Wireless network operation and maintenance method and device

Abstract

The invention discloses a wireless network operation and maintenance method and device. The method comprises the following steps: generating a test plan according to a task type; controlling the unmanned aerial vehicle carrying the test equipment to execute the task according to the test plan; when the network state diagnosis task is executed, the unmanned aerial vehicle is controlled to fly along a preset diagnosis route of the railway line, and field intensity data and service quality data of a wireless network are collected through the test equipment; performing network state diagnosis based on the acquired field intensity data and service quality data to obtain a diagnosis result; when an interference source positioning task is executed, the unmanned aerial vehicle is controlled to fly to an initial detection position according to a preset detection route, radio data of the position where the unmanned aerial vehicle is located is collected through test equipment, interference signal strength distribution is determined, and the interference direction is determined; and controlling the unmanned aerial vehicle to fly from the initial detection position to the next detection position along the interference direction. According to the invention, intelligent diagnosis of the wireless network state can be realized, and the accuracy of interference source positioning is improved.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to wireless network operation and maintenance methods and apparatus. Background Technology

[0002] With the increase in railway operating mileage, the operation and maintenance of GSM-R wireless networks are facing enormous pressure. Currently, network maintenance mainly relies on regularly scheduled inspection trains and manual boarding of operating trains for testing. These traditional methods have long testing intervals, high labor costs, and are limited by train operating schedules, making it difficult to achieve flexible and efficient daily monitoring.

[0003] More importantly, existing methods struggle to accurately reflect the real-world network environment during train operation. The antenna type, installation location, and height used in testing often differ from those of operating trains, leading to inherent biases in the collected wireless signal data. Relying on this data for network optimization and fault diagnosis compromises accuracy and reliability.

[0004] Troubleshooting unexplained interference in a network is particularly difficult. Currently, the main method relies on maintenance personnel carrying equipment and conducting foot searches in suspected areas, which is inefficient and virtually impossible in mountainous, bridge, and tunnel terrain. This allows some interference sources to persist, posing a potential threat to traffic safety. Furthermore, locating faults involving base station towers traditionally requires personnel to climb the towers, which is not only highly dangerous but also heavily dependent on weather and environmental conditions, becoming a significant challenge in maintenance work. Existing technologies cannot meet the needs for intelligent diagnostics of wireless network status and the accuracy required for interference source location.

[0005] Therefore, a wireless network operation and maintenance method is urgently needed to solve the above problems. Summary of the Invention

[0006] This invention provides a wireless network operation and maintenance method to achieve intelligent diagnosis of wireless network status, improve the accuracy of interference source location, and overcome the inherent defects of low efficiency and high risk of manual troubleshooting. The method includes: Test plans are generated based on task types, including network status diagnosis tasks and interference source location tasks. Control the drone equipped with testing equipment to perform tasks according to the test plan; When performing network status diagnosis tasks, the drone is controlled to fly along a preset diagnostic route along the railway line to simulate the train's trajectory. The drone collects wireless network field strength data and service quality data through testing equipment. Based on the collected field strength data and service quality data, network status diagnosis is performed to obtain the diagnosis results. When performing the task of locating the interference source, the UAV is controlled to fly to the initial detection position along a preset detection route, and the following steps are iteratively executed until the positioning conditions are met: radio data of the location of the UAV is collected by the directional antenna in the test equipment, and the interference signal intensity distribution is determined based on the radio data to determine the direction of interference; the UAV is controlled to fly from the initial detection position to the next detection position along the direction of interference; wherein, when the positioning conditions are met, the current detection position is determined as the location of the interference source.

[0007] This invention also provides a wireless network operation and maintenance device for intelligent diagnosis of wireless network status, improving the accuracy of interference source location, and overcoming the inherent defects of low efficiency and high risk of manual troubleshooting. The device includes: The test plan generation module is used to generate test plans based on task types, including network status diagnosis tasks and interference source location tasks. The test plan execution module is used to control the drone equipped with the test equipment to perform tasks according to the test plan; When performing network status diagnosis tasks, the drone is controlled to fly along a preset diagnostic route along the railway line to simulate the train's trajectory. The drone collects wireless network field strength data and service quality data through testing equipment. Based on the collected field strength data and service quality data, network status diagnosis is performed to obtain the diagnosis results. When performing the task of locating the interference source, the UAV is controlled to fly to the initial detection position along a preset detection route, and the following steps are iteratively executed until the positioning conditions are met: radio data of the location of the UAV is collected by the directional antenna in the test equipment, and the interference signal intensity distribution is determined based on the radio data to determine the direction of interference; the UAV is controlled to fly from the initial detection position to the next detection position along the direction of interference; wherein, when the positioning conditions are met, the current detection position is determined as the location of the interference source.

