Method and device for measuring work parameters of base station, medium and product

Abstract

The embodiment of the invention provides a method and device for measuring work parameters of a base station, a medium and a product, and relates to the technical field of communication. The method comprises the following steps: acquiring original observation data of a satellite based on a sensing base station, and converting the original observation data into a format file of a preset protocol; the preset protocol is a lightweight protocol including time, longitude and latitude; determining difference correction data according to the format file and a dynamic grid difference algorithm based on spatial-temporal clustering; determining position data according to the differential correction data, the original observation data and preset ephemeris data on that day; and according to the position data, obtaining target work parameter data needing to be measured. According to the scheme provided by the invention, the problems of insufficient precision and limited data real-time performance of the existing base station antenna work parameter measurement are solved.

Description

Technical Field

[0001] This application relates to the field of communication technology, specifically to a method, apparatus, medium, and product for measuring base station operating parameters. Background Technology

[0002] In current technologies, the measurement of base station antenna engineering parameters such as latitude, longitude, altitude, and azimuth mainly relies on manual measurement, UAV surveying, or satellite-based real-time dynamic differential positioning (RTK) methods. Manual measurement is limited by the environment, has low accuracy, and is inefficient; UAVs require professional operation and cannot monitor around the clock; RTK methods are limited by distance, rely on real-time data links, and have difficulty in backtracking historical data. These existing methods generally suffer from insufficient accuracy (meter-level) and real-time limitations, making it difficult to meet the high-precision engineering parameter requirements of sensing base stations. Summary of the Invention

[0003] At least one embodiment of this application provides a method, apparatus, medium, and product for measuring base station engineering parameters, which addresses the problems of insufficient measurement accuracy and data real-time limitations in existing base station antenna engineering parameter measurement schemes.

[0004] To solve the above-mentioned technical problems, this application is implemented as follows:

[0005] In a first aspect, embodiments of this application provide a method for measuring base station operating parameters, including:

[0006] The system collects raw observation data from satellites based on a sensor base station and converts the raw observation data into a format file with a preset protocol; the preset protocol is a lightweight protocol that includes time, longitude, and latitude.

[0007] Based on the format file and the dynamic grid difference algorithm based on spatiotemporal clustering, the difference correction data is determined;

[0008] The location data is determined based on the differential correction data, the original observation data, and the preset ephemeris data for the day;

[0009] Based on the location data, obtain the target engineering parameter data to be measured.

[0010] Optionally, after obtaining the target engineering parameter data to be measured, the method further includes:

[0011] Monitor the position fluctuation of the sensing base station according to a preset period. If the first sum of positive accumulation or the second sum of negative accumulation determined by the cumulative sum control chart algorithm is greater than or equal to a preset trigger threshold, execute the step of collecting the original observation data of the satellite based on the sensing base station, and at the same time reset the positive accumulation and the negative accumulation to 0.

[0012] Optionally, the format of the raw observation data is a preset binary encoding format, and the raw observation data includes at least one of the following:

[0013] Satellite system information;

[0014] Satellite number;

[0015] Positioning and correction information;

[0016] Satellite range information;

[0017] Signal pseudorange;

[0018] Signal phase data.

[0019] Optionally, based on the format file and the dynamic grid difference algorithm based on spatiotemporal clustering, the differential correction data is determined, including:

[0020] Based on the dynamic grid difference algorithm based on spatiotemporal clustering, the format file is subjected to trajectory noise removal processing to obtain preprocessed data;

[0021] Based on the trajectory length and grid number in the preprocessed data, the trajectory type is classified;

[0022] Based on the trajectory type, the corresponding grid parameters are adjusted and the trajectory is clustered to determine the differential correction data.

[0023] Optionally, the trajectory types include a first trajectory type, a second trajectory type, and a third trajectory type with sequentially increasing trajectory lengths. Based on the trajectory type, corresponding grid parameter adjustments and trajectory clustering are performed to determine the differential correction data, including:

[0024] When the trajectory type is represented as a first trajectory type, the trajectories of the first trajectory type are clustered into single grid point objects, and the differential correction data corresponding to the first trajectory type is determined based on the position information of the single grid point objects.

[0025] When the trajectory type is represented as the second trajectory type, the ionospheric state of each initial grid corresponding to the trajectory of the second trajectory type is obtained; the ionospheric state includes at least three levels, and the ionospheric state of each initial grid is determined according to the principle of the highest level;

[0026] The initial grid is encrypted according to the priority of the ionospheric state from high to low to obtain the encrypted grid; wherein, during the encryption process, if the total number of encrypted grids exceeds a preset maximum value, the grid of the current highest priority ionospheric state is dynamically adjusted and the encryption of grids of low priority ionospheric states is stopped.

[0027] Based on the encrypted grid, the trajectories of the second trajectory type are clustered to obtain multiple first cluster grid points. Based on the position information of the first cluster grid points, the corresponding differential correction data is determined.

[0028] When the trajectory type is represented as the third trajectory type, the initial grid corresponding to the trajectory of the third trajectory type is expanded and adjusted, and the total number of grids after adjustment does not exceed the preset maximum value, and the expanded grid is obtained.

[0029] Based on the expanded grid, the trajectories of the third trajectory type are clustered to obtain multiple third cluster grid points. The corresponding differential correction data is determined according to the position information of the third cluster grid points.

[0030] Optionally, position data is determined based on the differential correction data, the original observation data, and the preset ephemeris data for the day, including:

[0031] Preprocessing operations such as data filtering, noise removal, and data interpolation are performed on the differential correction data, the original observation data, and the daily ephemeris data to obtain the preprocessed differential correction data, the original observation data, and the daily ephemeris data.

[0032] The location data is determined based on the differential correction data after the preprocessing operation, the original observation data, the ephemeris data of the day, and the preset post-processing solution algorithm.

[0033] Optionally, based on the location data, the target engineering parameter data to be measured is obtained, including:

[0034] Extract the raw data points from the location data;

[0035] Based on the original data points, determine the mean and standard deviation of the original data points;

[0036] The upper and lower limits are determined based on the mean, the standard deviation, and the preset confidence interval.

[0037] Based on the upper limit and the lower limit, obtain the target engineering parameter data to be measured.

[0038] Secondly, embodiments of this application provide a base station operating parameter measurement device, comprising:

[0039] The first processing module is used to collect raw observation data from satellites based on the sensing base station and convert the raw observation data into a format file with a preset protocol; the preset protocol is a lightweight protocol that includes time, longitude, and latitude.

[0040] The first determining module is used to determine the differential correction data based on the format file and the dynamic grid difference algorithm based on spatiotemporal clustering.

[0041] The second determining module is used to determine the position data based on the differential correction data, the original observation data, and the preset ephemeris data for the day;

[0042] The second processing module is used to obtain the target engineering parameter data to be measured based on the location data.

[0043] Thirdly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method as described in any one of the first aspects.

[0044] Fourthly, embodiments of this application provide a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the method as described in any one of the first aspects.

