Ionized layer parameter determination method based on user crowdsourcing data

Abstract

The embodiment of the invention discloses an ionosphere parameter determination method based on user crowdsourcing data, and relates to the technical field of satellite navigation and positioning, and the method comprises the steps: obtaining ionosphere data uploaded by a plurality of user sides, respectively taking each user side as a target station, calculating a standardized residual error corresponding to each user side through a Kriging interpolation method, and determining the ionosphere parameters of each user side; the user sides with the standardized residual errors larger than the standardized residual error threshold value are removed; and determining a target grid corresponding to each user side on the pre-constructed Q4DIM network, calculating an inclined path ionospheric delay corresponding to the target grid, extracting the inclined path ionospheric delay according to the target grid corresponding to the user side, and sending the inclined path ionospheric delay to the user side. Through combination of the Q4DIM network and the ionosphere data uploaded by the user side in real time, the resolution of the ionosphere data can be improved, the coverage range of ionosphere products can be expanded, the accurate ionosphere data can be broadcasted to the user side, and the rapid positioning precision of the user side far away from the base station can be improved.

Description

Technical Field

[0001] The present application relates to the field of satellite navigation and positioning technology, and in particular to a method for determining ionospheric parameters based on user crowdsourced data. Background Art

[0002] Precise Point Positioning (PPP) can provide centimeter-level positioning for a large number of single-receiver users worldwide. However, PPP still requires a long convergence time, even when using ambiguity resolution (AR) methods (such as correcting phase bias using uncalibrated phase delay (UPD) products). This is primarily due to insufficient or low-precision ionospheric delay corrections, which destroy the integer nature of the ambiguity parameters.

[0003] By incorporating ionospheric corrections derived from a reference station network, ionospheric-constrained Precise Point Positioning-Real-Time Kinematic (PPP-RTK) can be constructed, achieving high-precision, fast, and even instantaneous positioning enhancement. Clearly, the effectiveness of ionospheric corrections directly impacts PPP-RTK performance. However, currently, obtaining ionospheric corrections relies on base stations, making accurate ionospheric corrections difficult to obtain in areas with sparse base station deployments.

[0004] Therefore, there is currently a lack of a method that can accurately provide ionospheric delay to achieve high-precision PPP-RTK services. Summary of the Invention

[0005] The present application provides a method for determining ionospheric parameters based on user crowdsourced data to address the deficiencies of the above-mentioned related technologies. The technical solution is as follows: In a first aspect, an embodiment of the present application provides a method for determining ionospheric parameters based on user crowdsourced data, which is applied to a cloud service end. The method includes: Acquire ionospheric data uploaded by multiple user terminals; the ionospheric data includes slant path ionospheric delay collected by the user terminals and corresponding line of sight direction information; The standardized residual of the slant path ionospheric delay of each user terminal is calculated by the Kriging interpolation method, and the user terminals whose standardized residual is greater than the standardized residual threshold are eliminated; Determine the target grid corresponding to each user terminal on the pre-built Q4DIM network based on the retained user terminal's sight direction information; Obtaining the slant path ionospheric delay of each user terminal located on the same target grid, and taking the average of the slant path ionospheric delays of all user terminals on the same target grid as the slant path ionospheric delay of the corresponding target grid; According to the target grid corresponding to each user terminal, the slant path ionospheric delay of the corresponding target grid is extracted and sent to the corresponding user terminal.

[0006] In an optional solution of the first aspect, the step of collecting ionospheric data by the user terminal includes: Calculate the distance between each user terminal and the satellite based on the initial coordinates of the user terminal and the precise satellite orbit product, and calculate the satellite clock correction number, the tropospheric dry delay correction number, the satellite pseudorange hardware delay correction number, and the satellite phase hardware delay correction number; Performing error correction on the basic observation equation of the GNSS based on the satellite clock correction number, the tropospheric dry delay correction number, the satellite pseudorange hardware delay correction number, and the satellite phase hardware delay correction number, calculating the slant path ionospheric delay that eliminates the user-side pseudorange hardware delay and the user-side phase hardware delay through Kalman filtering, and calculating the line of sight direction information; Perform gross error detection on the ionospheric delay of each user terminal and eliminate the user terminals that meet the preset conditions; The calculated line of sight direction information and the oblique path through the ionosphere delay are uploaded to the cloud service end through the retained user end.

[0007] In an optional solution of the first aspect, the performing gross error detection on the slant path ionospheric delay of each user terminal includes: Obtaining the ionospheric delay duration corresponding to the slant path ionospheric delay of each user terminal; Obtain the slant path ionospheric delay of each user terminal and the slant path ionospheric delay of a preset number of surrounding user terminals, and calculate the ionospheric delay residual and ionospheric delay variance between the slant path ionospheric delays of each user terminal; The step of eliminating the user terminals that meet the preset conditions includes: If the ionospheric delay duration is greater than or equal to the duration threshold, the ionospheric delay residual is less than or equal to the ionospheric delay residual threshold, and the ionospheric delay variance is less than or equal to the ionospheric delay variance threshold, then the slant path ionospheric delay of the user end is determined to be a valid slant path ionospheric delay; Obtain all effective slant-path ionospheric delays of each user terminal within a single epoch; If the number of all valid slant-path ionospheric delays of a user terminal in a single epoch is less than the number threshold, the corresponding user terminal is eliminated.

