A method and system for transmitting images during the flight of a drone

Abstract

The application relates to the technical field of unmanned aerial vehicle image transmission, and particularly provides an unmanned aerial vehicle flight process image transmission method and system, which comprises the following steps: acquiring electromagnetic environment information, channel state information and unmanned aerial vehicle internal parameter information; generating a comprehensive state index according to the electromagnetic environment information, the channel state information and the unmanned aerial vehicle internal parameter information; querying a mapping relationship table about state index and transmission parameter combination which is constructed in advance according to the comprehensive state index to obtain a target transmission parameter combination; and encoding, encapsulating, modulating and sending images collected by the unmanned aerial vehicle according to the target transmission parameter combination; the method can effectively solve the problems that the image stream received by a ground station appears picture freezing, image frame loss, picture mosaic or color distortion and the like due to interference or enhanced channel attenuation.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) image transmission technology, and more specifically, to a method and system for transmitting images during the flight of an UAV. Background Technology

[0002] As a flexible and efficient platform, drones are playing an increasingly important role in tasks such as inspecting urban infrastructure. In order to enable ground operators to monitor in real time, control remotely with precision, and make immediate judgments on the status of targets, drones need to transmit high-resolution image data collected during flight to the ground control station in real time with low latency via wireless communication links.

[0003] However, the electromagnetic environment and channel conditions encountered by drones when flying in different areas are dynamic (the type, intensity, direction and duration of interference sources may be different). That is, the interference and channel attenuation encountered by drones during flight are dynamic. For example, drones may face strong industrial electromagnetic interference when approaching industrial areas, a lot of Wi-Fi and Bluetooth signal interference when passing through dense residential or commercial areas, and strong electromagnetic interference of specific frequencies and intensities when approaching high-voltage power lines.

[0004] Existing UAV image transmission methods typically employ fixed image transmission strategies (using fixed encoding, encapsulation, modulation, and transmission parameters). These fixed strategies cannot effectively adapt to dynamically changing transmission environments, leading to numerous limitations. For example, using a fixed strong error correction coding rate to enhance robustness results in wasted bandwidth and UAV computing resources when channel conditions are good and interference is weak. Conversely, using a fixed weak error correction coding rate renders the system unable to resist sudden strong interference or severe fading, resulting in significant image data loss. Because interference and channel attenuation experienced by UAVs during flight are dynamic, and existing UAV image transmission methods use fixed strategies, current technologies suffer from issues such as image stuttering, frame loss, pixelation, or color distortion in the image stream received by the ground station due to increased interference or channel attenuation. This degrades image stream quality, potentially leading to delayed defect detection or misjudgments.

[0005] Currently, there is no effective technical solution to the above-mentioned problems. It should be noted that the information disclosed in this section is only for understanding the background of the present invention and therefore may include information that does not constitute prior art. Summary of the Invention

[0006] The purpose of this application is to provide a method and system for transmitting images during the flight of an unmanned aerial vehicle (UAV), which can effectively solve the problems of image stream received by the ground station experiencing stuttering, image frame loss, pixelation, or color distortion due to interference or increased channel attenuation.

[0007] In a first aspect, this application provides a method for transmitting images during the flight of a UAV, used to transmit images acquired by the UAV, comprising the following steps: S1. Acquire electromagnetic environment information, channel status information, and UAV internal parameter information; S2. Generate a comprehensive status index based on electromagnetic environment information, channel status information, and UAV internal parameter information; S3. Based on the comprehensive status index, query the pre-built mapping relationship table of status index and transmission parameter combination to obtain the target transmission parameter combination. The target transmission parameter combination includes modulation order, forward error correction coding rate, transmission power, image frame rate, data packet size, image resolution and compression ratio. S4. Encode, encapsulate, modulate, and transmit the images acquired by the UAV according to the target transmission parameter combination.

[0008] This application provides a method for transmitting images during UAV flight. First, it integrates electromagnetic environment information, channel state information, and UAV internal parameter information to generate a comprehensive state index reflecting the overall state of the UAV. Then, it dynamically combines the target transmission parameters based on this comprehensive index. Finally, it encodes, encapsulates, modulates, and transmits the images acquired by the UAV according to the target transmission parameter combination. In essence, this application is equivalent to first understanding the interference situation in the UAV's environment, the current transmission link quality, and the UAV's own state, and then adaptively adjusting the image transmission strategy based on these factors. Therefore, even if the interference and channel attenuation experienced by the UAV during flight are dynamic, this application can select an appropriate image transmission strategy based on the dynamically changing interference and channel attenuation. This effectively solves the problems of image stream stuttering, image frame loss, pixelation, or color distortion caused by increased interference or channel attenuation, thus effectively improving the image stream quality and preventing the failure to detect defects or misjudgments due to decreased image stream quality.

[0009] Secondly, this application also provides an image transmission system for the flight process of an unmanned aerial vehicle (UAV), used for transmitting images acquired by the UAV, comprising: The information acquisition module is used to acquire electromagnetic environment information, channel status information, and internal parameters of the UAV. The comprehensive status index acquisition module is used to generate comprehensive status indices based on electromagnetic environment information, channel status information, and UAV internal parameter information. The transmission parameter combination determination module is used to query a pre-built mapping table of status indicators and transmission parameter combinations based on the comprehensive status indicators to obtain the target transmission parameter combination. The target transmission parameter combination includes modulation order, forward error correction coding rate, transmission power, image frame rate, data packet size, image resolution, and compression ratio. The image processing module is used to encode, encapsulate, modulate, and transmit images acquired by the UAV based on the target transmission parameter combination.