[0045] Compared with existing technologies, the base station engineering parameter measurement method, device, medium and product provided in this application collects raw satellite observation data through a sensing base station and converts it into a lightweight protocol file containing time, latitude and longitude. It generates differential correction data by using a dynamic grid differential algorithm of spatiotemporal clustering. Then, based on the differential correction data, raw observation data and daily ephemeris, the location data is determined, and finally the target engineering parameters are obtained. This solves the problems of insufficient measurement accuracy and limited real-time data of existing base station antenna engineering parameters. Attached Figure Description

[0046] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0047] Figure 1 A flowchart illustrating the method for measuring base station operating parameters provided in this application embodiment;

[0048] Figure 2 A flowchart illustrating a specific embodiment of the method for measuring base station operating parameters provided in this application;

[0049] Figure 3 This is a schematic diagram of the structure of the base station engineering parameter measuring device provided in the embodiments of this application. Detailed Implementation

[0050] The terms "first," "second," etc., used in this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same class, without limiting the number of objects; for example, the first object can be one or more. Furthermore, "or" in this application indicates at least one of the connected objects. For example, "A or B" covers three scenarios: Scenario 1: including A but not B; Scenario 2: including B but not A; Scenario 3: including both A and B. The character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0051] The term "instruction" in this application can be either a direct instruction (or explicit instruction) or an indirect instruction (or implicit instruction). A direct instruction can be understood as one in which the sender explicitly informs the receiver of specific information, the operation to be performed, or the requested result, etc.; an indirect instruction can be understood as one in which the receiver determines the corresponding information based on the instruction sent by the sender, or makes a judgment and determines the operation to be performed or the requested result, etc., based on the judgment result.

[0052] To enable those skilled in the art to better understand the embodiments of this application, the following description is provided first:

[0053] Base station: Also known as a base station site or base station equipment, it is one of the key devices in a wireless communication network. Base stations are used to communicate and transmit data with mobile devices (such as mobile phones, wireless network cards, etc.) to achieve wireless communication coverage.

[0054] Sensing base stations are a type of communication infrastructure that not only provides traditional communication services such as voice and data transmission, but also integrates sensing capabilities. They can collect and process various types of information from the environment, such as the location, speed, and trajectory of low-altitude drones. Sensing base stations can provide more intelligent and personalized services through this sensing data. They integrate communication and sensing functions, enabling target recognition, trajectory tracking, and intrusion detection, thus supporting low-altitude economic development, low-altitude security, airway management, sea surface monitoring, and integrated ground-air communication. To improve sensing accuracy, high-precision parameter data is required for sensing base stations to significantly enhance the accuracy of object location sensing.

[0055] Antenna parameters: These are the parameters of a base station antenna, including its location (longitude, latitude, altitude), azimuth, and downtilt angle. These parameters have a decisive impact on the electromagnetic coverage of the base station. Therefore, the accuracy of antenna parameters is of great importance in network planning and optimization.

[0056] Post-processing differential: Using the carrier phase observations of the satellite from the synchronous observation base station receiver and the mobile receiver, instead of establishing real-time data transmission as in real-time differential dynamic positioning, the observations are linearly combined afterward to form virtual carrier phase observations, which are then used to calculate the precise position.

[0057] Post-Processed Kinematic (PPK): The relative position of the receiver is determined by using the raw observation data of the Global Navigation Satellite System (GNSS) from historical satellite signal receiving equipment and virtual carrier phase observations, and then the physical position is derived based on the coordinate system and epoch.

[0058] It should be noted that the traditional manual method of measuring base station parameters has the following drawbacks: (1) Due to the complex on-site environment, the mechanical compass has many limitations in high-altitude measurement, making it inconvenient to measure the site and difficult to measure accurately; (2) Currently, the latitude and longitude measurement records of antennas are recorded as one latitude and longitude per station rather than one latitude and longitude per antenna, which is not very accurate for later analysis. At the same time, it is inconvenient to carry multiple measuring tools, the operation steps are cumbersome and the work efficiency is low; (3) The current engineering management methods are relatively primitive, based on word-of-mouth construction and email construction receipts, lacking effective engineering progress and quality management methods. At the same time, the antenna database design does not match the actual value and the updates are not timely, which brings great drawbacks to subsequent work.

[0059] It should also be noted that the drone-based base station parameter measurement method has the following drawbacks: it requires drone equipment, demands certain skills from drone operators, and requires significant human and material resources; it requires a certain level of accuracy in the collected data, which greatly reduces data collection efficiency and makes it difficult to meet the increasing demand for base station parameter collection from the growing number of base stations; and drone measurement cannot provide full-time coverage and cannot achieve dynamic real-time monitoring.

[0060] It should also be noted that there are drawbacks to using satellite base station-based parameter measurement: real-time dynamic positioning faces a series of limitations as the distance between the rover and the base station increases, affecting the accuracy and reliability of RTK positioning; using satellite data for calculation requires real-time processing, needing to receive differential correction signals at every moment, and the received satellite observation data and differential data must be matched one by one before processing, but the base station may not be able to send and receive observation data in real time due to network limitations, and it is also impossible to backtrack historical data; the amount of satellite observation data is relatively large, requiring a lot of network resources to transmit to the calculation module.

[0061] As described in the background section, in the existing technology, the measurement of base station antenna parameters (latitude, longitude, altitude, azimuth, etc.) mainly relies on manual measurement, UAV surveying, or real-time satellite RTK. Manual measurement is limited by the environment, has low accuracy, and is inefficient; UAVs require professional operation and cannot monitor around the clock; RTK is limited by distance, relies on real-time data links, and has difficulty in backtracking historical data. These methods generally suffer from insufficient accuracy (meter-level), real-time limitations, high costs, and fragmented data management, making it difficult to meet the high-precision parameter requirements of sensing base stations. To solve at least one of the above problems, embodiments of this application provide a method, device, medium, and product for measuring base station parameters, which can reduce or avoid the occurrence of the above situations, improve the accuracy of low-altitude target perception, and contribute to scenarios such as low-altitude economy, security monitoring, and airway management.

[0062] This application provides a method, apparatus, medium, and product for measuring base station engineering parameters. The method and apparatus are based on the same concept, and since the principles by which they solve the problem are similar, their implementations can be referred to interchangeably; repeated details will not be repeated.

[0063] Please refer to Figure 1 This application provides a method for measuring base station operating parameters, including:

[0064] Step 11: Collect raw observation data from satellites based on the sensing base station, and convert the raw observation data into a format file of a preset protocol; the preset protocol is a lightweight protocol that includes time, longitude, and latitude.

[0065] Here, the sensing base station combines communication and sensing functions. It collects raw observation data from satellites (such as BeiDou and GPS), which may include core observations like pseudorange and carrier phase. This raw data is then converted into a pre-defined lightweight protocol file. The pre-defined protocol only includes core fields such as time, longitude, and latitude, eliminating redundant information and reducing data transmission and parsing overhead. This application standardizes the data format, reduces transmission latency, lays the foundation for subsequent real-time calculations, and simplifies the data acquisition link by relying on the sensing base station.

[0066] Step 12: Determine the differential correction data based on the format file and the dynamic grid difference algorithm based on spatiotemporal clustering.

[0067] In this embodiment, based on the standardized format file from step 11, a dynamic grid differential algorithm using spatiotemporal clustering is applied for data processing to generate differentially corrected data. The dynamic grid differential algorithm based on spatiotemporal clustering can eliminate abnormal data caused by signal obstruction and noise using spatiotemporal clustering. Furthermore, it can utilize a dynamic grid to adaptively adjust the grid size according to satellite distribution and terrain, avoiding the accuracy limitations of a fixed grid. This can correct systematic errors such as satellite clock bias and atmospheric delay, improving the accuracy of subsequent positioning data.