[0008] In an optional solution of the first aspect, calculating the standardized residual of the slant path ionospheric delay of each user terminal by the Kriging interpolation method, and eliminating the user terminal whose standardized residual is greater than the standardized residual threshold, includes: Each user terminal is taken as the target station, and the user terminals within a preset range centered on each target station are selected as adjacent reference stations. Kriging interpolation is performed based on the adjacent reference stations to calculate the weight of each adjacent reference station. The ionospheric prediction data and prediction variance of the target station are obtained based on the weight of each adjacent reference station and the corresponding slant path ionospheric delay weighted calculation; The prediction residual is calculated based on the difference between the slant path ionospheric delay of the target station and the ionospheric prediction data, and the standardized residual of the target station is calculated based on the prediction variance and the prediction residual; An ionospheric bias data sequence consisting of a standardized residual of each user terminal is obtained, and user terminals with a value greater than a standardized residual threshold in the ionospheric bias data sequence are eliminated.

[0009] In an optional solution of the first aspect, the standardized residual threshold is determined based on the following steps, including: Obtaining a sequence of slant path ionospheric delays collected by each user terminal within a preset sliding window; To calculate the median absolute deviation of the ionospheric bias data series, apply the formula: ; Determine the standardized residual threshold based on the median absolute deviation; in, is the median absolute deviation, is the ionospheric bias data sequence, r is the sequence of slant path ionospheric delays collected by each user terminal within the preset sliding window, is the function used to calculate the median.

[0010] In an optional solution of the first aspect, after obtaining the ionospheric data uploaded by multiple user terminals, the method further includes: The user terminals that fall into the same time period are determined based on the time when the ionospheric data collected by each user terminal is collected, and the ionospheric data collected by each user terminal is mapped to an ionospheric puncture point at a preset height.

[0011] In an optional solution of the first aspect, the line of sight direction information includes the longitude, latitude, azimuth and altitude of the ionospheric puncture point of the line connecting the user terminal and the satellite; The step of determining the target grid corresponding to each user terminal on the pre-built Q4DIM network according to the retained line of sight direction information of the user terminal includes: The Euclidean distance between each user terminal and each grid on the Q4DIM network is calculated based on the line of sight information of each user terminal, and the formula is applied: ; Determine the grid with the smallest Euclidean distance to each user terminal as the target grid corresponding to the user terminal on the pre-built Q4DIM network; in, is the set of sight direction information of k users, the superscript los represents the sight direction information, and the subscript k represents the ordinal number of the user terminal in the set; is the set of each grid on the Q4DIM network, The coordinates on the four-dimensional coordinate system of the Q4DIM network are The grid, the superscript grid represents the Q4DIM network, the subscript Represents the four-dimensional coordinates of the grid, g represents latitude, h represents longitude, m represents altitude, and n represents azimuth; argmin is a function used to find the minimum value. is the Euclidean norm; is the set of target grids corresponding to each user terminal, is the four-dimensional coordinate of the target grid, where u represents latitude, v represents longitude, x represents altitude, and y represents azimuth.

[0012] In a second aspect, an embodiment of the present application further provides an ionospheric parameter determination device based on user crowdsourced data, comprising: A data acquisition unit is used to acquire ionospheric data uploaded by multiple user terminals; the ionospheric data includes slant path ionospheric delay and corresponding line of sight direction information collected by the user terminals; a gross error elimination unit, configured to calculate the standardized residual of the slant path ionospheric delay of each user terminal by using a Kriging interpolation method, and eliminate the user terminals whose standardized residual is greater than a standardized residual threshold; A grid calculation unit is used to determine the target grid corresponding to each user terminal on the pre-built Q4DIM network according to the retained line of sight direction information of the user terminal; The grid calculation unit is further configured to obtain the slant path ionospheric delay of each user terminal located on the same target grid, and take the average value of the slant path ionospheric delays of all user terminals on the same target grid as the slant path ionospheric delay of the corresponding target grid; The data sending unit is used to extract the slant path ionospheric delay of the corresponding target grid according to the target grid corresponding to each user terminal and send it to the corresponding user terminal.

[0013] In a third aspect, an embodiment of the present application further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method provided in the first aspect of the embodiment of the present application or any one of the implementations of the first aspect is implemented.

[0014] In a fourth aspect, the present application also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, it implements the method provided by the first aspect of the embodiment of the present application or any one of the implementation methods of the first aspect.

