Real-time view coding and wireless transmission optimization method and system for unmanned aerial vehicle monitoring platform

Abstract

The invention discloses a real-time view coding and wireless transmission optimization method and system for an unmanned aerial vehicle monitoring platform, and relates to the technical field of unmanned aerial vehicle video coding optimization and wireless transmission scheduling. According to the invention, through a video coding regulation and control mechanism driven by attitude data, real-time perception and adaptive coding adjustment of flight dynamics of the unmanned aerial vehicle are realized, and the matching degree between coding parameters and attitude and heading changes is effectively improved, so that code rate sudden increase and transmission congestion caused by picture jitter are reduced; meanwhile, a time-space multi-stage reconstruction link triggered based on the flight path change rate can preferentially generate a low-resolution reconstruction frame capable of being quickly recovered in a violent maneuvering scene, and the lowest visual continuity is ensured; furthermore, an iterative packet reordering strategy dynamically associated with flight is utilized, so that the key data packets obtain a more preferential scheduling opportunity in an unstable link environment, and the overall transmission robustness and recovery speed are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of unmanned aerial vehicle video encoding optimization and wireless transmission scheduling, in particular to a real-time view encoding and wireless transmission optimization method and system for an unmanned aerial vehicle monitoring platform. BACKGROUND

[0002] With the rapid popularization of unmanned aerial vehicles in emergency management, traffic inspection, power line inspection, security monitoring and other scenarios, video monitoring platforms based on unmanned aerial vehicles have gradually become important infrastructure for aerial information collection and situation awareness. In recent years, high-definition video encoding, adaptive bit rate control and low-latency transmission protocols have all achieved significant development, enabling video streams on mobile platforms to maintain good continuity and image quality under dynamic network conditions.

[0003] However, compared with ground fixed camera systems, unmanned aerial vehicles have random attitude disturbances, frequent flight path changes, and strong channel fading during flight, resulting in stronger time-varying of video content, rapid fluctuations in encoding complexity, and uncontrollable buffer latency and bandwidth. To address these issues, domestic and foreign researches have used motion vector analysis, bit rate feedback, and channel state prediction techniques for optimization, but they are usually based on the structural characteristics of video content itself or on network layer throughput feedback, making it difficult to truly realize active sensing of the encoding layer for unmanned aerial vehicle flight dynamics. SUMMARY

[0004] In view of the problems existing in the prior art of unmanned aerial vehicle video encoding and wireless transmission, the present application is proposed.

[0005] Therefore, the problem to be solved by the present application is how to solve the technical problems of insufficient video encoding stability and wireless transmission reliability in the high dynamic flight environment of unmanned aerial vehicles.

[0006] To solve the above technical problems, the present application provides the following technical solutions: In a first aspect, the present application provides a real-time view encoding and wireless transmission optimization method for an unmanned aerial vehicle monitoring platform, which comprises: S1: obtaining attitude data and flight path change rate of the unmanned aerial vehicle; S2: calculating envelope fluctuation intensity based on the attitude data, and dynamically adjusting encoding parameters of a video encoder according to the envelope fluctuation intensity; S3: when the flight path change rate exceeds a set change rate threshold, the video encoder starts a time-space multi-level reconstruction link to generate unsent encoding data packets containing low-resolution fast reconstruction frames and subsequent enhancement layer data; S4: performing iterative packet reordering on the unsent encoding data packets according to the real-time flight path change rate.

[0007] As a preferred scheme of the real-time view encoding and wireless transmission optimization method for the unmanned aerial vehicle monitoring platform, the S1 comprises: reading original data sequences of a pitch angle, a roll angle and a yaw angle from an inertial measurement unit of the unmanned aerial vehicle, and simultaneously acquiring position coordinates of adjacent time stamps from a satellite positioning module of the unmanned aerial vehicle; and performing first-order difference calculation on the position coordinates to generate a flight path change rate.

