Wireless network coverage monitoring system based on AI
Through the AI-based wireless network coverage monitoring system, drones and coverage models are used to perform coverage difference analysis and tracing flights, which solves the problems of low efficiency of wireless network coverage monitoring and difficulty in locating interference sources, and achieves efficient and accurate interference source positioning and network recovery.
Patent Information
- Application Number
- CN202511138091.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-14
- Publication Date
- 2025-09-26
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Existing wireless network coverage monitoring methods are inefficient, difficult to fully detect in complex terrain areas, unable to dynamically reflect changes in network coverage, and difficult to locate interference sources, resulting in prolonged network recovery time and increased maintenance costs.
An AI-based wireless network coverage monitoring system is used, and drones are used for coverage monitoring. Combined with the wireless network coverage model, the actual positioning of the drone is compared with the signal base station to determine the coverage difference and lock the interference source, and the drone is controlled to trace the source for precise positioning.
It achieves efficient and accurate monitoring of wireless network coverage, quickly locks interference sources, improves network anti-interference capabilities and service quality, and reduces maintenance time and costs.
Smart Images
Figure CN120711432A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of wireless network coverage monitoring, and more specifically, to an AI-based wireless network coverage monitoring system. Background Art
[0002] With the rapid development of information technology, wireless networks play a vital role in people's daily lives, work, and various industries. Whether individuals use devices such as smartphones and tablets for communication, entertainment, and work anytime, anywhere, or businesses rely on wireless networks for remote monitoring, data transmission, and IoT applications, there are extremely high requirements for stable and high-quality wireless network coverage.
[0003] Wireless network coverage is constantly expanding, from bustling urban areas to remote rural and mountainous areas. The number of base stations is increasing, and the network environment is becoming increasingly complex. However, in actual wireless network operations, many issues often affect network coverage quality, with wireless network interference being a particularly prominent problem. The presence of these interference sources can cause signal weakening and coverage blind spots, severely impacting the user experience.
[0004] Traditional wireless network coverage monitoring methods rely primarily on manual drive testing and the deployment of detection equipment at fixed monitoring points to collect network coverage data. While manual drive testing can provide on-site measurements of network signal conditions at various locations, it suffers from low efficiency, limited coverage, and difficulty performing comprehensive monitoring in complex terrain (such as mountainous areas and urban areas with densely populated high-rise buildings). Furthermore, it consumes significant manpower, material resources, and time. Fixed monitoring points, on the other hand, only provide limited information at specific locations and fail to dynamically and comprehensively reflect changes in network coverage that may occur at any time across the entire coverage area. This makes it difficult to promptly detect and accurately locate temporary or dynamic interference sources.
[0005] Furthermore, when faced with network coverage anomalies, locating interference sources and determining their causes is complex and difficult. Current technologies often lack systematic analysis methods, making it difficult to quickly and accurately identify interference sources from a wide range of possible interference factors and locations. This results in lengthy interference troubleshooting cycles and delays in restoring normal network operations, causing long-term inconvenience for users and increasing network operation and maintenance costs.
[0006] In summary, given the many challenges currently faced by wireless network coverage monitoring and the limitations of existing technologies, combined with the development trends and advantages of artificial intelligence technology, there is an urgent need for an innovative, AI-based wireless network coverage monitoring system to achieve efficient and accurate monitoring of wireless network coverage, quickly and accurately locate interference sources, improve the anti-interference capability and service quality of the entire wireless network, and meet the needs of different users for stable and reliable wireless network services in various scenarios. Summary of the Invention
[0007] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide an AI-based wireless network coverage monitoring system.
[0008] To achieve the above object, the present invention provides the following technical solutions: An AI-based wireless network coverage monitoring system, including a wireless network coverage model building module, a wireless network coverage monitoring module, a coverage difference cause locking module, and an interference source positioning and tracing module; The wireless network coverage model building module is used to determine the coverage area of the wireless network and build a wireless network coverage model for the coverage area; The wireless network coverage monitoring module regularly arranges drones to monitor the coverage of the coverage area. During the coverage monitoring process, each duration, determine the actual positioning of the UAV, and then determine the network coverage difference index of the actual positioning. Based on the comparison result of the network coverage difference index and the network coverage difference standard index, determine whether to start the coverage difference cause locking step; The coverage difference cause locking module generates a source location monitoring sequence after starting the coverage difference cause locking step; The interference source positioning and tracing module controls the UAV to fly sequentially to the possible source positioning within the source positioning monitoring sequence, and determines each possible source positioning as a pre-selected source positioning or an erroneous source positioning.
