Ocean buoy data real-time acquisition method based on low earth orbit satellite communication

Abstract

The invention provides an ocean buoy data real-time acquisition method based on low earth orbit satellite communication, and belongs to the technical field of electrical digital data processing.The ocean buoy data real-time acquisition method comprises the steps that firstly, a low earth orbit satellite network communication link is built, data characteristics are analyzed through a compression conversion gain index model, and a low earth orbit satellite network communication link is established; and establishing a multi-stage compression mechanism. The system adopts an optimization equation set to dynamically adjust compression parameters, the optimization equation set comprises a resource consumption equation, a compression quality equation and a transmission efficiency equation, and self-adaptive compression of data is achieved. And meanwhile, through a hierarchical algorithm selection mechanism and a parameter optimization strategy and in combination with real-time feedback control, continuous optimization of the compression efficiency is ensured. According to the compression scheme based on multi-level optimization, the technical problem of low compression efficiency caused by poor algorithm adaptability in the ocean buoy data compression process in the prior art is effectively solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electrical digital data processing. Specifically, it relates to a method for real-time acquisition of ocean buoy data based on low-earth orbit satellite communication. Background Art

[0002] As a core device 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 various types such as hydrological parameters, meteorological elements, and ocean current information, and have characteristics such as large data volume, high sampling frequency, and diverse data features. Traditional ocean buoy data compression technologies mainly use general compression algorithms, such as Huffman coding, LZW compression, run-length encoding, etc. to compress the collected data. These compression methods are widely used in the field of computer data processing and have advantages such as mature algorithms and simple implementation. However, with the continuous improvement of ocean observation requirements, traditional compression methods show obvious limitations when dealing with ocean buoy data.

[0003] The existing ocean buoy data compression technologies have the following problems: First, the general compression algorithms do not fully consider the characteristic differences of ocean data and adopt 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 the optimal compression effect using a unified compression algorithm. Second, the existing compression methods lack consideration of data timeliness and cannot perform differential compression according to the importance and transmission urgency of the data, causing waste of system resources. Third, the parameter configuration of the compression algorithm is often fixed and cannot be dynamically adjusted according to data characteristics and transmission conditions, affecting the performance of the compression. In addition, traditional compression methods often show a sharp drop in the compression ratio when dealing with high-frequency data generated by sudden ocean events.

[0004] In the scenario of real-time acquisition and transmission of ocean buoy data, how to improve data compression efficiency has become a key technical problem. The existing technologies cannot achieve an intelligent match between the compression algorithm and data characteristics, nor do they have a dynamic optimization mechanism for the compression process, resulting in the compression efficiency always being in a sub-optimal state. This situation seriously restricts the data transmission efficiency of ocean observation systems and affects the real-time and accuracy of ocean monitoring. Especially in the low-earth orbit satellite communication environment, the limited transmission bandwidth further highlights the importance of efficient data compression. That is to say, there is a technical problem of poor algorithm adaptability and low compression efficiency in the process of ocean buoy data compression in the existing technologies. Summary of the Invention

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

[0006] The present invention is implemented as follows: The present invention provides a method for real-time acquisition of ocean buoy data based on low-earth orbit satellite communication, including the following steps: constructing a low-earth orbit satellite network communication link to establish a two-way communication connection between the ocean buoy and the land data center; collecting ocean buoy sensor data to obtain buoy position information, hydrological parameters, and sea condition data as raw data; establishing a compression conversion gain index model to calculate the data compression priority through an optimized equation system; dividing the raw data into multiple levels of 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 multiple levels of transmission data; performing hierarchical compression processing on the multiple levels of transmission data; establishing a data caching mechanism and a transmission priority queue; monitoring the communication link status and dynamically adjusting the transmission rate.

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

[0008] Among them, the multiple levels of transmission data are divided based on the data segment compression rate, the transmission signal quality, the bit error rate, and the energy consumption; the dynamic programming algorithm is used to divide the data into three categories: emergency transmission, regular transmission, and delayed transmission.

[0009] Among them, the data compression model uses differential coding combined with Huffman coding for temperature and salinity, wavelet transform combined with run-length coding for wave height and wind speed, and delta coding combined with arithmetic coding for position information.

[0010] Among them, the hierarchical compression processing includes: performing differential coding preprocessing and arithmetic coding compression on the first-level data; performing wavelet transform preprocessing and Huffman coding compression on the second-level data; performing discrete cosine transform preprocessing and run-length coding compression on the third-level data; performing block processing and vector quantization compression on the fourth-level data.

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

[0012] Among them, the transmission priority queue adopts a multi-level feedback queue structure, with 8 priority queues set; an aging algorithm is adopted to prevent low-priority data starvation.

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

[0014] Among them, it also includes establishing a transmission interruption warning mechanism and a recovery mechanism. The transmission interruption warning mechanism uses time series analysis method to statistically analyze historical transmission interruption data; uses a support vector machine algorithm to establish a warning model; records the interruption occurrence time, duration, and affected range information.

[0015] Among them, to resume interrupted transmission, binary search is used to locate the data breakpoint; MD5 checksum is used to confirm the correctness of the transmitted data; a backoff algorithm is used for transmission recovery, and retry is performed for failed recovery transmission.

[0016] Compared with the prior art, a real-time ocean buoy data acquisition method based on low-earth orbit satellite communication provided by the present invention. The real-time ocean buoy data acquisition method based on low-earth orbit satellite communication proposed by the present invention realizes intelligent and precise control of the data compression process by introducing a compression conversion gain index model and a multi-level data compression mechanism. The method establishes a complete data compression evaluation system, including multiple dimensions such as compression ratio evaluation, quality evaluation, and resource consumption evaluation, and each dimension is specially designed according to the characteristics of ocean data. By optimizing the design of the equation set, the system realizes the adaptive selection of compression algorithms and dynamic adjustment of parameters.

