A method and system for screening rtmcs for use in PDT

Abstract

The application discloses an RTCM screening method and system applied to PDT, a base station obtains an elevation angle, a signal-to-noise ratio and a PDOP value through a GNSS receiver analyzing GSV and GSA statements, a processor establishes a time period probability, a historical quality and a historical PDOP model, removes a satellite to be optimized by taking a star list as an initial placeholder, constructs a candidate satellite set and sorts, supplements to a preset number to obtain a candidate list, dynamically determines a broadcast satellite number M1 according to the historical PDOP to make a dual-frequency observation value not exceed a single-time-slot net load rate, generates two-way RTCM data, a first way takes dual-frequency observation values of the first M1 satellites to adapt to an idle state, a second way takes single-frequency observation values of the first preset number of satellites to adapt to a service state, and the two-way satellites are consistent, and the processor outputs historical statistical model data to a storage to be persisted. The application solves the contradiction between service-state voice and data and RTCM transmission, adapts to dual-bandwidth constraints, and guarantees that a terminal continuously performs centimeter-level positioning.

Description

Technical Field

[0001] This invention belongs to the fields of digital trunking communication technology and satellite navigation technology, and particularly relates to an RTCM screening method and system applied to PDT. Background Technology

[0002] The PDT (Police Digital Trunking) system employs TDMA (Time Division Multiple Access) technology, dividing each radio frequency carrier into two time slots. The effective net payload rate per time slot is limited by the coding method. In the PDT system, the base station needs to broadcast RTCM differential data to terminals within the coverage area to support high-precision positioning. However, the RTCM data volume is enormous, conflicting with the limited wireless channel resources of the PDT system. Existing satellite selection technologies are primarily designed for terminal-specific positioning calculations, reducing the computational burden by filtering visible satellites. The selection results are only for local use and do not address scenarios where the base station broadcasts RTCM data to multiple terminals, nor do they consider the bandwidth constraints of the communication system.

[0003] The prior art patent CN102540214B discloses a smooth satellite selection method for signal sources in navigation satellite systems. This method includes a first satellite selection step and subsequent satellite selection steps. In each subsequent selection step, it employs methods such as "using satellites selected in the previous selection that are visible this time as the selected satellites for this selection," "first selecting a maximum of NUM satellites before determining the GDOP value," and "if reselection is needed after adding satellites, only the newly selected satellites are reselected." Furthermore, it introduces new rules for determining "rising and falling satellites," along with their priority ranking rules and corresponding processing procedures, in subsequent satellite selections. This method limits the time interval between two adjacent satellite selections to within 30 minutes, using the satellite selection result of the previous epoch as the preferred combination for the current epoch, maintaining list stability by reducing the number of satellites changing between adjacent selections. However, this method adopts a passive maintenance strategy, relying solely on the results of the previous epoch for smoothing. It does not establish a multi-dimensional statistical model based on historical data and cannot proactively identify long-term quality defects of the satellite. At the same time, this method only serves the terminal's own positioning calculation, does not involve the RTCM data broadcasting problem on the base station side, and does not consider the bandwidth constraints and service transmission limitations of the communication system. Summary of the Invention

[0004] Existing smoothing methods for satellite selection employ a passive, hold-on strategy, relying solely on the previous epoch's results for smoothing. They lack a multi-dimensional statistical model based on historical data and cannot proactively identify long-term satellite quality defects. Furthermore, current satellite selection technologies are all designed for terminal-specific positioning calculations, neglecting scenarios where base stations broadcast RTCM data to multiple terminals and failing to consider the bandwidth constraints of the PDT system. The challenge lies in efficiently filtering GNSS RTCM data under the single-slot net payload rate constraint of the PDT system, particularly under stringent transmission conditions where RTCM data can only be received via SLC signaling in service mode with an actual usable rate of only 200 bps. This would ensure the filtered data volume adapts to the dual bandwidth constraints of the PDT system, while simultaneously maintaining the stability of the filtering list to guarantee seamless RTK fixed solution maintenance during terminal transitions between idle and service modes.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an RTCM screening method applied to PDT, comprising: S1. The base station parses GSV and GSA statements to obtain elevation angle, signal-to-noise ratio, satellite selection list and PDOP value, and establishes and maintains historical statistical models. S2. Using the selected satellite list as the initial placeholder, identify and remove satellites to be optimized based on the historical statistical model, construct a candidate set and sort it according to the quality score, and supplement it to the preset number to obtain the candidate list; S3. Dynamically determine the number of broadcast satellites M1 based on historical statistical models, so that the amount of dual-frequency observation data of M1 satellites does not exceed the net payload rate of a single time slot. S4. Generate two RTCMs: The first path takes the dual-frequency observations of the first M1 satellites to adapt to the idle state, and the second path takes the single-frequency observations of the first preset number of satellites in the candidate list to adapt to the service state, and this preset number of satellites is consistent with the corresponding satellites in the first path.

[0006] Furthermore, the historical statistical model includes a time-period probability model, a historical quality model, and a historical PDOP model. The time-period probability model records the frequency of each satellite appearing in the satellite selection list for 24 time periods and updates it using a sliding window or exponential smoothing method. The historical quality model records the average elevation angle and average signal-to-noise ratio of each satellite in each time period. The historical PDOP model records the average value and standard deviation of the PDOP value in each time period. Each model is constructed based on historical data before the current epoch. The model data is persistently stored and loaded when the base station starts.

