Real-time monitoring and adjustment method of intelligent antenna feeder system based on Beidou differential positioning

Abstract

The present invention relates to the field of communication engineering technology, and discloses a real-time monitoring and adjustment method for an intelligent antenna feed system based on Beidou differential positioning. The positioning data and operating parameters of the target antenna feed system are synchronously collected through a Beidou differential positioning module and an antenna feed parameter sensor. The data error is corrected by a dynamic error compensation model to obtain calibration data, which is then input into a signal coverage optimization model to generate a beam pointing adjustment instruction, and the servo mechanism is driven to adjust and update the parameter state library. When constructing a dynamic error compensation model, historical data is used to divide the training subset to train the model. The collected positioning data is processed by spatial alignment and the like, and a compensation algorithm is selected according to the data characteristics. The signal coverage optimization model realizes dynamic adjustment by constructing a distribution map and calculating weights, and verifies the adjustment results. The parameter state library classifies and stores data and sets access keys to ensure security. It can also optimize the model to improve performance, effectively improve the monitoring and adjustment accuracy of the antenna feed system, and improve the signal coverage effect.

Description

Technical Field

[0001] The present invention relates to the field of communication engineering technology, and in particular to a real-time monitoring and adjustment method for an intelligent antenna feeder system based on Beidou differential positioning. Background Art

[0002] In modern communications, antenna systems, as a key component of wireless communication networks, have a significant impact on communication quality and signal coverage. With the rapid advancement of communication technology, from 2G to 5G and even 6G, users are placing increasingly high demands on communication networks. They expect not only high-speed and stable data transmission but also excellent signal coverage in a variety of complex environments. However, traditional antenna systems have numerous limitations in terms of monitoring and adjustment.

[0003] Traditional antenna systems struggle to achieve real-time, accurate monitoring. First, conventional positioning technologies are severely impacted by multipath effects. Multipath refers to the phenomenon in which signals, during propagation, are reflected and refracted along different paths before reaching the receiver. This causes interference and errors in the received signal, leading to deviations in the antenna system's positioning and, in turn, affecting accurate assessment of its status. For example, in urban environments with densely populated high-rise buildings, signals can reflect multiple times between buildings, resulting in significant errors in positioning data. Second, sensor noise can also interfere with the accurate collection of operating parameters. Over long-term operation, sensors in antenna systems are affected by factors such as ambient temperature and electromagnetic interference, generating noise. This can lead to deviations in collected operating parameters such as signal strength, standing wave ratio, and feeder line loss, making it difficult to accurately reflect the antenna system's actual operating status.

[0004] Traditional antenna and feeder system adjustment methods are not intelligent and efficient. Previous adjustment methods are often based on fixed empirical rules or manual adjustments, lacking the ability to adapt to complex and changing communication environments and real-time signal requirements. When signal coverage requirements in the target area change, such as during large-scale events when demand for communication capacity and signal strength surges in crowded areas, or when there is environmental interference (such as a nearby new interference source), traditional antenna and feeder systems are unable to quickly and accurately adjust beam pointing, resulting in insufficient signal coverage or severe interference, impacting the user's communication experience. Furthermore, manual adjustment is not only inefficient but also prone to human error, making it difficult to meet the timeliness and accuracy requirements of modern communication networks.

[0005] Traditional antenna systems also face challenges in data management and utilization. Data storage lacks rational classification and efficient organization, and different types of parameter data are often mixed, making data query and analysis difficult and preventing rapid support for system adjustments. Furthermore, insufficient data security measures and a lack of comprehensive access rights management mechanisms can easily lead to data leaks, threatening the secure and stable operation of communication networks.