[0008] As a preferred scheme of the real-time view encoding and wireless transmission optimization method for the unmanned aerial vehicle monitoring platform, the envelope fluctuation intensity is generated by: calculating standard deviations of the pitch angle, the roll angle and the yaw angle in a sliding window to obtain pitch angle deviation, roll angle deviation and yaw angle deviation; performing weighted summation on the pitch angle deviation, the roll angle deviation and the yaw angle deviation, wherein the yaw angle deviation is given the highest weight coefficient; and performing normalization processing on a result of the weighted summation to obtain a scalar value as the attitude envelope fluctuation intensity.

[0009] As a preferred scheme of the real-time view encoding and wireless transmission optimization method for the unmanned aerial vehicle monitoring platform, the adjustment of the encoding parameters of the video encoder comprises: dividing the envelope fluctuation intensity by a predefined base step to obtain a first reduction coefficient; multiplying the first reduction coefficient with a reference frame span used in encoding of a previous frame or a previous group of images to obtain a first reduction amount; deducting the first reduction amount from the reference frame span to form a reduced reference frame span; inputting the first reduction coefficient into a Sigmoid function to perform nonlinear transformation and calculate a second reduction coefficient; using the second reduction coefficient to perform weighted compression to an upper limit value and a lower limit value of a quantization parameter interval used in encoding of the previous frame or the previous group of images, respectively, to form a compressed quantization parameter interval; and configuring the reduced reference frame span and the compressed quantization parameter interval into the video encoder to encode a next frame of image following the same.

[0010] As a preferred scheme of the real-time view encoding and wireless transmission optimization method for the unmanned aerial vehicle monitoring platform, the time-space multi-level reconstruction link comprises: using an upper limit value of the compressed quantization parameter interval as a first quantization parameter to encode a current input frame to generate a low-resolution fast reconstruction frame that can be independently decoded; and using a lower limit value of the compressed quantization parameter interval as a second quantization parameter to encode the same current input frame to generate subsequent enhancement layer data packets that depend on a decoding result of the low-resolution fast reconstruction frame.

[0011] As a preferred scheme of the real-time view encoding and wireless transmission optimization method for the unmanned aerial vehicle monitoring platform, the generation of the unsent encoded data packet comprises: encoding the low-resolution fast reconstruction frame into an independent data packet, and encapsulating the subsequent enhancement layer data into one or more associated data packets, and collectively marking as belonging to the same view unit; the transmission priority identifier is assigned to the view unit, and collectively constitutes the unsent encoded data packet; the value of the transmission priority identifier is obtained by linear mapping of the absolute value of the current flight path change rate.

[0012] As a preferred scheme of the real-time view encoding and wireless transmission optimization method for the unmanned aerial vehicle monitoring platform, the iteration packet reordering comprises: before each sending scheduling, checking the type of each unsent encoded data packet in the transmission queue: if it is an unsent encoded data packet containing a low-resolution fast reconstruction frame, it is marked as a reference packet; if it is an unsent encoded data packet containing only subsequent enhancement layer data, it is marked as an enhancement packet; the ratio of the number of reference packets to the number of unsent encoded data packets in the current transmission queue is calculated as the reference packet ratio; the absolute value of the flight path change rate is multiplied by the reference packet ratio to obtain a dynamic control factor; according to the value of the dynamic control factor, the sorting score of each unsent encoded data packet in the transmission queue is updated: the initial value of the sorting score of each unsent encoded data packet is the transmission priority identifier of the view unit to which it belongs; when updating, the initial value of the sorting score of each reference packet is added to the dynamic control factor, and the sorting score of the enhancement packet remains unchanged; according to the updated sorting score, all unsent encoded data packets are sorted in descending order to form the sending order of this iteration.

