Ocean buoy data aggregation method

Abstract

The invention discloses a method for aggregating ocean buoy data, which comprises the following steps of: establishing a space-time correlation model according to the spatial distribution density of buoy clusters and the dynamic characteristics of an ocean environment, and adaptively dividing buoy groups with a data compression cooperative relationship; performing a data-related compression operation within the buoy group; constructing a transmission priority dynamic sorting engine, and calculating a dynamic weight coefficient; performing hierarchical packaging processing on the differential coding data, distributing enhanced forward error correction coding resources for high-priority data, and reserving redundant transmission time slots; the method comprises the following steps: receiving a hierarchical data packet through a low-orbit satellite and executing on-satellite preprocessing, reconstructing an original data stream by using a distributed decoder, and meanwhile, fusing multi-dimensional space-time related data to generate an aggregated data product; and finally, the aggregated data product is transmitted to a ground data center through a satellite downlink. According to the invention, uplink data volume is reduced, disaster monitoring timeliness is improved, ground station processing load is reduced, and system environment adaptability is enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of low-orbit satellite Internet of Things data transmission, and particularly relates to a method for aggregating ocean buoy data. BACKGROUND

[0002] Ocean environment monitoring is a core means for understanding and predicting global climate change, ocean disasters and ecosystem dynamics. In recent years, with the rapid development of low-orbit satellite constellation and Internet of Things technology, a large-scale, real-time ocean data acquisition and backhaul system based on a buoy cluster has become an important infrastructure in this field. The traditional system mainly relies on each buoy to independently and directly connect to a satellite for raw data transmission, and a ground station is used to complete data reception, processing and fusion. With the continuous expansion of the observation network scale, the coverage of the sea area is increasingly wide, the data acquisition density and frequency are continuously improved, and the data volume generated by the system presents an exponential growth, which poses a serious challenge to the limited satellite uplink bandwidth and the star-ground collaborative processing capability. Under this background, the research on a new data aggregation method that can significantly reduce the data transmission volume, improve the bandwidth utilization efficiency, and enhance the system response speed has become a clear trend in the field.

[0003] However, the prior art has many obvious deficiencies. First, at the data transmission level, the buoy generally uses an independent direct transmission mode, which fails to effectively utilize the inherent spatiotemporal correlation of ocean parameters, and the highly similar redundant data between adjacent buoys is repeatedly transmitted, which seriously wastes the valuable satellite uplink bandwidth resources. Secondly, at the transmission scheduling level, most systems use static or semi-static priority strategies, which cannot be adaptively adjusted according to dynamic scenarios such as disaster event outbreaks and satellite overtop window changes, resulting in insufficient timeliness of critical data transmission and structural mismatch of bandwidth resources. Thirdly, at the system architecture level, the computing load is highly concentrated in the ground station, and the satellite only serves as a transparent forwarding channel, so that the downlink transmission of massive raw data not only occupies a large amount of bandwidth but also introduces significant processing delay, which is difficult to meet the minute-level response requirement of disaster warning and other applications. In addition, the existing collaborative compression mechanism is mostly based on fixed grouping and static parameters, and lacks the adaptive ability to dynamic mutations of the ocean environment (such as sudden changes in ocean currents and topological separation), so the system robustness is poor. SUMMARY

[0004] To solve the problems in the prior art, the purpose of the present application is to provide a method for aggregating ocean buoy data, which reduces the uplink data volume, improves the timeliness of disaster monitoring, and at the same time, reduces the processing load of the ground station and enhances the environmental adaptability of the system.

[0005] To achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows: a method for aggregating ocean buoy data, comprising the following steps:

[0006] Step 1, a space-time correlation model is established according to the spatial distribution density of the buoy cluster and the dynamic characteristics of the marine environment, and a buoy group with data compression cooperation relationship is adaptively divided;

[0007] Step 2, a data correlation compression operation is implemented in the buoy group, the main buoy collects original sensing data and broadcasts a reference data frame and compression control parameters through a satellite link, and the slave buoy generates a differential encoding data stream based on the space-time correlation model;

[0008] Step 3, a transmission priority dynamic sorting engine is constructed, and dynamic weight coefficients are calculated by fusing data timeliness requirements, disaster warning factors, satellite overhead time windows and data reconstruction error thresholds;

[0009] Step 4, differential encoding data is implemented by hierarchical packaging processing according to the dynamic weight, and enhanced forward error correction coding resources are allocated for high-priority data and redundant transmission time slots are reserved;

[0010] Step 5, the low-orbit satellite receives the hierarchical data packet and performs on-board preprocessing, reconstructs the original data stream using a distributed decoder, and generates an aggregated data product by fusing multi-dimensional space-time related data; finally, the aggregated data product is transmitted to the ground data center through the satellite downlink.