[0007] Furthermore, the identification conditions for the satellite to be optimized in step S2 are as follows: the satellite simultaneously meets the following conditions: the historical occurrence probability is lower than the preset probability threshold and the number of recent consecutive occurrences is lower than the preset number; the historical average elevation angle is lower than the preset historical elevation angle threshold or the historical average signal-to-noise ratio is lower than the preset historical signal-to-noise ratio threshold; the current elevation angle is lower than the preset current elevation angle threshold or the current signal-to-noise ratio is lower than the preset current signal-to-noise ratio threshold.

[0008] Furthermore, in step S2, the number of satellites to be optimized removed at one time shall not exceed a preset upper limit, and the remaining satellites to be optimized shall be removed gradually in subsequent epochs.

[0009] Furthermore, in step S2, the comprehensive quality score is calculated based on the satellite's instantaneous elevation angle, instantaneous signal-to-noise ratio, and historical statistical model. Satellites with better historical performance receive a higher score bonus.

[0010] Furthermore, in step S3, the value of M1 is dynamically determined by the historical average PDOP value and PDOP standard deviation of the current time period. When the average PDOP value and standard deviation are both lower than the corresponding lower limit, M1 takes the minimum value. When the average PDOP value or standard deviation is higher than the corresponding upper limit, M1 takes the maximum value. The minimum and maximum values ​​are determined by the constraint that the amount of dual-frequency observation data does not exceed the net load rate of a single time slot.

[0011] Furthermore, the preset number is 12, the minimum value of M1 is 12, and the maximum value is 16. In step S2, the candidate set also includes visible satellites that are not included in the initial placeholder list in the current epoch. In step S3, if M1 is greater than the preset number, satellites are selected from the candidate set to be added to the candidate list until M1 is reached.

[0012] This invention also provides an RTCM screening system for PDT, including a GNSS receiver. The GNSS receiver parses GSV and GSA statements, obtains a satellite selection list, and outputs it to a processor. The processor establishes and maintains a historical statistical model, reviews and replaces the satellite selection list based on the historical statistical model, determines the number of broadcast satellites M1, ensuring that the amount of dual-frequency observation data of M1 satellites does not exceed the single-slot net payload rate, and generates two RTCMs: the first path takes the dual-frequency observations of the first M1 satellites to adapt to the idle state, and the second path takes the single-frequency observations of the first preset number of satellites to adapt to the service state, and this preset number of satellites is consistent with the corresponding satellites in the first path. The historical statistical model data is output to a memory; the memory persistently stores the historical statistical model data.

[0013] Furthermore, when the processor identifies a satellite to be optimized, it determines whether the satellite simultaneously meets the following conditions: its historical occurrence probability is lower than a preset probability threshold and its recent consecutive occurrence count is lower than a preset number; its historical average elevation angle is lower than a preset historical elevation angle threshold or its historical average signal-to-noise ratio is lower than a preset historical signal-to-noise ratio threshold; its current elevation angle is lower than a preset current elevation angle threshold or its current signal-to-noise ratio is lower than a preset current signal-to-noise ratio threshold.

[0014] Furthermore, when the processor updates the historical statistical model, it includes the data of the current epoch into the historical database after the satellite selection decision is completed; the memory persistently stores the probability of the time period, historical quality and historical PDOP data in 24 time periods, and loads the stored data when the base station starts up to guide the satellite selection.

[0015] The beneficial effects of this invention are: 1. This invention uses the GSA satellite selection list as the initial occupancy benchmark, and actively identifies and replaces satellites to be optimized by combining historical statistical models, forming a closed-loop optimization mechanism of "benchmark reference - quality review - active replacement", which is different from the passive smoothing strategy of existing technologies. 2. This invention establishes three historical statistical models: time period probability, historical quality, and historical PDOP. It adopts a 24-hour time period division and a lag statistical mechanism to realize the active prediction and maintenance of satellite long-term stability on the base station side. The system does not need to relearn after a power outage and restart. 3. This invention dynamically determines the number of broadcast satellites M1 based on the historical average PDOP value and standard deviation, achieving an adaptive balance between geometric configuration quality and bandwidth constraints, which differs from the reactive adjustment based on instantaneous PDOP values ​​in existing technologies; 4. This invention generates dual-channel RTCM data simultaneously through one screening, and the preset number of satellites in both channels are completely identical, ensuring that the terminal does not need to re-acquire satellites when switching between idle and service modes, and the RTK fixed solution can be seamlessly maintained. 5. This invention reduces the number of broadcast satellites from a total of 30 to 12 to 16, reduces the amount of dual-frequency observation data by 45% to 60%, reduces the amount of single-frequency observation data by 65% ​​to 82%, and controls the amount of single-frequency observation data to about 120 bytes / 5 seconds, which fully adapts to the 200bps transmission constraint of PDT service SLC signaling. 6. This invention is based on the standard NMEA and RTCM protocols, and can directly utilize the output data of the existing low-cost GNSS receivers in the base station without opening the GDOP calculation interface or adding additional computing resources, resulting in low engineering implementation costs. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method of the present invention.

[0017] Figure 2 This is a system block diagram of the present invention. Detailed Implementation

[0018] Example 1: An RTCM screening method applied to PDT, such as Figure 1 As shown, it includes the following steps: S1, the base station parses GSV and GSA statements to obtain elevation angle, signal-to-noise ratio, satellite selection list and PDOP value, and establishes and maintains historical statistical models; S2: Using the selected satellite list as the initial placeholder, identify and remove satellites to be optimized based on the historical statistical model, construct a set of candidate satellites and sort them according to quality scores, and supplement them to a preset number to obtain the candidate list; S3, dynamically determine the number of broadcast satellites M1 based on historical statistical models, so that the amount of dual-frequency observation data of M1 satellites does not exceed the net payload rate of a single time slot; S4, generate two RTCMs: the first path takes the dual-frequency observations of the first M1 satellites to adapt to the idle state, and the second path takes the single-frequency observations of the first preset number of satellites in the candidate list to adapt to the service state, and this preset number of satellites is consistent with the corresponding satellites in the first path.

