Underwater platform data acquisition and transmission system

Abstract

The invention discloses an underwater platform data acquisition and transmission system. The system comprises the following steps: an underwater data acquisition module acquires underwater environment information through a heterogeneous sensor and performs standardization processing; the channel modeling module analyzes underwater channel characteristics by using a self-adaptive channel model and generates channel estimation parameters; the channel estimation module is combined with a quantum interference measurement method to correct a channel state and optimize a channel model; the channel optimization and data fusion module constructs a joint feature matching network of underwater acoustic signals and quantum state signals to realize signal fusion and channel feature dynamic optimization; the data transmission optimization module adjusts a data transmission strategy based on the optimized transmission parameters, and the transmission stability is improved; and the energy management module adopts a dynamic power control strategy to realize low-power-consumption operation. According to the method, quantum measurement, sparse Bayesian channel modeling and an adaptive optimization method are combined, and the underwater channel estimation precision, the data transmission efficiency and the system stability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data acquisition, and in particular to an underwater platform data acquisition and transmission system. Background Art

[0002] With the development of underwater communication and ocean exploration technologies, the demand for data collection and transmission from underwater platforms is increasing. However, due to the complexity of the underwater environment, traditional data collection and transmission methods often fail to meet the requirements for efficient and stable communication when faced with problems such as high attenuation, multipath effects, time-varying channel characteristics, and limited bandwidth.

[0003] Currently, traditional underwater data transmission systems rely primarily on underwater acoustic communication technology, which focuses on fixed channel modeling, traditional Bayesian channel estimation, and signal optimization methods based on fixed rules. However, in complex underwater environments, existing technologies have significant shortcomings in the following aspects: 1. Low channel estimation accuracy: Existing underwater communication systems mostly use classical Bayesian methods or statistical channel modeling for channel estimation, which cannot effectively cope with the rapid changes of underwater channels, resulting in large channel state prediction errors and affecting the stability of data transmission.

[0004] 2. Weak multipath interference suppression capability: Underwater channels have strong multipath effects. Traditional underwater acoustic communication systems find it difficult to effectively distinguish between direct path signals and multipath reflection signals, resulting in severe signal interference and affecting data reception quality.

[0005] 3. Insufficient data fusion capabilities: Existing technologies mostly rely on a single type of signal for data transmission, making it difficult to achieve multimodal information fusion, especially in the optimization matching and joint transmission between underwater acoustic signals and quantum state signals, and lack effective feature fusion methods.

[0006] 4. Lagging channel optimization methods: Traditional underwater acoustic channel optimization methods usually rely on preset rules or static optimization strategies, which are difficult to dynamically adjust according to real-time changes in the underwater environment. This results in poor system adaptability and inability to efficiently optimize channel characteristics.

[0007] 5. Low energy management efficiency: Existing underwater data acquisition and transmission systems lack optimization strategies for low-power operation. Especially in long-distance ocean exploration missions, the energy management mechanism for long-term operation of equipment is imperfect, making it difficult to effectively improve the system's endurance.

[0008] 6. Insufficient data security: Traditional underwater acoustic communication systems are easily affected by environmental noise and external interference, and lack physical-level encryption mechanisms for information security protection. They are vulnerable to eavesdropping and attacks, and cannot meet the needs of high-security data transmission.

[0009] Therefore, how to provide an underwater platform data acquisition and transmission system is a problem that those skilled in the art urgently need to solve. Summary of the Invention

[0010] One purpose of the present invention is to propose an underwater platform data acquisition and transmission system. The present invention adopts quantum state interferometry, sparse Bayesian channel modeling, joint feature matching network and adaptive channel optimization method to dynamically optimize the channel characteristics in complex underwater environments, and fuse underwater acoustic signals and quantum state signals to improve the stability and reliability of data transmission. The implementation scheme of channel modeling, channel estimation, channel optimization and data fusion is described in detail, and it has the advantages of high-precision channel estimation, strong anti-multipath interference capability, outstanding data fusion optimization capability and low-power adaptive transmission.

