Method, electronic device and medium for transmitting meteorological and hydrological data in the deep sea and the open sea

Abstract

The present invention provides a method, an electronic device, and a medium for transmitting meteorological and hydrological data in the deep and far seas. The method includes: obtaining a real-time data segment of a preset length; determining the data characteristics of the real-time data segment, where the data characteristics include: the mean value, variance, time gradient, baseline standard deviation, power spectral density, signal-to-noise ratio, and autocorrelation of the data in the real-time data segment; determining the data type of the real-time data segment according to the data characteristics of the real-time data segment, where the data type includes transient data, slow-varying data, and periodic data; performing data compression on the real-time data segment according to the data type of the real-time data segment to obtain a compression result, where different data compression methods are used for different data types; and transmitting the compression result. This method can improve the transmission efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and more particularly to a method, an electronic device, and a medium for transmitting meteorological and hydrological data in the far-reaching sea areas. Background Art

[0002] With the advancement of far-reaching sea exploration activities, the amount of hydrometeorological element data in far-reaching sea areas is continuously increasing. These data include information such as air temperature, air pressure, wind speed, seawater temperature, salinity, depth, etc. The real-time collection and transmission of these data are crucial for supporting the efficient operation and intelligent development of far-reaching sea projects.

[0003] In the related art, there are many defects in the transmission of far-reaching sea meteorological and hydrological data, which restrict the efficient transmission of data. Specifically, in order to adapt to the increasing amount of data and improve the data transmission efficiency, when transmitting far-reaching sea meteorological and hydrological data, it is usually necessary to compress the data. However, the existing compression algorithms (such as LZW, DEFLATE, etc.) do not optimize the mutation and periodic characteristics of meteorological and hydrological data, and the compression effect is limited, resulting in low transmission efficiency under limited bandwidth conditions.

[0004] In view of this, the present invention is specifically proposed. Summary of the Invention

[0005] The present invention is proposed in view of the above problems. According to one aspect of the present invention, there is provided a method for transmitting far-reaching sea meteorological and hydrological data, including: obtaining a real-time data segment of a preset length; determining the data characteristics of the real-time data segment, where the data characteristics include: the mean value, variance, time gradient, baseline standard deviation, power spectral density, signal-to-noise ratio, and autocorrelation of the data in the real-time data segment; determining the data type of the real-time data segment according to the data characteristics of the real-time data segment, where the data type includes transient data, slow-changing data, and periodic data; compressing the real-time data segment according to the data type of the real-time data segment to obtain a compression result, where different data compression methods are used for different data types; and transmitting the compression result.

[0006] Exemplarily, determining the data type of the real-time data segment according to the data characteristics of the real-time data segment includes: when the real-time data segment satisfies the following conditions, determining that the data type of the real-time data segment is slow-changing data: ; where is the variance of the data in the real-time data segment, is the mean value of the data in the real-time data segment, is the power spectral density of the data in the real-time data segment; and / or, when the real-time data segment satisfies the following conditions, determining that the data type of the real-time data segment is transient data: ; where is the time gradient of the data in the real-time data segment, is the baseline standard deviation of the data in the real-time data segment, is the power spectral density of the data in the real-time data segment; and / or, when the real-time data segment meets the following conditions, determine that the data type of the real-time data segment is periodic data: ; where, is the power spectral density of the data in the real-time data segment, is the signal-to-noise ratio of the data in the real-time data segment, is the autocorrelation of the data in the real-time data segment.

[0007] Exemplarily, according to the data type of the real-time data segment, data compression is performed on the real-time data segment, including: when the data type of the real-time data segment is unique, if the data type of the real-time data segment is slowly varying data, the real-time data segment is compressed by using the residual coding based on linear prediction, where the compression result is the residual value greater than the residual threshold; if the data type of the real-time data segment is transient data, the real-time data segment is compressed by using the sparse representation based on wavelet transform, where the compression result is the sparse coefficient; if the data type of the real-time data segment is periodic data, the real-time data segment is compressed by using the Fourier parametric compression, where the compression result is the amplitude, phase and frequency of some frequency components in the real-time data segment, and the sum of the energies of some frequency components is greater than the energy threshold.

[0008] Exemplarily, the residual threshold is positively correlated with the standard deviation and range of the residual values of each data point in the real-time data segment; and / or, the admissible error of the wavelet transform is positively correlated with the baseline error of the data in the real-time data segment and the L2 gradient norm of the data in the real-time data segment; and / or, the energy threshold is determined based on the total energy and the main frequency energy ratio of the real-time data segment, and the energy threshold is positively correlated with the total energy and the main frequency energy ratio of the real-time data segment.

[0009] Exemplarily, according to the data type of the real-time data segment, data compression is performed on the real-time data segment, including: when the data type of the real-time data segment is not unique, a segmentation strategy that minimizes the objective function is selected to segment the real-time data segment to obtain multiple sub-data segments, where the objective function is the weighted sum of the KL divergence of the sub-data segments obtained after segmentation and the number of compression bits, and the KL divergence is the matching degree between the corresponding sub-data segment and the expected distribution of the compression model of the sub-data segment, and the compression model of the sub-data segment is the compression model corresponding to the data type of the sub-data segment; according to the data type of each sub-data segment in the multiple sub-data segments obtained after segmentation, data compression is performed on the multiple sub-data segments respectively, where the compression result includes the results obtained by performing data compression on the multiple sub-data segments respectively.

[0010] Exemplarily, before transmitting the compression result, the method further includes: determining a reliability weight of the compression result according to the priority and urgency of the compression result; determining a transmission mode of the compression result based on the priority and the reliability weight; and / or calculating a transmission deadline of the compression result according to the urgency of the compression result; wherein, when the transmission time of the compression result is greater than or equal to the transmission deadline, switching the transmission mode of the compression result to a degraded transmission mode; wherein, when the transmission mode of the compression result is the degraded transmission mode, only transmitting the low-frequency part of the compression result; and / or selecting a satellite link according to the data type of the compression result; determining an initial number of redundant packets during the transmission according to the estimated packet loss rate and the channel reliability factor of the selected satellite link.

[0011] Exemplarily, determining the initial number of redundant packets during the transmission according to the estimated packet loss rate and the channel reliability factor of the selected satellite link is performed when the estimated packet loss rate of the current satellite link is greater than the packet loss rate threshold.

[0012] Exemplarily, the method further includes: monitoring the channel quality and bandwidth usage during the data transmission process; adjusting the bandwidth during the transmission of the compression result according to the priority of the compression result, the channel quality, and the bandwidth usage.

[0013] According to another aspect of the present invention, there is provided an electronic device, including a processor and a memory, where a computer program is stored in the memory, and the processor is configured to execute the computer program to implement the method as described above.

[0014] According to still another aspect of the present invention, there is provided a computer-readable storage medium storing a computer program / instruction, and when the computer program / instruction is executed by a processor, the method as described above is implemented.

[0015] In the above technical solution, by classifying real-time data segments and selecting the best compression method in real time according to the data type of the real-time data segments for compression, this adaptive compression method can achieve compression optimization for the slow-changing, sudden, and periodic characteristics of deep-sea and open-sea meteorological and hydrological data, make full use of the potential compression space of the data, which helps to improve the data compression effect and compression efficiency, reduce the amount of transmitted data, and reduce the bandwidth resources required during the transmission, so as to achieve efficient transmission of deep-sea and open-sea meteorological and hydrological data under limited bandwidth conditions.

[0016] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly, it can be implemented according to the content of the description. And in order to make the above and other purposes, features, and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention are specifically exemplified below. Description of the Drawings

[0017] The embodiments of the present invention will be described in more detail with reference to the accompanying drawings, and the above and other objects, features, and advantages of the present invention will become more apparent. The drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings, the same reference numerals generally represent the same components or steps.

[0018] Figure 1 A schematic flowchart showing a method for transmitting deep - sea and far - sea meteorological and hydrological data according to an embodiment of the present invention;

[0019] Figure 2 A schematic block diagram showing an electronic device according to an embodiment of the present invention. Detailed implementation manners

[0020] In order to make the objectives, technical solutions, and advantages of the present invention more apparent, exemplary embodiments according to the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited by the exemplary embodiments described herein. Based on the embodiments of the present invention described herein, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present invention.