[0019] In S1, the PDT base station uses its local GNSS receiver to parse the GSV and GSA statements of the NMEA-0183 protocol in real time. The GSV statement outputs the PRN number, elevation angle, azimuth angle, and signal-to-noise ratio (SNR) of all visible satellites in the current epoch. The elevation angle ranges from 0 to 90 degrees, the azimuth angle from 0 to 359 degrees, and the SNR from 0 to 99 dBHz. The GSA statement outputs the list of satellites currently participating in the positioning calculation (i.e., the satellite selection list), as well as the PDOP, HDOP, and VDOP values. The PDOP value reflects the impact of the current satellite geometry on positioning accuracy; a smaller PDOP value indicates a better geometry. The base station obtains the instantaneous elevation angle and instantaneous SNR of each visible satellite from the GSV statement and the satellite selection list and PDOP value from the GSA statement. The base station establishes and maintains a historical statistical model, which includes three sub-models: a time-period probability model, a historical quality model, and a historical PDOP model. The base station divides the day into 24 time slots, each lasting one hour. This division is based on the fact that the orbital period of BeiDou satellites is approximately 24 hours, and IGSO and MEO satellites appear in the sky at roughly the same position at the same time each day, thus the hourly statistics reflect physical regularity. A time-slot probability model records the frequency with which each satellite appears in the satellite selection list during each time slot. For example, after one week of operation, statistics show that PRN1 has a 95% probability of appearing in the satellite selection list between 8:00 AM and 9:00 AM, PRN2 has an 85% probability, while PRN5 has only a 15% probability. This probability is continuously updated using a sliding window or exponential smoothing method, with a smoothing coefficient of 0.3, allowing the model to both track changes and maintain stability. A historical quality model records the average elevation angle and average signal-to-noise ratio (SNR) of each satellite in each time slot. For example, PRN5 has an average elevation angle of 8 degrees and an average SNR of 32 dB during the 8:00 AM to 9:00 AM time slot, significantly lower than most other satellites. The historical PDOP model records the average and standard deviation of PDOP values ​​for each time period. The standard deviation is calculated by taking the square root of the sum of the squares of the deviations of all PDOP values ​​from the average value within each time period. A smaller standard deviation indicates less PDOP fluctuation and a more stable geometric configuration. For example, the average PDOP value for the period from 8:00 AM to 9:00 AM is 2.1, with a standard deviation of 0.6, indicating a stable geometric configuration during this period; while the average PDOP value for the period from 2:00 PM to 3:00 PM is 4.5, with a standard deviation of 1.2, indicating a poorer geometric configuration and greater fluctuation. Each model is built based on historical data prior to the current epoch, employing a lag statistical mechanism. The probability, quality, and PDOP data used in decision-making are all based on historical data prior to the current epoch. Data for the current epoch is only added to the historical database after the decision is made, for use in subsequent epoch statistics. This lag mechanism avoids the excessive influence of single, occasional events on decision-making, ensuring the priority principle of list stability. Model data is periodically written to non-volatile storage media for persistent storage, and accumulated historical data is loaded when the base station starts up.When historical data is insufficient in the initial stage of system operation, supplementary decisions mainly rely on the instantaneous elevation angle and signal-to-noise ratio of the GSV; after 24 hours of data accumulation, the complete historical statistical model is automatically activated. If the memory data fails, a cold start mode is entered, relying on the instantaneous quality of the GSV for screening, and the accumulation of statistics is restarted.