[0011] According to an embodiment of the present invention, a system for collecting and transmitting underwater platform data includes the following steps: S1, underwater data acquisition module, uses heterogeneous sensor networks to monitor underwater environmental parameters in real time, collect underwater environmental information data and perform standardized processing to form multimodal underwater data; S2, channel modeling module, based on multimodal underwater data, uses sparse Bayesian method to extract channel features, build an adaptive channel model, and generate channel estimation parameters; S3, channel estimation module, based on channel estimation parameters, combines quantum state interferometry measurement method to perform channel detection, correct the adaptive channel model, and generate channel optimization parameters; S4, channel optimization and data fusion module, builds a joint feature matching network of underwater acoustic signals and quantum state signals based on channel optimization parameters, dynamically optimizes channel characteristics, fuses underwater acoustic signals and quantum state signals, and generates optimized transmission parameters; S5, data transmission optimization module, based on the optimized transmission parameters, uses dynamic error correction and adaptive modulation strategy to optimize the data transmission path and generate transmission control instructions; S6, the energy management module, dynamically adjusts data acquisition and transmission power based on transmission control instructions, optimizes energy consumption distribution, and achieves low-power operation.

[0012] Optionally, the S2 specifically includes: S21, channel data preprocessing unit, receives multimodal underwater data, performs time synchronization, denoising and standardization conversion, removes acquisition errors and environmental interference, and generates channel input data matrix , and extract the signal frequency; S22, channel feature extraction unit, based on the channel input data matrix , using sparse Bayesian learning method to construct channel feature vector ,in, Indicates the The channel gain parameters of each path are calculated, the main characteristics and time variables of the multipath channel are extracted, and sparse modeling optimization is performed: ; in, Indicates the The channel gain parameters of the paths, is the measurement signal vector, is the channel measurement matrix, is the regularization factor, is the sparse Bayesian prior parameter, is the total number of effective multipath propagation paths in the underwater channel, that is, the channel eigenvector Dimensions; S23, adaptive channel modeling unit, based on the channel feature vector Building an adaptive channel model , establish a time-varying feature map of the channel dynamics: ; in, is the signal frequency, is the time variable, For the The propagation delay of each path, For the The signal attenuation factor of each path, is an imaginary unit, satisfying , is the total number of effective multipath propagation paths in the underwater channel, is pi, is an exponential function; S24, channel parameter optimization unit, based on adaptive channel model , an adaptive optimization strategy is used to update the channel eigenvector to correct the effects of multipath fading and time-varying noise: ; in, For the The channel gain parameter during the round iteration, For the The updated value of the channel gain parameter after round iteration, is the step size factor, is the channel estimation error, defined as: ; in, is the channel estimation error, are the elements of the channel measurement matrix, For the A measurement signal, is the total number of effective multipath propagation paths in the underwater channel, is the number of measurement signals; S25, channel estimation parameter output unit receives the updated channel gain parameter , calculate the channel estimation parameters and transmit them to the channel estimation module.

[0013] Optionally, the S22 specifically includes: S221, channel data analysis unit, receiving channel input data matrix , analyze its time domain and frequency domain characteristics, perform preliminary statistics on the channel state of the channel input data matrix, and generate the measurement signal vector and channel measurement matrix ; S222, sparse Bayesian modeling unit, based on the measurement signal vector and channel measurement matrix , establish a sparse channel feature estimation model, use the variational Bayesian inference method to sparsely represent the channel features, and solve the channel feature vector ; S223, channel feature iterative optimization unit, using iterative optimization strategy to update the channel feature vector , optimize the channel sparsity according to the maximum a posteriori estimation principle and calculate the posterior probability: ; in, represents the likelihood probability of the measured signal, is the prior distribution of the channel feature vector, is the posterior probability of the channel feature; S224, the measurement matrix adaptive adjustment unit adjusts the channel measurement matrix according to the channel characteristic estimation result. Adjust to minimize the channel estimation error so that the measurement matrix meets the optimal estimation criterion: ; in, is the optimized channel measurement matrix, are the elements of the channel measurement matrix, For the A measurement signal, Indicates the The channel gain parameters of the paths, is the total number of effective multipath propagation paths in the underwater channel, is the number of measurement signals; S225, channel feature output unit, based on the optimized channel measurement matrix , adjust the channel eigenvector , and output it to the adaptive channel modeling unit.