[0008] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described wireless network operation and maintenance method.

[0009] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned wireless network operation and maintenance method.

[0010] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the aforementioned wireless network operation and maintenance method.

[0011] In this embodiment of the invention, a test plan is generated based on task types, including network status diagnosis tasks and interference source location tasks. A drone equipped with testing equipment is controlled to execute tasks according to the test plan. When performing a network status diagnosis task, the drone is controlled to fly along a preset diagnostic route along a railway line to simulate train operation, and the test equipment collects wireless network field strength data and service quality data. Based on the collected field strength data and service quality data, network status diagnosis is performed to obtain diagnostic results. When performing an interference source location task, the drone is controlled to fly along a preset detection route to the initial detection position, iteratively executing the following steps until the location conditions are met: radio data of the drone's location is collected using a directional antenna in the test equipment; the interference signal strength distribution is determined based on the radio data to determine the interference direction; the drone is controlled to fly along the interference direction from the initial detection position to the next detection position; wherein, when the location conditions are met, the current detection position is determined as the location of the interference source. In the above process, this embodiment of the invention generates a test plan based on task types, realizing intelligent planning of operation and maintenance tasks; and controls the drone equipped with testing equipment to execute tasks according to the plan, realizing automated execution of operations. When performing network status diagnosis tasks, the UAV is controlled to fly along a preset diagnostic route to simulate train operation, ensuring that the collected field strength and service quality data accurately reflect the network status during actual train operation. Network status diagnosis is then performed based on the field strength and service quality data, achieving intelligent diagnosis. When performing interference source localization tasks, the UAV is controlled to fly to the initial detection position and iteratively update the next detection position, enabling the search for interference sources. Finally, when the localization conditions are met, the current detection position is determined as the interference source location, allowing the UAV to accurately locate the interference source. This fundamentally overcomes the inherent shortcomings of manual investigation, such as low efficiency and high risk. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 This is a flowchart of the wireless network operation and maintenance method in an embodiment of the present invention; Figure 2 This is a structural diagram of the intelligent operation and maintenance device for wireless networks in an embodiment of the present invention; Figure 3 This is a flowchart illustrating the process of obtaining network diagnostic results in an embodiment of the present invention; Figure 4 This is a flowchart illustrating the process of determining the direction of interference in an embodiment of the present invention; Figure 5 This is a schematic diagram of a wireless network operation and maintenance device in an embodiment of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0014] Figure 1 This is a flowchart of a wireless network operation and maintenance method in an embodiment of the present invention. The method includes: Step 101: Generate a test plan according to the task type, which includes network status diagnosis task and interference source location task; Step 102: Control the drone equipped with the test equipment to perform the task according to the test plan; Step 1021: When performing the network status diagnosis task, control the UAV to fly along the preset diagnosis route of the railway line to simulate the train running trajectory, and collect the field strength data and service quality data of the wireless network through the test equipment; perform network status diagnosis based on the collected field strength data and service quality data to obtain the diagnosis result; Step 1022: When performing the interference source localization task, control the UAV to fly to the initial detection position along the preset detection route, and iteratively execute the following steps until the positioning conditions are met: collect radio data of the UAV's location through the directional antenna in the test equipment, determine the interference signal strength distribution based on the radio data, and determine the interference direction; control the UAV to fly from the initial detection position to the next detection position along the interference direction; wherein, when the positioning conditions are met, the current detection position is determined as the location of the interference source.

[0015] Each step is explained in detail below.

[0016] In step 101, a test plan is generated according to the task type, which includes network status diagnosis tasks and interference source location tasks.