[0010] This application provides an image transmission system for unmanned aerial vehicles (UAVs) during flight. First, it integrates electromagnetic environment information, channel state information, and UAV internal parameter information to generate a comprehensive state index reflecting the overall state of the UAV. Then, based on this comprehensive index, it dynamically combines the target transmission parameters. Finally, it encodes, encapsulates, modulates, and transmits the images acquired by the UAV according to the target transmission parameter combination. In essence, this application first understands the interference situation in the UAV's environment, the current transmission link quality, and the UAV's own state. Then, it adaptively adjusts the image transmission strategy based on these factors. Therefore, even if the interference and channel attenuation experienced by the UAV during flight are dynamic, this application can select an appropriate image transmission strategy based on the dynamically changing interference and channel attenuation. This effectively solves the problems of image stream stuttering, image frame loss, pixelation, or color distortion caused by increased interference or channel attenuation, thus effectively improving the image stream quality and preventing the failure to detect defects or misjudgments due to degraded image stream quality.

[0011] As can be seen from the above, the UAV flight process image transmission method and system provided in this application first integrates electromagnetic environment information, channel state information, and UAV internal parameter information to generate a comprehensive state index that reflects the overall state of the UAV. Then, based on this comprehensive index, the target transmission parameter combination of the comprehensive state index is dynamically integrated. Finally, the images acquired by the UAV are encoded, encapsulated, modulated, and transmitted according to the target transmission parameter combination. That is, this application is equivalent to first understanding the interference situation of the environment in which the UAV is located, the current transmission link quality, and the UAV's own state, and then adaptively adjusting the image transmission strategy according to the interference situation, the current transmission link quality, and the UAV's own state. Therefore, even if the interference and channel attenuation experienced by the UAV during flight are dynamic, this application can select an appropriate image transmission strategy for image transmission according to the dynamically changing interference and channel attenuation. This effectively solves the problems of image stream receiving by the ground station experiencing stuttering, image frame loss, pixelation, or color distortion due to interference or increased channel attenuation, thereby effectively improving the image stream quality and effectively avoiding the situation where defects cannot be detected in time or misjudgments occur due to the decline in image stream quality. Attached Figure Description

[0012] Figure 1 A flowchart illustrating a method for transmitting images during the flight of a drone, as provided in an embodiment of this application.

[0013] Figure 2 This is a schematic diagram of the structure of an image transmission system for a drone during flight, provided in an embodiment of this application.

[0014] Reference numerals in the attached diagram: 1. Information acquisition module; 2. Comprehensive status index acquisition module; 3. Transmission parameter combination determination module; 4. Image processing module. Detailed Implementation

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0016] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0017] In traditional UAV image transmission methods, the interference and channel attenuation encountered by UAVs during flight are dynamic due to the varying types, intensities, directions, and durations of interference sources encountered in different areas. Existing UAV image transmission methods employ fixed image transmission strategies, namely fixed encoding, encapsulation, modulation, and transmission strategies. This fixed image transmission strategy leads to a waste of bandwidth and UAV computing resources when using strong error correction coding rates to enhance robustness under good channel conditions and weak interference. However, when encountering sudden strong interference or severe fading, the weak error correction coding rates used are insufficient to resist interference, resulting in significant image data loss. Consequently, the image stream received by the ground station may exhibit image frame loss, pixelation, or color distortion, leading to a degraded image stream quality.

[0018] For example, suppose a drone is performing an inspection mission on urban infrastructure. The drone takes off from an open area (with good channel conditions and minimal interference), then flies into an industrial area where it faces strong industrial electromagnetic interference. Next, it flies over a densely populated residential area, where it encounters significant Wi-Fi and Bluetooth signal interference. Finally, it approaches a high-voltage power line, where it is subjected to strong electromagnetic interference of specific frequencies and intensities. If a fixed modulation order, forward error correction coding rate, transmit power, image frame rate, data packet size, image resolution, and compression ratio (the image transmission strategy is composed of these parameters) are used for image transmission, high-resolution image transmission may consume excessive resources in open areas. However, in industrial areas, residential areas, or near high-voltage power lines, due to increased interference or severe channel attenuation, the fixed image transmission strategy cannot guarantee reliable data transmission. The image data packet loss rate increases sharply, and the image stream received by the ground station exhibits stuttering, incomplete images, or abnormal colors.

[0019] If the above problems are not resolved, the degraded image quality will directly affect the user's immediate judgment of the target status. For example, image distortion or loss may cause the user to misjudge the situation on site, resulting in the failure to detect subtle defects in the infrastructure or the neglect of security risks.

[0020] In this regard, firstly, such as Figure 1 As shown, this application provides a method for transmitting images during the flight of a UAV, used to transmit images acquired by the UAV, which includes the following steps: S1. Acquire electromagnetic environment information, channel status information, and UAV internal parameter information; S2. Generate a comprehensive status index based on electromagnetic environment information, channel status information, and UAV internal parameter information; S3. Based on the comprehensive status index, query the pre-built mapping relationship table of status index and transmission parameter combination to obtain the target transmission parameter combination. The target transmission parameter combination includes modulation order, forward error correction coding rate, transmission power, image frame rate, data packet size, image resolution and compression ratio. S4. Encode, encapsulate, modulate, and transmit the images acquired by the UAV according to the target transmission parameter combination.