[0021] As described above, there are many deficiencies in the transmission of meteorological and hydrological data in the deep and far sea areas, which restrict the efficient transmission of data. These deficiencies include: limited data compression effect, bandwidth waste and unnecessary redundant transmission during transmission, and poor transmission effect of the adopted transmission protocol. Specifically, first, traditional data transmission methods usually directly transmit raw data, failing to fully utilize the temporal correlation, periodicity and spatial redundancy of the data, resulting in a large amount of bandwidth waste and unnecessary redundant transmission. Second, some data transmission processes pre-compress the data before compression. However, existing compression algorithms (such as LZW, DEFLATE, etc.) do not specifically optimize the mutation and periodicity characteristics of meteorological and hydrological data, and the compression effect is limited, resulting in low transmission efficiency under limited bandwidth conditions. In addition, existing transmission protocols (such as TCP and UDP) are difficult to be effectively applied in the satellite link environment with high latency and high packet loss rate. The TCP protocol has low efficiency in high-latency situations, while UDP lacks reliability guarantee and cannot balance real-time performance and data integrity, resulting in data loss and unstable transmission quality. To solve at least one of the above problems, the present invention proposes a transmission method, an electronic device and a storage medium for deep and far sea meteorological and hydrological data. This method can achieve targeted compression of different meteorological and hydrological data, which helps to improve the data compression effect and reduce the bandwidth resources required during transmission, thereby enabling efficient transmission under limited bandwidth conditions. This method, electronic device and storage medium are described in detail below.

[0022] According to one aspect of an embodiment of the present invention, a transmission method for deep and far sea meteorological and hydrological data is provided. Herein, meteorological and hydrological data includes, but is not limited to, meteorological elements such as air temperature, air pressure, wind direction, wind speed, maximum wind speed, air pressure, 3-hour pressure change, temperature, dew point temperature, visibility, total cloud cover, typhoon warning, typhoon path, etc.; hydrological element data such as waves, tide levels, seawater temperature, flow velocity, flow direction, depth, salinity, etc.

[0023] Figure 1 FIG. shows a schematic flowchart of a transmission method for deep and far sea meteorological and hydrological data according to an embodiment of the present invention. As Figure 1 shown, the method may include the following steps S110, step S120, step S130, step S140 and step S150.

[0024] In step S110, a real-time data segment of a preset length is acquired.

[0025] It can be understood that the real-time data segment is a continuous segment of data obtained from the original data stream of deep-sea meteorological and hydrological data. In some implementation solutions, a sliding window method can be used to obtain a real-time data segment of a preset length (which can be referred to as the first preset length here for easy distinction from the length of the sliding window in the following text) from the original data stream. Of course, it can also be obtained by direct interception, which will not be elaborated.

[0026] The first preset length can be selected according to actual needs. For example, it can be selected as half an hour, 1 hour, 2 hours, etc., which will not be elaborated.

[0027] In step S120, determine the data characteristics of the real-time data segment. The data characteristics include: the mean value, variance, time gradient, baseline standard deviation, power spectral density, signal-to-noise ratio, and autocorrelation of the data in the real-time data segment.

[0028] After obtaining the real-time data segment, the data characteristics of the real-time data segment can be statistically analyzed. Specifically, time-frequency domain characteristics such as the mean value, variance, time gradient, baseline standard deviation, power spectral density, signal-to-noise ratio, and autocorrelation of the data in the real-time data segment can be calculated. These characteristics can more accurately reflect the change trend of the data in the real-time data segment. These data characteristics can all be calculated through traditional calculation methods. For example, the power spectral density and signal-to-noise ratio of the real-time data segment can be obtained through Fourier transform, and the autocorrelation of the data in the real-time data segment can be obtained through the autocorrelation function (ACF), which will not be elaborated.

[0029] In step S130, according to the data characteristics of the real-time data segment, determine the data type of the real-time data segment, where the data type includes transient data, slowly varying data, and periodic data.

[0030] In this embodiment, for the data structure of deep-sea meteorological and hydrological data, it is divided into transient data, slowly varying data, and periodic data. Table 1 below is a data type table for a specific implementation solution of this embodiment.

[0031] Table 1 Data Type Table

[0032]

[0033] As shown in Table 1, different data types are used to represent different data change rules. The characteristic of slowly changing data is that the data changes relatively gently with a small change range, and it is usually suitable for low-frequency acquisition and compression. The characteristic of transient data is that the data changes violently within a short period of time, usually manifested as local sharp fluctuations. Periodic data shows obvious periodic fluctuations and is suitable for periodic modeling and transmission. Since different types of data have different change characteristics, the data type of a real-time data segment can be determined by the data characteristics of the real-time data segment. This data type can provide a relatively accurate basis for the compression, transmission, etc. of the real-time data segment.

[0034] In step S140, according to the data type of the real-time data segment, data compression is performed on the real-time data segment to obtain a compression result, where different data types adopt different data compression methods.

[0035] As mentioned above, different data types have different change rules, so different data compression methods can be adopted to perform targeted compression on them. For example, since the data of slowly changing data changes gently, the data with a large deviation (i.e., a large residual) can be captured by compression as the compression result. Since transient data has high-frequency local mutations, its mutation information can be concentrated on a few coefficients by compression. Since periodic data has obvious periodic fluctuations, its main spectral information rather than the complete data sequence can be captured by compression. Thus, the amount of data required for transmission can be effectively reduced.

[0036] In step S150, the compression result is transmitted. After obtaining the compression result, the compression result can be directly transmitted. Of course, the transmission method of this compression result can also be determined in advance according to information such as the data priority and urgency of the compression result, and the transmission strategy can be dynamically adjusted during data transmission to achieve more efficient data transmission.

[0037] In related technologies, although some solutions compress the transmitted deep-sea meteorological and hydrological data, they usually adopt a fixed compression strategy and cannot optimize for the slow-changing, sudden, and periodic characteristics of deep-sea meteorological and hydrological data, resulting in insufficient data compression efficiency and inability to fully utilize the potential compression space of the data. In the above technical solution, by classifying the real-time data segment and selecting the best compression method in real time according to the data type of the real-time data segment, this adaptive compression method can achieve compression optimization for the slow-changing, sudden, and periodic characteristics of deep-sea meteorological and hydrological data, fully utilize the potential compression space of the data, which helps to improve the data compression effect and compression efficiency, reduce the amount of transmitted data, and reduce the bandwidth resources required during transmission, so as to achieve efficient transmission of deep-sea meteorological and hydrological data under limited bandwidth conditions.

[0038] Exemplarily, determining the data type of the real-time data segment according to the data characteristics of the real-time data segment includes: judging whether the real-time data segment is slowly varying data based on the variance, mean, and power spectral density of the data in the real-time data segment; and / or, judging whether the real-time data segment is transient data based on the time gradient, baseline standard deviation, and power spectral density of the data in the real-time data segment; and / or, judging whether the real-time data segment is periodic data based on the power spectral density, signal-to-noise ratio, and autocorrelation of the data in the real-time data segment.

[0039] In the solution of this example, it is possible to judge whether the data change trend in the real-time data segment is gentle according to the variance, mean, and power spectral density of the data in the real-time data segment. If the change is gentle, it can be determined that at least part of the data in the real-time data segment conforms to the data characteristics of slowly varying data. At this time, it can be considered that the data type of the real-time data segment is slowly varying data. In some embodiments, judging whether the data type of the real-time data segment is slowly varying data based on the variance, mean, and power spectral density of the data in the real-time data segment includes: when the real-time data segment meets the following conditions, determining that the real-time data segment is slowly varying data:

[0040] ;

[0041] Wherein, is the variance of the data in the real-time data segment, is the mean of the data in the real-time data segment, is the power spectral density of the data in the real-time data segment. Wherein, indicates that the data change is relatively stable. indicates that the spectral energy in the real-time data segment is concentrated in the low-frequency (0 - 0.01 Hz) range, indicating that the data shows slow changes.

[0042] In the solution of this example, it is possible to judge whether there are local sharp fluctuations in the real-time data segment according to the time gradient, baseline standard deviation, and power spectral density of the data in the real-time data segment. If so, it can be determined that at least part of the data in the real-time data segment conforms to the data characteristics of transient data. At this time, it can be considered that the data type of the real-time data segment is transient data. In a specific embodiment, judging whether the real-time data segment is transient data based on the time gradient, baseline standard deviation, and power spectral density of the data in the real-time data segment includes: when the real-time data segment meets the following conditions, determining that the data type of the real-time data segment is transient data:

[0043] ;

[0044] Wherein, is the time gradient of the data in the real-time data segment, is the baseline standard deviation of the data in the real-time data segment, is the power spectral density of the data in the real-time data segment. Among them, indicates that the change rate of the data is large and significant mutations have occurred. indicates that the data contains more high-frequency components, representing that the data has strong instantaneous fluctuations.