[0013] In a second aspect, the present application provides a real-time view encoding and wireless transmission optimization system for an unmanned aerial vehicle monitoring platform, which comprises: An attitude acquisition module for acquiring attitude data and flight path change rate of the unmanned aerial vehicle; An envelope parameter adjustment module for calculating envelope fluctuation intensity based on the attitude data, and dynamically adjusting the encoding parameters of the video encoder according to the envelope fluctuation intensity; A reconstruction triggering module for starting the time-space multi-level reconstruction link of the video encoder when the flight path change rate exceeds a set change rate threshold, to generate an unsent encoded data packet containing a low-resolution fast reconstruction frame and subsequent enhancement layer data; A packet reordering module for performing iteration packet reordering on the unsent encoded data packet according to the real-time flight path change rate.

[0014] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and wherein the computer program instructs the processor to implement the steps of the real-time view encoding and wireless transmission optimization method for a UAV monitoring platform according to the first aspect of the present application.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program, wherein the computer program instructs a processor to implement the steps of the real-time view encoding and wireless transmission optimization method for a UAV monitoring platform according to the first aspect of the present application.

[0016] The present application has the following beneficial effects: the present application realizes real-time perception and adaptive encoding adjustment of UAV flight dynamics through the attitude data-driven video encoding regulation mechanism, effectively improves the matching degree between the encoding parameters and the changes in flight attitude, thereby reducing the sudden increase in code rate and transmission congestion caused by picture jitter; at the same time, the time-space multi-level reconstruction link triggered based on the flight path change rate can generate low-resolution reconstruction frames that can be quickly recovered in a severe maneuvering scene, ensuring the minimum visual continuity; further, the iterative packet reordering strategy associated with flight dynamics enables key data packets to obtain a more preferential scheduling opportunity in an unstable link environment, improving the robustness and recovery speed of overall transmission.

[0017] In summary, the present application significantly enhances the stability, continuity and anti-link fluctuation capability of UAV monitoring video in high dynamic flight scenes. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0019] Figure 1 The flowchart of the real-time view encoding and wireless transmission optimization method for a UAV monitoring platform.

[0020] Figure 2 The structural diagram of the real-time view encoding and wireless transmission optimization system for a UAV monitoring platform. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned objects, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification.

[0022] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without the specific details set forth in this description. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily obscure aspects of the present application.

[0023] It should also be noted that, as used herein, "the embodiment" refers to a particular feature, structure, or characteristic described herein. A discussion of "the embodiment" and a discussion of "one embodiment" are not necessarily mutually exclusive. For example, a feature, structure, or characteristic described herein as "the embodiment" can also be described using the phrase "one embodiment." Likewise, a discussion of an embodiment can also apply to the discussion of another embodiment.

[0024] Figure 1 A flowchart of a real-time view encoding and wireless transmission optimization method for a UAV monitoring platform according to an embodiment of the present application. As shown in FIG. 1, in the real-time view encoding and wireless transmission optimization method for a UAV monitoring platform, the method comprises the following steps: Figure 1 S1: Obtain attitude data and flight path change rate of a UAV.

[0025] The obtaining of the attitude data of the UAV comprises the following steps: Read the original data sequence of the pitch angle, roll angle, and yaw angle from the inertial measurement unit of the UAV, and simultaneously obtain the position coordinates of adjacent time stamps from the satellite positioning module of the UAV.

[0026] The calculation of the flight path change rate comprises the following steps: Perform first-order difference calculation on the position coordinates to generate the flight path change rate.

[0027] Specifically, read the position coordinate sequence (latitude, longitude, or planar projection coordinates) from the satellite positioning module according to the time stamps synchronized with the attitude sampling; perform first-order difference on the position coordinates of adjacent time stamps to obtain the instantaneous movement vector, and calculate the heading angle (in radians or degrees) therefrom.

[0028] As can be seen, unlike the conventional method of using only the instantaneous angular velocity or single heading difference, the present application couples the time window statistics of the three-axis attitude with the normalized path change rate to obtain the stability identifier and path correlation quantity that can be directly used for decision-making on the encoding side, thereby providing accurate driving parameters for the encoding-transmission linkage.