[0068] Step 13: Determine the position data based on the differential correction data, the original observation data, and the preset ephemeris data for the day;

[0069] Step 14: Based on the location data, obtain the target engineering parameter data to be measured.

[0070] In this embodiment, differential correction data, raw observation data, and preset daily ephemeris data are used to output position data through a solution model (such as the least squares method). The preset daily ephemeris data includes orbital parameters, clock error parameters, and other data. Daily ephemeris has smaller errors than long-term ephemeris, and the complementary nature of multi-source data offsets the limitations of a single data source. This method of obtaining high-precision position data for the base station antenna installation point is the core basis for engineering parameter calculation. Based on the precise position data obtained in step 13, combined with antenna structure parameters, base station reference point coordinates, etc., the target engineering parameter data to be measured is derived through a geometric model. The target engineering parameter data may include longitude, latitude, elevation information, etc. This application transforms high-precision position data into engineering parameter results required for actual engineering, completing the measurement closed loop.

[0071] Optionally, the format of the raw observation data is a preset binary encoding format, and the raw observation data includes at least one of the following:

[0072] Satellite system information;

[0073] Satellite number;

[0074] Positioning and correction information;

[0075] Satellite range information;

[0076] Signal pseudorange;

[0077] Signal phase data.

[0078] In this embodiment, the sensing base station is equipped with a satellite receiver acquisition module to collect GNSS satellite signals and record raw observation data, including satellite system, satellite number, basic positioning correction, satellite coarse range, signal pseudorange, signal phase, and other data. The acquired data format is generally the Radio Technical Commission for Maritime Services (RTCM) format, which is binary data encoding and is a standard protocol for differential correction data transmission in Global Navigation Satellite Systems (GNSS). To reduce the amount of data exchanged between modules, the raw observation data in RTCM format is converted into a preset protocol format file, such as the GPGGA file format. GPGGA is a lightweight NMEA protocol that includes time, latitude, longitude, and altitude data, and its data volume accounts for only about 7.7% of that in RTCM.

[0079] It should be noted that satellite system information can be identification data indicating which satellite navigation system it comes from. It can also be used for subsequent differential algorithms to adapt corresponding error correction models based on satellite system type, supporting multi-system data fusion, improving observation data redundancy, and enhancing positioning stability. Satellite ID is used to uniquely identify a single satellite, matching the ephemeris data of the day to accurately obtain the satellite's real-time orbit and clock bias information. The satellite ID is used to filter valid satellites, avoiding invalid data from affecting calculation accuracy. Positioning correction information is preliminary correction data from the satellite or base station, providing a basic correction benchmark for differential correction in step 12, reducing the computational load of the differential algorithm, offsetting some systematic errors in advance, and improving the accuracy of subsequent differential correction. Satellite range information is related to the satellite's spatial position relative to the sensing base station. Signal pseudorange is the observation distance from the sensing base station receiver to the satellite, including errors such as satellite clock bias, atmospheric delay, and multipath effects. It is the core calculation object for differential correction in step 12 and the core input information for position calculation in step 13. Signal phase data is the observed phase difference between the satellite carrier signal and the receiver's local oscillation signal. Compared to pseudorange, phase data has higher observation accuracy (millimeter level) and is the core basis for high-precision positioning. It can be used in step 12 to improve the accuracy of the correction data and ultimately support centimeter-level or decimeter-level position data.

[0080] Optionally, after step 14 above, the method further includes:

[0081] Monitor the position fluctuation of the sensing base station according to a preset period. If the first sum of positive accumulation or the second sum of negative accumulation determined by the cumulative sum control chart algorithm is greater than or equal to a preset trigger threshold, execute the step of collecting the original observation data of the satellite based on the sensing base station, and at the same time reset the positive accumulation and the negative accumulation to 0.

[0082] In this embodiment, this step is a dynamic closed-loop calibration mechanism for the location accuracy of the sensing base station. Through periodic monitoring and trend deviation identification, it avoids long-term distortion of the location data of the base station due to environmental changes, and ensures that subsequent engineering parameter measurements are always based on the accurate and latest base station location. Essentially, it is a full life cycle accuracy guarantee of one-time calibration, continuous monitoring, and deviation over-limit recalibration.

[0083] Furthermore, the location data of the sensing base station output in step 14 is used as the reference value. All subsequent location fluctuations are based on the real-time monitoring of the deviation between the location and this reference value. Every preset period, the real-time current location of the sensing base station is obtained without the need for a complete recalculation. This can be achieved through simplified observation or real-time signal fitting, taking efficiency into account. The deviation between the real-time location and the reference location is then calculated.

[0084] Optionally, the preset cycle length needs to balance accuracy stability and resource consumption; there is no fixed value, and it should be adapted to the scenario. For example, in high-precision scenarios (e.g., parameter error ≤ 1cm): the cycle is set to a short cycle (e.g., 5 minutes, 10 minutes, etc.) to avoid the accumulation of small deviations; in ordinary-precision scenarios (e.g., error ≤ 5cm): the cycle is set to a long cycle (e.g., 30 minutes, 1 hour, etc.) to reduce computation and transmission overhead; in extreme scenarios (e.g., the base station is installed in a stable environment and is not touched by external forces): the cycle can be extended to several hours, adjusted as needed.

[0085] It should be noted that this application employs the Cumulative Sum Control Chart (CUSUM) algorithm. CUSUM is a statistical algorithm for identifying small and persistent trend deviations. Unlike single-deviation threshold judgments, it is better at capturing slow drifts. Here, it covers bidirectional deviation trends through positive and negative accumulation. The CUSUM algorithm can determine the first sum for positive accumulation and the second sum for negative accumulation. The first sum is the total amount of deviations in the real-time position from the reference position in the positive direction. The deviation value can be calculated each time. If the deviation is positive, it is added to the first sum; if it is negative, the first sum is not reduced, and only the positive trend is accumulated. The first sum is used to capture unidirectional positive drift. The second sum is the total amount of deviations in the real-time position from the reference position in the negative direction. The deviation value can be calculated each time. If the deviation is negative, it is added to the second sum, and the absolute value is accumulated, or the trend is marked with a negative sign within the algorithm; if it is positive, the second sum is not reduced. The second sum is used to capture unidirectional negative drift.

[0086] In one specific implementation, after the base station is turned on, a normal state benchmark is established by collecting high-precision parameters 10 times (with an interval of ≥5 minutes), and the benchmark mean μ0 and standard deviation σ are calculated. The standard deviation σ is used to represent the normal fluctuation range. High-precision parameter data Xi is collected once according to a preset period (the preset period can be set to daily), and compared with the benchmark value μ0 to monitor whether the position has a continuous drift rather than a single accidental fluctuation.

