A low-orbit satellite communication-based real-time acquisition method for ocean buoy data

Abstract

The application provides a kind of ocean buoy data real-time acquisition method based on low-orbit satellite communication, belongs to the technical field of electric digital data processing, the ocean buoy data real-time acquisition method provided by the application first constructs low-orbit satellite network communication link, data characteristics are analyzed by compression conversion gain index model, and multi-stage compression mechanism is established.System uses optimization equation set to dynamically adjust compression parameters, including resource consumption equation, compression quality equation and transmission efficiency equation, adaptive compression of data is realized.At the same time, through hierarchical algorithm selection mechanism and parameter optimization strategy, combined with real-time feedback control, the continuous optimization of compression efficiency is ensured.This compression scheme based on multi-level optimization effectively solves the technical problems of low compression efficiency caused by poor algorithm adaptability in the compression process of ocean buoy data in the prior art.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of digital data processing, and specifically relates to a real-time ocean buoy data acquisition method based on low-orbit satellite communication. BACKGROUND

[0002] As the core equipment of modern ocean observation systems, ocean buoys need to collect and transmit a large amount of ocean environmental data in real time. These data include hydrological parameters, meteorological elements, and ocean current information, and have characteristics such as large data volume, high sampling frequency, and diverse data characteristics. Traditional ocean buoy data compression techniques mainly use general compression algorithms such as Huffman coding, LZW compression, and run-length encoding to compress the collected data. These compression methods are widely used in computer data processing and have advantages such as mature algorithms and simple implementation. However, with the increasing demand for ocean observation, traditional compression methods have obvious limitations in processing ocean buoy data.

[0003] The existing ocean buoy data compression techniques have the following problems: First, the general compression algorithm does not fully consider the characteristic differences of ocean data, and uses the same compression strategy for different types of data, resulting in unstable compression efficiency. For example, ocean current data and temperature data have different variation characteristics and numerical distributions, and it is difficult to achieve optimal compression effect using a unified compression algorithm. Second, the existing compression methods lack consideration of data timeliness, and cannot perform differentiated compression according to the importance and transmission urgency of data, resulting in waste of system resources. Third, the parameter configuration of the compression algorithm is usually fixed and cannot be dynamically adjusted according to data characteristics and transmission conditions, affecting the performance of the compression algorithm. In addition, traditional compression methods often experience a sharp decrease in compression ratio when processing high-frequency data generated by sudden ocean events.

[0004] In the real-time acquisition and transmission of ocean buoy data, how to improve the data compression efficiency becomes a key technical problem. The existing technology cannot achieve intelligent matching of compression algorithms and data characteristics, and lacks a dynamic optimization mechanism for the compression process, resulting in suboptimal compression efficiency. This situation seriously restricts the data transmission efficiency of the ocean observation system and affects the real-time and accuracy of ocean monitoring. In particular, in the low-orbit satellite communication environment, the limited transmission bandwidth highlights the importance of efficient data compression. That is, the existing technology has the technical problem of low compression efficiency caused by poor algorithm adaptability in the ocean buoy data compression process. SUMMARY

[0005] Therefore, the present application provides a low-orbit satellite communication-based ocean buoy data real-time acquisition method, which can solve the technical problem of low compression efficiency caused by poor algorithm adaptability in the compression process of ocean buoy data in the prior art.

[0006] The present application is implemented as follows: The present application provides a low-orbit satellite communication-based ocean buoy data real-time acquisition method, which comprises the following steps: constructing a low-orbit satellite network communication link to establish a two-way communication connection between an ocean buoy and a land data center; acquiring ocean buoy sensor data to obtain buoy position information, hydrological parameters and sea state data as raw data; establishing a compression conversion gain index model to calculate data compression priorities through an optimization equation set; dividing the raw data into multi-level transmission data according to the compression conversion gain index model; establishing a data compression model to select a compression algorithm according to the characteristics of the multi-level transmission data; performing hierarchical compression processing on the multi-level transmission data; establishing a data caching mechanism and a transmission priority queue; and monitoring the communication link state and dynamically adjusting the transmission rate.

[0007] The compression conversion gain index model comprises a resource consumption equation, a transmission efficiency equation and a quality evaluation equation; the inputs of the resource consumption equation include data compression rate, processor occupancy rate, memory usage rate and power consumption rate, and the output is a resource consumption index; the inputs of the transmission efficiency equation include channel capacity, bit error rate, signal-to-noise ratio, transmission delay and channel utilization rate, and the output is a transmission efficiency index; the inputs of the quality evaluation equation include data integrity, compression distortion, transmission success rate, link stability and recovery cost, and the output is a quality evaluation index.

[0008] The multi-level transmission data is divided based on data segment compression rate, transmission signal quality, bit error rate and energy consumption; and the dynamic programming algorithm is used to divide the data into three categories: emergency transmission, regular transmission and delayed transmission.

[0009] The data compression model adopts differential encoding combined with Huffman encoding for temperature and salinity, wave height and wind speed, adopts wavelet transform combined with run-length encoding, and adopts incremental encoding combined with arithmetic encoding for position information.

[0010] The hierarchical compression processing comprises: differential encoding preprocessing and arithmetic encoding compression for first-level data; wavelet transform preprocessing and Huffman encoding compression for second-level data; discrete cosine transform preprocessing and run-length encoding compression for third-level data; and block processing and vector quantization compression for fourth-level data.

[0011] The data caching mechanism adopts a hierarchical storage structure, including a cache area, a main storage area and a backup storage area; adopts a B+ tree structure to organize indexes, taking the compression conversion gain index as a key value; and adopts the least recently used algorithm for cache replacement.

[0012] The transmission priority queue adopts a multi-stage feedback queue structure, and eight priority queues are set; an aging algorithm is used to prevent low-priority data from being starved.

[0013] The link evaluation equation set includes a channel capacity equation, a bit error rate prediction equation and a transmission rate optimization equation; the input of the channel capacity equation includes signal power, noise power, bandwidth utilization and channel gain; the input of the bit error rate prediction equation includes historical bit error rate, signal strength, atmospheric attenuation and Doppler shift; the input of the transmission rate optimization equation includes theoretical channel capacity, predicted bit error rate, buffer capacity and energy constraint.

[0014] The method further comprises establishing a transmission interruption early warning mechanism and a recovery mechanism, wherein the transmission interruption early warning mechanism uses a time series analysis method to statistically analyze historical transmission interruption data; a support vector machine algorithm is used to establish an early warning model; and the time of interruption occurrence, duration and influence range information are recorded.