[0017] In a specific embodiment, Figure 2 This is a structural diagram of the intelligent operation and maintenance device for wireless networks in an embodiment of the present invention. This device is an integrated, lightweight airborne testing equipment specifically designed for the operation and maintenance of railway GSM-R networks. Its core components include: AC220V Interface: Provides AC mains power input for the entire system. System Power Supply: Converts the input AC220V power to the operating voltage (e.g., DC12V) required by each unit within the system, and performs power distribution and management. System Display: A human-machine interface used to display mission plans, real-time data, diagnostic results, UAV status, and system alarms. System Controller: Receives operator instructions and sends control commands to each unit within the system; it is the human control entry point for the system. Real-time Processing Unit: Serves as the ground data processing and intelligent decision-making center, responsible for mission planning, in-depth data analysis, algorithm computation (such as empirical mode decomposition and dynamic time warping algorithms), and the generation of comprehensive diagnostic results.

[0018] Unmanned Aerial Vehicle (UAV): Serves as a flight carrier and mobile platform, providing power, flight control, and equipment mounting capabilities. Positioning Module: Integrated on the UAV, used to acquire accurate latitude, longitude, altitude, flight speed, and attitude angle information in real time. Acquisition Unit: As an aerial data aggregation node, responsible for scheduling, buffering, and initially encapsulating data generated by the measurement receiver and test terminal. Multifunctional Measurement Receiver: The core airborne RF measurement hardware, responsible for receiving, down-converting, and digitizing wireless signals, and performing functions such as field strength measurement, spectrum analysis, and frequency sweep decoding. GSM-R Antenna: Connected to the multifunctional measurement receiver and dedicated test terminal, used to receive downlink signals from the GSM-R network during diagnostic tasks. Dedicated Test Terminal: Used to initiate calls, conduct service tests, and generate network signaling interactions during diagnostic tasks. Directional Antenna: Connected to the multifunctional measurement receiver, used for directional signal reception and scanning during interference source localization tasks.

[0019] Air interface: refers to the wireless data link and remote control link connecting the ground human-machine interface unit with the airborne UAV and its payload, used to transmit control commands, telemetry data and acquisition results.

[0020] These modules are interconnected via electrical and data interfaces and are installed together on the UAV platform to form an integrated system capable of performing aerial testing missions.

[0021] In a specific embodiment, the operator views the maintenance requirements through the system display and inputs task commands (such as selecting a segment or specifying a task type) through the system controller. The real-time processing unit receives the commands and, combining historical data and the network model, automatically generates a detailed test plan (including task type, diagnostic / probe route, and test parameters). The test plan is distributed via the air interface. Ground commands first reach the UAV's flight control system. Simultaneously, the planned test configuration commands are distributed to the multi-functional measurement receiver and dedicated test terminal through the acquisition unit, and each device initializes. The system power supply ensures normal power supply to all ground and air units. The UAV, carrying the positioning module, acquisition unit, multi-functional measurement receiver, dedicated test terminal, and GSM-R antenna / directional antenna, takes off and begins to autonomously execute the mission according to the plan.

[0022] In step 102, the drone equipped with the test equipment is controlled to perform the task according to the test plan.

[0023] In step 1021, when performing the network status diagnosis task, the UAV is controlled to fly along the preset diagnosis route of the railway line to simulate the train running trajectory, and the field strength data and service quality data of the wireless network are collected through the test equipment; based on the collected field strength data and service quality data, network status diagnosis is performed to obtain the diagnosis result.

[0024] In a specific embodiment, a drone is controlled to fly to a designated location near the base station tower antenna for fixed-point monitoring and to collect uplink data of the wireless network for network status diagnosis. The network status diagnosis task also includes a fixed-point monitoring mode. In this mode, based on operational needs, such as a specific base station fault alarm, a monitoring task is generated containing the latitude, longitude, and a specified altitude of the target base station tower. After receiving the command, the drone flies to a designated spatial location near the base station antenna, for example, within 1-3 meters of the antenna, and hovers or performs micro-scanning. The uplink signal data collected at this location can reproduce the actual reception environment at the base station antenna to the greatest extent possible, thereby accurately perceiving and diagnosing the uplink coverage quality and interference status. This mode changes the high-risk, low-efficiency operation method of traditional maintenance that requires manual climbing of towers for testing, and is suitable for rapid verification and location of base station uplink problems.

[0025] In one embodiment, controlling a drone to fly along a preset diagnostic route along a railway line to simulate a train's trajectory includes: The test antenna mounted on the UAV is controlled to maintain a set altitude and direction relative to the ground. The altitude relative to the ground is consistent with the installation height of the antenna on the roof of the running train, and the direction is consistent with the tangential direction of the UAV's flight direction.