[0021] The electromagnetic environment information in step S1 refers to the distribution of electromagnetic waves and interference in the space where the UAV is located. Step S1 can obtain electromagnetic environment information by using equipment such as spectrum analyzers and interference source detectors to measure the signal strength of specific frequency bands and identify the source and characteristics of interference signals. This embodiment can understand the impact of external electromagnetic interference on wireless communication through electromagnetic environment information. The channel state information in step S1 refers to the transmission characteristics and quality status of the wireless communication link between the UAV and the ground station. Step S1 can obtain channel state information by measuring the received signal strength, calculating the signal-to-noise ratio, statistically analyzing the transmission error rate, or analyzing the time difference of signal arrival through different paths. This channel state information directly reflects the current quality of the wireless communication link (signal attenuation, interference level, and multipath effect). This embodiment can more accurately assess the carrying capacity and stability of the current transmission link through channel state information. Since the carrying capacity and stability of the current transmission link are related to the transmission quality of wireless communication, this embodiment can understand the impact of the current transmission link on wireless communication by obtaining channel state information. The internal parameter information of the UAV in this embodiment refers to the UAV's own resource usage and operating status. Since existing UAVs are usually equipped with an operating system or a dedicated flight control system, these operating systems and flight control systems usually provide APIs (Application Programming Interfaces) to allow developers to access various internal parameters of the UAV. Therefore, this embodiment can obtain the UAV's internal parameter information through the APIs provided by the UAV's operating system or flight control system. This embodiment can also use sensors integrated into the UAV to obtain the UAV's internal parameter information. Since the UAV continuously sends telemetry data to the ground station during flight, this data usually contains various status information of the UAV, including the UAV's internal parameters. Therefore, this embodiment can also obtain the UAV's internal parameter information by analyzing the UAV's telemetry data. This embodiment can understand the UAV's ability to perform image processing and transmission tasks through the UAV's internal parameter information. It should be understood that since the UAV's ability to perform image processing and transmission tasks affects the transmission quality of wireless communication, this embodiment can understand the impact of the UAV's own characteristics on wireless communication by obtaining the UAV's internal parameter information.

[0022] The comprehensive status index in step S2 is one or a set of values ​​obtained by comprehensively evaluating electromagnetic environment information, channel status information, and UAV internal parameter information. This embodiment can generate the comprehensive status index by directly integrating electromagnetic environment information, channel status information, and UAV internal parameter information. For example, when the electromagnetic environment information is strong narrowband interference, the signal status information is poor quality of the current transmission link, and the UAV internal parameter information is 25% remaining power, the comprehensive status index generated in step S2 is "strong narrowband interference, severe channel attenuation, and low remaining power". Step S2 can also employ methods such as direct summation, weighted summation, fuzzy logic judgment, or machine learning models to generate a comprehensive state index based on electromagnetic environment information, channel state information, and UAV internal parameter information. For example, based on electromagnetic environment information, a pre-built mapping table of electromagnetic environment and transmission quality impact scores is queried to obtain a first score; based on channel state information, a pre-built mapping table of channel state and transmission quality impact scores is queried to obtain a second score; based on UAV internal parameter information, a pre-built mapping table of UAV internal parameters and transmission quality impact scores is queried to obtain a third score; the first, second, and third scores are summed to obtain the comprehensive state index. It should be understood that step S2 is equivalent to integrating multiple influencing factors (the impact of external electromagnetic interference on wireless communication, the impact of current transmission link quality on wireless communication, and the impact of UAV's own characteristics on wireless communication) into a unified metric to facilitate subsequent transmission parameter decisions.

[0023] The mapping table for state indicators and transmission parameter combinations in step S3 can be a mapping table established before system deployment through experiments, simulations, or expert experience. That is, the data in this mapping table can be experimental data, simulation data, or empirical data. This mapping table stores the transmission parameter combinations corresponding to different state indicators. The target transmission parameter combination in step S3 refers to the transmission parameter combination obtained after querying the mapping table based on the comprehensive state indicators. This target transmission parameter combination is used to guide a series of parameter settings for the current image transmission. Specifically, the target transmission parameter combination includes modulation order, forward error correction coding rate, transmission power, image frame rate, data packet size, image resolution, and compression ratio. It should be understood that, since the target transmission parameter combination in this embodiment is determined based on a comprehensive state index, the comprehensive state in this embodiment is one or a set of values ​​obtained after comprehensively evaluating electromagnetic environment information, channel state information, and UAV internal parameter information. Therefore, this embodiment is equivalent to dynamically confirming a suitable image transmission strategy based on reducing the impact of external electromagnetic interference, current transmission link quality (equivalent to the degree of channel attenuation), and the characteristics of the UAV itself on wireless communication, so as to eliminate or minimize the impact of external electromagnetic interference, current transmission link quality, and the characteristics of the UAV itself on wireless communication.

[0024] Step S4, which encodes, encapsulates, modulates, and transmits the images acquired by the UAV according to the target transmission parameter combination, is preferably an existing technology, and its working principle and workflow will not be discussed in detail here.

