A SSR comprehensive correction number forecasting method and related equipment

Abstract

The present application provides an SSR comprehensive correction number prediction method and related equipment, which relate to the field of satellite navigation technology. After obtaining the predicted correction amount and target SSR comprehensive correction data of at least one target navigation satellite that meets the correction number prediction conditions supported by the satellite navigation terminal through data preprocessing, the present application will perform correction number gray prediction or polynomial fitting prediction based on the target SSR comprehensive correction data for each target navigation satellite according to the test result of whether the target SSR comprehensive correction data of the target navigation satellite meets the non-negative quasi-exponential law, and obtain the correction number prediction result of the target navigation satellite, and perform data superposition processing on the predicted correction amount of the target navigation satellite and the target SSR predicted correction number in the corresponding correction number forecast result, so that the predicted SSR comprehensive correction number can ensure that the corresponding navigation positioning result has a sufficiently high positioning accuracy to meet the real-time positioning requirements of autonomous driving as much as possible.

Description

Technical Field

[0001] The present application relates to the field of satellite navigation technology, and in particular to a method for predicting SSR comprehensive correction numbers and related equipment. Background Art

[0002] With the continuous advancement of science and technology, autonomous driving technology is increasingly being applied across various industries. High-precision navigation and positioning technology is an indispensable component of autonomous driving. Currently, high-precision navigation and positioning technology has advanced to the point where it uses satellite communications to broadcast "comprehensive error corrections" to achieve global satellite-based enhanced positioning. This technology has evolved from traditional RTK (Real-Time Kinematic) positioning technology to network RTK positioning technology and then to PPP (Precise Point Positioning)-RTK positioning technology. PPP-RTK positioning is a high-precision positioning technology based on SSR (State Space Representation). It comprehensively processes satellite base station data to generate a set of state corrections for satellite positioning errors, including satellite clock error, satellite orbit error, ionospheric error, and tropospheric error. These corrections are then transmitted to mobile stations for position calculation, enabling satellite navigation positioning for mobile stations (e.g., satellite navigation vehicle-mounted terminals or personal navigation devices).

[0003] As for PPP-RTK positioning technology, it integrates the advantages of PPP technology and traditional RTK positioning technology, and can provide real-time, fast, centimeter-level positioning services to a large number of users worldwide. At the same time, under the same base station density and the same base station distribution, the positioning accuracy and convergence time of PPP-RTK positioning technology are basically equivalent to those of traditional RTK positioning technology. Compared with traditional RTK positioning technology, it has higher positioning reliability. The SSR comprehensive correction number output to the mobile station using PPP-RTK positioning technology can guarantee positioning accuracy for a certain period of time in the event of a network outage.

[0004] However, it is worth noting that during the implementation of PPP-RTK positioning technology, the mobile station may not be able to receive the latest SSR comprehensive correction number for a period of time due to problems such as Internet anomalies, satellite-based differential signal obstruction or differential server anomalies. In this case, even if the mobile station uses the most recently received SSR comprehensive correction number for navigation and positioning, the corresponding navigation and positioning result can only maintain a relatively low positioning accuracy for a short period of time. As time goes by, the positioning accuracy of the final navigation and positioning result will drop sharply, which cannot meet the real-time positioning requirements of autonomous driving. Summary of the Invention

[0005] In view of this, the purpose of this application is to provide an SSR comprehensive correction number prediction method and device, a satellite navigation terminal and a readable storage medium, which can select a suitable correction number prediction scheme according to the data characteristics of historical SSR comprehensive correction data for forecasting when the satellite navigation terminal fails to receive the SSR comprehensive correction number for a long time, so that the final predicted SSR comprehensive correction number can ensure that the corresponding navigation positioning result has a sufficiently high positioning accuracy, so as to meet the real-time positioning needs of autonomous driving as much as possible.

[0006] In order to achieve the above objectives, the technical solutions adopted in the embodiments of the present application are as follows:

[0007] In a first aspect, the present application provides a method for predicting SSR comprehensive correction numbers, which is applied to a satellite navigation terminal, and the method comprises:

[0008] Performing data preprocessing on the historical SSR comprehensive correction data of all navigation satellites supported by the satellite navigation terminal to obtain a predicted correction value and target SSR comprehensive correction data of at least one target navigation satellite that meets the correction number forecast conditions;

[0009] For each target navigation satellite, detect whether the target SSR integrated correction data corresponding to the target navigation satellite satisfies the non-negative quasi-exponential law;

[0010] If it is detected that the target SSR comprehensive correction data satisfies the non-negative quasi-exponential law, gray prediction of the correction number is performed based on the target SSR comprehensive correction data to obtain a correction number prediction result of the target navigation satellite; otherwise, polynomial fitting prediction is performed based on the target SSR comprehensive correction data to obtain a correction number prediction result of the target navigation satellite;

[0011] The forecast correction amount of the target navigation satellite and the target SSR forecast correction number in the corresponding correction number forecast result are subjected to data superposition processing to obtain the forecast SSR comprehensive correction number of the target navigation satellite in the current navigation positioning period, wherein the target SSR forecast correction number is the SSR forecast correction number in the correction number forecast result of the target navigation satellite that matches the current navigation positioning period.

[0012] In an optional embodiment, the step of performing data preprocessing on the historical SSR comprehensive correction data of each of all navigation satellites supported by the satellite navigation terminal to obtain the predicted correction value and target SSR comprehensive correction data of at least one target navigation satellite that meets the correction number forecast conditions includes:

[0013] For each navigation satellite, the gross error data of the historical SSR comprehensive correction data corresponding to the navigation satellite are eliminated to obtain the valid SSR comprehensive correction data of the navigation satellite;

[0014] Calculate the correction number jump amount of the navigation satellite at the valid SSR comprehensive correction data according to the target historical SSR comprehensive correction number of the closest current navigation positioning cycle included in the valid SSR comprehensive correction data, and calculate the jump amount threshold of the navigation satellite according to the valid SSR comprehensive correction data;

[0015] According to the correction number jump variables of each navigation satellite belonging to the same satellite navigation system, detecting whether the satellite navigation system meets the system prediction conditions;

[0016] When it is detected that the satellite navigation system meets the system prediction conditions, each navigation satellite under the satellite navigation system is used as a target navigation satellite, and the valid SSR integrated correction data of each navigation satellite under the satellite navigation system is used as the corresponding target SSR integrated correction data;

[0017] According to the correction number jump amount and jump amount threshold of each navigation satellite in the satellite navigation system, the forecast correction amount corresponding to each navigation satellite in the satellite navigation system is determined.

[0018] In an optional embodiment, the step of determining the forecast correction corresponding to each navigation satellite in the satellite navigation system according to the correction jump amount and jump amount threshold of each navigation satellite in the satellite navigation system includes:

[0019] Detecting whether the correction number jump value of each navigation satellite under the satellite navigation system exceeds the corresponding jump value threshold;

[0020] If it is detected that the correction number jump values ​​of each navigation satellite under the satellite navigation system exceed the corresponding jump value threshold, the correction number jump values ​​of each navigation satellite under the satellite navigation system are respectively used as the corresponding predicted correction values; otherwise, the predicted correction values ​​corresponding to each navigation satellite under the satellite navigation system are set to zero.

[0021] In an optional embodiment, for each target navigation satellite, the step of detecting whether the target SSR integrated correction data corresponding to the target navigation satellite satisfies a non-negative quasi-exponential law includes:

[0022] Sampling the target SSR integrated correction data according to a preset sampling time interval to obtain a correction number sampling sequence of the target navigation satellite;

[0023] Check whether the positive and negative signs of all sequence elements included in the correction number sampling sequence of the target navigation satellite are consistent;

[0024] If it is detected that the positive and negative signs of all sequence elements are not consistent, it is determined that the target SSR comprehensive correction data does not satisfy the non-negative quasi-exponential law; otherwise, the correction number sampling sequence of the target navigation satellite is non-negatively processed to obtain a non-negative correction number sequence of the target navigation satellite;

[0025] Performing a cumulative generation process on the non-negative correction number sequence of the target navigation satellite to obtain a cumulative generation sequence of the target navigation satellite;

[0026] Calculate the smoothness ratio data of the non-negative correction number sequence of the target navigation satellite relative to the sequence generated by the single accumulation, and detect whether the calculated smoothness ratio data conforms to the quasi-exponential law;

[0027] If it is detected that the calculated smoothness ratio data does not meet the quasi-exponential law, it is determined that the target SSR comprehensive correction data does not meet the non-negative quasi-exponential law; otherwise, it is determined that the target SSR comprehensive correction data meets the non-negative quasi-exponential law.