[0011] As a further improvement of the application, in step 1, the space-time correlation model is established as follows:

[0012] The covariance matrix of temperature, salinity and flow rate parameters between buoys is calculated, and the maximum allowed distance threshold of buoys in the group is determined dynamically according to the covariance eigenvalue distribution range and the sea current motion vector; the effective duration interval of the cooperative compression group is predicted combined with the satellite orbit parameters, and when the ocean parameter mutation gradient exceeds the predetermined range, the topology structure reorganization in the group is automatically triggered and the compression parameters are updated.

[0013] As a further improvement of the application, in step 2, the data correlation compression operation in the buoy group is implemented as follows:

[0014] The main buoy periodically broadcasts a reference data frame including quantization step range and frequency domain characteristics, and the slave buoy generates a differential data stream using an adaptive residual encoding algorithm; when the residual energy of consecutive multiple sampling periods exceeds the dynamically changing threshold, it switches to an independent transmission mode, and sends a cooperative group state update instruction to the satellite to re-elect a main buoy.

[0015] As a further improvement of the application, in step 3, the calculation method of the dynamic weight coefficient is as follows:

[0016] A time-efficiency function with nonlinear characteristics is configured for disaster warning data, and a satellite overhead time constraint factor is superimposed to form a basic weight; a value evaluation matrix based on spatial coverage density is introduced to generate a spatial weight component; and a fusion proportion coefficient of the basic weight and the spatial weight is dynamically adjusted according to a fluctuation range of a satellite channel available bandwidth to form a final transmission priority ranking.

[0017] As a further improvement of the application, in step 4, the hierarchical packaging process is specifically as follows:

[0018] A multi-layer forward error correction protection mechanism is configured for high-priority data, and a preset multiple of redundant transmission resources is allocated; a probabilistic discard strategy is implemented for medium and low-priority data, and the length interval of packaged data units is dynamically adjusted; and the error correction coding strength and packaging structure parameters are optimized in real time based on satellite link quality feedback to ensure complete transmission of critical data.

[0019] As a further improvement of the application, in step 5, the on-board preprocessing is specifically as follows:

[0020] Spatial interpolation reconstruction is performed on missing data using buoy position information and ocean parameter gradient field; time series of multi-source heterogeneous ocean parameters are aligned based on sliding time window algorithm; regional characteristic parameters including three-dimensional gradient field distribution characteristic vectors, abnormal region boundary topological structure and parameter correlation measurement matrix are extracted; and aggregated data products with a volume reduced to a predetermined compression ratio range of original data are generated.

[0021] As a further improvement of the application, the generation process of the regional characteristic parameters is specifically as follows:

[0022] The data dimension is compressed and a predetermined proportion of feature information is retained through principal component analysis algorithm; the convolutional neural network is used to identify the abnormal region boundary topological structure; the parameter correlation measurement matrix is constructed to describe the coupling relationship of multiple physical fields, and finally the lightweight feature data packet is generated.

[0023] As a further improvement of the application, it further includes: dynamic management of cache between data compression and satellite transmission, specifically as follows:

[0024] Incremental compression algorithm is used to store local data during satellite overhead gap, and a multi-level storage area dynamic coverage mechanism is established according to data priority; when the storage space occupancy reaches a preset threshold, a selective deletion strategy based on spatio-temporal correlation is started and a data missing marker is recorded for on-board reconstruction.

[0025] As a further improvement of the application, it also includes: through a triple synergy mechanism to realize end-to-end bandwidth optimization: only the master buoy in the cooperative compression group transmits complete reference data, and the differential encoding data amount of the subordinate buoy is controlled within a predetermined proportion range of the reference data amount; the on-board feature extraction processing reduces the downlink data volume to a predetermined compression ratio interval of the original data amount; the dynamic transmission scheduling mechanism maintains the retransmission rate of high-value data below a predetermined threshold, and the overall uplink bandwidth consumption is reduced to a predetermined proportion range of the original demand.