[0025] This application provides a method for transmitting images during UAV flight. First, it integrates electromagnetic environment information, channel state information, and UAV internal parameter information to generate a comprehensive state index reflecting the overall state of the UAV. Then, it dynamically combines the target transmission parameters based on this comprehensive index. Finally, it encodes, encapsulates, modulates, and transmits the images acquired by the UAV according to the target transmission parameter combination. In essence, this application is equivalent to first understanding the interference situation in the UAV's environment, the current transmission link quality, and the UAV's own state, and then adaptively adjusting the image transmission strategy based on these factors. Therefore, even if the interference and channel attenuation experienced by the UAV during flight are dynamic, this application can select an appropriate image transmission strategy based on the dynamically changing interference and channel attenuation. This effectively solves the problems of image stream stuttering, image frame loss, pixelation, or color distortion caused by increased interference or channel attenuation, thus effectively improving the image stream quality and preventing the failure to detect defects or misjudgments due to decreased image stream quality. It should be understood that, since this application can adaptively adjust the image transmission strategy according to the interference situation of the UAV's environment, the current transmission link quality and the UAV's own state, this application can improve transmission efficiency when the channel conditions are good, so as to make full use of bandwidth and resources, and enhance the robustness of transmission when the channel conditions are poor, so as to effectively resist interference and channel attenuation and reduce data loss.

[0026] In some preferred embodiments, step S3 includes: S31. Based on the comprehensive status index, query the pre-built mapping relationship table of status index and transmission parameter combination to obtain the preliminary transmission parameter combination; S32. Analyze whether there are buildings obstructing the images collected by the UAV. If so, proceed to step S33. If not, use the preliminary transmission parameter combination as the target transmission parameter combination. S33. Obtain the current pose information and flight altitude information of the UAV. Based on the current pose information, flight altitude information and pre-built 3D city model, predict the degree of influence of building occlusion on image transmission to obtain the first degree of influence on image transmission. S34. Based on the first image transmission influence level, query the pre-constructed mapping relationship table of image transmission influence level and transmission parameter combination adjustment strategy to obtain the first transmission parameter combination adjustment strategy. Then, adjust the initial transmission parameter combination according to the first transmission parameter combination adjustment strategy to obtain the target transmission parameter combination.

[0027] In this embodiment, analyzing the presence of building obstructions in images collected by the UAV refers to processing the image data transmitted by the UAV to determine if there are any building structures in the image content that may obstruct the wireless signal transmission path. This embodiment can employ image recognition technology, computer vision algorithms, or deep learning models to analyze the presence of building obstructions in images collected by the UAV. The current pose information of the UAV in this embodiment refers to the position and attitude information of the UAV in three-dimensional space. This embodiment can obtain the current pose information of the UAV by fusing sensor data such as Global Positioning System (GPS) data, Inertial Measurement Unit (IMU) data, or visual odometry. The flight altitude information in this embodiment refers to the altitude of the UAV relative to a reference plane (e.g., ground or sea level). This embodiment can obtain the flight altitude information using barometric altimeter, radar altimeter, or GPS altitude data. The pre-built 3D city model in this embodiment refers to a digital model containing information such as spatial location, height, and shape, formed by digitally modeling buildings, terrain, etc., within a specific urban area. This 3D city model can be an existing 3D city model, and can be constructed using LiDAR scanning, photogrammetry, or Geographic Information System (GIS) data. The prediction of the impact of building occlusion on image transmission in this embodiment refers to calculating or estimating the probability, penetration distance, or degree of obstruction of the signal transmission path through buildings based on the relative position between the UAV and the ground station and the city's 3D model, thereby quantifying the impact of building obstruction on wireless signal attenuation. Specifically, the process of predicting the impact of building occlusion on image transmission based on the UAV's current pose information, flight altitude information, and a pre-constructed city 3D model can be as follows: determine the precise position of the UAV in the city's 3D model based on the UAV's current pose information and flight altitude information; emit a virtual ray from the precise position of the UAV in the city's 3D model to the precise position (known value) of the ground station in the city's 3D model, which represents the main path of wireless signal propagation; obtain the penetration length and building material corresponding to each building penetrated by the ray; for each penetrated building, query a pre-constructed mapping table of penetration length and building material, building material and transmission impact score based on its corresponding penetration length and building material to obtain an image transmission impact score; directly sum or weightedly sum all image transmission impact scores, and use the summation result as the degree of impact of building occlusion on image transmission. The first image transmission impact level in this embodiment refers to the quantitative index of the predicted building occlusion impact on image transmission. The first image transmission impact level can be expressed as a signal attenuation value, a link quality prediction value, or a discrete occlusion level.This embodiment stores a mapping table between the degree of image transmission impact and the transmission parameter combination adjustment strategy. This table stores the transmission parameter combination adjustment strategies corresponding to different degrees of image transmission impact. These strategies may include instructions or values ​​for increasing, decreasing, or keeping unchanged parameters such as modulation order, forward error correction coding rate, transmit power, image frame rate, data packet size, image resolution, or compression ratio. This embodiment can use parameter superposition, parameter replacement, or rule-based parameter modification methods to adjust the initial transmission parameter combination according to the first transmission parameter combination adjustment strategy.