[0009] Furthermore, the network coverage difference index of actual positioning is specifically determined as follows: determine all the signal base stations that can be monitored by the drone under actual positioning, mark all the signal base stations that can be monitored as actual signal base stations, obtain the received signal strength indication and ID of each actual signal base station, input the corresponding coordinates of the actual positioning into the wireless network coverage model, obtain all the signal base station entities that can be monitored under virtual positioning in the wireless network coverage model, mark all the signal base station entities that can be monitored as virtual signal base stations, obtain the received signal strength indication and ID of each virtual signal base station, compare each virtual signal base station with all the actual signal base stations, and when a virtual signal base station has the same ID as the actual signal base station, obtain the signal strength difference indication Indi (cs), set the signal strength difference indication coefficient to ws, and obtain the number of network coverage differences Occunu. Get the network coverage difference index of the actual positioning .
[0010] Furthermore, the network coverage difference number Occunu is specifically obtained as follows: when a virtual signal base station and an actual signal base station do not have the same ID, the network coverage difference number is increased by one, and the network coverage difference number is marked as Occunu.
[0011] Furthermore, the signal strength difference indication Indi(cs) is specifically obtained as follows: the absolute difference between the received signal strength indication of the actual signal base station with the same ID and the received signal strength indication of the virtual signal base station is calculated to obtain the signal strength difference indication Indi(cs), where c=1, 2, ..., C-1, C, where c represents the corresponding signal strength difference indication and C is the total number of signal strength difference indications.
[0012] Furthermore, a source positioning monitoring sequence is generated, specifically: controlling the wireless network coverage model to perform interference simulation of multiple interference source locations, obtaining the interference source simulation performance index of each interference source location, setting the interference source simulation performance threshold index, and when the interference source simulation performance index of the interference source location is greater than or equal to the interference source simulation performance threshold index, marking the corresponding interference source location as a possible source location, and sorting all possible source locations in order from large to small according to the value of the interference source simulation performance index to generate a source positioning monitoring sequence.
[0013] Furthermore, the interference source simulation performance index of the interference source positioning is obtained as follows: add an interference source to a location in the wireless network coverage model, control the interference source to perform multiple interference simulations, obtain the network coverage difference index of the actual positioning under each interference simulation, calculate the sum and average of the network coverage difference index of the actual positioning under all interference simulations, and calculate the average interference coverage difference index Diage (AVE), obtain the number of network coverage possible overlaps Vsdp, and pass Calculate the interference source simulation performance index of the interference source location .
[0014] Furthermore, the number of network coverage possibility overlaps Vsdp is specifically obtained as follows: the network coverage difference indexes of the actual positioning under each interference simulation are compared pairwise, and the absolute difference between the network coverage difference indexes of the actual positioning under the two compared interference simulations are calculated to obtain the network coverage difference distance index, and the network coverage difference distance threshold index is set. When the network coverage difference distance index is less than the network coverage difference distance threshold index, the number of network coverage possibility overlaps is increased by one, and the number of network coverage possibility overlaps is marked as Vsdp.
[0015] Furthermore, the UAV is controlled to fly towards the possible source that ranks first in the source location monitoring sequence. During the tracing flight, each duration, determine the real-time positioning of the UAV, and synchronously obtain the network coverage difference index of the real-time positioning. When the UAV flies to the possible source positioning that ranks first in the ranking, obtain the source locking index of the possible source positioning, set the source locking threshold index, and when the source locking index of the possible source positioning is greater than or equal to the source locking threshold index, mark the possible source positioning as the pre-selected source positioning; when the source locking index of the possible source positioning is less than the source locking threshold index, mark the possible source positioning as the wrong source positioning, and then control the UAV to fly towards the next possible source positioning in the source positioning monitoring sequence for tracing.