[0026] The application aims at the technical problems of serious limitation of satellite uplink bandwidth and disaster response delay caused by massive ocean buoy data, realizes data correlation compression by establishing a space-time correlation model of buoy clusters, greatly reduces redundant transmission; adopts a dynamic priority engine to fuse disaster warning factors, satellite overhead windows and data value coefficients to generate transmission weights, realizes adaptive allocation of bandwidth resources; innovatively migrates data processing to the satellite, reconstructs data streams through distributed decoders and extracts gradient field features, abnormal boundary topologies and other core parameters to generate lightweight aggregated products. This method reduces uplink data volume, improves disaster monitoring timeliness, reduces ground station processing load and enhances system environmental adaptability.

[0027] The beneficial effects of the application are:

[0028] 1. The application realizes efficient grouping and cooperative compression of data by constructing a space-time correlation model of buoy clusters. The system dynamically divides cooperative groups according to the covariance characteristics of environmental parameters between buoys and the sea current motion vector, and the master buoy broadcasts the reference frame, and the subordinate buoy only transmits differential encoded data. This method significantly reduces the repeated transmission of data caused by spatial redundancy, controls the uplink data volume within the group at a low level, and improves the utilization efficiency of the satellite uplink.

[0029] 2. The application adopts a dynamic priority sorting engine to fuse disaster warning factors, satellite overhead windows and data value coefficients to generate transmission weights. By allocating enhanced error correction resources and redundant time slots to high-priority data and probabilistically discarding low-priority data, the application realizes adaptive optimization of bandwidth resource allocation. This mechanism effectively guarantees the timely and reliable transmission of key data and improves the response capability of the system in sudden disaster scenarios.

[0030] 3. The application migrates data processing tasks to the satellite for execution, uses a distributed decoder to reconstruct data streams and extract gradient field features, abnormal area boundary topologies and other core parameters to generate lightweight aggregated products. This design greatly reduces the downlink data transmission volume and ground station processing load, while enhancing the timeliness and usability of data, realizing the whole process optimization from raw data transmission to feature product downlink. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1This is a system framework diagram of an embodiment of the present invention;

[0032] Figure 2 This is a flowchart illustrating the data-related compression process in an embodiment of the present invention;

[0033] Figure 3 This is a flowchart of the dynamic priority scheduling process in an embodiment of the present invention;

[0034] Figure 4 This is a flowchart of the on-board preprocessing process in an embodiment of the present invention. Detailed Implementation

[0035] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0036] Example

[0037] like Figures 1-4 As shown, a method for aggregating ocean buoy data includes the following steps:

[0038] S1. Based on the spatial distribution density of buoy clusters and the dynamic characteristics of the marine environment, a spatiotemporal correlation model is established to adaptively divide buoy groups with data compression cooperative relationships. Further, the position coordinates and real-time environmental parameters of each node in the buoy cluster, including temperature, salinity, and current velocity information, are collected. A weighted graph model is constructed with buoys as vertices and environmental parameter similarity as edges. A spectral clustering algorithm is used to segment the graph model, and the initial cluster centers are determined based on the distribution of feature vectors. The ocean current dynamics equation is introduced as a constraint condition to calculate the motion correlation coefficient between buoys and dynamically adjust the cluster boundaries. A maximum intra-group distance threshold is set, which is jointly determined by the gradient of the ocean current velocity field and the eigenvalues ​​of the parameter covariance matrix. The parameter differences between buoys within a group are monitored in real time. If the adaptive threshold is exceeded, a group splitting or merging operation is triggered, the cooperative groups are re-divided, and the master-slave relationship within the group is updated. Finally, a buoy cooperative group structure with high spatiotemporal correlation is output, providing a grouping basis for subsequent differential compression.

[0039] S2. Within the buoy group, data-related compression operations are implemented. The main buoy collects raw sensor data and broadcasts a reference data frame and compression control parameters via satellite link. Subordinate buoys generate differentially coded data streams based on a spatiotemporal correlation model. Further, the main buoy collects raw environmental parameter data within the group at fixed intervals, extracts frequency domain features using wavelet transform, and generates a reference data frame containing quantization step size, frequency band energy distribution, and timestamp. This frame is then multicast to all subordinate buoys within the group via satellite link. After receiving the reference frame, the subordinate buoys use an adaptive differential pulse code modulation algorithm to calculate the residual between the locally collected data and the reference data, generating a differentially coded sequence. A dynamic threshold for residual energy is set, which is adaptively adjusted based on historical data fluctuations and the current ocean state. If the residual energy exceeds the threshold for multiple consecutive sampling periods, a sudden change in the current environment is determined, and the subordinate buoy switches to independent coding mode, directly uploads raw data fragments, and sends a group state anomaly signal to the satellite. Upon receiving the anomaly signal, the satellite initiates a main buoy reselection process, elects a new master node based on signal strength and data quality assessment results, updates the group compression parameters, and restores the collaborative compression state.

