Compression encoding processing method and system for low-orbit satellite observation data

Abstract

The application discloses a low-orbit satellite observation data compression encoding processing method and system. The method relates to the technical field of compression encoding processing and comprises the following steps: task perception analysis and cooperative compression, task performance driven regulation and control, cooperative state monitoring and evaluation, and cooperative determination constraint. The application generates target detection results and ROI masks through intelligent analysis on the satellite, and differentiates compression according to the task perception. Whether to start task performance regulation is decided based on the calculated end-to-end task performance evaluation value, then the link state is monitored in real time and the on-satellite cooperative processing certainty is evaluated. Finally, whether to perform cooperative accuracy regulation is decided according to the certainty, and a high-reliable compression result is output, realizing the driven change from data fidelity to task performance, and solving the problem of low reliability of low-orbit satellite observation data compression encoding processing caused by the cooperative fragmentation between on-orbit intelligent processing and compression encoding in the prior art.

Description

Technical Field

[0001] This invention relates to the field of compression coding technology, and in particular to a method and system for compression coding of low-orbit satellite observation data. Background Technology

[0002] After acquiring observation data (including optical images and radar echo signals) through optical and radar payloads, low-orbit satellites perform data format standardization, noise suppression, and region segmentation. For optical images, the SCS-LBP operator combined with the advantages of sparse coding is used to segment the background and foreground, separating static background regions such as deep space background and stars from dynamic foreground regions such as debris and spacecraft. At the same time, the radar echo data is preprocessed by logarithmic transformation. For multi-satellite collaborative observation scenarios, the SIFT (Scale-Invariant Feature Transform) operator is used to extract image feature points and descriptors, identify overlapping regions of adjacent satellite observation images, and then use the area equivalence method to determine the overlap width and boundary coordinates.

[0003] The CCSDS (Consultative Committee for Space Data Systems) standard algorithm, which is mainstream in the spaceborne field, is adopted. The discrete wavelet transform of the 9 / 7 wavelet lifting format is used to decompose the image into multi-scale components, converting pixel information into low-frequency approximate components and high-frequency detail components. Then, non-uniform logarithmic quantization is performed on the high-frequency components. Subsequently, the quantization coefficients are scanned in parallel bit-plane coding and entropy coding is performed on the coefficients in combination with adaptive arithmetic coding. The quantization bit depth is dynamically adjusted according to the statistical characteristics of the data. For scenarios that need to retain complete information, the JPEG GLS algorithm is selected. Lossless / near lossless compression is completed through context modeling, prediction error calculation and Golomb-Grice coding. For data in the overlapping area of ​​multiple satellites, the aggregation sparse coding technique is adopted. After initializing the dictionary tensor and auxiliary tensor, the dictionary tensor is obtained through iterative update and Fourier transform. Then, the dictionary tensors of adjacent satellites are aggregated. During the encoding process, real-time performance is ensured through an FPGA (Field-Programmable Gate Array) hardware platform. A block compression strategy is adopted, and parallel data processing is achieved by combining pipeline and parallel architecture. On-chip RAM (Random Access Memory) is used to cache the dictionary and intermediate data to reduce external storage access latency. At the same time, CSD (Canonical Signed Digit) encoding is used to replace multiplication operations to reduce resource consumption.

[0004] The encoded coefficient tensors are quantized into integer values ​​and rearranged into importance and level mappings. An overlap-aware adaptive binary arithmetic coding algorithm is used for overlapping regions. Combined with the pixel context update probability model of the current view and the reference view, a compressed bitstream containing transition tensors and position tensors is output. The segmented background region data is transmitted simultaneously. The bitstream is then downlinked to the ground station after establishing a link through the onboard laser communication payload.

[0005] For example, Chinese invention patent CN103227920B discloses a lossless compression method for multi-channel satellite images, which includes: for multi-channel satellite image data, based on existing lossless compression coding technology, making full use of the similarity of satellite cloud images between different channels, first calculating the difference between the images, and then performing lossless compression coding.

[0006] For example, the Chinese invention patent with announcement number CN105163121B discloses a high compression ratio satellite remote sensing image compression method based on a deep autoencoder network, which includes: cascading and stacking multiple autoencoders to form a deep autoencoder network; inputting a set of training image data into the deep autoencoder network, training the network to obtain optimized network parameters, and obtaining a deep compression network and a deep decompression network; sending the remote sensing image to be compressed into the deep compression network to obtain high-order sparse features, quantizing and encoding the features to obtain the final compressed bitstream; dequantizing and encoding the received bitstream to obtain high-order sparse features and sending them into the deep decompression network, and the final output of the network is the decompressed remote sensing image.

[0007] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems:

[0008] In existing technologies, traditional on-board data compression technology is based on the classic Shannon rate distortion theory. Its optimization goal is to minimize the overall mathematical error between the reconstructed image and the original image at the pixel level under a given bit rate. This paradigm treats the image as an undifferentiated set of signals and strives to retain all pixel information, while completely ignoring the semantic information extraction requirements of subsequent intelligent analysis tasks (such as target detection and land cover classification). It encodes and transmits background pixels (such as large areas of uniform ocean, clouds, and desert) with very low or even zero task value, while the truly critical target information is submerged in redundant data and transmitted together. As a result, from the perspective of information utility, most of the downlink bandwidth is occupied by invalid data, and the end-to-end efficiency of the system is extremely low.

[0009] In terms of engineering implementation, the compression module adopts a pre-fixed, globally unified encoding strategy (such as fixed quantization parameters), which cannot dynamically and differentiate the allocation of bitrate resources based on the semantic importance perceived in real time by the image content or analysis module (such as the confidence of the detected target area and region of interest). This further aggravates the processing latency and limits the system's flexibility in dealing with multi-tasking and dynamic scenarios.

[0010] In the existing serial architecture, both highly complex intelligent analysis models and high-performance compression encoders require independent use of already scarce on-board computing and storage resources, and their simple cascading increases end-to-end processing latency. More importantly, the "black box" nature and performance uncertainty of intelligent analysis models (especially deep learning models) inherently contradict the stringent requirements of aerospace systems for high reliability and determinism. This significantly increases the risks of designing, verifying, and maintaining deep synergistic integration between the two, and poses a problem of low reliability in the compression encoding of low-Earth orbit satellite observation data due to a disconnect between on-orbit intelligent processing and compression encoding. Summary of the Invention

[0011] To address the low reliability of low-Earth orbit (LEO) satellite observation data compression and encoding processing due to the disconnect between on-orbit intelligent processing and compression encoding in existing technologies, this invention provides a method and system for LEO satellite observation data compression and encoding processing. The technical solution is as follows:

[0012] On the one hand, a compression coding method for low-Earth orbit (LEO) satellite observation data is provided. This method includes: S1, intelligent analysis of the observation data in orbit to generate target detection results and region-of-interest (ROI) masks containing semantic information; based on the target detection results and ROI masks, task-aware differential compression coding is performed on the raw observation data to obtain task performance data. This data quantifies the efficiency of converting LEO satellite observation data into effective information from the initial acquisition of raw observation data from the satellite to the final generation of preset application requirements on the ground, resulting in an end-to-end task performance evaluation value; S2, determining whether to execute intelligent task performance control based on the end-to-end task performance evaluation value. This achieves a shift from data fidelity-driven to task utility-driven approaches, thereby improving information processing efficiency and task success rate. In the optimal coordination phase, if executed, the downlink bandwidth utilization and on-board processing pipeline latency status are monitored in real time after adjustment; otherwise, the downlink bandwidth utilization and on-board processing pipeline latency status are directly monitored in real time. In phase S3, during the real-time monitoring of downlink bandwidth utilization and on-board processing pipeline latency status, coordinated processing data is acquired to quantify the coordination accuracy of on-orbit intelligent analysis and compressed coding coordination on the low-Earth orbit satellite, thus obtaining on-board coordinated processing determinism. In phase S4, based on the on-board coordinated processing determinism, it is determined whether to execute intelligent adjustment of coordinated processing accuracy, thereby maintaining the reliability of the on-board processing link and the credibility of the output results. If executed, the compressed coding processing result is output after adjustment; otherwise, the compressed coding processing result is directly output.