[0028] Specifically, this method first obtains a preliminary combination of transmission parameters based on comprehensive state indicators. This preliminary combination reflects the optimal or suboptimal transmission configuration under the current unobstructed state. Then, the system analyzes real-time images captured by the UAV to determine if there are any signs of building obstruction in the image content. If the image analysis indicates building obstruction, a further fine-tuning process is triggered. At this point, the system acquires the UAV's precise position and attitude information, as well as its flight altitude, and analyzes the signal path from the UAV to the ground station using a pre-constructed 3D city model to predict the specific impact of buildings on signal transmission. Then, based on the specific impact, a corresponding first transmission parameter adjustment strategy is obtained. Finally, the previously obtained preliminary transmission parameter combination is modified according to this strategy, such as increasing forward error correction capability, reducing the bit rate or resolution, thereby obtaining the final target parameter combination used for image transmission. If the image analysis does not detect building obstruction, the preliminary transmission parameter combination is directly adopted as the target transmission parameter combination to avoid unnecessary calculations. Because this embodiment can identify building occlusion situations encountered by UAVs when flying in urban environments, and make targeted adjustments to image transmission parameters based on the predicted degree of occlusion impact, this embodiment is equivalent to adding the identification and targeted response mechanism for building occlusion problems unique to urban environments to the adaptive capability based on comprehensive state indicators. Therefore, this embodiment can make the confirmation of image transmission parameters more precise and robust, thereby effectively improving the accuracy and reliability of target transmission parameter combinations, and thus effectively improving the accuracy and reliability of image transmission strategy adjustment.

[0029] In some preferred embodiments, step S31 includes: S311. Based on the comprehensive status index, query the pre-built mapping relationship table of status index and transmission parameter combination to obtain the original transmission parameter combination; S312. Obtain real-time meteorological data, and then query a pre-built mapping table of meteorological data and transmission parameter combination adjustment strategy based on the real-time meteorological data to obtain the second transmission parameter combination adjustment strategy. S313. Adjust the original transmission parameter combination according to the second transmission parameter combination adjustment strategy to obtain the preliminary transmission parameter combination.

[0030] The real-time meteorological data in this embodiment is the meteorological information of the environment in which the UAV is located. This real-time meteorological data is preferably meteorological information related to wireless signal propagation (e.g., rainfall, haze concentration, temperature). This embodiment can obtain real-time meteorological data through meteorological forecast information pre-released by meteorological observatories, or by querying real-time meteorological data released by meteorological observatories. The mapping table for meteorological data and transmission parameter combination adjustment strategies in this embodiment stores the transmission parameter combination adjustment strategies corresponding to different meteorological data. This embodiment can construct the mapping table for meteorological data and transmission parameter combination adjustment strategies by analyzing the impact of different meteorological conditions on wireless signal propagation before the actual flight of the UAV. The second transmission parameter combination adjustment strategy in this embodiment refers to a set of specific rules or instructions determined based on real-time meteorological data to guide how to modify the original transmission parameter combination. This second transmission parameter combination adjustment strategy may include instructions to add, reduce, or replace one or more image transmission parameters in the original transmission parameter combination. This embodiment can use parameter superposition, parameter replacement, or rule-based parameter modification methods to adjust the original transmission parameter combination according to the second transmission parameter combination adjustment strategy. Because this embodiment can dynamically adjust image transmission parameters according to real-time meteorological conditions by first determining the second transmission parameter combination adjustment strategy based on real-time meteorological data, and then adjusting the original transmission parameter combination according to the second transmission parameter combination adjustment strategy, this embodiment can enable the initial transmission parameter combination to better adapt to signal attenuation and interference caused by complex meteorological environments such as rainfall and haze, thereby reducing the bit error rate and reducing image frame loss and image distortion, thus further improving the accuracy and reliability of the target transmission parameter combination, and further improving the accuracy and reliability of image transmission strategy adjustment.

[0031] In some preferred embodiments, step S313 includes: A1. Obtain the current task type of the UAV, and then query the pre-built mapping table of task type and transmission parameter combination adjustment strategy according to the current task type of the UAV to obtain the third transmission parameter combination adjustment strategy; A2. Adjust the original transmission parameter combination according to the second and third transmission parameter combination adjustment strategies to obtain the initial transmission parameter combination.

[0032] The current task type of the UAV in this embodiment refers to the specific flight task being performed by the UAV (e.g., power line inspection, urban mapping, security monitoring, or logistics delivery). This embodiment can obtain the current task type of the UAV through the UAV's mission planning system or ground station. The mapping table of task type and transmission parameter combination adjustment strategy in this embodiment stores the transmission parameter combination adjustment strategies corresponding to different task types. The third transmission parameter combination adjustment strategy in this embodiment refers to a set of rules or instructions obtained from the mapping table based on the current task type of the UAV, used to adjust the original transmission parameter combination. This third transmission parameter combination adjustment strategy may include specific instructions or adjustments to increase, decrease, or keep unchanged parameters such as modulation order, forward error correction coding rate, transmission power, image frame rate, data packet size, image resolution, and compression ratio. The purpose of introducing the third transmission parameter combination adjustment strategy in this embodiment is to ensure that the initial setting of the transmission parameters better matches the specific requirements of the current task for image transmission performance. For example, for rescue tasks requiring high real-time performance, the third transmission parameter combination adjustment strategy instructs to reduce the image resolution and compression ratio to increase the transmission rate; for inspection tasks requiring high definition, the third transmission parameter combination adjustment strategy instructs to increase the resolution and compression ratio. This embodiment can employ parameter superposition, parameter replacement, or rule-based parameter modification methods to adjust the original transmission parameter combination according to the second and third transmission parameter combination adjustment strategies. Because this embodiment can first determine the third transmission parameter combination adjustment strategy based on the current task type of the UAV, and then adjust the original transmission parameter combination according to the second and third transmission parameter combination adjustment strategies, it achieves targeted optimization of image transmission parameters based on the current task type of the UAV. Therefore, this embodiment enables the UAV to better balance the real-time performance, clarity, robustness, and other performance indicators of image transmission when performing different types of tasks, thereby improving the efficiency and quality of image transmission to better meet the specific needs of different application scenarios, and further improving the accuracy and reliability of the target transmission parameter combination and image transmission strategy adjustment.