[0040] S3. Construct a dynamic ranking engine for transmission priority, integrating data timeliness requirements, disaster early warning factors, satellite overpass time windows, and data reconstruction error thresholds to calculate dynamic weight coefficients. Further, establish a multi-dimensional weight factor database, including data generation time, disaster type identifier, satellite overpass time series, and reconstruction error tolerance. Design an exponential timeliness decay function for disaster early warning data, causing its weight to increase non-linearly over time after the warning is issued. Introduce a satellite overpass time window function to calculate the proportion of remaining transmittable time as a time urgency factor. Construct a value assessment matrix based on buoy spatial distribution density, assigning higher weights to data in sparse areas. Use the analytic hierarchy process (AHP) to determine the initial weight ratios of each factor, then dynamically adjust the fusion coefficients based on real-time channel bandwidth conditions. Finally, calculate the transmission priority score of each data unit using a weighted summation method to form a global dynamic ranking queue.

[0041] Dynamic transfer weight calculation model:

[0042] Where W is the transmission weight score, used for priority ranking; α, β, γ are weighting coefficients, representing the weights of timeliness, spatial value, and bandwidth adaptive factor, respectively, which can be dynamically adjusted according to channel conditions; t remain The remaining time before the satellite passes overhead is denoted by f; f is a nonlinear time-dependent function that increases sharply as the remaining time decreases; I event This is a disaster event indicator factor, with a value of 1 when a disaster occurs and 0 otherwise; D spare This is a spatial sparsity assessment value, reflecting the degree of data scarcity in the current buoy's location; B currentB represents the currently available uplink bandwidth. max To maximize theoretical uplink lending, this formula comprehensively considers the urgency of timeliness, the importance of the event, spatial value, and real-time bandwidth conditions to generate dynamic transmission weights. It achieves priority assessment through multi-factor coupling, overcomes the problem of static priority setting in traditional methods, and improves the scientific nature of bandwidth allocation and disaster response speed.

[0043] S4. Based on dynamic weights, differentially coded data undergoes hierarchical encapsulation processing. Enhanced forward error correction coding resources are allocated to high-priority data, and redundant transmission time slots are reserved. Furthermore, data is divided into high, medium, and low priority levels. For high-priority data, a Reed-Solomon coding and convolutional code concatenation error correction scheme is used, allocating multiple redundant symbols and independent retransmission time slots. Medium-priority data uses adaptive-length Reed-Solomon coding, dynamically adjusting coding redundancy based on link quality. Low-priority data employs a probabilistic discarding strategy, setting a discard probability function to actively discard some data during channel congestion. The encapsulation unit length is dynamically adjusted based on the channel signal-to-noise ratio (SNR). The unit length is increased to improve efficiency at high SNR and decreased to enhance robustness at low SNR. Channel state information fed back from the satellite is received in real-time to update coding parameters and encapsulation strategies, achieving adaptive transmission across the link.

[0044] S5. Receive hierarchical data packets via low-Earth orbit satellites and perform on-board preprocessing. Reconstruct the original data stream using a distributed decoder, and simultaneously integrate multi-dimensional spatiotemporal related data to generate aggregated data products. Finally, transmit the aggregated data products to the ground data center via satellite downlink.

[0045] Furthermore, the satellite's onboard processing unit receives data packets uploaded by each buoy group, first performing error correction decoding and data reconstruction to restore the differential coded sequence; based on the baseline data and differential data within the group, it uses a distributed least squares reconstruction algorithm to restore the original data sequence of each buoy; it performs spatiotemporal alignment on the reconstructed multi-source data, using a sliding window mechanism to unify timestamps and spatial coordinates; it uses a Kriging interpolation algorithm to spatially fill in missing data, forming a complete spatiotemporal data field; it extracts the three-dimensional gradient features, anomalous region boundary point sets, and multi-parameter correlation matrices of the data field; it performs Huffman coding and quantization compression on the feature parameters to generate a lightweight aggregated data product; and finally, it transmits the data to the ground station via a downlink, completing the end-to-end data aggregation process.