[0087] The advantage of the CUSUM algorithm is its ability to detect small but persistent systematic drifts. Single small deviations may be normal noise, while continuously accumulating deviations are the anomalies that require correction. The first sum of the positive accumulation determined by the CUSUM algorithm is denoted as Shi. When the daily collected Xi > μ0, the accumulated positive drift deviation is reduced by K = 0.5σ as a noise tolerance to avoid accidental triggering due to random fluctuations. If the accumulated sum is negative, it is reset to 0, focusing only on persistent positive drift. The second sum of the negative accumulation determined by the CUSUM algorithm can be denoted as SLi: When the daily collected Xi < μ0, the accumulated negative drift deviation is reduced by K = 0.5σ as a noise tolerance. If the accumulated sum is negative, it is also reset to 0, focusing only on persistent negative drift.

[0088] When the positive cumulative sum SXi is greater than or equal to the preset trigger threshold H (e.g., H=4σ), the base station continues to drift in the positive direction and the total deviation reaches the critical value; or when the negative cumulative sum SLi is greater than or equal to H, the base station continues to drift in the negative direction and the total deviation reaches the critical value. This indicates that the drift of the base station position has exceeded the normal fluctuation range. If it is not corrected, it will affect the stability of the system. Therefore, the correction process is initiated.

[0089] The complete steps of re-executing the original satellite observation data, solving the GPGGA results, and calculating high-precision parameters are re-executed. The base station position is calibrated with the latest accurate data to offset the accumulated drift. Both SHi and SLi are reset to 0, which means that the base station has returned to a stable state after correction. The subsequent accumulation of deviation will start again based on the new stable state to avoid the impact of historical drift values ​​on subsequent monitoring.

[0090] This application solves the problem of small and continuous drift in base station location by using the logic of continuous monitoring (preset period), precise detection (CUSUM algorithm), automatic correction (recalculation of original data), and cyclic reset (accumulation and zeroing). It avoids accidental correction triggered by single fluctuations (by filtering noise with K=0.5σ) and can promptly capture systemic drifts that affect system stability (by triggering correction with H=4σ). Ultimately, it ensures the long-term stability of the high-precision parameters of the sensing base station and supports the continuous and reliable operation of the system.

[0091] Optionally, after obtaining the target engineering parameter data to be measured, this application also includes: archiving and storing the data. Historical data such as the time period of each calculation, raw observation data, GPGGA input data, GPGGA solution results, and high-precision engineering parameter results are archived and stored for easy retrospective analysis or prediction later.

[0092] Optionally, step 12 above includes:

[0093] Based on the dynamic grid difference algorithm based on spatiotemporal clustering, the format file is subjected to trajectory noise removal processing to obtain preprocessed data;

[0094] Based on the trajectory length and grid number in the preprocessed data, the trajectory type is classified;

[0095] Based on the trajectory type, the corresponding grid parameters are adjusted and the trajectory is clustered to determine the differential correction data.

[0096] It should be noted that the first trajectory type represents a small trajectory with a length ≤ 10 km; the second trajectory type represents a regular trajectory with a length > 10 km, or a trajectory length covering an initial grid number ≤ 32; and the third trajectory type represents an ultra-long trajectory with a trajectory length covering an initial grid number > 32. The value 32 here is only an example and can be adjusted according to specific needs; this application does not impose any restrictions.

[0097] In this embodiment, accurate clustering is achieved by differentiating trajectory types and dynamically adjusting the grid, ultimately generating reliable differential correction data. The processing steps for each trajectory type are designed around adapting the grid to trajectory characteristics and controlling the total number of grid cells.

[0098] Furthermore, the trajectory types include a first trajectory type, a second trajectory type, and a third trajectory type with sequentially increasing trajectory lengths. Based on the trajectory type, corresponding grid parameter adjustments and trajectory clustering are performed to determine the differential correction data, including:

[0099] When the trajectory type is represented as a first trajectory type, the trajectories of the first trajectory type are clustered into single grid point objects, and the differential correction data corresponding to the first trajectory type is determined based on the position information of the single grid point objects.

[0100] When the trajectory type is represented as the second trajectory type, the ionospheric state of each initial grid corresponding to the trajectory of the second trajectory type is obtained; the ionospheric state includes at least three levels, and the ionospheric state of each initial grid is determined according to the principle of the highest level;

[0101] The initial grid is encrypted according to the priority of the ionospheric state from high to low to obtain the encrypted grid; wherein, during the encryption process, if the total number of encrypted grids exceeds a preset maximum value, the grid of the current highest priority ionospheric state is dynamically adjusted and the encryption of grids of low priority ionospheric states is stopped.

[0102] Based on the encrypted grid, the trajectories of the second trajectory type are clustered to obtain multiple first cluster grid points. Based on the position information of the first cluster grid points, the corresponding differential correction data is determined.

[0103] When the trajectory type is represented as the third trajectory type, the initial grid corresponding to the trajectory of the third trajectory type is expanded and adjusted, and the total number of grids after adjustment does not exceed the preset maximum value, and the expanded grid is obtained.

[0104] Based on the expanded grid, the trajectories of the third trajectory type are clustered to obtain multiple third cluster grid points. The corresponding differential correction data is determined according to the position information of the third cluster grid points.

[0105] In one implementation of this application, the processing steps for the first trajectory type (i.e., small trajectory) specifically include:

[0106] When the trajectory type is represented as the first trajectory type, the trajectories of the first trajectory type are clustered into single grid point objects. Based on the location information of the single grid point objects, the differential correction data corresponding to the first trajectory type is determined. The type determination premise is: based on the trajectory noise removal and preprocessing data in the previous steps, the trajectory length is calculated. If the trajectory length is ≤10 kilometers, it is determined to be the first trajectory type. Since the coverage area of ​​the small trajectory is small, there is no need for complex grid division. Single grid point adaptation is directly used, and the small trajectory is clustered into a single grid point object. The entire preprocessed small trajectory data is aggregated into a unified grid point object. The location information of this grid point integrates the spatiotemporal characteristics of all valid points of the trajectory. Based on the location information of this single grid point, combined with subsequent baseline calculation, atmospheric correction and other subsequent processes, the differential correction data corresponding to the small trajectory is calculated and output.

[0107] In another implementation of this application, the processing steps for the second trajectory type (i.e., a regular trajectory) specifically include:

[0108] After preprocessing, if the trajectory length is >10 km, or the number of initial grid cells corresponding to the trajectory length coverage area is ≤32 (the maximum coverage area of ​​the trajectory length grid cells can be adjusted according to actual needs), it is determined to be the second trajectory type. The ionospheric state of the initial grid cells is then obtained, i.e., all initial grid cells covered by the regular trajectory are first determined, along with the basic grid cells before encryption. For each initial grid cell, its corresponding ionospheric state is obtained. If multiple ionospheric states exist within an initial grid cell, such as some areas being calm and others active, only the highest-level state is retained. For example, if a grid cell contains both moderate and active states, it is ultimately determined to be active.

[0109] Optionally, the initial grid can be encrypted according to priority. The ionospheric state level of this application can be set to five levels, as shown in Table 1:

[0110] Table 1:

[0111]

[0112] Referring to the levels in Table 1, this application sets encryption priority rules, with ionospheric state levels from high to low (Level 5 > Level 4 > Level 3 > Level 2 > Level 1).

[0113] During the encryption process, the total number of grid cells is counted in real time. If the total number of grid cells of the current priority exceeds the preset maximum value (default 32) after encryption, the encryption of subsequent lower priority grid cells is immediately stopped. The density of the current highest priority grid cell is dynamically adjusted: its encryption density is flexibly adjusted within the corresponding basic density range. For example, if the basic encryption density of a level 5 grid cell is 1 kilometer, and the total number of grid cells exceeds 32 after encryption, it is adjusted to a suitable density between 1 and 5 kilometers to ensure that the total number of grid cells is ≤32.