[0033] In some preferred embodiments, step S2 includes: S21. Obtain the current task type of the UAV, and then query the pre-built mapping table of task type and weighted weight combination according to the current task type of the UAV to obtain the weighted weights corresponding to electromagnetic environment information, channel state information and UAV internal parameter information respectively. S22. Calculate the comprehensive status index based on electromagnetic environment information and its corresponding weighting, channel state information and its corresponding weighting, and UAV internal parameter information and its corresponding weighting.

[0034] The mapping table for task types and weighted weight combinations in this embodiment stores weighted weight combinations corresponding to different task types. These weighted weight combinations include weighted weights corresponding to electromagnetic environment information, channel state information, and UAV internal parameter information. Therefore, this embodiment can obtain the weighted weights corresponding to electromagnetic environment information, channel state information, and UAV internal parameter information by querying the mapping table for task types and weighted weight combinations based on the current task type of the UAV. It should be understood that the weighted weight combinations in this embodiment can reflect the relative importance of electromagnetic environment information, channel state information, and UAV internal parameter information under a specific task type. For example, in power line inspection tasks, electromagnetic interference may be more critical, so the weight of electromagnetic environment information may be higher; while in long-distance mapping tasks, channel attenuation and signal-to-noise ratio may be more important, so the weight of channel state information may be higher. Since the impact of electromagnetic environment information, channel state information, and UAV internal parameter information on UAV image transmission varies depending on the type of task being performed by the UAV, this embodiment can dynamically adjust the weighting of electromagnetic environment information, channel state information, and UAV internal parameter information when calculating the comprehensive status index based on the type of task currently being performed by the UAV. This allows the comprehensive status index to more accurately reflect the key factors and overall status affecting image transmission in the current task scenario, providing a more reliable basis for selecting a more suitable combination of image transmission parameters. This effectively improves the accuracy and reliability of target transmission parameter combinations and image transmission strategy adjustments, thereby effectively improving the quality and reliability of UAV image transmission.

[0035] In some preferred embodiments, electromagnetic environment information includes the UAV's operating frequency band and adjacent frequency bands, spectrum occupancy, interference signal frequency bands, interference signal bandwidth, and interference signal strength. The operating frequency band and adjacent frequency bands in this embodiment refer to the communication frequency band currently being used by the UAV and other frequency bands immediately adjacent to it. This embodiment can acquire the operating frequency band and adjacent frequency bands by configuring the scanning range of the spectrum sensing module. For example, if the UAV's operating frequency band is 2.4 GHz, the scanning range of the spectrum sensing module can be set to 2.3 GHz to 2.5 GHz to cover adjacent frequency bands. Then, by analyzing the scanning results of the spectrum sensing module, the signal strength, frequency occupancy, and potential interference signals within the operating frequency band and adjacent frequency bands can be determined. This embodiment can understand the potential co-channel or adjacent-channel interference that the UAV's communication may be subject to based on the UAV's operating frequency band and adjacent frequency bands. The spectrum occupancy in this embodiment refers to the presence and distribution of radio signals within a specific frequency band. This embodiment can obtain the spectrum occupancy by using existing spectrum analysis techniques to measure the signal power equation at different frequency points. This embodiment can understand which frequency bands are occupied and which are idle based on the spectrum occupancy. The interference signal frequency band in this embodiment refers to the frequency range where the detected interference signal is located. This embodiment can use existing peak detection or threshold judgment algorithms to obtain the interference signal frequency band. For example, this embodiment can use the frequency range where the signal strength exceeds a certain threshold as the interference signal frequency band. The interference signal bandwidth in this embodiment refers to the frequency width occupied by the interference signal. This embodiment can use the bandwidth measurement function of an existing spectrum analyzer or signal processing algorithm to obtain the interference signal bandwidth. The interference signal strength in this embodiment refers to the power of the interference signal. This embodiment can use an existing spectrum analyzer to obtain the interference signal strength.

[0036] In some preferred embodiments, channel state information includes received signal strength indication (RSI), signal-to-noise ratio (SNR), bit error rate (BER), and multipath delay spread. The RSI in this embodiment refers to the signal power measured at the receiver. This embodiment can utilize existing RF front-end power detection circuits to obtain the RSI, which reflects the overall strength of the received signal and is an important indicator for judging signal coverage and signal attenuation. The SNR in this embodiment refers to the ratio of received signal power to noise power. This embodiment can obtain the SNR by using a baseband processing unit to analyze and calculate the received signal. The SNR reflects signal quality; a higher SNR indicates less noise interference and higher image transmission quality. The BER in this embodiment refers to the proportion of erroneous bits during transmission to the total number of transmitted bits. This embodiment can obtain the BER by using error detection at the receiver or by estimating the BER by sending a known sequence. The BER reflects the reliability of data transmission; a lower BER indicates fewer data transmission errors and higher image transmission quality. In this embodiment, multipath delay spread refers to the maximum or root mean square value of the time difference between the arrival of a signal at the receiver through different propagation paths. This embodiment can obtain multipath delay spread by analyzing the received pilot signal or training sequence using channel estimation technology. Multipath delay spread reflects the time difference between the arrival of a signal at the receiver through different paths. The larger the multipath delay spread, the more severe the signal interference and the worse the image transmission quality.