[0006] In the face of these problems, there is an urgent need for an innovative technical solution that can achieve real-time and accurate monitoring and intelligent and efficient adjustment of the antenna feed system, improve the overall performance and reliability of the communication system, and meet the growing communication needs. This is also the research background and starting point of this invention. Summary of the Invention

[0007] The purpose of the present invention is to provide a real-time monitoring and adjustment method for an intelligent antenna feed system based on Beidou differential positioning to solve the problems raised in the above background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a real-time monitoring and adjustment method for an intelligent antenna feeder system based on Beidou differential positioning, the method comprising:

[0009] The Beidou differential positioning module and antenna parameter sensor are used to synchronously collect positioning data and operating parameters of the target antenna system. The positioning data includes real-time latitude, longitude and elevation information, and the operating parameters include signal strength, standing wave ratio and feeder loss rate.

[0010] Inputting the positioning data and operating parameters into a preset dynamic error compensation model to correct multipath effect errors and sensor noise in the positioning data to obtain calibration data, wherein the dynamic error compensation model dynamically adjusts the compensation coefficient based on historical error distribution characteristics;

[0011] Inputting the calibration data into a preset signal coverage optimization model to generate real-time beam pointing adjustment instructions for the antenna feed system, wherein the signal coverage optimization model determines the adjustment weights based on the signal coverage requirements and environmental interference characteristics of the target area;

[0012] The servo mechanism of the antenna feed system is driven to perform dynamic adjustment based on the adjustment instruction, and a parameter state library of the antenna feed system is updated.

[0013] Preferably, the step of constructing the dynamic error compensation model includes: obtaining a historical positioning data set and corresponding error annotation data, wherein the error annotation data includes a multipath effect type and an error amplitude; dividing training subsets according to the multipath effect type and the error amplitude, each training subset corresponding to a typical error scenario; using the training subsets to train an initial compensation model in parallel, and stopping the training until the compensation accuracy of the initial compensation model for each error scenario is greater than or equal to a preset first threshold, thereby obtaining an intermediate compensation model; inputting the historical positioning data set into the intermediate compensation model, and verifying whether the compensation result output by the intermediate compensation model meets the preset error tolerance; if so, determining the intermediate compensation model as the dynamic error compensation model.

[0014] Preferably, collecting positioning data through the Beidou differential positioning module includes being implemented in the following manner: establishing a communication connection with a target Beidou receiver, wherein the target Beidou receiver is deployed at a preset monitoring node of the antenna feed system; periodically reading the original positioning data stream of the target Beidou receiver according to a preset sampling frequency, and marking the positioning timestamp based on the timing characteristics of the data stream; according to the spatial topological relationship of the antenna feed system, spatially aligning the original positioning data streams of different monitoring nodes at the same timestamp to form a spatially associated positioning data set.

[0015] Preferably, inputting the positioning data into a preset dynamic error compensation model includes: extracting abnormal offset segments from the positioning data, wherein the abnormal offset segments are data segments in which the position offset in a continuous time window exceeds a preset offset threshold; generating an error evaluation index based on the duration and offset amplitude of the abnormal offset segments; dynamically selecting a corresponding compensation algorithm based on the error evaluation index, wherein the Kalman filtering algorithm is used for short-term high-amplitude offsets, and the least squares fitting algorithm is used for long-term low-amplitude offsets.

[0016] Preferably, the method also includes: after correcting the multipath effect error, performing data integrity verification on the calibration data; if the verification finds that the data missing rate exceeds a preset second threshold, triggering the signal coverage optimization model to perform priority compensation for the missing data, wherein high-priority missing data is a data segment whose continuous missing duration exceeds a preset duration.

[0017] Preferably, the signal coverage optimization model includes the following adjustment steps: constructing a spatial signal strength distribution map based on the beam coverage range of the antenna feed system, wherein each distribution node corresponds to a preset geographic coordinate; calculating the coverage optimization weight based on the signal strength gradient difference of adjacent distribution nodes; and dynamically adjusting the target beam pointing in combination with the time series changes of the operating parameters.

[0018] Preferably, the method also includes: after generating the adjustment instruction, performing consistency verification on the adjustment result, wherein the verification method includes comparing the deviation between the adjusted signal strength and the preset target strength; if the deviation exceeds the preset third threshold, recalculating the coverage optimization weight and iteratively adjusting it until the deviation is less than the third threshold.