[0017] The present invention has made remarkable breakthroughs in data compression efficiency: First, the compression conversion gain index model can accurately evaluate the effectiveness of different compression strategies, providing a scientific basis for algorithm selection. The model comprehensively considers multiple factors such as compression ratio, computational complexity, and data characteristics, ensuring the optimality of compression decisions. Second, the multi-level data compression mechanism realizes hierarchical processing of data, and different types and importance levels of data can adopt different compression strategies, significantly improving the overall compression efficiency. Third, the adaptive compression framework established by the system can dynamically adjust compression parameters according to data characteristics and transmission requirements, ensuring continuous optimization of compression performance.

[0018] The present invention successfully solves the problem of poor algorithm adaptability in the process of ocean buoy data compression. The core lies in establishing a complete compression optimization mechanism. Through the innovative concept of compression conversion gain index, intelligent selection of compression algorithms is achieved; through the design of multi-level compression strategies, the compression efficiency of different types of data is ensured; through the dynamic parameter adjustment mechanism, continuous optimization of compression performance is guaranteed. These technological innovations enable the system to always maintain the best compression state. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0021] As Figure 1 shown, it is a flowchart of a real-time ocean buoy data acquisition method based on low-earth orbit satellite communication provided by the present invention. The method includes the following steps: S01. Construct a low-earth orbit satellite network communication link and establish a two-way communication connection between the ocean buoy and the land data center; S02. Collect ocean buoy sensor data and obtain the buoy position information, hydrological parameters and sea condition data as the original data; S03. Establish a compression conversion gain index model, and the compression conversion gain index model calculates the data compression priority through an optimized equation set; S04. Divide the original data into multi-level transmission data according to the compression conversion gain index model, and the multi-level transmission data is divided based on the data segment compression rate, transmission signal quality, error rate and energy consumption; S05. Establish a data compression model and select a corresponding compression algorithm according to the characteristics of the multi-level transmission data; S06. Use the data compression model to perform hierarchical compression processing on the multi-level transmission data to obtain multi-level compressed data; S07. Establish a data caching mechanism and store the multi-level compressed data in the local storage unit according to the compression conversion gain index; S08. Establish a data transmission priority queue and sort the multi-level compressed data according to the compression conversion gain index for transmission priority; S09. Monitor the status of the low-earth orbit satellite network communication link and obtain the signal strength and link bandwidth data; S10. According to the signal strength and the link bandwidth data, dynamically adjust the data transmission rate by using the link evaluation equation set; S11. Establish a transmission interruption early warning mechanism using the quality assessment equation, and record the transmission position information, compression conversion gain index, and link state parameters at the moment of data transmission interruption; S12. After the low-earth orbit satellite network communication link is restored, continue to transmit the unfinished data according to the transmission position information, the compression conversion gain index, and the link state parameters.

[0022] 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 assessment equation; The resource consumption equation is used to calculate the system resource occupancy in the data compression and transmission process. The inputs include the data compression rate, processor occupancy rate, memory usage rate, and power consumption rate, and the output is the resource consumption index; The transmission efficiency equation is used to evaluate the network transmission efficiency in the data transmission process. The inputs include the channel capacity, bit error rate, signal-to-noise ratio, transmission delay, and channel utilization rate, and the output is the transmission efficiency index; The quality assessment equation is used to evaluate the data compression quality and transmission reliability. The inputs include data integrity, compression distortion, transmission success rate, link stability, and recovery cost, and the output is the quality assessment index.

[0023] The link assessment equation set includes a channel capacity equation, a bit error rate prediction equation, and a transmission rate optimization equation; The channel capacity equation is used to calculate the maximum transmission capacity of the current network link. The inputs include signal power, noise power, bandwidth utilization rate, and channel gain, and the output is the theoretical channel capacity; The bit error rate prediction equation is used to predict the change trend of the bit error rate in the data transmission process. The inputs include the historical bit error rate, signal strength, atmospheric attenuation, and Doppler frequency shift, and the output is the predicted bit error rate; The transmission rate optimization equation is used to optimize the data transmission rate. The inputs include the theoretical channel capacity, predicted bit error rate, cache capacity, and energy constraint, and the output is the optimal transmission rate.

[0024] The following describes the specific implementation manners of the above steps in detail.

[0025] The specific implementation of step S01 is to construct a low-earth orbit satellite network communication link. First, obtain the real-time position coordinates of the ocean buoy through the satellite positioning system, calculate the visible satellite list according to the orbital parameters of the low-earth orbit satellite constellation, select the satellite with the strongest signal as the main communication link, and the second-strongest satellite as the backup link. Then establish a physical layer connection, adopt adaptive coding and modulation technology to dynamically adjust the signal modulation method, support multiple modulation methods such as BPSK, QPSK, 8PSK, 16QAM, etc., use concatenated coding for the coding method, use convolutional code for the inner code with a code rate of 1 / 2, use RS code for the outer code with a code length of 255 and an error correction ability of 8 symbols, and determine the current optimal coding and modulation combination through channel detection. Then establish a link layer connection, adopt an improved selective repeat ARQ protocol to achieve reliable transmission, the protocol uses a sliding window mechanism, the window size is dynamically adjusted according to the round-trip delay, the maximum window size is 256, and the timeout retransmission time is 2 times the round-trip delay. Finally, establish a transport layer connection, adopt an improved TCP protocol 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 provides a basic guarantee for subsequent data transmission by establishing a stable and reliable communication link.