[0015] The method further comprises using a binary search to locate a data breakpoint for recovering the interrupted transmission; using an MD5 check code to confirm the correctness of the transmitted data; and using a backoff algorithm to recover the transmission, and retrying the recovery transmission in case of failure.

[0016] Compared with the prior art, the present application provides a real-time ocean buoy data acquisition method based on low-orbit satellite communication, which realizes intelligent and accurate control of the data compression process by introducing a compression conversion gain index model and a multi-stage data compression mechanism. The method establishes a complete data compression evaluation system, including compression rate evaluation, quality evaluation, resource consumption evaluation and other dimensions, each of which is specially designed for the characteristics of ocean data. Through the design of the optimization equation set, the system realizes adaptive selection of the compression algorithm and dynamic adjustment of the parameters.

[0017] The present application has made a significant breakthrough in data compression efficiency: first, the compression conversion gain index model can accurately evaluate the performance of different compression strategies, providing a scientific basis for algorithm selection. The model takes into account multiple factors such as compression rate, computational complexity, data characteristics, etc., ensuring the optimality of the compression decision. Secondly, the multi-stage data compression mechanism realizes the hierarchical processing of data, and different types and importance of data can use different compression strategies, significantly improving the overall compression efficiency. Thirdly, the adaptive compression framework established by the system can dynamically adjust the compression parameters according to the data characteristics and transmission requirements, ensuring the continuous optimization of compression performance.

[0018] The application successfully solves the problem of poor algorithm adaptability in the data compression process of the marine buoy, and the core is to establish a complete compression optimization mechanism. Through the innovative compression conversion gain index concept, intelligent selection of the compression algorithm is realized; through the design of the multi-level compression strategy, the compression efficiency of different types of data is ensured; through the dynamic parameter adjustment mechanism, the continuous optimization of the compression performance is ensured. These technical innovations enable the system to always maintain the best compression state. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 A flowchart of the method of the application. DETAILED DESCRIPTION

[0020] To make the purpose, technical scheme and advantages of the embodiments of the application clearer, the technical scheme in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application.

[0021] As Figure 1 shown is a flowchart of a marine buoy data real-time acquisition method based on low-orbit satellite communication provided by the application, and the method comprises the following steps:

[0022] S01, a low-orbit satellite network communication link is constructed to establish a two-way communication connection between the marine buoy and the land data center;

[0023] S02, marine buoy sensor data is acquired to obtain buoy position information, hydrological parameters and sea state data as original data;

[0024] S03, a compression conversion gain index model is established, and the compression conversion gain index model is calculated by an optimization equation group to obtain a data compression priority;

[0025] S04, the original data is divided into multi-level transmission data according to the compression conversion gain index model, and the multi-level transmission data is divided based on data segment compression rate, transmission signal quality, error rate and energy consumption;

[0026] S05, a data compression model is established, and a corresponding compression algorithm is selected according to the multi-level transmission data characteristics;

[0027] S06, the multi-level transmission data is processed by hierarchical compression using the data compression model to obtain multi-level compression data;

[0028] S07, a data caching mechanism is established, and the multi-level compression data is stored in a local storage unit according to the compression conversion gain index;

[0029] S08, a data transmission priority queue is established, and the multi-level compression data is sorted in transmission priority according to the compression conversion gain index.

[0030] S09, monitoring a low-orbit satellite network communication link state, acquiring signal strength and link bandwidth data;

[0031] S10, dynamically adjusting a data transmission rate by using a link evaluation equation set according to the signal strength and the link bandwidth data;

[0032] S11, establishing a transmission interruption early warning mechanism by using the quality evaluation equation, recording transmission position information, compression conversion gain index and link state parameters at a data transmission interruption moment;

[0033] S12, continuing to transmit incomplete data according to the transmission position information, the compression conversion gain index and the link state parameters after the low-orbit satellite network communication link is recovered.

[0034] The compression conversion gain index is a data transmission efficiency evaluation parameter, used for quantifying comprehensive benefits in a data compression and transmission process, and an optimization equation set for calculating the compression conversion gain index includes a resource consumption equation, a transmission efficiency equation and a quality evaluation equation;

[0035] The resource consumption equation is used for calculating a system resource occupation in a data compression and transmission process, and input includes a data compression rate, a processor occupation rate, a memory usage rate and a power consumption rate, and output is a resource consumption index;

[0036] The transmission efficiency equation is used for evaluating a network transmission efficiency in a data transmission process, and input includes a channel capacity, a bit error rate, a signal-to-noise ratio, a transmission delay and a channel utilization rate, and output is a transmission efficiency index;

[0037] The quality evaluation equation is used for evaluating data compression quality and transmission reliability, and input includes data integrity, compression distortion, transmission success rate, link stability and recovery cost, and output is a quality evaluation index.

[0038] The link evaluation equation set includes a channel capacity equation, a bit error rate prediction equation and a transmission rate optimization equation;

[0039] The channel capacity equation is used for calculating a maximum transmission capacity of a current network link, and input includes signal power, noise power, bandwidth utilization rate and channel gain, and output is a theoretical channel capacity;

[0040] The bit error rate prediction equation is used for predicting a bit error rate change trend in a data transmission process, and input includes historical bit error rate, signal strength, atmospheric attenuation and Doppler shift, and output is a predicted bit error rate;

[0041] The transmission rate optimization equation is used for optimizing data transmission rate, input includes theoretical channel capacity, predicted bit error rate, buffer capacity and energy constraint, and output is optimal transmission rate.

[0042] The specific implementation of the above steps is described in detail below.

[0043] The specific implementation of step S01 is to construct a low-orbit satellite network communication link. First, the real-time position coordinates of the ocean buoy are obtained through the satellite positioning system, and the visible satellite list is calculated according to the orbit parameters of the low-orbit satellite constellation. The satellite with the strongest signal is selected as the main communication link, and the satellite with the second strongest signal is selected as the backup link. Then, the physical layer connection is established, and the adaptive coding and modulation technology is used to dynamically adjust the signal modulation mode, supporting multiple modulation modes such as BPSK, QPSK, 8PSK, and 16QAM. The coding mode uses concatenated coding, the inner code uses convolutional code with a code rate of 1 / 2, and the outer code uses RS code with a code length of 255 and an error correction capability of 8 symbols. The current optimal coding and modulation combination is determined through channel detection. Then, the link layer connection is established, and the improved selective repeat ARQ protocol is used to realize reliable transmission. The protocol uses a sliding window mechanism, and the window size is dynamically adjusted according to the round-trip delay. The maximum window size is 256, and the timeout retransmission time is twice the round-trip delay. Finally, the transmission layer connection is established, and the improved TCP protocol is used to adapt to the characteristics of satellite communication, including setting the slow start threshold to 1 / 4 of the receive window and setting the congestion window growth factor to 1. This step establishes a stable and reliable communication link, providing a basic guarantee for subsequent data transmission.