[0020] After completing data acquisition and model initialization in S1, the process proceeds to S2 to review the satellite selection list and construct the candidate satellite set. The base station uses the satellite selection list in the GSA statement as the initial placeholder list. For example, in a typical epoch, the GSV resolution result shows that there are currently 20 visible satellites, and the GSA resolution result provides a selection list containing 12 satellites from PRN1 to PRN12, with a PDOP value of 2.3. The base station records this list as the initial placeholder list. Each satellite in the initial placeholder list is traversed, and its historical occurrence probability, historical average elevation angle, and historical average signal-to-noise ratio (SNR) for the current time period are queried based on the historical statistical model. Simultaneously, the instantaneous elevation angle and instantaneous SNR of the satellite are obtained based on the current GSV data. If a satellite simultaneously meets the following conditions, it is marked as a satellite to be optimized: its historical occurrence probability is lower than a preset probability threshold and its recent consecutive occurrence count is lower than a preset number; its historical average elevation angle is lower than a preset historical elevation angle threshold or its historical average SNR is lower than a preset historical SNR threshold; its current elevation angle is lower than a preset current elevation angle threshold or its current SNR is lower than a preset current SNR threshold. For example, PRN5 has a historical occurrence probability of 15%, a recent consecutive occurrence count of 1, a historical average elevation angle of 8 degrees, a current elevation angle of 9 degrees, a historical average signal-to-noise ratio of 32 dB, and a current signal-to-noise ratio of 30 dB. Since all three conditions are met, it is marked as needing optimization. The remaining 11 satellites do not meet all three conditions and are not marked. Satellites marked as needing optimization are removed from the initial placeholder list and added to the candidate satellite set. Simultaneously, all visible satellites in the current epoch that were not included in the initial placeholder list are also added to the candidate satellite set. All satellites in the candidate satellite set are then uniformly sorted from highest to lowest based on their comprehensive quality score. The comprehensive quality score is calculated based on the satellite's instantaneous elevation angle, instantaneous signal-to-noise ratio, and historical statistical model; satellites with better historical performance receive higher score bonuses. For example, a weighted method can be used to calculate the score, where instantaneous elevation angle and instantaneous signal-to-noise ratio are the base scores, and satellites with high historical occurrence probabilities, high historical average elevation angles, and high historical average signal-to-noise ratios receive additional bonuses. Satellites are selected from the candidate satellite set in descending order of their overall quality score to supplement the initial placeholder list until the list reaches a preset number, resulting in the candidate list. The preset number is set at 12 satellites. The quantitative basis for selecting 12 satellites is that each satellite's single-frequency MSM4 observation data volume is 67 bits, and the single-frequency observation data volume of 12 satellites is 804 bits, approximately 100.5 bytes. With a message header overhead of 20 bytes, the total data volume is approximately 120.5 bytes, which precisely meets the stringent constraint of transmitting 125 bytes within 5 seconds for SLC signaling at 200bps in operational mode. When removing satellites to be optimized, the number removed at one time does not exceed a preset limit; for example, a maximum of 2 satellites can be removed in a single epoch. Priority is given to removing the satellite with the lowest overall quality score, and the remaining satellites to be optimized are processed gradually in subsequent epochs. This rule is symmetrical to selecting the highest quality satellite from the candidate satellite set, reflecting the optimization principle of eliminating inferior satellites and selecting the best.In this embodiment, after PRN5 is removed, the initial placeholder list has 11 satellites remaining. PRN15, which has the highest overall quality score, is added from the candidate satellite set, restoring the list to 12 satellites.

[0021] After S2 is completed, S3 is entered to dynamically determine the number of broadcast satellites. The base station dynamically determines the number of broadcast satellites M1 based on historical PDOP information in the historical statistical model. The historical average PDOP value and PDOP standard deviation for the current time period are queried. When both the average PDOP value and standard deviation are below the corresponding lower limit, it indicates that the current geometric configuration is very stable, and M1 takes the minimum value; when the average PDOP value or standard deviation is above the corresponding upper limit, it indicates that the geometric configuration is poor or unstable, and M1 takes the maximum value. The minimum and maximum values ​​are determined by the constraint that the amount of dual-frequency observation data does not exceed the net load rate of a single time slot. The preset number is 12 satellites, the minimum value of M1 is 12 satellites, and the maximum value is 16 satellites. Calculations show that when M1 is 12 satellites, the amount of dual-frequency observation data is 169 bits plus 116 bits of dual-frequency observation data per satellite, totaling 1561 bits, approximately 1.56 kbps, which is less than the net load rate of a single time slot. When M1 is 16 satellites, the dual-frequency observation data volume is 169 bits plus the dual-frequency observation data from 16 satellites, totaling 2025 bits, approximately 2.025 kbps, which is also less than the net payload rate of a single time slot, satisfying the bandwidth constraint. The value of M1 is dynamically determined by the historical average PDOP value and PDOP standard deviation for the current time period, and the aforementioned lower and upper limits are configurable parameters, whose values ​​are set according to the stability requirements of the satellite geometry configuration in the actual deployment environment. In this embodiment, the historical average PDOP for the current time period is 2.1, and the standard deviation is 0.6, which is an intermediate case, so M1 is set to 14 satellites. Since M1 is greater than 12, the base station continues to select PRN16 and PRN17, which have higher comprehensive quality scores, from the candidate satellite set to add to the candidate list, expanding it to 14 satellites. If M1 equals 12, no expansion is required.

[0022] After S3 is completed, S4 is executed to generate dual-channel RTCM data. The base station generates two channels of RTCM data based on the candidate list. The first channel takes the dual-frequency observations of the first M1 satellites in the candidate list, adapts to the idle state broadcast of the PDT system, and sends them to all terminals in the coverage area through the secondary control channel. In this embodiment, M1 is 14, and the first channel takes the dual-frequency observations of all 14 satellites, encapsulates them according to the RTCM standard MSM4 message format, and has a data volume of approximately 1.79 kbps, adapting to the idle state single-slot net payload rate. The second channel takes the single-frequency observations of the first preset number of satellites in the candidate list, adapts to the PDT system service state broadcast, and transmits them through SLC signaling carried in the CACH channel. This preset number of satellites is consistent with the corresponding satellites in the first channel, ensuring that the terminal does not need to reacquire satellites when switching between idle state and service state, and the RTK fixed solution can be seamlessly maintained. In this embodiment, the second channel contains the single-frequency observations of the first 12 satellites, with a data volume of 804 bits, approximately 100.5 bytes. The message header overhead is estimated at 20 bytes, and the total data volume is approximately 120.5 bytes. The SLC signaling transmission capacity is 200bps. The time required to transmit 120.5 bytes of data is 120.5 multiplied by 8 divided by 200, which equals 4.82 seconds. This is less than the maximum differential age requirement of 5 seconds for RTK differential data, thus meeting the transmission timeliness constraint. The consistency of the two data streams is crucial: the first 12 satellites in the first data stream are identical to the 12 satellites in the second data stream. Therefore, when the terminal receives the first data stream in idle mode and establishes an RTK fixed solution, if it switches to service mode, the satellite list in the received second data stream will be completely consistent with the previous list. The terminal does not need to reacquire satellites or reinitialize the RTK solution, and the RTK fixed solution can be seamlessly maintained. For ephemeris messages and base station coordinate messages, no filtering is performed; the full amount of data is directly retained. After the filtering decision is completed at each epoch, the base station uses the data of the current epoch to update the historical statistical model, incorporating the elevation angle, signal-to-noise ratio, and other data of each satellite into the time-period probability model and the historical quality model, and incorporating the PDOP value of the current epoch into the historical PDOP model. This delayed statistical mechanism, which prioritizes decision-making before updates, ensures that isolated, unforeseen events do not excessively influence decision-making, thus upholding the principle of prioritizing list stability.