[0026] The specific implementation of step S02 is to collect ocean buoy sensor data. First, obtain the buoy position information through the GPS module, including longitude, latitude, altitude, speed, and direction, with a sampling period of 60 seconds. Then collect hydrological parameters, including measuring the water temperature with a temperature sensor with a measurement range of -20 to 50 degrees Celsius and an accuracy of 0.1 degrees Celsius, with a sampling period of 300 seconds; measuring the salinity with a salinity sensor with a measurement range of 0 to 40‰ and an accuracy of 0.01‰, with a sampling period of 300 seconds; measuring the dissolved oxygen content with a dissolved oxygen sensor with a measurement range of 0 to 20mg / L and an accuracy of 0.1mg / L, with a sampling period of 300 seconds. Then collect sea condition data, including measuring the wave height with a wave height sensor with a measurement range of 0 to 20 meters and an accuracy of 0.1 meters, with a sampling period of 60 seconds; measuring the wind speed with a wind speed sensor with a measurement range of 0 to 60 meters per second and an accuracy of 0.1 meters per second, with a sampling period of 60 seconds; measuring the air pressure with an air pressure sensor with a measurement range of 800 to 1100 hPa and an accuracy of 0.1 hPa, with a sampling period of 300 seconds. Finally, preprocess the collected data, including outlier detection, data smoothing, and timestamp addition. Outlier detection uses the 3-fold standard deviation method, and data smoothing uses a 5-point moving average. This step obtains comprehensive ocean environment monitoring data through multi-sensor collaborative acquisition.

[0027] The specific implementation of step S03 is to establish a compression conversion gain index model. First, a resource consumption equation is constructed, and the resource consumption index is calculated using the weighted summation method. The weight coefficients are determined by the fuzzy analytic hierarchy process. The weight of the data compression rate is 0.3, the weight of the processor occupancy rate is 0.2, the weight of the memory usage rate is 0.2, and the weight of the power consumption rate is 0.3. Then, a transmission efficiency equation is constructed, and the normalization method is used to unify each parameter to the range of 0 to 1. The channel capacity is calculated using the Shannon formula, the bit error rate threshold is set to 10⁻ 6 , the signal-to-noise ratio threshold is set to 10 dB, the transmission delay threshold is set to 1 s, and the channel utilization threshold is set to 80%. Then, a quality evaluation equation is constructed, and the multi-objective optimization method is used to comprehensively evaluate the data quality. The data integrity requirement is greater than 99%, the compression distortion is evaluated using the mean square error, and the threshold is set to 0.01. The transmission success rate requirement is greater than 95%, the link stability is evaluated by the signal fluctuation variance, and the recovery cost is calculated according to the amount of retransmitted data. Finally, the genetic algorithm is used to solve the optimization equations. The population size is set to 100, the number of iterations is 1000, the crossover probability is 0.8, and the mutation probability is 0.1. This step realizes the quantitative evaluation of data transmission efficiency by establishing a mathematical model.

[0028] The specific implementation of step S04 is to divide the multi-level transmission data. First, it is classified according to the data segment compression rate. The data with a compression rate greater than 80% is the first-level data, the data with a compression rate between 60% and 80% is the second-level data, the data with a compression rate between 40% and 60% is the third-level data, and the data with a compression rate less than 40% is the fourth-level data. Then, it is further divided according to the transmission signal quality. The data with a signal-to-noise ratio greater than 15 dB is classified as high priority, the data with a signal-to-noise ratio between 10 and 15 dB is classified as medium priority, and the data with a signal-to-noise ratio less than 10 dB is classified as low priority. Then, it is adjusted according to the bit error rate. The data with a bit error rate less than 10⁻ 6 maintains the original priority, the data with a bit error rate between 10⁻ 6 and 10⁻ 4 has its priority reduced by one level, and the data with a bit error rate greater than 10⁻ 4 has its priority reduced by two levels. Finally, it is optimized according to the energy consumption, and the dynamic programming algorithm is used to calculate the optimal transmission strategy, and the data is divided into three categories: emergency transmission, regular transmission, and delayed transmission. This step realizes the reasonable hierarchical transmission of data through multi-dimensional evaluation.

[0029] The specific implementation of step S05 is to establish a data compression model. First, perform feature analysis on the original data, including data distribution characteristics, temporal correlation, and spatial correlation, and use statistical methods to calculate the entropy value, autocorrelation coefficient, and cross-correlation coefficient of the data. Then, select a compression algorithm according to the data characteristics. For continuously varying data such as temperature and salinity, use differential coding combined with Huffman coding. For fluctuating data such as wave height and wind speed, use wavelet transform combined with run-length coding. For position information, use delta coding combined with arithmetic coding. Next, optimize the parameters of each compression algorithm. Set the prediction order of differential coding to 3, use the db4 wavelet for wavelet transform, the decomposition level to 3, and the threshold to 0.5 times the standard deviation. Set the minimum number of repetitions of run-length coding to 4. Finally, establish a compression algorithm evaluation mechanism, including three indicators: compression ratio, computational complexity, and reconstruction error, and dynamically adjust the compression strategy according to the real-time monitoring results. This step realizes the efficient compression of data through algorithm selection and optimization.

[0030] The specific implementation of step S06 is hierarchical compression processing. First, compress the first-level data using a lossless compression scheme, including differential coding preprocessing, and then use arithmetic coding to achieve the final compression, with a compression ratio requirement of greater than 2:1. Then, compress the second-level data using an approximately lossless compression scheme, including wavelet transform preprocessing, retaining the first 90% of the coefficients in absolute value of the high-frequency coefficients, and then using Huffman coding for compression, with a compression ratio requirement of greater than 4:1. Next, compress the third-level data using a lossy compression scheme, including discrete cosine transform preprocessing, retaining the coefficients with an energy ratio of 90%, and then using run-length coding for compression, with a compression ratio requirement of greater than 8:1. Finally, compress the fourth-level data using a high compression ratio scheme, including block processing with a block size of 8×8, and then using vector quantization for 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.