[0044] The specific implementation of step S02 is to collect marine buoy sensor data. First, the buoy position information is obtained through the GPS module, including longitude, latitude, height, speed and direction, and the sampling period is 60 seconds. Then, the hydrological parameters are collected, including the water temperature measured by the temperature sensor, the measurement range is -20 to 50 degrees Celsius, the accuracy is 0.1 degrees Celsius, and the sampling period is 300 seconds; the salinity measured by the salinity sensor, the measurement range is 0 to 40 ‰, the accuracy is 0.01 ‰, and the sampling period is 300 seconds; the dissolved oxygen content measured by the dissolved oxygen sensor, the measurement range is 0 to 20 mg / L, the accuracy is 0.1 mg / L, and the sampling period is 300 seconds. Then, the sea state data is collected, including the wave height measured by the wave height sensor, the measurement range is 0 to 20 meters, the accuracy is 0.1 meters, and the sampling period is 60 seconds; the wind speed measured by the wind speed sensor, the measurement range is 0 to 60 meters per second, the accuracy is 0.1 meters per second, and the sampling period is 60 seconds; the air pressure measured by the air pressure sensor, the measurement range is 800 to 1100 hundred pascals, the accuracy is 0.1 hundred pascals, and the sampling period is 300 seconds. Finally, the collected data is preprocessed, including outlier detection, data smoothing and timestamp addition, the outlier detection uses the 3 times standard deviation method, and the data smoothing uses the 5 point moving average. This step cooperates with multiple sensors to collect comprehensive marine environment monitoring data.

[0045] The specific implementation of step S03 is to establish a compression conversion gain index model. First, a resource consumption equation is constructed, and a weighted summation method is used to calculate the resource consumption index, and the weight coefficients are determined by fuzzy analytic hierarchy process, the data compression rate weight is 0.3, the processor occupancy rate weight is 0.2, the memory usage rate weight is 0.2, and the power consumption rate weight is 0.3. Then, a transmission efficiency equation is constructed, and a normalization processing method is used to unify each parameter to the interval of 0 to 1, the channel capacity is calculated by Shannon formula, the bit error rate threshold is set to 10⁻ 6 , the signal-to-noise ratio threshold is set to 10 decibels, the transmission delay threshold is set to 1 second, and the channel utilization rate threshold is set to 80%. Then, a quality evaluation equation is constructed, and a multi-objective optimization method is used to comprehensively evaluate the data quality, the data integrity requirement is greater than 99%, the compression distortion degree is evaluated by mean square error, the threshold is set to 0.01, the transmission success rate requirement is greater than 95%, the link stability is evaluated by signal fluctuation variance, and the recovery cost is calculated according to the retransmission data amount. Finally, a genetic algorithm is used to solve the optimization equation set, the population size is set to 100, the iteration number is 1000, the crossover probability is 0.8, and the mutation probability is 0.1. This step establishes a mathematical model to realize the quantitative evaluation of data transmission efficiency.

[0046] The specific implementation of step S04 is to divide the multi-level transmission data. First, according to the data compression rate, the compression rate greater than 80% is first-level data, the compression rate between 60% and 80% is second-level data, the compression rate between 40% and 60% is third-level data, and the compression rate less than 40% is fourth-level data. Then, according to the transmission signal quality, the signal-to-noise ratio greater than 15 decibels is divided into high priority, the signal-to-noise ratio between 10 and 15 decibels is divided into medium priority, and the signal-to-noise ratio less than 10 decibels is divided into low priority. Then, according to the bit error rate, the bit error rate less than 10⁻ 6 maintains the original priority, the bit error rate between 10⁻ 6 and 10⁻ 4 reduces one priority, and the bit error rate greater than 10⁻ 4 reduces two priorities. Finally, according to the energy consumption, the optimal transmission strategy is calculated by using a dynamic programming algorithm, and the data is divided into three categories: emergency transmission, regular transmission and delay transmission. This step realizes the reasonable hierarchical transmission of data through multi-dimensional evaluation.

[0047] The specific implementation of step S05 is to establish a data compression model. First, the original data is analyzed for features, including data distribution characteristics, time correlation and spatial correlation. The entropy value, autocorrelation coefficient and cross-correlation coefficient of the data are calculated using statistical methods. Then, according to the data characteristics, the compression algorithm is selected. For continuous data such as temperature and salinity, difference coding combined with Huffman coding is used. For fluctuating data such as wave height and wind speed, wavelet transform combined with run-length coding is used. For position information, incremental coding combined with arithmetic coding is used. Then, the parameters of each compression algorithm are optimized. The prediction order of difference coding is set to 3, the wavelet transform uses db4 wavelet, the decomposition level is 3, the threshold is set to 0.5 times the standard deviation, and the minimum repetition number of run-length coding is set to 4. Finally, an evaluation mechanism for compression algorithms is established, including compression rate, computational complexity and reconstruction error. According to the real-time monitoring results, the compression strategy is dynamically adjusted. This step realizes the efficient compression of data through algorithm selection and optimization.

[0048] The specific implementation of step S06 is a hierarchical compression process. First, the primary data is compressed using a lossless compression scheme, including differential encoding preprocessing, and then arithmetic encoding is used to achieve final compression, with a compression ratio requirement of greater than 2:1. Then the secondary data is compressed using an approximate lossless compression scheme, including wavelet transform preprocessing, retaining the top 90% of the absolute value of the high-frequency coefficient, and then using Huffman coding compression, with a compression ratio requirement of greater than 4:1. Next, the tertiary data is compressed using a lossy compression scheme, including discrete cosine transform preprocessing, retaining the 90% energy coefficient, and then using run-length encoding compression, with a compression ratio requirement of greater than 8:1. Finally, the fourth-level data is compressed using a high compression ratio scheme, including block processing with a block size of 8x8, and then using vector quantization compression with a codebook size of 256, with a compression ratio requirement of greater than 16:1. This step balances the compression ratio and data quality through a hierarchical compression strategy.