[0023] Example 2: Based on Example 1, this example improves the fine-grained identification and progressive removal mechanism for satellites to be optimized, the recent trend assessment and vicious cycle avoidance mechanism, and the calculation rules for the comprehensive quality score.

[0024] Regarding the refined identification of satellites to be optimized, Example 1 outlines the steps for identifying and removing satellites to be optimized based on historical statistical models. This example strictly limits the identification conditions. A satellite is marked as a satellite to be optimized and removed only if it simultaneously meets the following three conditions: First, poor long-term availability, meaning the satellite's historical probability of occurrence is lower than a preset probability threshold, and the number of times it has appeared consecutively in the satellite selection list recently is lower than a preset number; Second, poor long-term quality, meaning the satellite's historical average elevation angle is lower than a preset historical elevation angle threshold, or its historical average signal-to-noise ratio is lower than a preset historical signal-to-noise ratio threshold; Third, poor current quality, meaning the satellite's current elevation angle is lower than a preset current elevation angle threshold, or its current signal-to-noise ratio is lower than a preset current signal-to-noise ratio threshold. The use of AND logic for all three conditions being met simultaneously, rather than OR logic, is to avoid misjudgments caused by fluctuations in a single-dimensional indicator. If a satellite has a low historical probability but an extremely high current signal-to-noise ratio, it may be a newly launched important satellite and should not be removed; only satellites that perform poorly in all three aspects are considered truly unstable satellites. This multi-condition joint review mechanism ensures the stability of the list while preventing low-probability satellites from becoming trapped in a vicious cycle and unable to recover. For example, the preset probability threshold can be 30%, the preset number of occurrences can be 5 consecutive occurrences, the preset historical elevation angle threshold can be 10 degrees, the preset historical signal-to-noise ratio threshold can be 35dB, the preset current elevation angle threshold can be 10 degrees, and the preset current signal-to-noise ratio threshold can be 35dB. Only when all three conditions are met simultaneously—a satellite's historical occurrence probability is below 30% and the recent consecutive occurrence count is below 5, its historical average elevation angle is below 10 degrees or its historical average signal-to-noise ratio is below 35dB, and its current elevation angle is below 10 degrees or its current signal-to-noise ratio is below 35dB—can it be marked as needing optimization.

[0025] Regarding the removal mechanism for satellites to be optimized, to avoid drastic changes in the broadcast list caused by removing too many satellites in a single epoch, which could lead to RTK lockout on the terminal, this embodiment employs a gradual removal strategy. When removing satellites to be optimized, the number removed in a single instance does not exceed a preset limit, for example, a maximum of two satellites can be removed in a single epoch. When the number of satellites to be optimized exceeds this limit, the satellite with the lowest overall quality score is removed first, and the remaining satellites are temporarily retained in a placeholder list and gradually processed and removed in subsequent epochs. This smooth transition mechanism, combined with a lag statistical model, greatly maintains the long-term stability of the satellite list.

[0026] Regarding the calculation rules for the comprehensive quality score, Example 1 mentioned sorting by quality score, and this example clarifies the specific calculation logic of this score. The comprehensive quality score is calculated jointly based on the satellite's instantaneous elevation angle, instantaneous signal-to-noise ratio, and historical statistical model. Its core principle is that satellites with better historical performance receive a higher score bonus. Specifically, the instantaneous elevation angle and instantaneous signal-to-noise ratio are first normalized and weighted summed to obtain the instantaneous base score; simultaneously, a historical bonus coefficient is calculated based on historical occurrence probability and historical average quality, with a value between zero and one; finally, the instantaneous base score and the historical bonus coefficient are combined to obtain the total score. This calculation method ensures that, given similar instantaneous quality, satellites with long-term stable and reliable performance are prioritized for retention or inclusion in the list, further consolidating the long-term availability of the base station broadcast list.

[0027] Regarding maintaining the stability of the satellite list, when there are differences between the candidate list and the list from the previous epoch, the historical quality model is used to determine whether to retain or replace a satellite, thus maintaining list stability. Specifically, the processor queries the average elevation angle and average signal-to-noise ratio of each satellite in the historical quality model, compares the quality of differing satellites, and prioritizes retaining satellites with better historical quality, avoiding frequent list changes due to instantaneous fluctuations. This mechanism complements the comprehensive quality scoring and ranking, jointly ensuring the long-term stability of the list from both statistical regularity and real-time scoring dimensions.