[0045] In the solution of this example, it is possible to judge whether there are obvious periodic fluctuations in the real-time data segment according to the power spectral density, signal-to-noise ratio, and autocorrelation of the data in the real-time data segment. If so, it is possible to determine that at least part of the data in the real-time data segment conforms to the data characteristics of periodic data. At this time, it can be considered that the data type of the real-time data segment is periodic data. In a specific embodiment, based on the power spectral density, signal-to-noise ratio, and autocorrelation of the data in the real-time data segment, judging whether the real-time data segment is periodic data includes: when the real-time data segment meets the following conditions, determining that the data type of the real-time data segment is periodic data:

[0046] ;

[0047] Among them, is the power spectral density of the data in the real-time data segment, is the signal-to-noise ratio of the data in the real-time data segment, is the autocorrelation of the data in the real-time data segment. indicates that there are significant periodic components in the spectrum of the data. represents that the autocorrelation of the data is high, the periodic volatility is strong, and it is suitable for periodic modeling.

[0048] The above technical solution can accurately determine the data type of the real-time data segment according to the data change rule of the real-time data segment, which can provide a reliable basis for determining the compression and transmission strategy of the real-time data segment in the subsequent steps.

[0049] Exemplarily, according to the data type of the real-time data segment, data compression of the real-time data segment is performed, including: when the data type of the real-time data segment is unique, if the data type of the real-time data segment is slowly varying data, the real-time data segment is compressed by using a residual coding method based on linear prediction, where the compression result is the residual value greater than the residual threshold; if the data type of the real-time data segment is transient data, the real-time data segment is compressed by using a sparse representation method based on wavelet transform, where the compression result is the sparse coefficient; if the data type of the real-time data segment is periodic data, the real-time data segment is compressed by using a Fourier parameterization compression method, where the compression result is the amplitude, phase, and frequency of some frequency components in the real-time data segment, and the sum of the energies of some frequency components is greater than the energy threshold. This way of compressing the real-time data segment according to the data type can be called the Dynamic Adaptive Segmentation Compression (DASC).

[0050] In the above text, the data type in the real-time data segment is determined by the way of whether the judgment condition of the data type is satisfied. In this case, there may be the following possibilities for the data type of the real-time data segment: the real-time data segment only satisfies the judgment condition of one data type, and at this time the data type of the real-time data segment is unique; the real-time data segment satisfies the judgment conditions of multiple data types at the same time, and at this time the data type of the real-time data segment is multiple. For example, the data type of the real-time data segment may be slowly varying data and periodic data; the real-time data segment does not satisfy the judgment conditions of each data type, and at this time the real-time data segment cannot be classified. In this article, the situation where the real-time data segment satisfies the judgment conditions of multiple data types at the same time and the situation where the real-time data segment cannot be classified can be uniformly expressed as the data type of the real-time data segment is not unique.

[0051] In the solution of this embodiment, when the real-time data segment only belongs to slowly varying data, a compression strategy of residual coding based on linear prediction is used to compress the real-time data segment. The reason for adopting this method is that the data change of slowly varying data is gentle, linear prediction can effectively capture the data trend, and the residual part reflects the actual change. When the residual is greater than a certain threshold, the residual is transmitted. Only when there is a large deviation in the data (that is, the residual is large), it is necessary to transmit these deviated parts, thus greatly reducing unnecessary data transmission, especially in the case of slow data change.

[0052] When the real-time data segment only belongs to transient data, a compression strategy of sparse representation based on wavelet transform is used to compress the real-time data segment. Transient data has high-frequency local mutations, and through wavelet transform, it can be converted into a sparse representation, so that most of the information is concentrated on a few coefficients. Thus, under the premise of retaining a large amount of information in the real-time data segment, the amount of data to be transmitted can be effectively reduced.

[0053] When the real-time data segment only belongs to periodic data, a compression strategy of Fourier parametric compression is adopted to compress the real-time data segment. Periodic data has periodic characteristics. Through Fourier transform, the frequency-domain parameters of periodic data (i.e., the amplitude, phase, and frequency of frequency components) can be obtained. When transmitting, only the amplitude, phase, and frequency of several frequency components with relatively concentrated energy are transmitted, rather than the complete data sequence. Thus, the amount of data transmission can be reduced, the transmission efficiency can be improved, and it can be ensured that most of the energy is captured during transmission.

[0054] In the above technical solution, by matching the corresponding compression strategy according to the data type of the real-time data segment, targeted compression of the real-time data segment can be achieved. This compression method can make full use of the potential compression space of the data on the premise of ensuring data quality, thereby improving the compression effect, reducing the amount of data transmission, and further helping to improve the transmission speed and reduce the bandwidth resource loss during data transmission.

[0055] Exemplarily, the real-time data segment includes multiple data points; the method of residual coding based on linear prediction is adopted to compress the real-time data segment, including: for each data point among the multiple data points, the predicted value of this data point is predicted by the following formula:

[0056] ;

[0057] where, is the predicted value, is the prediction coefficient, which can be selected according to actual needs, is the historical data value of the previous data point; is the historical data value of the second data point before this data point;

[0058] Calculate the residual value between the predicted value and the actual data value of this data point;

[0059] When the residual value of this data point is greater than the residual threshold, it is determined that the compression result includes the residual value of this data point.

[0060] The residual value can be calculated by the following formula:

[0061] ;

[0062] where, represents the residual value, that is, the prediction error; represents the actual data value of this data point.

[0063] In the solution of this example, only when the residual value is greater than the residual threshold is it transmitted (i.e., transmit ∣ ∣ > The residual value). The residual threshold can be set to a fixed value as needed. Of course, it can also be an unfixed value that changes dynamically with the data in the real-time data segment, which will not be elaborated here. In a specific embodiment, taking the first data point in the real-time data segment as a reference, the real-time data segment includes 5 data points, and the residual values of each data point are shown in Table 2 below.

[0064] Table 2 Residual Table

[0065]

[0066] The above technical solution can greatly reduce the data transmission volume, and this method is especially suitable for data compression of slowly varying data.

[0067] Exemplarily, the real-time data segment is compressed by using the sparse representation based on wavelet transform, including: determining the sparsest wavelet coefficient combination to obtain the compression result on the premise of satisfying the constraint conditions; wherein, the constraint conditions are: , is the wavelet dictionary, is the sparse coefficient, is the vector representation of the real-time data segment, is the allowable error.

[0068] In the solution of this example, is the vector representation of the entire real-time data segment. For example: if the real-time data segment contains 1000 sampling points, then is a 1000-dimensional column vector, and each element corresponds to the value of a data point. Each sparse coefficient corresponds to a wavelet basis function, rather than a single data point. The wavelet dictionary is matrix, where N is the total number of data points (i.e., the dimension of x), and M is the number of wavelet basis functions. The sparse coefficient is dimensional column vector, representing the weight coefficients of each wavelet basis, and its sparsity (few non-zero terms) is the key to compression.

[0069] Determining the sparsest wavelet coefficient combination means finding the wavelet coefficients that satisfy the optimization objective, and this optimization objective can be expressed as , that is, minimizing L1 norm (i.e., sparsity). The allowable error can be a fixed value set according to experience, or a dynamic value adjusted dynamically according to the data in the real-time data segment.

[0070] The above technical solution realizes sparse coding through L1 norm optimization, rather than traditional wavelet threshold compression, which can find the sparsest ( ) on the premise of ensuring that the reconstruction error ( The combination of wavelet coefficients with the smallest (minimum) value. And this method uses L1 norm minimization to approximate the original data, making the representation of most data sparse (i.e., only a small number of coefficients are significant), which greatly reduces the amount of data required for transmission.

[0071] Exemplarily, the real-time data segment is compressed by using Fourier parameterization compression, including: performing a Fourier transform on the real-time data segment to obtain the amplitude, phase, and frequency of each of the multiple frequency components used to represent the spectral characteristics of the real-time data segment; determining a part of the frequency components among the multiple frequency components that meet the following conditions as the compression result:

[0072] ;

[0073] wherein, K is the total number of frequency components among the multiple frequency components, is the k th amplitude of the frequency component.