[0114] For example, the initial grid consists of 20 grids (10 level 5, 5 level 3, and 5 level 1). First, the level 5 grids are encrypted: the 10 level 5 grids are encrypted at a density of 1 km, resulting in 40 grids (exceeding 32). The density of the level 5 grids is then adjusted to 2 km (from 10 to 20). The total number of grids is 20 ≤ 32. Encryption of level 3 and level 1 grids is then stopped. Finally, the number of encrypted grids is 20.

[0115] Further, trajectory clustering and differential data generation are performed. The effective data points of the regular trajectory are spatiotemporally clustered using an encrypted grid, with trajectory points within each grid aggregating into a first cluster grid point. The location information of all first cluster grid points is collected, and combined with baseline calculation, atmospheric correction, and other processes, differential correction data corresponding to the regular trajectory is generated.

[0116] In another implementation of this application, the processing steps for the third trajectory type (i.e., ultra-long trajectory) specifically include:

[0117] After preprocessing, if the number of initial grid cells corresponding to the trajectory length coverage is greater than 32, it is determined to be a third trajectory type. Due to the wide coverage of the ultra-long trajectory and the excessive number of initial grid cells, there is no need to judge the ionospheric state; instead, grid expansion is directly performed to increase the coverage of individual grid cells and reduce the total number of grid cells. The total number of grid cells after adjustment must be less than or equal to the preset maximum value (default 32) to ensure the efficiency of subsequent processing. The expanded grid cells are used to perform spatiotemporal clustering on the effective data points of the ultra-long trajectory, and the trajectory points in each expanded grid cell are aggregated into a third cluster grid cell. Based on the location information of these cluster grid cells, combined with baseline calculation, atmospheric correction and other processes, differential correction data corresponding to the ultra-long trajectory is generated.

[0118] This application addresses the problem of accurately generating differential data for trajectories of different lengths by employing a type-specific adaptive grid strategy. For short trajectories (Type 1), a single grid point is used to simplify calculations and ensure coverage. For regular trajectories (Type 2), dynamic encryption driven by ionospheric state balances accuracy and efficiency. For ultra-long trajectories (Type 3), the grid is enlarged to reduce the number of grids and ensure processing feasibility. By employing rules of prioritizing high-order data, optimizing encryption, and dynamically adjusting for excess data, the reliability and computational efficiency of the differential correction data are ensured.

[0119] Optionally, position data is determined based on the differential correction data, the original observation data, and the preset ephemeris data for the day, including:

[0120] Preprocessing operations such as data filtering, noise removal, and data interpolation are performed on the differential correction data, the original observation data, and the daily ephemeris data to obtain the preprocessed differential correction data, the original observation data, and the daily ephemeris data.

[0121] The location data is determined based on the differential correction data after the preprocessing operation, the original observation data, the ephemeris data of the day, and the preset post-processing solution algorithm.

[0122] In this embodiment, the original satellite observation data, differential correction data, and daily ephemeris data are used as inputs. First, preprocessing is performed to ensure the quality of the input data. Then, error correction and accurate calculation are performed by post-processing algorithms to eliminate signal propagation errors, and finally, high-precision position data is output.

[0123] Among them, the preset post-processing algorithm can be selected as a satellite post-processing algorithm, which is used to realize error correction and positioning; the pre-processing operation can be selected as data quality check and screening, noise and gross error removal, and data interpolation, which are used to purify the input data and eliminate invalid or interfering data; the daily ephemeris data is used to provide basic parameters of satellite orbit and atmospheric environment.

[0124] The preprocessing operations for the differentially corrected data, raw observation data, and daily ephemeris data, including data filtering, noise removal, and data interpolation, involve removing invalid data, correcting outlier data, and filling in missing data. Specifically, these operations may include:

[0125] (1) Remove satellite data with incomplete dual-frequency pseudorange or carrier phase from the original observation data. For example, pseudorange and carrier phase are core observation values ​​for positioning, and they cannot be calculated if missing. Remove satellite data with an elevation angle <5°, as satellite signals with low elevation angles have long propagation paths, more interference, and lower accuracy. Remove data with missing satellite orbit parameters or clock errors from the ephemeris data, as the ephemeris is the basis for calculating the real-time position of the satellite, and it cannot be used for positioning if missing. Compare the observation data from different stations to verify the rationality of the baseline length (straight-line distance between two stations). If it exceeds the physical possibility range, it should be removed to avoid data anomalies. Statistically calculate the reception ratio of differential correction data. If the transmission is interrupted, use historical differential correction data to establish a mathematical model to fill in the missing values ​​and ensure the continuity of differential data.

[0126] (2) Eliminate random noise and obvious errors (gross errors) in the data to avoid interfering with positioning accuracy. Use the statistical method of ephemeris prediction accuracy range to eliminate random noise in satellite laser ranging (SLR) data, such as small data fluctuations caused by electromagnetic interference during signal propagation. Gross error removal can include: setting reasonable thresholds for observation values ​​(such as pseudorange and carrier phase) and removing abnormal data that exceed the thresholds, such as pseudorange abrupt changes caused by signal blockage and erroneous values ​​caused by equipment failure.

[0127] (3) Data imputation. This involves filling in the remaining small amount of missing data after filtering and denoising to ensure data integrity. For example, the least squares method can be used to interpolate or fit the missing data. Based on the valid data before and after the missing data, a mathematical model is established to calculate reasonable values ​​for the missing locations, such as missing values ​​after a short interruption in differential correction data or local missing values ​​in ephemeris data. Specifically, this involves reading the first epoch data, checking data quality, generating model correction errors, and removing unqualified data through cycle slips and chi-square tests. Common-view satellites are calculated, and residuals are calculated using real-valued solutions of the state; then, ambiguity is updated using filtering algorithms (such as Kalman filtering); a fixed solution or a non-fixed floating-point solution is output; the next epoch data is looped until all data has been looped; the encoding format is converted based on the calculated results, and the final result is output.

[0128] Preprocessing output results: After three operations of filtering, denoising, and interpolation, three types of core data with high quality, no missing data, and low interference are obtained, providing reliable input for post-processing calculation.

[0129] Based on the differential correction data after the preprocessing operation, the original observation data, the ephemeris data of the day, and the preset post-processing solution algorithm, the position data is determined. This is the calculation step. Through multiple rounds of error correction and algorithm iteration, various errors in signal propagation (ionospheric delay, tropospheric delay, satellite clock error, etc.) are eliminated, and finally high-precision position data is obtained. Specifically, the calculation is performed in cycles according to epochs. An epoch is a unit of time for satellite observation, such as one epoch every 1 second.

[0130] For example, the initialization and first-round data verification are performed. The preprocessed data of the first epoch is read, an error correction model is generated, and unqualified data from the first round is removed. The raw observation data, differentially corrected data, ephemeris data, and ionospheric data of the current epoch are read; based on the ephemeris and ionospheric data, model correction errors are generated to pre-correct known error sources, such as satellite orbit errors and ionospheric delay errors; cycle slip detection is used to identify discontinuous jumps in carrier phase observations, often caused by signal interruptions; and chi-square detection is performed to verify the consistency between the observations and model predictions, removing unqualified data from the current epoch, such as incompletely eliminated gross errors and cycle slip data.