[0014] Optionally, the S3 specifically includes: S31, channel sounding data acquisition unit, receives channel estimation parameters, extracts time-varying state information of the channel, obtains channel attenuation, phase offset, delay distribution and noise characteristics, and generates a channel state matrix ; S32, quantum state interference measurement unit, based on the channel state matrix , using quantum interferometry to obtain the interferometry phase matrix and channel amplitude adjustment factor , and construct the quantum measurement matrix : ; in, represents the interferometric phase matrix, is the channel amplitude adjustment factor, is an imaginary unit; S33, channel state optimization calculation unit, based on quantum measurement matrix and the channel state matrix , using adaptive optimization strategy to adjust the channel state, combined with dynamic feedback mechanism to calculate the optimized channel state matrix : ; in, To optimize the adjustment coefficient, is the optimized channel state matrix; S34, adaptive channel correction unit, based on the optimized channel state matrix , adjust the adaptive channel model , correct the channel gain parameters and use the phase compensation strategy to optimize the interference error caused by phase mismatch: ; in, is the modified adaptive channel model, For the The propagation delay of each path, For the The signal attenuation factor of each path, is the signal frequency, is the time variable; S35, channel optimization parameter output unit, receives the modified adaptive channel model , generate channel optimization parameters and output them to the channel optimization and data fusion module.

[0015] Optionally, the S32 specifically includes: S321, quantum state channel analysis unit, receiving channel state matrix , analyze the time-varying characteristic parameters of the channel state matrix, including path loss, phase offset, amplitude variation and noise interference, and convert them into quantum state input signals; S322, single photon interferometry unit, based on the quantum state input signal, uses the single photon interferometry method to perform interference measurement on the coherence characteristics of the channel and obtain the interference measurement phase matrix and channel amplitude adjustment factor ; S323, quantum state phase compensation unit, receiving interference measurement phase matrix , calculate the phase offset based on the channel path characteristics , correct the channel phase error and obtain the compensated phase matrix: ; in, is the phase offset, is the phase matrix after compensation; S324, quantum measurement matrix generation unit, based on the compensated phase matrix and channel amplitude adjustment factor , construct the final quantum measurement matrix .

[0016] Optionally, the S33 specifically includes: S331, channel state data calculation unit, receiving channel state matrix and the quantum measurement matrix , based on the joint calculation of the channel state matrix and the quantum measurement matrix to optimize the parameter set : ; in, is the initial estimation function optimized for the channel state, To optimize the parameter set, including channel state adjustment factor, noise estimation and channel adaptive weight matrix ; S332, adaptive optimization parameter calculation unit, based on the optimization parameter set , using adaptive optimization strategy to calculate channel adjustment coefficient , the optimization goal is to minimize the channel state error: ; in, is the ideal channel state matrix, To optimize the objective function, is the channel adaptive weight matrix, which comes from the optimized parameter set , used to adjust the weights of different channel paths, channel adjustment coefficient Obtained by solving the following equation: ; in, is the gradient of the channel state error; When direct solution is not feasible, an iterative optimization method can be used: ; in, For the The optimized channel adjustment coefficient of the round iteration, is the step size factor, For the Optimized channel adjustment coefficients for rounds of iterations; S333, dynamic feedback adjustment unit, channel adjustment coefficient calculated based on the optimization objective function , combined with the channel error feedback mechanism, the channel state matrix Perform iterative adjustments and dynamically update the optimization adjustment coefficient , calculate the error correction : ; in, For the The error feedback value of the channel path, is the error adjustment coefficient; S334, optimize the channel state matrix calculation unit, based on the optimization adjustment coefficient , calculate the optimized channel state matrix .