[0046] In this embodiment, the process of establishing the spatiotemporal correlation model includes: calculating the covariance matrix of temperature, salinity and current velocity parameters between buoys; dynamically determining the maximum allowable spacing threshold of buoys in the group based on the distribution range of covariance eigenvalues ​​and ocean current motion vectors; predicting the effective duration interval of the collaborative compression group by combining satellite orbit parameters; and automatically triggering topological restructuring within the group and updating the compression parameters when the gradient of ocean parameters abruptly exceeds a predetermined range.

[0047] Furthermore, historical environmental parameter data of the buoy swarm are collected to construct a multivariate time series dataset; the covariance matrix of temperature, salinity, and current velocity among each buoy is calculated, and the principal component direction and variance contribution rate are extracted through eigenvalue decomposition; the relevant radius of the parameter space is determined based on the eigenvalue distribution and used as the initial intra-group distance threshold; ocean current motion vector field data is introduced to calculate the relative motion trend among buoys, and the distance threshold is dynamically adjusted to reflect changes in the flow field; satellite ephemeris data is used to calculate the satellite visibility time window, and the effective duration of the group is predicted in combination with the spatial range of the buoy swarm; changes in parameter gradients within the group are monitored in real time, and if the temperature or salinity gradient exceeds the historical statistical threshold, it is determined that a sudden change in the environment has occurred, triggering the intra-group topology reconstruction process; the reconstruction process includes recalculating the covariance matrix, updating the intra-group master node, adjusting compression parameters, and broadcasting a new reference frame to ensure that the intra-group data coordination efficiency adapts to environmental changes.

[0048] Criteria for determining spatiotemporal correlation within a buoy group:

[0049] If If so, then "whether to recombine = 1";

[0050] Else "Whether it is sufficient = 0";

[0051] in, The spatial gradient of current ocean parameters (such as temperature and salinity); θ dynamic The dynamic adjustment coefficient is determined by both the ocean current velocity and the eigenvalue of the covariance; σ hist This represents the standard deviation of the historical fluctuation range of this parameter. It serves as a quantitative criterion for dynamically determining whether a buoy group needs regrouping. When environmental parameters change drastically beyond a certain proportion of the historical fluctuation range, intra-group topology regrouping is automatically triggered to ensure that cooperative compression remains effective and enhances the system's adaptability to sudden changes in the marine environment.

[0052] In this embodiment, the specific implementation of the data-related compression operation includes: the main buoy periodically broadcasts a reference data frame containing the quantization step size range and frequency domain characteristics; the subordinate buoy generates a differential data stream using an adaptive residual coding algorithm; when the residual energy of multiple consecutive sampling periods exceeds the dynamic change threshold, it switches to independent transmission mode and sends a cooperative group status update command to the satellite to re-elect the main buoy.

[0053] Furthermore, the main buoy collects environmental data within the group at a preset period, extracts frequency domain features through discrete cosine transform, and generates a reference frame containing global quantization step size, frequency band energy distribution, and time synchronization information. This reference frame is then transmitted to all subordinate buoys via satellite link in a multicast manner. After receiving the reference frame, the subordinate buoys use an adaptive predictor based on minimum mean square error to predict local data and calculate residual sequences. The residuals are then scalar-quantized and entropy-encoded to generate differential bit streams. A residual energy monitoring mechanism based on a sliding window is set up to calculate the recent residual sum of squares and compare it with a dynamic threshold. This threshold is jointly determined by historical residual statistics and the current ocean state index. If the residual energy exceeds the limit for several consecutive periods, a transmission mode switch is triggered, and the subordinate buoys stop differential encoding and switch to uploading raw data fragments. Simultaneously, a status update command containing the group identifier and anomaly type is sent to the satellite. Upon receiving the command, the satellite initiates the main buoy reselection algorithm, comprehensively evaluates factors such as buoy data quality, signal stability, and remaining energy, selects a new master node, and issues a group reconstruction command to update the group compression parameters and reference frame broadcast strategy.

[0054] In this embodiment, the calculation process of the dynamic weight coefficient includes: configuring a time-effect function with nonlinear characteristics for disaster early warning data, and superimposing a satellite overpass time constraint factor to form a basic weight; introducing a value assessment matrix based on spatial coverage density to generate spatial weight components; and dynamically adjusting the fusion ratio coefficient of the basic weight and spatial weight according to the fluctuation range of available bandwidth of the satellite channel to form the final transmission priority ranking.