[0031] 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 storage units, including a cache area, a main storage area, and a backup storage area. The cache uses a solid-state drive with a capacity of 128 GB, the main storage uses a large-capacity hard disk with a capacity of 1 TB, and the backup storage uses flash memory with a capacity of 256 GB. Then, a data storage index is established, and the B+ tree structure is used to organize the index. The leaf nodes store data block pointers, and the non-leaf nodes store key value ranges. The order of the tree is set to 128, and the compression conversion gain index is used as the key value. Next, data block management is implemented. The least recently used algorithm is used for cache replacement, the cache block size is set to 4 KB, and the write-back strategy is used for writing. When the cache utilization rate exceeds 90%, replacement is triggered. Finally, a data consistency maintenance mechanism is established. The two-phase commit protocol is used to ensure data integrity, and the asynchronous logging method is used to record the operation history. The log files roll over daily and are kept for 30 days. This step ensures the reliable storage of data by establishing an efficient storage mechanism.

[0032] The specific implementation of step S08 is to establish a data transmission priority queue. First, a multi-level feedback queue is constructed, with 8 priority queues set. Each queue adopts the first-in-first-out strategy, and preemptive scheduling is used between queues. Then, a priority mapping rule is designed according to the compression conversion gain index. Those with an index greater than 0.8 are mapped to queues 1 and 2, those with an index between 0.6 and 0.8 are mapped to queues 3 and 4, those with an index between 0.4 and 0.6 are mapped to queues 5 and 6, and those with an index less than 0.4 are mapped to queues 7 and 8. Next, 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 raised by one level. The threshold is initially set to 300 seconds and decreases with the waiting time. Finally, a priority queue monitoring mechanism is established to record metrics such as queue length, waiting time, and processing time. When the backlog of a certain queue exceeds 1000 data packets or the waiting time exceeds 600 seconds, an alarm is triggered. This step realizes the orderly transmission of data through reasonable queue management.

[0033] The specific implementation of step S09 is to monitor the communication link status. First, a signal strength detection mechanism is established. The pilot signal is used to measure the signal strength, with a measurement period of 1 second. The mean and variance of the signal strength are calculated, and the signal strength alarm threshold is set to -85 dBm. Then, bandwidth detection is implemented. The probing packet technology is used to measure the available bandwidth. The size of the probing packet is 1 KB, and the sending interval is 10 seconds. The round-trip delay and packet loss rate are calculated through timestamps. Next, a link quality assessment mechanism is established. Considering the signal strength, bandwidth utilization, packet loss rate, and delay jitter comprehensively, 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 status report mechanism is implemented. The link status report is generated regularly, with a report period of 60 seconds, including basic parameter statistics, quality rating, and trend analysis. This step monitors the link status changes in real time.

[0034] 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 the signal power value range from 0 to 30 dBm, the noise power is set to -100 dBm, the upper limit of bandwidth utilization is 95%, and the channel gain is 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, with a window size of 100 sampling points. The autoregressive moving average model is used to predict the future bit error rate. Next, the optimal transmission rate is calculated using the transmission rate optimization equation. The constraint conditions include that the remaining cache capacity is not less than 10% and the remaining battery 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 size is 10% of the current rate. The additive increase multiplicative decrease strategy is adopted. When the network quality improves, the rate is linearly increased, and when congestion occurs, it is exponentially backed off. This step optimizes the transmission performance through adaptive control.

[0035] The specific implementation of step S11 is to establish a transmission interruption warning mechanism. First, a historical data analysis model is established. Using the time series analysis method, the historical transmission interruption data is statistically analyzed, and the interruption characteristic parameters are extracted, including the interruption frequency, duration, and impact range. Then, an early warning index system is constructed, with three-level early warning levels set. According to indicators such as link status parameters, compression conversion gain index, and transmission queue status, a support vector machine algorithm is used to establish an early warning model, and the number of training samples is 10,000. Next, an early warning trigger mechanism is implemented. When the early warning level reaches above level two, the data transmission backup program is started, and the data that has not been transmitted completely is backed up to local storage, and the transmission breakpoint information is recorded. Finally, an interruption event recording mechanism is established. Information such as the interruption occurrence time, duration, and impact range is recorded, stored in a structured log format, and an interruption cause analysis mechanism is established. This step reduces the losses caused by transmission interruptions through the warning mechanism.

[0036] The specific implementation of step S12 is to resume the interrupted transmission. First, locate the interruption point according to the recorded transmission position information, and use binary search to quickly locate the data break point. Then, verify the data integrity and confirm the correctness of the transmitted data using the MD5 checksum. Next, analyze the link status parameters to evaluate whether the current network environment meets the transmission conditions, including that the signal strength is not lower than -85 dBm, the signal-to-noise ratio is not lower than 10 dB, and the available bandwidth is not lower than 50% of the link capacity. Then, formulate a transmission recovery strategy. For high-priority data, resume the transmission immediately. For low-priority data, use the backoff algorithm with an initial backoff time of 1 second and a maximum backoff time of 60 seconds. Finally, establish a recovery monitoring mechanism to monitor the network quality, transmission rate, and completion progress during the resumed transmission. When the resumed transmission fails, record the failure reason and trigger the retry mechanism with a maximum retry count of 3 times. This step ensures the integrity of data transmission through a reliable recovery mechanism.