[0037] In some preferred embodiments, the drone's internal parameter information includes the drone's remaining battery power, CPU load, and memory usage. In this embodiment, the remaining battery power refers to the current charge level of the drone's battery, reflecting its continuous operating capability. This embodiment can obtain the remaining battery power by reading data from the drone's battery management system. In this embodiment, the CPU load refers to the workload of the drone's central processing unit, reflecting its task processing capability. This embodiment can obtain the CPU load by reading performance data provided by the drone's operating system or monitoring software. In this embodiment, the memory usage rate refers to the proportion of the drone's random access memory (RAM) that is occupied, reflecting the drone's data caching and processing capabilities. This embodiment can also obtain the memory usage rate by reading performance data provided by the drone's operating system or monitoring software.

[0038] As can be seen from the above, the UAV flight image transmission method provided in this application first integrates electromagnetic environment information, channel state information, and UAV internal parameter information to generate a comprehensive state index that reflects the overall state of the UAV. Then, based on this comprehensive index, it dynamically integrates the target transmission parameter combination of the state index. Finally, it encodes, encapsulates, modulates, and transmits the images acquired by the UAV according to the target transmission parameter combination. That is, this application is equivalent to first understanding the interference situation of the environment in which the UAV is located, the current transmission link quality, and the UAV's own state, and then adaptively adjusting the image transmission strategy according to the interference situation, the current transmission link quality, and the UAV's own state. Therefore, even if the interference and channel attenuation experienced by the UAV during flight are dynamic, this application can select an appropriate image transmission strategy for image transmission according to the dynamically changing interference and channel attenuation. This effectively solves the problems of image stream receiving by the ground station experiencing stuttering, image frame loss, pixelation, or color distortion due to interference or increased channel attenuation, thereby effectively improving the image stream quality and effectively avoiding the situation where defects cannot be detected in time or misjudgments occur due to the decline in image stream quality.

[0039] Secondly, such as Figure 2 As shown, this application also provides an image transmission system for the flight process of a UAV, used for transmitting images acquired by the UAV, comprising: Information acquisition module 1 is used to acquire electromagnetic environment information, channel status information, and UAV internal parameter information; The comprehensive status index acquisition module 2 is used to generate comprehensive status indices based on electromagnetic environment information, channel status information, and UAV internal parameter information. The transmission parameter combination determination module 3 is used to query a pre-built mapping table of status indicators and transmission parameter combinations based on the comprehensive status indicators to obtain the target transmission parameter combination. The target transmission parameter combination includes modulation order, forward error correction coding rate, transmission power, image frame rate, data packet size, image resolution, and compression ratio. Image processing module 4 is used to encode, encapsulate, modulate, and transmit images acquired by the UAV according to the target transmission parameter combination.

[0040] This application provides a UAV flight process image transmission system, which includes an information acquisition module 1, a comprehensive status index acquisition module 2, a transmission parameter combination determination module 3, and an image processing module 4. The UAV flight process image transmission system provided in this embodiment is used to perform the steps in the UAV flight process image transmission method provided in the first aspect above. The principle of the UAV flight process image transmission system provided in this embodiment is the same as the principle of the UAV flight process image transmission method provided in the first aspect above, and will not be discussed in detail here.

[0041] In some preferred embodiments, the process of querying a pre-built mapping table of status indicators and transmission parameter combinations based on comprehensive status indicators to obtain the target transmission parameter combination includes: B1. Based on the comprehensive status index, query the pre-built mapping table of status index and transmission parameter combination to obtain the preliminary transmission parameter combination; B2. Analyze the images collected by the UAV to see if there are any buildings obstructing the view. If so, proceed to step B3. If not, use the initial transmission parameter combination as the target transmission parameter combination. B3. Obtain the current pose and flight altitude information of the UAV, and predict the degree of impact of building occlusion on image transmission based on the current pose and flight altitude information of the UAV and the pre-built 3D city model to obtain the first degree of impact on image transmission. B4. Based on the first image transmission impact level, query the pre-constructed mapping table of image transmission impact level and transmission parameter combination adjustment strategy to obtain the first transmission parameter combination adjustment strategy. Then, adjust the initial transmission parameter combination according to the first transmission parameter combination adjustment strategy to obtain the target transmission parameter combination.