[0013] On the other hand, a compression coding and processing system for low-Earth orbit (LEO) satellite observation data is provided. This system includes: a mission-aware analysis and collaborative compression module, a mission performance-driven control module, a collaborative status monitoring and evaluation module, and a deterministic constraint-based collaborative control module. Specifically, the mission-aware analysis and collaborative compression module performs intelligent analysis of observation data in orbit, generating target detection results and region-of-interest (ROI) masks containing semantic information. Based on these results, it performs mission-aware differentiated compression coding on the raw observation data to obtain mission performance data. This data quantifies the efficiency of converting LEO satellite observation data into effective information from the initial acquisition of raw observation data from the satellite to the final generation of preset application requirements on the ground, yielding an end-to-end mission performance evaluation value. The mission performance-driven control module determines whether to execute intelligent mission performance control based on the end-to-end mission performance evaluation value, realizing a shift from data fidelity-driven to mission-constrained control. The shift towards service utility-driven transformation achieves optimal synergy between information processing efficiency and task success rate. If executed, the downlink bandwidth utilization and on-board processing pipeline latency status are monitored in real time after adjustment; otherwise, the downlink bandwidth utilization and on-board processing pipeline latency status are directly monitored in real time. The collaborative status monitoring and evaluation module acquires collaborative processing data during real-time monitoring of downlink bandwidth utilization and on-board processing pipeline latency status. This data is used to quantify the collaborative accuracy of on-orbit intelligent analysis and compression coding collaboration on the low-Earth orbit satellite, obtaining on-board collaborative processing determinism. The deterministic constraint collaborative adjustment module determines whether to perform intelligent adjustment of collaborative processing accuracy based on the on-board collaborative processing determinism, thereby maintaining the reliability of the on-board processing link and the credibility of the output results. If executed, the compressed coding processing result is output after adjustment; otherwise, the compressed coding processing result is directly output.

[0014] Beneficial effects

[0015] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0016] 1. By intelligently analyzing observation data in orbit, target detection results and region-of-interest (ROI) masks containing semantic information are generated. Based on the target detection results and ROI masks, task-aware differentiated compression coding is performed on the raw observation data to obtain task performance data. This data is used to quantify the efficiency of converting low-Earth orbit (LEO) satellite observation data into effective information from the initial acquisition of raw observation data from the satellite to the final generation of preset application requirements (such as target identification reports and disaster assessment maps) on the ground. An end-to-end task performance evaluation value is obtained. Based on the end-to-end task performance evaluation value, it is determined whether to perform intelligent task performance adjustment. This realizes the transformation from data fidelity-driven to task utility-driven, thereby achieving the optimal synergy between information processing efficiency and task success rate. In the process of real-time monitoring of downlink bandwidth utilization and on-board processing pipeline latency, collaborative processing data is obtained. This data is used to quantify the collaborative accuracy of on-orbit intelligent analysis and compression coding on the LEO satellite, obtaining on-board collaborative processing determinism. Based on the on-board collaborative processing determinism, it is determined whether to perform intelligent adjustment of collaborative processing accuracy, thereby maintaining the reliability of the on-board processing link and the credibility of the output results, and thus improving the reliability of compression coding processing.

[0017] 2. By implementing dynamic window scheduling for space-ground collaboration oriented towards mission timeliness, ground processing latency is incorporated into the real-time decision-making closed loop of the onboard transmission window. Through dynamic compression or adaptation of transmission time slots, it ensures that even under tight end-to-end time constraints, the space-ground system can still prioritize the completion of the entire process from acquisition to application of high-value data within the mission timeliness threshold. This transforms limited transit window resources into deterministic information delivery capabilities. Through adaptive background compression based on channel congestion awareness, channel state (bandwidth occupancy) is used as a direct control variable for the compression intensity of the background area. By establishing an adaptive mapping relationship between occupancy and compression intensity, the potential value of background information is preserved when the channel is idle, and extreme compression or even selective discarding is performed when the channel is congested. This intelligently releases downlink bandwidth pressure while maintaining the quality of critical foreground information, achieving dynamic optimal allocation of communication link resources between valuable information and redundant background data, thereby improving the reliability of compression coding processing.

[0018] 3. Through adaptive queue control based on processing latency awareness, the status of ROI mask generation latency—a key bottleneck in the processing pipeline—is used as a forward-looking indicator for queue management. By establishing a latency-overflow rate mapping relationship, the water level is dynamically lowered and the discard strategy is switched, realizing a shift from "passively dealing with overflow" to "actively preventing backlog." This allows for the early reorganization of buffer queues and priority protection of high-value data streams when delays occur in intelligent processing, ensuring the continuous smoothness and overall stability of the data processing link. Through adaptive trigger noise tolerance control based on jitter awareness, the real-time jitter statistics of collaborative trigger latency and the determinism of collaborative processing required by the system are used as joint inputs to dynamically increase the noise tolerance threshold for trigger judgment. This effectively filters out false trigger signals caused by spatial environmental interference or system load fluctuations, suppresses the disorder of inter-module collaboration caused by false triggers, ensures the timing accuracy and reliability of the interaction between intelligent analysis and compression encoding, and thus improves the reliability of compression encoding processing. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating the compression encoding process for low-orbit satellite observation data provided in this application embodiment;

[0021] Figure 2 A flowchart of the adaptive background compression method based on channel congestion awareness for the compression coding processing method of low-orbit satellite observation data provided in the embodiments of this application;

[0022] Figure 3 A flowchart of an adaptive queue control method based on processing delay awareness for a low-orbit satellite observation data compression coding processing method provided in this application embodiment;

[0023] Figure 4 This is a schematic diagram of the structure of the low-orbit satellite observation data compression encoding processing system provided in the embodiments of this application. Detailed Implementation

[0024] The technical solution provided in this application will now be described with reference to the accompanying drawings.

[0025] To facilitate understanding of the embodiments of this application, the following points will be explained first:

[0026] First, in this application, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates an "or" relationship between the preceding and following related objects, but it does not exclude the possibility of indicating an "and" relationship; the specific meaning can be understood in context. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c; a and b; a and c; b and c; or a and b and c. Here, a, b, and c can be single or multiple.

[0027] Second, the use of prefixes such as "first" and "second" in this application is solely for the purpose of distinguishing and describing different things belonging to the same category, and does not constrain the order, size, or quantity of things. For example, "first message" and "second message" are simply different messages, and there is no chronological, size, or priority relationship between them.

[0028] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0029] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0030] like Figure 1 The diagram shown is a flowchart of a low-orbit satellite observation data compression and encoding processing method provided in this application embodiment. The method includes the following steps:

[0031] S1 performs intelligent analysis of observation data in orbit, generating target detection results and region of interest masks containing semantic information. Based on the target detection results and region of interest masks, it performs task-aware differentiated compression encoding on the raw observation data to obtain task performance data. This data is used to quantify the efficiency of converting low-orbit satellite observation data into effective information from the time the satellite acquires raw observation data to the final generation of preset application requirements such as target identification reports and disaster assessment maps on the ground, thus obtaining an end-to-end task performance evaluation value.

