是上述存储器的组合。
处理器602，可以包括一个或多个中央处理单元(central processing unit，CPU)或者为数字处理单元等等。处理器602，用于调用存储器601中存储的计算机程序时实现上述定位数据处理方法。
通讯模块603用于与终端设备和其他服务器进行通信。
本申请实施例中不限定上述存储器601、通讯模块603和处理器602之间的具体连接介质。本申请实施例在图6中以存储器601和处理器602之间通过总线604连接，总线604在图6中以粗线描述，其它部件之间的连接方式，仅是进行示意性说明，并不引以为限。总线604可以分为地址总线、数据总线、控制总线等。为便于描述，图6中仅用一条粗线描述，但并不描述仅有一根总线或一种类型的总线。
存储器601中存储有计算机存储介质，计算机存储介质中存储有计算机可执行指令，计算机可执行指令用于实现本申请实施例的定位数据处理方法。处理器602用于执行上述的定位数据处理方法，如图2A所示。
在另一种实施例中，计算机设备也可以是其他计算机设备，如图1A所示的终端设备110。在该实施例中，计算机设备的结构可以如图7所示，包括：通信组件710、存储器720、显示单元730、摄像头740、传感器750、音频电路760、蓝牙模块770、处理器780等部件。
存储器720可用于存储软件程序及数据。处理器780通过运行存储在存储器720的软件程序或数据，从而执行终端设备110的各种功能以及数据处理。存储器720可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他易失性固态存储器件。存储器720存储有使得终端设备110能运行的操作系统。本申请中存储器720可以存储操作系统及各种应用程序，还可以存储执行本申请实施例定位数据处理方法的计算机程序。
显示单元730可以包括设置在终端设备110正面的显示屏732。显示单元730还可以包括设置在终端设备110正面的触控屏731。
终端设备还可以包括至少一种传感器750，比如加速度传感器751、距离传感器752、指纹传感器753、温度传感器754。
音频电路760、扬声器761、传声器762可提供对象与终端设备110之间的音频接口。。
处理器780是终端设备的控制中心，利用各种接口和线路连接整个终端的各个部分，通过运行或执行存储在存储器720内的软件程序，以及调用存储在存储器720内的数据，执行终端设备的各种功能和处理数据。在一些实施例中，处理器780可包括一个或多个处理单元；处理器780还可以集成应用处理器和基带处理器。
在一些可能的实施方式中，本申请提供的定位数据处理方法的各个方面还可以实现为一种程序产品的形式，其包括计算机程序，当程序产品在计算机设备上运行时，计算机程序用于使计算机设备执行本说明书上述描述的根据本申请各种示例性实施方式的定位数据处理方法中的步骤，例如，计算机设备可以执行如图2A中所示的步骤。
程序产品可以采用一个或多个可读介质的任意组合。可读介质可以是可读信号介质或者可读存储介质。可读存储介质例如可以是但不限于电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。可读存储介质的更具体地例子(非穷举的列表)包括：具有一个或多个导线的电连接、便携式盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。
应当注意，尽管在上文详细描述中提及了装置的若干单元或子单元，但是这种划分仅仅是示例性的并非强制性的。实际上，根据本申请的实施方式，上文描述的两个或更多单元的特征和功能可以在一个单元中具体化。反之，上文描述的一个单元的特征和功能可以进一步划分为由多个单元来具体化。
此外，尽管在附图中以特定顺序描述了本申请方法的操作，但是，这并非要求或者暗示必须按照该特定顺序来执行这些操作，或是必须执行全部所示的操作才能实现期望的结果。附加地或备选地，可以省略某些步骤，将多个步骤合并为一个步骤执行，和/或将一个步骤分解为多个步骤执行。
本领域内的技术人员应明白，本申请的实施例可提供为方法、系统、或计算机程序产品。因此，本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用计算机程序的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本申请是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序命令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序命令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的命令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序命令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的命令产生包括命令装置的制造品，该命令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序命令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的命令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