[0055] Furthermore, a disaster early warning database is established, including event types such as typhoons, tsunamis, and red tides, along with their timeliness requirements. An exponential or S-shaped timeliness function is designed for each type of early warning data, causing its weight to rise rapidly in the early stages of the event and then stabilize. The proportion of remaining time before satellite overhead is calculated as a time urgency factor and multiplied by the timeliness function to form the basic weight component. A buoy spatial distribution density map is constructed, and the coverage scarcity index of each buoy's area is calculated to generate spatial value weights. A weighted fusion method is used to combine the basic weights and spatial weights, with the initial weight ratio set by expert experience. The available bandwidth of the satellite uplink channel is monitored in real time; if bandwidth is sufficient, the spatial weight ratio is increased to encourage the transmission of data from sparse areas; if bandwidth is tight, the basic weight ratio is increased to prioritize disaster-related data. Finally, a transmission priority score is generated for each data unit through normalization processing, and transmission scheduling queues are generated based on this score.

[0056] In this embodiment, the hierarchical encapsulation processing includes: configuring a multi-layer forward error correction protection mechanism for high-priority data and allocating redundant transmission resources at a preset multiple; implementing a probabilistic discarding strategy for medium and low-priority data and dynamically adjusting the length range of encapsulated data units; and optimizing the error correction coding strength and encapsulation structure parameters in real time based on satellite link quality feedback to ensure the complete transmission of critical data.

[0057] Furthermore, the data stream is divided into three categories: high, medium, and low priority. High-priority data uses concatenated error correction coding, with an outer layer of Reed-Solomon codes and an inner layer of convolutional codes, and is allocated multiple redundant symbols and dedicated retransmission time slots. Medium-priority data uses variable-rate Reed-Solomon coding, dynamically adjusting the redundancy based on real-time channel quality. Low-priority data has a drop probability function, the probability of which increases with channel load. The length of the encapsulated data unit is adaptively adjusted according to the channel signal-to-noise ratio (SNR). Longer units are used to improve transmission efficiency at high SNR, and shorter units are used to enhance reliability at low SNR. Channel state information fed back from the satellite in real time, including bit error rate, SNR, and jitter indicators, is received to dynamically update the coding scheme and encapsulation parameters. A feedback control loop is established to periodically evaluate transmission performance and adjust error correction strength and unit length parameters to achieve link self-optimization.

[0058] In this embodiment, the on-board preprocessing includes the following sequential operations: spatial interpolation reconstruction of missing data using buoy position information and ocean parameter gradient fields; alignment of time series of multi-source heterogeneous ocean parameters based on a sliding time window algorithm; extraction of regional feature parameters including three-dimensional gradient field distribution feature vectors, anomalous region boundary topology, and parameter correlation metric matrices; and generation of aggregated data products with volume reduced to a predetermined compression ratio range of the original data.

[0059] Furthermore, the onboard processing unit receives data packets from each buoy group, decodes and reassembles them to obtain the position coordinates and time series data of each buoy; for missing data points, a spatial imputation algorithm based on Kriging interpolation is used to estimate the missing values ​​using surrounding buoy data and parameter gradient fields; a sliding time window mechanism is used to align the timestamps of different buoys, with the window size adaptively adjusted according to the data sampling rate and motion speed; a three-dimensional gradient vector is calculated for the aligned data field, including the rate of change of parameters in the horizontal and vertical directions; an edge detection algorithm is used to identify the boundaries of abnormal regions and generate a topological sequence of boundary points; the Pearson correlation coefficient matrix between multiple parameters is calculated to describe the coupling relationship between parameters such as temperature, salinity, and flow velocity; the gradient vector, boundary topology, and correlation coefficient matrix are quantized and encoded to generate a lightweight feature data packet with a compression ratio controlled within a preset range, and finally transmitted to the ground station via the downlink.

[0060] In this embodiment, dynamic cache management is implemented between data compression and satellite transmission: incremental compression algorithm is used to store local data during satellite overhead intervals, and a multi-level storage area dynamic overlay mechanism is established according to data priority; when the storage space occupancy reaches a preset threshold, a selective deletion strategy based on spatiotemporal correlation is initiated and data missing markers are recorded for on-board reconstruction.