[0028] In an optional embodiment, the step of performing gray prediction of correction numbers based on the target SSR comprehensive correction data to obtain a correction number forecast result of the target navigation satellite includes:

[0029] Performing a close-by mean generation process on the once accumulated generation sequence of the target navigation satellite to obtain a close-by mean generation sequence of the target navigation satellite;

[0030] Solve the grey differential equation of the grey prediction model according to the immediate neighborhood mean value generation sequence and the non-negative correction number sequence of the target navigation satellite to obtain the estimated development coefficient and the estimated grey action of the target navigation satellite;

[0031] Calculating an estimated initial condition of the grey prediction model according to a primary accumulation generation sequence, an estimated development coefficient and an estimated grey action of the target navigation satellite;

[0032] The estimated initial conditions are substituted into the prediction restoration function of the grey prediction model to perform correction number prediction, and obtain the correction number prediction result of the target navigation satellite.

[0033] In an optional embodiment, the prediction restoration function of the grey prediction model is expressed as follows:

[0034]

[0035] in, It is used to represent the SSR forecast correction number corresponding to the nth sampling time point in the correction number forecast result obtained by using the grey prediction model, It is used to represent the estimated development coefficient of the grey prediction model, It is used to represent the estimated initial conditions of the grey prediction model.

[0036] In an optional embodiment, after the step of performing grey prediction of correction numbers based on the target SSR comprehensive correction data, the method further comprises:

[0037] Calculate the correction number fitting residual of the target navigation satellite based on the correction number prediction result of the target navigation satellite and the non-negative correction number sequence of the corresponding target SSR integrated correction data;

[0038] Check whether the correction number fitting residual of the target navigation satellite meets the grey prediction accuracy requirement;

[0039] If it is detected that the correction number fitting residual of the target navigation satellite does not meet the gray forecast accuracy requirement, a polynomial fitting prediction is performed based on the target SSR comprehensive correction data to obtain the correction number forecast result of the target navigation satellite. Otherwise, the process jumps to the step of performing data superposition processing on the forecast correction amount of the target navigation satellite and the target SSR forecast correction number in the corresponding correction number forecast result.

[0040] In a second aspect, the present application provides an SSR comprehensive correction number prediction device, which is applied to a satellite navigation terminal, and the device includes:

[0041] a correction number preprocessing module, configured to perform data preprocessing on the historical SSR comprehensive correction data of all navigation satellites supported by the satellite navigation terminal to obtain a predicted correction value and target SSR comprehensive correction data of at least one target navigation satellite that meets the correction number forecast conditions;

[0042] The data law verification module is used to detect, for each target navigation satellite, whether the target SSR integrated correction data corresponding to the target navigation satellite meets the non-negative quasi-exponential law;

[0043] a data fitting prediction module, configured to, when the data regularity verification module detects that the target SSR comprehensive correction data satisfies a non-negative quasi-exponential law, perform correction number grey prediction based on the target SSR comprehensive correction data to obtain a correction number prediction result of the target navigation satellite; or, when the data regularity verification module detects that the target SSR comprehensive correction data does not satisfy a non-negative quasi-exponential law, perform polynomial fitting prediction based on the target SSR comprehensive correction data to obtain a correction number prediction result of the target navigation satellite;

[0044] The correction number output module is used to perform data superposition processing on the predicted correction amount of the target navigation satellite and the target SSR predicted correction number in the corresponding correction number forecast result to obtain the predicted SSR comprehensive correction number of the target navigation satellite in the current navigation positioning period, wherein the target SSR predicted correction number is the SSR predicted correction number in the correction number forecast result of the target navigation satellite that matches the current navigation positioning period.

[0045] In an optional embodiment, the device further comprises:

[0046] A fitting residual calculation module is used to calculate the correction number fitting residual of the target navigation satellite according to the correction number prediction result of the target navigation satellite and the non-negative correction number sequence of the corresponding target SSR comprehensive correction data when the correction number prediction result of the target navigation satellite is obtained through the correction number grey prediction operation;

[0047] A forecast accuracy verification module is used to detect whether the correction number fitting residual of the target navigation satellite meets the grey prediction accuracy requirement when the correction number forecast result of the target navigation satellite is obtained through the correction number grey prediction operation;

[0048] The data fitting prediction module is also used to perform polynomial fitting prediction based on the target SSR comprehensive correction data to obtain the correction number prediction result of the target navigation satellite when the forecast accuracy verification module detects that the correction number fitting residual of the target navigation satellite does not meet the grey forecast accuracy requirement, or to drive the correction number output module to run when the forecast accuracy verification module detects that the correction number fitting residual of the target navigation satellite meets the grey forecast accuracy requirement.

[0049] In a third aspect, the present application provides a satellite navigation terminal comprising a processor and a memory, wherein the memory stores a computer program executable by the processor, and the processor can execute the computer program to implement the SSR comprehensive correction number prediction method described in any one of the aforementioned embodiments.

[0050] In a fourth aspect, the present application provides a readable storage medium having a computer program stored thereon. When the computer program is executed by a satellite navigation terminal, the SSR comprehensive correction number prediction method described in any one of the aforementioned embodiments is implemented.

[0051] In this case, the beneficial effects of the embodiments of the present application may include the following:

[0052] The present application performs data preprocessing on the historical SSR comprehensive correction data of all navigation satellites supported by the satellite navigation terminal to obtain the predicted correction value and target SSR comprehensive correction data of at least one target navigation satellite that meets the correction number prediction conditions. For each target navigation satellite, when it is detected that the target SSR comprehensive correction data of the target navigation satellite meets the non-negative quasi-exponential law, the correction number gray prediction is performed based on the target SSR comprehensive correction data to obtain the correction number prediction result of the target navigation satellite. Otherwise, a polynomial fitting prediction is performed based on the target SSR comprehensive correction data to obtain the correction number of the target navigation satellite. The forecast result of the number is obtained, and then the forecast correction amount of the target navigation satellite and the target SSR forecast correction number in the corresponding correction number forecast result are processed by data superposition to obtain the forecast SSR comprehensive correction number of the target navigation satellite in the current navigation positioning cycle. In this way, when the satellite navigation terminal fails to receive the SSR comprehensive correction number for a long time, a suitable correction number forecast scheme is selected according to the data characteristics of the historical SSR comprehensive correction data for forecasting, so that the final forecast SSR comprehensive correction number can ensure that the corresponding navigation positioning result has a sufficiently high positioning accuracy to meet the real-time positioning needs of autonomous driving as much as possible.

[0053] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0055] Figure 1 A schematic diagram of the composition of a satellite navigation terminal provided in an embodiment of the present application;

[0056] Figure 2 One of the flow charts of the SSR comprehensive correction number forecasting method provided in the embodiment of the present application;

[0057] Figure 3 for Figure 2 A schematic flow chart of the sub-steps included in step S210;

[0058] Figure 4 for Figure 2 A schematic flow chart of the sub-steps included in step S220;

[0059] Figure 5 for Figure 2A schematic flow chart of the sub-steps included in step S230;

[0060] Figure 6 The second flow chart of the SSR comprehensive correction number forecasting method provided in the embodiment of the present application;

[0061] Figure 7 This is a schematic diagram of the composition of the SSR comprehensive correction number forecasting device provided in an embodiment of the present application;

[0062] Figure 8 This is the second schematic diagram of the composition of the SSR comprehensive correction number forecasting device provided in an embodiment of the present application.

[0063] Icons: 10-satellite navigation terminal; 11-memory; 12-processor; 13-communication unit; 100-SSR comprehensive correction number forecast device; 110-correction number preprocessing module; 120-data regularity verification module; 130-data fitting prediction module; 140-correction number output module; 150-fitting residual calculation module; 160-forecast accuracy verification module. DETAILED DESCRIPTION

[0064] To make the objectives, technical solutions, and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Generally, the components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0065] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for protection, but merely represents selected embodiments of the present application. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments in the present application without creative work are within the scope of protection of the present application.