[0026] In a specific embodiment, signals are received via a GSM-R antenna, and the received signal level is measured and recorded in real time by a multi-functional measurement receiver to generate field strength data. A dedicated test terminal performs a call test, and the multi-functional measurement receiver simultaneously decodes Layer 3 signaling to generate quality of service data.

[0027] Figure 3 This is a flowchart illustrating the process of obtaining network diagnostic results in an embodiment of the present invention. In one embodiment, network status diagnosis is performed based on collected field strength data and service quality data to obtain diagnostic results, including: Step 301: Using the empirical mode decomposition algorithm, the trend component characterizing the gain characteristics of the repeater is obtained based on the field strength data; Step 302: Analyze the changing characteristics of the trend component. When the trend component has a local maximum value, it is determined that the repeater network coverage is normal; otherwise, it is determined that the network coverage is abnormal.

[0028] The implementation of network status diagnosis includes two levels: real-time problem diagnosis based on single test data, and comprehensive evaluation of maintenance effectiveness based on multiple test data.

[0029] 1. Real-time problem diagnosis based on single test data: First, based on the real-time data collected in a single task, the immediate state of the network is identified and classified, specifically including field strength issues and quality of service issues.

[0030] (1) Diagnosis of field strength problem

[0031] Field strength issues include inadequate field strength coverage and abnormal coverage.

[0032] Network coverage failure: If the received signal level is lower than the preset threshold (e.g., -92dBm for CTCS-2 level lines and -98dBm for CTCS-3 level lines), the network coverage failure is determined.

[0033] Abnormal field strength coverage (e.g., repeater malfunction): The specific diagnostic process is as follows: Based on the repeater's location, extract the field strength waveform x(t) within a 1km radius above and below it; use the Empirical Mode Decomposition (EMD) algorithm to extract the intrinsic mode function trend component y(t); solve for the first derivative z'(t) of the trend component; if z'(t) changes from positive to negative, a peak is determined to exist (i.e., the repeater is working normally and the coverage is normal); otherwise, a coverage abnormality is determined. Here, the EMD algorithm is used to filter and extract trend features from the single-acquired non-stationary field strength signal, realizing direct and automated diagnosis of the equipment status.

[0034] (2) Diagnosis of service quality issues

[0035] Service quality issues are determined in real time based on the decoded Layer 3 signaling and measurement reports, including: Call drop: If a disconnection signal is detected, a call drop is determined to have occurred.

[0036] Handover failure: If a handover failure signal is detected, it is determined that a handover failure has occurred.

[0037] Handover anomaly: If multiple ping-pong handovers occur at the coverage boundary of two base stations, or if the handover target cell is inconsistent with the network planning data, then a handover anomaly is determined to exist.

[0038] Poor quality: If the reception quality parameter in the measurement report is higher than the set threshold N times in a row, it is determined that there is poor quality.

[0039] 2. Comprehensive evaluation of repair effectiveness based on multiple test data: After network maintenance is completed, a comprehensive evaluation of the maintenance effectiveness is necessary to objectively confirm whether the network status has been restored to normal. This evaluation is achieved by merging and comparing the test data after maintenance with historical test data before maintenance, as well as records of known network problems.

[0040] (1) Field strength problem recovery assessment

[0041] If a single diagnostic test determines that the network coverage is still unsatisfactory or abnormal, the overall assessment conclusion is that the network status has not been restored. If no problems are found in a single diagnostic test, further quantitative assessment of the degree of recovery is required.

[0042] The specific method is as follows: Select field strength data sequences of the same line section before and after maintenance, and use the Dynamic Time Warping (DTW) algorithm to calculate the similarity between the two sequences. The calculation process includes: constructing a distance matrix D; dynamically programming to calculate the cumulative distance matrix DP; calculating the DTW distance and normalizing it to obtain the similarity (similarity = 1 / (1+DTW distance)). If the similarity is greater than a preset threshold, the network is considered to have fully recovered; otherwise, the network status is considered to still be abnormal. Here, the DTW algorithm is specifically used to solve the sequence alignment problem caused by differences in the test starting point and sampling points, thereby achieving effective and quantitative comparison of historical and current test data. Its core objective is to verify the maintenance effect.