[0049] The specific implementation of step S07 is to establish a data caching mechanism. First, a hierarchical storage structure is designed according to the characteristics of the storage unit, including a cache area, a main storage area, and a backup storage area. The cache uses a solid-state drive with a capacity of 128GB, the main storage uses a large-capacity hard drive with a capacity of 1TB, and the backup storage uses a flash memory with a capacity of 256GB. Then, a data storage index is established using a B+ tree structure to organize the index, with leaf nodes storing data block pointers and non-leaf nodes storing key value ranges. The tree order is set to 128, and the compression transformation gain index is used as the key value. Next, data block management is implemented using the least recently used algorithm for cache replacement, with a cache block size of 4KB and a write-back strategy. When the cache usage rate exceeds 90%, replacement is triggered. Finally, a data consistency maintenance mechanism is established using a two-phase commit protocol to ensure data integrity and an asynchronous log method to record operation history. Log files are rolled over daily, with a 30-day retention period. This step ensures reliable data storage by establishing an efficient storage mechanism.

[0050] The specific implementation of step S08 is to establish a data transmission priority queue. First, a multi-level feedback queue is constructed, and 8 priority queues are set. Each queue adopts a first-in-first-out strategy, and the queues adopt preemptive scheduling. Then, a priority mapping rule is designed according to the compression transformation gain index. The index greater than 0.8 is mapped to queue 1 and 2, the index between 0.6 and 0.8 is mapped to queue 3 and 4, the index between 0.4 and 0.6 is mapped to queue 5 and 6, and the index less than 0.4 is mapped to queue 7 and 8. Then, dynamic priority adjustment is implemented. The aging algorithm is used to prevent low priority data starvation. When the waiting time exceeds the threshold, the priority is increased by one. The threshold is initially set to 300 seconds and decreases with the waiting time. Finally, a priority queue monitoring mechanism is established to record queue length, waiting time and processing time, etc. When the backlog of a certain queue exceeds 1000 data packets or the waiting time exceeds 600 seconds, an alarm is triggered. Through reasonable queue management, this step realizes the orderly transmission of data.

[0051] The specific implementation of step S09 is to monitor the communication link state. First, a signal strength detection mechanism is established. The pilot signal is used to measure the signal strength, and the measurement period is 1 second. The mean and variance of the signal strength are calculated, and the signal strength alarm threshold is set to -85 decibel milliwatts. Then, bandwidth detection is implemented. The probe packet technology is used to measure the available bandwidth. The probe packet size is 1KB, and the sending interval is 10 seconds. The round-trip time and packet loss rate are calculated by timestamp. Then, a link quality evaluation mechanism is established. The signal strength, bandwidth utilization, packet loss rate and delay jitter are considered comprehensively, and the fuzzy comprehensive evaluation method is used to obtain the link quality level, which is divided into four levels: excellent, good, medium and poor. Finally, a state reporting mechanism is implemented. The link state report is generated periodically, and the report period is 60 seconds. It includes basic parameter statistics, quality rating and trend analysis. Through real-time monitoring, this step masters the link state changes.

[0052] The specific implementation of step S10 is to dynamically adjust the transmission rate. First, the theoretical transmission rate upper limit is calculated based on the channel capacity equation. The input parameters include signal power value range of 0 to 30 decibel milliwatts, noise power set to -100 decibel milliwatts, bandwidth utilization upper limit of 95%, and channel gain obtained through channel estimation. Then, the current channel quality is estimated according to the bit error rate prediction equation. A historical bit error rate sliding window is established, and the window size is 100 sampling points. The autoregressive moving average model is used to predict the future bit error rate. Then, the optimal transmission rate is calculated using the transmission rate optimization equation. The constraint conditions include that the remaining capacity of the buffer is not less than 10%, and the remaining power is not less than 20%. The Lagrange multiplier method is used to solve the optimization problem. Finally, a rate adjustment mechanism is implemented. The adjustment step is 10% of the current rate. The additive increase and multiplicative decrease strategy is adopted. When the network quality improves, the rate is increased linearly, and when congestion occurs, the rate is exponentially backed off. Through adaptive control, this step optimizes the transmission performance.

[0053] The specific implementation of step S11 is to establish a transmission interruption early warning mechanism. First, a historical data analysis model is established, time series analysis method is adopted to statistically analyze historical transmission interruption data, interruption characteristic parameters are extracted, including interruption frequency, duration and influence range. Then, an early warning index system is constructed, three early warning levels are set, according to link state parameters, compression transformation gain index and transmission queue state, an early warning model is established by using support vector machine algorithm, and the number of training samples is 10000. Then, the early warning triggering mechanism is realized, when the early warning level reaches two levels or more, the data transmission backup program is started, the data not completed is backed up to the local storage, and the transmission breakpoint information is recorded. Finally, the interruption event recording mechanism is established, the information such as interruption time, duration and influence range is recorded, the structured log format is used for storage, and the interruption cause analysis mechanism is established. Through the early warning mechanism, the loss caused by transmission interruption is reduced.

[0054] The specific implementation of step S12 is to recover the interrupted transmission. First, the breakpoint is located according to the recorded transmission position information, the binary search is used to quickly locate the data breakpoint, and then the data integrity is verified, the MD5 check code is used to confirm the correctness of the transmitted data. Then, the link state parameters are analyzed, and it is evaluated whether the current network environment meets the transmission conditions, including that the signal strength is not less than-85 decibel milliwatt, the signal-to-noise ratio is not less than 10 decibels, and the available bandwidth is not less than 50% of the link capacity. Then, the transmission recovery strategy is formulated, the high priority data is immediately recovered, and the low priority data uses the backoff algorithm, the initial backoff time is 1 second, and the maximum backoff time is 60 seconds. Finally, the recovery monitoring mechanism is established, the network quality, transmission rate and completion progress during the recovery transmission process are monitored, when the recovery transmission fails, the failure reason is recorded and the retry mechanism is triggered, and the maximum number of retries is 3 times. Through the reliable recovery mechanism, the integrity of data transmission is ensured.

[0055] The mathematical model or calculation process involved in the present application is described in detail below.