[0066] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0067] In the description of the present application, it should be understood that relational terms such as the terms "first" and "second" are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also include elements inherent to such process, method, article or equipment. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or equipment comprising the elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0068] Currently, when a mobile station is unable to receive the latest SSR comprehensive corrections for a period of time, a differential service provider typically forecasts satellite-based differential data using a single forecast method at the differential service end. The differential service end then feeds the forecasted SSR comprehensive corrections back to the mobile station for navigation and positioning, thereby improving the mobile station's positioning accuracy. However, it is worth noting that this SSR comprehensive correction forecast solution relies on the internet and the differential service end, and cannot address the problem of low mobile station positioning accuracy caused by internet or differential service end anomalies.

[0069] In this case, in order to solve the above problems, the embodiment of the present application provides an SSR comprehensive correction number forecasting scheme applied to the mobile station, so that the mobile station can select a suitable correction number forecasting scheme based on the data characteristics of the historical SSR comprehensive correction data when it fails to receive the SSR comprehensive correction number for a long time, so as to ensure that the final predicted SSR comprehensive correction number can ensure that the corresponding navigation positioning result has a sufficiently high positioning accuracy, so as to meet the real-time positioning requirements of the autonomous driving as much as possible, and at the same time, the problem of low positioning accuracy of the mobile station caused by abnormal Internet network or differential service end is effectively solved by the mobile station's self-forecasting of correction numbers. The mobile station can be, but is not limited to, a satellite navigation vehicle-mounted terminal device, a satellite navigation personal terminal device, etc.

[0070] The following describes some embodiments of the present application in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0071] Please refer to Figure 1 , Figure 1It is a schematic diagram of the composition of the satellite navigation terminal 10 provided in the embodiment of the present application. In the embodiment of the present application, the satellite navigation terminal 10 can be used to select a suitable correction number forecasting scheme based on the data characteristics of the historical SSR comprehensive correction data when no SSR comprehensive correction number is received for a long time, so as to ensure that the final forecast SSR comprehensive correction number can ensure that the corresponding navigation positioning result has a sufficiently high positioning accuracy to meet the real-time positioning requirements of autonomous driving as much as possible, while effectively reducing the impact of Internet anomalies and differential service-side anomalies on the navigation positioning accuracy of the satellite navigation terminal 10. Among them, the satellite navigation terminal 10 can be, but is not limited to, a satellite navigation vehicle-mounted terminal device, a satellite navigation personal terminal device, etc.

[0072] In an embodiment of the present application, the satellite navigation terminal 10 may include a memory 11, a processor 12, and a communication unit 13. The memory 11, the processor 12, and the communication unit 13 are electrically connected to each other, directly or indirectly, to enable data transmission or interaction. For example, the memory 11, the processor 12, and the communication unit 13 may be electrically connected to each other via one or more communication buses or signal lines.

[0073] In this embodiment, the memory 11 may be, but is not limited to, a random access memory (RAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc. The memory 11 is used to store a computer program, and the processor 12 may execute the computer program accordingly after receiving an execution instruction.

[0074] In this embodiment, the processor 12 can be an integrated circuit chip with signal processing capabilities. The processor 12 can be a general-purpose processor, including at least one of a central processing unit (CPU), a graphics processing unit (GPU), a network processor (NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor, etc., which can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application.

[0075] In this embodiment, the communication unit 13 is used to establish a communication connection between the satellite navigation terminal 10 and other electronic devices through a network, and to send and receive data through the network, wherein the network includes a wired communication network and a wireless communication network. For example, the satellite navigation terminal 10 can obtain the actual SSR comprehensive correction number corresponding to each of the different navigation satellites supported by the satellite navigation terminal 10 from the differential service end through the communication unit 13, wherein the types of satellite navigation systems supported by the satellite navigation terminal 10 can include at least one or more combinations of the GPS (Global Positioning System) satellite navigation system, the BDS (BeiDou Navigation Satellite System) satellite navigation system, and the Galileo (Galileo) satellite navigation system. The actual SSR comprehensive correction number of a single navigation satellite can be represented by the LOS (Line of Sight) correction number between the satellite navigation terminal 10 and the navigation satellite provided by the differential service end.

[0076] In an embodiment of the present application, the satellite navigation terminal 10 may further include an SSR comprehensive correction number forecasting device 100. The SSR comprehensive correction number forecasting device 100 may include at least one software function module that can be stored in the memory 11 in the form of software or firmware or solidified in the operating system of the satellite navigation terminal 10. The processor 12 may be used to execute the executable modules stored in the memory 11, such as the software function modules and computer programs included in the SSR comprehensive correction number forecasting device 100. When the SSR comprehensive correction number forecasting device 100 has not received any SSR comprehensive correction number for a long time, the satellite navigation terminal 10 may select a suitable correction number forecasting scheme based on the data characteristics of the historical SSR comprehensive correction data for forecasting, so that the final forecasted SSR comprehensive correction number can ensure that the corresponding navigation positioning result has a sufficiently high positioning accuracy, so as to meet the real-time positioning requirements of the autonomous driving as much as possible, while effectively reducing the impact of Internet anomalies and differential server anomalies on the navigation positioning accuracy of the satellite navigation terminal 10.

[0077] It is understandable that Figure 1 The block diagram shown is only a schematic diagram of the composition of the satellite navigation terminal 10. The satellite navigation terminal 10 may also include Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown. Figure 1 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0078] In the present application, in order to ensure that the satellite navigation terminal 10 can select an appropriate correction number forecasting scheme based on the data characteristics of the historical SSR comprehensive correction data when it has not received the SSR comprehensive correction number for a long time, so that the final predicted SSR comprehensive correction number can ensure that the corresponding navigation positioning result has a sufficiently high positioning accuracy to meet the real-time positioning needs of autonomous driving as much as possible, and effectively reduce the impact of Internet anomalies and differential service end anomalies on the navigation positioning accuracy of the satellite navigation terminal 10, the embodiment of the present application provides an SSR comprehensive correction number forecasting method applied to the satellite navigation terminal 10 to achieve the above-mentioned purpose. The SSR comprehensive correction number forecasting method provided by the present application is described in detail below.

[0079] Please refer to Figure 2 , Figure 2 This is one of the flow charts of the SSR comprehensive correction number forecasting method provided in the embodiment of the present application. In the embodiment of the present application, the SSR comprehensive correction number forecasting method may include steps S210 to S250.

[0080] Step S210 , preprocessing the historical SSR comprehensive correction data of all navigation satellites supported by the satellite navigation terminal to obtain the predicted correction value and target SSR comprehensive correction data of at least one target navigation satellite that meets the correction number prediction conditions.

[0081] In this embodiment, the satellite navigation terminal 10 can detect whether the differential age between itself and the differential server becomes larger, and whether the satellite navigation terminal 10 receives the actual SSR comprehensive correction number within the preset time period before the current navigation positioning cycle. When it is determined that the corresponding differential age becomes larger and the actual SSR comprehensive correction number is not received within the corresponding preset time period, it determines that it needs to perform the SSR comprehensive correction number forecast operation in the current navigation positioning cycle. At this time, the satellite navigation terminal 10 will perform data preprocessing on the historical SSR comprehensive correction data cached before the current navigation positioning cycle for all the navigation satellites supported by it, so as to determine which navigation satellites among the many navigation satellites supported by the satellite navigation terminal 10 are target navigation satellites that meet the correction number forecast conditions, and determine the predicted correction amount and target SSR comprehensive correction data of each target navigation satellite in the correction number forecast process, wherein the historical SSR comprehensive correction data of a single navigation satellite includes multiple actual SSR comprehensive correction numbers corresponding to the navigation satellite received by the satellite navigation terminal 10 before the current navigation positioning cycle, wherein each actual SSR comprehensive correction number corresponds to a time point separately.

[0082] Alternatively, see Figure 3 , Figure 3 yes Figure 2 FIG2 is a flow chart of the sub-steps included in step S210 of FIG2. In this embodiment, step S210 may include sub-steps S211 to S215 to screen target navigation satellites that meet the correction number forecast conditions from the plurality of navigation satellites supported by the satellite navigation terminal 10, and effectively extract the forecast correction value and target SSR comprehensive correction data of each target navigation satellite during the correction number forecast process.