[0043] (2) Service quality problem recovery assessment

[0044] If a single diagnostic test after maintenance still detects issues such as dropped calls, handover failures, abnormal handover processes, or poor service quality, the network status is considered not to have recovered. If a single diagnostic test does not detect the above issues, the service quality is considered to have returned to normal.

[0045] In summary, the Empirical Mode Decomposition (EMD) algorithm in this invention is specifically applied to feature extraction and direct fault diagnosis based on single real-time data; while the Dynamic Time Warping (DTW) algorithm is specifically applied to the comparison and similarity assessment of multiple test data before and after maintenance. The two algorithms have a clear division of labor and work together at different levels to realize a complete intelligent operation and maintenance process from real-time network problem perception to closed-loop verification of maintenance effect.

[0046] In step 1022, when performing the interference source localization task, the UAV is controlled to fly to the initial detection position along a preset detection route, and the following steps are iteratively executed until the positioning conditions are met: radio data of the UAV's location is collected through the directional antenna in the test equipment, and the interference signal strength distribution is determined based on the radio data to determine the interference direction; the UAV is controlled to fly from the initial detection position to the next detection position along the interference direction; wherein, when the positioning conditions are met, the current detection position is determined as the location of the interference source.

[0047] Figure 4 This is a flowchart illustrating the process of determining the direction of interference in an embodiment of the present invention. In one embodiment, determining the direction of interference based on radio data to determine the intensity distribution of the interference signal includes: Step 401: Calculate the spatial distribution of interference signal intensity at the current detection location based on radio data; Step 402: Perform frequency scanning and decoding on the radio data to extract the characteristic information of the interference signal source; Step 403: Determine the direction of interference based on spatial distribution and characteristic information.

[0048] In one embodiment, the positioning conditions include at least one of the following: The spatial gradient change in the intensity of interference signals collected by the drone at multiple detection locations in succession is less than a preset change threshold; and / or, The UAV maintains consistent characteristics of the interference signal sources obtained through frequency scanning and decoding at multiple detection locations it continuously flies to.

[0049] In a specific embodiment, radio data of the airspace surrounding the UAV is collected using a directional antenna in the testing equipment. A multi-functional compact measurement receiver simultaneously performs the following operations: 1) measuring and recording the intensity of interference signals in each direction; 2) performing frequency sweep decoding on the signals, supporting multiple standards such as GSM-R and LTE, and possessing full-band blind sweep capability from 870MHz to 960MHz, to analyze the characteristic information of the interference signal source, including base station identification and abnormal signal standards.

[0050] Based on spatial distribution and feature information, the direction of interference is determined, specifically including: prioritizing the direction in which the spatial gradient direction is consistent with the direction of the interference signal; if there are multiple gradient directions, the direction with the highest feature information matching degree is selected as the direction of interference.

[0051] In a specific embodiment, after determining the direction of interference, a preset step size is established. This step size can be a fixed distance (e.g., 50 meters) or adaptively adjusted based on the magnitude of the signal strength gradient (e.g., a larger gradient allows for a larger step size to quickly approximate the target; a smaller gradient allows for a smaller step size to improve positioning accuracy). Based on the coordinates of the current detection position, the direction of interference, and the step size, the target coordinates (latitude, longitude, and altitude) of the next detection position are calculated using geometric calculations. The calculated target coordinates are sent to the UAV, which plans a local flight path from its current position to the target point and controls the UAV to autonomously fly to the target coordinates.

[0052] This invention also provides a wireless network operation and maintenance device, as described in the following embodiments. Since the principle by which this device solves the problem is similar to that of the wireless network operation and maintenance method, the implementation of this device can refer to the implementation of the wireless network operation and maintenance method; repeated details will not be elaborated further.

[0053] Figure 5 This is a schematic diagram of a wireless network operation and maintenance device in an embodiment of the present invention. The device includes: The test plan generation module 501 is used to generate a test plan according to the task type, which includes network status diagnosis task and interference source location task. The test plan execution module 502 is used to control the drone equipped with test equipment to execute tasks according to the test plan. When performing network status diagnosis tasks, the drone is controlled to fly along a preset diagnostic route along the railway line to simulate the train's trajectory. The drone collects wireless network field strength data and service quality data through testing equipment. Based on the collected field strength data and service quality data, network status diagnosis is performed to obtain the diagnosis results. When performing the task of locating the interference source, the UAV is controlled to fly to the initial detection position along a preset detection route, and the following steps are iteratively executed until the positioning conditions are met: radio data of the location of the UAV is collected by the directional antenna in the test equipment, and the interference signal intensity distribution is determined based on the radio data to determine the direction of interference; the UAV is controlled to fly from the initial detection position to the next detection position along the direction of interference; wherein, when the positioning conditions are met, the current detection position is determined as the location of the interference source.