[0032] It is important to understand that the specific steps to obtain the end-to-end task performance evaluation value are as follows:

[0033] Mission performance data includes downlink transmission latency of low-Earth orbit satellite observation data, end-to-end information utility bandwidth ratio, and low-Earth orbit satellite observation data compression latency. Among them, the downlink transmission delay of low-Earth orbit (LEO) satellite observation data refers to the total time taken for LEO satellite observation data to travel from the onboard payload output end, through the satellite-to-ground / inter-satellite downlink, to the data demodulation completion end of the ground receiving station. This is achieved by embedding timestamp tags in the observation data frames, extracting the timestamps after demodulation at the ground receiving station, and calculating the difference between the ground reception time and the onboard transmission time. The end-to-end information utility bandwidth ratio refers to the ratio of the effective data transmission bandwidth obtained by collecting the actual available bandwidth of the downlink through the spectrum analyzer at the ground receiving station, deducting signaling and error correction overhead, and recording it as the end-to-end information utility bandwidth ratio. The compression delay of LEO satellite observation data refers to the total computation time taken for LEO satellite observation data from entering the compression stage to outputting the compressed data frame. This is achieved by embedding timestamps at the input and output ends of the onboard compression module, transmitting the timestamp data to the ground through the onboard telemetry system, and calculating the difference between the two timestamps.

[0034] The transmission delay correction factor is combined with the ratio of the transmission delay threshold to the downlink transmission delay of low-Earth orbit satellite observation data to obtain the transmission delay impact component.

[0035] The bandwidth ratio correction factor is combined with the ratio of the end-to-end information utility bandwidth ratio to the bandwidth ratio threshold to obtain the bandwidth ratio influence component. The compression delay correction factor is combined with the ratio of the compression delay of low-Earth orbit satellite observation data to the compression delay threshold to obtain the compression delay influence component. The transmission delay influence component, the bandwidth ratio influence component, and the compression delay influence component are coupled to obtain the end-to-end mission performance evaluation value. The specific constraint expression for the end-to-end mission performance evaluation value is as follows:

[0036] ;

[0037] ;

[0038] In the formula, R represents the end-to-end mission performance evaluation value; s1 represents the transmission delay correction factor obtained from the compressed coding database; s2 represents the bandwidth ratio correction factor obtained from the compressed coding database; s3 represents the compression delay correction factor obtained from the compressed coding database; N0 represents the transmission delay threshold obtained from the compressed coding database; E0 represents the bandwidth ratio threshold obtained from the compressed coding database; F0 represents the compression delay threshold obtained from the compressed coding database; N represents the downlink transmission delay of low-Earth orbit satellite observation data; E represents the end-to-end information utility bandwidth ratio; and F represents the compression delay of low-Earth orbit satellite observation data.

[0039] It should be understood that the longer the downlink transmission latency of low-Earth orbit (LEO) satellite observation data, the lower the number of effective frames / resolution / update frequency that can be transmitted per unit time, and the lower the end-to-end information utility bandwidth ratio. The longer the downlink transmission latency of LEO satellite observation data, the more severe the data throughput capacity of the satellite-to-ground channel is, resulting in serious data backlog. This "forces" the compression latency of LEO satellite observation data to be shortened. The shorter the compression latency of LEO satellite observation data, the more complex and refined the intelligent processing and analysis being performed on the satellite, leading to a significant improvement in the end-to-end information utility bandwidth ratio. Meanwhile, there is a negative correlation between the downlink transmission latency of low-Earth orbit (LEO) satellite observation data and the end-to-end mission performance evaluation value. The longer the downlink transmission latency of LEO satellite observation data, the more incomplete key frames are transmitted, the more time-sensitive the data becomes, and the less effective the data is, resulting in a lower end-to-end mission performance evaluation value. Conversely, there is a positive correlation between the end-to-end information utility bandwidth ratio and the end-to-end mission performance evaluation value. The higher the end-to-end information utility bandwidth ratio, the more effective information can be transmitted with the same bandwidth (e.g., higher resolution, more frames, shorter update cycles), thus improving both the accuracy and timeliness of the mission, resulting in a higher end-to-end mission performance evaluation value. Furthermore, there is a positive correlation between the compression latency of LEO satellite observation data and the end-to-end mission performance evaluation value. The longer the compression latency of LEO satellite observation data, the more complex and higher compression ratio compression strategy is used on-board / ground, and this strategy does not cause data distortion or exceed the mission timeliness threshold, ultimately improving the end-to-end mission performance, resulting in a higher end-to-end mission performance evaluation value.

[0040] S2 determines whether to perform intelligent task performance control based on end-to-end task performance evaluation values, realizing the transformation from data fidelity-driven to task utility-driven, thereby achieving optimal synergy between information processing efficiency and task success rate. If executed, the downlink bandwidth utilization and on-board processing pipeline latency status are monitored in real time after the control is implemented. If not, the downlink bandwidth utilization and on-board processing pipeline latency status are monitored directly in real time.

[0041] Furthermore, the specific steps for determining whether to perform intelligent task performance control are as follows: if the end-to-end task performance evaluation value is greater than or equal to the task performance benchmark value, then intelligent task performance control is not performed; otherwise, dynamic window scheduling based on satellite-ground coordination and adaptive background compression based on channel congestion awareness are performed, oriented towards task timeliness.

[0042] It should be further explained that the specific steps for performing mission-time-oriented space-ground collaborative dynamic window scheduling are as follows:

[0043] For each low-Earth orbit satellite observation mission, a maximum end-to-end allowable delay threshold is predefined. The maximum end-to-end allowable delay threshold represents the maximum total time allowed from the time of low-Earth orbit satellite observation data acquisition to the time of final delivery to the end user.

[0044] Before and during each satellite transit, the current ground processing and distribution latency is monitored and acquired in real time. Based on the maximum permissible end-to-end latency threshold and the ground processing and distribution latency, the total time window available for satellite-ground collaboration for on-board processing and downlink transmission is obtained. The total time window available for satellite-ground collaboration represents the difference between the maximum permissible end-to-end latency threshold and the ground processing and distribution latency.

[0045] Executing mission-time-oriented, space-ground collaborative dynamic window scheduling also includes:

[0046] Based on the end-to-end mission performance evaluation value and the total time window, combined with the preset time window for satellite transit, the adjusted actual scheduling and transmission time window is dynamically determined and instructed to be executed by the satellite through the following logic:

[0047] If the total time window is greater than or equal to the preset time window, it indicates that the ground processing margin is sufficient, and the satellite can be instructed to carry out low-orbit satellite observation data transmission in the most stable manner within the entire preset transit time window. That is, the actual scheduled transmission time window is the preset time window.

[0048] If the total time window is less than the preset time window, it indicates that the total time is tight. The transmission window is compressed to reserve enough time for ground processing. Based on the end-to-end mission performance evaluation value and the current total time window, it is input into the predefined distribution delay-transmission time window mapping relationship, outputting the actual scheduled transmission time window, and instructing the satellite to transmit high-priority data only within the actual scheduled transmission time window. High-priority data refers to data units that are marked by the system as having the highest information value and time urgency for completing the preset observation task during onboard intelligent processing and collaborative compression, and are therefore given priority transmission rights.

[0049] The start and end times of the actual scheduled transmission time window, along with the corresponding data transmission priority strategy, are encapsulated into a scheduling instruction. When the satellite enters the previous ground station's visible window, it is instructed to execute low-orbit satellite observation data transmission according to the adjusted actual scheduled transmission time window.