[0056] 1. The resource consumption equation is specifically represented as follows:

[0057] ;

[0058] In the formula, is the resource consumption index; is the data compression rate; is the processor occupancy rate; is the memory usage rate; is the power consumption change rate; is the weight coefficient, and satisfies ; is an error term; the reciprocal term reflects the nonlinear growth when the processor is close to saturation, the exponential term indicates the rapid growth of memory pressure, and the square term indicates the cumulative effect of energy consumption.

[0059] 2. The transmission efficiency equation is specifically expressed as follows:

[0060] ;

[0061] wherein, is a transmission efficiency index; is a signal power; is a noise power; is a bit error rate; is a channel utilization rate; is a transmission delay; is a maximum allowable delay; is an attenuation coefficient; is a weight coefficient, and satisfies ; is an error term; the exponential decay term reflects the serious impact of the bit error rate, the square term indicates the nonlinear benefit of channel utilization, and the square root term indicates the decreasing marginal effect of delay.

[0062] 3. The quality evaluation equation is specifically expressed as follows:

[0063] ;

[0064] wherein, is a quality evaluation index; is data integrity; is a mean square error; is a maximum allowable error; is a transmission success rate change over time; is a link stability; is a recovery cost; is a maximum allowable recovery cost; is a weight coefficient, and satisfies ; is an error term; the integral term indicates the cumulative effect of the transmission success rate, and the derivative term reflects the trend of the link stability.

[0065] 4. The channel capacity equation is specifically expressed as follows:

[0066] ;

[0067] wherein, is a channel capacity; is a bandwidth; is a transmission power; is a channel gain; is a noise power spectral density; is the signal-to-noise ratio; is the signal-to-noise ratio threshold; is the adjustment coefficient; is the error term; the Sigmoid function is introduced to reflect the sharp decline of channel capacity in the low signal-to-noise ratio region.

[0068] 5. The bit error rate prediction equation is specifically expressed as follows:

[0069] ;

[0070] In the formula, is the predicted bit error rate; is the historical bit error rate; is the signal strength; is the maximum signal strength; is the atmospheric attenuation; is the Doppler shift; is the weight coefficient, and satisfies ; is the error term; the square term is introduced to represent the nonlinear influence of signal strength attenuation, the square root term reflects the gradual change characteristics of atmospheric attenuation, and the partial derivative term represents the dynamic change of the Doppler shift.

[0071] 6. The transmission rate optimization equation is specifically expressed as follows:

[0072] ;

[0073] In the formula, is the optimal transmission rate; is the theoretical channel capacity; is the remaining buffer capacity; is the expected transmission time; is the current power; is the maximum power; is the adjustment coefficient; is the error term; the exponential term is introduced to represent the nonlinear inhibitory effect of bit error rate and power on transmission rate.

[0074] 7. The abnormal value detection equation is specifically expressed as follows:

[0075] ;

[0076] In the formula, is the abnormal value judgment result; is the data to be detected; is the data mean value; is the data standard deviation; is the sensitivity coefficient; is the data change rate; is the error term; the sigmoid function is introduced to adjust the threshold and consider the influence of data change rate.

[0077] 8. The data smoothing equation is specifically expressed as follows:

[0078] ;

[0079] wherein, is the smoothed data; is the original data; is the weight coefficient, and satisfies ; is the decay coefficient; is the error term; the exponential decay weight is introduced to emphasize the influence of neighboring points.

[0080] 9. The aging priority calculation equation is specifically expressed as follows:

[0081] ;

[0082] wherein, is the new priority; is the initial priority; is the waiting time; is the priority promotion threshold; is the acceleration coefficient; is the error term; the logarithmic term is introduced to represent the marginal decreasing effect of priority promotion, and the second derivative term reflects the accelerated change of the waiting time.

[0083] 1. The establishment process of the resource consumption equation is described in detail as follows:

[0084] First, an initial linear model is constructed: ;

[0085] wherein the resource vector is expressed as:

[0086] ;

[0087] Considering that the system performance sharply decreases when the processor occupancy approaches 100%, the reciprocal term is introduced.

[0088] Considering the system jitter phenomenon after the memory usage exceeds the threshold, the exponential term is introduced.

[0089] Considering the cumulative effect of power consumption, the is replaced by .

[0090] Finally, the nonlinear equation is obtained: .

[0091] 2. The establishment process of the transmission efficiency equation is described in detail as follows:

[0092] Based on the Shannon capacity formula: ;

[0093] Considering the exponential decay effect of the bit error rate: ;

[0094] Considering the nonlinear gain of channel utilization: ;

[0095] Considering the marginal effect of delay: ;

[0096] The final equation is obtained by combining.

[0097] 3. The establishment process of the quality evaluation equation is described in detail as follows:

[0098] First, construct the quality evaluation index matrix:

[0099] ;

[0100] Normalize the mean square error: , introduce the square term to enhance the punishment of large error;

[0101] Consider the cumulative effect of transmission success rate: ;

[0102] Consider the dynamic characteristics of link stability: ;

[0103] Consider the exponential decay of recovery cost: ;

[0104] Finally, the nonlinear evaluation equation is obtained by combining.

[0105] 4. The establishment process of the channel capacity equation is described in detail as follows:

[0106] Based on the Shannon capacity theory, construct the basic model: ;

[0107] Introduce the signal-to-noise ratio matrix:

[0108] ;

[0109] Considering the sudden change characteristics of channel quality, introduce the Sigmoid function: ;

[0110] Finally, the capacity equation in the form of product is obtained.

[0111] 5. The establishment process of the bit error rate prediction equation is described in detail as follows:

[0112] Constructing the error rate influencing factor matrix:

[0113] ;

[0114] Considering the nonlinear attenuation of signal strength: ;

[0115] Considering the gradual change of atmospheric attenuation: ;

[0116] Introducing the dynamic change of Doppler shift: ;

[0117] Combining each item through the weight matrix .

[0118] 6. The establishment process of the transmission rate optimization equation is described in detail as follows:

[0119] First, determine the upper limit of the rate: ;

[0120] Construct the constraint term:

[0121] ;

[0122] Introducing the exponential decay of error rate: ;

[0123] Introducing the nonlinear constraint of power: ;

[0124] Finally, the optimization equation considering multiple constraints is obtained.

[0125] 7. The establishment process of the abnormal value detection equation is described in detail as follows:

[0126] Construct the basic detection standard: ;

[0127] Introducing the data change rate matrix:

[0128] ;

[0129] Designing an adaptive threshold function: ;

[0130] Finally, the dynamic threshold detection equation is obtained.