[0061] Furthermore, during periods without satellite coverage, the buoy employs an incremental compression algorithm to compress and store locally collected data. This compression algorithm utilizes a hybrid scheme based on differential coding and entropy coding. Multiple cache areas are defined according to data priority, with high-priority data stored in a high-speed cache and medium-to-low-priority data stored in a regular storage area. Storage space utilization is monitored in real time, and if a preset threshold is reached, a data cleanup process is initiated. The cleanup strategy is based on spatiotemporal correlation assessment, prioritizing the deletion of historical data fragments with high spatiotemporal redundancy and low value. The operation records the time range and spatial location of missing data, generating a missing data marker file. When a satellite subsequently passes overhead, the missing data marker file is uploaded to the satellite for reference by the onboard reconstruction algorithm. The onboard processing unit uses interpolation and extrapolation algorithms combined with surrounding data to recover missing fragments, ensuring the continuity and integrity of the data product.

[0062] In this embodiment, the process of generating the regional feature parameters includes: compressing the data dimension and retaining a predetermined proportion of feature information through principal component analysis algorithm; identifying the topological structure of abnormal region boundaries using convolutional neural network; constructing a parameter correlation metric matrix to describe the multi-physics coupling relationship, and finally generating a lightweight feature data package;

[0063] Furthermore, principal component analysis was performed on the reconstructed multidimensional ocean parameter data to calculate the covariance matrix and eigenvectors. The principal components with the highest variance contribution rates were selected as feature representations, retaining most of the original information. A pre-trained convolutional neural network model was used to scan the parameter field, identifying anomalous regions such as vortices and fronts, and outputting the boundary point coordinate sequence. Mutual information and correlation coefficients among multiple physical fields such as temperature, salinity, and current velocity were calculated to construct a parameter correlation metric matrix. The principal component eigenvectors, boundary topology sequences, and correlation matrices were binary encoded and quantized for compression, encapsulating them into a lightweight feature data package. The data package size was compressed to below a predetermined proportion of the original data while retaining the key feature information required for scientific analysis and disaster early warning.

[0064] In this embodiment, end-to-end bandwidth optimization is achieved through a triple collaborative mechanism: within the collaborative compression group, only the primary buoy transmits complete reference data, and the differential coded data volume of the subordinate buoys is controlled within a predetermined proportion of the reference data volume; on-board feature extraction processing reduces the downlink data volume to a predetermined compression ratio range of the original data volume; and the dynamic transmission scheduling mechanism maintains the retransmission rate of high-value data below a predetermined threshold, reducing the overall uplink bandwidth consumption to a predetermined proportion of the original demand.

[0065] Furthermore, within the buoy coordination group, the primary buoy transmits complete reference data, while the subordinate buoys transmit only differentially coded data. The data volume is maintained below a set proportion of the reference data volume through a residual control algorithm. The on-board processing unit reconstructs and extracts features from the received data, compressing the volume of the generated feature data packets to below a set proportion of the original data volume. The dynamic transmission scheduling engine allocates transmission resources according to priority, allocating redundant transmission opportunities and error correction resources to high-value data, ensuring that its retransmission rate is below a set threshold. Through the synergistic effect of these three mechanisms, the overall uplink data volume is reduced to below a set proportion of the original demand, and the downlink data volume is also significantly reduced, thereby achieving efficient utilization of end-to-end bandwidth resources.

[0066] The embodiments described above are merely illustrative of specific implementations of the present invention, and while the descriptions are detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A method for aggregating ocean buoy data, characterized in that, Includes the following steps: Step 1: Establish a spatiotemporal correlation model based on the spatial distribution density of buoy clusters and the dynamic characteristics of the marine environment, and adaptively divide buoy groups with data compression and cooperative relationships; Step 2: Perform data correlation compression operation within the buoy group. The main buoy collects raw sensor data and broadcasts reference data frames and compression control parameters through the satellite link. The subordinate buoys generate differential coded data streams based on the spatiotemporal correlation model. Step 3: Construct a dynamic priority ranking engine for transmission, and calculate dynamic weight coefficients by integrating data timeliness requirements, disaster early warning factors, satellite overpass time windows, and data reconstruction error thresholds; Step 4: Perform hierarchical encapsulation processing on the differentially encoded data according to the dynamic weights, allocate enhanced forward error correction coding resources for high-priority data and reserve redundant transmission time slots; Step 5: Receive hierarchical data packets via low-orbit satellites and perform on-board preprocessing. Reconstruct the original data stream using a distributed decoder and integrate multi-dimensional spatiotemporal related data to generate aggregated data products. Finally, transmit the aggregated data products to the ground data center via satellite downlink.