[0054] In one embodiment, the test plan execution module includes a network status diagnostic unit, specifically used for: The test antenna mounted on the UAV is controlled to maintain a set altitude and direction relative to the ground. The altitude relative to the ground is consistent with the installation height of the antenna on the roof of the running train, and the direction is consistent with the tangential direction of the UAV's flight direction.

[0055] In one embodiment, the network status diagnosis unit is specifically used for: The empirical mode decomposition algorithm is used to obtain the trend components characterizing the gain characteristics of the repeater based on the field strength data; Analyzing the changing characteristics of the trend component, if the trend component has a local maximum, the repeater network coverage is determined to be normal; otherwise, the network coverage is determined to be abnormal.

[0056] In one embodiment, the test plan execution module includes an interference source localization unit, specifically used for: Based on radio data, calculate the spatial distribution of interference signal strength at the current detection location; Radio data is scanned and decoded to extract the characteristic information of the interference signal source; The direction of interference is determined based on spatial distribution and characteristic information.

[0057] In one embodiment, the positioning conditions include at least one of the following: The spatial gradient change in the intensity of interference signals collected by the drone at multiple detection locations in succession is less than a preset change threshold; and / or, The UAV maintains consistent characteristics of the interference signal sources obtained through frequency scanning and decoding at multiple detection locations it continuously flies to.

[0058] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described wireless network operation and maintenance method.

[0059] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned wireless network operation and maintenance method.

[0060] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the aforementioned wireless network operation and maintenance method.

[0061] In this embodiment of the invention, a test plan is generated based on task types, including network status diagnosis tasks and interference source location tasks. A drone equipped with testing equipment is controlled to execute tasks according to the test plan. When performing a network status diagnosis task, the drone is controlled to fly along a preset diagnostic route along a railway line to simulate train operation, and the test equipment collects wireless network field strength data and service quality data. Based on the collected field strength data and service quality data, network status diagnosis is performed to obtain diagnostic results. When performing an interference source location task, the drone is controlled to fly along a preset detection route to the initial detection position, iteratively executing the following steps until the location conditions are met: radio data of the drone's location is collected using a directional antenna in the test equipment; the interference signal strength distribution is determined based on the radio data to determine the interference direction; the drone is controlled to fly along the interference direction from the initial detection position to the next detection position; wherein, when the location conditions are met, the current detection position is determined as the location of the interference source. In the above process, this embodiment of the invention generates a test plan based on task types, realizing intelligent planning of operation and maintenance tasks; and controls the drone equipped with testing equipment to execute tasks according to the plan, realizing automated execution of operations. When performing network status diagnosis tasks, the UAV is controlled to fly along a preset diagnostic route to simulate train operation, ensuring that the collected field strength and service quality data accurately reflect the network status during actual train operation. Network status diagnosis is then performed based on the field strength and service quality data, achieving intelligent diagnosis. When performing interference source localization tasks, the UAV is controlled to fly to the initial detection position and iteratively update the next detection position, enabling the search for interference sources. Finally, when the localization conditions are met, the current detection position is determined as the interference source location, allowing the UAV to accurately locate the interference source. This fundamentally overcomes the inherent shortcomings of manual investigation, such as low efficiency and high risk.

[0062] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0063] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0064] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0065] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0066] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A wireless network operation and maintenance method, characterized in that, include: Test plans are generated based on task types, including network status diagnosis tasks and interference source location tasks. Control the drone equipped with testing equipment to perform tasks according to the test plan; When performing network status diagnostic tasks, the drone is controlled to fly along a preset diagnostic route along the railway line to simulate the train's trajectory and collect wireless network field strength data and service quality data through testing equipment. Network status diagnosis is performed based on the collected field strength data and service quality data to obtain diagnostic results; When performing the task of locating the interference source, the UAV is controlled to fly to the initial detection position along a preset detection route, and the following steps are iteratively executed until the positioning conditions are met: radio data of the location of the UAV is collected by the directional antenna in the test equipment, and the interference signal intensity distribution is determined based on the radio data to determine the direction of interference; the UAV is controlled to fly from the initial detection position to the next detection position along the direction of interference; wherein, when the positioning conditions are met, the current detection position is determined as the location of the interference source.