[0028] To prevent satellites with low historical probability from becoming permanently unrecoverable due to a vicious cycle, this embodiment introduces a recent trend assessment mechanism. The base station maintains a counter for the number of times each satellite appears consecutively in the satellite selection list. When a satellite is marked as needing optimization and removed from the placeholder list, the base station does not permanently exclude it but checks its status in each subsequent epoch. If the satellite subsequently appears in the satellite selection list multiple times consecutively, for example, five times consecutively, or if its historical appearance probability rises above a preset threshold, for example, above 40%, it is automatically removed from the needing optimization state, allowing it to re-enter the placeholder list during subsequent candidate satellite set sorting and replenishment. Taking PRN5 as an example, assuming that PRN5's elevation angle and signal-to-noise ratio gradually improve over several epochs and it appears in the satellite selection list five times consecutively, the base station restores its status from needing optimization to normal in the sixth epoch. In the subsequent candidate satellite set construction, PRN5 will participate in the comprehensive quality score sorting along with other visible satellites. If its score is high enough, it can be re-selected into the broadcast list. This mechanism achieves a balance between long-term stability and dynamic adaptation.

[0029] Regarding the cold start and persistence of the historical statistical model, this embodiment further details the initialization and persistence process of the historical statistical model. When a base station is deployed for the first time or historical data is corrupted, the system enters cold start mode. The cold start period is set to 24 hours, based on the physical law that the orbital period of GNSS satellites such as BeiDou is approximately 24 hours. During the cold start period, there is no available historical data for the time-period probability model, historical quality model, and historical PDOP model. At this time, the satellite identification to be optimized is based solely on the current quality conditions, without using historical probability and historical quality conditions. The supplementary ranking of the candidate satellite set also mainly relies on the instantaneous elevation angle and instantaneous signal-to-noise ratio provided by the GSV. During the 24 hours of cold start, the base station records the satellite data and PDOP value for each epoch, gradually filling the historical statistical table for each time period. For example, in the first hour, the base station records the number of times each satellite appears in the satellite selection list, as well as the elevation angle and signal-to-noise ratio at each appearance, and simultaneously records the PDOP value for each epoch within that time period. After 24 hours of accumulation, the statistical sample size for each time period is sufficient, and the base station automatically switches from cold start mode to normal mode, enabling the complete three-condition identification mechanism for satellites to be optimized.

[0030] To ensure historical data is not lost after a power outage and restart of the base station, this embodiment employs a database storage mechanism for persistence. At each hourly interval, the base station writes the time-period probability table, historical quality table, and historical PDOP statistics table to non-volatile storage media. Upon base station startup, it first checks the database for valid historical data. If valid data exists and its timestamp is within the last 7 days, it is directly loaded and updates continue. If data is missing or expired, and has not been updated for more than 7 days, the data table is cleared and the cold start mode is re-entered. The memory persistently stores time-period probability, historical quality, and historical PDOP data in 24 time periods. Time-period probability data includes the frequency of each satellite appearing in the satellite selection list for each time period; historical quality data includes the average elevation angle and average signal-to-noise ratio of each satellite for each time period; historical PDOP data includes the average and standard deviation of the PDOP values ​​for each time period. The memory loads the stored data upon base station startup, and the processor directly guides satellite selection based on the loaded historical data, eliminating the need to re-enter the cold start cycle.

[0031] Regarding the detailed implementation of lag statistics, Example 1 mentioned a lag statistics mechanism of decision-making followed by updates. This example further refines the specific implementation of this mechanism. In S2, when the base station needs to query the historical probability of a satellite in the current time period, it uses the probability value that was statistically calculated up to the end of the previous epoch. Whether the satellite is included in the GSA list of the current epoch, as well as the satellite's elevation angle and signal-to-noise ratio at the current epoch, are not included in the calculation of the current decision. After the decision is completed, the base station adds the data of the current epoch to the corresponding statistical counter for use in the decision of the next epoch. Through the above improvements, this example can not only actively identify and replace unstable satellites, but also provide a recovery channel for satellites with poor historical performance but recent real improvement, while ensuring that historical data is still available after a power outage and restart, making the system's self-learning capability continuous and reliable.

[0032] Example 3: This example improves upon Example 1 in terms of the dynamic determination strategy for the number of broadcast satellites M1, as well as the hardware implementation and data interaction of the system.

[0033] Regarding the hierarchical dynamic adjustment strategy based on historical PDOP, Example 1 provides an example of the rules for M1 values. This example further extends these rules to a more refined three-level hierarchical strategy. The base station divides the time periods into three levels based on the average PDOP and standard deviation recorded by the historical PDOP model for each time period. The first level is excellent: when the average is less than 2.0 and the standard deviation is less than 0.5, it indicates that the satellite geometry is very stable and the positioning accuracy is good. In this case, M1 is set to the minimum value of 12 satellites to maximize bandwidth savings, while the single-frequency observations of 12 satellites are sufficient to meet the service transmission requirements. The second level is good: when the average value is between 2.0 and 4.0 and the standard deviation is less than 1.0, it indicates that the geometric configuration is within an acceptable range but there is some fluctuation. At this time, M1 is 14 satellites. Two redundant satellites are added on top of the 12 satellites to deal with the slight instability of the geometric configuration. It is verified that the dual-frequency observation data of 14 satellites is 169 bits plus 116 bits per satellite multiplied by 14, totaling 1793 bits, about 1.79kbps, which is still less than 2.4kbps. The third level is considered poor: when the average value is greater than 4.0 or the standard deviation is greater than or equal to 1.0, it indicates poor geometry or extreme instability, requiring more satellites to participate in the calculation to ensure positioning accuracy. In this case, M1 is set to the maximum value of 16 satellites, providing 4 redundant satellites. The dual-frequency observation data from these 16 satellites is 169 bits plus 116 bits per satellite, totaling 2025 bits, approximately 2.025 kbps. This leaves a bandwidth margin of approximately 0.375 kbps to ensure transmission reliability. Furthermore, these 16 satellites do not broadcast dual frequencies during operational mode; they only broadcast single-frequency observations from 12 of them. Therefore, operational bandwidth is unaffected. In this embodiment, assuming the current time period is from 2 PM to 3 PM, historical statistics show that the average value for this period is 4.5 and the standard deviation is 1.2, falling into the third level. Therefore, M1 is set to 16 satellites. Since there are still satellites available in the candidate satellite set, the base station selects four more satellites from the candidate satellite set based on their overall quality score, from highest to lowest. These four satellites include PRN18, PRN19, and other high-ranking satellites from the candidate satellite set. These four satellites are then added to the basic candidate list, expanding it to 16 satellites. The first stream of RTCM data generated ultimately contains dual-frequency observations from these 16 satellites, while the second stream of RTCM data still only contains single-frequency observations from the first 12 satellites.