[0019] Preferably, updating the parameter status library includes: dividing the first-level storage tags according to the parameter type, wherein the first-level storage tags include positioning class, signal strength class and standing wave ratio class; under each level of storage tags, further dividing the second-level storage sub-tags based on the parameter value range; storing the classified parameter data in different partitions of the distributed database according to the tag level.

[0020] Preferably, the method further includes: configuring the access key of the storage tag according to preset data permissions; upon receiving a data query request, verifying whether the key provided by the requester matches the access key of the target storage tag; if so, opening the data access interface of the corresponding storage tag.

[0021] Preferably, the method also includes optimizing the signal coverage optimization model in the following manner: statistically analyzing the adjustment error distribution of the calibration data in different time periods; determining the parameter correction amount of the optimization model based on the error distribution, wherein the coverage optimization weight is increased in the high error period and the interference suppression weight is increased in the low error period; iteratively optimizing the signal coverage optimization model based on the parameter correction amount until the adjustment error rate is less than a preset fourth threshold.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] During the monitoring phase, the Beidou differential positioning module and antenna parameter sensors synchronize data collection, significantly improving data acquisition accuracy and real-time performance. Beidou differential positioning technology effectively mitigates the impact of multipath. Compared to traditional positioning methods, the acquired real-time latitude, longitude, and elevation information is more accurate, reducing positioning errors and laying the foundation for accurate determination of the antenna system's position. Simultaneously, the antenna parameter sensors simultaneously collect operational parameters such as signal strength, standing wave ratio, and feeder loss rate, enabling real-time awareness of the antenna system's omnidirectional status. Furthermore, a pre-set dynamic error compensation model dynamically adjusts compensation coefficients based on historical error distribution characteristics to correct for multipath errors and sensor noise in the positioning data. This model's construction, such as by dividing the training set into parallel training steps for the initial compensation model, enables it to better adapt to different error scenarios, effectively improving data accuracy and providing a reliable basis for subsequent precision adjustments.

[0024] In terms of regulation, the signal coverage optimization model determines adjustment weights based on the target area's signal coverage requirements and environmental interference characteristics, generating real-time beam pointing adjustment instructions. This adjustment method, based on actual needs and environmental characteristics, enables precise control of the antenna system's beam pointing. By constructing a spatial signal strength distribution map, calculating coverage optimization weights, and dynamically adjusting them based on time-series changes in operating parameters, the antenna system can quickly respond to changes in signal coverage and improve signal coverage quality. For example, signal strength can be enhanced in weak signal areas, while beams can be adjusted to avoid interference sources in areas of severe interference, effectively improving the overall performance of the communication system. Furthermore, after generating adjustment instructions, consistency verification is performed. If the adjustment result deviation exceeds a threshold, the weights are recalculated and iterative adjustments are made, ensuring the accuracy and stability of the regulation.

[0025] In terms of data management, a reasonable classification and storage method is adopted when updating the parameter status library. First-level storage tags are divided according to parameter type, and second-level storage sub-tags are divided according to parameter value range. Data is stored in different partitions of the distributed database according to the tag level, facilitating data query, statistics, and analysis, and improving data utilization efficiency. At the same time, data access keys are set, and data access interfaces are only opened after verifying that the requesting party's key matches. This effectively ensures data security, prevents data leakage, and improves the security and reliability of the entire communication system.

[0026] Furthermore, the signal coverage optimization model's optimization mechanism adjusts the error distribution according to the calibration data over different time periods to determine parameter corrections. This mechanism increases the coverage optimization weight during high-error periods and the interference suppression weight during low-error periods. Through iterative optimization, the adjusted error rate is kept below a preset threshold, continuously improving model performance and ensuring the long-term stable and efficient operation of the antenna feeder system to meet ever-changing communication needs. Overall, this invention offers innovations across multiple aspects, from monitoring and regulation to data management and model optimization, comprehensively improving the performance of intelligent antenna feeder systems and possessing extremely high application value and socioeconomic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a working principle diagram of the real-time monitoring and adjustment method of the intelligent antenna feed system based on Beidou differential positioning according to the present invention;