[0029] S2: Calculate the envelope fluctuation intensity based on the attitude data, and dynamically adjust the encoding parameters of the video encoder according to the envelope fluctuation intensity.

[0030] ​It should be noted that in the unmanned agency map platform, the fluctuation degree of the attitude data provided by the flight control system directly affects the motion complexity of the collected picture, and the motion complexity determines the load of the video encoder in the motion estimation and quantization level. The envelope fluctuation intensity is constructed based on the attitude data to reflect the degree of flight disturbance in a short time, and the coding parameters are dynamically adjusted according to the intensity.

[0031] Compared with the angular velocity or instantaneous deviation commonly used in the prior art, the present application combines the statistical distribution of three-axis angles in the sliding time window, adopts a weighted fluctuation structure with enhanced weight of the yaw axis, and according to the convergence rule of double coefficients-double level reduction, the coding parameters can be more finely adapted to the change of flight state.

[0032] First, the standard deviations of the pitch angle, roll angle and yaw angle in the sliding window are calculated (using the standard deviation formula) to obtain the pitch angle deviation, roll angle deviation and yaw angle deviation; the pitch angle deviation, roll angle deviation and yaw angle deviation are weighted and summed, wherein the yaw angle deviation is assigned the highest weight coefficient; the result of the weighted sum is normalized to obtain a scalar value as the attitude envelope fluctuation intensity.

[0033] The normalization process can be performed by dividing the weighted sum result by a predefined normalization factor and truncating it to the interval 0-1.

[0034] Further, based on the envelope fluctuation intensity and the reference frame span and quantization parameter interval configuration used by the video encoder when encoding the previous frame or the previous group of pictures, the updated parameters for encoding the next frame or the next group of pictures are calculated: The envelope fluctuation intensity is divided by a predefined base step to obtain a first reduction coefficient ; the first reduction coefficient is multiplied by the reference frame span used when encoding the previous frame or the previous group of pictures to obtain a first reduction amount; the first reduction amount is deducted from the reference frame span to form a reduced reference frame span, and the reduction can suppress the motion mismatch caused by long-span reference frames during attitude fluctuation.

[0035] It should be noted that the reference frame span is derived from the initial configuration or default value of the video encoder. For example, when the system starts, the encoder will load a set of default coding parameters (including reference frame span, initial quantization parameter QP, etc.); from the second frame or the second GOP, the parameters will be dynamically adjusted based on the values of the previous frame / GOP and the calculated envelope fluctuation intensity.

[0036] The first reduction coefficient is input into a Sigmoid function for nonlinear transformation to calculate a second reduction coefficient between 0 and 1 ; using the second reduction factor, the upper limit value of the quantization parameter interval used in the encoding of the previous frame or the previous group of pictures is weighted to an intermediate compression and the lower limit value is weighted to an intermediate compression to form a compressed quantization parameter interval.

[0037] By way of example, the weighted intermediate compression can be expressed as ; ; wherein is the interval median value, is the upper limit value of the compressed quantization parameter interval, is the lower limit value of the compressed quantization parameter interval. This compression mechanism converges the upper and lower limits to the median value, so that the quantization accuracy remains more consistent under flight disturbances.

[0038] The reduced reference frame span and the compressed quantization parameter interval are jointly configured into the video encoder for encoding the next frame of image.

[0039] It should be noted that the present application adopts the attitude standard deviation + yaw enhancement weight method, which is superior to the direct judgment method of angular velocity commonly used in the prior art, and can more accurately reflect the short-term attitude drift trend; and the double coefficient, two-stage reduction structure is adopted, so that the reference frame span and the quantization interval are respectively subjected to linear reduction and nonlinear compression, avoiding the excessive response caused by simple proportional reduction of the traditional method.