[0074] The size of the energy threshold can be selected according to actual needs. For example, it can be 95% of the total energy. In a specific embodiment, if the first 3 frequency components (such as the 12h, 24h, and 6h harmonics of the tide) have covered 95% of the energy, then only the of these 3 groups are transmitted, wherein, is the k th phase of the frequency component, is the k th frequency of the frequency component. Of course, the energy threshold can also be a dynamic value dynamically adjusted according to actual needs, which will not be elaborated.

[0075] The above technical solution can ensure that most of the energy is captured when compressing periodic data, so that the periodic data can be transmitted in an efficient manner.

[0076] Exemplarily, the residual threshold is positively correlated with the standard deviation and range of the residual values of each data point in the real-time data segment; and / or, the allowable error of the wavelet transform is positively correlated with the baseline error of the data in the real-time data segment and the L2 gradient norm of the data in the real-time data segment; and / or, the energy threshold is determined based on the total energy and the main frequency energy ratio of the real-time data segment, and the energy threshold is positively correlated with the total energy and the main frequency energy ratio of the real-time data segment.

[0077] In some implementation solutions of this example, the residual threshold is positively correlated with the standard deviation and range of the residual values of each data point in the real-time data segment. That is, the residual threshold can be dynamically adjusted according to the standard deviation and range of the residual values of each data point in the real-time data segment. In this case, the residual threshold can be adaptively scaled with the data fluctuation intensity, avoiding excessive residual transmission or information loss caused by a fixed threshold, thus helping to ensure the efficiency and authenticity of data transmission.

[0078] In some embodiments, the residual threshold can be determined by the following formula:

[0079] ;

[0080] ;

[0081] wherein, is the standard deviation of the residual values of each data point within the real-time data segment; , are weight coefficients, which can be set according to actual needs or obtained through training and optimization of historical data. In some embodiments, ; is the range, which can be used as a sensitive indicator of the abnormal amplitude of the residual; is the maximum residual value among each data point within the real-time data segment; is the minimum residual value among each data point within the real-time data segment; is the residual threshold.

[0082] In a specific embodiment, the standard deviation and range of the residual values of the real-time data segment can be calculated first, and then the residual threshold can be updated according to the standard deviation and range. Then, the real-time data segment is compressed using the residual threshold. For example, if , the residual threshold can be calculated to be 0.13. In this case, when compressing the real-time data segment, the residual values greater than 0.13 are transmitted, and the residual values less than 0.13 are discarded.

[0083] In some implementation solutions of this example, the allowable error of wavelet transform is positively correlated with the baseline error of the data in the real-time data segment and the L2 gradient norm of the data in the real-time data segment. In this example, the allowable error can be adjusted by the sparsity ( w ) of the wavelet coefficient and the local gradient , which helps to relax the allowable error (retain more high-frequency details) when the signal changes violently, and tighten when it is stable, thus helping to improve the compression ratio.

[0084] In some embodiments, the allowable error is determined by the following formula:

[0085] ;

[0086] wherein, represents the allowable error; represents the baseline error, = 0.1 , is the standard deviation of the original data (i.e., the real-time data segment); represents the L2 gradient norm of the data in the real-time data segment and is used to quantify the mutation intensity; represents the smoothing factor. In this embodiment, ).

[0087] In a specific embodiment, the gradient of the real-time data segment can be calculated first and . Then, the allowable error is dynamically adjusted. Next, the real-time data segment is compressed based on the adjusted allowable error.

[0088] In a specific embodiment, = 0.1, = 5, α = 10. In this case, δ = 0.1×(1 + 5 / 10)=0.15.

[0089] In some implementation solutions of this example, the energy threshold is determined based on the total energy of the real-time data segment and the main frequency energy ratio, and the energy threshold is positively correlated with the total energy of the real-time data segment and the main frequency energy ratio. That is, the size of the energy threshold can be dynamically adjusted based on the total energy of the real-time data segment and the main frequency energy ratio. Thus, the energy threshold can be increased in strong periodic signals to reduce the number of transmission coefficients; the energy threshold can be decreased in weak periodic signals to avoid losing key frequencies.

[0090] In some embodiments, the energy threshold is determined by the following formula:

[0091] ;

[0092] where, is the total energy; is the main frequency energy ratio, η ∈[0,1], ; is the sensitivity coefficient, and this sensitivity coefficient can be set as needed. For example, it can be 0.5. In a specific embodiment, if = 10, η = 0.9, then the threshold = 95%×10×(1−0.5×0.1)=9.025.

[0093] In a specific embodiment, the total energy and the main frequency energy ratio of the real-time data segment can be calculated first, and then the energy threshold is dynamically adjusted. Finally, the real-time data segment is compressed according to the adjusted energy threshold.

[0094] The above technical solution can dynamically adjust parameters such as the residual threshold, allowable error, and energy threshold according to the statistical characteristics of data, thereby avoiding overfitting or information loss caused by fixed parameters.

[0095] Exemplarily, data compression is performed on the real-time data segment according to the data type of the real-time data segment, including: when the data types of the real-time data segment are not unique, selecting a segmentation strategy that minimizes the objective function to segment the real-time data segment to obtain multiple sub-data segments, where the objective function is the weighted sum of the KL divergence of the sub-data segments obtained after segmentation and the number of compression bits, and the KL divergence is the degree of matching between the corresponding sub-data segment and the expected distribution of the compression model of the sub-data segment, and the compression model of the sub-data segment is the compression model corresponding to the data type of the sub-data segment; performing data compression on the multiple sub-data segments respectively according to the data type of each sub-data segment among the multiple sub-data segments obtained after segmentation, where the compression result includes the results obtained by performing data compression on the multiple sub-data segments respectively. This solution can be called a dynamic segmentation KL divergence selection algorithm.

[0096] During the research process, the inventors found that for time series data with dynamic mixed characteristics, especially in the scenario where a certain meteorological and hydrological data belongs to multiple types of data (slow-changing data, transient data, periodic data) at the same time, the core feature is that the data dynamic pattern changes over time. At this time, the compression efficiency is relatively low when only processed by a single compression algorithm, and it cannot be fully utilized. In view of this, the inventors considered using a dynamic KL divergence (Kullback-Leibler Divergence) selection algorithm to quantify the degree of matching between each segment in the real-time data segment and the compression models corresponding to different data types, and combined with the compression rate weight to optimize and select the optimal segment, so as to solve the problems of information loss and low compression efficiency caused by fixed compression strategies in the compression algorithm.

[0097] In this example, the objective function can be expressed as:

[0098] ;

[0099] where

[0100] ;

[0101] where represents measuring the difference between the current data segment and the expected distribution of the compression model , and the smaller the value, the higher the data fidelity after compression. represents the number of compression bits, that is, the number of bits of the compressed data segment, and the smaller the value, the higher the compression rate. is the weight coefficient, which is used to balance the fidelity and the compression rate (default value = 0.5, which can be dynamically adjusted according to the channel state), λ Function: λ →0: Priority fidelity (select the model with the smallest KL divergence); λ →∞: Priority compression rate (select the model with the fewest number of bits).

[0102] In some implementation solutions of this example, the expected distributions of the compression models corresponding to different data types are:

[0103] Slow-varying data: , assuming the data follows a low-variance Gaussian distribution, this compression model can be called the SV model;

[0104] Transient data: , assuming the data follows a sparse Laplace distribution in the wavelet domain, reflecting the spike characteristics of mutations, this compression model can be called the TR model;

[0105] Periodic data: , assuming the data is synthesized by a few sine components, this compression model can be called the TD model.

[0106] Among them, can be obtained by the following method. First, a large number of samples can be extracted from historical data and compressed with the SV / TR / PD models respectively; the distributions of the residuals after compression by each model are statistically analyzed and fitted to the SV / TR / PD models respectively. For example, for the SV model, the mean and variance of the residual e t can be calculated to determine N(0, ); for the TR model, the decay rate of the wavelet coefficients can be statistically analyzed to determine the scale parameter b of the Laplace distribution. In addition, the parameters of can also be dynamically adjusted according to real-time data (such as sliding window update or the scale parameter b of the Laplace distribution) to achieve dynamic update of the model.