[0028] Figure 2 Workflow diagram for inputting dynamic error compensation model for positioning data;

[0029] Figure 3 A workflow diagram for consistency verification of adjustment results;

[0030] Figure 4 This is a workflow diagram for data permission control. DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0032] See also Figures 1-4 The present invention provides a technical solution: a real-time monitoring and adjustment method for an intelligent antenna feeder system based on Beidou differential positioning, the method comprising:

[0033] Beidou differential positioning modules and antenna parameter sensors are used to simultaneously collect positioning data and operating parameters of the target antenna system. Positioning data includes real-time latitude, longitude, and elevation information, which accurately determine the antenna system's geographic location. Operating parameters include signal strength, standing wave ratio, and feeder loss rate, which reflect the antenna system's operating status. For example, in a certain communication base station, Beidou differential positioning modules are installed at key locations in the antenna system, and antenna parameter sensors are deployed to ensure accurate data acquisition.

[0034] The collected positioning data and operating parameters are fed into a pre-set dynamic error compensation model. This model dynamically adjusts the compensation coefficient based on historical error distribution characteristics, thereby correcting for multipath errors and sensor noise in the positioning data, ultimately generating calibrated data. In real-world applications, multipath can cause deviations in positioning data, and sensors can also generate noise due to various factors. The dynamic error compensation model effectively addresses these issues.

[0035] The calibration data is fed into a pre-set signal coverage optimization model. This model determines adjustment weights based on the target area's signal coverage requirements and environmental interference characteristics, generating real-time beam pointing adjustments for the antenna system. Different target areas have different signal coverage requirements and varying ambient interference conditions. The signal coverage optimization model considers these factors to determine the appropriate adjustment plan.

[0036] The generated adjustment instructions drive the antenna system's servo mechanism to perform dynamic adjustments, adjusting the antenna system's beam pointing according to the instructions. After the adjustment is completed, the antenna system's parameter status database is promptly updated, and the adjusted parameter information is accurately recorded for subsequent analysis and management.

[0037] The present invention will be further described below in conjunction with Examples 1 to 5:

[0038] Example 1:

[0039] This embodiment describes in detail the process of building a dynamic error compensation model. When building a dynamic error compensation model, first obtain the historical positioning data set and the corresponding error annotation data, where the error annotation data includes the multipath effect type and error amplitude. Assume that the historical positioning data set is D, and each positioning data sample is d i , i=1,2,…,n (n is the number of data samples), the corresponding error annotation data is e i , e i Contains multipath effect type t i and error amplitude a iThe training subsets are divided according to the multipath effect type and error amplitude, and each training subset corresponds to a typical error scenario. For example, the multipath effect type is divided into direct wave and reflected wave interference type, multiple reflected wave interference type, etc., and the training subsets are divided for data of different types with similar error amplitude ranges. Let the divided training subset be S j , j==1,2,…,m (m is the number of training subsets). These training subsets are used to train the initial compensation model in parallel until the initial compensation model's compensation accuracy for each error scenario is greater than or equal to the preset first threshold T1. Training is then terminated to obtain the intermediate compensation model. During training, model parameters are continuously adjusted to improve compensation accuracy. The historical positioning dataset D is input into the intermediate compensation model, and the compensation results output by the intermediate compensation model are verified to meet the preset error tolerance. If so, the intermediate compensation model is designated as the dynamic error compensation model. This dynamic error compensation model can effectively adapt to different error scenarios and improve its ability to compensate for positioning data errors.