[0016] Furthermore, the source locking index of possible source positioning is specifically obtained as follows: all network coverage difference indices obtained during the tracing flight are sorted in the order of the time of acquisition, and the next adjacent network coverage difference index after sorting is compared with the previous network coverage difference index. When the next network coverage difference index is greater than the previous network coverage difference index in the comparison, the number of coverage difference increments is increased by one, the number of coverage difference increments is marked as nutt, and the total number of network coverage difference indices is marked as nucc. All network coverage difference indices are summed and averaged to obtain the average network coverage difference index Pht (AVE). Calculate the source locking index of the possible source location .
[0017] Compared with the prior art, the present invention has the following beneficial effects: The system of the present invention regularly monitors the coverage area of the wireless network by setting a wireless network coverage model construction module and a wireless network coverage monitoring module. During the coverage monitoring process, based on the actual positioning of the drone and combined with the modeling of the wireless network coverage, it deeply analyzes whether there is any wireless network coverage anomaly at the current positioning, and efficiently determines whether there is wireless network interference in the wireless network coverage area, providing strong technical support for achieving high-quality wireless network coverage in the entire area, ensuring that users at different positioning levels can enjoy stable and reliable network services, setting a coverage difference cause locking module and an interference source positioning and tracing module, and performing interference simulation of multiple interference source positioning by controlling the wireless network coverage model, comprehensively considering the network interference effects that may be caused by different interference source positioning for the drone's positioning, efficiently locking the positioning of possible interference sources, reasonably formulating the tracing flight sequence of the drone, and further improving the accuracy and credibility of the interference source positioning, ensuring the precise guidance of the interference source investigation work, improving the quality and efficiency of the entire wireless network anti-interference maintenance work, and always maintaining the effective response capability to complex and changeable interference sources and network coverage anomalies. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] Figure 1 This is an overview diagram of an AI-based wireless network coverage monitoring system; Figure 2 It is a flowchart of the operation process of the system of the present invention; Figure 3 Generate a flow chart for the positioning monitoring sequence. DETAILED DESCRIPTION
[0019] Reference Figures 1 to 3 , an AI-based wireless network coverage monitoring system, including a wireless network coverage model building module, a wireless network coverage monitoring module, a coverage difference cause locking module, and an interference source positioning and tracing module.
[0020] The wireless network coverage model building module determines the coverage area of the wireless network and builds a wireless network coverage model for the coverage area.
[0021] Construct a wireless network coverage model for the coverage area: Import the geographic information data of the coverage area (such as CAD drawings, DEM terrain data, and 3D building models) into general finite element analysis software, define the spatial distribution of obstacles such as terrain, buildings, and vegetation, and form a 3D model of the coverage area. Based on the signal base stations and basic information contained in the coverage area (including signal base station location, signal base station ID, antenna height, transmission power, transmission direction angle, etc.), add corresponding signal base station entities to the 3D model and assign corresponding parameters to each signal base station entity. Define the radiation pattern of the base station antenna (such as the gain distribution of an omnidirectional or directional antenna), set free space boundary conditions, simulate the infinite propagation of signals, and assign electromagnetic parameters (dielectric constant, conductivity) to different media (air, concrete, metal, etc.) in the 3D model. Finally, construct a wireless network coverage model.
[0022] The wireless network coverage monitoring module regularly arranges drones to monitor the wireless network coverage of the coverage area (drones fly according to the set path and are equipped with wireless network signal detection sensors). During the coverage monitoring process, each Duration ( is a preset duration, which is adjusted according to system requirements), determine the actual positioning of the drone, and then determine the network coverage difference index of the actual positioning, set the network coverage difference standard index (the network coverage difference standard index is a preset index used for comparison with the network coverage difference index), and when the network coverage difference index of the actual positioning is greater than or equal to the network coverage difference standard index, start the coverage difference cause locking step (when the network coverage difference index of the actual positioning is less than the network coverage difference standard index, no further processing is performed).