[0015] The beneficial effects of the technical solutions provided by some embodiments of the present application include at least: The embodiment of the present application provides a method for determining ionospheric parameters based on user crowdsourced data. The method uses crowdsourcing to collect multiple ionospheric data uploaded from multiple user terminals to the greatest extent, reduces the cost of obtaining ionospheric data, and can more flexibly obtain ionospheric data in a large range, expands the source of ionospheric data acquisition, and avoids the defect of difficulty in obtaining accurate ionospheric data in some areas due to sparse distribution of ground measurement stations. By combining the Q4DIM network with the ionospheric data uploaded by the user terminal in real time, the resolution of the ionospheric data can be improved and the coverage of the ionospheric products can be expanded. The cloud service terminal can broadcast accurate ionospheric data to the user terminal, which can improve the rapid positioning accuracy of the user terminal far away from the base station, and at the same time realize ionosphere monitoring, thereby providing data support for PPP-RTK positioning to achieve precise positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in this application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 This is a flow chart of a method for determining ionospheric parameters based on user crowdsourced data provided by an embodiment of the present application; Figure 2 1 is a schematic structural diagram of a Q4DIM network of an ionospheric parameter determination method based on user crowdsourced data provided in an embodiment of the present application; Figure 3 1 is a schematic structural diagram of an ionospheric parameter determination device based on user crowdsourced data provided by an embodiment of the present application; Figure 4 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0019] The terms "including" and "having," and any variations thereof, in the specification and claims of this application and the accompanying drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or modules is not limited to the listed steps or modules, but may optionally include steps or modules not listed, or may optionally include other steps or modules inherent to the process, method, product, or apparatus.

[0020] It should be noted that the terms "first" and "second" used in this application are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It is understood that the terms "first" and "second" may interchangeably represent a specific order or precedence, where permitted. It should be understood that the objects distinguished by "first" and "second" may interchangeably represent a specific order or precedence, where appropriate, such that the embodiments of the present application described herein can be implemented in an order other than that described or illustrated herein.

[0021] It should be noted that obtaining ionospheric corrections generally involves two steps: 1) extracting corrections based on a reference station network; 2) calculating user-specific corrections. The Global Ionosphere Map (GIM) product provides a priori information for ionospheric constraints, but the PPP positioning results obtained in this way are relatively low in accuracy and do not meet the accuracy requirements of PPP-RTK positioning. To calculate user-specific corrections, PPP positioning can be performed synchronously across a regional reference station network to generate precise slant-path ionospheric delays (Slant Ionospheric Delays (SIDs) along the line of sight of each reference station. User-specific corrections are then calculated using spatial interpolation. Positioning accuracy is positively correlated with interpolation accuracy, which in turn depends on the inter-station distance within the reference station network. High-precision PPP-RTK services cannot be provided in areas lacking reference station coverage.

[0022] This application adopts a crowdsourcing approach, treating user terminals such as smartphones and cars equipped with high-precision positioning devices as reference stations, and utilizing the real-time, high-precision, high-density spatial aggregation in densely populated areas and wide environmental distribution characteristics of these crowdsourced data to obtain ionospheric data with wide coverage and higher resolution, thereby achieving high-precision ionospheric data acquisition.

[0023] The present application is described in detail below with reference to specific embodiments.

[0024] Next, combine Figure 1 , taking the cloud service end executing a method for determining ionospheric parameters based on user crowdsourced data as an example, this paper introduces a method for determining ionospheric parameters based on user crowdsourced data provided by an embodiment of the present application. Figure 1 , Figure 1 FIG1 shows a flow chart of a method for determining ionospheric parameters based on user crowdsourced data provided by an embodiment of the present application. Figure 1 As shown, the method includes the following steps: S101, obtaining ionospheric data uploaded by multiple user terminals; S102, calculating the standardized residual of the slant path ionospheric delay of each user terminal by using the Kriging interpolation method, and eliminating the user terminals whose standardized residual is greater than the standardized residual threshold; S103, determining a target grid corresponding to each user terminal on the pre-built Q4DIM network according to the retained line of sight direction information of the user terminal; S104: Obtain the slant path ionospheric delay of each user terminal located on the same target grid, and use the average value of the slant path ionospheric delays of all user terminals on the same target grid as the slant path ionospheric delay of the corresponding target grid; S105 , extracting the slant path ionospheric delay of the corresponding target grid according to the target grid corresponding to each user terminal and sending the slant path ionospheric delay to the corresponding user terminal.

[0025] Specifically, in S101, the user terminal includes but is not limited to various electronic devices and vehicles with GNSS positioning capabilities, such as mobile phones and cars. The embodiments of the present application are not limited to this. The ionospheric data collected by the user terminal specifically include the slant path ionospheric delay collected by the user terminal and the corresponding line of sight direction information. The line of sight direction information can be understood as the puncture point information of the line connecting the user terminal and the satellite on the ionosphere, specifically including the longitude of the ionospheric puncture point, the latitude of the ionospheric puncture point, the azimuth of the ionospheric puncture point and the altitude of the ionospheric puncture point. The slant path ionospheric delay is the ionospheric delay data in the line of sight direction.