[0040] In practical applications, a large amount of historical positioning data, such as the antenna feed system of a city's communication network base station, was collected. This data includes positioning data and corresponding error annotations for various weather conditions, time periods, and surrounding environments. Through analysis and processing of this data, multiple training subsets were generated, corresponding to different multipath effect types and error amplitude ranges. The initial compensation model was trained in parallel using these training subsets. After multiple iterations, training was terminated when the model's compensation accuracy for the error scenarios corresponding to each training subset reached a preset first threshold, T1 (e.g., 90%). The intermediate compensation model was then trained by re-entering the historical positioning data set into the intermediate compensation model for verification. If the compensation results met the preset error tolerance (e.g., within ±5 meters), the intermediate compensation model was determined to be the final dynamic error compensation model. This model played a key role in subsequent error compensation of the antenna feed system's positioning data, effectively reducing the impact of multipath error and sensor noise on positioning data.

[0041] Example 2:

[0042] This embodiment focuses on the specific implementation method of collecting positioning data through the Beidou differential positioning module. First, establish a communication connection with the target Beidou receiver, and the target Beidou receiver is deployed at the preset monitoring node of the antenna feed system. During actual installation, according to the structure and monitoring requirements of the antenna feed system, the location of the monitoring node is reasonably selected to ensure that the Beidou receiver can accurately obtain the location information of the antenna feed system. The original positioning data stream of the target Beidou receiver is periodically read according to the preset sampling frequency f, and the positioning timestamp is marked based on the timing characteristics of the data stream. Assume that at t k The original positioning data stream read at the moment is P k , then the timestamp is tk According to the spatial topology of the antenna feed system, the original positioning data streams of different monitoring nodes at the same timestamp are spatially aligned to form a spatially associated positioning data set. For example, there are N monitoring nodes in the antenna feed system. At time t, the original positioning data streams obtained from each monitoring node are P 1,t 、P 2,t ,…,P N,t , through the spatial alignment algorithm, these data streams are integrated into a spatially associated positioning dataset P t The positioning data set processed in this way can more comprehensively and accurately reflect the position status of the antenna feeder system, providing a reliable data basis for subsequent data analysis and processing.

[0043] In the construction of a communication base station in a mountainous area, due to the complex terrain, the position monitoring of the antenna feed system is particularly important. The antenna feed system of the base station is set up with 5 preset monitoring nodes, each of which is equipped with a target BeiDou receiver. The preset sampling frequency f is set to 10Hz, that is, the raw positioning data stream is read every 0.1 seconds. At a certain time t, the raw positioning data streams obtained from the 5 monitoring nodes are P 1,t 、P 2,t 、P 3,t 、P 4,t 、P 5,t According to the spatial topology of the antenna system, a specially designed spatial alignment algorithm is used to integrate these data streams. The algorithm first determines the relative position relationship of each monitoring node, and then matches and merges the data streams with the same timestamp according to the coordinate information in the positioning data, and finally forms a spatially associated positioning data set P. t Collecting and processing positioning data in this way effectively improves the accuracy of antenna and feeder system position monitoring in complex mountainous environments, providing strong support for subsequent real-time monitoring and adjustment.

[0044] Example 3:

[0045] This embodiment focuses on the specific operation of inputting positioning data into the preset dynamic error compensation model. First, the abnormal offset segments in the positioning data are extracted. The abnormal offset segments are data segments whose position offset exceeds the preset offset threshold Δ within the continuous time window W. Assuming that the positioning data sequence is L(t), within the time window [t1, t1+W], if , then this data segment is an abnormal offset segment. An error evaluation index E is generated based on the duration T and offset amplitude A of the abnormal offset segment, where E = f(T, A) (f is a functional relationship determined according to the actual situation). The corresponding compensation algorithm is dynamically selected according to the error evaluation index. For short-term high-amplitude offsets (i.e., T < T0 and A > A0, where T0 and A0 are set duration and amplitude thresholds), the Kalman filter algorithm is used, and for long-term low-amplitude offsets (i.e., T ≥ T0 and A ≤ A0), the least squares fitting algorithm is used. In practical applications, for different types of errors, using appropriate compensation algorithms can more effectively correct the errors in the positioning data and improve the accuracy of the data.