[0040] S3: When the flight path change rate exceeds the set change rate threshold, the video encoder starts a time-space multi-level reconstruction link to generate an unsent encoded data packet containing a low-resolution fast reconstruction frame and subsequent enhancement layer data.

[0041] It should be noted that in the case of dramatic changes in the flight attitude of the unmanned aerial vehicle, the conventional video encoder usually maintains stable quantization parameters and reference frame configurations, and cannot respond immediately to the path acceleration changes occurring in a short time, resulting in problems such as failure of long-distance prediction chain, increase of compression noise, accumulation of local detail errors, etc. Based on the upper limit value and the lower limit value of the compressed quantization parameter interval obtained in the previous step, the present application performs two-level encoding processes of different accuracy levels on the same input frame, so that the encoder maintains a reliable video output channel in a high dynamic environment and provides enhancement layer data that can be gradually recovered. The specific operation is as follows: S3.1: The upper limit value of the compressed quantization parameter interval is used as the first quantization parameter to encode the current input frame to generate a low-resolution fast reconstruction frame that can be independently decoded.

[0042] When the absolute value of the flight path change rate exceeds the set change rate threshold, the video encoder is immediately called to use the upper bound of the compressed quantization parameter interval as the first quantization parameter to perform encoding on the current input frame. The first quantization parameter corresponds to a stronger compression strength, allowing the current frame to retain basic structural texture information and not rely on previous reference frames when decoded independently.

[0043] The set change rate threshold is determined based on historical flight path change rate statistics (e.g., mean plus N times standard deviation) of the UAV in the normal cruise mode or a fixed threshold preset according to the task scenario.

[0044] The encoding result forms a low-resolution fast reconstruction frame, which has independent decoding properties and can be transmitted as the smallest available video unit in subsequent transmission.

[0045] The fast reconstruction frame obtained by the first quantization parameter encoding has a relatively stable global shape structure and will not have significant block artifacts due to the broken prediction link, allowing the receiving end to obtain continuous video pictures in high dynamic scenarios such as rapid turning and intense acceleration, improving the stability of the basic picture under high dynamic conditions and providing necessary reference for the progressive recovery of the enhancement layer.

[0046] S3.2: Encode the same current input frame using the lower bound of the compressed quantization parameter interval as the second quantization parameter to generate subsequent enhancement layer data packets that depend on the decoding result of the low-resolution fast reconstruction frame.

[0047] Due to the smaller value of the second quantization parameter, the encoding output has higher fidelity, but its decoding depends on the decoding result of the fast reconstruction frame to ensure that the data volume is within a controllable range and avoid repeated encoding of basic structural information.

[0048] It should be further noted that the operation of encoding the same input frame with the first and second quantization parameters will introduce additional computational overhead and single-frame processing delay. To meet the real-time requirements of the UAV monitoring platform, one or more of the following methods can be used to overcome this problem: (1) Use the parallel computing capabilities of the encoder hardware unit (such as GPU, dedicated video encoding chip) to perform synchronous or pipelined processing on different quantization parameter versions of the same frame data; (2) In system design, the limited delay (usually within one to several frame periods) generated by this double encoding process is included in the overall link delay budget, at the cost of significantly improving the transmission robustness and decodability of the video base layer in high dynamic scenarios, which is of key value to avoid picture interruption. Those skilled in the art can trade off between delay increase and reliability enhancement based on actual hardware performance and delay tolerance.

[0049] S3.3: encode the low-resolution fast reconstruction frame as an independent data packet, and encapsulate the subsequent enhancement layer data as one or more associated data packets, and mark both as belonging to the same view unit.

[0050] S3.4: assign a transmission priority identifier to the view unit, collectively forming unsent encoded data packets.

[0051] The value of the transmission priority identifier is obtained by linear mapping of the absolute value of the current flight path rate of change. Specifically, according to the absolute value of the current flight path rate of change, linear mapping is performed to a set priority interval to obtain a data packet transmission priority identifier for the view unit, so that the key base frame in the high dynamic scene can obtain a higher sending sequence.