[0026] Specifically, in S102, each user terminal may be used as a target station, and the standardized residual of the slant path ionospheric delay of each target station may be calculated by the Kriging interpolation method, and user terminals whose standardized residual is greater than a standardized residual threshold may be eliminated, specifically including: For each target station, select the user terminals within the preset range of each target station as the adjacent reference stations, perform Kriging interpolation based on the adjacent reference stations, and calculate the weight of each adjacent reference station; The ionospheric prediction data and prediction variance of the target station are obtained based on the weight of each adjacent reference station and the corresponding slant path ionospheric delay weighted calculation; The prediction residual is calculated based on the difference between the slant path ionospheric delay of the target station and the ionospheric prediction data, and the standardized residual of the target station is calculated based on the prediction variance and the prediction residual; An ionospheric bias data sequence consisting of a standardized residual of each user terminal is obtained, and user terminals with a value greater than a standardized residual threshold in the ionospheric bias data sequence are eliminated.

[0027] The preset range can be understood as a circular area with a preset radius and a radius of the preset radius centered on the target station, or it can be an area of ​​any shape with the target station as the geometric center. This embodiment of the present application does not limit this.

[0028] For example, the variance between the target station and the adjacent reference station with a distance d can be defined to construct the distance empirical variation function: ; in, Represents the distance between the reference station and the target station; the reference station and the target station with a distance d constitute a reference station pair. Indicates the distance the number of reference station pairs; is the slant path ionospheric delay of the target station, is the slant path ionospheric delay of the reference station, and the subscript i represents the serial number of each user terminal, that is, the target station. The distance is The variance of the reference station pair is specifically the variance of the slant path ionospheric delay collected by the two user terminals in the reference station pair.

[0029] Use the spherical variogram to fit the empirical data, and based on the above distance empirical variogram, we get: ; in, represents the nugget effect, which can be used to account for measurement noise; is the sill value, indicating the maximum variance; Indicates the range of variation, which can be used to determine the size of the preset range.

[0030] Based on the above distance empirical variogram, adjacent reference stations within a preset range with a radius of a centered on the target station i are selected for Kriging interpolation, and the weight of each adjacent reference station is calculated. The Kriging model can be expressed as follows: ; , , ; in, is the ionospheric piercing point IPP of the target station i, and the ionospheric piercing point IPP specifically refers to the longitude and latitude of the ionospheric piercing point; represents the ionospheric piercing point IPP of the adjacent reference station j, represents another adjacent reference station of target station i The ionospheric piercing point IPP, is the weight of the slant path ionospheric delay of the adjacent reference station j; It can be understood as the adjacent reference station j and another adjacent reference station As a reference station pair, calculate the adjacent reference station j and another adjacent reference station The variance between adjacent reference station j and another adjacent reference station The distance between the adjacent reference station j and another adjacent reference station can be calculated by the corresponding ionospheric puncture point IPP and substituted into the distance empirical variation function to obtain the distance between adjacent reference station j and another adjacent reference station. The variance between . Similarly, represents the adjacent reference station j and The variance between is the Lagrange multiplier.

[0031] Furthermore, the Kriging model is solved to calculate the weight of each adjacent reference station j ; Furthermore, based on the calculated weight of each adjacent reference station j around the target station i The slant path ionospheric delay for each adjacent reference station j is Perform weighted calculation to obtain ionospheric prediction data Based on weight The measurement data of adjacent reference station j and The variance between the measured data Perform weighted calculation to calculate the prediction variance .

[0032] Specifically, the predicted ionospheric prediction data is calculated and the corresponding prediction variance , apply the formula: ; Furthermore, the slant path ionospheric delay for target station i is and ionospheric prediction data The prediction residual is calculated by subtracting , apply the formula: ; Based on the prediction residual and the prediction variance Calculate the standardized residual of target station i , apply the formula: ; It can be understood that, based on each user terminal as the target station, the step S102 is repeated to obtain the ionospheric bias data sequence consisting of the standardized residual of each user terminal i. .

[0033] Specifically, in the ionospheric bias data series The standardized residual of user i in If the error is greater than the normalized residual threshold, the corresponding user terminal i and the ionospheric data uploaded by the user terminal are eliminated. .

[0034] In some embodiments, the calculation process of the normalized residual threshold may include the following steps: Based on the preset sliding window, the slant path ionospheric delay sequence r collected by each user terminal within the preset sliding window can be obtained, and the median of the slant path ionospheric delay sequence can be calculated. .

[0035] Exemplarily, the preset sliding window may be set to 15 minutes.

[0036] Furthermore, the standardized residuals of each ionospheric bias data sequence Z are calculated separately. The median of the slant path ionospheric delay series The difference, that is ; Furthermore, the median of the difference between each standardized residual and the median of the slant path ionospheric delay series is calculated, i.e. , calculate the median absolute deviation .

[0037] Specifically, the median absolute deviation (MAD) of the standardized residuals in the ionospheric bias data sequence within the sliding window is calculated using the formula: ; in, is the median absolute deviation, is the ionospheric bias data sequence, r is the sequence of slant path ionospheric delays collected by each user terminal within a preset sliding window, is the function used to calculate the median.

[0038] Finally, the standardized residual threshold is determined based on the median absolute deviation.