[0083] Sub-step S211 , for each navigation satellite, removing gross error data from the historical SSR integrated correction data corresponding to the navigation satellite to obtain valid SSR integrated correction data for the navigation satellite.

[0084] In this embodiment, for each navigation satellite, gross error detection can be performed on the historical SSR comprehensive correction data of the navigation satellite based on the quality index of the SSR comprehensive correction number and the differential age of the navigation satellite in the current navigation positioning cycle to determine all gross error data in the historical SSR comprehensive correction data of the navigation satellite, and then all gross error data in the historical SSR comprehensive correction data are correspondingly eliminated to obtain the valid SSR comprehensive correction data of the navigation satellite.

[0085] Sub-step S212, calculates the correction number jump amount of the navigation satellite at the valid SSR comprehensive correction data based on the target historical SSR comprehensive correction number of the closest current navigation positioning cycle included in the valid SSR comprehensive correction data, and calculates the jump amount threshold of the navigation satellite based on the valid SSR comprehensive correction data.

[0086] In the present embodiment, the target historical SSR comprehensive correction number is the actual SSR comprehensive correction number that is closest to the current navigation positioning cycle at the time point corresponding to the valid SSR comprehensive correction data; the correction number change between the target historical SSR comprehensive correction number and at least one actual SSR comprehensive correction number in the valid SSR comprehensive correction data that is adjacent to the target historical SSR comprehensive correction number can be calculated, and then satellite LOS jump detection is performed based on the calculated correction number change, and when the satellite LOS jump phenomenon is detected, the calculated correction number change is subjected to mathematical operations (including mean operations, weighted sum operations, etc.) to obtain the correction number jump amount of the navigation satellite at the corresponding valid SSR comprehensive correction data. Optionally, in one implementation of the present embodiment, for a single navigation satellite, the correction number jump amount of the navigation satellite can be represented by the correction number change between the corresponding target historical SSR comprehensive correction number and the actual SSR comprehensive correction number that is closest to the target historical SSR comprehensive correction number.

[0087] For each navigation satellite, the effective SSR comprehensive correction data corresponding to the navigation satellite can be processed by differential sequence generation to obtain the corresponding first-order correction number differential sequence. Then, based on the normal distribution characteristics of the first-order correction number differential sequence, data analysis is performed on the first-order correction number differential sequence to obtain the jump value threshold of the navigation satellite.

[0088] Sub-step S213: detecting whether the satellite navigation system meets the system prediction condition according to the correction number jump variables of each navigation satellite belonging to the same satellite navigation system.

[0089] In this embodiment, the system forecast condition is used to determine whether the corresponding satellite navigation system is suitable for correction number forecasting; after the satellite navigation terminal 10 calculates the corresponding correction number jump variables and jump variable thresholds for all the navigation satellites supported by it, it will group all the calculated correction number jump variables according to the type of satellite navigation system to which each navigation satellite belongs, so that the correction number jump variables of each navigation satellite belonging to the same satellite navigation system are divided into the same data group, and then for each data group, it is detected whether the numerical distribution of all the correction number jump variables included in the data group conforms to the data law represented by the system forecast condition (for example, small-scale disordered fluctuations, small-scale sinusoidal fluctuations, small-scale square fluctuations and other data laws), and when it is detected that the data group substantially conforms to the data law represented by the system forecast condition, it is determined that the satellite navigation system corresponding to the data group meets the system forecast condition; otherwise, it is determined that the satellite navigation system corresponding to the data group does not meet the system forecast condition. Optionally, in one implementation of this embodiment, the system forecast condition may be expressed as "the actual jump value differences between the correction number jump values ​​of each navigation satellite under the same satellite navigation system are all within the preset jump value difference range corresponding to the satellite navigation system."

[0090] Among them, if a certain satellite navigation system meets the system forecast conditions, it means that the correction number jump variables of all navigation satellites under the satellite navigation system belong to normal baseline jump data, and the valid SSR comprehensive correction data of all navigation satellites under the satellite navigation system can be used to implement the correction number forecast operation, that is, all navigation satellites under the satellite navigation system can be used as target navigation satellites that meet the correction number forecast conditions; if a certain satellite navigation system does not meet the system forecast conditions, it means that the correction number jump variables of at least some of the navigation satellites under the satellite navigation system belong to abnormal baseline jump data, and all navigation satellites under the satellite navigation system cannot be used as target navigation satellites that meet the correction number forecast conditions.

[0091] Sub-step S214, when it is detected that the satellite navigation system meets the system forecast conditions, each navigation satellite under the satellite navigation system is used as a target navigation satellite, and the valid SSR comprehensive correction data of each navigation satellite under the satellite navigation system is used as the corresponding target SSR comprehensive correction data.

[0092] In this embodiment, if it is detected that a certain satellite navigation system meets the system prediction conditions, each navigation satellite under the satellite navigation system can be directly used as a target navigation satellite, and the effective SSR comprehensive correction data of the navigation satellite can be used as the target SSR comprehensive correction data of the corresponding target navigation satellite.

[0093] Sub-step S215, determining the forecast correction value corresponding to each navigation satellite in the satellite navigation system according to the correction jump value and jump value threshold of each navigation satellite in the satellite navigation system.

[0094] In this embodiment, when it is determined that each navigation satellite under a certain satellite navigation system can be used as a target navigation satellite, the predicted correction amount of each navigation satellite under the satellite navigation system in the subsequent correction number forecast process can be determined by performing data analysis on the correction number jump variables and jump variable thresholds of each navigation satellite under the satellite navigation system (for example, correction number jump variable distribution analysis, jump variable threshold distribution analysis, analysis of the numerical size relationship between the correction number jump variable and the jump variable threshold, and other analysis methods), so as to ensure that the predicted SSR comprehensive correction number finally obtained for each target navigation satellite is as close as possible to the actual correction number situation.

[0095] Optionally, in one implementation of this embodiment, to simplify the data analysis process and improve the real-time performance of the final satellite navigation positioning, the sub-step S215 may include:

[0096] Detecting whether the correction number jump value of each navigation satellite under the satellite navigation system exceeds the corresponding jump value threshold;

[0097] If it is detected that the correction number jump values ​​of each navigation satellite under the satellite navigation system exceed the corresponding jump value threshold, the correction number jump values ​​of each navigation satellite under the satellite navigation system are respectively used as the corresponding predicted correction values; otherwise, the predicted correction values ​​corresponding to each navigation satellite under the satellite navigation system are set to zero.

[0098] Therefore, the present application can screen out target navigation satellites that meet the correction number forecast conditions from the numerous navigation satellites supported by the satellite navigation terminal 10 by executing the above-mentioned sub-steps S211 to S215, and effectively extract the forecast correction amount and target SSR comprehensive correction data of each target navigation satellite during the correction number forecast process.

[0099] Step S220 , for each target navigation satellite, detecting whether the target SSR integrated correction data corresponding to the target navigation satellite satisfies a non-negative quasi-exponential law.

[0100] In this embodiment, after determining the target SSR integrated correction data of each target navigation satellite that currently meets the correction number forecast conditions, the satellite navigation terminal 10 will perform data analysis on the target SSR integrated correction data of each target navigation satellite to confirm whether the target SSR integrated correction data of the target navigation satellite meets the non-negative quasi-exponential law. If it is confirmed that the target SSR integrated correction data of a certain target navigation satellite meets the non-negative quasi-exponential law, it means that the target navigation satellite is substantially suitable for correction number prediction using a gray prediction model. At this time, the satellite navigation terminal 10 will execute step S230 corresponding to the target navigation satellite; if it is confirmed that the target SSR integrated correction data of a certain target navigation satellite does not meet the non-negative quasi-exponential law, it means that the target navigation satellite is substantially suitable for correction number prediction using a polynomial fitting model. At this time, the satellite navigation terminal 10 will execute step S240 corresponding to the target navigation satellite.