[0046] During the monitoring of the antenna feeder system of a communication base station in a coastal area, the positioning data is significantly affected by the multipath effect in the marine environment. During a certain period, abnormal offsets in the positioning data are detected. By setting the continuous time window W to 10 seconds and the preset offset threshold Δ to 10 meters, it is found that there are some data segments whose position offset exceeds 10 meters within 10 seconds, and these data segments are abnormal offset segments. Analyze these abnormal offset segments and calculate their duration T and offset amplitude A. For example, there is an abnormal offset segment with a duration T of 5 seconds and an offset amplitude A of 15 meters. Since T < T0 (let T0 be 8 seconds) and A > A0 (let A0 be 12 meters), it belongs to a short-term high-amplitude offset, so the Kalman filter algorithm is used for compensation. Another abnormal offset segment has a duration T of 12 seconds and an offset amplitude A of 8 meters. Because T ≥ T0 and A ≤ A0, it belongs to a long-term low-amplitude offset, so the least squares fitting algorithm is used for compensation. By dynamically selecting the compensation algorithm according to the error characteristics in this way, the correction effect of the positioning data error is significantly improved, providing more accurate data for subsequent signal coverage optimization.

[0047] Embodiment 4:

[0048] This embodiment details the adjustment steps of the signal coverage optimization model. The signal coverage optimization model first constructs a spatial signal strength distribution map according to the beam coverage range of the antenna feeder system. Assume that there are M preset geographical coordinate points within the beam coverage range of the antenna feeder system, and each coordinate point is G i , i = 1, 2, …, M, and each distribution node corresponds to a preset geographical coordinate. The signal strength value S i is measured or calculated at each coordinate point, thereby constructing a spatial signal strength distribution map. Calculate the coverage optimization weight based on the signal strength gradient difference between adjacent distribution nodes. Let the signal strengths of adjacent nodes G i and G i+1 be S i and S i+1 respectively, and the signal strength gradient difference is , and the coverage optimization weight W is calculated through a certain algorithm based on this differencei , g is a function relationship determined based on actual conditions. The target beam pointing is dynamically adjusted based on the time series changes of operating parameters. Operating parameters such as signal strength and standing wave ratio change over time. By analyzing the time series of these parameters and comprehensively considering coverage optimization weights, the adjustment direction and amplitude of the target beam pointing are determined, thereby dynamically optimizing the antenna feed system's beam pointing to meet the signal coverage requirements of the target area.

[0049] Coverage optimization weight W i The calculation of is realized by algorithm g, and the specific steps are as follows:

[0050] First, define the adjacent distribution node G i With G i+1 Signal intensity gradient difference , where S i 、S i+1 Node G i , G i+1 Signal strength value (unit: dBm). The maximum theoretical value is 20dBm (determined based on the historical signal strength fluctuation range of the target area), and the weight W i The value range is [0,1].

[0051] The specific calculation logic of algorithm g is: Normalize it and map it to the weight interval. The specific formula is ,in When W i Calculate according to the linear relationship; if (In actual scenarios, Based on historical data settings, this situation rarely occurs), then W i Take 1.

[0052] In a communication network optimization project in a certain city's commercial district, 100 preset geographic coordinate points were set within the antenna feed system beam coverage area. Signal strength monitoring equipment was installed at these coordinate points to obtain the signal strength value S at each point. i , and then construct a spatial signal strength distribution map. For example, the signal strengths of two adjacent coordinate points G5 and G6 are S5 and S6 respectively, and the signal strength gradient difference is calculated. According to the pre-set algorithm g, the coverage optimization weight W5 is calculated. Assume that the actual monitoring signal strength of G5 is , G6 signal strength ,but ,at this time At the same time, the operating parameters of the antenna and feeder system, such as signal strength and standing wave ratio, are monitored over a long period of time to obtain their time series data. Analysis of this time series data reveals that during peak hours on weekdays, signal strength fluctuates significantly and the standing wave ratio also increases. Combined with coverage optimization weights, the target beam direction is dynamically adjusted. During peak hours, the beam intensity directed towards densely populated areas in commercial areas is appropriately increased, and the beam angle is adjusted to avoid interference sources, thereby effectively improving the signal coverage quality in the area and meeting users' demand for communication signals during peak hours.