[0131] Identifying common-view satellites (satellites simultaneously observed by both the base station and the mobile terminal) is crucial for differential positioning. Calculating observation residuals verifies data consistency. In the current epoch, satellites simultaneously observed by both the base station and the target observation point (e.g., the mobile terminal) are selected (common-view satellites). Only observation data from common-view satellites can be used for differential correction. The state-based real-valued solution method is used to calculate the residuals (differences) between the observed values ​​(pseudorange, carrier phase) and the model predictions, further verifying data quality. If the residuals exceed a threshold, the data needs to be re-verified.

[0132] Filtering algorithm optimization and ambiguity update. The impact of random errors is reduced by using filtering algorithms to update ambiguity parameters. Kalman filtering or other filtering algorithms are employed, combined with the results from historical epochs, to smooth the observation data of the current epoch, reducing the impact of random noise on the positioning results. The ambiguity parameters of the carrier phase are updated and fixed. The accurate solution of ambiguity directly determines the positioning accuracy. After filtering algorithm optimization, the ambiguity is fixed to integers as much as possible; successful fixing results in higher positioning accuracy.

[0133] Output the solution result for the current epoch. Based on the ambiguity resolution result, output the positioning solution for the current epoch. The result types include fixed solutions and non-fixed floating-point solutions. Fixed solutions indicate that the ambiguity has been successfully fixed to integers, resulting in high positioning accuracy, such as centimeter or decimeter level. Non-fixed floating-point solutions indicate that the ambiguity has not been successfully fixed, resulting in only floating-point values, with relatively lower positioning accuracy (such as meter level).

[0134] The algorithm iterates through all epochs, re-executes the initialization and first-round data verification steps, and processes the preprocessed data of each epoch in turn until the data of all observation epochs has been solved, ensuring the continuity of position data throughout the entire observation period.

[0135] Finally, the encoding format is converted and the final output is performed. The solution results (fixed or floating-point solutions) of all epochs are converted into a standard encoding format, and the final high-precision location data is output. The output results can include the three-dimensional coordinates (longitude, latitude, and elevation) and positioning accuracy indicators of each epoch, which can be directly used for subsequent applications, such as trajectory reconstruction and high-precision mapping.

[0136] The entire process of this application not only reflects the error correction value of differential correction data, but also maximizes the accuracy potential of the original observation data through multiple rounds of optimization of the post-processing algorithm, and finally outputs reliable high-precision location data.

[0137] Optionally, based on the location data, the target engineering parameter data to be measured is obtained, including:

[0138] Extract the raw data points from the location data;

[0139] Based on the original data points, determine the mean and standard deviation of the original data points;

[0140] The upper and lower limits are determined based on the mean, the standard deviation, and the preset confidence interval.

[0141] Based on the upper limit and the lower limit, obtain the target engineering parameter data to be measured.

[0142] It should be noted that the original data points can be longitude, latitude, or elevation from location data, and the steps for each type of information can be applied to this specific step.

[0143] In this embodiment of the application, the calculated location data is used as input. The process of extracting the original data, calculating statistical indicators, defining reasonable intervals, removing outliers, and taking the accurate mean is used to filter discrete abnormal data and obtain high-precision and high-reliability target engineering parameter data. The target engineering parameter data can be such as corrected longitude, latitude, and elevation.

[0144] Specific operations include extracting raw data points for the longitude dimension: the longitude values ​​of all valid epochs after calculation, denoted as the set {N1, N2, ..., Nn}, where n is the total number of longitude data points. Alternatively, extracting raw data points for the latitude dimension: the latitude values ​​of all valid epochs after calculation. Or, extracting raw data points for the elevation dimension: the elevation values ​​of all valid epochs after calculation. This provides independent and clean basic data for subsequent dimensional statistical calculations, avoiding interference from data from different dimensions.

[0145] Based on the original data points, determine the mean and standard deviation of the original data points. The arithmetic mean μ = (N1 + N2 + ... + Nn) / n can be calculated, and the standard deviation σ = sqrt( For each dimension of the original data points, the arithmetic mean (μ) and the sample standard deviation (σ) are calculated respectively. The two indicators together reflect the central tendency and dispersion of the data.

[0146] Based on the mean, standard deviation, and pre-set confidence interval, determine the upper and lower limits. Here, the pre-set confidence interval can be selected as Z-score (standard score) = 1.96, upper limit = μ + 1.96 * σ, lower limit = μ - 1.96 * σ. Using the mean μ as the center and the standard deviation σ as the scale, combined with the pre-set confidence interval, define a reasonable range for normal data. Data exceeding this range are considered outliers. Here, the pre-set confidence interval is set to 95%, meaning that 95% of normal data will fall within this range, and only 5% of extreme data (outliers) will be removed. Establishing outlier screening criteria provides clear boundaries for subsequent removal of discrete data and purification of valid data.

[0147] Based on the upper and lower limits, obtain the target working parameter data to be measured, filter all data points, and remove values ​​outside the upper and lower limit ranges. That is, retain the value Fj with lower limit ≤ Ni ≤ upper limit. Then calculate the arithmetic mean of Fj as the final value of this calculation, μF=(F1+F2+...+Fj) / j. Filter valid data points according to the defined upper and lower limits, remove outliers, and recalculate the mean to obtain the final target working parameter data.

[0148] Repeat the above steps to obtain the target engineering parameter data, such as the corrected longitude, latitude, and elevation. These three values ​​together constitute the target engineering parameter data to be measured and are then entered into the final engineering parameter result. Three key design features ensure the accuracy of the engineering parameters: independent calculations for each dimension, with longitude, latitude, and elevation not interfering with each other, avoiding the impact of cross-dimensional data dispersion on the accuracy of a single engineering parameter, and ensuring the accuracy and usability of the target engineering parameter data.

[0149] Reference Figure 2 As shown, this application also provides a method for measuring base station positioning parameters, specifically including a full-link process from satellite data acquisition to monitoring and correction, utilizing... Figure 2 The modules shown achieve high-precision engineering parameter calculation and feedback optimization through data interaction. The specific steps are as follows:

[0150] The satellite data acquisition phase begins with the activation of the satellite data acquisition module, which is responsible for acquiring raw satellite observation data and transmitting the data in two paths: one path transmits the data in RTCM format to the data decoding module; the other path transmits the data directly in OMC satellite observation data format to the satellite post-processing module.

[0151] During the data decoding and differential generation phase, the data decoding module receives RTCM format data, decodes it, and then transmits the data to the post-differential generation module in OMC GPGGA format. Based on the GPGGA data, the post-differential generation module generates virtual differential data through baseline calculation, atmospheric correction, and a dynamic grid differential algorithm based on spatiotemporal clustering, and then transmits this virtual differential data to the satellite post-processing module.

[0152] In the satellite post-processing and engineering parameter calculation stage, the satellite post-processing module combines OMC satellite observation data, virtual differential data, as well as daily ephemeris and ionospheric data, to execute post-processing calculation algorithms, such as data preprocessing, cycle slip detection, and Kalman filtering, and outputs the OMC calculation results to the high-precision engineering parameter calculation module.