以上实施例的各技术特征可以进行任意的组合，为使描述简洁，未对上述实施例中的各个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本说明书记载的范围。
尽管已描述了本申请的优选实施例，但本领域内的技术人员一旦得知了基本创造性概念，则可对这些实施例做出另外的变更和修改。所以，所附权利要求意欲解释为包括优选实施例以及落入本申请范围的所有变更和修改。
显然，本领域的技术人员可以对本申请进行各种改动和变型而不脱离本申请的精神和范围。这样，倘若本申请的这些修改和变型属于本申请权利要求及其等同技术的范围之内，则本申请也意图包含这些改动和变型在内。

Claims (19)

  1. 一种定位数据处理方法，由服务器执行，包括：
    获取来自预设类型的智能终端的多组原始观测数据，其中，每组原始观测数据包括：对智能终端进行定位时生成的导航定位信息与信噪比，导航定位信息是关于定位所使用的资源的信息；
    将每组原始观测数据中的导航定位信息映射到一个索引；
    针对每个索引，通过异常检验从与所述索引关联的原始观测数据中、信噪比大于第一设定阈值的至少两组原始观测数据中剔除异常数据，得到观测数据子集，并基于所述观测数据子集中的每个信噪比在所述观测数据子集中出现的次数，得到所述观测数据子集中信噪比的统计特征；
    通过使用所述观测数据子集及所述统计特征调整预先构建的随机模型，确定所述随机模型的至少一个模型参数的值，所述至少一个模型参数的值被提供用于配置所述预设类型的智能终端中用于定位的随机模型。
  2. 如权利要求1所述的方法，其中，所述通过异常检验从与所述索引关联的原始观测数据中、信噪比大于第一设定阈值的至少两组原始观测数据中剔除异常数据，得到观测数据子集，包括：
    利用至少两个信噪比子集，对所述至少两组原始观测数据中的信噪比进行单峰检验，以及，基于所述至少两组原始观测数据中的信噪比的初始峰度与初始偏度，对所述至少两组原始观测数据中的信噪比进行正态检验，其中，所述至少两个信噪比子集是对所述至少两组原始观测数据中的信噪比按照数值进行分组而得到的；
    在所述至少两组原始观测数据中的信噪比通过所述单峰检验与所述正态检验时，对所述至少两组原始观测数据中的信噪比进行离群值检验，并删除信噪比未位于设定离群值范围的原始观测数据，得到所述观测数据子集。
  3. 如权利要求2所述的方法，其中，所述对所述至少两组原始观测数据中的信噪比进行单峰检验，包括：
    对所述至少两组原始观测数据中的信噪比进行聚类，得到所述至少两个信噪比子集；
    分别确定所述至少两个信噪比子集中每两个信噪比子集的聚类中心之间的信噪比差值；
    当至少一个信噪比差值不小于所述第二设定阈值时，确定未通过单峰检验；当各信噪比差值均小于所述第二设定阈值时，确定通过单峰检验。
  4. 如权利要求2所述的方法，其中，所述对所述至少两组原始观测数据中的信噪比进行正态检验，包括：
    确定所述至少两组原始观测数据中的信噪比的初始峰度与初始偏度；其中，所述初始峰度用于反映信噪比的正态分布图像的尖锐程度，所述初始偏度用于反映信噪比的正态分布图像的对称程度；
    分别对所述初始峰度与所述初始偏度进行归一化，获得参考峰度与参考偏度；
    通过对所述参考峰度与所述参考偏度进行正态检验，得到所述至少两组原始观测数据中的信噪比的正态检验结果。
  5. 如权利要求1所述的方法，其中，所述基于所述观测数据子集中的每个信噪比在所述观测数据子集中出现的次数，得到所述观测数据子集中信噪比的统计特征，包括：
    获得所述观测数据子集中信噪比的最大信噪比、平均值、中位数及标准差中的至少一个，作为所述统计特征。
  6. 如权利要求1～5任一项所述的方法，其中，所述将每组原始观测数据中的导航定位信息映射到一个索引，包括：
    基于星座类型与导航定位系统之间的转换关系，将所述导航定位信息中的星座类型映射为相应的导航定位系统；
    基于导航定位系统、频率与频点之间的转换关系，将所述导航定位信息中的频率映射为相应的频点；
    将映射得到的导航定位系统与频点关联成的索引对作为所述索引。
  7. 如权利要求1～5任一项所述的方法，其中，所述通过使用所述观测数据子集及所述统计特征调整预先构建的随机模型，确定所述随机模型的至少一个模型参数的值，包括：
    利用所述观测数据子集中的信噪比以及所述统计特征，确定所述随机模型的频点间差异参数的值；
    将预先构建的随机模型转换为输出值与信噪比为线性关系的权重模型，所述权重模型包括所述至少一个模型参数；
    利用所述观测数据子集中的信噪比以及所述统计特征，对所述权重模型进行拟合，得到所述至少一个模型参数的值。
  8. 如权利要求2所述的方法，其中，在所述至少两组原始观测数据中的信噪比未通过所述单峰检验与所述正态检验中的至少一个时，执行以下操作，直至所述第一设定阈值达到预设的最大阈值，或者所述至少两组原始观测数据中的信噪比通过所述单峰检验与所述正态检验时为止：
    使所述第一设定阈值增加设定的阈值增长量；