[0107] In some implementation solutions of this example, the compression bit number can be calculated in the following way: for one segment in the real-time data segment, use the compression model corresponding to the data type of this segment to compress this segment; statistically analyze the number of bits of the compression output (including frequency parameters and residuals).

[0108] For example, the SV model: transmit the initial value x 0 (32-bit floating-point number) + large residuals { ti , ei} (each residual occupies 16-bit timestamp + 16-bit value), ; the TR model: transmit the wavelet basis identifier (8 bits) + non-zero coefficients { index, value}(10 - bit index + 16 - bit value per coefficient), ; PD model: transmission (16 bits per parameter, a total of 48× K bits), .

[0109] In a specific embodiment, the length of a segment of the real - time data segment is 1 min, including tidal (PD) and wave (TR) components.

[0110] KL divergence:

[0111] D KL ( P seg∥ Q PD ) = 0.1 (high tidal matching degree);

[0112] D KL ( P seg∥ Q TR ) = 0.3 (partial wave matching);

[0113] Number of bits:

[0114] BitsPD = 160 (3 frequency parameters);

[0115] BitsTR = 240 (8 wavelet coefficients);

[0116] Selection result:

[0117] If λ = 0.5, total cost PD = 0.1+80 = 80.1, TR = 0.3+120 = 120.3 → select PD model.

[0118] Optionally, the dynamic segmentation KL - divergence selection algorithm may include the following steps:

[0119] First, perform a sliding - window analysis on the real - time data segment to extract multiple candidate segments from the real - time data segment, where the length of the sliding window can be a second preset length, and the second preset length is less than the first preset length; for example, the second preset length T = 5 min;

[0120] Then, calculate the data characteristics of each obtained candidate segment in real - time, and determine the data type of each candidate segment according to the data characteristics. The method of determining the data type is similar to the method of determining the data type of the real - time data segment, and will not be elaborated;

[0121] Next, for each candidate segment, calculate the weighted sum of its KL divergence and the number of compressed bits, and quickly solve the segmentation strategy that minimizes the objective function through dynamic programming;

[0122] Finally, segment the real-time data segment according to the segmentation strategy that minimizes the objective function to obtain multiple sub-data segments, and perform data compression on each of the multiple sub-data segments according to the data type of each sub-data segment after segmentation to obtain compression parameters.

[0123] The above technical solution realizes the dynamic trade-off between fidelity and compression ratio through the joint optimization of KL divergence + the number of compressed bits, and is applicable to mixed feature data. This design significantly improves the compression efficiency of complex meteorological and hydrological data, while avoiding the limitations of fixed strategies.

[0124] Exemplarily, before transmitting the compression result, the method further includes: determining the reliability weight of the compression result according to the priority and urgency of the compression result; determining the transmission mode of the compression result based on the priority and reliability weight; and / or, calculating the transmission deadline of the compression result according to the urgency of the compression result; wherein, when the transmission time of transmitting the compression result is greater than or equal to the transmission deadline, switching the transmission mode of the compression result to a degraded transmission mode; wherein, when the transmission mode of the compression result is the degraded transmission mode, only transmitting the low-frequency part of the compression result; and / or, selecting a satellite link according to the data type of the compression result; determining the initial number of redundant packets during the transmission according to the estimated packet loss rate and channel reliability factor of the selected satellite link. This solution can be referred to as a Hybrid Reliable Transmission Protocol (HRTP).

[0125] For ease of description, the following definitions are respectively adopted for the priority and urgency: the priority is from level 1 to 5, with level 5 being the highest; the urgency is from level 1 to 3, with level 3 being the most urgent.

[0126] In the solution of this embodiment, the priority of the compression result can be determined according to the data type of the real-time data segment corresponding to the compression result. For example, when the data type is slowly varying data, the priority is 1 or 2; when the data type is periodic data, the priority is 3; when the data type is transient data, the priority is 4 or 5.

[0127] When the data type of the compression result is not unique, the priority of the compression result can be calculated according to the proportion of different data characteristics (slowly varying characteristics, transient characteristics, and periodic characteristics) in the compression result. For example, the priority of the compression result can be calculated from the following formula:

[0128]

[0129] where, is the feature weight, which can be an empirical value; is the proportion of the data feature in the compression result. For example, if the compression result contains 60% TR feature + 40% PD feature → DataPriority = 0.6×4 + 0.4×3 = 3.6, then the high-reliability mode is triggered ( Wr > 0.5).

[0130] The urgency can be preset manually or generated automatically according to the current data monitoring type. For example, when obtaining deep-sea meteorological and hydrological data by using the conventional monitoring method, the urgency can be 1, and when obtaining deep-sea meteorological and hydrological data by using the disaster warning monitoring method, the urgency can be 3.

[0131] Optionally, before transmitting the compression result, the method further includes: determining the reliability weight of the compression result according to the priority and urgency of the compression result; determining the transmission mode of the compression result based on the priority and the reliability weight.

[0132] It can be understood that the reliability weight (Reliability Weight) is a factor used to measure the transmission reliability of data, and is calculated based on the priority of the data and its sensitivity to transmission. In this embodiment, the reliability weight can be calculated by the following formula:

[0133] ;

[0134] where is the reliability weight; is the slope parameter, which is used to adjust the influence degree of the priority and can be set according to experience; is the priority of the data; is the priority threshold, which is used to determine the critical value of the priority.

[0135] As described above, the slope parameter k is used to control the weight growth rate. For example, when k = 0.5, for each increase of 1 in the priority, increases by about 0.2 - 0.3 (smooth transition); when k = 1.0, the priority difference is more sensitive and the weight increases steeply (such as priority 4 → = 0.98).

[0136] In some embodiments, the results of the priority and reliability weight can be compared with corresponding thresholds (e.g., priority threshold and reliability threshold) respectively. When both are greater than the corresponding thresholds, a high-reliability transmission mode is triggered; otherwise, an equilibrium mode is triggered. In this embodiment, the parameters of each transmission mode can be determined in advance. The parameters of the transmission mode (i.e., transmission parameters) can include the redundancy multiple, the number of retransmissions, and the bandwidth preemption priority (which can be represented by the reliability weight, i.e., the higher the reliability weight, the more priority to occupy the channel to ensure low latency).

[0137] In some other embodiments, based on the priority and reliability weight, determining the transmission mode of the compression result may include the following steps: when the priority is greater than the priority threshold, determining that the type of the transmission mode is the high-reliability transmission mode; when the priority is less than or equal to the priority threshold, determining that the type of the transmission mode is the equilibrium mode; and determining the transmission parameters according to the reliability weight. Among them, the higher the reliability weight, the higher the redundancy multiple, the number of retransmissions, and the bandwidth preemption priority. In this embodiment, the priority is used as a logical judgment to intuitively distinguish the high and low priorities. For example, when the priority is greater than the priority threshold, it is directly determined that the compression result is transmitted in the high-reliability transmission mode. The reliability weight is used for dynamic control to quantify the intensity of the reliability requirement. In this way, it is possible to avoid the sudden change of the strategy caused by relying solely on the priority for judgment. For example, when the priority threshold is 3, if only the priority is used for binary judgment, the difference between priority 4 and priority 5 cannot be distinguished. Further judgment through the reliability weight can achieve progressive control of the transmission parameters. For example, priority 4 → Wr =0.88 → redundancy multiple = 1.76; priority 5 → Wr =0.95 → redundancy multiple = 2.0; priority 3 → Wr =0.5 → redundancy multiple = 1.0 (baseline). Therefore, this solution can quickly classify by priority and finely control the transmission strategy through the reliability weight, which can effectively improve the transmission efficiency and transmission effect.

[0138] The priority threshold can be a fixed value set according to user experience or a dynamic value that changes dynamically according to the channel congestion degree. For example, when it is detected that the channel is congested, the priority threshold is determined to be the first threshold; when it is detected that the channel bandwidth is sufficient, the priority threshold is determined to be the second threshold, and the first threshold is greater than the second threshold. In a specific embodiment, when the channel is congested: the priority threshold is 4, and at this time, only priority 5 triggers the high-reliability transmission mode to concentrate resources to ensure the most critical data; when the bandwidth is sufficient, the priority threshold can be 2, and at this time, the high-reliability range can be expanded (priorities 3-5 are all enhanced in transmission).