[0101] Alternatively, see Figure 4 , Figure 4 yes Figure 2 Flowchart of the sub-steps included in step S220. In this embodiment, step S220 may include sub-steps S221 to S227 to effectively analyze the data characteristics of the historical SSR comprehensive correction data of a single target navigation satellite, thereby facilitating the selection of an appropriate correction number prediction scheme for the target navigation satellite to perform correction number prediction.

[0102] Sub-step S221, sampling the target SSR integrated correction data according to a preset sampling time interval to obtain a correction number sampling sequence of the target navigation satellite.

[0103] In this embodiment, the period length of the current navigation and positioning cycle is greater than or equal to a positive integer multiple of the preset sampling time interval. For example, the period lengths of the preset sampling time interval and the current navigation and positioning cycle are both 5 seconds.

[0104] Sub-step S222: Check whether the positive and negative signs of all sequence elements included in the correction number sampling sequence of the target navigation satellite are consistent.

[0105] Among them, if the positive and negative signs of all sequence elements included in the correction number sampling sequence of a single target navigation satellite are consistent, it means that all sequence elements included in the correction number sampling sequence of the target navigation satellite are negative or positive, and the correction number sampling sequence of the target navigation satellite can execute sub-step S223 accordingly; if the positive and negative signs of all sequence elements included in the correction number sampling sequence of a single target navigation satellite are not consistent, it means that all sequence elements included in the correction number sampling sequence of the target navigation satellite are negative and positive at the same time, and the target SSR comprehensive correction data of the target navigation satellite does not actually meet the non-negative quasi-exponential law, and sub-step S227 can be executed accordingly at this time.

[0106] Sub-step S223, performing non-negative processing on the correction number sampling sequence of the target navigation satellite to obtain a non-negative correction number sequence of the target navigation satellite.

[0107] If all sequence elements of the correction number sampling sequence of a single target navigation satellite are negative, then all sequence elements of the correction number sampling sequence can be inverted to obtain a non-negative correction number sequence for the target navigation satellite; if all sequence elements of the correction number sampling sequence of a single target navigation satellite are positive, then the correction number sampling sequence can be directly used as the non-negative correction number sequence for the target navigation satellite. (0) You can use {x (0) (1),x (0) (2),…,x (0) (N)}, where N is used to represent the total sampling times of the target SSR integrated correction data of the target navigation satellite. The total sampling times corresponding to different target navigation satellites can be the same or different.

[0108] Sub-step S224, performing a cumulative generation process on the non-negative correction number sequence of the target navigation satellite to obtain a cumulative generation sequence of the target navigation satellite.

[0109] In this embodiment, the first-order Accumulated Generating Operation (1-AGO) sequence X of a single target navigation satellite is (1) You can use {x (1) (1),x (1) (2),…,x (1) (N)}, where the kth (k=1, 2, ..., N)th sequence element x in the generated sequence is accumulated once. (1) (k) Can be used Calculated.

[0110] Sub-step S225, calculating the smoothing ratio data of the non-negative correction number sequence of the target navigation satellite relative to the sequence generated by the single accumulation, and detecting whether the calculated smoothing ratio data conforms to the quasi-exponential law.

[0111] In this embodiment, the smoothness ratio data {ρ(2), ρ(3), ..., ρ(N)} of the non-negative correction number sequence of a single target navigation satellite relative to the sequence generated by one accumulation can be expressed as “ρ(k)=x (0) (k) / x (1) (k-1)" is calculated. The quasi-exponential rule can be expressed as follows: rule (1) the percentage value of the number of smooth ratios with a value less than 0.5 in the corresponding smooth ratio data relative to the total number of smooth ratios must be greater than 60%; rule (2) the percentage value of the number of smooth ratios with a value less than 0.5 excluding ρ(2) and ρ(3) relative to the total number of smooth ratios must be greater than 90%. Among them, if the smooth ratio data corresponding to a single target navigation satellite simultaneously satisfies the rules (1) and (2) included in the aforementioned quasi-exponential rule, it indicates that the target SSR comprehensive correction data corresponding to the target navigation satellite satisfies the non-negative quasi-exponential rule, and sub-step S226 will be executed accordingly at this time; if the smooth ratio data corresponding to a single target navigation satellite only satisfies the rules (1) or (2) included in the aforementioned quasi-exponential rule, or the smooth ratio data corresponding to a single target navigation satellite does not satisfy the rules (1) and (2) included in the aforementioned quasi-exponential rule, it indicates that the target SSR comprehensive correction data corresponding to the target navigation satellite does not satisfy the non-negative quasi-exponential rule, and sub-step S227 will be executed accordingly at this time.

[0112] Sub-step S226: determining whether the target SSR comprehensive correction data satisfies the non-negative quasi-exponential law.

[0113] Sub-step S227, determining that the target SSR comprehensive correction data does not satisfy the non-negative quasi-exponential law.

[0114] Therefore, the present application can effectively analyze the data characteristics of the historical SSR comprehensive correction data of a single target navigation satellite by executing the above sub-steps S221 to S227, and determine whether the historical SSR comprehensive correction data of the target navigation satellite is suitable for correction number prediction using a gray prediction model, so as to facilitate the selection of a suitable correction number prediction scheme for the target navigation satellite for correction number prediction.

[0115] Step S230 , performing correction number grey prediction based on the target SSR comprehensive correction data to obtain the correction number prediction result of the target navigation satellite.

[0116] In this embodiment, when the satellite navigation terminal 10 determines that the target SSR integrated correction data of a target navigation satellite conforms to the non-negative quasi-exponential law, it indicates that the target SSR integrated correction data of the target navigation satellite can be used to predict the correction number using the gray prediction model (GM (1,1) model), and the correction number prediction result of the target navigation satellite based on the gray prediction model is obtained, wherein the correction number prediction result based on the gray prediction model is Can be used To express, Used to indicate the corresponding correction number forecast result The SSR forecast correction number at the kth (k=1, 2, ..., N, N+1, ...) sampling time point in the , and the time interval between two adjacent sampling time points is consistent with the above-mentioned preset sampling time interval.

[0117] Alternatively, see Figure 5 , Figure 5 yes Figure 2 In this embodiment, step S230 may include sub-steps S231 to S234 to perform correction number grey prediction for target SSR comprehensive correction data that conforms to the non-negative quasi-exponential law.

[0118] Sub-step S231 , performing a close-by mean generation process on the once accumulated generation sequence of the target navigation satellite to obtain the close-by mean generation sequence of the target navigation satellite.

[0119] Among them, the adjacent mean value generation sequence Z of a single target navigation satellite is (1) You can use {z (1) (2),z (1) (3),…,z (1) (N)}, where the sequence element z in the adjacent mean generation sequence is (1) (k) Can be used Calculated.

[0120] Sub-step S232, solving the grey differential equation of the grey prediction model based on the immediate neighborhood mean generation sequence and the non-negative correction number sequence of the target navigation satellite to obtain the estimated development coefficient and estimated grey action of the target navigation satellite.

[0121] Among them, the gray differential equation of the gray prediction model is "x (0) (k)+az (1) (k) = b, where k = 2, 3, ..., N; a is used to represent the development coefficient of the grey prediction model, and b is used to represent the grey action of the grey prediction model. The grey differential equation can be represented by a matrix Y = B × r, where r = [a, b] T,

[0122] After the satellite navigation terminal 10 determines the adjacent mean value generation sequence and the non-negative correction number sequence of a single target navigation satellite, the determined adjacent mean value generation sequence and the non-negative correction number sequence can be substituted into the matrix expression of the above-mentioned grey differential equation, and the matrix expression of the above-mentioned grey differential equation is estimated and solved based on the least squares method to obtain the estimated model parameters of the target navigation satellite that match the grey prediction model. in Used to represent the estimated development coefficient of the target navigation satellite, Used to represent the estimated grey action of the target navigation satellite.

[0123] Sub-step S233, calculating the estimated initial conditions of the grey prediction model according to the one-time accumulation generation sequence, the estimated development coefficient and the estimated grey action of the target navigation satellite.

[0124] Among them, for the grey prediction model, the whitening equation of the grey prediction model can be expressed as The solution of the whitening equation is the time response function of the grey prediction model in Used to represent the estimated initial conditions of the corresponding grey prediction model".