[0053] Example 5:

[0054] This embodiment mainly describes the update of the parameter status library and related data management operations. When updating the parameter status library, the first-level storage tags are divided according to the parameter type. The first-level storage tags include positioning class, signal strength class and standing wave ratio class, etc. Under each level of storage tags, the second-level storage sub-tags are further divided based on the parameter value range. For example, under the positioning class tag, the second-level sub-tags are divided according to the longitude and latitude range; under the signal strength class tag, the second-level sub-tags are divided according to the strong and weak intervals of the signal strength. The classified parameter data is stored in different partitions of the distributed database according to the tag level. This storage method facilitates data management and query. At the same time, the access key of the storage tag is configured according to the preset data permissions. Assuming that different user roles have different access rights, a corresponding access key K is set for each storage tag. j , j=1,2,…,q (q is the number of storage tags). When receiving a data query request, verify the key K provided by the requester req Whether the access key of the target storage tag matches. req =K j , then the data access interface of the corresponding storage tag is opened, otherwise access is denied, thereby ensuring the security and privacy of the data.

[0055] In the network management system of a large telecommunications operator, the parameters of antenna feeder systems distributed in various locations are managed. The parameters of the antenna feeder system are divided into first-level storage tags such as positioning, signal strength, and standing wave ratio. Under the positioning tag, the world is divided into multiple regions according to the longitude and latitude range, and each region corresponds to a second-level storage sub-tag; under the signal strength tag, it is divided into multiple intervals such as strong signal, medium signal, and weak signal according to the signal strength value, and each interval corresponds to a second-level storage sub-tag. The collected antenna feeder system parameter data is stored in different partitions of the distributed database according to these tag levels. For example, the positioning data of a certain antenna feeder system is stored in the corresponding second-level sub-tag partition under the positioning tag according to its longitude and latitude information; its signal strength data is stored in the corresponding second-level sub-tag partition under the signal strength tag according to the signal strength value. At the same time, in order to ensure data security, different access keys are configured according to different user roles, such as network administrators, technical maintenance personnel, etc. When the network administrator sends a data query request, the system will verify the key K provided by it. req Whether to store the access key K of the tag with the target j If there is a match, the corresponding data access interface is opened, and the administrator can obtain the required data for network analysis and management; if there is a mismatch, access is denied, effectively preventing data leakage and illegal access, and ensuring the security and stability of communication network data.

[0056] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0057] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A real-time monitoring and adjustment method for an intelligent antenna feeder system based on Beidou differential positioning, characterized in that: include: The Beidou differential positioning module and antenna parameter sensor are used to synchronously collect positioning data and operating parameters of the target antenna system. The positioning data includes real-time latitude, longitude and elevation information, and the operating parameters include signal strength, standing wave ratio and feeder loss rate. Inputting the positioning data and operating parameters into a preset dynamic error compensation model to correct multipath effect errors and sensor noise in the positioning data to obtain calibration data, wherein the dynamic error compensation model dynamically adjusts the compensation coefficient based on historical error distribution characteristics; the steps of constructing the dynamic error compensation model include: obtaining a historical positioning data set and corresponding error annotation data, wherein the error annotation data includes multipath effect type and error amplitude; dividing training subsets according to the multipath effect type and error amplitude, each training subset corresponding to a typical error scenario; using the training subsets to train an initial compensation model in parallel until the compensation accuracy of the initial compensation model for each error scenario is greater than or equal to a preset first threshold, stopping the training to obtain an intermediate compensation model; inputting the historical positioning data set into the intermediate compensation model, and verifying whether the compensation result output by the intermediate compensation model meets the preset error tolerance; if so, determining the intermediate compensation model as the dynamic error compensation model; Inputting the calibration data into a preset signal coverage optimization model to generate real-time beam pointing adjustment instructions for the antenna feed system, wherein the signal coverage optimization model determines an adjustment weight based on the signal coverage requirements and environmental interference characteristics of the target area; the signal coverage optimization model includes the following adjustment steps: constructing a spatial signal strength distribution map based on the beam coverage range of the antenna feed system, wherein each distribution node corresponds to a preset geographic coordinate; calculating a coverage optimization weight based on the signal strength gradient difference between adjacent distribution nodes; and dynamically adjusting the target beam pointing in combination with the time series changes of the operating parameters; The servo mechanism of the antenna feed system is driven to perform dynamic adjustment based on the adjustment instruction, and a parameter state library of the antenna feed system is updated.