[0050] In this embodiment, firstly, a maximum permissible end-to-end latency threshold is predefined for each observation task. This threshold establishes a global time constraint from data acquisition to user reception. Before and during each satellite transit, the current ground processing and distribution latency is monitored and acquired in real time. Subsequently, this ground latency is subtracted from the maximum permissible latency to obtain a coordinated total time window dedicated to onboard processing and downlink transmission. This approach incorporates the variable processing latency of the ground system into the real-time planning of onboard transmission, overcoming the time-sensor disconnect problem caused by traditional independent satellite-ground planning. Next, based on the calculated total time window and the theoretically visible window of the satellite transit (preset time window), combined with the real-time evaluated end-to-end mission efficiency value, dynamic decision-making is made through a predefined distribution delay-transmission time window mapping relationship: if the total time window is sufficient (greater than or equal to the preset window), the satellite is instructed to transmit with the most stable parameters throughout the transit window, making full use of channel resources to maximize data throughput; if the total time window is tight (less than the preset window), it indicates that the transmission time must be compressed to reserve margin for ground processing. At this time, the system calculates the compressed actual scheduling transmission time window based on the mission efficiency value and the tight time window through the mapping relationship, and instructs the satellite to prioritize the transmission of high-priority data (i.e., data units with the highest information value and time urgency marked by on-board intelligent processing) only within this compressed window. This mechanism ensures that when the end-to-end time limit is about to be exceeded, the system can automatically focus resources to ensure the timely delivery of core mission information, realizing the leap from "best effort transmission" to "quality and timeliness". Finally, the calculated actual window parameters and priority strategy are encapsulated into scheduling instructions, which are then injected uplink when the satellite enters the previous ground station's visible window, forming a closed-loop control for satellite-ground collaboration. This enables the satellite's transmission behavior to proactively adapt to changes in the ground processing status, ultimately transforming the limited transit time into deterministic mission timeliness at the system level.

[0051] It should be noted that, as Figure 2The diagram shows the adaptive background compression flowchart based on channel congestion awareness for the low-orbit satellite observation data compression coding processing method provided in this application embodiment. The specific process is as follows: First, the real-time monitoring and evaluation phase is started from "Start Monitoring" to continuously collect the downlink total bandwidth occupancy rate; then, through the "Bandwidth Occupancy Rate Judgment" node, the real-time occupancy rate is compared with a preset threshold value. If the occupancy rate is less than or equal to the threshold value, it is determined that the link is in "channel idle state". At this time, the compression intensity of the current background area is directly maintained, the current regulation is completed, and the process enters "End Regulation"; if the occupancy rate is greater than the threshold value, it is determined that the link is in "channel congestion state", and then the joint decision input phase is entered. The two core parameters, "downlink total bandwidth occupancy rate" and "end-to-end task performance evaluation value", are integrated. Combined with the predefined "occupancy rate-maximum compression intensity mapping relationship", the maximum compression intensity of the target background area is calculated and output. Finally, the maximum compression is applied to the background area to release bandwidth resources. After the regulation is completed, the process also enters "End Regulation". The entire process dynamically adjusts the background area compression strategy by differentiating channel states, ensuring data quality during idle periods and prioritizing bandwidth release during congestion, thus achieving a balance between bandwidth utilization efficiency and task performance.

[0052] It should be further explained that the specific steps of adaptive background compression based on channel congestion awareness are as follows:

[0053] The downlink total bandwidth utilization is monitored in real time. Based on the real-time monitored downlink total bandwidth utilization and the end-to-end task performance evaluation value, the maximum allowable compression intensity of the background area is dynamically adjusted. Specifically, if the downlink total bandwidth utilization is less than or equal to the bandwidth utilization threshold, it is determined that the channel idleness is high and the downlink total bandwidth resources are sufficient, and the maximum allowable compression intensity of the current background area is maintained.

[0054] If the downlink total bandwidth utilization rate is greater than the bandwidth utilization rate threshold, the channel is determined to be congested. Based on the downlink total bandwidth utilization rate and the end-to-end task performance evaluation value, the input is fed into the predefined utilization rate-maximum compression intensity mapping relationship, and the maximum compression intensity of the target background area is output. The maximum compression is applied to the background area to reduce its data volume and reserve bandwidth for critical information.

[0055] In this embodiment, firstly, the total downlink bandwidth occupancy rate is continuously monitored in real time. This indicator comprehensively reflects the overall consumption of the channel by downlink data streams from the local satellite and all satellites sharing the same ground station resources. Through global bandwidth occupancy awareness, the system can accurately determine the real-time tension of channel resources, providing a precise basis for dynamic resource allocation. When the total downlink bandwidth occupancy rate is detected to be less than or equal to a preset bandwidth occupancy rate threshold, the system determines that the current channel idleness is high and bandwidth resources are sufficient. At this time, the system maintains the current compression intensity of the background area. This is intended to moderately preserve the potential value of background information (such as background change analysis for long-term environmental monitoring) when channel resources are ample, avoiding the loss of future application potential due to excessive compression, reflecting a resource elastic management strategy. When the total downlink bandwidth occupancy rate is detected to be greater than the threshold, the system determines that the channel has entered a congested state. At this time, the system does not simply and unilaterally enhance compression, but instead inputs the total downlink bandwidth occupancy rate and the end-to-end task performance evaluation value reflecting the global task completion efficiency into a predefined occupancy rate-maximum compression intensity mapping relationship for joint decision-making. This mapping relationship intelligently calculates and outputs an optimal maximum compression intensity for the target background region based on the channel congestion level and the sensitivity of the current task performance. Subsequently, the system applies corresponding levels, or even maximum levels, of compression to the background region according to this intensity, directly and intelligently transforming the channel congestion state into a "squeezing" instruction for redundant background data. This rapidly and significantly reduces the volume of invalid data while maintaining the absolute priority of the quality of critical foreground information, proactively "clearing obstacles" and "making way" for the transmission of high-value information. This achieves resource rebalancing under bandwidth shortage crises and ensures that the core performance of the task is still given priority even when the channel is congested.

[0056] S3 acquires collaborative processing data during real-time monitoring of downlink bandwidth utilization and on-board processing pipeline latency. This data is used to quantify the collaborative accuracy of on-orbit intelligent analysis and compression coding collaboration on low-Earth orbit satellites, thereby obtaining on-board collaborative processing determinism.

[0057] It needs to be explained that the specific steps to obtain the determinism of on-board collaborative processing are as follows:

[0058] The collaborative processing data includes the effective value of task performance, the latency for ROI content discrimination, and the collaborative completion rate of the over-the-top window. The effective value of task performance refers to the re-acquired end-to-end task performance evaluation value if intelligent task performance adjustment was implemented; otherwise, the current end-to-end task performance evaluation value is used. The latency for ROI content discrimination refers to the total time taken by the onboard intelligent analysis module of the low-Earth orbit satellite from receiving the first byte of the original observation data frame to outputting the corresponding region of interest mask. High-precision timestamps are embedded in the data input port and ROI output port of the onboard intelligent analysis module. The timestamp data is transmitted to the ground station along with the telemetry frame, and the difference between the two timestamps is the ROI content discrimination latency. The region of interest (ROI) refers to a local area within the original observation data frame that has core value for a specific task and needs to be retained and analyzed. These areas are the carriers of the task objectives and are also the priority objects for subsequent compression coding and downlink transmission. The over-the-top window collaborative completion rate refers to the proportion of the number of tasks that successfully complete the entire collaborative process of original data acquisition, ROI content identification, compression coding, and downlink transmission within a single over-the-top window of a low-orbit satellite over a ground target, relative to the total number of tasks within that window period. By extracting information such as task number, acquisition time, and data volume within the window period from the onboard payload telemetry logs, the total number of tasks is counted, and the ratio of the total number of tasks to the total number of tasks within that window period is recorded as the over-the-top window collaborative completion rate.