[0131] 8. The establishment process of the data smoothing equation is described in detail as follows:

[0132] Constructing a sliding window:

[0133] ;

[0134] Designing an exponential decay weight: ;

[0135] Weight normalization: ;

[0136] Finally, the non-uniformly weighted smoothing equation is obtained.

[0137] 9. The establishment process of the aging priority calculation equation is described in detail as follows:

[0138] Build the basic priority model: ;

[0139] Consider the logarithmic growth of waiting time: ;

[0140] Introduce the acceleration term: ;

[0141] Finally, the priority calculation equation with acceleration characteristics is obtained.

[0142] Specifically, the principle of the present application is: the technical principle of the present application is based on data compression theory and adaptive system control principle. The design of the compression transformation gain index is derived from the entropy coding theory in information theory, and the mapping relationship between data characteristics and compression effect is established to realize the quantitative evaluation of compression efficiency. The index is calculated by a system of optimization equations, in which the resource consumption equation evaluates the calculation overhead of the compression process, the quality evaluation equation measures the fidelity of the compressed data, and the transmission efficiency equation considers the influence of the compression result on the transmission performance. The synergistic effect of the three equations enables the system to select the optimal compression strategy in a complex data environment.

[0143] In terms of compression algorithm design, the present application adopts a hierarchical processing architecture. The system first performs feature analysis on the original data, including data distribution characteristics, time correlation, spatial correlation, etc. Based on these characteristics, the system selects the most suitable compression method from the preset algorithm library. The algorithm library contains compression algorithms optimized for different data types, such as Fourier transform compression for periodic data and wavelet transform compression for burst data. At the same time, the system also designs a parameter adaptive mechanism that can adjust algorithm parameters in real time according to the compression effect.

[0144] The compression optimization mechanism of the present application is based on feedback control theory. The system monitors the compression effect in real time, including compression rate, calculation time, compression quality, etc., forming a complete feedback loop. These feedback information is used to adjust the compression strategy and parameters to ensure that the system always runs in the optimal state. The design of the entire technical solution follows the basic principles of data compression, and through multi-level optimization and control, it realizes the efficient compression of ocean buoy data.

[0145] A specific embodiment 1 of the present application is provided below, and the specific implementation of each step in embodiment 1 is described in detail as follows.

[0146] The specific implementation of step S01 is to construct a low-orbit satellite network communication link. First, the position information of the ocean buoy is collected through the satellite navigation system to obtain the position vector: The longitude range is -180 to 180 degrees, the latitude range is -90 to 90 degrees, the height range is 0 to 100 meters, and the sampling period is 60 seconds. Then, the visible satellite list is calculated according to the low-orbit satellite orbit parameters, and the satellite visibility matrix is represented as: Wherein is the satellite elevation angle, ranging from 0 to 90 degrees, is the azimuth angle, ranging from 0 to 360 degrees, is the signal-to-noise ratio, ranging from 0 to 60 decibels. Then, the physical layer connection is established, and the adaptive coding and modulation technology is adopted. The modulation mode selection matrix is: Wherein is the signal-to-noise ratio threshold, is the modulation mode, is the coding efficiency. Finally, the link layer connection is established, and the improved selective retransmission ARQ protocol is adopted. The sliding window size is dynamically adjusted according to the round-trip time : Wherein is the channel capacity, is the maximum segment size, set to 1024 bytes. This step establishes a multi-level link to ensure communication reliability and efficiency.

[0147] The specific implementation of step S02 is to collect ocean buoy sensor data. First, the ocean environment parameter matrix is collected: The temperature ranges from -20 to 50 degrees Celsius, the salinity ranges from 0 to 40 ‰, and the dissolved oxygen ranges from 0 to 20 mg / L. Then, the sea state data matrix is collected: The wave height ranges from 0 to 20 meters, the wind speed ranges from 0 to 60 meters per second, and the air pressure ranges from 800 to 1100 hundred pascals. Then, the original data is preprocessed, and the 3 times standard deviation method is used for outlier detection: Wherein is the data to be detected, is the data mean, is the data standard deviation, is the sensitivity coefficient, and the value is 0.1. Finally, the data is smoothed: Wherein is a weight coefficient, is a decay coefficient, taking a value of 0.5. This step obtains reliable monitoring data through multi-sensor cooperative acquisition and data preprocessing.

[0148] The specific implementation of step S03 is to establish a compression conversion gain index model. First, a resource consumption equation is constructed: where the resource vector is represented as: Then, a transmission efficiency equation is established: Next, a quality assessment equation is constructed: Finally, a genetic algorithm is used to solve the optimization equation set, with a population size of 100, a crossover probability of 0.8, a mutation probability of 0.1, and a maximum number of iterations of 1000. This step realizes quantitative evaluation of data transmission efficiency by establishing a mathematical model.

[0149] The specific implementation of step S04 is to divide multi-level transmission data. First, a data feature matrix is constructed: where is the compression rate, is the signal quality, is the bit error rate, is the energy consumption. Then, preliminary classification is performed according to the compression rate, and a classification threshold matrix is set: Next, priority adjustment is performed based on signal quality, and the signal quality assessment function is: Finally, a dynamic programming algorithm is used to optimize the transmission strategy, and a state transition matrix is constructed: represents the cost from state to state . This step realizes reasonable hierarchical transmission of data through multi-dimensional evaluation and optimization.

[0150] The specific implementation of step S05 is to establish a data compression model. First, a data feature analysis matrix is constructed: where is the data entropy value, is the autocorrelation coefficient, is the cross-correlation coefficient. Then, differential encoding preprocessing is performed on continuous change data: Huffman coding is performed on the preprocessed data, and a probability distribution matrix is constructed: Next, wavelet transform is performed on fluctuating data, and a discrete wavelet transform basis function is selected: where is the scale parameter, is the translation parameter, and the wavelet coefficient is: Finally, a compression algorithm evaluation index system is established, including compression rate , computational complexity , reconstruction error This step realizes efficient compression of data through the combination of multiple compression algorithms.

[0151] The specific implementation of step S06 is a hierarchical compression process. First, lossless compression is performed on the first-level data using differential encoding preprocessing: Then, arithmetic encoding is used for compression, and the cumulative probability distribution function is: The encoding interval is: Then, the second-level data is approximately lossless compressed. The wavelet transform coefficient selection matrix is: The coefficients that account for the top 90% of the energy are retained. Next, the third-level data is lossy compressed using discrete cosine transform: Finally, the fourth-level data is processed with high compression ratio. The vector quantization encoding design is: This step balances the compression rate and data quality through hierarchical compression strategy.