[0017] Optionally, the S4 specifically includes: S41, channel optimization parameter parsing unit, receives channel optimization parameters, parses channel correction factors, and extracts channel feature adjustment vectors : ; in, Indicates the Phase offset correction value of the channel path, is the amplitude compensation factor, is the propagation delay correction, is the total number of channel paths; S42, underwater acoustic-quantum joint feature matching network construction unit, based on channel feature adjustment vector , extract the underwater acoustic signal matrix and quantum state signal matrix Feature distribution, build a joint feature matching network, and calculate the feature mapping matrix : ; in, is the feature transformation function, is the feature mapping matrix, is the transpose of the underwater acoustic signal matrix; S43, channel characteristic adaptive optimization unit, based on the feature mapping matrix and channel characteristic adjustment vector , adjust the channel adaptive weight matrix , calculate the optimized channel feature matrix : ; in, Optimize the weight coefficient for the channel, is the optimized channel feature matrix, is the channel feature matrix, is the channel adaptive weight matrix, is the channel characteristic correction factor; S44, signal fusion calculation unit, based on the optimized channel feature matrix , for the underwater acoustic signal matrix and quantum state signal matrix Perform weighted fusion and calculate the fusion signal matrix : ; in, are the fusion weight factors of the underwater acoustic signal matrix and the quantum state signal matrix, satisfying , Optimize impact factors for channels; S45, optimize the transmission parameter calculation unit, based on the fusion signal matrix , extract the channel adaptive transmission characteristics, calculate the optimized transmission parameter set, and output it to the data transmission optimization module.

[0018] The beneficial effects of the present invention are: (1) This paper combines quantum interferometry, sparse Bayesian channel modeling, adaptive channel optimization, joint feature matching network, and multimodal data fusion to construct an underwater data acquisition and transmission system. This system dynamically optimizes channel characteristics in complex underwater environments and improves the stability and accuracy of data transmission. Quantum interferometry technology is used to perform high-precision estimation of channel phase, amplitude, and propagation characteristics, effectively reducing channel estimation errors in traditional underwater acoustic communication systems and improving channel adaptability and robustness.

[0019] (2) This invention achieves efficient fusion of underwater acoustic signals and quantum state signals by constructing a joint feature matching network for underwater acoustic signals and quantum state signals, and dynamically optimizing channel characteristics based on channel optimization parameters. This method effectively alleviates the problem of traditional underwater acoustic communication systems being limited by a single signal source, improves the reliability and anti-interference capability of data transmission, and enables the system to adapt to extreme underwater environments such as deep-sea turbulence and thermoclines.

[0020] (3) This invention uses an adaptive optimization strategy to dynamically adjust the channel state and optimizes the channel characteristic matrix using a feedback mechanism. This allows the system to adjust channel parameters in real time based on underwater channel changes, thereby reducing the impact of multipath interference on data transmission. Compared with traditional static channel optimization methods, this method can maintain high signal quality in complex underwater environments and effectively improve the stability and continuity of data transmission.

[0021] (4) This invention uses a low-power adaptive transmission strategy, combined with dynamic error correction and energy management optimization, to achieve intelligent adjustment of the data transmission path, improving the energy efficiency of long-distance underwater data transmission. By optimizing the energy consumption distribution of data acquisition and transmission through an intelligent power control strategy, the system can adapt to long-term ocean observation and underwater exploration missions, extending the equipment's endurance. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings are used to provide a further understanding 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 of the present invention. In the accompanying drawings: Figure 1 This is the overall framework diagram of the underwater platform data acquisition and transmission system proposed by the present invention. DETAILED DESCRIPTION

[0023] The present invention will now be described in further detail with reference to the accompanying drawings, which are simplified schematic diagrams that illustrate the basic structure of the present invention in a schematic manner.