[0153] The high-precision engineering parameter calculation module extracts latitude, longitude, elevation and other data from the solution results. By extracting the original data points, calculating the mean and standard deviation, defining the 95% confidence interval, removing outliers, and taking the average twice, the high-precision engineering parameter results are generated.

[0154] During the monitoring, correction, and data storage phase, the monitoring / correction module receives high-precision engineering parameter results, performs periodic checks, and sends the check feedback information back to the satellite data acquisition module to optimize the accuracy of subsequent satellite data acquisition.

[0155] Meanwhile, the intermediate data from the post-differential generation module and the final engineering parameter results from the high-precision engineering parameter calculation module are all transmitted to the data storage and management module to complete the data storage and management.

[0156] exist Figure 2 Throughout the entire process, a closed-loop design involving acquisition, decoding, differential processing, calculation, parameter processing, monitoring, and storage was implemented to achieve end-to-end processing from raw satellite data to high-precision parameters. Furthermore, a feedback mechanism was used to continuously optimize the accuracy of data acquisition and calculation.

[0157] The proposed solution, after continuous field testing, showed relatively stable engineering parameter data. The maximum deviation of latitude and longitude of the base station engineering parameters was 9 cm, and the maximum deviation of elevation was 7 cm. The horizontal accuracy of the sensing position was improved from the level of 100 meters to within 15 meters, and the vertical accuracy was improved from 30 meters to within 13 meters.

[0158] It should be noted that satellite post-processing technology (including post-processing differential VRS) has a wide range of applications. Based on the high-precision sensing requirements of sensing base stations, such as accurately sensing the location information of drones, high-precision parameters are needed. This proposal utilizes satellite post-processing technology (including post-processing differential VRS) to calculate high-precision positioning coordinates and output high-precision position parameters (latitude, longitude, and altitude). In satellite post-processing, differential VRS technology selects historical data from multiple physical stations (CORS stations) within a certain area. After data quality inspection, the optimal three stations are selected to form a network, creating a mesh coverage. Based on the latitude and longitude of the user's rover data, a virtual reference station is created nearby, and the differential data of this virtual station is output to achieve high-precision positioning. Traditional post-processing technologies typically rely on data from a single or a few reference stations and require customers to establish temporary reference stations, which is more time-consuming and costly.

[0159] This application utilizes a network of multiple stations, mitigating the impact of system and ionospheric errors, resulting in higher accuracy. Users no longer need to establish temporary reference stations, saving time and costs and improving operational efficiency. It does not rely on real-time data communication links and supports a data acquisition-before-processing mode, allowing use in environments without real-time data transmission, such as remote areas or areas with signal obstruction. Since multiple stations are typically located within a region, even if one fails, other stations can still network and perform calculations, enhancing system stability. An optimized satellite post-processing algorithm solves the problem of conveniently calculating high-precision parameters of base stations. By continuously monitoring high-precision parameter data and supporting dynamic correction strategies, it addresses the issue of maintaining stable high-precision parameter accuracy of base stations and resolves the problem of inaccurate object sensing accuracy by base stations.

[0160] It should also be noted that GPS accuracy is generally within 10 meters. This application, by utilizing the satellite post-processing capabilities of the integrated navigation post-processing platform, improves the position accuracy from 10 meters to 10 centimeters. This application, by leveraging the historical differential source data of the integrated navigation post-processing platform, can perform parameter position calculations at any given time without manual intervention; even historical observation data can be calculated and used for retrospective analysis.

[0161] In summary, this application uses satellite post-processing technology to replace traditional measuring tools such as GPS measuring instruments, achieving precise measurement of antenna engineering parameters, improving accuracy from 10 meters to centimeter level; post-processing of data reduces the limitations of real-time data processing. Traditional measurement methods are susceptible to deviations due to complex environments and human factors, while the solution in this application is unaffected by these factors, ensuring the accuracy of the measurement results. The measurement results of this application can also be encrypted before being uploaded to a server, thus ensuring the security of the antenna engineering parameter data; this application can also be used in conjunction with a data management platform to achieve closed-loop, electronic management of work assignment, measurement, receipt, and data processing.

[0162] This application addresses the technical challenge of accurately and conveniently calculating the high-precision parameters of base stations using satellite post-processing technology. Previous 4G or 5G communication base stations only performed communication functions, providing public calls and information transmission. This application's solution utilizes satellite post-processing technology to achieve precise calculation of high-precision positional parameters (latitude, longitude, and altitude). This solution leverages data received from the satellite navigation system, combined with ground reference station data, and employs post-processing algorithms to calculate coordinates, eliminating various error sources and improving positioning accuracy.

[0163] The various methods of the embodiments of this application have been described above. Apparatus for implementing the above methods will now be provided.

[0164] Please refer to Figure 3 This application embodiment also provides a base station operating parameter measurement device, including:

[0165] The first processing module 31 is used to collect raw observation data from satellites based on the sensing base station and convert the raw observation data into a format file with a preset protocol; the preset protocol is a lightweight protocol that includes time, longitude, and latitude.

[0166] The first determining module 32 is used to determine the differential correction data based on the format file and the dynamic grid difference algorithm based on spatiotemporal clustering.

[0167] The second determining module 33 is used to determine the position data based on the differential correction data, the original observation data, and the preset daily ephemeris data;

[0168] The second processing module 34 is used to obtain the target engineering parameter data to be measured based on the location data.

[0169] Optionally, the base station operating parameter measuring device of this application further includes:

[0170] The third processing module is used to monitor the position fluctuation of the sensing base station according to a preset period. If the first sum of positive accumulation or the second sum of negative accumulation determined by the cumulative sum control chart algorithm is greater than or equal to a preset trigger threshold, the module executes the step of collecting the original observation data of the satellite based on the sensing base station, and simultaneously resets the positive accumulation and the negative accumulation to 0.

[0171] It should be noted that the format of the raw observation data is a preset binary encoding format, and the raw observation data includes at least one of the following:

[0172] Satellite system information;

[0173] Satellite number;

[0174] Positioning and correction information;

[0175] Satellite range information;

[0176] Signal pseudorange;

[0177] Signal phase data.

[0178] Optionally, the first determining module 32 described above includes:

[0179] The first acquisition unit is used to perform trajectory noise removal processing on the format file according to the dynamic grid difference algorithm based on spatiotemporal clustering, and acquire the preprocessed data;

[0180] The first processing unit is used to classify trajectory types based on the trajectory length and grid number in the preprocessed data;

[0181] The first determining unit is used to adjust the corresponding grid parameters and perform trajectory clustering according to the trajectory type to determine the differential correction data.

[0182] Optionally, the trajectory types include a first trajectory type, a second trajectory type, and a third trajectory type with sequentially increasing trajectory lengths, and the first determining unit is specifically used for:

[0183] When the trajectory type is represented as a first trajectory type, the trajectories of the first trajectory type are clustered into single grid point objects, and the differential correction data corresponding to the first trajectory type is determined based on the position information of the single grid point objects.