[0023] The network coverage difference index of actual positioning is determined as follows: determine all the signal base stations that can be monitored by the drone under actual positioning, mark all the signal base stations that can be monitored as actual signal base stations, obtain the received signal strength indicator and ID of each actual signal base station (the received signal strength indicator is the RSSI value, which is usually between -120 dBm and -30 dBm), input the corresponding coordinates of the actual positioning into the wireless network coverage model (after the corresponding coordinates of the actual positioning are input, the virtual positioning is obtained), obtain all the signal base station entities that can be monitored under the virtual positioning in the wireless network coverage model, mark all the signal base station entities that can be monitored as virtual signal base stations, obtain the received signal strength indication and ID of each virtual signal base station, compare each virtual signal base station with all the actual signal base stations, when a virtual signal base station has the same ID as the actual signal base station (that is, there is an actual signal base station with the same ID as the virtual signal base station among all the actual signal base stations), calculate the absolute difference between the received signal strength indication of the actual signal base station with the same ID and the received signal strength indication of the virtual signal base station (the same ID indicates the same signal base station, but one is the actual signal base station and the other is the virtual signal base station), and calculate the signal strength difference indication Indi (cs), c = 1, 2, ..., C-1, C, c represents the corresponding signal strength difference indication, C is the total number of signal strength difference indications, set the signal strength difference indication coefficient to ws, s=1, 2, ..., S-1, S, w1<w2<…<wS-1<wS, each signal strength difference indication coefficient corresponds to a range of signal strength difference indications, the range of signal strength difference indication includes (0, Indi (c1)], (Indi (c1), Indi (c2)], ..., (Indi (cS-1), Indi (cS)], when the signal strength difference indication Indi (cs) ∈ (0, Indi (c1)], the signal strength difference indication coefficient is w1, when a virtual signal base station does not have the same ID as the actual signal base station (that is, the ID of the virtual signal base station does not exist in all actual signal base stations), the network coverage difference number is increased by one, and the network coverage difference number is marked as Occunu, through Get the network coverage difference index of the actual positioning .
[0024] A wireless network coverage model construction module and a wireless network coverage monitoring module are set up to regularly monitor the coverage area of the wireless network. During the coverage monitoring process, based on the actual positioning of the drone and combined with the modeling of wireless network coverage, an in-depth analysis is conducted to determine whether there is any abnormal wireless network coverage at the current location, and to efficiently determine whether there is any wireless network interference in the wireless network coverage area. This provides strong technical support for achieving high-quality wireless network coverage across the entire area, ensuring that users at different locations can enjoy stable and reliable network services.
[0025] The coverage difference cause locking module, when the coverage difference cause locking step is started, controls the wireless network coverage model to perform interference simulation of multiple interference source locations, obtains the interference source simulation performance index of each interference source location, sets the interference source simulation performance threshold index (the interference source simulation performance threshold index is a preset index used for comparison with the interference source simulation performance index), when the interference source simulation performance index of the interference source location is greater than or equal to the interference source simulation performance threshold index, marks the corresponding interference source location as a possible source location (when the interference source simulation performance index of the interference source location is less than the interference source simulation performance threshold index, no marking is performed), and sorts all possible source locations in descending order according to the value of the interference source simulation performance index to generate a source location monitoring sequence.
[0026] The interference source simulation performance index of interference source positioning is specifically obtained as follows: an interference source is added to a positioning in the wireless network coverage model, and the interference source is controlled to perform multiple interference simulations (the difference in each interference simulation lies in the difference in the interference source transmission power, bandwidth, and frequency band range, which is used to simulate the difference in network interference for virtual positioning under different interference source modes under the corresponding positioning), the network coverage difference index of the actual positioning under each interference simulation is obtained, the network coverage difference index of the actual positioning under all interference simulations is summed and averaged, and the average interference coverage difference index Diage (AVE) is calculated. The network coverage difference index of the actual positioning under each interference simulation is compared pairwise, and the absolute difference between the network coverage difference indexes of the actual positioning under the two compared interference simulations is calculated to obtain the network coverage difference distance index, and the network coverage difference distance threshold index is set (the network coverage difference distance threshold index is a preset index used for comparison with the network coverage difference distance threshold index). When the network coverage difference distance index is less than the network coverage difference distance threshold index, the number of network coverage possibility overlaps is increased once (when the network coverage difference distance index is greater than or equal to the network coverage difference distance threshold index, no further processing is performed), and the number of network coverage possibility overlaps is marked as Vsdp. Calculate the interference source simulation performance index of the interference source location .