[0024] refer to Figure 1 , an underwater platform data acquisition and transmission system, comprising the following steps: S1, underwater data acquisition module, uses heterogeneous sensor networks to monitor underwater environmental parameters in real time, collect underwater environmental information data and perform standardized processing to form multimodal underwater data; This implementation builds a heterogeneous sensor network to monitor underwater environmental parameters in real time, including key physical and chemical parameters such as temperature, salinity, pressure, flow rate, and dissolved oxygen content. This data is collected using a variety of sensor types, including acoustic sensors, optical sensors, and quantum state sensors. The collected data undergoes standardization, unifying the scale and format of the different sensor data, removing outliers and noise, and generating multimodal underwater data with temporal synchronization and spatial consistency. This method ensures the integrity and consistency of underwater environmental data, providing a high-quality data foundation for subsequent channel modeling, channel estimation, and data fusion. This improves the system's adaptability to complex underwater environments and enhances the stability and accuracy of underwater data transmission.

[0025] S2, channel modeling module, based on multimodal underwater data, uses sparse Bayesian method to extract channel features, build an adaptive channel model, and generate channel estimation parameters; S3, channel estimation module, based on channel estimation parameters, combines quantum state interferometry measurement method to perform channel detection, correct the adaptive channel model, and generate channel optimization parameters; S4, channel optimization and data fusion module, builds a joint feature matching network of underwater acoustic signals and quantum state signals based on channel optimization parameters, dynamically optimizes channel characteristics, fuses underwater acoustic signals and quantum state signals, and generates optimized transmission parameters; S5, data transmission optimization module, based on the optimized transmission parameters, uses dynamic error correction and adaptive modulation strategy to optimize the data transmission path and generate transmission control instructions; This implementation optimizes underwater data transmission paths based on optimized transmission parameters, combined with dynamic error correction and adaptive modulation strategies. The dynamic error correction mechanism improves data transmission reliability by monitoring channel status in real time and adjusting the error correction coding method. The adaptive modulation strategy dynamically adjusts the modulation method based on channel bandwidth, noise level, and signal attenuation to optimize data transmission rate and stability. This method ensures efficient data transmission in complex underwater environments, reduces signal attenuation and bit error rate, improves communication quality, and reduces power consumption, providing stable data transmission for long-distance underwater exploration and long-term monitoring missions.

[0026] S6, the energy management module, dynamically adjusts data acquisition and transmission power based on transmission control instructions, optimizes energy consumption distribution, and achieves low-power operation.

[0027] This embodiment uses an energy management module to dynamically adjust data acquisition and transmission power according to transmission control instructions to optimize the energy consumption distribution of the underwater platform. In actual operation, the module monitors the channel status, data transmission requirements and equipment power consumption in real time, and combines the adaptive power adjustment algorithm to reduce unnecessary power consumption while ensuring data transmission quality. This method can intelligently switch transmission modes according to changes in the underwater environment, such as reducing transmission power when channel quality is better and enhancing power compensation when signal attenuation is severe, so as to extend the battery life of the equipment and improve the energy utilization of long-range underwater detection missions, making it suitable for long-term ocean monitoring and underwater autonomous systems.

[0028] In this embodiment, S2 specifically includes: S21, channel data preprocessing unit, receives multimodal underwater data, performs time synchronization, denoising and standardization conversion, removes acquisition errors and environmental interference, and generates channel input data matrix , and extract the signal frequency; S22, channel feature extraction unit, based on the channel input data matrix , using sparse Bayesian learning method to construct channel feature vector ,in, Indicates the The channel gain parameters of each path are calculated, the main characteristics and time variables of the multipath channel are extracted, and sparse modeling optimization is performed: ; in, Indicates the The channel gain parameters of the paths, is the measurement signal vector, is the channel measurement matrix, is the regularization factor, is the sparse Bayesian prior parameter, is the total number of effective multipath propagation paths in the underwater channel, that is, the channel eigenvector Dimensions; This formula extracts channel features based on a sparse Bayesian learning method to optimize the accuracy and robustness of channel estimation. The system constructs an error minimization objective function and extracts the optimal channel feature vector by minimizing the error between the measured signal and the channel model's prediction. To enhance the adaptability of the channel model, the system introduces a sparse regularization term to promote a sparse structure in the channel feature vector, thereby reducing interference from environmental noise and invalid channel paths. During this optimization process, the system iteratively optimizes the channel characteristics by solving for optimal parameters, resulting in a more accurate channel gain parameter. This parameter can effectively address multipath effects, attenuation, and interference issues in complex underwater channel conditions, providing reliable input for subsequent channel modeling and optimization.