[0042] As can be seen from the above, the UAV flight process image transmission method and system provided in this application first integrates electromagnetic environment information, channel state information, and UAV internal parameter information to generate a comprehensive state index that reflects the overall state of the UAV. Then, based on this comprehensive index, the target transmission parameter combination of the comprehensive state index is dynamically integrated. Finally, the images acquired by the UAV are encoded, encapsulated, modulated, and transmitted according to the target transmission parameter combination. That is, this application is equivalent to first understanding the interference situation of the environment in which the UAV is located, the current transmission link quality, and the UAV's own state, and then adaptively adjusting the image transmission strategy according to the interference situation, the current transmission link quality, and the UAV's own state. Therefore, even if the interference and channel attenuation experienced by the UAV during flight are dynamic, this application can select an appropriate image transmission strategy for image transmission according to the dynamically changing interference and channel attenuation. This effectively solves the problems of image stream receiving by the ground station experiencing stuttering, image frame loss, pixelation, or color distortion due to interference or increased channel attenuation, thereby effectively improving the image stream quality and effectively avoiding the situation where defects cannot be detected in time or misjudgments occur due to the decline in image stream quality.

[0043] In the embodiments provided in this application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of the above units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple units or components may be combined or integrated into another robot, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0044] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0045] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0046] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for transmitting images during the flight of a UAV, used to transmit images acquired by the UAV, characterized in that, The method for transmitting images during UAV flight includes the following steps: S1. Acquire electromagnetic environment information, channel status information, and UAV internal parameter information; S2. Generate a comprehensive status index based on the electromagnetic environment information, the channel state information, and the UAV internal parameter information; S3. Based on the comprehensive status index, query the pre-constructed mapping relationship table of status index and transmission parameter combination to obtain the target transmission parameter combination, which includes modulation order, forward error correction coding rate, transmission power, image frame rate, data packet size, image resolution and compression ratio. S4. Encode, encapsulate, modulate, and transmit the images acquired by the UAV according to the target transmission parameter combination; Step S3 includes: S31. Based on the comprehensive status index, query the pre-constructed mapping table of status index and transmission parameter combination to obtain the preliminary transmission parameter combination; S32. Analyze whether there are buildings obstructing the images collected by the UAV. If so, proceed to step S33. If not, use the preliminary transmission parameter combination as the target transmission parameter combination. S33. Obtain the current pose information and flight altitude information of the UAV, and predict the degree of influence of building occlusion on image transmission based on the current pose information of the UAV, the flight altitude information and the pre-constructed 3D city model to obtain the first degree of influence on image transmission. S34. Based on the first image transmission influence level, query the pre-constructed mapping relationship table about the image transmission influence level and the transmission parameter combination adjustment strategy to obtain the first transmission parameter combination adjustment strategy. Then, adjust the preliminary transmission parameter combination according to the first transmission parameter combination adjustment strategy to obtain the target transmission parameter combination. Step S31 includes: S311. Query the pre-built mapping table of status indicators and transmission parameter combinations according to the comprehensive status indicators to obtain the original transmission parameter combinations; S312. Obtain real-time meteorological data, and then query a pre-constructed mapping table of meteorological data and transmission parameter combination adjustment strategy based on the real-time meteorological data to obtain the second transmission parameter combination adjustment strategy. S313. Adjust the original transmission parameter combination according to the second transmission parameter combination adjustment strategy to obtain a preliminary transmission parameter combination; Step S313 includes: A1. Obtain the current task type of the UAV, and then query the pre-built mapping table of task type and transmission parameter combination adjustment strategy according to the current task type of the UAV to obtain the third transmission parameter combination adjustment strategy; A2. Adjust the original transmission parameter combination according to the second transmission parameter combination adjustment strategy and the third transmission parameter combination adjustment strategy to obtain a preliminary transmission parameter combination.

2. The method for transmitting images during UAV flight according to claim 1, characterized in that, Step S2 includes: S21. Obtain the current task type of the UAV, and then query the pre-built mapping table of task type and weighted weight combination according to the current task type of the UAV to obtain the weighted weights corresponding to the electromagnetic environment information, the channel state information and the UAV internal parameter information respectively. S22. Calculate the comprehensive status index based on the electromagnetic environment information and its corresponding weighted weight, the channel state information and its corresponding weighted weight, and the UAV internal parameter information and its corresponding weighted weight.

3. The method for transmitting images during UAV flight according to claim 1, characterized in that, The electromagnetic environment information includes the operating frequency band and adjacent frequency bands of the UAV, spectrum occupancy, interference signal frequency band, interference signal bandwidth, and interference signal strength.

4. The method for transmitting images during UAV flight according to claim 1, characterized in that, The channel state information includes received signal strength indication, signal-to-noise ratio, bit error rate, and multipath delay spread.

5. The method for transmitting images during UAV flight according to claim 1, characterized in that, The internal parameters of the drone include the drone's remaining battery power, CPU load, and memory usage.

6. A drone flight process image transmission system for transmitting images acquired by the drone, characterized in that, The UAV flight process image transmission system is used to perform the steps in the UAV flight process image transmission method according to any one of claims 1-5, and the UAV flight process image transmission system includes: The information acquisition module is used to acquire electromagnetic environment information, channel status information, and internal parameters of the UAV. The comprehensive status index acquisition module is used to generate a comprehensive status index based on the electromagnetic environment information, the channel status information, and the UAV internal parameter information. The transmission parameter combination determination module is used to query a pre-built mapping table of state indicators and transmission parameter combinations based on the comprehensive state indicators to obtain the target transmission parameter combination, which includes modulation order, forward error correction coding rate, transmission power, image frame rate, data packet size, image resolution, and compression ratio. The image processing module is used to encode, encapsulate, modulate, and transmit the images acquired by the UAV according to the target transmission parameter combination.

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