[0037] The following details the mathematical models or calculation processes involved in the present invention.

[0038] 1. The resource consumption equation is specifically expressed as follows: ; In the formula, is the resource consumption index; is the data compression ratio; is the processor occupancy rate; is the memory usage rate; is the power consumption change rate; is the weight coefficient and satisfies ; is the error term; the reciprocal term is introduced to reflect the non-linear growth when the processor is approaching saturation, the exponential term represents the rapid growth of memory pressure, and the square term represents the cumulative effect of energy consumption.

[0039] 2. The transmission efficiency equation is specifically expressed as follows: ; In the formula, is the transmission efficiency index; is the signal power; is the noise power; is the bit error rate; is the channel utilization rate; is the transmission delay; is the maximum allowable delay; is the attenuation coefficient; is the weight coefficient and satisfies ; is the error term; the exponential decay is introduced to reflect the serious impact of the bit error rate, the square term represents the non-linear benefit of channel utilization, and the square root term represents the diminishing marginal effect of delay.

[0040] 3. The quality assessment equation is specifically expressed as follows: ; In the formula, is the quality assessment index; is the data integrity; is the mean square error; is the maximum allowable error; is the change of transmission success rate over time; is the link stability; is the recovery cost; is the maximum allowable recovery cost; is the weight coefficient, and it satisfies ; is the error term; the integral term is introduced to represent the cumulative effect of the transmission success rate, and the derivative term reflects the changing trend of the link stability.

[0041] 4. The channel capacity equation is specifically expressed as follows: ; In the formula, 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.

[0042] 5. The bit error rate prediction equation is specifically expressed as follows: ; 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 frequency shift; is the weight coefficient, and it satisfies ; is the error term; the square term is introduced to represent the non-linear effect of signal strength attenuation, the square root term reflects the gradual change characteristic of atmospheric attenuation, and the partial derivative term represents the dynamic change of Doppler frequency shift.

[0043] 6. The transmission rate optimization equation is specifically expressed as follows: ; In the formula, is the optimal transmission rate; is the theoretical channel capacity; The remaining capacity of the cache; 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 the transmission rate.

[0044] 7. The outlier detection equation is specifically expressed as follows: ; In the formula, is the outlier judgment result; is the data to be tested; is the data mean; 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 the data change rate.

[0045] 8. The data smoothing equation is specifically expressed as follows: ; In the formula, is the smoothed data; is the original data; is the weight coefficient and satisfies ; is the attenuation coefficient; is the error term; an exponential decay weight is introduced to emphasize the influence of neighboring points.

[0046] 9. The aging priority calculation equation is specifically expressed as follows: ; In the formula, For the new priority; is the initial priority; For waiting time; Raise the threshold for priority; is the acceleration factor; is the error term; the logarithmic term is introduced to represent the marginal decreasing effect of priority improvement, and the second-order derivative term reflects the accelerated change of waiting time.

[0047] 1. The process of establishing the resource consumption equation is described in detail as follows: First, construct an initial linear model: ; Among them, the resource vector is expressed as: ; Considering that the system performance drops sharply when the processor occupancy is close to 100%, the reciprocal term ; Considering the system thrashing phenomenon after the memory usage exceeds the threshold, the exponential term ; Considering the cumulative effect of power consumption, is replaced by ; Finally, the non - linear equation is obtained: .

[0048] 2. The establishment process of the transmission efficiency equation is described in detail as follows: Based on the Shannon capacity formula: ; Considering the exponential decay effect of the bit error rate: ; Considering the non - linear gain of channel utilization: ; Considering the marginal effect of delay: ; Combined to obtain the final equation.

[0049] 3. The establishment process of the quality evaluation equation is described in detail as follows: First, construct the quality evaluation index matrix: ; Normalize the mean square error: , introducing a square term to enhance the penalty for large errors; Considering the cumulative effect of the transmission success rate: ; Considering the dynamic characteristics of link stability: ; Considering the exponential decay of the recovery cost: ; Finally, combined to obtain the non - linear evaluation equation.

[0050] 4. The establishment process of the channel capacity equation is described in detail as follows: Construct the basic model based on the Shannon capacity theory: ; Introduce the signal - to - noise ratio matrix: ; Considering the mutation characteristics of channel quality, introduce the Sigmoid function: ; Finally, obtain the capacity equation in product form.

[0051] The establishment process of the bit error rate prediction equation is described in detail as follows: Construct the matrix of factors affecting the bit error rate: ; Consider the non-linear attenuation of the signal strength: ; Consider the gradual change characteristics of the atmospheric attenuation: ; Introduce the dynamic change of the Doppler frequency shift: ; Through the weight matrix Combine each item.

[0052] The establishment process of the transmission rate optimization equation is described in detail as follows: First, determine the rate upper limit: ; Construct the constraint condition items: ; Introduce the exponential decay of the bit error rate: ; Introduce the non-linear constraint of the power consumption: ; Finally, obtain the optimization equation considering multiple constraints.

[0053] The establishment process of the outlier detection equation is described in detail as follows: Construct the basic detection standard: ; Introduce the data change rate matrix: ; Design the adaptive threshold function: ; Finally, obtain the dynamic threshold detection equation.

[0054] The establishment process of the data smoothing equation is described in detail as follows: Construct the sliding window: ; Design the exponentially decaying weights: ; Normalize the weights: ; Finally, obtain the non-uniform weighted smoothing equation.

[0055] The establishment process of the aging priority calculation equation is described in detail as follows: Construct the basic priority model: ; Consider the logarithmic growth of the waiting time: ; Introduce the acceleration term: ; Finally, a priority calculation equation with acceleration characteristics is obtained.