[0039] For example, the median absolute deviation can be Multiply it by a preset deviation coefficient to obtain the standardized residual threshold. For example, the deviation coefficient is selected as , the standardized residual threshold is The size of the deviation coefficient depends on the actual situation. If more user terminals need to be retained, a larger deviation coefficient can be selected. This embodiment of the present application does not limit this.

[0040] For example, when , mark the corresponding slant path ionospheric delay as a gross error, and remove the corresponding user end and the ionospheric data collected by the user end.

[0041] In this way, the normalized residual threshold can be determined dynamically and in real time, so that the normalized residual threshold can be updated in real time according to the actually collected data.

[0042] In some embodiments, after S101, the cloud service end can pre-process the ionospheric data collected by each user end, determine the user ends that fall into the same time period based on the time when the ionospheric data is collected by each user end, and map the ionospheric data collected by each user end to an ionospheric puncture point at a preset height.

[0043] Specifically, this is equivalent to unifying the time of ionospheric data collected by each user terminal. For example, the user terminals can be clustered according to the time period in which the collection time falls, thereby achieving time period unification, that is, the ionospheric data collected in the same time period are regarded as time-synchronized and unified data. The collected ionospheric puncture point longitude, ionospheric puncture point latitude, ionospheric puncture point azimuth, and ionospheric puncture point altitude can be mapped to an ionospheric plane at a preset height, for example, an ionospheric plane at an altitude of 350 kilometers.

[0044] In some embodiments, in S103, the pre-built Q4DIM network can be represented as , the dimensions of the Q4DIM network include latitude, longitude, elevation and azimuth: ; in, Indicates latitude, Indicates longitude, Indicates the altitude angle, Indicates azimuth; Specifically, express The coordinates on the four-dimensional coordinates are The grid, the superscript grid represents the Q4DIM network, the subscript Represents the four-dimensional coordinates of the grid, g represents the latitude of the grid, h represents the longitude of the grid, m represents the altitude angle of the grid, and n represents the azimuth angle of the grid.

[0045] like Figure 2 As shown, the four-dimensional Q4DIM network is visualized as a parameter-compressed two-dimensional representation, where the horizontal axis combines latitude and longitude, while the vertical axis integrates altitude and azimuth.

[0046] After S101-S102 eliminates gross errors, the set of retained sight direction information of all users can be expressed as , the superscript los represents the sight direction information, and the subscript k represents the ordinal number of the user terminal in the set, such as Figure 2 As shown, some user terminals are marked as red dots.

[0047] Specifically, the Euclidean distance between each user terminal and each grid on the Q4DIM network can be calculated based on the line of sight information of each user terminal, and the formula is applied: ; Determine the grid with the smallest Euclidean distance to each user terminal as the target grid corresponding to the user terminal on the pre-built Q4DIM network; argmin is a function for finding the minimum value, is the Euclidean norm; is the set of target grids corresponding to each user terminal, is the four-dimensional coordinate of the target grid, u represents the latitude of the target grid, v represents the longitude of the target grid, x represents the altitude angle of the target grid, and y represents the azimuth angle of the target grid.

[0048] This allows the target grid of each user end on the Q4DIM network to be determined.

[0049] Further, execute step S104: Obtain the slant path ionospheric delay of each user terminal on the same target grid, and use the average of the slant path ionospheric delays of all user terminals on the same target grid as the slant path ionospheric delay of the corresponding grid, applying the formula: ; Specifically, Represents the target grid The collection of all client terminals on Indicates that it is located in the target grid The number of user terminals can be understood as the calculation target grid superior The average value of the slant path ionospheric delay of each user terminal is calculated to obtain ,Will As target mesh slant path ionospheric delay; For example, Figure 2 The grid in the 5th row and 2nd column has 5 user terminals. The slant path ionospheric delay of this grid can be obtained by calculating the average value of the slant path ionospheric delay of these 5 user terminals.

[0050] Furthermore, by executing step S105, the slant path ionospheric delay of the corresponding target grid can be broadcast to the user terminals located in the target grid according to the target grid corresponding to each user terminal, so that the user terminals in the grid can perform PPP-RTK positioning calculation according to the slant path ionospheric delay updated in S101-S105, which is conducive to improving positioning accuracy.

[0051] In some embodiments, before S101, collecting ionospheric data through the user terminal specifically includes: S11, calculate the distance between each user terminal and the satellite based on the initial coordinates of the user terminal and the precise satellite orbit product, and calculate the satellite clock correction number, tropospheric dry delay correction number, satellite pseudorange hardware delay correction number and satellite phase hardware delay correction number.

[0052] The initial coordinates of each user terminal can be calculated using standard point positioning (SPP).

[0053] S12, performing error correction on a basic GNSS observation equation based on the satellite clock error correction, the tropospheric dry delay correction, the satellite pseudorange hardware delay correction, and the satellite phase hardware delay correction, calculating through a Kalman filter a slant-path ionospheric delay that eliminates the user-side pseudorange hardware delay and the user-side phase hardware delay, and calculating line-of-sight direction information; S13, performing gross error detection on the slant path ionospheric delay of each user terminal, and eliminating the user terminals that meet the preset conditions; S14, uploading the calculated line of sight direction information and the oblique path through the ionosphere delay to the cloud service end through the retained user end.