[0052] The priority identifier is embedded in the view unit, which is used for scheduling by the subsequent transmission end under bandwidth limited conditions, so that the base frame and its necessary enhancement layer can reach the receiving end in the shortest time delay.

[0053] S4: According to the real-time flight path rate of change, the unsent encoded data packets are iteratively reordered.

[0054] S4.1: Before each sending scheduling, check the type of each unsent encoded data packet in the transmission queue: if it is an unsent encoded data packet containing a low-resolution fast reconstruction frame, it is marked as a reference packet; if it is an unsent encoded data packet containing only subsequent enhancement layer data, it is marked as an enhancement packet.

[0055] Through the above marking process, each unsent encoded data packet in the transmission queue has a clear classification label, providing a data basis for the differential processing and sorting score in the subsequent steps based on the dynamic control factor, avoiding the problem that the traditional queue cannot correctly identify the key data packet affecting video continuity in the high-speed maneuvering scene.

[0056] S4.2: The ratio of the number of reference packets to the number of unsent encoded data packets in the current transmission queue is calculated as the reference packet ratio. The reference packet ratio directly reflects whether the current queue structure is biased towards the base layer or the enhancement layer, providing a variable basis for constructing a dynamic control factor in combination with the flight path rate of change.

[0057] S4.3: Multiply the absolute value of the flight path rate of change by the reference packet ratio to obtain a dynamic control factor.

[0058] It should be noted that the conventional scheduling strategy usually adjusts the order according to fixed weights or fixed priorities, and does not have the ability to adapt to the flight state. The flight path rate of change and the queue structure parameters are combined to form a new control factor, which has a higher control amplitude for high dynamic scenes.

[0059] Specifically, the absolute value of the flight path change rate at the current time is read, multiplied by the reference packet ratio, and a dynamic control factor for the current round of sorting is generated. The dynamic control factor is adjusted accordingly when the flight attitude changes dramatically (high change rate) or there is a shortage of reference packets in the queue (low reference packet ratio), and the value directly reflects the urgency of the current need to improve the priority of the base frame.

[0060] The dynamic control factor is not dependent on external fixed rules, but is determined by the flight dynamics and the queue structure, so that the scheduling strategy can be adjusted adaptively with the change of time, and is not disturbed by a single parameter.

[0061] S4.4: According to the value of the dynamic control factor, the sorting score of each unsent encoded data packet in the transmission queue is updated: the initial value of the sorting score of each unsent encoded data packet is the transmission priority identifier of the view unit to which it belongs; when updating, the initial value of the sorting score of each reference packet is added to the dynamic control factor, and the sorting score of the enhancement packet remains unchanged.

[0062] The above operation forms a one-way weighting of the reference packet, so that the sorting score is significantly higher than that of the enhancement packet under the condition of dramatic attitude change, thereby obtaining a more forward transmission order in the sorting stage.

[0063] S4.5: According to the updated sorting score, all unsent encoded data packets are sorted in descending order, that is, according to the updated sorting score of each unsent encoded data packet in this round, the entire transmission queue is sorted in descending order, so that the reference packet with a higher sorting score is arranged in front of the queue, and the enhancement packet with a lower sorting score is arranged later, forming the sending order of this iteration.

[0064] The above sorting makes the system have an interpretable priority structure consistent with the current flight dynamics before each transmission, improving the robustness of the video link under high-speed maneuvering conditions.