[0125] In this process, the estimated initial conditions of a single target navigation satellite that match the grey prediction model can be calculated using any of the following four calculation formulas:

[0126] Calculation formula 1:

[0127] Calculation 2:

[0128] Calculation 3: in

[0129] Calculation 4: where β k Used to represent the kth (k=1, 2, ..., N) sequence element x in the corresponding cumulative generated sequence (1) The weight of (k) can be Calculated; α is a constant, which can be used Calculated, where S can be expressed as Calculated.

[0130] In sub-step S234, the estimated initial conditions are substituted into the prediction restoration function of the grey prediction model to perform correction number prediction, thereby obtaining the correction number prediction result of the target navigation satellite.

[0131] The prediction restoration function of the grey prediction model can be obtained by the time response function of the grey prediction model through a first-order cumulative subtraction operation, that is, At this time, the prediction restoration function of a single target navigation satellite that matches the grey prediction model can be expressed as follows:

[0132]

[0133] in, It is used to represent the SSR forecast correction number corresponding to the nth (n=1, 2, 3, ..., N, N+1, ...) sampling time point in the correction number forecast result obtained by using the grey prediction model, It is used to represent the estimated development coefficient of the grey prediction model, It is used to represent the estimated initial conditions of the grey prediction model; when l is greater than or equal to N+1, and l is used to represent the current navigation positioning cycle, it can be used It represents the target SSR forecast correction number of the corresponding target navigation satellite in the correction number forecast result, wherein the target SSR forecast correction number is the SSR forecast correction number in the correction number forecast result of the target navigation satellite that matches the current navigation positioning period.

[0134] Therefore, the present application can perform correction number grey prediction for the target SSR comprehensive correction data of a single target navigation satellite that conforms to the non-negative quasi-exponential law by executing the above sub-steps S231 to S234.

[0135] Step S240: Perform polynomial fitting prediction based on the target SSR comprehensive correction data to obtain the correction number prediction result of the target navigation satellite.

[0136] In this embodiment, when the satellite navigation terminal 10 determines that the target SSR integrated correction data of a target navigation satellite does not conform to the non-negative quasi-exponential law, it indicates that the target SSR integrated correction data of the target navigation satellite can be used to predict the correction number using a polynomial fitting model, and the correction number prediction result of the target navigation satellite based on the polynomial fitting model is obtained, wherein the correction number prediction result based on the polynomial fitting model is Can be used To express, Used to indicate the corresponding correction number forecast result The SSR forecast correction number of the i-th (i=1, 2, …, W, W+1, …) positioning time point in , W is used to represent the order of the positioning time points corresponding to the target historical SSR comprehensive correction number in the corresponding target SSR comprehensive correction data.

[0137] In this process, the polynomial fitting model can be expressed as follows:

[0138] x i =a0+a1(t i -t0)+a2(t i -t0) 2 +…+a m (t i -t0) m +Δ i , where 1≤i≤W;

[0139] Among them, x i Used to indicate the i-th positioning time point t in the corresponding target SSR comprehensive correction data i The actual SSR comprehensive correction number, a0, a1, ..., a m is the m+1 model coefficient of the polynomial fitting model, m is used to represent the order of the polynomial fitting model, t0 is used to represent the positioning time point corresponding to the first actual SSR comprehensive correction number in the corresponding target SSR comprehensive correction data, Δ i Used to represent the fitted model error.

[0140] Therefore, the present application can utilize the model expression of the above-mentioned polynomial fitting model, combined with the target SSR comprehensive correction data of the target navigation satellite to perform model fitting prediction, and obtain the correction number prediction result corresponding to the target navigation satellite that matches the polynomial fitting model.

[0141] Step S250, performing data superposition processing on the forecast correction value of the target navigation satellite and the target SSR forecast correction number in the corresponding correction number forecast result to obtain the forecast SSR comprehensive correction number of the target navigation satellite in the current navigation positioning cycle.

[0142] In this embodiment, the target SSR forecast correction number is the SSR forecast correction number in the correction number forecast result of a single target navigation satellite that matches the current navigation positioning period; when the satellite navigation terminal 10 determines the correction number forecast result of a target navigation satellite supported by itself, it extracts at least one target SSR forecast correction number that matches the current navigation positioning period from the correction number forecast result (for example, if the correction number forecast result is obtained based on a gray prediction model, the number of corresponding target SSR forecast correction numbers is at least one; if the correction number forecast result is obtained based on a polynomial fitting model, the number of corresponding target SSR forecast correction numbers is multiple, and at this time, the positioning time corresponding to each of the multiple target SSR forecast correction numbers is The time point is within the current navigation positioning cycle), and then the satellite navigation terminal 10 will use the predicted correction amount of the target navigation satellite to perform data superposition processing on each extracted target SSR forecast correction number, and obtain at least one predicted SSR comprehensive correction number of the target navigation satellite within the current navigation positioning cycle, so that the satellite navigation terminal 10 can use the predicted SSR comprehensive correction number of the target navigation satellite within the current navigation positioning cycle to achieve high-precision satellite navigation positioning operation, ensuring that the corresponding navigation positioning result has a sufficiently high positioning accuracy to meet the real-time positioning needs of autonomous driving as much as possible, wherein the number of the at least one predicted SSR comprehensive correction number is consistent with the number of the at least one target SSR forecast correction number.

[0143] Therefore, the present application can execute the above steps S210 to S250, and when the satellite navigation terminal 10 has not received the SSR comprehensive correction number for a long time, drive the satellite navigation terminal 10 to select an appropriate correction number forecasting scheme based on the data characteristics of the historical SSR comprehensive correction data for forecasting, so that the final forecast SSR comprehensive correction number can ensure that the corresponding navigation positioning result has a sufficiently high positioning accuracy, so as to meet the real-time positioning needs of autonomous driving as much as possible, and at the same time effectively reduce the impact of Internet anomalies and differential server anomalies on the navigation positioning accuracy of the satellite navigation terminal 10.

[0144] Alternatively, see Figure 6 , Figure 6 This is the second flow chart of the SSR comprehensive correction number forecasting method provided in the embodiment of the present application. Figure 2 Compared with the SSR comprehensive correction number prediction method shown in the figure, Figure 6 The SSR comprehensive correction number prediction method shown can also include steps S260 and S270 after step S230 to provide a backup prediction scheme (i.e., a polynomial fitting prediction scheme) for correction number forecasting for target navigation satellites where gray prediction of correction numbers fails, to ensure that the SSR comprehensive correction number prediction scheme provided in this application has stronger scheme completeness.

[0145] Step S260 , calculating the correction fitting residual of the target navigation satellite based on the correction prediction result of the target navigation satellite and the non-negative correction sequence of the corresponding target SSR integrated correction data.

[0146] Among them, when the satellite navigation terminal 10 uses the gray prediction model to obtain the correction number prediction result of a target navigation satellite, it can be based on the average relative residual calculation formula of the gray prediction model, extract the correction number prediction result of the target navigation satellite and the matching data in the non-negative correction number sequence according to the sampling time point to perform average relative residual calculation to obtain the correction number fitting residual of the target navigation satellite.

[0147] Step S270: Check whether the correction fitting residual of the target navigation satellite meets the grey prediction accuracy requirement.

[0148] Among them, the gray forecast accuracy requirement can be described as "the correction number fitting residual of the corresponding target navigation satellite is less than or equal to the first preset residual threshold (for example, 20% or 10%)"; when the correction number fitting residual of a target navigation satellite does not meet the gray forecast accuracy requirement, it means that the target SSR comprehensive correction data of the target navigation satellite actually has a correction number gray prediction failure phenomenon, at this time the satellite navigation terminal 10 will execute the above-mentioned step S240 corresponding to the target SSR comprehensive correction data of the target navigation satellite, so as to provide a backup forecast scheme (i.e., a polynomial fitting forecast scheme) for correction number forecasting for the target navigation satellite for which the correction number gray prediction fails; when the correction number fitting residual of a target navigation satellite meets the gray forecast accuracy requirement, it means that the target SSR comprehensive correction data of the target navigation satellite actually has a correction number gray prediction success phenomenon, at this time the satellite navigation terminal 10 will execute the above-mentioned step S250 corresponding to the target navigation satellite.