2. The method for aggregating ocean buoy data according to claim 1, characterized in that, In step 1, the method for establishing the spatiotemporal correlation model is as follows: The covariance matrix of temperature, salinity, and current velocity parameters between buoys is calculated. The maximum allowable spacing threshold of buoys in the group is dynamically determined based on the distribution range of covariance eigenvalues ​​and ocean current motion vectors. The effective duration interval of the collaborative compression group is predicted by combining satellite orbit parameters. When the gradient of ocean parameters changes abruptly beyond the predetermined range, the topology restructuring within the group is automatically triggered and the compression parameters are updated.

3. The method for aggregating ocean buoy data according to claim 1, characterized in that, In step 2, the data compression operation performed within the buoy group is as follows: The main buoy periodically broadcasts a reference data frame including the quantization step size range and frequency domain characteristics. The subordinate buoys use an adaptive residual coding algorithm to generate differential data streams. When the residual energy of multiple consecutive sampling periods exceeds the dynamic change threshold, it switches to independent transmission mode and sends a cooperative group status update command to the satellite to re-elect the main buoy.

4. The method for aggregating ocean buoy data according to claim 1, characterized in that, In step 3, the calculation method for the dynamic weighting coefficient is as follows: A time-response function with nonlinear characteristics is configured for disaster early warning data, and a satellite overpass time constraint factor is superimposed to form the basic weight; a value assessment matrix based on spatial coverage density is introduced to generate spatial weight components. The fusion ratio coefficient of the basic weight and spatial weight is dynamically adjusted according to the fluctuation range of the available bandwidth of the satellite channel to form the final transmission priority ranking.

5. The method for aggregating ocean buoy data according to claim 1, characterized in that, In step 4, the hierarchical packaging process is specifically as follows: Configure a multi-layer forward error correction protection mechanism for high-priority data and allocate redundant transmission resources at a preset multiple; implement a probabilistic discarding strategy for medium and low-priority data and dynamically adjust the encapsulated data unit length range; Real-time optimization of error correction coding strength and encapsulation structure parameters based on satellite link quality feedback ensures the complete transmission of critical data.

6. The method for aggregating ocean buoy data according to claim 1, characterized in that, In step 5, the on-board preprocessing is specifically as follows: Spatial interpolation reconstruction of missing data is performed using buoy location information and ocean parameter gradient fields; time series of multi-source heterogeneous ocean parameters are aligned based on a sliding time window algorithm; regional feature parameters including three-dimensional gradient field distribution feature vectors, anomalous region boundary topology, and parameter correlation metric matrices are extracted; and aggregated data products with volume reduced to the predetermined compression ratio range of the original data are generated.

7. The method for aggregating ocean buoy data according to claim 6, characterized in that, The specific process for generating the region feature parameters is as follows: The data dimensionality is compressed by principal component analysis while retaining a predetermined proportion of feature information; convolutional neural networks are used to identify the boundary topology of abnormal regions; a parameter correlation metric matrix is ​​constructed to describe the coupling relationship of multiple physics fields, and finally a lightweight feature data package is generated.

8. The method for aggregating ocean buoy data according to claim 1, characterized in that, Also includes: Dynamic cache management is implemented between data compression and satellite transmission, as detailed below: During satellite overhead gaps, incremental compression algorithms are used to store local data, and a multi-level storage area dynamic overlay mechanism is established based on data priority. When the storage space occupancy reaches a preset threshold, a selective deletion strategy based on spatiotemporal correlation is initiated and data missing markers are recorded for on-board reconstruction.

9. The method for aggregating ocean buoy data according to claim 1, characterized in that, Also includes: End-to-end bandwidth optimization is achieved through a triple collaborative mechanism: within the collaborative compression group, only the primary buoy transmits complete reference data, while the differentially encoded data volume of the subordinate buoys is controlled within a predetermined proportion of the reference data volume; on-board feature extraction processing reduces the downlink data volume to a predetermined compression ratio range of the original data volume; and the dynamic transmission scheduling mechanism maintains the retransmission rate of high-value data below a predetermined threshold, reducing the overall uplink bandwidth consumption to a predetermined proportion of the original demand.

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