[0056] Specifically, the principle of the present invention is as follows: The technical principle of the present invention is based on the data compression theory and the adaptive system control principle. The design of the compression conversion gain index stems from the entropy coding theory in information theory. By establishing the mapping relationship between data characteristics and compression effects, the quantitative evaluation of compression efficiency is realized. This index is calculated through an optimized equation set, where the resource consumption equation evaluates the computational overhead of the compression process, the quality evaluation equation measures the data fidelity after compression, and the transmission efficiency equation considers the impact of the compression result on the transmission performance. The synergistic effect of these three equations enables the system to select the optimal compression strategy in a complex data environment.

[0057] In terms of the compression algorithm design, the present invention adopts a hierarchical processing architecture. The system first analyzes the characteristics of the original data, including data distribution characteristics, time correlation, space correlation, etc. Based on these characteristics, the system selects the most suitable compression method from a preset algorithm library. The algorithm library contains compression algorithms optimized for different data types, such as Fourier transform compression for periodic data, wavelet transform compression for burst data, etc. At the same time, the system also designs a parameter adaptive mechanism that can adjust the algorithm parameters in real time according to the compression effect.

[0058] The compression optimization mechanism of the present invention is based on the feedback control theory. The system forms a complete feedback loop by real-time monitoring of compression effects, including indicators such as compression ratio, computational time consumption, and compression quality. These feedback information is used to adjust the compression strategy and parameters to ensure that the system always operates in the optimal state. The design of the entire technical solution follows the basic principle of data compression, and through multi-level optimization and control, the efficient compression of ocean buoy data is realized.

[0059] A specific embodiment 1 of the present invention is provided below. The specific implementation of each step in this embodiment 1 is described in detail as follows.

[0060] The specific implementation of step S01 is to construct a low-earth orbit satellite network communication link. First, collect the ocean buoy position information 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 calculate the visible satellite list according to the low-earth orbit satellite orbit parameters. The satellite visibility matrix is expressed as: , where is the satellite elevation angle, with a range of 0 to 90 degrees, is the azimuth angle, with a range of 0 to 360 degrees, is the signal-to-noise ratio, ranging from 0 to 60 decibels. Then, a physical layer connection is established, and adaptive coding and modulation technology is adopted. The modulation mode selection matrix is: , where is the signal-to-noise ratio threshold, is the modulation mode, is the coding efficiency. Finally, a link layer connection is established, and an improved selective repeat ARQ protocol is adopted. The sliding window size is dynamically adjusted according to the round-trip delay : , where is the channel capacity, is the maximum segment size, set to 1024 bytes. This step ensures communication reliability and efficiency through multi-level link establishment.

[0061] The specific implementation of step S02 is to collect ocean buoy sensor data. First, collect the ocean environmental parameter matrix: , temperature ranges from -20 to 50 degrees Celsius, salinity ranges from 0 to 40‰, dissolved oxygen ranges from 0 to 20 mg / L. Then, collect the sea condition data matrix: , wave height ranges from 0 to 20 meters, wind speed ranges from 0 to 60 meters per second, air pressure ranges from 800 to 1100 hPa. Then, preprocess the original data. The outlier detection uses the 3-sigma method: , where is the data to be detected, is the data mean, is the data standard deviation, is the sensitivity coefficient, with a value of 0.1. Finally, perform data smoothing: , where is the weight coefficient, is the attenuation coefficient, with a value of 0.5. This step obtains reliable monitoring data through multi-sensor collaborative acquisition and data preprocessing.

[0062] The specific implementation of step S03 is to establish a compression conversion gain index model. First, construct the resource consumption equation: , where the resource vector is expressed as: . Then, establish the transmission efficiency equation: . Next, construct the quality evaluation equation: Finally, the genetic algorithm is used to solve the optimization equations. The population size is set to 100, the crossover probability is 0.8, the mutation probability is 0.1, and the maximum number of iterations is 1000. This step realizes the quantitative evaluation of data transmission efficiency by establishing a mathematical model.

[0063] The specific implementation of step S04 is to divide the data into multiple levels for transmission. First, a data feature matrix is constructed: , where is the compression ratio, is the signal quality, is the bit error rate, is the energy consumption. Then, a preliminary classification is performed according to the compression ratio, and a classification threshold matrix is set: . Next, the priority is adjusted based on the signal quality, and the signal quality evaluation function is: . Finally, the 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 the reasonable hierarchical transmission of data through multi-dimensional evaluation and optimization.

[0064] 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 coding preprocessing is performed on the continuously varying data: , and Huffman coding is performed on the preprocessed data to construct a probability distribution matrix: . Next, wavelet transform is performed on the fluctuating data, and the discrete wavelet transform basis function is selected: , where is the scale parameter, is the translation parameter, and the wavelet coefficient is: . Finally, an evaluation index system for the compression algorithm is established, including the compression ratio , the computational complexity , and the reconstruction error . This step realizes the efficient compression of data through the combination of multiple compression algorithms.

[0065] The specific implementation of step S06 is hierarchical compression processing. First, lossless compression is performed on the primary data, and differential coding preprocessing is used: , and then arithmetic coding is used for compression. The cumulative probability distribution function is: , and the coding interval is: . Then, near-lossless compression is performed on the secondary data, and the wavelet transform coefficient selection matrix is: , retain the coefficients with the top 90% of the energy ratio. Then perform lossy compression on the three-level data, discrete cosine transform: . Finally, perform high compression ratio processing on the four-level data, vector quantization codebook design: . This step balances the compression ratio and data quality through a hierarchical compression strategy.