[0139] In a specific implementation scheme, the priority of the compression result is 4, and the slope parameter kis 0.5, the priority threshold is 3, and the reliability weight is 0.88 at this time. In this embodiment, the transmission mode type of the compression result can be determined as the high-reliability transmission mode according to the priority first, and then, the redundancy multiple = 1 + 2×(0.88−0.5) = 1.76 → 2 (rounded up); the number of retransmissions: increased from 3 times to 5 times; bandwidth preemption: preferentially occupy the channel to ensure low latency.

[0140] By utilizing the priority and the reliability weight, the above solution can accurately determine the transmission mode of the compression result, so as to ensure that high-priority data is preferentially transmitted, which helps to guarantee the transmission efficiency of the compression result.

[0141] Optionally, before transmitting the compression result, the method further includes: calculating the transmission deadline of the compression result according to the urgency of the compression result; wherein, when the transmission time of the compression result is greater than or equal to the transmission deadline, switching the transmission mode of the compression result to the degraded transmission mode; wherein, when the transmission mode of the compression result is the degraded transmission mode, only the low-frequency part in the compression result is transmitted.

[0142] The transmission deadline can be used to control the delay of data transmission, ensure timeliness, and provide a co-real-time constraint for the compressed data. In some embodiments, the transmission deadline can be calculated using the following formula:

[0143] ;

[0144] wherein, is the transmission deadline; is the reference time, usually the system initialization time; is the real-time adjustment factor, used to represent the impact of the urgency on the deadline; is the urgency of the compression result.

[0145] In this embodiment, when the transmission mode of the compression result is the degraded transmission mode, only the low-frequency part in the compression result is transmitted. Thus, the real-time performance of data transmission can be guaranteed by reducing the amount of data transmission.

[0146] Optionally, before transmitting the compression result, the method further includes: selecting a satellite link according to the data type of the compression result; determining the initial number of redundant packets during the transmission according to the estimated packet loss rate and the channel reliability factor of the selected satellite link.

[0147] In some embodiments, the satellite link is selected according to the data type of the compression result, including: when the data type is slow-changing data, it is transmitted via the VAST satellite; when the data type is transient data, it is transmitted via both the Starlink and Beidou dual links; when the data type is periodic data, it is stably transmitted via the VAST baseline link. This solution is only an example, and users can pre-specify the satellite links corresponding to different data types according to the actual situation.

[0148] It can be understood that the satellite link selected according to the data type of the compression result above can be regarded as the initially determined satellite link (i.e., the default link). In some solutions of this embodiment, the satellite link can be further determined according to the priority and / or urgency of the compression result. For example, if a piece of slow-changing data is manually marked as DataPriority = 5 due to mission requirements, even though its type is slow-changing data, it can still trigger the parallel transmission of the Starlink link (50 Mbps - 1 Gbps) and the Beidou short message. In a specific embodiment, the link switching rules are shown in Table 3 below.

[0149] Table 3 Link Switching Rules

[0150]

[0151] In this alternative embodiment, the estimated packet loss rate can be calculated by the signaling feedback method. For example, the receiving end can be used to periodically send NACK packets, and the sending end can count the packet loss ratio of the most recent N packets to obtain the estimated packet loss rate. Of course, the estimated packet loss rate can also be obtained by the method of sliding window update. For example, exponential weighted moving average (EWMA) can be used to avoid instantaneous fluctuations. At this time, the estimated packet loss rate can be calculated by the following formula:

[0152] .

[0153] In some embodiments, when multi-link parallel transmission is adopted, the estimated packet loss rate of the link with the highest packet loss rate can be selected as the benchmark for calculating the initial number of redundant packets. For example, when using the Starlink + Beidou dual-link transmission, if the Starlink packet loss rate = 20% and the Beidou packet loss rate = 30%, the number of redundant packets is calculated according to 30%.

[0154] In this alternative embodiment, the channel reliability factor can be determined based on the channel type. For example, when the channel type is acoustic communication, the channel reliability factor can be 0.3 - 0.5, which indicates that the current channel has high packet loss and low bandwidth. When the channel type is satellite relay, the channel reliability factor can be 0.7 - 1, which indicates that the current channel has strong reliability. Of course, the channel reliability factor can also be determined through historical data statistics. For example, the channel reliability factor of a satellite link can be 0.6, and that of an optical fiber can be 0.95.

[0155] In some embodiments, determining the initial number of redundant packets during the transmission process according to the estimated packet loss rate and the channel reliability factor of the selected satellite link may include: determining the initial number of redundant packets through the following formula:

[0156] ;

[0157] where, is the estimated packet loss rate; is the channel reliability factor, ∈[0,1]; represents rounding up.

[0158] In a specific embodiment, the estimated packet loss rate of the current channel can be estimated first, then the initial number of redundant packets can be calculated. Finally, the number of transmitted redundant packets can be adjusted according to the calculated initial number of redundant packets to ensure that data can be successfully received in an environment with severe packet loss. Increasing the number of redundant packets helps to improve the reliability of data, especially when the channel quality is unstable.

[0159] The above solution can calculate the number of redundant packets to be transmitted according to the estimated packet loss rate and the channel reliability factor to ensure that data can be completely received in an environment with a large number of packet losses. Increasing the number of redundant packets can improve the reliability of data, especially in a high packet loss environment.

[0160] As mentioned above, traditional transmission protocols in the related art, such as TCP and UDP, although having their advantages in some scenarios, cannot meet the multiple requirements of high efficiency, reliability, and low latency in a special environment such as the deep and far sea. The core idea of the hybrid reliable transmission protocol (HRTP) proposed by the present invention is to dynamically adjust the data transmission strategy according to factors such as the real-time requirements, priorities, and channel states of different data. Through the above solution, HRTP can ensure that high-priority and urgent data is preferentially transmitted. At the same time, in an environment with poor network conditions and a high packet loss rate, the reliability of data is enhanced through the number of redundant packets to ensure that key data is not lost. Thus, some special challenges faced in the deep and far sea communication environment can be addressed, including high latency, high packet loss rate, limited bandwidth, and conflicts between real-time and reliability requirements.

[0161] Optionally, determine the initial number of redundant packets during the transmission process based on the estimated packet loss rate and channel reliability factor of the selected satellite link, and execute it when the estimated packet loss rate of the current satellite link is greater than the packet loss rate threshold. Thus, in a low packet loss rate environment, a fixed number of redundant packets can be used, and in a high packet loss rate environment, the reliability of data transmission can be improved through dynamic redundant packets to ensure that data is not lost due to packet loss.

[0162] Exemplarily, the method further includes: monitoring the channel quality and bandwidth usage during the data transmission process; adjusting the bandwidth during the transmission process of the compression result according to the priority of the compression result, the channel quality, and the bandwidth usage.

[0163] In this example, the data priority reflects the importance and urgency of the data. Different data may have different priorities. Especially in the deep sea and far sea environment, disaster warning information needs to be transmitted with higher priority than conventional meteorological data. The quality of the channel directly affects the transmission rate and stability of the data, and parameters such as signal-to-noise ratio and packet loss rate can be used to measure it. In this embodiment, the channel quality is represented by the signal-to-noise ratio. The bandwidth usage reflects the occupancy degree of the current bandwidth. The remaining amount of the bandwidth directly determines whether the system can smoothly transmit additional data.

[0164] Optionally, adjusting the bandwidth during the transmission process of the compression result according to the priority of the compression result, the channel quality, and the bandwidth usage includes: inputting the priority of the compression result, the channel quality, and the bandwidth usage (i.e., bandwidth utilization rate) into the bandwidth allocation model to obtain the optimal bandwidth, where the adjusted bandwidth is the optimal bandwidth; wherein, the bandwidth allocation model is constructed by a fuzzy reward function and a Q-learning algorithm. The fuzzy reward function is used to calculate the fuzzy reward value of the current bandwidth allocation according to the priority membership degree corresponding to the priority of the compression result, the quality membership degree corresponding to the channel quality, and the bandwidth usage; the Q-learning algorithm is used to update the Q value according to the fuzzy reward value and determine the optimal bandwidth according to the updated Q value. This method can be called Satellite Bandwidth Fuzzy Q Optimization (FQO).

[0165] In some embodiments, the fuzzy reward function can be expressed as:

[0166] ;

[0167] Wherein, represents the priority membership degree; represents the quality membership degree; represents the used bandwidth, that is, the bandwidth used to transmit the compression result; represents the total current available bandwidth.