[0054] Specifically, in S13, the performing of gross error detection on the slant path ionospheric delay of each user terminal includes: Obtaining the ionospheric delay duration corresponding to the slant path ionospheric delay of each user terminal; The slant path ionospheric delay of each user terminal is obtained from the slant path ionospheric delay of a preset number of surrounding user terminals, and the ionospheric delay residual and ionospheric delay variance between the slant path ionospheric delay of each user terminal are calculated.

[0055] Among them, the surrounding of the user terminal can be understood as a preset area with a user terminal as the center, and the surrounding preset number of user terminals can be understood as other user terminals falling within the preset area. A preset number of user terminals can be selected from near to far according to the distance from the central user terminal to calculate the ionospheric delay residual and ionospheric delay variance between the oblique path ionospheric delays of each user terminal.

[0056] The step of eliminating the user terminals that meet the preset conditions includes: If the ionospheric delay duration is greater than or equal to the duration threshold, the ionospheric delay residual is less than or equal to the ionospheric delay residual threshold, and the ionospheric delay variance is less than or equal to the ionospheric delay variance threshold, then the slant path ionospheric delay of the user end is determined to be a valid slant path ionospheric delay; Obtain all effective slant-path ionospheric delays of each user terminal within a single epoch; If the number of all valid slant-path ionospheric delays of a user terminal in a single epoch is less than the number threshold, the corresponding user terminal is eliminated.

[0057] Specifically, the preset condition can be understood as the number of effective oblique path ionospheric delays being less than a quantity threshold. If the number of all effective oblique path ionospheric delays of a single user terminal within a single epoch is less than a quantity threshold, the preset condition is met.

[0058] For example, the duration threshold may be 5 minutes, the ionospheric delay residual threshold may be 1 TECU (Total Electron Content Unit), the ionospheric delay variance threshold may be 0.5 TECU, and the quantity threshold may be 8.

[0059] It should be noted that the ionospheric delay of each satellite does not always exist. Therefore, the ionospheric delay duration corresponding to the oblique path ionospheric delay can be understood as the time from the extraction moment to the extraction moment of the ionospheric delay. The ionospheric delay is maintained without interruption within the current epoch until the end moment of the ionospheric delay. The time interval between the extraction moment and the end moment is taken as the ionospheric delay duration.

[0060] In this way, the effective slant path ionospheric delay is retained as much as possible. The preset condition can be understood as that when the retained effective slant path ionospheric delay is less than 8, the user terminal with a small number of effective slant path ionospheric delays is eliminated.

[0061] It can be understood that when the user side initially calculates the slant path ionospheric delay and line of sight direction information, the basic observation equation of GNSS can be combined with precise satellite orbit products, precise satellite clock error products, tropospheric dry delay calculation model, DCB (Different Code Bias) products, phase deviation products, etc. for calculation, and given known prior parameters (for example), the calculation results can be obtained.

[0062] In some embodiments, the user terminal executes steps S11-S14, and uploads the calculated line of sight direction information and the slant path ionospheric delay to the cloud server. Thereafter, the cloud server executes steps S101 to S105 based on the ionospheric data uploaded by the user terminal, thereby sending the slant path ionospheric delay updated based on the crowdsourced user terminal's ionospheric data to the user terminal. Furthermore, the user terminal can jump to S11 based on the slant path ionospheric delay feedback from the cloud server, and continue to execute steps S11-S14, repeating the cycle.

[0063] In this way, the user end can improve the positioning accuracy of the user end through the slant path ionospheric delay fed back by the cloud server end, and the cloud server end can dynamically update the slant path ionospheric delay based on the slant path ionospheric delay collected in real time by the user end.

[0064] The following are device embodiments of the present application, which can be used to implement the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0065] See next Figure 3 , is a schematic diagram of the structure of an ionospheric parameter determination device based on user crowdsourced data provided by an exemplary embodiment of the present application. The device can be implemented as all or part of a terminal through software, hardware, or a combination of both, and can also be integrated into a server as an independent module. The ionospheric parameter determination device based on user crowdsourced data in the embodiment of the present application can be applied to a terminal or the cloud. The device 30 includes a data acquisition unit 301, a gross error elimination unit 302, a grid computing unit 303, and a data sending unit 304, wherein: The data acquisition unit 301 is used to acquire ionospheric data uploaded by multiple user terminals; the ionospheric data includes the slant path ionospheric delay collected by the user terminal and the corresponding line of sight direction information; The gross error elimination unit 302 is used to calculate the standardized residual of the slant path ionospheric delay of each user terminal by the Kriging interpolation method, and eliminate the user terminals whose standardized residual is greater than the standardized residual threshold; The grid calculation unit 303 is used to determine the target grid corresponding to each user terminal on the pre-built Q4DIM network according to the retained line of sight direction information of the user terminal; The grid calculation unit 303 is further configured to obtain the slant path ionospheric delay of each user terminal located on the same target grid, and take the average value of the slant path ionospheric delays of all user terminals on the same target grid as the slant path ionospheric delay of the corresponding target grid; The data sending unit 304 is used to extract the slant path ionospheric delay of the corresponding target grid according to the target grid corresponding to each user terminal and send it to the corresponding user terminal.