[0066] The specific implementation of step S07 is to establish a data caching mechanism. First, construct a storage hierarchy matrix: . Then establish a B+ tree index structure, node structure: , where is the compression conversion gain exponent, is the data block pointer. Then 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 the reliable storage of data by establishing an efficient storage mechanism.

[0067] The specific implementation of step S08 is to establish a data transmission priority queue. First, construct a multi-level feedback queue structure, queue state matrix: . Then design a priority mapping rule, compression conversion gain exponent mapping function: . Then 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 metric matrix: . This step realizes the orderly transmission of data through reasonable queue management.

[0068] The specific implementation of step S09 is to monitor the communication link status. First, establish a signal strength detection mechanism, construct a signal feature matrix: , signal strength calculation formula: , where is the received power, is the transmitted power, is the transmit gain, is the receive gain, is the path loss, is other losses. Then implement bandwidth detection, using the probe packet technology, probe packet timestamp matrix: , round-trip delay calculation: , available bandwidth estimation: . Then establish a link quality assessment mechanism, fuzzy comprehensive evaluation matrix: Finally, implement a status reporting mechanism, with the report content matrix: This step monitors in real time to grasp the changes in the link status.

[0069] The specific implementation of step S10 is to dynamically adjust the transmission rate. First, based on the channel capacity equation: Calculate the upper limit of the theoretical transmission rate. Then, according to the bit error rate prediction equation: Estimate the channel quality. Next, use the transmission rate optimization equation: Calculate the optimal transmission rate. Finally, implement a rate adjustment mechanism, with the adjustment step matrix: This step optimizes the transmission performance through adaptive control.

[0070] The specific implementation of step S11 is to establish a transmission interruption warning mechanism. First, construct a historical data analysis model, with the interruption feature matrix: Then, establish a warning index system, with the support vector machine model: , where is the kernel function, and the radial basis kernel function is selected: Next, implement a warning trigger mechanism, with the warning level matrix: Finally, establish an interruption event recording mechanism, with the event log structure: This step reduces the losses caused by transmission interruptions through the warning mechanism.

[0071] The specific implementation of step S12 is to resume interrupted transmission. First, use binary search to locate the data breakpoint, with the search process status matrix: Then, perform data integrity verification, with the MD5 check matrix: Next, formulate a transmission recovery strategy, with the backoff time calculation: , where is the base backoff time, set to 1 second, is the number of retry attempts, is the maximum backoff time, set to 60 seconds. Finally, establish a recovery monitoring mechanism, with the monitoring status matrix: This step ensures the integrity of data transmission through a reliable recovery mechanism.

[0072] To better understand and implement the present invention, the following provides Example 2 of a specific application scenario of the present invention: Researchers deployed an ocean ecological environment monitoring buoy system in the Yellow Sea. This system includes various types of sensor devices such as meteorological, hydrological, and water quality sensors, and uses low-earth orbit satellite communication to achieve data backhaul. As shown in Table 1: Table 1 Ocean buoy sensor configuration table

[0073] The system realizes real-time data acquisition and transmission through the following steps.

[0074] First, construct a low-earth orbit satellite network communication link and achieve a stable two-way communication connection through adaptive coding and modulation technology. The channel capacity equation is: , where is set to 400 kHz, is set to 6 dB. As shown in Table 2: Table 2 Communication Link Parameter Configuration Table

[0075] Then, collect ocean environment data based on multiple sensors. The measurement range of the temperature sensor is -20 to 50 degrees Celsius, and the accuracy is 0.1 degree Celsius; the measurement range of the salinity sensor is 0 to 40‰, and the accuracy is 0.01‰; the measurement range of the barometric pressure sensor is 800 to 1100 hPa, and the accuracy is 0.1 hPa. As shown in Table 3: Table 3 Data Preprocessing Parameter Table

[0076] Next, establish a compression conversion gain index model. The resource consumption equation is: , where , , , . As shown in Table 4: Table 4 Compression Efficiency Evaluation Table

[0077] Divide the data into multi-level transmission data according to the compression conversion gain index. Based on the compression ratio, signal quality, bit error rate, and energy consumption for division, the dynamic programming state transition matrix is shown in Table 5: Table 5 Data Hierarchical Transmission Matrix

[0078] Establish a data compression model. Meteorological and hydrological data use differential coding combined with Huffman coding, water quality data use wavelet transform combined with run-length coding, and image data use discrete cosine transform combined with vector quantization. As shown in Table 6: Table 6 Compression Algorithm Configuration Table

[0079] Perform hierarchical compression processing on the multi-level transmission data and adopt different compression strategies according to different levels. As shown in Table 7: Table 7 Hierarchical Compression Processing Strategy Table

[0080] Establish a data caching mechanism and adopt a hierarchical storage structure. As shown in Table 8: Table 8 Storage Hierarchy Table

[0081] Establish a data transmission priority queue and adopt an aging algorithm: , where is set to 300 seconds, is set to 0.1. As shown in Table 9: Table 9 Queue Priority Configuration Table

[0082] Monitor the communication link status and implement signal strength and bandwidth detection. The bit error rate prediction equation is: , where , , , . As shown in Table 10: Table 10 Link Status Monitoring Parameter Table

[0083] Dynamically adjust the transmission rate according to the signal strength and link bandwidth. The transmission rate optimization equation is: , where , . As shown in Table 11: Table 11 Transmission Rate Adjustment Strategy Table

[0084] Establish a transmission interruption warning mechanism and adopt a support vector machine algorithm to establish a warning model. As shown in Table 12: Table 12 Warning Mechanism Configuration Table

[0085] When a transmission interruption occurs, the system records the interruption information and waits for the link to recover. As shown in Table 13: Table 13 Interruption Recovery Strategy Table

[0086] Through actual application tests, the actual operation effect of the system in the Yellow Sea area is shown in Table 14: Table 14 System Operation Effect Comparison Table

[0087] In this embodiment, by establishing a compression conversion gain index model, the quantitative evaluation of data compression efficiency is realized; through a multi-level data hierarchical transmission mechanism, the reasonable allocation of resources is achieved; through the cooperation of multiple compression algorithms, the overall compression efficiency is improved. The operation results of the system show that this method can effectively solve the technical problem of low compression efficiency caused by poor algorithm adaptability in the process of ocean buoy data compression.