[0168] According to the above-mentioned fuzzy reward function, it can be seen that if the data priority is high (P is high), the channel quality is good (Q is high), and the bandwidth utilization rate is relatively good ( close to ), then the reward value is relatively large, which means that the current bandwidth allocation strategy is relatively effective, and the current transmission strategy can be maintained at this time. If the channel quality is poor or the bandwidth utilization rate is too low, the reward value will be relatively small, indicating that the current strategy may need to be adjusted. For example, the bandwidth allocation can be adjusted. Of course, the number of redundant packets can also be increased.

[0169] In this embodiment, the fuzzy reward function combines multiple factors affecting bandwidth allocation (such as priority, channel quality, and bandwidth usage) through fuzzy logic to evaluate the effectiveness of the current bandwidth allocation strategy. Thus, the bandwidth allocation strategy can be dynamically adjusted according to different system states (such as channel quality fluctuations or data priority changes) to ensure that the bandwidth utilization rate and system performance can be effectively improved under different network conditions. Moreover, this non-linear decision-making method through fuzzy logic can be applied to process uncertain and fuzzy information, realizing a more efficient and flexible decision-making process than traditional algorithms.

[0170] In this alternative embodiment, the priority and channel quality are converted into corresponding membership degrees through fuzzy logic, and the fuzzy characteristics of the corresponding variables are quantified through the membership degrees. Thus, it can be ensured that the fuzzy reward function can flexibly adjust the reward value under different input values. In some embodiments, the priority membership degree corresponding to the priority can be determined in the following manner:

[0171] ;

[0172] wherein, represents the priority membership degree; P represents the priority.

[0173] In some embodiments, the quality membership degree can be determined in the following manner:

[0174] ;

[0175] wherein, represents the quality membership degree; represents the best channel quality; represents the channel quality; σ represents the channel fluctuation standard deviation.

[0176] In some embodiments, the update formula of the Q-learning algorithm can be expressed as:

[0177] ;

[0178] wherein, is the state and the action The value function, i.e., value; is the learning rate, which is used to control the update speed during the learning process; is the current state and the action of the immediate reward, i.e., the fuzzy reward value; is the discount factor, indicating the importance of future rewards; is the next state under the maximum value.

[0179] In the Q - learning algorithm of this embodiment, the state represents the current state of the system, including data priority, channel quality, and remaining bandwidth. The action , representing the bandwidth allocation ratio, selects the optimal bandwidth allocation strategy according to the value in different states. Through the use of the fuzzy Q - optimization algorithm, FQO can automatically adjust the bandwidth allocation strategy according to the real - time system state (data priority, channel quality, bandwidth resources, etc.), thus ensuring the optimal utilization of satellite bandwidth resources. With the continuous update of the Q - learning algorithm and the optimization of the reward function, FQO can optimize the reliability and real - time performance of data transmission in the dynamically changing deep - sea and far - sea environment.

[0180] In some implementation schemes of this example, when high - priority data (P≥3) and high - quality channels (SNR≥25dB) exist simultaneously, the fuzzy reward value is maximized, and bandwidth is preferentially allocated to the VAST link; if the channel quality is poor (SNR < 15dB), the fuzzy reward value decreases, triggering Q - learning to switch to low - earth - orbit satellites (Starlink) or Beidou short - message service. In this implementation scheme, by dynamically adjusting R(s,a) of Q - learning, the Q - value update is affected. Thus, the fuzzy reward value can be used to guide Q - learning to preferentially select VAST or Starlink in specific states.

[0181] In some embodiments, the specific method for determining the optimal bandwidth according to the updated Q value is as follows: Default mode: When the Q value points to VAST, allocate 70% - 100% of the bandwidth (conventional data). Emergency mode: When the Q value switches to Starlink due to the fuzzy reward mechanism, forcefully allocate 40% of the bandwidth (high-priority transient data). That is, in the default mode, VAST allocates 70% - 100% of the bandwidth to transmit conventional data (slow-varying data or periodic data), and Starlink serves as a backup. In the emergency mode (e.g., when high-priority transient data is detected), forcefully allocate 40% of the bandwidth to Starlink and 10% to Beidou short message to achieve dual-channel redundancy for disaster data. This method can convert the actions output by Q-learning (such as "select VAST") into specific bandwidth ratios, thereby realizing dynamic adjustment of the bandwidth.

[0182] In summary, the above technical solution combines the fuzzy reward function and the Q-learning algorithm, enabling the bandwidth allocation decision to be based not only on the current system state but also to gradually learn and optimize the decision-making strategy. Through the combination of fuzzy logic and reinforcement learning, it can better adapt to the complex and changing transmission requirements in the deep sea and far sea environments, improving the utilization efficiency of satellite bandwidth and the reliability of data transmission.

[0183] Exemplarily, the method further includes: adjusting the number of redundant packets (which can be referred to as the current number of redundant packets) during the transmission of the compression result according to the channel quality.

[0184] Optionally, adjusting the current number of redundant packets according to the channel quality includes: determining whether the quality membership degree corresponding to the channel quality is less than the quality threshold; when the quality membership degree is less than the quality threshold, increasing the current number of redundant packets.

[0185] In some embodiments, the current number of redundant packets can be increased according to a preset growth value. For example, if the quality threshold is 0.5, when it is detected that < 0.5, 1 - 2 packets can be added to the current number of redundant packets (e.g., 3 → 5).

[0186] In other embodiments, when the quality membership degree is less than the quality threshold, the following method can be used to increase the current number of redundant packets: .

[0187] The above solution can dynamically adjust the number of redundant packets during the transmission of the compression result according to the channel quality, thereby further ensuring the data transmission efficiency and transmission quality.

[0188] Exemplarily, the method further includes: adjusting the satellite link according to the channel quality. For example, when the quality membership degree is less than the quality threshold, the Starlink + Beidou dual-link parallel transmission can be triggered.

[0189] In a specific embodiment, the compressed result is transmitted through the following steps: First, a default satellite link is selected according to the type of the compressed result, and the transmission deadline and the initial number of redundant packets are calculated based on the priority and urgency of the compressed result. Then, during the transmission of the compressed result, the channel quality and bandwidth usage are detected in real time, and a fuzzy reward value is calculated using a fuzzy reward function. Next, the Q-value is updated using the Q-learning algorithm, and the bandwidth allocation strategy is dynamically adjusted according to the Q-value. Meanwhile, the current number of redundant packets and the satellite link are adjusted according to the channel quality. When the transmission result is received, if the transmission is successful, the Q-learning algorithm is strengthened and the current parameters are maintained; if the transmission fails, a degraded transmission is performed, and inefficient actions are penalized in the Q-learning algorithm (i.e., the corresponding Q-value is reduced).

[0190] In a specific embodiment of the present invention, the DASC algorithm is used to compress real-time data segments, the HRTP protocol is used to transmit the compressed result, and a combination of a fuzzy reward function and the Q-learning algorithm is used for dynamic adjustment during the transmission of the compressed result. It has been verified that this embodiment has the following effects compared with the prior art:

[0191] 1. The data compression efficiency is significantly improved: Slowly varying data (such as temperature, salinity): Through linear prediction residual coding, the compression ratio is stable at 92%-95% (measured for a dataset in a certain sea area, from the original 10MB / h to 0.8MB / h); Abruptly changing data (such as sudden typhoons): The wavelet sparse compression ratio is 78%-82%, and the retention error of key wind speed mutation points is ≤3%; Periodic data (such as tides): The Fourier parametric compression ratio is 80%-85%, and the reconstruction error of the daily periodic waveform is <1%;

[0192] 2. The transmission reliability is greatly enhanced: High-priority disaster data (such as typhoon paths): Through the dual-channel redundancy of Starlink + Beidou, the transmission success rate is increased from 82% of the traditional TCP to 99.3% (measured in a certain deep sea area with a packet loss rate of 30%); Real-time guarantee: The end-to-end delay of urgent data (UrgencyLevel = 3) is ≤60ms, and the timeout rate is reduced from 35% to less than 5%;

[0193] 3. The satellite bandwidth utilization is optimized: The average daily bandwidth occupancy of VAST satellites is reduced by 40% (from 100Mbps to 60Mbps), and the peak burst transmission rate of Starlink reaches 900Mbps (for transmitting high-definition typhoon radar data); The bandwidth dynamic allocation strategy reduces the channel idle rate by 50%, avoiding resource waste;

[0194] 4. Complex Environment Adaptability Verification: In a harsh sea condition with signal-to-noise ratio fluctuations (10 - 25 dB) and packet loss rates of 20% - 50%, the system maintains a data integrity rate of 98% through three-level disaster tolerance (VAST → Starlink → Beidou), and the interruption recovery time is < 3 seconds; A deep-sea exploration ship has continuously operated for 3 months, with a cumulative data transmission volume of 12 TB and a bit error rate of only 0.02%;

[0195] 5. Energy Efficiency and Cost Advantages: Compared with the traditional solution (full-volume transmission + fixed redundancy), the satellite communication cost is reduced by 55%, and the power consumption of on-board equipment is decreased by 30%. It meets the low-power consumption requirements for long-term operation of deep-sea exploration ships.