[0065] Further, as shown in Figure 2 The embodiment also provides a real-time view encoding and wireless transmission optimization system for a UAV monitoring platform, which comprises, An attitude acquisition module for acquiring attitude data and flight path change rate of the UAV; An envelope parameter adjustment module for calculating envelope fluctuation intensity based on the attitude data and dynamically adjusting the encoding parameters of the video encoder according to the envelope fluctuation intensity; A reconstruction triggering module for starting a time-space multi-level reconstruction link of the video encoder to generate unsent encoded data packets containing low-resolution fast reconstruction frames and subsequent enhancement layer data when the flight path change rate exceeds a set change rate threshold. The packet reordering module is configured to perform iterative packet reordering on the unsent encoded data packets according to a real-time flight path change rate.

[0066] The embodiment also provides a computer device suitable for the real-time view encoding and wireless transmission optimization method for a UAV monitoring platform, which comprises a memory and a processor; the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions to implement the real-time view encoding and wireless transmission optimization method for the UAV monitoring platform as proposed in the above embodiment.

[0067] The computer device can be a terminal, which comprises a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The communication interface of the computer device is configured to perform wired or wireless communication with external terminals. The wireless communication can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0068] The embodiment also provides a storage medium having a computer program stored thereon, which is executed by a processor to implement the real-time view encoding and wireless transmission optimization method for the UAV monitoring platform as proposed in the above embodiment.

[0069] To sum up, the video encoding regulation and control mechanism driven by attitude data realizes real-time perception and adaptive encoding adjustment of the flight dynamics of the UAV, effectively improves the matching degree between the encoding parameters and the changes in the flight attitude, thereby reducing the sudden increase in code rate and transmission congestion caused by picture jitter; at the same time, the time-space multi-level reconstruction link triggered based on the flight path change rate can generate low-resolution reconstruction frames that can be quickly recovered under a severe maneuvering scene, ensuring the minimum visual continuity; further, the iterative packet reordering strategy associated with the flight dynamics enables the key data packets to obtain a more preferential scheduling opportunity in an unstable link environment, improving the robustness and recovery speed of the overall transmission.

[0070] In summary, the present application significantly enhances the stability, continuity and anti-link fluctuation capability of the UAV monitoring video in a high dynamic flight scene.

[0071] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced, without departing from the spirit and scope of the technical solutions of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. A method for optimizing real-time view encoding and wireless transmission for unmanned aerial vehicle (UAV) monitoring platforms, characterized in that: include: S1: Acquire the attitude data and flight path change rate of the UAV; S2: Calculate the envelope fluctuation intensity based on the attitude data, and dynamically adjust the encoding parameters of the video encoder according to the envelope fluctuation intensity; S3: When the flight path change rate exceeds the set change rate threshold, the video encoder starts the time-space multi-level reconstruction link to generate unsent encoded data packets containing low-resolution fast reconstruction frames and subsequent enhancement layer data. S4: Perform iterative packet reordering on the unsent encoded data packets based on the real-time flight path change rate.

2. The real-time view encoding and wireless transmission optimization method for UAV monitoring platforms as described in claim 1, characterized in that: S1 includes: The original data sequences of pitch angle, roll angle and yaw angle are read from the inertial measurement unit of the UAV, and the position coordinates of adjacent timestamps are obtained from the satellite positioning module of the UAV. The first-order difference calculation is performed on the position coordinates to generate the flight path change rate.

3. The real-time view encoding and wireless transmission optimization method for UAV monitoring platforms as described in claim 2, characterized in that: The generation of the envelope wave intensity: Calculate the standard deviations of pitch angle, roll angle and yaw angle within the sliding window to obtain pitch angle deviation, roll angle deviation and yaw angle deviation; The pitch angle deviation, roll angle deviation, and yaw angle deviation are weighted and summed, with the yaw angle deviation being assigned the highest weighting coefficient. The result of the weighted summation is normalized to obtain a scalar value as the attitude envelope fluctuation intensity.