[0029] S23, adaptive channel modeling unit, based on the channel feature vector Building an adaptive channel model , establish a time-varying feature map of the channel dynamics: ; in, is the signal frequency, is the time variable, For the The propagation delay of each path, For the The signal attenuation factor of each path, is an imaginary unit, satisfying , is the total number of effective multipath propagation paths in the underwater channel, is pi, is an exponential function; This formula simulates the complex propagation environment of underwater acoustic channels by superimposing multipath signals. The gain, phase offset, and delay of each signal path are accurately modeled to reflect the multipath effects of underwater signals. Furthermore, to characterize the temporal evolution of the channel, the model introduces an exponential decay factor to describe the energy loss caused by environmental factors during underwater propagation. This modeling approach accurately captures the time-frequency characteristics of underwater channels, providing precise channel state information for subsequent channel optimization and data transmission.

[0030] S24, channel parameter optimization unit, based on adaptive channel model , an adaptive optimization strategy is used to update the channel eigenvector to correct the effects of multipath fading and time-varying noise: ; in, For the The channel gain parameter during the round iteration, For the The updated value of the channel gain parameter after round iteration, is the step size factor, is the channel estimation error, defined as: ; in, is the channel estimation error, are the elements of the channel measurement matrix, For the A measurement signal, is the total number of effective multipath propagation paths in the underwater channel, is the number of measurement signals; This formula iteratively updates channel characteristic parameters using a gradient descent algorithm to optimize the accuracy of the channel model. The system calculates the channel estimation error based on channel measurement data and constructs an error loss function to evaluate the accuracy of the channel eigenvector. Using a gradient descent optimization strategy, the system dynamically adjusts the channel gain parameters by calculating the gradient of the loss function, gradually approaching the optimal value, thereby reducing the impact of errors on channel estimation. This optimization method can effectively reduce the impact of signal attenuation, multipath interference, and environmental noise on the channel model, making the final channel characteristic estimate more stable and providing more accurate input for subsequent channel optimization and data fusion.

[0031] S25, channel estimation parameter output unit receives the updated channel gain parameter , calculate the channel estimation parameters and transmit them to the channel estimation module.

[0032] This embodiment models underwater channels based on a sparse Bayesian learning method. A channel data preprocessing unit performs time synchronization, denoising, and normalization on multimodal underwater data, eliminating acquisition errors and environmental interference and ensuring the accuracy of channel input data. A channel feature extraction unit uses a sparse Bayesian method to construct channel feature vectors and perform sparse modeling optimization on multipath channels to reduce computational complexity and extract key channel characteristics. An adaptive channel modeling unit establishes a time-varying channel model based on the channel feature vectors, capturing channel dynamic characteristics and optimizing the channel state description. A channel parameter optimization unit uses an adaptive optimization strategy to iteratively update channel feature parameters, reducing the impact of multipath fading and time-varying noise and improving the accuracy of channel estimation. A channel estimation parameter output unit receives the optimized channel feature parameters, generates high-precision channel estimation parameters, and transmits them to a channel estimation module. By introducing sparse Bayesian learning and an adaptive optimization strategy, this method improves the accuracy and robustness of underwater channel modeling, effectively enhancing the system's adaptability to complex underwater environments and optimizing the stability and efficiency of data transmission.