[0184] When the trajectory type is represented as the second trajectory type, the ionospheric state of each initial grid corresponding to the trajectory of the second trajectory type is obtained; the ionospheric state includes at least three levels, and the ionospheric state of each initial grid is determined according to the principle of the highest level;

[0185] The initial grid is encrypted according to the priority of the ionospheric state from high to low to obtain the encrypted grid; wherein, during the encryption process, if the total number of encrypted grids exceeds a preset maximum value, the grid of the current highest priority ionospheric state is dynamically adjusted and the encryption of grids of low priority ionospheric states is stopped.

[0186] Based on the encrypted grid, the trajectories of the second trajectory type are clustered to obtain multiple first cluster grid points. Based on the position information of the first cluster grid points, the corresponding differential correction data is determined.

[0187] When the trajectory type is represented as the third trajectory type, the initial grid corresponding to the trajectory of the third trajectory type is expanded and adjusted, and the total number of grids after adjustment does not exceed the preset maximum value, and the expanded grid is obtained.

[0188] Based on the expanded grid, the trajectories of the third trajectory type are clustered to obtain multiple third cluster grid points. The corresponding differential correction data is determined according to the position information of the third cluster grid points.

[0189] Optionally, the second determining module 33 described above includes:

[0190] The second processing unit is used to perform preprocessing operations such as data filtering, noise removal and data interpolation on the differential correction data, the original observation data and the daily ephemeris data to obtain the preprocessed differential correction data, the original observation data and the daily ephemeris data.

[0191] The second determining unit is used to determine the location data based on the differential correction data after the preprocessing operation, the original observation data, the ephemeris data of the day, and the preset post-processing solution algorithm.

[0192] Optionally, the second processing module 34 described above includes:

[0193] The third processing unit is used to extract the original data points from the location data;

[0194] The third determining unit is used to determine the mean and standard deviation of the original data points based on the original data points;

[0195] The fourth determining unit is used to determine the upper limit and lower limit values ​​based on the mean, the standard deviation, and the preset confidence interval;

[0196] The second acquisition unit is used to acquire the target engineering parameter data to be measured based on the upper limit value and the lower limit value.

[0197] It should be noted that the device in this embodiment corresponds to the device used in the above-described measurement method for base station engineering parameters. The implementation methods in the above embodiments are all applicable to the embodiments of this device and can achieve the same technical effect. The device provided in this application embodiment can implement all the method steps implemented in the above method embodiments and can achieve the same technical effect. Therefore, the parts that are the same as those in the method embodiments and the beneficial effects will not be described in detail here.

[0198] This application also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the various processes of the above-described method for measuring base station parameters, achieving the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0199] This application also provides a computer program product, including computer instructions. When the computer instructions are executed by a processor, they implement the various processes of the above-described method for measuring base station parameters and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0200] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0201] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0202] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for measuring base station operating parameters, characterized in that, The method comprises the following steps: collecting satellite raw observation data based on a common sense base station, and converting the raw observation data into a format file of a preset protocol; the preset protocol is a lightweight protocol containing time, longitude, and latitude; determining differential correction data according to the format file and a dynamic grid differential algorithm based on space-time clustering; determining position data according to the differential correction data, the raw observation data, and preset ephemeris data of the day; obtaining target work parameter data required for measurement according to the position data.

2. The method of claim 1, wherein, After obtaining the target work parameter data required for measurement, the method further comprises the following steps: monitoring position fluctuation of the common sense base station at a preset period, and in the case that a first sum value of forward accumulation or a second sum value of reverse accumulation determined according to a cumulative and control chart algorithm is greater than or equal to a preset trigger threshold, performing the step of collecting satellite raw observation data based on the common sense base station, and resetting the forward accumulation and the reverse accumulation to 0 at the same time.

3. The method of claim 1, wherein, The format of the raw observation data is a preset binary encoding format, and the raw observation data comprises at least one of the following: satellite system information; satellite number; position correction information; satellite range information; signal pseudo-range; signal phase data.

4. The method of claim 1, wherein, According to the format file and the dynamic grid differential algorithm based on space-time clustering, the differential correction data is determined, which comprises the following steps: performing trajectory noise elimination processing on the format file according to the dynamic grid differential algorithm based on space-time clustering to obtain preprocessed data; dividing trajectory types according to the trajectory length and the grid number in the preprocessed data; performing corresponding grid parameter adjustment and trajectory clustering according to the trajectory types to determine the differential correction data.

5. The method of claim 4, wherein, The trajectory types comprise a first trajectory type, a second trajectory type, and a third trajectory type in which the trajectory length is sequentially increased, and the differential correction data is determined according to the corresponding grid parameter adjustment and trajectory clustering of the trajectory types, which comprises the following steps: in the case that the trajectory type is the first trajectory type, clustering the trajectory of the first trajectory type into a single grid point object, and determining the corresponding differential correction data of the first trajectory type according to the position information of the single grid point object; in the case that the trajectory type is the second trajectory type, obtaining ionospheric states of each initial grid corresponding to the trajectory of the second trajectory type; the ionospheric states comprise at least three levels, and the ionospheric state of each initial grid is determined according to the principle of high level first; encrypting the initial grid according to the priority of the ionospheric state from high to low, and obtaining an encrypted grid; wherein, in the encryption process, if the total number of encrypted grids exceeds a preset maximum value, the grid of the highest priority ionospheric state is dynamically adjusted, and the encryption of the grid of the low priority ionospheric state is stopped; clustering the trajectory of the second trajectory type based on the encrypted grid to obtain a plurality of first clustered grid points, and determining the corresponding differential correction data according to the position information of the first clustered grid points; In a case where the trajectory type is represented as a third trajectory type, an initial grid corresponding to a trajectory of the third trajectory type is expanded and adjusted, and a total number of adjusted grids does not exceed the preset maximum value, to obtain an expanded grid; Based on the expanded grid, the trajectory of the third trajectory type is clustered to obtain a plurality of third clustered grid points, and corresponding differential correction data is determined according to position information of the third clustered grid points.

6. The method of claim 1, wherein, According to the differential correction data, the original observation data, and preset ephemeris data of the day, position data is determined, including: The differential correction data, the original observation data, and the ephemeris data of the day are subjected to a preprocessing operation of data filtering, noise elimination, and data interpolation, to obtain the differential correction data, the original observation data, and the ephemeris data of the day after the preprocessing operation; According to the differential correction data, the original observation data, the ephemeris data of the day after the preprocessing operation, and a preset post-processing calculation algorithm, the position data is determined.

7. The method of claim 1, wherein, According to the position data, target work parameter data required for measurement is obtained, including: Original data points in the position data are extracted; According to the original data points, a mean value and a standard deviation of the original data points are determined; According to the mean value, the standard deviation, and a preset confidence interval, an upper limit value and a lower limit value are determined; According to the upper limit value and the lower limit value, the target work parameter data required for measurement is obtained.

8. A base station operating parameter measuring device, characterized by, Including: A first processing module is configured to collect original observation data of a satellite based on a common sense base station, and convert the original observation data into a format file of a preset protocol; The preset protocol is a lightweight protocol containing time, longitude, and latitude; A first determining module is configured to determine differential correction data according to the format file and a dynamic grid difference algorithm based on space-time clustering; A second determining module is configured to determine position data according to the differential correction data, the original observation data, and preset ephemeris data of the day; A second processing module is configured to obtain target work parameter data required for measurement according to the position data.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 7.

10. A computer program product, characterised in that, The computer instructions are executed by the processor to implement the steps of the method of any one of claims 1 to 7.

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