2. The method as described in claim 1, characterized in that, Controlling a drone to fly along a pre-set diagnostic route along a railway line to simulate train trajectory includes: The test antenna mounted on the UAV is controlled to maintain a set altitude and direction relative to the ground. The altitude relative to the ground is consistent with the installation height of the antenna on the roof of the running train, and the direction is consistent with the tangential direction of the UAV's flight direction.

3. The method as described in claim 1, characterized in that, Network status diagnosis is performed based on the collected field strength data and service quality data, and the diagnostic results are obtained, including: The empirical mode decomposition algorithm is used to obtain the trend components characterizing the gain characteristics of the repeater based on the field strength data; Analyzing the changing characteristics of the trend component, if the trend component has a local maximum, the repeater network coverage is determined to be normal; otherwise, the network coverage is determined to be abnormal.

4. The method as described in claim 1, characterized in that, Determining the intensity distribution of interference signals based on radio data to determine the direction of interference includes: Based on radio data, calculate the spatial distribution of interference signal strength at the current detection location; Radio data is scanned and decoded to extract the characteristic information of the interference signal source; The direction of interference is determined based on spatial distribution and characteristic information.

5. The method as described in claim 1, characterized in that, The positioning conditions include at least one of the following: The spatial gradient change in the intensity of interference signals collected by the drone at multiple detection locations in succession is less than a preset change threshold; and / or, The UAV maintains consistent characteristics of the interference signal sources obtained through frequency scanning and decoding at multiple detection locations it continuously flies to.

6. A wireless network operation and maintenance device, characterized in that, include: The test plan generation module is used to generate test plans based on task types, including network status diagnosis tasks and interference source location tasks. The test plan execution module is used to control the drone equipped with the test equipment to perform tasks according to the test plan; When performing network status diagnostic tasks, the drone is controlled to fly along a preset diagnostic route along the railway line to simulate the train's trajectory and collect wireless network field strength data and service quality data through testing equipment. Network status diagnosis is performed based on the collected field strength data and service quality data to obtain diagnostic results; When performing the task of locating the interference source, the UAV is controlled to fly to the initial detection position along a preset detection route, and the following steps are iteratively executed until the positioning conditions are met: radio data of the location of the UAV is collected by the directional antenna in the test equipment, and the interference signal intensity distribution is determined based on the radio data to determine the direction of interference; the UAV is controlled to fly from the initial detection position to the next detection position along the direction of interference; wherein, when the positioning conditions are met, the current detection position is determined as the location of the interference source.

7. The apparatus as claimed in claim 6, characterized in that, The test plan execution module includes a network status diagnostic unit, specifically used for: The test antenna mounted on the UAV is controlled to maintain a set altitude and direction relative to the ground. The altitude relative to the ground is consistent with the installation height of the antenna on the roof of the running train, and the direction is consistent with the tangential direction of the UAV's flight direction.

8. The apparatus as claimed in claim 6, characterized in that, The network status diagnostic unit is specifically used for: The empirical mode decomposition algorithm is used to obtain the trend components characterizing the gain characteristics of the repeater based on the field strength data; Analyzing the changing characteristics of the trend component, if the trend component has a local maximum, the repeater network coverage is determined to be normal; otherwise, the network coverage is determined to be abnormal.

9. The apparatus as claimed in claim 6, characterized in that, The test plan execution module includes an interference source localization unit, which is specifically used for: Based on radio data, calculate the spatial distribution of interference signal strength at the current detection location; Radio data is scanned and decoded to extract the characteristic information of the interference signal source; The direction of interference is determined based on spatial distribution and characteristic information.

10. The apparatus as claimed in claim 6, characterized in that, The positioning conditions include at least one of the following: The spatial gradient change in the intensity of interference signals collected by the drone at multiple detection locations in succession is less than a preset change threshold; and / or, The UAV maintains consistent characteristics of the interference signal sources obtained through frequency scanning and decoding at multiple detection locations it continuously flies to.

11. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 5.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 5.

13. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 5.

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