[0149] Therefore, the present application can provide a backup forecasting scheme (i.e., a polynomial fitting forecasting scheme) for correction number forecasting for target navigation satellites for which gray prediction of correction numbers fails by executing steps S260 and S270 respectively after executing step S230, to ensure that the SSR comprehensive correction number forecasting scheme provided by the present application has stronger scheme completeness.

[0150] It is understood that in one embodiment of the present application, Figure 6Compared with the SSR comprehensive correction number prediction method shown in the figure, after executing step S240, the satellite navigation terminal 10 calculates the correction number fitting residual between the correction number prediction result of a single target navigation satellite and the target SSR comprehensive correction data (refer to the execution process of step S260), and detects whether the calculated correction number fitting residual meets the polynomial fitting accuracy requirement (for example, the correction number fitting residual of the corresponding target navigation satellite is less than or equal to the second preset residual threshold (for example, 30%)), and then executes the above-mentioned step S250 for the target navigation satellite to ensure that the correction number prediction result obtained based on the polynomial fitting model can meet the expected positioning accuracy standard.

[0151] In this application, to ensure that the satellite navigation terminal 10 can effectively execute the above-mentioned SSR comprehensive correction number forecasting method, this application implements the above-mentioned functions by dividing the SSR comprehensive correction number forecasting device 100 stored in the satellite navigation terminal 10 into functional modules. The specific components of the SSR comprehensive correction number forecasting device 100 provided in this application and applied to the above-mentioned satellite navigation terminal 10 are described below.

[0152] Please refer to Figure 7 , Figure 7 This is one of the schematic diagrams of the composition of the SSR comprehensive correction number forecasting device 100 provided in the embodiment of the present application. In the embodiment of the present application, the SSR comprehensive correction number forecasting device 100 may include a correction number preprocessing module 110, a data regularity verification module 120, a data fitting prediction module 130, and a correction number output module 140.

[0153] The correction number preprocessing module 110 is used to preprocess the historical SSR comprehensive correction data of all navigation satellites supported by the satellite navigation terminal to obtain the predicted correction value and target SSR comprehensive correction data of at least one target navigation satellite that meets the correction number prediction conditions.

[0154] The data law checking module 120 is used to detect, for each target navigation satellite, whether the target SSR integrated correction data corresponding to the target navigation satellite satisfies the non-negative quasi-exponential law.

[0155] The data fitting prediction module 130 is used to perform gray prediction of the correction number based on the target SSR comprehensive correction data when the data regularity verification module 120 detects that the target SSR comprehensive correction data satisfies the non-negative quasi-exponential law, so as to obtain the correction number prediction result of the target navigation satellite, or to perform polynomial fitting prediction based on the target SSR comprehensive correction data when the data regularity verification module 120 detects that the target SSR comprehensive correction data does not satisfy the non-negative quasi-exponential law, so as to obtain the correction number prediction result of the target navigation satellite.

[0156] The correction number output module 140 is used to perform data superposition processing on the predicted correction amount of the target navigation satellite and the target SSR predicted correction number in the corresponding correction number forecast result to obtain the predicted SSR comprehensive correction number of the target navigation satellite in the current navigation positioning period, wherein the target SSR predicted correction number is the SSR forecast correction number in the correction number forecast result of the target navigation satellite that matches the current navigation positioning period.

[0157] Alternatively, see Figure 8 , Figure 8 This is the second schematic diagram of the composition of the SSR comprehensive correction number forecasting device 100 provided in the embodiment of the present application. In the embodiment of the present application, the SSR comprehensive correction number forecasting device 100 may further include a fitting residual calculation module 150 and a forecast accuracy verification module 160.

[0158] The fitting residual calculation module 150 is used to calculate the correction number fitting residual of the target navigation satellite based on the correction number prediction result of the target navigation satellite and the non-negative correction number sequence of the corresponding target SSR comprehensive correction data when the correction number prediction result of the target navigation satellite is obtained through the correction number gray prediction operation.

[0159] The prediction accuracy checking module 160 is used to detect whether the correction number fitting residual of the target navigation satellite meets the grey prediction accuracy requirement when the correction number prediction result of the target navigation satellite is obtained through the correction number grey prediction operation.

[0160] The data fitting prediction module 130 is also used to perform polynomial fitting prediction based on the target SSR comprehensive correction data to obtain the correction number prediction result of the target navigation satellite when the forecast accuracy verification module 160 detects that the correction number fitting residual of the target navigation satellite does not meet the gray forecast accuracy requirement, or to drive the correction number output module 140 to run when the forecast accuracy verification module 160 detects that the correction number fitting residual of the target navigation satellite meets the gray forecast accuracy requirement.

[0161] In one implementation of the present application, the fitting residual calculation module 150 may also be used to calculate the correction fitting residual of the target navigation satellite based on the correction forecast result of the target navigation satellite and the target SSR comprehensive correction data when the correction forecast result of the target navigation satellite is obtained through a polynomial fitting prediction operation. In this case, the prediction accuracy verification module 160 may also be used to detect whether the correction fitting residual of the target navigation satellite meets the polynomial fitting accuracy requirements when the correction forecast result of the target navigation satellite is obtained through a polynomial fitting prediction operation, and drive the correction output module 140 to operate when it is detected that the correction fitting residual of the target navigation satellite meets the polynomial fitting accuracy requirements.

[0162] It should be noted that the basic principles and technical effects of the SSR comprehensive correction number forecasting device 100 provided in the embodiment of the present application are the same as those of the aforementioned SSR comprehensive correction number forecasting method. For the sake of brevity, any details not mentioned in this embodiment can be referred to the description of the aforementioned SSR comprehensive correction number forecasting method.

[0163] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to the embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0164] In addition, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part. If the various functions provided by the present application are implemented in the form of software functional modules and sold or used as independent products, they can be stored in a storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a readable storage medium, including several instructions for enabling a satellite navigation terminal (which can be a smart phone, an intelligent driving vehicle terminal, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned readable storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0165] In summary, in an SSR comprehensive correction number prediction method and device, a satellite navigation terminal and a readable storage medium provided in an embodiment of the present application, the present application performs data preprocessing on the historical SSR comprehensive correction data of all navigation satellites supported by the satellite navigation terminal to obtain the predicted correction amount and target SSR comprehensive correction data of at least one target navigation satellite that meets the correction number prediction conditions, and for each target navigation satellite, when it is detected that the target SSR comprehensive correction data of the target navigation satellite meets the non-negative quasi-exponential law, a correction number gray prediction is performed based on the target SSR comprehensive correction data to obtain the correction number prediction result of the target navigation satellite, otherwise a polynomial fitting prediction is performed based on the target SSR comprehensive correction data to obtain the correction number prediction result of the target navigation satellite. The forecast correction value of the target navigation satellite and the target SSR forecast correction number in the corresponding correction number forecast result are then processed by data superposition to obtain the forecast SSR comprehensive correction number of the target navigation satellite in the current navigation positioning cycle. In this way, when the satellite navigation terminal fails to receive the SSR comprehensive correction number for a long time, a suitable correction number forecast scheme is selected according to the data characteristics of the historical SSR comprehensive correction data for forecasting, so that the final forecast SSR comprehensive correction number can ensure that the corresponding navigation positioning result has a sufficiently high positioning accuracy to meet the real-time positioning needs of autonomous driving as much as possible. At the same time, the problem of low positioning accuracy of the mobile station caused by Internet anomalies or differential server anomalies is effectively solved by the mobile station's self-forecasting of correction numbers.

[0166] The above are merely various embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for forecasting SSR comprehensive correction numbers, characterized in that: Applied to a satellite navigation terminal, the method includes: Performing data preprocessing on the historical SSR comprehensive correction data of all navigation satellites supported by the satellite navigation terminal to obtain a predicted correction value and target SSR comprehensive correction data of at least one target navigation satellite that meets the correction number forecast conditions; For each target navigation satellite, detect whether the target SSR integrated correction data corresponding to the target navigation satellite satisfies the non-negative quasi-exponential law; If it is detected that the target SSR comprehensive correction data satisfies the non-negative quasi-exponential law, gray prediction of the correction number is performed based on the target SSR comprehensive correction data to obtain a correction number prediction result of the target navigation satellite; otherwise, polynomial fitting prediction is performed based on the target SSR comprehensive correction data to obtain a correction number prediction result of the target navigation satellite; The forecast correction amount of the target navigation satellite and the target SSR forecast correction number in the corresponding correction number forecast result are subjected to data superposition processing to obtain the forecast SSR comprehensive correction number of the target navigation satellite in the current navigation positioning period, wherein the target SSR forecast correction number is the SSR forecast correction number in the correction number forecast result of the target navigation satellite that matches the current navigation positioning period.