[0152] The specific implementation of step S07 is to establish a data caching mechanism. First, build the storage hierarchy matrix: Then, establish the B+ tree index structure, node structure: Where is the compression conversion gain index, is the data block pointer. Next, implement the least recently used algorithm for cache replacement, access record matrix: Finally, establish a two-phase commit protocol, transaction state transition matrix: This step ensures reliable storage of data by establishing an efficient storage mechanism.

[0153] The specific implementation of step S08 is to establish a data transmission priority queue. First, build a multi-level feedback queue structure, queue state matrix: Then, design the priority mapping rule, compression conversion gain index mapping function: Next, implement the aging algorithm: Where is the new priority, is the initial priority, is the waiting time, is the priority promotion threshold. Finally, establish a queue monitoring mechanism, monitoring index matrix: This step realizes the orderly transmission of data through reasonable queue management.

[0154] The specific implementation of step S09 is to monitor the communication link state. First, establish a signal strength detection mechanism, build a signal feature matrix: The signal strength calculation formula is: Where is the received power, is the transmit power, is the transmit gain, is the receive gain, is the path loss, is the other loss. Then the bandwidth detection is implemented, using the probe packet technology, the probe packet timestamp matrix: , the round-trip delay calculation: , the available bandwidth estimation: . Next, the link quality evaluation mechanism is established, the fuzzy comprehensive judgment matrix: . Finally, the state reporting mechanism is implemented, the reporting content matrix: . This step realizes real-time monitoring and grasps the link state changes.

[0155] The specific implementation of step S10 is to dynamically adjust the transmission rate. First, the theoretical transmission rate upper limit is calculated based on the channel capacity equation: . Then the channel quality is estimated according to the bit error rate prediction equation: . Next, the optimal transmission rate is calculated using the transmission rate optimization equation: . Finally, the rate adjustment mechanism is implemented, and the adjustment step matrix is: . This step optimizes the transmission performance through adaptive control.

[0156] The specific implementation of step S11 is to establish a transmission interruption early warning mechanism. First, the historical data analysis model is constructed, and the interruption feature matrix is: . Then the early warning index system is established, and the support vector machine model is: , where is the kernel function, and the radial basis kernel function is selected: . Next, the early warning trigger mechanism is implemented, and the early warning level matrix is: . Finally, the interruption event recording mechanism is established, and the event log structure is: . This step reduces the loss caused by transmission interruption through the early warning mechanism.

[0157] The specific implementation of step S12 is to recover the interrupted transmission. First, the data breakpoint is located using binary search, and the search process state matrix is: . Then the data integrity is verified, and the MD5 verification matrix is: . Next, the transmission recovery strategy is developed, and the backoff time is calculated: , where is the basic backoff time, set to 1 second, is the number of retries, is the maximum backoff time, set to 60 seconds. Finally, the recovery monitoring mechanism is established, and the monitoring state matrix is: . This step ensures the integrity of data transmission through a reliable recovery mechanism.

[0158] To better understand and implement the present application, the following provides an embodiment 2 of a specific application scenario of the present application: researchers deploy a set of marine ecological environment monitoring buoy systems in the Yellow Sea, which contains multiple types of sensor equipment such as weather, hydrology, water quality, etc., and uses low-orbit satellite communication mode to realize data backhaul. As shown in Table 1:

[0159] Table 1 Marine buoy sensor configuration table

[0160]

[0161] The system realizes real-time collection and transmission of data by the following steps.

[0162] First, build a low-orbit satellite network communication link to realize stable two-way communication connection through adaptive coding and modulation technology. The channel capacity equation is: wherein is set to 400 kHz, is set to 6 dB. As shown in Table 2:

[0163] Table 2 Communication link parameter configuration table

[0164]

[0165] Then, collect marine environmental data based on multiple sensors. The temperature sensor measures -20 to 50 degrees Celsius, with an accuracy of 0.1 degrees Celsius; the salinity sensor measures 0 to 40 ‰, with an accuracy of 0.01 ‰; the barometric pressure sensor measures 800 to 1100 hundred Pa, with an accuracy of 0.1 hundred Pa. As shown in Table 3:

[0166] Table 3 Data preprocessing parameter table

[0167]

[0168] Next, establish a compression conversion gain index model. The resource consumption equation is: wherein , , , . As shown in Table 4:

[0169] Table 4 Compression efficiency evaluation table

[0170]

[0171] According to the compression conversion gain index, the data is divided into multiple levels of transmission data. Based on compression rate, signal quality, bit error rate and energy consumption, the dynamic planning state transition matrix is shown in Table 5:

[0172] Table 5 Data hierarchical transmission matrix

[0173]

[0174] The data compression model is established, the meteorological and hydrological data adopts differential encoding combined with Huffman encoding, the water quality data adopts wavelet transform combined with run-length encoding, and the image data adopts discrete cosine transform combined with vector quantization. As shown in Table 6:

[0175] Table 6 Compression algorithm configuration table

[0176]

[0177] The multi-level transmission data is processed by hierarchical compression, and different compression strategies are adopted according to different levels. As shown in Table 7:

[0178] Table 7 Hierarchical compression processing strategy table

[0179]

[0180] The data cache mechanism is established, and a hierarchical storage structure is adopted. As shown in Table 8:

[0181] Table 8 Storage hierarchy table

[0182]

[0183] The data transmission priority queue is established, and the aging algorithm is adopted: , wherein is set to 300 seconds, is set to 0.1. As shown in Table 9:

[0184] Table 9 Queue priority configuration table

[0185]

[0186] The communication link state is monitored to realize signal strength and bandwidth detection. The bit error rate prediction equation is: , wherein , , , . As shown in Table 10:

[0187] Table 10 Link state monitoring parameter table

[0188]

[0189] According to the signal strength and link bandwidth, the transmission rate is dynamically adjusted. The transmission rate optimization equation is: , wherein , . As shown in Table 11:

[0190] Table 11 transmission rate adjustment strategy table

[0191]

[0192] An early warning mechanism for transmission interruption is established, and a support vector machine algorithm is used to establish an early warning model.