[0033] In this embodiment, the S22 specifically includes: S221, channel data analysis unit, receiving channel input data matrix , analyze its time domain and frequency domain characteristics, perform preliminary statistics on the channel state of the channel input data matrix, and generate the measurement signal vector and channel measurement matrix ; S222, sparse Bayesian modeling unit, based on the measurement signal vector and channel measurement matrix , establish a sparse channel feature estimation model, use the variational Bayesian inference method to sparsely represent the channel features, and solve the channel feature vector ; S223, channel feature iterative optimization unit, using iterative optimization strategy to update the channel feature vector , optimize the channel sparsity according to the maximum a posteriori estimation principle and calculate the posterior probability: ; in, represents the likelihood probability of the measured signal, is the prior distribution of the channel feature vector, is the posterior probability of the channel feature; S224, the measurement matrix adaptive adjustment unit adjusts the channel measurement matrix according to the channel characteristic estimation result. Adjust to minimize the channel estimation error so that the measurement matrix meets the optimal estimation criterion: ; in, is the optimized channel measurement matrix, are the elements of the channel measurement matrix, For the A measurement signal, Indicates the The channel gain parameters of the paths, is the total number of effective multipath propagation paths in the underwater channel, is the number of measurement signals; This formula optimizes the channel measurement matrix based on the results of channel characteristic estimation to minimize the channel estimation error and improve the accuracy of channel modeling. The optimization process takes the optimal estimation criterion as the goal and adjusts the elements of the measurement matrix so that it can more accurately map the channel characteristics. The system calculates the error between the measurement signal and the output of the channel estimation model, and uses this error as the optimization target to adjust the parameters of the measurement matrix so that the measurement matrix can more accurately match the actual state of the channel. Through this optimization, the measurement matrix can dynamically adapt to changes in complex underwater environments, improve the accuracy of channel estimation, reduce the impact of multipath effects and environmental noise, and thus ensure the stability and reliability of subsequent channel modeling and optimization.

[0034] S225, channel feature output unit, based on the optimized channel measurement matrix , adjust the channel eigenvector , and output it to the adaptive channel modeling unit.

[0035] This implementation analyzes the time and frequency domain characteristics of the channel input data to extract the measurement signal vector and channel measurement matrix. It then uses variational Bayesian inference to sparsely model the channel characteristics, optimizing the stability of the channel estimation. Through an iterative optimization strategy, the sparsity of the channel characteristics is dynamically adjusted according to the maximum a posteriori estimation principle to improve the accuracy of channel state prediction. Based on the channel characteristic estimation results, the measurement matrix is adaptively adjusted to minimize the estimation error, ensuring the adaptability of the channel model to complex underwater environments, and outputting the optimized channel characteristic vector. This method, combining sparse Bayesian learning with a dynamic optimization strategy, overcomes the problem of the traditional underwater channel modeling method's lack of adaptability to complex environments, significantly improving the channel estimation accuracy, noise resistance, and stability of underwater data transmission.

[0036] In this embodiment, S3 specifically includes: S31, channel sounding data acquisition unit, receives channel estimation parameters, extracts time-varying state information of the channel, obtains channel attenuation, phase offset, delay distribution and noise characteristics, and generates a channel state matrix ; S32, quantum state interference measurement unit, based on the channel state matrix , using quantum interferometry to obtain the interferometry phase matrix and channel amplitude adjustment factor , and construct the quantum measurement matrix : ; in, represents the interferometric phase matrix, is the channel amplitude adjustment factor, is an imaginary unit; S33, channel state optimization calculation unit, based on quantum measurement matrix and the channel state matrix , using adaptive optimization strategy to adjust the channel state, combined with dynamic feedback mechanism to calculate the optimized channel state matrix : ; in, To optimize the adjustment coefficient, is the optimized channel state matrix; S34, adaptive channel correction unit, based on the optimized channel state matrix , adjust the adaptive channel model , correct the channel gain parameters and use the phase compensation strategy to optimize the interference error caused by phase mismatch: ; in, is the modified adaptive channel model, For the The propagation delay of each path, For the The signal attenuation factor of each path, is the signal frequency, is the time variable; This implementation utilizes a phase compensation strategy based on an optimized channel state matrix to optimize the interference error caused by channel phase mismatch. By dynamically adjusting the channel gain and phase compensation, the adaptive channel model can correct channel characteristic deviations in real time. In practice, the system extracts the signal phase drift based on the channel state matrix and calculates the optimal phase compensation value, which is applied to the channel model to reduce the phase interference error between multipath signals and optimize signal coherence. This method effectively improves the stability of the channel model, enabling precise correction of signal propagation delay and phase offset, enhancing the channel equalization capability of underwater data transmission, and reducing the impact of signal attenuation on communication quality, providing reliable guarantees for efficient communication in complex underwater environments.