[0059] The effective performance correction factor is combined with the ratio of the effective task performance value to the effective performance threshold to obtain the effective performance impact component; the discrimination delay correction factor is combined with the ratio of the discrimination delay threshold to the ROI content discrimination delay to obtain the discrimination delay impact component; the collaborative completion rate correction factor is combined with the ratio of the over-the-top window collaborative completion rate to the collaborative completion rate threshold to obtain the collaborative completion rate impact component; the effective performance impact component, the discrimination delay impact component, and the collaborative completion rate impact component are coupled to obtain the on-board collaborative processing determinism. The specific constraint expression for the on-board collaborative processing determinism is as follows:

[0060] ;

[0061] ;

[0062] In the formula, Q represents the determinism of on-board collaborative processing; v1 represents the performance effective correction factor obtained from the compressed coding database; v2 represents the discrimination delay correction factor obtained from the compressed coding database; v3 represents the collaborative completion rate correction factor obtained from the compressed coding database; D0 represents the performance effective threshold obtained from the compressed coding database; T0 represents the discrimination delay threshold obtained from the compressed coding database; P0 represents the collaborative completion rate threshold obtained from the compressed coding database; D represents the task performance effective value; T represents the ROI content discrimination delay; and P represents the over-the-top window collaborative completion rate.

[0063] It should be understood that the higher the completion rate of over-the-top window collaboration, the more tasks are completed within the window period, the larger the amount of effective data that can be obtained from the ground, the higher the data update frequency, and the higher the effective value of task performance. The longer the ROI content discrimination latency, the longer the processing time of a single task exceeds the window period threshold, and a large number of tasks overflow due to the inability to complete compression and download in time, the lower the completion rate of over-the-top window collaboration. This leads to a significant decrease in the accuracy of ROI discrimination (such as target omission and mask misalignment), and the lower the effective value of task performance. Meanwhile, there is a positive correlation between the effective value of task performance and the determinism of on-board collaborative processing. The higher the effective value of task performance, the stronger the alignment accuracy and process stability of intelligent analysis and compression, the more controllable the quality and quantity of effective data output, and the higher the determinism of on-board collaborative processing. There is a negative correlation between the ROI content discrimination latency and the determinism of on-board collaborative processing. The longer the ROI content discrimination latency, the more chaotic the collaborative timing becomes, and the lower the determinism of on-board collaborative processing. There is a positive correlation between the over-the-top window collaborative completion rate and the determinism of on-board collaborative processing. The higher the over-the-top window collaborative completion rate, the more stable the single task processing latency, the controllable collaborative triggering timing, the lower the queue overflow rate, and the higher the determinism of on-board collaborative processing.

[0064] S4 determines whether to perform intelligent adjustment of the accuracy of on-board collaborative processing based on the deterministic judgment of on-board collaborative processing, thereby maintaining the reliability of the on-board processing link and the credibility of the output results. If it is performed, the compressed encoding processing result is output after adjustment; otherwise, the compressed encoding processing result is output directly.

[0065] Furthermore, the specific steps for determining whether to perform intelligent control of collaborative processing accuracy are as follows: if the on-board collaborative processing determinism is greater than or equal to the collaborative processing benchmark value, then intelligent control of collaborative processing accuracy will not be performed; otherwise, adaptive queue control based on processing delay perception and adaptive trigger noise tolerance control based on jitter perception will be performed.

[0066] It should be noted that, as Figure 3The diagram shows an adaptive queue control flowchart based on processing delay awareness for the low-orbit satellite observation data compression coding processing method provided in this application embodiment. The specific process is as follows: First, the real-time monitoring and evaluation phase is initiated from "Start Monitoring," continuously collecting the "current ROI mask generation delay." Then, through the "Delay Status Judgment" node, the real-time delay is compared with a preset benchmark value. If the delay is less than or equal to the benchmark value, it is determined that "intelligent processing is running normally." At this time, the current queue management strategy and standard overflow rate threshold are maintained, allowing the queue to transmit fluctuations downstream, completing this control and entering "End Adjustment." If the delay is greater than the benchmark value, it is determined that "intelligent processing has a delay," and then the joint decision input phase is entered, integrating the two core parameters of "ROI mask generation delay" and "on-board collaborative processing determinism," combined with the predefined "generation delay-overflow rate mapping relationship," to output the target queue management and overflow rate threshold. Subsequently, by "lowering the overflow protection level and switching the data discard strategy," and "preemptively triggering data flow to reserve buffer space for data backlog," the risk of queue congestion caused by processing delay is alleviated. After completing the control, it also enters "End Adjustment." The entire process dynamically monitors the ROI mask generation latency and combines it with collaborative processing to deterministically adjust queue management strategies. This ensures data transmission flexibility during normal processing and proactively optimizes queue buffering and data scheduling when delays occur, effectively maintaining the stability of the onboard intelligent processing link and the continuity of data processing.

[0067] As further explained in detail, the specific steps for implementing adaptive queue control based on processing latency awareness are as follows:

[0068] In the onboard intelligent processing pipeline, the ROI mask generation latency of the current frame is monitored in real time. Based on the real-time monitored ROI mask generation latency and the determinism of onboard collaborative processing, the overflow prevention waterline and data discarding strategy of the queue are dynamically adjusted, specifically as follows:

[0069] If the ROI mask generation delay is less than or equal to the generation delay baseline value, the intelligent processing is determined to be operating normally. The current on-board buffer queue management and overflow rate are set to the on-board buffer queue management and overflow rate threshold to allow the data stored in the queue to smooth downstream transmission fluctuations.

[0070] If the ROI mask generation delay is greater than the generation delay baseline, intelligent processing will be delayed. Based on the determinism of ROI mask generation delay and on-board collaborative processing, the data is input into a predefined generation delay-overflow rate mapping relationship. The target on-board buffer queue management and overflow rate are output, and the on-board buffer queue management and overflow rate are adjusted downward to trigger data flow in advance, thus reserving buffer space for the backlog of low-orbit satellite observation data that may be caused by processing deceleration.

[0071] In this embodiment, the ROI mask generation latency of the current frame is monitored in real time within the onboard intelligent processing pipeline. This latency directly reflects the processing speed of the intelligent analysis module and is a key indicator for determining whether the data production rate is normal. By continuously monitoring the latency of this key node, the system can proactively perceive the throughput change risks that the entire processing pipeline will face. When the ROI mask generation latency is detected to be less than or equal to a preset generation latency benchmark value, the system determines that the intelligent processing module is operating normally and the data production rate is stable. At this time, the system maintains the overflow prevention water level and management strategy of the onboard buffer queue at a preset high threshold. This allows the queue to accumulate more data to effectively absorb and buffer short-term rate fluctuations that may occur in downstream compression, encoding, or transmission stages, thereby improving the overall smoothness and throughput efficiency of the system and avoiding frequent data interruptions caused by minor downstream instabilities. When the ROI mask generation latency is detected to be greater than the baseline value, the system determines that intelligent processing has been delayed, indicating a decrease in data production rate. Without intervention, the queue may gradually be emptied due to production falling behind consumption, potentially causing a pipeline interruption. More seriously, if the downlink transmission rate remains unchanged, the emptied queue will not provide sufficient data, resulting in valuable downlink bandwidth being idle. To address this risk, the system does not passively wait but instead inputs both the "ROI mask generation latency" and the "on-board collaborative processing determinism," which characterizes the system's collaborative stability, into a predefined generation latency-overflow rate mapping. This mapping intelligently calculates the target queue management and overflow rate thresholds to be lowered based on the severity of the latency and its impact on system determinism. The system then lowers the overflow prevention threshold accordingly and switches to a matching, more aggressive data discarding or priority flow strategy. This transforms the queue management mechanism from a passive "overflow response" to a proactive "pre-deceleration prevention." By triggering data flow or selectively discarding low-value data before the water level bottoms out, the system proactively "reserves" buffer space for the inevitable data backlog caused by the continuous slowdown in processing, while ensuring that high-priority data is processed first. This is like stockpiling "data provisions" in advance for the upcoming "low tide of data production," thereby avoiding pipeline interruptions and bandwidth idleness. When intelligent processing experiences performance fluctuations, it maximizes the continuity of the data processing chain and overall throughput efficiency, ensuring the system's robust operation under non-ideal conditions.