    基于所述至少两个信噪比子集，对所述至少两组原始观测数据中的信噪比进行单峰检验，以及，基于所述至少两组原始观测数据中的信噪比的初始峰度与初始偏度，对所述至少两组原始观测数据中的信噪比进行正态检验。
  9. 一种定位数据处理装置，包括：
    数据采集模块，用于获取来自预设类型的智能终端的多组原始观测数据，其中，每组原始观测数据包括：对智能终端进行定位时生成的导航定位信息与信噪比，导航定位信息是关于定位所使用的资源的信息；
    数据处理模块，用于将每组原始观测数据中的导航定位信息映射到一个索引；
    特征提取模块，用于针对每个索引，通过异常检验从与所述索引关联的原始观测数据中、信噪比大于第一设定阈值的至少两组原始观测数据中剔除异常数据，得到观测数据子集，并基于所述观测数据子集中的每个信噪比在所述观测数据子集中出现的次数，得到所述观测数据子集中信噪比的统计特征；
    参数拟合模块，用于通过使用所述观测数据子集及所述统计特征调整预先构建的随机模型，确定所述随机模型的至少一个模型参数的值，所述至少一个模型参数的值被提供用于配置所述预设类型的智能终端中用于定位的随机模型。
  10. 如权利要求9所述的装置，其中，所述特征提取模块用于：
    利用至少两个信噪比子集，对所述至少两组原始观测数据中的信噪比进行单峰检验，以及，基于所述至少两组原始观测数据中的信噪比的初始峰度与初始偏度，对所述至少两组原始观测数据中的信噪比进行正态检验，其中，所述至少两个信噪比子集是对所述至少两组原始观测数据中的信噪比按照数值进行分组而得到的；
    在所述至少两组原始观测数据中的信噪比通过所述单峰检验与所述正态检验时，对所述至少两组原始观测数据中的信噪比进行离群值检验，并删除信噪比未位于设定离群值范围的原始观测数据，得到所述观测数据子集。
  11. 如权利要求9所述的装置，其中，所述特征提取模块用于：
    获得所述观测数据子集中信噪比的最大信噪比、平均值、中位数和标准差中的至少一个，作为所述统计特征。
  12. 如权利要求9～11任一项所述的装置，其中，所述数据处理模块用于：
    基于星座类型与导航定位系统之间的转换关系，将所述导航定位信息中的星座类型映射为相应的导航定位系统；
    基于导航定位系统、频率与频点之间的转换关系，将所述导航定位信息中的频率映射为相应的频点；
    将所述导航定位系统与所述频点关联成的索引对作为所述索引。
  13. 如权利要求9～11任一项所述的装置，其中，所述参数拟合模块用于：
    利用所述观测数据子集中的信噪比以及所述统计特征，确定所述随机模型的频点间差异参数的值；
    将预先构建的随机模型转换为输出值与信噪比为线性关系的权重模型，所述权重模型包括所述至少一个模型参数；
    利用所述观测数据子集中的信噪比以及所述统计特征，对所述权重模型进行拟合，得到所述至少一个模型参数的值。
  14. 如权利要求10所述的装置，其中，所述特征提取模块用于：
    对所述至少两组原始观测数据中的信噪比进行聚类，得到所述至少两个信噪比子集；
    分别确定所述至少两个信噪比子集中每两个信噪比子集的聚类中心之间的信噪比差值；
    当至少一个信噪比差值不小于所述第二设定阈值时，确定未通过单峰检验；当各信噪比差值均小于所述第二设定阈值时，确定通过单峰检验。
  15. 如权利要求10所述的装置，其中，所述特征提取模块用于：
    确定所述至少两组原始观测数据中的信噪比的初始峰度与初始偏度；其中，所述初始峰度用于反映信噪比的正态分布图像的尖锐程度，所述初始偏度用于反映信噪比的正态分布图像的对称程度；
    分别对所述初始峰度与所述初始偏度进行归一化，获得参考峰度与参考偏度；
    通过对所述参考峰度与所述参考偏度进行正态检验，得到所述至少两组原始观测数据中的信噪比的正态检验结果。
  16. 如权利要求10所述的装置，其中，在所述至少两组原始观测数据中的信噪比未通过所述单峰检验与所述正态检验中的至少一个时，执行以下操作，直至所述第一设定阈值达到预设的最大阈值，或者所述至少两组原始观测数据中的信噪比通过所述单峰检验与所述正态检验时为止：
    使所述第一设定阈值增加设定的阈值增长量；
    基于所述至少两个信噪比子集，对由所述至少两组原始观测数据中的信噪比进行单峰检验，以及，基于所述至少两组原始观测数据中的信噪比的初始峰度与初始偏度，对所述至少两组原始观测数据中的信噪比进行正态检验。
  17. 一种计算机设备，包括处理器和存储器，其中，所述存储器存储有程序代码，当所述程序代码被所述处理器执行时，使得所述处理器执行权利要求1～8中任一项所述方法的步骤。
  18. 一种计算机可读存储介质，包括程序代码，当所述程序代码在计算机设备上运行时，所述程序代码用于使所述计算机设备执行权利要求1～8中任一项所述方法的步骤。
  19. 一种计算机程序产品，包括计算机指令，所述计算机指令被处理器执行时实现权利要求1～8中任一项所述方法的步骤。
PCT/CN2025/080118 2024-03-14 2025-03-03 定位数据处理方法、装置、设备及存储介质 Pending WO2025190094A1 (zh)

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