2. The method according to claim 1, characterized in that The step of performing data preprocessing on the historical SSR comprehensive correction data of all navigation satellites supported by the satellite navigation terminal to obtain the predicted correction value and target SSR comprehensive correction data of at least one target navigation satellite that meets the correction number forecast conditions includes: For each navigation satellite, the gross error data of the historical SSR comprehensive correction data corresponding to the navigation satellite are eliminated to obtain the valid SSR comprehensive correction data of the navigation satellite; Calculate the correction number jump amount of the navigation satellite at the valid SSR comprehensive correction data according to the target historical SSR comprehensive correction number of the closest current navigation positioning cycle included in the valid SSR comprehensive correction data, and calculate the jump amount threshold of the navigation satellite according to the valid SSR comprehensive correction data; According to the correction number jump variables of each navigation satellite belonging to the same satellite navigation system, detecting whether the satellite navigation system meets the system prediction conditions; When it is detected that the satellite navigation system meets the system prediction conditions, each navigation satellite under the satellite navigation system is used as a target navigation satellite, and the valid SSR integrated correction data of each navigation satellite under the satellite navigation system is used as the corresponding target SSR integrated correction data; According to the correction number jump amount and jump amount threshold of each navigation satellite in the satellite navigation system, the forecast correction amount corresponding to each navigation satellite in the satellite navigation system is determined.

3. The method according to claim 2, characterized in that The step of determining the forecast correction amount corresponding to each navigation satellite in the satellite navigation system according to the correction number jump amount and jump amount threshold of each navigation satellite in the satellite navigation system includes: Detecting whether the correction number jump value of each navigation satellite under the satellite navigation system exceeds the corresponding jump value threshold; If it is detected that the correction number jump values ​​of each navigation satellite under the satellite navigation system exceed the corresponding jump value threshold, the correction number jump values ​​of each navigation satellite under the satellite navigation system are respectively used as the corresponding predicted correction values; otherwise, the predicted correction values ​​corresponding to each navigation satellite under the satellite navigation system are set to zero.

4. The method according to claim 1, wherein For each target navigation satellite, the step of detecting whether the target SSR integrated correction data corresponding to the target navigation satellite satisfies a non-negative quasi-exponential law comprises: Sampling the target SSR integrated correction data according to a preset sampling time interval to obtain a correction number sampling sequence of the target navigation satellite; Check whether the positive and negative signs of all sequence elements included in the correction number sampling sequence of the target navigation satellite are consistent; If it is detected that the positive and negative signs of all sequence elements are not consistent, it is determined that the target SSR comprehensive correction data does not satisfy the non-negative quasi-exponential law; otherwise, the correction number sampling sequence of the target navigation satellite is non-negatively processed to obtain a non-negative correction number sequence of the target navigation satellite; Performing a cumulative generation process on the non-negative correction number sequence of the target navigation satellite to obtain a cumulative generation sequence of the target navigation satellite; Calculate the smoothness ratio data of the non-negative correction number sequence of the target navigation satellite relative to the sequence generated by the single accumulation, and detect whether the calculated smoothness ratio data conforms to the quasi-exponential law; If it is detected that the calculated smoothness ratio data does not meet the quasi-exponential law, it is determined that the target SSR comprehensive correction data does not meet the non-negative quasi-exponential law; otherwise, it is determined that the target SSR comprehensive correction data meets the non-negative quasi-exponential law.

5. The method according to claim 4, characterized in that The step of performing grey prediction of correction numbers based on the target SSR comprehensive correction data to obtain a correction number forecast result of the target navigation satellite comprises: Performing a close-by mean generation process on the once accumulated generation sequence of the target navigation satellite to obtain a close-by mean generation sequence of the target navigation satellite; Solve the grey differential equation of the grey prediction model according to the immediate neighborhood mean value generation sequence and the non-negative correction number sequence of the target navigation satellite to obtain the estimated development coefficient and the estimated grey action of the target navigation satellite; Calculating an estimated initial condition of the grey prediction model according to a primary accumulation generation sequence, an estimated development coefficient and an estimated grey action of the target navigation satellite; The estimated initial conditions are substituted into the prediction restoration function of the grey prediction model to perform correction number prediction, and obtain the correction number prediction result of the target navigation satellite.

6. The method according to any one of claims 1 to 5, characterized in that After the step of performing grey prediction of correction numbers based on the target SSR comprehensive correction data, the method further comprises: Calculate the correction number fitting residual of the target navigation satellite based on the correction number prediction result of the target navigation satellite and the non-negative correction number sequence of the corresponding target SSR integrated correction data; Check whether the correction number fitting residual of the target navigation satellite meets the grey prediction accuracy requirement; If it is detected that the correction number fitting residual of the target navigation satellite does not meet the gray forecast accuracy requirement, a polynomial fitting prediction is performed based on the target SSR comprehensive correction data to obtain the correction number forecast result of the target navigation satellite. Otherwise, the process jumps to the step of performing data superposition processing on the forecast correction amount of the target navigation satellite and the target SSR forecast correction number in the corresponding correction number forecast result.

7. An SSR comprehensive correction number forecasting device, characterized in that: Applied to a satellite navigation terminal, the device comprises: a correction number preprocessing module, configured to perform data preprocessing on the historical SSR comprehensive correction data of all navigation satellites supported by the satellite navigation terminal to obtain a predicted correction value and target SSR comprehensive correction data of at least one target navigation satellite that meets the correction number forecast conditions; The data law verification module is used to detect, for each target navigation satellite, whether the target SSR integrated correction data corresponding to the target navigation satellite meets the non-negative quasi-exponential law; a data fitting prediction module, configured to, when the data regularity verification module detects that the target SSR comprehensive correction data satisfies a non-negative quasi-exponential law, perform correction number grey prediction based on the target SSR comprehensive correction data to obtain a correction number prediction result of the target navigation satellite; or, when the data regularity verification module detects that the target SSR comprehensive correction data does not satisfy a non-negative quasi-exponential law, perform polynomial fitting prediction based on the target SSR comprehensive correction data to obtain a correction number prediction result of the target navigation satellite; The correction number output module is used to perform data superposition processing on the predicted correction amount of the target navigation satellite and the target SSR predicted correction number in the corresponding correction number forecast result to obtain the predicted SSR comprehensive correction number of the target navigation satellite in the current navigation positioning period, wherein the target SSR predicted correction number is the SSR predicted correction number in the correction number forecast result of the target navigation satellite that matches the current navigation positioning period.

8. The device according to claim 7, characterized in that The device further comprises: A fitting residual calculation module is used to calculate the correction number fitting residual of the target navigation satellite according to the correction number prediction result of the target navigation satellite and the non-negative correction number sequence of the corresponding target SSR comprehensive correction data when the correction number prediction result of the target navigation satellite is obtained through the correction number grey prediction operation; A forecast accuracy verification module is used to detect whether the correction number fitting residual of the target navigation satellite meets the grey prediction accuracy requirement when the correction number forecast result of the target navigation satellite is obtained through the correction number grey prediction operation; The data fitting prediction module is also used to perform polynomial fitting prediction based on the target SSR comprehensive correction data to obtain the correction number prediction result of the target navigation satellite when the forecast accuracy verification module detects that the correction number fitting residual of the target navigation satellite does not meet the grey forecast accuracy requirement, or to drive the correction number output module to run when the forecast accuracy verification module detects that the correction number fitting residual of the target navigation satellite meets the grey forecast accuracy requirement.

9. A satellite navigation terminal, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program executable by the processor, and the processor can execute the computer program to implement the SSR comprehensive correction number prediction method described in any one of claims 1 to 6.

10. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a satellite navigation terminal, the SSR comprehensive correction number prediction method described in any one of claims 1 to 6 is implemented.

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