[0034] In calculating the standard deviation, the square of the difference between the PDOP value at each epoch and the mean is first calculated. Then, the average of these squared values ​​is calculated. Finally, the square root of this average is taken to obtain the standard deviation, which quantifies the degree of PDOP fluctuation. The mean thresholds of 2.0 and 4.0, and the standard deviation thresholds of 0.5 and 1.0 used in this embodiment are exemplary values ​​based on engineering practice and are not intended to limit the invention. Specifically, a standard deviation less than 0.5 indicates extremely small PDOP fluctuations and a very stable geometric configuration, allowing for a reduction in the number of satellites to optimize bandwidth; a standard deviation between 0.5 and 1.0 indicates acceptable fluctuations, maintaining a conventional number of satellites; a standard deviation greater than 1.0 indicates significant fluctuations and an unstable geometric configuration, requiring an increase in the number of satellites to provide redundancy. Those skilled in the art can adjust these thresholds according to the actual deployment environment.

[0035] Regarding system hardware architecture and data interaction, this embodiment provides an RTCM screening system applied to PDT to implement the above method. For example... Figure 2 As shown, the system includes a GNSS receiver, processor, memory, and communication module. The GNSS receiver uses a low-cost commercial module, connected to the processor via a standard interface, and outputs GSV and GSA statements of the NMEA-0183 protocol at a fixed frequency. The receiver does not perform GDOP calculation internally, only providing basic functions for satellite tracking and positioning. This solution directly utilizes the PDOP value and satellite selection list output by the GSA statement, leveraging the receiver's inherent output to avoid calculations on the base station side, reducing hardware costs and improving system reliability. The GNSS receiver parses the GSV and GSA statements, obtains the satellite selection list, and outputs it to the processor.

[0036] The processor is the core computing unit of this system, responsible for establishing and maintaining historical statistical models, and reviewing and replacing the satellite selection list output by the GNSS receiver based on these models. When identifying satellites to be optimized, the processor executes a refined three-condition discrimination logic: determining whether the satellite simultaneously meets the following conditions: its historical occurrence probability is lower than a preset probability threshold and its recent consecutive occurrence count is lower than a preset number; its historical average elevation angle is lower than a preset historical elevation angle threshold or its historical average signal-to-noise ratio is lower than a preset historical signal-to-noise ratio threshold; and its current elevation angle is lower than a preset current elevation angle threshold or its current signal-to-noise ratio is lower than a preset current signal-to-noise ratio threshold. Once a satellite to be optimized is identified, the processor removes it and adds high-quality satellites from the candidate satellite set to ensure the list is stable and of optimal quality. Subsequently, the processor dynamically determines the number of broadcast satellites M1 based on the historical PDOP model, ensuring that the amount of dual-frequency observation data for M1 does not exceed the net payload rate of a single time slot. Ultimately, the processor generates two RTCM data streams: the first stream uses dual-frequency observations from the first M1 satellites to adapt to idle bandwidth, and the second stream uses single-frequency observations from a predetermined number of satellites to adapt to ultra-low bandwidth in operational mode, ensuring that this predetermined number of satellites is completely consistent with the corresponding satellites in the first stream. After completing the above calculations and decisions, the processor outputs the historical statistical model data to the memory. When updating the historical statistical model, the processor follows the principle of lag statistics, adding the data of the current epoch to the historical database after the satellite selection decision is completed, preventing current data from interfering with the current decision.

[0037] The memory is responsible for persistently storing historical statistical model data. It stores time-period probability, historical quality, and historical PDOP data in 24 time periods. Time-period probability data includes the frequency with which each satellite appears in the satellite selection list for each time period; historical quality data includes the average elevation angle and average signal-to-noise ratio of each satellite for each time period; historical PDOP data includes the average and standard deviation of PDOP values ​​for each time period. All data is stored in a structured format on non-volatile storage media and is written periodically to ensure data integrity after power failure. When the base station starts up, the processor first loads the stored data from the memory to guide satellite selection. If there is no valid data in the memory, the system enters a cold start mode, relying solely on instantaneous observations for selection. After 24 hours of data accumulation, it automatically switches to a mode guided by the complete historical statistical model. This persistence and self-learning mechanism makes the system increasingly accurate with use and provides power failure recovery capabilities, meeting the high reliability requirements of PDT systems in industrial applications.