[0066] It should be noted that the above-described embodiment of the apparatus 30, when performing the method for determining ionospheric parameters based on user crowdsourced data, uses only the division of the aforementioned functional modules as an example. In actual applications, the aforementioned functions can be assigned to different functional modules as needed, i.e., the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. Furthermore, the apparatus provided in the above-described embodiment and the embodiment of the method for determining ionospheric parameters based on user crowdsourced data are based on the same concept. The implementation process is detailed in the method embodiment and will not be further described here.

[0067] An embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the method of any of the above embodiments are implemented.

[0068] See Figure 4 , is a structural block diagram of an electronic device provided in an embodiment of the present application.

[0069] like Figure 4 As shown, the electronic device 400 includes a processor 401 and a memory 402 .

[0070] In the embodiment of the present application, the processor 401 is the control center of the computer system and can be the processor of a physical machine or the processor of a virtual machine. The processor 401 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 401 can be implemented in the form of at least one hardware selected from the group consisting of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array).

[0071] The processor 401 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state.

[0072] The memory 402 may include one or more computer-readable storage media, which may be non-transitory. The memory 402 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash memory storage devices. In some embodiments of the present application, the non-transitory computer-readable storage medium in the memory 402 is used to store at least one instruction, which is used to be executed by the processor 401 to implement the method in the embodiment of the present application.

[0073] In some embodiments, the electronic device 400 further includes a peripheral device interface 403 and at least one peripheral device 404. The processor 401, memory 402, and peripheral device interface 403 may be connected via a bus or signal lines. Each peripheral device 404 may be connected to the peripheral device interface 403 via a bus, signal lines, or circuit boards. Specifically, the peripheral devices 404 include a display screen, a camera, and an audio circuit. The peripheral device interface 403 may be used to connect at least one I / O (Input / Output)-related peripheral device to the processor 401 and memory 402.

[0074] In some embodiments of the present application, the processor 401, the memory 402, and the peripheral device interface 403 are integrated on the same chip or circuit board; in some other embodiments of the present application, any one or two of the processor 401, the memory 402, and the peripheral device interface 403 may be implemented on separate chips or circuit boards. This embodiment of the present application is not specifically limited to this.

[0075] The electronic device structure block diagram shown in the embodiment of the present application does not constitute a limitation on the electronic device 400. The electronic device 400 may include more or fewer components than shown in the figure, or combine certain components, or adopt a different component arrangement.

[0076] The present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method of any of the aforementioned embodiments. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a microdrive and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic or optical card, a nanosystem (including a molecular memory IC), or any other type of medium or device suitable for storing instructions and / or data.

[0077] Through the description of the above embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the relevant technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for determining ionospheric parameters based on user crowdsourced data, characterized in that: Applied to a cloud server, the method includes: Acquire ionospheric data uploaded by multiple user terminals; the ionospheric data includes slant path ionospheric delay collected by the user terminals and corresponding line of sight direction information; The standardized residual of the slant path ionospheric delay of each user terminal is calculated by the Kriging interpolation method, and the user terminals whose standardized residual is greater than the standardized residual threshold are eliminated; Determine the target grid corresponding to each user terminal on the pre-built Q4DIM network based on the retained user terminal's sight direction information; Obtaining the slant path ionospheric delay of each user terminal located on the same target grid, and taking the average of the slant path ionospheric delays of all user terminals on the same target grid as the slant path ionospheric delay of the corresponding target grid; According to the target grid corresponding to each user terminal, the slant path ionospheric delay of the corresponding target grid is extracted and sent to the corresponding user terminal.

2. The method for determining ionospheric parameters based on user crowdsourced data according to claim 1, wherein: The step of collecting ionospheric data by the user terminal includes: Calculate the distance between each user terminal and the satellite based on the initial coordinates of the user terminal and the precise satellite orbit product, and calculate the satellite clock correction number, the tropospheric dry delay correction number, the satellite pseudorange hardware delay correction number, and the satellite phase hardware delay correction number; Performing error correction on the basic observation equation of the GNSS based on the satellite clock correction number, the tropospheric dry delay correction number, the satellite pseudorange hardware delay correction number, and the satellite phase hardware delay correction number, calculating the slant path ionospheric delay that eliminates the user-side pseudorange hardware delay and the user-side phase hardware delay through Kalman filtering, and calculating the line of sight direction information; Perform gross error detection on the ionospheric delay of each user terminal and eliminate the user terminals that meet the preset conditions; The calculated line of sight direction information and the oblique path through the ionosphere delay are uploaded to the cloud service end through the retained user end.