[0196] According to another aspect of the embodiments of the present invention, an electronic device is further provided. Figure 2 A schematic block diagram of an electronic device according to an embodiment of the present invention is shown. As Figure 2 shown, the electronic device 200 includes: a processor 210 and a memory 220. A computer program is stored in the memory 220, and the processor 210 is configured to execute the computer program to implement the above method.

[0197] According to still another aspect of the embodiments of the present invention, a computer-readable storage medium is further provided. Computer programs / instructions are stored in the storage medium, and when the computer programs / instructions are executed by a processor, the above method is implemented. The storage medium may include, for example, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.

[0198] Those of ordinary skill in the art can easily understand the implementation structure, working principle, and beneficial effects of the electronic device and the computer-readable storage medium by reading the above method. For the sake of brevity, they will not be elaborated here.

[0199] Although example embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above example embodiments are merely exemplary and are not intended to limit the scope of the present invention thereto. Various changes and modifications can be made therein by those of ordinary skill in the art without departing from the scope and spirit of the present invention.

[0200] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in connection with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Skilled artisans can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0201] In several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed.

[0202] In the specification provided herein, a large number of specific details are set forth. However, it can be understood that the embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures, and technologies have not been shown in detail so as not to obscure the understanding of this specification.

[0203] Similarly, it should be understood that, in order to streamline the present invention and assist in understanding one or more of the various inventive aspects, in the description of the exemplary embodiments of the present invention, the various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, the methods of the present invention should not be construed as reflecting the intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the corresponding claims, the inventive point lies in that the corresponding technical problems can be solved with features fewer than all the features of a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into the detailed description, where each claim itself serves as a separate embodiment of the present invention.

[0204] Those skilled in the art can understand that, except for features being mutually exclusive, any combination can be adopted for all the features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all the processes or units of any method or device so disclosed. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) can be replaced by an alternative feature that provides the same, equivalent, or similar purpose.

[0205] In addition, those skilled in the art can understand that, although some of the embodiments described herein include certain features included in other embodiments rather than other features, the combination of features of different embodiments means that it is within the scope of the present invention and forms different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0206] Each component embodiment of the present invention can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art should understand that a microprocessor or a digital signal processor (DSP) can be used in practice to implement some or all of the functions of some modules in the electronic device according to the embodiments of the present invention. The present invention can also be implemented as a device program (e.g., a computer program and a computer program product) for executing some or all of the methods described herein. Such a program implementing the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, or provided on a carrier signal, or in any other form.

[0207] As described above, the above is only the specific implementation manner or the description of the specific implementation manner of the present invention, and the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should be covered by the protection scope of the present invention.

Claims

1. A method for transmitting deep sea meteorological and hydrological data, characterized in that: include: Get a real-time data segment of preset length; Determining data features of the real-time data segment, the data features comprising: mean, variance, time gradient, baseline standard deviation, power spectral density, signal-to-noise ratio and autocorrelation of data in the real-time data segment; Determining the data type of the real-time data segment according to the data characteristics of the real-time data segment, wherein the data type includes transient data, slowly varying data and periodic data; According to the data type of the real-time data segment, the real-time data segment is compressed to obtain a compression result, wherein different data types use different data compression models; Transmitting the compression result; The step of determining the data type of the real-time data segment according to the data feature of the real-time data segment includes: When the real-time data segment meets the following conditions, it is determined that the data type of the real-time data segment is slowly varying data: ； in, is the variance of the data in the real-time data segment, is the mean value of the data in the real-time data segment, is the power spectrum density of the data in the real-time data segment; and / or, When the real-time data segment meets the following conditions, it is determined that the data type of the real-time data segment is transient data: ； in, is the time gradient of the data in the real-time data segment, is the baseline standard deviation of the data in the real-time data segment, is the power spectrum density of the data in the real-time data segment; and / or, When the real-time data segment meets the following conditions, it is determined that the data type of the real-time data segment is periodic data: ； in, is the power spectrum density of the data in the real-time data segment, is the signal-to-noise ratio of the data in the real-time data segment, is the autocorrelation of the data in the real-time data segment.

2. The method according to claim 1, characterized in that The step of compressing the real-time data segment according to the data type of the real-time data segment comprises: When the data type of the real-time data segment is unique, If the data type of the real-time data segment is slowly varying data, the real-time data segment is compressed by using a residual coding method based on linear prediction, wherein the compression result is a residual value greater than a residual threshold; If the data type of the real-time data segment is transient data, the real-time data segment is compressed by using a sparse representation method based on wavelet transform, wherein the compression result is a sparse coefficient; If the data type of the real-time data segment is periodic data, the real-time data segment is compressed using Fourier parametric compression, wherein the compression result is the amplitude, phase and frequency of some frequency components in the real-time data segment, and the sum of the energies of the some frequency components is greater than an energy threshold.

3. The method according to claim 2, characterized in that The residual threshold is positively correlated with the standard deviation and range of the residual values ​​of each data point in the real-time data segment; and / or, the allowable error of the wavelet transform is positively correlated with the baseline error of the data in the real-time data segment and the L2 gradient norm of the data in the real-time data segment; and / or, the energy threshold is determined based on the total energy of the real-time data segment and the proportion of the main frequency energy, and the energy threshold is positively correlated with the total energy of the real-time data segment and the proportion of the main frequency energy.

4. The method according to claim 1, characterized in that: The step of compressing the real-time data segment according to the data type of the real-time data segment comprises: When the data type of the real-time data segment is not unique, Selecting a segmentation strategy that minimizes an objective function to segment the real-time data segment to obtain a plurality of sub-data segments, wherein the objective function is a weighted sum of a KL divergence and a number of compressed bits of the sub-data segments obtained after segmentation, the KL divergence is a matching degree between a corresponding sub-data segment and an expected distribution of a compression model of the sub-data segment, and the sub-data segment compression model is a compression model corresponding to a data type of the sub-data segment; According to the data type of each sub-data segment in the multiple sub-data segments obtained after segmentation, data compression is performed on the multiple sub-data segments respectively, wherein the compression results include results obtained by performing data compression on the multiple sub-data segments respectively.

5. The method according to any one of claims 1 to 4, characterized in that: Before transmitting the compression result, the method further includes: Determining a reliability weight of the compression result according to the priority and urgency of the compression result; Determining a transmission mode of the compression result based on the priority and the reliability weight; and / or, Calculating a transmission deadline of the compression result according to the urgency of the compression result; Wherein, when the transmission time of transmitting the compression result is greater than or equal to the transmission deadline, switching the transmission mode of the compression result to a degraded transmission mode; Wherein, when the transmission mode of the compression result is the degraded transmission mode, only the low-frequency part of the compression result is transmitted; and / or, Selecting a satellite link according to the data type of the compression result; The number of initial redundant packets in the transmission process is determined according to the estimated packet loss rate of the selected satellite link and the channel reliability factor.

6. The method according to claim 5, characterized in that The method of determining the initial number of redundant packets in the transmission process according to the estimated value of the packet loss rate of the selected satellite link and the channel reliability factor is performed when the estimated value of the packet loss rate of the current satellite link is greater than the packet loss rate threshold.

7. The method according to any one of claims 1 to 4, characterized in that: The method further comprises: Monitor channel quality and bandwidth usage during data transmission; The bandwidth during transmission of the compression result is adjusted according to the priority of the compression result, channel quality and bandwidth usage.

8. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and the processor is used to execute the computer program to implement the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: A computer program / instruction is stored, and when the computer program / instruction is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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