[0072] As further explained in detail, the specific steps of adaptive trigger noise tolerance control based on jitter perception are as follows:

[0073] A predefined baseline value for collaborative trigger delay jitter is defined. During on-board collaborative processing, the actual delay from analysis completion to compression trigger is collected and recorded each time. The current jitter statistics of the actual delay sequence within a preset window are calculated in real time. Based on the determinism of on-board collaborative processing and the current jitter statistics, the noise margin threshold used to determine the signal completion of analysis is dynamically adjusted. Specifically:

[0074] If the current jitter statistic is less than or equal to the collaborative trigger delay jitter baseline value, the collaborative trigger timing is determined to be stable, and the current trigger threshold noise margin threshold is maintained.

[0075] The jitter-aware adaptive trigger noise tolerance control also includes:

[0076] If the current jitter statistic is greater than the collaborative trigger delay jitter benchmark value, it is determined that there is abnormal jitter in the collaborative trigger timing. The trigger threshold noise margin threshold is increased to suppress false triggering events such as premature or delayed triggering caused by noise. Specifically, based on the on-board collaborative processing determinism and the current jitter statistic, it is input into the predefined delay jitter-margin threshold mapping relationship, and the output adjustment gain coefficient is used. The adjustment gain coefficient is combined with the trigger threshold noise margin threshold to obtain the target trigger threshold noise margin threshold, thereby raising the threshold for trigger determination and ensuring the accuracy and determinism of the collaborative action in timing.

[0077] In this embodiment, a predefined baseline value for collaborative triggering delay jitter is first defined. This baseline value characterizes the allowable timing fluctuation range from the completion of processing by the intelligent analysis module to the successful triggering of the compression module—a key collaborative action—under normal system calibration conditions. During onboard collaborative processing, the system continuously and accurately collects and records the actual delay from "analysis completion" to "compression triggering" each time, and calculates its standard deviation or mean absolute difference in real time based on the delay sequence within a sliding time window as the current jitter statistic. This continuous monitoring mechanism provides the system with a direct means to quantitatively assess the timing stability of the collaborative link, enabling it to detect latent interference caused by single-event effects, power supply noise, or software scheduling uncertainties. When the calculated current jitter statistic is less than or equal to the preset baseline value, the system determines that the collaborative triggering timing is in a stable state and maintains the existing noise margin threshold unchanged. This avoids unnecessary adjustments when the system is running smoothly, reduces additional uncertainties that may be introduced by frequent threshold changes, and ensures optimal system performance under normal conditions. When the current jitter statistic exceeds the baseline value, the system determines that abnormal jitter has occurred in the coordinated triggering timing, indicating an increase in interference levels and a significant increase in the risk of false triggering (early or delayed). At this point, the system does not simply increase the threshold by a fixed step size. Instead, it inputs the "current jitter statistic" and the "on-board coordinated processing determinism," which reflects the overall coordinated reliability of the system, into a predefined time-delay jitter-margin threshold mapping relationship. This mapping relationship intelligently decides and outputs an "adjustment gain coefficient" based on the severity of the jitter (interference intensity) and the system's current requirement for determinism (reliability tolerance). Subsequently, the system combines this gain coefficient with the current noise margin threshold (e.g., multiplication or addition) to generate a new, higher "target noise margin threshold." This achieves "intelligent noise tolerance," which effectively filters out false triggering signals or glitches caused by space environment interference or increased internal noise, whose amplitude is above the original threshold but below the new threshold, thereby fundamentally suppressing false triggering events. More importantly, its adjustment range is not fixed, but rather adaptive through a "jitter-determinism" joint mapping: in tasks requiring high determinism, the system has "zero tolerance" for jitter and uses a larger gain coefficient to quickly raise the threshold, strictly ensuring collaborative accuracy; in scenarios where a certain degree of uncertainty is tolerable, a relatively gentle adjustment is used to avoid delays in the response of legitimate trigger signals due to excessively high thresholds. This ensures that the handshake between the two core modules of onboard intelligent analysis and compression coding maintains high timing accuracy and collaborative determinism even in complex space interference environments, laying a solid foundation for the reliable operation of the entire onboard processing link.

[0078] like Figure 4The diagram shows the structure of a low-Earth orbit satellite observation data compression and encoding processing system provided in this application embodiment. It includes: a task-aware analysis and collaborative compression module, a task performance-driven control module, a collaborative state monitoring and evaluation module, and a deterministic constraint-based collaborative control module. The task-aware analysis and collaborative compression module performs intelligent analysis of observation data in orbit, generating target detection results and region-of-interest masks containing semantic information. Based on the target detection results and region-of-interest masks, it performs task-aware differentiated compression and encoding on the original observation data to obtain task performance data. This data quantifies the efficiency of converting low-Earth orbit satellite observation data into effective information from the initial acquisition of original observation data from the satellite to the final generation of preset application requirements on the ground, resulting in an end-to-end task performance evaluation value. The task performance-driven control module determines whether to perform intelligent task performance control based on the end-to-end task performance evaluation value, achieving data fidelity... The shift from a task-driven to a mission-utility-driven approach achieves optimal synergy between information processing efficiency and mission success rate. If executed, the downlink bandwidth utilization and on-board processing pipeline latency are monitored in real time after adjustment; otherwise, the downlink bandwidth utilization and on-board processing pipeline latency are directly monitored in real time. The collaborative status monitoring and evaluation module acquires collaborative processing data during real-time monitoring of downlink bandwidth utilization and on-board processing pipeline latency. This data is used to quantify the collaborative accuracy of on-orbit intelligent analysis and compression coding collaboration on the low-Earth orbit satellite, obtaining on-board collaborative processing determinism. The deterministic constraint collaborative adjustment module determines whether to perform intelligent adjustment of collaborative processing accuracy based on the on-board collaborative processing determinism, thereby maintaining the reliability of the on-board processing link and the credibility of the output results. If executed, the compressed coding processing result is output after adjustment; otherwise, the compressed coding processing result is directly output.

[0079] In this embodiment, through the coordinated linkage of the task perception analysis and collaborative compression module, the task performance-driven control module, the collaborative status monitoring and evaluation module, and the deterministic constraint collaborative control module, a fundamental shift from "data fidelity-driven" to "task utility-driven" in low-Earth orbit satellite on-board data processing is achieved, significantly improving the conversion efficiency of on-board observation data to effective ground information and the reliability and stability of the on-board processing link. Among them, the mission perception analysis and collaborative compression module generates target detection results and ROI masks through on-orbit intelligent analysis, and implements differentiated compression coding in combination with mission requirements to accurately mine the mission value of observation data, effectively improving the end-to-end mission performance evaluation value and ensuring the efficient implementation of ground-based pre-set application requirements (such as target identification reports and disaster assessment maps); the mission performance-driven control module dynamically decides whether to execute intelligent control based on the end-to-end mission performance evaluation value, taking into account the optimal synergy between information processing efficiency and mission success rate, and avoiding bandwidth waste and computing power redundancy caused by excessive pursuit of data fidelity; the collaborative status monitoring and evaluation module accurately quantifies the collaborative accuracy of on-orbit intelligent analysis and compression coding based on real-time monitoring of downlink bandwidth utilization and on-board processing pipeline latency, providing a core basis for the stability evaluation of on-orbit processing links; the deterministic constraint collaborative control module performs collaborative accuracy control based on the deterministic dynamic execution of on-orbit collaborative processing, effectively avoiding problems such as processing result distortion and link unreliability caused by collaborative deviation, and ensuring the credibility and consistency of on-orbit processing output results. The four modules form a closed-loop link of perception, compression, evaluation, and regulation, which maximizes the mission value of low-orbit satellite observation data and ensures the long-term stability and reliability of the on-board processing link.