2. The method for real-time monitoring and adjustment of an intelligent antenna feed system based on Beidou differential positioning according to claim 1 is characterized in that: Collecting positioning data through the Beidou differential positioning module includes the following implementation: establishing a communication connection with a target Beidou receiver, wherein the target Beidou receiver is deployed at a preset monitoring node of the antenna feed system; periodically reading the original positioning data stream of the target Beidou receiver according to a preset sampling frequency, and marking a positioning timestamp based on the timing characteristics of the data stream; and spatially aligning the original positioning data streams of different monitoring nodes at the same timestamp according to the spatial topological relationship of the antenna feed system to form a spatially associated positioning data set.

3. The method for real-time monitoring and adjustment of an intelligent antenna feed system based on Beidou differential positioning according to claim 1, characterized in that: Inputting the positioning data into a preset dynamic error compensation model includes: extracting abnormal offset segments from the positioning data, wherein the abnormal offset segments are data segments whose position offset exceeds a preset offset threshold within a continuous time window; generating an error evaluation index based on the duration and offset amplitude of the abnormal offset segments; and dynamically selecting a corresponding compensation algorithm based on the error evaluation index, wherein a Kalman filtering algorithm is used for short-term high-amplitude offsets and a least squares fitting algorithm is used for long-term low-amplitude offsets.

4. The method for real-time monitoring and adjustment of an intelligent antenna feed system based on Beidou differential positioning according to claim 3 is characterized in that: The method also includes: after correcting the multipath effect error, performing a data integrity check on the calibration data; if the check finds that the data missing rate exceeds a preset second threshold, triggering the signal coverage optimization model to perform priority compensation on the missing data, wherein high-priority missing data is a data segment whose continuous missing duration exceeds a preset duration.

5. The method for real-time monitoring and adjustment of an intelligent antenna feed system based on Beidou differential positioning according to claim 1, characterized in that: The method also includes: after generating the adjustment instruction, performing consistency verification on the adjustment result, wherein the verification method includes comparing the deviation between the adjusted signal strength and the preset target strength; if the deviation exceeds a preset third threshold, recalculating the coverage optimization weight and iteratively adjusting it until the deviation is less than the third threshold.

6. The method for real-time monitoring and adjustment of an intelligent antenna feed system based on Beidou differential positioning according to claim 1, characterized in that: Updating the parameter status library includes: dividing the first-level storage tags according to the parameter type, wherein the first-level storage tags include positioning class, signal strength class and standing wave ratio class; under each level of storage tags, further dividing the second-level storage sub-tags based on the parameter value range; storing the classified parameter data in different partitions of the distributed database according to the tag level.

7. The method for real-time monitoring and adjustment of an intelligent antenna feed system based on Beidou differential positioning according to claim 6, characterized in that: The method further includes: configuring an access key of the storage tag according to preset data permissions; upon receiving a data query request, verifying whether the key provided by the requester matches the access key of the target storage tag; if so, opening a data access interface of the corresponding storage tag.

8. The method for real-time monitoring and adjustment of an intelligent antenna feed system based on Beidou differential positioning according to claim 1, characterized in that: The method also includes optimizing the signal coverage optimization model in the following manner: statistically analyzing the adjustment error distribution of the calibration data in different time periods; determining the parameter correction amount of the optimization model based on the error distribution, wherein the coverage optimization weight is increased in the high error period and the interference suppression weight is increased in the low error period; and iteratively optimizing the signal coverage optimization model based on the parameter correction amount until the adjustment error rate is less than a preset fourth threshold.

Images