[0027] Example: The network coverage difference index of the actual positioning under an interference simulation is obtained as follows: the interference source is controlled to perform an interference simulation. During the interference simulation, all virtual signal base stations under the virtual positioning in the wireless network coverage model are determined, and all actual signal base stations under the actual positioning of the drone are simultaneously obtained, thereby obtaining the network coverage difference index of the actual positioning under the interference simulation.
[0028] The interference source positioning and tracing module controls the UAV to fly towards the possible source that is ranked first in the source positioning monitoring sequence. During the tracing flight, each Duration ( is a preset duration, which is adjusted according to system requirements), determines the real-time positioning of the UAV, and simultaneously obtains the network coverage difference index of the real-time positioning. When the UAV flies to the possible source positioning that ranks first in the ranking, obtains the source locking index of the possible source positioning, and sets the source locking threshold index (the source locking threshold index is a preset index used for comparison with the source locking index). When the source locking index of the possible source positioning is greater than or equal to the source locking threshold index, the possible source positioning is marked as the pre-selected source positioning (the corresponding personnel can then be arranged to investigate the interference source for the pre-selected source positioning). When the source locking index of the possible source positioning is less than the source locking threshold index, the possible source positioning is marked as an erroneous source positioning, and then the UAV is controlled to fly towards the next possible source positioning in the source positioning monitoring sequence for tracing.
[0029] The source locking index of possible source positioning is specifically obtained as follows: all network coverage difference indices obtained during the tracing flight are sorted in the order of the time of acquisition, and the next adjacent network coverage difference index after sorting is compared with the previous network coverage difference index. When the next network coverage difference index is greater than the previous network coverage difference index in the comparison, the number of coverage difference increments is increased once, the number of coverage difference increments is marked as nutt, and the total number of network coverage difference indices is marked as nucc. All network coverage difference indices are summed and averaged to obtain the average network coverage difference index Pht (AVE). Calculate the source locking index of the possible source location , where a1 is the first coefficient, and the value of a1 is 0.87.
[0030] A coverage difference cause locking module and an interference source positioning and tracing module are set up. By controlling the wireless network coverage model, interference simulation of multiple interference source locations is performed. The network interference impact that may be caused by different interference source positioning for the drone's positioning is comprehensively considered. The location of possible interference sources is efficiently locked, and the drone's tracing flight sequence is reasonably formulated. The accuracy and reliability of interference source positioning are further improved, ensuring precise guidance of interference source investigation work, improving the quality and efficiency of the entire wireless network anti-interference maintenance work, and always maintaining the ability to effectively respond to complex and changeable interference sources and network coverage anomalies.
[0031] The above formulas are all dimensionless and numerically calculated, and the preset parameters in the formulas are set by technicians in this field according to actual conditions.
[0032] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.
[0033] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.
[0034] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0035] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.
[0036] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.
[0037] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.
[0038] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.
Claims
1. An AI-based wireless network coverage monitoring system, characterized in that: It includes wireless network coverage model building module, wireless network coverage monitoring module, coverage difference cause locking module, and interference source positioning and tracing module; The wireless network coverage model building module is used to determine the coverage area of the wireless network and build a wireless network coverage model for the coverage area; The wireless network coverage monitoring module regularly arranges drones to monitor the coverage of the coverage area. During the coverage monitoring process, each duration, determine the actual positioning of the UAV, and then determine the network coverage difference index of the actual positioning. Based on the comparison result of the network coverage difference index and the network coverage difference standard index, determine whether to start the coverage difference cause locking step; The coverage difference cause locking module generates a source location monitoring sequence after starting the coverage difference cause locking step; The interference source positioning and tracing module controls the UAV to fly sequentially to the possible source positioning within the source positioning monitoring sequence, and determines each possible source positioning as a pre-selected source positioning or an erroneous source positioning.
2. The AI-based wireless network coverage monitoring system according to claim 1, characterized in that: The network coverage difference index of actual positioning is specifically determined as follows: determine all the signal base stations that can be monitored by the drone under actual positioning, mark all the signal base stations that can be monitored as actual signal base stations, obtain the received signal strength indication and ID of each actual signal base station, input the corresponding coordinates of the actual positioning into the wireless network coverage model, obtain all the signal base station entities that can be monitored under virtual positioning in the wireless network coverage model, mark all the signal base station entities that can be monitored as virtual signal base stations, obtain the received signal strength indication and ID of each virtual signal base station, compare each virtual signal base station with all the actual signal base stations, and when a virtual signal base station has the same ID as the actual signal base station, obtain the signal strength difference indication Indi (cs), set the signal strength difference indication coefficient to ws, and obtain the number of network coverage differences Occunu. Get the network coverage difference index of the actual positioning .