[0080] The various features and processes described above can be used independently of each other or can be combined in various ways. All possible combinations and sub-combinations are intended to fall within the scope of this disclosure. Furthermore, certain method or process blocks may be omitted in some embodiments. The methods and processes described herein are not limited to any particular order, and the blocks or states associated with them may be performed in other suitable orders. For example, the described blocks or states may be performed in an order different from the order specifically disclosed, or multiple blocks or states may be combined in a single block or state. Example blocks or states may be performed serially, in parallel, or in some other manner. Blocks or states may be added to or removed from the disclosed example embodiments. The exemplary systems and components described herein may be configured differently from those described. For example, elements may be added to, removed from, or rearranged compared to the disclosed example embodiments.

[0081] The various operations of the example methods described herein can be performed at least in part by an algorithm. This algorithm can be contained in program code or instructions stored in memory (e.g., the aforementioned non-transitory computer-readable storage medium). Such an algorithm may include a machine learning algorithm. In some embodiments, the machine learning algorithm may not be explicitly programmed into the computer to perform the function, but can learn from training data to create a predictive model that performs the function.

[0082] The various operations of the example methods described herein can be performed, at least in part, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors can constitute the engine of a processor implementation that operates to perform one or more of the operations or functions described herein.

[0083] Similarly, the methods described herein can be implemented at least in part by a processor, where one or more specific processors are examples of hardware. For example, at least some operations of a method can be performed by one or more processors or an engine implemented by a processor. Furthermore, one or more processors can also be operated to support the performance of related operations in a “cloud computing” environment or as “Software as a Service” (SaaS). For example, at least some operations can be performed by a set of computers (as an example of a machine including processors), where these operations are accessible via a network (e.g., the Internet) and via one or more suitable interfaces (e.g., application programming interfaces (APIs)).

[0084] The performance of certain operations can be distributed across processors, residing not only within a single machine but also deployed across multiple machines. In some example embodiments, the processor or processor-implemented engine may reside in a single geographic location (e.g., within a home environment, office environment, or server cluster). In other example embodiments, the processor or processor-implemented engine may be distributed across multiple geographic locations.

[0085] In this specification, multiple instances may implement components, operations, or structures described as single instances. Although individual operations of one or more methods are shown and described as separate operations, one or more of the separate operations may be performed simultaneously and do not need to be performed in the order shown. Structures and functions presented as separate components in the example configuration may be implemented as composite structures or components. Similarly, structures and functions presented as single components may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of this document.

[0086] While an overview of the subject matter has been described with reference to specific example embodiments, various modifications and changes can be made to these embodiments without departing from the broader scope of embodiments of this disclosure. Such embodiments of the subject matter are referred to herein, individually or collectively, by the term "invention," and are used for convenience only and are not intended to limit the scope of this application to any single disclosure or concept, should more than one disclosure or concept be disclosed in fact.

[0087] The embodiments described herein have been described in sufficient detail to enable those skilled in the art to practice the disclosed teachings. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. Therefore, the detailed description should not be construed as limiting, and the scope of the various embodiments is defined only by the full scope of the appended and their assigned equivalents.

Claims

1. A method for compressing and encoding low-orbit satellite observation data, characterized in that, Includes the following steps: S1. Perform intelligent analysis of observation data in orbit to generate target detection results and region of interest masks containing semantic information. Based on the target detection results and region of interest masks, perform task-aware differential compression encoding on the original observation data to obtain task performance data. This data is used to quantify the efficiency of converting low-orbit satellite observation data into effective information from the time the satellite acquires the original observation data to the time the preset application requirements are generated on the ground, and obtain an end-to-end task performance evaluation value. S2, based on the end-to-end task performance evaluation value, determine whether to perform intelligent task performance regulation, realizing the transformation from data fidelity-driven to task performance-driven, thereby achieving the optimal synergy between information processing efficiency and task success rate. If executed, the downlink bandwidth utilization and on-board processing pipeline latency status are monitored in real time after regulation. If not, the downlink bandwidth utilization and on-board processing pipeline latency status are directly monitored in real time. S3 acquires collaborative processing data during real-time monitoring of downlink bandwidth utilization and on-board processing pipeline latency. This data is used to quantify the collaborative accuracy of on-orbit intelligent analysis and compression coding collaboration on low-Earth orbit satellites, thereby obtaining on-board collaborative processing determinism. S4. Based on the on-board collaborative processing deterministic judgment, whether to perform intelligent adjustment of the collaborative processing accuracy is to maintain the reliability of the on-board processing link and the credibility of the output results. If it is performed, the compressed encoding processing result is output after adjustment; otherwise, the compressed encoding processing result is output directly. The specific steps for obtaining the end-to-end task performance evaluation value are as follows: The mission performance data includes the downlink transmission latency of low-Earth orbit satellite observation data, the end-to-end information utility bandwidth ratio, and the compression latency of low-Earth orbit satellite observation data. The transmission delay correction factor is combined with the ratio of the transmission delay threshold to the downlink transmission delay of low-Earth orbit satellite observation data to obtain the transmission delay impact component. The bandwidth ratio correction factor is combined with the ratio of the end-to-end information utility bandwidth ratio to the bandwidth ratio critical value to obtain the bandwidth ratio influence component. The compression delay correction factor is combined with the ratio of the compression delay to the compression delay threshold of the low-orbit satellite observation data to obtain the compression delay influence component. The transmission delay impact component, bandwidth ratio impact component, and compression delay impact component are coupled and processed to obtain the end-to-end task performance evaluation value. The specific steps for determining whether to perform intelligent task performance control are as follows: If the end-to-end task performance evaluation value is greater than or equal to the task performance benchmark value, then intelligent task performance control will not be performed; otherwise, dynamic window scheduling based on satellite-ground coordination and adaptive background compression based on channel congestion awareness will be performed to meet task timeliness.

2. The compression encoding processing method for low-orbit satellite observation data as described in claim 1, characterized in that, The specific steps for performing task-time-oriented space-ground collaborative dynamic window scheduling are as follows: For each low-Earth orbit satellite observation mission, a maximum end-to-end allowable delay threshold is predefined. The maximum end-to-end allowable delay threshold represents the maximum total time allowed from the time of low-Earth orbit satellite observation data acquisition to the time of final delivery to the end user. Before and during each satellite transit, the current ground processing and distribution latency is monitored and acquired in real time. Based on the end-to-end maximum allowable latency threshold and the ground processing and distribution latency, a total time window available for satellite-ground collaboration is obtained for on-board processing and downlink transmission. The total time window available for satellite-ground collaboration represents the difference between the end-to-end maximum allowable latency threshold and the ground processing and distribution latency.