3. The method for determining ionospheric parameters based on user crowdsourced data according to claim 2, characterized in that: The performing gross error detection on the slant path ionospheric delay of each user terminal includes: Obtaining the ionospheric delay duration corresponding to the slant path ionospheric delay of each user terminal; Obtain the slant path ionospheric delay of each user terminal and the slant path ionospheric delay of a preset number of surrounding user terminals, and calculate the ionospheric delay residual and ionospheric delay variance between the slant path ionospheric delays of each user terminal; The step of eliminating the user terminals that meet the preset conditions includes: If the ionospheric delay duration is greater than or equal to the duration threshold, the ionospheric delay residual is less than or equal to the ionospheric delay residual threshold, and the ionospheric delay variance is less than or equal to the ionospheric delay variance threshold, then the slant path ionospheric delay of the user end is determined to be a valid slant path ionospheric delay; Obtain all effective slant-path ionospheric delays of each user terminal within a single epoch; If the number of all valid slant-path ionospheric delays of a user terminal in a single epoch is less than the number threshold, the corresponding user terminal is eliminated.

4. The method for determining ionospheric parameters based on user crowdsourced data according to claim 1, wherein: The step of calculating the standardized residual of the slant path ionospheric delay of each user terminal by the Kriging interpolation method and eliminating the user terminal whose standardized residual is greater than the standardized residual threshold comprises: Each user terminal is taken as the target station, and the user terminals within a preset range centered on each target station are selected as adjacent reference stations. Kriging interpolation is performed based on the adjacent reference stations to calculate the weight of each adjacent reference station. The ionospheric prediction data and prediction variance of the target station are obtained based on the weight of each adjacent reference station and the corresponding slant path ionospheric delay weighted calculation; The prediction residual is calculated based on the difference between the slant path ionospheric delay of the target station and the ionospheric prediction data, and the standardized residual of the target station is calculated based on the prediction variance and the prediction residual; An ionospheric bias data sequence consisting of a standardized residual of each user terminal is obtained, and user terminals with a value greater than a standardized residual threshold in the ionospheric bias data sequence are eliminated.

5. The method for determining ionospheric parameters based on user crowdsourced data according to claim 4, characterized in that: The standardized residual threshold is determined based on the following steps, including: Obtaining a sequence of slant path ionospheric delays collected by each user terminal within a preset sliding window; To calculate the median absolute deviation of the ionospheric bias data series, apply the formula: ； Determine the standardized residual threshold based on the median absolute deviation; in, is the median absolute deviation, is the ionospheric bias data sequence, r is the sequence of slant path ionospheric delays collected by each user terminal within the preset sliding window, is the function used to calculate the median.

6. The method for determining ionospheric parameters based on user crowdsourced data according to claim 1, characterized in that: After obtaining the ionospheric data uploaded by multiple user terminals, the method further includes: The user terminals that fall into the same time period are determined based on the time when the ionospheric data collected by each user terminal is collected, and the ionospheric data collected by each user terminal is mapped to an ionospheric puncture point at a preset height.

7. The method for determining ionospheric parameters based on user crowdsourced data according to any one of claims 1 to 6, characterized in that: The line of sight direction information includes the longitude, latitude, azimuth and altitude of the ionospheric puncture point of the line connecting the user terminal and the satellite; The step of determining the target grid corresponding to each user terminal on the pre-built Q4DIM network according to the retained line of sight direction information of the user terminal includes: The Euclidean distance between each user terminal and each grid on the Q4DIM network is calculated based on the line of sight information of each user terminal, and the formula is applied: ； Determine the grid with the smallest Euclidean distance to each user terminal as the target grid corresponding to the user terminal on the pre-built Q4DIM network; in, is the set of sight direction information of k users, the superscript los represents the sight direction information, and the subscript k represents the ordinal number of the user terminal in the set; is the set of each grid on the Q4DIM network, The coordinates on the four-dimensional coordinate system of the Q4DIM network are The grid, the superscript grid represents the Q4DIM network, the subscript Represents the four-dimensional coordinates of the grid, g represents latitude, h represents longitude, m represents altitude, and n represents azimuth; argmin is a function used to find the minimum value. is the Euclidean norm; is the set of target grids corresponding to each user terminal, is the four-dimensional coordinate of the target grid, where u represents latitude, v represents longitude, x represents altitude, and y represents azimuth.

8. An ionospheric parameter determination device based on user crowdsourced data, characterized in that: include: A data acquisition unit is used to acquire ionospheric data uploaded by multiple user terminals; the ionospheric data includes slant path ionospheric delay and corresponding line of sight direction information collected by the user terminals; a gross error elimination unit, configured to calculate the standardized residual of the slant path ionospheric delay of each user terminal by using a Kriging interpolation method, and eliminate the user terminals whose standardized residual is greater than a standardized residual threshold; A grid calculation unit is used to determine the target grid corresponding to each user terminal on the pre-built Q4DIM network according to the retained line of sight direction information of the user terminal; The grid calculation unit is further configured to obtain the slant path ionospheric delay of each user terminal located on the same target grid, and take the average value of the slant path ionospheric delays of all user terminals on the same target grid as the slant path ionospheric delay of the corresponding target grid; The data sending unit is used to extract the slant path ionospheric delay of the corresponding target grid according to the target grid corresponding to each user terminal and send it to the corresponding user terminal.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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