[0088] Traditional ocean buoy data compression methods mainly rely on a single compression algorithm and cannot dynamically adjust the compression strategy according to data characteristics and network conditions, resulting in low compression efficiency. In this invention, by establishing a compression conversion gain index model, the quantitative evaluation of data compression efficiency is realized; through a multi-level data hierarchical transmission mechanism, the reasonable allocation of resources is achieved; through the cooperation of multiple compression algorithms, the overall compression efficiency is improved. After testing, compared with the traditional method, the data compression efficiency of this invention has increased by 45%, the transmission success rate has increased by 35%, and the system resource utilization rate has increased by 40%.

[0089] It should be noted that the detailed explanations of the variables involved in this invention are shown in Table 15 below.

[0090] Table 15 Variable Explanation Table

[0091] The above is only the specific implementation manner of this invention, but the protection scope of this invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by this invention can easily think of changes or substitutions, which should all be covered within the protection scope of this invention.

Claims

1. A real-time data acquisition method for ocean buoys based on low-earth orbit satellite communication, characterized in that, It includes the following steps: Construct a low-earth orbit satellite network communication link to establish a two-way communication connection between the ocean buoy and the land data center; collect ocean buoy sensor data to obtain buoy position information, hydrological parameters, and sea condition data as raw data; establish a compression conversion gain index model, and calculate the data compression priority through optimizing the equations; divide the raw data into multi-level transmission data according to the compression conversion gain index model; establish a data compression model, and select a compression algorithm according to the characteristics of the multi-level transmission data; perform hierarchical compression processing on the multi-level transmission data; establish a data caching mechanism and a transmission priority queue; monitor the communication link status and dynamically adjust the transmission rate.

2. The real-time ocean buoy data acquisition method based on low-earth orbit satellite communication according to claim 1, characterized in that The compression conversion gain index model includes a resource consumption equation, a transmission efficiency equation, and a quality evaluation equation; the input of the resource consumption equation includes the data compression rate, processor occupancy rate, memory usage rate, and power consumption rate, and the output is the resource consumption index; the input of the transmission efficiency equation includes the channel capacity, bit error rate, signal-to-noise ratio, transmission delay, and channel utilization rate, and the output is the transmission efficiency index; the input of the quality evaluation equation includes data integrity, compression distortion, transmission success rate, link stability, and recovery cost, and the output is the quality evaluation index.

3. The real-time data acquisition method for ocean buoys based on low-earth orbit satellite communication according to claim 2, wherein The multi-level transmission data is divided based on the data segment compression rate, transmission signal quality, bit error rate, and energy consumption; the dynamic programming algorithm is used to divide the data into three categories: emergency transmission, regular transmission, and delayed transmission.

4. The real-time ocean buoy data acquisition method based on low-earth orbit satellite communication according to claim 3, characterized in that The data compression model uses differential coding combined with Huffman coding for temperature and salinity, wavelet transform combined with run-length coding for wave height and wind speed, and delta coding combined with arithmetic coding for position information.

5. The real-time data acquisition method of the ocean buoy based on low-orbit satellite communication according to claim 4, characterized in that, The hierarchical compression processing includes: performing differential coding preprocessing and arithmetic coding compression on the first-level data; performing wavelet transform preprocessing and Huffman coding compression on the second-level data; performing discrete cosine transform preprocessing and run-length coding compression on the third-level data; performing block processing and vector quantization compression on the fourth-level data.

6. The real-time marine buoy data acquisition method based on low-orbit satellite communication according to claim 5, 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; uses a B+ tree structure to organize the index, with the compression conversion gain index as the key value; and adopts the least recently used algorithm for cache replacement.

7. The real-time data acquisition method for ocean buoys based on low-earth orbit satellite communication according to claim 6, characterized in that The transmission priority queue adopts a multi-level feedback queue structure, with 8 priority queues set; and adopts an aging algorithm to prevent low-priority data starvation.

8. The real-time data acquisition method for ocean buoys based on low-earth orbit satellite communication according to claim 7, characterized in that The link evaluation equations include 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 rate, and channel gain; the input of the bit error rate prediction equation includes the historical bit error rate, signal strength, atmospheric attenuation, and Doppler shift; the input of the transmission rate optimization equation includes the theoretical channel capacity, predicted bit error rate, cache capacity, and energy constraint.

9. The real-time data acquisition method for ocean buoys based on low-earth orbit satellite communication according to claim 8, characterized in that, It also includes establishing a transmission interruption warning mechanism and a recovery mechanism, where the transmission interruption warning mechanism uses time series analysis method to statistically analyze the historical transmission interruption data; uses the support vector machine algorithm to establish a warning model; and records the interruption occurrence time, duration, and affected range information.

10. The real-time data acquisition method for ocean buoys based on low-earth orbit satellite communication according to claim 9, wherein, The resumption of interrupted transmission uses binary search to locate the data breakpoint; uses MD5 checksum to confirm the correctness of the transmitted data; uses a backoff algorithm for transmission recovery and retries in case of failed recovery transmission.

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