3. The AI-based wireless network coverage monitoring system according to claim 2, characterized in that: The network coverage difference count Occunu is specifically obtained as follows: when a virtual signal base station and an actual signal base station do not have the same ID, the network coverage difference count is increased by one, and the network coverage difference count is marked as Occunu.
4. The AI-based wireless network coverage monitoring system according to claim 2, characterized in that: The signal strength difference indicator Indi(cs) is obtained as follows: the absolute difference between the received signal strength indicator of the actual signal base station and the received signal strength indicator of the virtual signal base station with the same ID is calculated to obtain the signal strength difference indicator Indi(cs), where c = 1, 2, ..., C-1, C, where c represents the corresponding signal strength difference indicator and C is the total number of signal strength difference indicators.
5. The AI-based wireless network coverage monitoring system according to claim 1, characterized in that: Generate a source positioning monitoring sequence, specifically: control the wireless network coverage model to perform interference simulation of multiple interference source locations, obtain the interference source simulation performance index of each interference source location, set the interference source simulation performance threshold index, and when the interference source simulation performance index of the interference source location is greater than or equal to the interference source simulation performance threshold index, mark the corresponding interference source location as a possible source location, and sort all possible source locations in descending order according to the value of the interference source simulation performance index to generate a source positioning monitoring sequence.
6. The AI-based wireless network coverage monitoring system according to claim 5, characterized in that: The interference source simulation performance index of interference source positioning is obtained as follows: add an interference source to a location in the wireless network coverage model, control the interference source to perform multiple interference simulations, obtain the network coverage difference index of the actual positioning under each interference simulation, and calculate the average of the network coverage difference index of the actual positioning under all interference simulations to obtain the average interference coverage difference index Diage (AVE), obtain the number of network coverage possible overlaps Vsdp, and pass Calculate the interference source simulation performance index of the interference source location .
7. The AI-based wireless network coverage monitoring system according to claim 6, characterized in that: The specific acquisition process of the number of network coverage possibility overlaps Vsdp is as follows: the network coverage difference indexes of the actual positioning under each interference simulation are compared pairwise, and the absolute difference between the network coverage difference indexes of the actual positioning under the two compared interference simulations is calculated to obtain the network coverage difference distance index, and the network coverage difference distance threshold index is set. When the network coverage difference distance index is less than the network coverage difference distance threshold index, the number of network coverage possibility overlaps is increased by one, and the number of network coverage possibility overlaps is marked as Vsdp.
8. The AI-based wireless network coverage monitoring system according to claim 1, characterized in that: Control the UAV to fly towards the possible source that ranks first in the source location monitoring sequence. During the tracing flight, each duration, determine the real-time positioning of the UAV, and synchronously obtain the network coverage difference index of the real-time positioning. When the UAV flies to the possible source positioning that ranks first in the ranking, obtain the source locking index of the possible source positioning, set the source locking threshold index, and when the source locking index of the possible source positioning is greater than or equal to the source locking threshold index, mark the possible source positioning as the pre-selected source positioning; when the source locking index of the possible source positioning is less than the source locking threshold index, mark the possible source positioning as the wrong source positioning, and then control the UAV to fly towards the next possible source positioning in the source positioning monitoring sequence for tracing.
9. The AI-based wireless network coverage monitoring system according to claim 8, characterized in that: The source locking index of possible source positioning is specifically obtained as follows: all network coverage difference indices obtained during the tracing flight are sorted in the order of the time of acquisition, and the next adjacent network coverage difference index after sorting is compared with the previous network coverage difference index. When the next network coverage difference index is greater than the previous network coverage difference index in the comparison, the number of coverage difference increments is increased once, the number of coverage difference increments is marked as nutt, and the total number of network coverage difference indices is marked as nucc. All network coverage difference indices are summed and averaged to obtain the average network coverage difference index Pht (AVE). Calculate the source locking index of the possible source location .