[0038] The communication module includes a secondary control channel broadcast unit and an SLC signaling encapsulation unit. The secondary control channel broadcast unit encapsulates dual-frequency RTCM data into data bursts according to the PDT protocol and broadcasts them on the secondary control channel time slots. The SLC signaling encapsulation unit splits single-frequency RTCM data into 24-bit payload blocks, fills them into the payload area of ​​the short-link control signaling, and sends a complete SLC signaling message every 120 milliseconds, completing the transmission of all 12 satellite observations in approximately 4.82 seconds. The entire system has a compact architecture, low incremental hardware costs, and can be directly deployed in existing PDT base stations.

Claims

1. An RTCM screening method applied to PDT, characterized in that, include: S1. The base station parses GSV and GSA statements to obtain elevation angle, signal-to-noise ratio, satellite selection list and PDOP value, and establishes and maintains historical statistical models. S2. Using the selected satellite list as the initial placeholder, identify and remove satellites to be optimized based on the historical statistical model, construct a candidate set and sort it according to the quality score, and supplement it to the preset number to obtain the candidate list; S3. Dynamically determine the number of broadcast satellites M1 based on historical statistical models, so that the amount of dual-frequency observation data of M1 satellites does not exceed the net payload rate of a single time slot; S4. Generate two RTCMs: The first path takes the dual-frequency observations of the first M1 satellites to adapt to the idle state, and the second path takes the single-frequency observations of the first preset number of satellites in the candidate list to adapt to the service state, and this preset number of satellites is consistent with the corresponding satellites in the first path.

2. The RTCM screening method applied to PDT according to claim 1, characterized in that, The historical statistical model includes a time-period probability model, a historical quality model, and a historical PDOP model. The time-period probability model records the frequency of each satellite appearing in the satellite selection list for 24 time periods and updates it using a sliding window or exponential smoothing method. The historical quality model records the average elevation angle and average signal-to-noise ratio of each satellite in each time period. The historical PDOP model records the average value and standard deviation of the PDOP value in each time period. Each model is constructed based on historical data before the current epoch. The model data is persistently stored and loaded when the base station starts.

3. The RTCM screening method applied to PDT according to claim 1, characterized in that, The identification conditions for the satellite to be optimized in step S2 are as follows: the satellite simultaneously meets the following conditions: the historical occurrence probability is lower than the preset probability threshold and the number of recent consecutive occurrences is lower than the preset number; the historical average elevation angle is lower than the preset historical elevation angle threshold or the historical average signal-to-noise ratio is lower than the preset historical signal-to-noise ratio threshold; the current elevation angle is lower than the preset current elevation angle threshold or the current signal-to-noise ratio is lower than the preset current signal-to-noise ratio threshold.

4. The RTCM screening method applied to PDT according to claim 1 or 3, characterized in that, In step S2, the number of satellites to be optimized removed at one time shall not exceed the preset upper limit, and the remaining satellites to be optimized shall be removed gradually in subsequent epochs.

5. The RTCM screening method applied to PDT according to claim 1, characterized in that, In step S2, the comprehensive quality score is calculated based on the satellite's instantaneous elevation angle, instantaneous signal-to-noise ratio, and historical statistical model. Satellites with better historical performance receive a higher score bonus.

6. The RTCM screening method applied to PDT according to claim 2, characterized in that, In step S3, the value of M1 is dynamically determined by the historical average PDOP value and PDOP standard deviation of the current time period. When the average PDOP value and standard deviation are both lower than the corresponding lower limit, M1 takes the minimum value. When the average PDOP value or standard deviation is higher than the corresponding upper limit, M1 takes the maximum value. The minimum and maximum values ​​are determined by the constraint that the amount of dual-frequency observation data does not exceed the net load rate of a single time slot.

7. The RTCM screening method applied to PDT according to claim 1 or 2, characterized in that, The preset number is 12, the minimum value of M1 is 12, and the maximum value is 16. In step S2, the candidate set also includes visible satellites that are not included in the initial placeholder list in the current epoch. In step S3, if M1 is greater than the preset number, satellites are selected from the candidate set and added to the candidate list until M1 is reached.

8. An RTCM screening system applied to PDT, characterized in that, The system includes a GNSS receiver, which parses GSV and GSA statements, obtains a satellite selection list, and outputs it to the processor. The processor establishes and maintains a historical statistical model, reviews and replaces the satellite selection list based on the historical statistical model, determines the number of broadcast satellites M1, ensures that the amount of dual-frequency observation data of M1 satellites does not exceed the net payload rate of a single time slot, and generates two RTCMs: the first RTCM takes the dual-frequency observations of the first M1 satellites to adapt to the idle state, and the second RTCM takes the single-frequency observations of the first preset number of satellites to adapt to the service state, and this preset number of satellites is consistent with the corresponding satellites in the first RTCM. The historical statistical model data is then output to the memory. Historical statistical model data is persistently stored in memory.

9. The RTCM screening system applied to PDT according to claim 8, characterized in that, When the processor identifies a satellite to be optimized, it determines whether the satellite simultaneously meets the following conditions: its historical occurrence probability is lower than a preset probability threshold and its recent consecutive occurrence count is lower than a preset number; its historical average elevation angle is lower than a preset historical elevation angle threshold or its historical average signal-to-noise ratio is lower than a preset historical signal-to-noise ratio threshold; its current elevation angle is lower than a preset current elevation angle threshold or its current signal-to-noise ratio is lower than a preset current signal-to-noise ratio threshold.

10. The RTCM screening system applied to PDT according to claim 8, characterized in that, When the processor updates the historical statistical model, the data of the current epoch is included in the historical database after the satellite selection decision is completed; the memory persistently stores the probability of the time period, historical quality and historical PDOP data in 24 time periods, and loads the stored data when the base station starts up to guide the satellite selection.

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