4. The real-time view encoding and wireless transmission optimization method for UAV monitoring platforms as described in claim 3, characterized in that: The adjustment of the video encoder's encoding parameters includes: Divide the envelope fluctuation intensity by a predefined base step size to obtain a first reduction factor; multiply the first reduction factor by the reference frame span used when encoding the previous frame or the previous image group to obtain a first reduction amount; subtract the first reduction amount from the reference frame span to form the reduced reference frame span; The first reduction coefficient is input into the Sigmoid function and nonlinear transformation is performed to calculate the second reduction coefficient. The second reduction coefficient is used to weight and compress the upper and lower bounds of the quantization parameter range used in the encoding of the previous frame or the previous image group towards the middle to form the compressed quantization parameter range. The reduced reference frame span and the compressed quantization parameter range are configured together in the video encoder for encoding the next frame image.

5. The real-time view encoding and wireless transmission optimization method for UAV monitoring platforms as described in claim 4, characterized in that: The time-space multi-level reconstruction link includes: The upper bound of the compressed quantization parameter range is used as the first quantization parameter to encode the current input frame, generating a low-resolution fast reconstructed frame that can be independently decoded. The lower bound of the compressed quantization parameter range is used as the second quantization parameter to encode the same current input frame, generating subsequent enhancement layer data packets that depend on the decoding results of the low-resolution fast reconstructed frame.

6. The real-time view encoding and wireless transmission optimization method for UAV monitoring platforms as described in claim 5, characterized in that: The generation of the unsent encoded data packet includes: The low-resolution fast reconstruction frame is encoded into an independent data packet, and the subsequent enhancement layer data is encapsulated into one or more associated data packets, which are collectively marked as belonging to the same view unit; The view units are assigned transmission priority identifiers, which together constitute unsent encoded data packets; The value of the transmission priority identifier is obtained by linear mapping from the absolute value of the rate of change of the current flight path.

7. The real-time view encoding and wireless transmission optimization method for UAV monitoring platforms as described in claim 6, characterized in that: The iterative package reordering includes: Before each transmission scheduling, the type of each untransmitted encoded data packet in the transmission queue is checked: if it is an untransmitted encoded data packet containing a low-resolution fast reconstructed frame, it is marked as a base packet; if it is an untransmitted encoded data packet containing only subsequent enhancement layer data, it is marked as an enhancement packet. The ratio of the number of packets marked as reference packets to the number of untransmitted encoded data packets in the current transmission queue is used as the reference packet percentage. The dynamic control factor is obtained by multiplying the absolute value of the flight path change rate by the proportion of the baseline package. Based on the value of the dynamic adjustment factor, the sorting score of each untransmitted encoded data packet in the transmission queue is updated: the initial value of the sorting score of each untransmitted encoded data packet is the transmission priority identifier of the view unit to which it belongs; during the update, the initial value of the sorting score of each base packet is added to the dynamic adjustment factor, while the sorting score of the enhancement packet remains unchanged. Based on the updated sorting scores, all unsent encoded data packets are sorted in descending order to form the sending order for this iteration.

8. A real-time view encoding and wireless transmission optimization system for a drone monitoring platform, based on the real-time view encoding and wireless transmission optimization method for a drone monitoring platform as described in any one of claims 1 to 7, characterized in that: Also includes: The attitude acquisition module is used to acquire the attitude data and flight path change rate of the UAV. The envelope parameter tuning module is used to calculate the envelope fluctuation intensity based on the attitude data, and dynamically adjust the encoding parameters of the video encoder according to the envelope fluctuation intensity. The reconstruction trigger module is used to activate the time-space multi-level reconstruction link of the video encoder when the flight path change rate exceeds a set change rate threshold, and generate unsent encoded data packets containing low-resolution fast reconstruction frames and subsequent enhancement layer data. The packet reordering module is used to perform iterative packet reordering on the unsent encoded data packets based on the real-time flight path change rate.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the real-time view encoding and wireless transmission optimization method for UAV monitoring platforms as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the real-time view encoding and wireless transmission optimization method for UAV monitoring platforms as described in any one of claims 1 to 7.

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