[0193] Table 12 early warning mechanism configuration table

[0194]

[0195] When transmission interruption occurs, the system records the interruption information and waits for link recovery.

[0196] Table 13 interruption recovery strategy table

[0197]

[0198] Through actual application test, the actual operation effect of the system in the Yellow Sea is shown in Table 14:

[0199] Table 14 system operation effect comparison table

[0200]

[0201] The embodiment realizes quantitative evaluation of data compression efficiency by establishing a compression conversion gain index model, realizes reasonable allocation of resources through a multi-level data hierarchical transmission mechanism, and improves overall compression efficiency through cooperation of multiple compression algorithms. The system operation result shows that the method can effectively solve the technical problem of low compression efficiency caused by poor algorithm adaptability in the data compression process of the marine buoy.

[0202] The traditional marine buoy data compression method mainly relies on a single compression algorithm, and cannot dynamically adjust the compression strategy according to the data characteristics and network conditions, resulting in low compression efficiency. The present application realizes quantitative evaluation of data compression efficiency by establishing a compression conversion gain index model, realizes reasonable allocation of resources through a multi-level data hierarchical transmission mechanism, and improves overall compression efficiency through cooperation of multiple compression algorithms. Compared with the traditional method, the data compression efficiency of the present application is improved by 45%, the transmission success rate is improved by 35%, and the system resource utilization rate is improved by 40%.

[0203] It should be noted that the variables involved in the present application are explained in detail as shown in Table 15.

[0204] Table 15 variable explanation table

[0205]

[0206] The above merely illustrates the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be covered in the protection scope of the present application.

Claims

1. A method for real-time acquisition of ocean buoy data based on low-orbit satellite communication, characterized in that, The method comprises the following steps: S01, constructing a low-orbit satellite network communication link to establish a two-way communication connection between the ocean buoy and the land data center; S02, collecting ocean buoy sensor data to obtain buoy position information, hydrological parameters, and sea state data as raw data; S03, establishing a compression conversion gain index model, which is calculated by an optimization equation set to obtain data compression priority; S04, dividing the raw data into multi-level transmission data according to the compression conversion gain index model, which is divided based on data segment compression rate, transmission signal quality, error rate, and energy consumption; S05, establishing a data compression model to select a corresponding compression algorithm according to the characteristics of the multi-level transmission data; S06, using the data compression model to perform hierarchical compression processing on the multi-level transmission data to obtain multi-level compressed data; S07, establishing a data caching mechanism to store the multi-level compressed data in a local storage unit according to the compression conversion gain index; S08, establishing a data transmission priority queue to sort the multi-level compressed data according to the compression conversion gain index; S09, monitoring the low-orbit satellite network communication link state to obtain signal strength and link bandwidth data; S10, dynamically adjusting the data transmission rate using a link evaluation equation set according to the signal strength and link bandwidth data; S11, establishing a transmission interruption early warning mechanism using a quality evaluation equation to record the transmission position information, compression conversion gain index, and link state parameters at the data transmission interruption time; S12, after the low-orbit satellite network communication link is restored, continuing to transmit the incomplete data according to the transmission position information, compression conversion gain index, and link state parameters; The compression conversion gain index is a data transmission efficiency evaluation parameter used to quantify the comprehensive benefits in the data compression and transmission process. The optimization equation set for calculating the compression conversion gain index includes a resource consumption equation, a transmission efficiency equation, and a quality evaluation equation. The resource consumption equation is specifically expressed as follows: ； In the formula, is a resource consumption index; is a data compression rate; is a processor occupancy rate; is a memory usage rate; is a power consumption change rate; is a weight coefficient, and satisfies ; is an error term; The transmission efficiency equation is specifically expressed as follows: ； wherein, is a transmission efficiency index; is a signal power; is a noise power; is a bit error rate; is a channel utilization rate; is a transmission delay; is a maximum allowable delay; is an attenuation coefficient; is a weight coefficient, and satisfies ; is an error term; The quality evaluation equation is specifically expressed as follows: ； wherein, is a quality evaluation index; is data integrity; is a mean square error; is a maximum allowable error; is a change in transmission success rate over time; is link stability; is a recovery cost; is a maximum allowable recovery cost; is a weight coefficient, and satisfies ; is an error term; The optimization equation set is solved using a genetic algorithm to obtain the compression conversion gain index. The link evaluation equation set includes a channel capacity equation, an error rate prediction equation, and a transmission rate optimization equation.

2. The method for real-time acquisition of data from ocean buoys based on low-orbit satellite communication according to claim 1, characterized in that, The channel capacity equation is used to calculate the maximum transmission capacity of the current network link and is specifically expressed as follows: ； wherein, is the channel capacity; is the bandwidth; is the transmit power; is the channel gain; is the noise power spectral density; is the signal-to-noise ratio; is the signal-to-noise ratio threshold; is the adjustment coefficient; is the error term; the Sigmoid function is introduced to reflect the sharp decline of the channel capacity in the low signal-to-noise ratio region; The error rate prediction equation is used to predict the error rate trend during data transmission and is specifically expressed as follows: ； wherein, is the predicted bit error rate; is the historical bit error rate; is the signal strength; is the maximum signal strength; is the atmospheric attenuation; is the Doppler shift; is the weight coefficient, and satisfies ; is the error term; the square term indicates the nonlinear effect of signal strength attenuation, the square root term reflects the gradual change of atmospheric attenuation, and the partial derivative term represents the dynamic change of Doppler shift; The transmission rate optimization equation is used to optimize the data transmission rate and is specifically expressed as follows: ； wherein, is the optimal transmission rate; is the theoretical channel capacity; is the remaining buffer capacity; is the expected transmission time; is the current power; is the maximum power; is the adjustment coefficient; is the error term; the exponential term is introduced to represent the non-linear suppression effect of the error rate and power on the transmission rate.

3. The method for real-time acquisition of data from ocean buoys based on low-orbit satellite communication according to claim 2, characterized in that, The data caching mechanism adopts a hierarchical storage structure, including a cache area, a main storage area, and a backup storage area. It uses a B+ tree structure to organize indexes, takes the compression conversion gain index as the key value, and uses the least recently used algorithm for cache replacement.

4. The method for real-time acquisition of ocean buoy data based on low-orbit satellite communication according to claim 3, characterized in that, The transmission priority queue adopts a multi-level feedback queue structure with 8 priority queues. The aging algorithm is used to prevent low-priority data starvation.

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