3. The compression encoding processing method for low-orbit satellite observation data as described in claim 2, characterized in that, The execution of mission-time-oriented space-ground collaborative dynamic window scheduling also includes: Based on the end-to-end mission performance evaluation value and the total time window, combined with the preset time window for satellite transit, the adjusted actual scheduling and transmission time window is dynamically determined and instructed to be executed by the satellite through the following logic: If the total time window is greater than or equal to the preset time window, it indicates that the ground processing margin is sufficient, and the satellite can be instructed to carry out low-orbit satellite observation data transmission in the most stable manner within the entire preset transit time window. That is, the actual scheduled transmission time window is the preset time window. If the total time window is less than the preset time window, it indicates that the total time is tight. The transmission window is compressed to reserve enough time for ground processing. Based on the end-to-end mission performance evaluation value and the current total time window, it is input into the predefined distribution delay-transmission time window mapping relationship, outputs the actual scheduled transmission time window, and instructs the satellite to transmit high-priority data only within the actual scheduled transmission time window. The start and end times of the actual scheduled transmission time window, along with the corresponding data transmission priority strategy, are encapsulated into a scheduling instruction. When the satellite enters the previous ground station's visible window, it is instructed to execute low-orbit satellite observation data transmission according to the adjusted actual scheduled transmission time window.

4. The compression encoding processing method for low-orbit satellite observation data as described in claim 1, characterized in that, The specific steps of the adaptive background compression based on channel congestion awareness are as follows: The downlink total bandwidth utilization is monitored in real time. Based on the real-time monitored downlink total bandwidth utilization and the end-to-end task performance evaluation value, the maximum allowable compression intensity of the background area is dynamically adjusted. Specifically, if the downlink total bandwidth utilization is less than or equal to the bandwidth utilization threshold, it is determined that the channel idleness is high and the downlink total bandwidth resources are sufficient, and the maximum allowable compression intensity of the current background area is maintained. If the downlink total bandwidth utilization rate is greater than the bandwidth utilization rate threshold, the channel is determined to be congested. Based on the downlink total bandwidth utilization rate and the end-to-end task performance evaluation value, the input is fed into the predefined utilization rate-maximum compression intensity mapping relationship, and the maximum compression intensity of the target background area is output. The maximum compression is applied to the background area to reduce its data volume and reserve bandwidth for critical information.

5. The compression encoding and processing method for low-orbit satellite observation data as described in claim 1, characterized in that, The specific steps to obtain the deterministic nature of on-board collaborative processing are as follows: The collaborative processing data includes the effective value of task performance, the latency of ROI content discrimination, and the collaborative completion rate of the over-the-top window. The effective influence component is obtained by combining the ratio of the effective value of task effectiveness to the effective threshold value of effectiveness with the effective correction factor. The discrimination delay correction factor is combined with the ratio of the discrimination delay threshold to the ROI content discrimination delay to obtain the discrimination delay impact component; The collaborative completion rate correction factor is combined with the ratio of the collaborative completion rate to the critical value of the collaborative completion rate in the top window to obtain the collaborative completion rate influence component. By coupling the effective impact component of performance, the impact component of discrimination delay, and the impact component of collaborative completion rate, the deterministic nature of on-board collaborative processing is obtained. The specific steps for determining whether to perform intelligent adjustment of the accuracy of collaborative processing are as follows: If the on-board collaborative processing determinism is greater than or equal to the collaborative processing baseline value, then intelligent adjustment of collaborative processing accuracy will not be performed; otherwise, adaptive queue adjustment based on processing latency awareness and adaptive trigger noise tolerance adjustment based on jitter awareness will be performed.

6. The compression encoding processing method for low-orbit satellite observation data as described in claim 5, characterized in that, The specific steps for performing adaptive queue control based on processing latency awareness are as follows: In the onboard intelligent processing pipeline, the ROI mask generation latency of the current frame is monitored in real time. Based on the real-time monitored ROI mask generation latency and the determinism of onboard collaborative processing, the overflow prevention waterline and data discarding strategy of the queue are dynamically adjusted, specifically as follows: If the ROI mask generation delay is less than or equal to the generation delay baseline value, the intelligent processing is determined to be running normally. The current on-board buffer queue management and overflow rate are set to the on-board buffer queue management and overflow rate threshold to allow the data stored in the queue to smooth downstream transmission fluctuations. If the ROI mask generation delay is greater than the generation delay baseline, intelligent processing will be delayed. Based on the determinism of ROI mask generation delay and on-board collaborative processing, the data is input into a predefined generation delay-overflow rate mapping relationship. The target on-board buffer queue management and overflow rate are output, and the on-board buffer queue management and overflow rate are adjusted downward to trigger data flow in advance, thus reserving buffer space for the backlog of low-orbit satellite observation data caused by subsequent processing deceleration.

7. The compression encoding processing method for low-orbit satellite observation data as described in claim 5, characterized in that, The specific steps of the adaptive trigger noise tolerance control based on jitter perception are as follows: A predefined baseline value for collaborative triggering delay jitter is defined. During on-board collaborative processing, the actual delay from analysis completion to compression trigger is collected and recorded each time. The current jitter statistics of the actual delay sequence within a preset window are calculated in real time. Based on the determinism of on-board collaborative processing and the current jitter statistics, the noise margin threshold used to determine the signal completion of analysis is dynamically adjusted, specifically: If the current jitter statistic is less than or equal to the collaborative trigger delay jitter baseline value, the collaborative trigger timing is determined to be stable, and the current trigger threshold noise margin threshold is maintained.

8. The compression encoding processing method for low-orbit satellite observation data as described in claim 7, characterized in that, The adaptive trigger noise tolerance control based on jitter perception also includes: If the current jitter statistic is greater than the collaborative trigger delay jitter benchmark value, it is determined that there is abnormal jitter in the collaborative trigger timing. The trigger threshold noise margin threshold is increased to suppress premature or delayed false triggering events caused by noise. Specifically, based on the on-board collaborative processing determinism and the current jitter statistic, the data is input into a predefined delay jitter-margin threshold mapping relationship, and the output is an adjustment gain coefficient. The adjustment gain coefficient is combined with the trigger threshold noise margin threshold to obtain the target trigger threshold noise margin threshold, thereby raising the threshold for trigger determination and ensuring the accuracy and determinism of the collaborative action in timing.

9. A compression encoding and processing system for low-Earth orbit satellite observation data, wherein the low-Earth orbit satellite observation data compression encoding and processing system is used to implement the low-Earth orbit satellite observation data compression encoding and processing method as described in any one of claims 1-8, characterized in that, The system includes: a task perception analysis and collaborative compression module, a task performance-driven control module, a collaborative state monitoring and evaluation module, and a collaborative control module with deterministic constraints. The task-aware analysis and collaborative compression module is used to perform intelligent analysis of observation data in orbit, generate target detection results and region of interest masks containing semantic information, and perform task-aware differentiated compression encoding on the original observation data based on the target detection results and region of interest masks to obtain task performance data. This data is used to quantify the efficiency of converting low-orbit satellite observation data into effective information from the time the satellite acquires the original observation data to the time the preset application requirements are generated on the ground, and to obtain an end-to-end task performance evaluation value. The task performance-driven control module is used to determine whether to perform intelligent task performance control based on the end-to-end task performance evaluation value. This realizes the transformation from data fidelity-driven to task performance-driven, thereby achieving the optimal synergy between information processing efficiency and task success rate. If the control is performed, the downlink bandwidth utilization and the latency status of the on-board processing pipeline are monitored in real time after the control is implemented. If not, the downlink bandwidth utilization and the latency status of the on-board processing pipeline are monitored directly in real time. The collaborative status monitoring and evaluation module is used to acquire collaborative processing data during real-time monitoring of downlink bandwidth utilization and on-board processing pipeline latency. This data is used to quantify the collaborative accuracy of on-orbit intelligent analysis and compression coding collaboration on low-Earth orbit satellites, and to obtain on-board collaborative processing determinism. The deterministic constraint-based collaborative control module is used to determine whether to perform intelligent control of the accuracy of the collaborative processing based on the deterministic judgment of the on-board collaborative processing, thereby maintaining the reliability of the on-board processing link and the credibility of the output results. If it is executed, the compressed encoding processing result is output after the control; otherwise, the compressed encoding processing result is output directly.

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