[0037] S35, channel optimization parameter output unit, receives the modified adaptive channel model , generate channel optimization parameters and output them to the channel optimization and data fusion module.

[0038] This embodiment uses a channel sounding data acquisition unit to receive channel estimation parameters, analyze channel attenuation, phase offset, and noise characteristics, and construct a channel state matrix. A quantum interferometry unit uses interferometry to extract phase change and amplitude adjustment factors, generating a quantum measurement matrix to achieve high-precision measurement of channel characteristics. A channel state optimization calculation unit uses an adaptive optimization strategy based on the quantum measurement matrix and the channel state matrix to adjust the channel state. It also optimizes channel parameters using a dynamic feedback mechanism to improve the real-time adaptability of the channel model. An adaptive channel correction unit adjusts the channel model and corrects the channel gain based on the optimized channel state matrix, and reduces errors caused by channel mismatch through a phase compensation strategy. A channel optimization parameter output unit transmits the optimized channel model parameters to a channel optimization and data fusion module to ensure stable and reliable data transmission. This method achieves high-precision channel estimation, real-time optimization, and phase compensation, enhancing the underwater channel's ability to resist multipath interference and improving the stability and continuity of underwater data transmission. It also offers greater channel adaptability and robustness in complex underwater environments.

[0039] In this embodiment, the S32 specifically includes: S321, quantum state channel analysis unit, receiving channel state matrix , analyze the time-varying characteristic parameters of the channel state matrix, including path loss, phase offset, amplitude variation and noise interference, and convert them into quantum state input signals; S322, single photon interferometry unit, based on the quantum state input signal, uses the single photon interferometry method to perform interference measurement on the coherence characteristics of the channel and obtain the interference measurement phase matrix and channel amplitude adjustment factor ; S323, quantum state phase compensation unit, receiving interference measurement phase matrix , calculate the phase offset based on the channel path characteristics , correct the channel phase error and obtain the compensated phase matrix: ; in, is the phase offset, is the phase matrix after compensation; S324, quantum measurement matrix generation unit, based on the compensated phase matrix and channel amplitude adjustment factor , construct the final quantum measurement matrix .

[0040] This implementation parses the channel state matrix to extract path loss, phase offset, and noise interference information, converting them into quantum state input signals. Using single-photon interferometry, the interferometry phase matrix and channel amplitude adjustment factor are obtained. High-resolution measurements of the channel's coherence properties are performed. Combined with the channel path characteristics, the phase offset is calculated, and the channel phase error is dynamically compensated to ensure phase consistency. This compensated phase matrix is then obtained. Based on the compensated phase matrix and amplitude adjustment factor, an optimized quantum measurement matrix is constructed. This method leverages the ultra-high sensitivity of quantum state interferometry to significantly improve the accuracy of underwater channel estimation and enhance the ability to suppress multipath effects and noise interference, thereby improving the stability and reliability of data transmission.

[0041] In this embodiment, the S33 specifically includes: S331, channel state data calculation unit, receiving channel state matrix and the quantum measurement matrix , based on the joint calculation of the channel state matrix and the quantum measurement matrix to optimize the parameter set : ; in, is the initial estimation function optimized for the channel state, To optimize the parameter set, including channel state adjustment factor, noise estimation and channel adaptive weight matrix ; S332, adaptive optimization parameter calculation unit, based on the optimization parameter set , using adaptive optimization strategy to calculate channel adjustment coefficient , the optimization goal is to minimize the channel state error: ; in, is the ideal channel state matrix, To optimize the objective function, is the channel adaptive weight matrix, which comes from the optimized parameter set , used to adjust the weights of different channel paths, channel adjustment coefficient Obtained by solving the following equation: ; in, is the gradient of the channel state error; When direct solution is not feasible, an iterative optimization method can be used: ; in, For the The optimized channel adjustment coefficient of the round iteration, is the step size factor, For the Optimized channel adjustment coefficients for rounds of iterations; The formula calculates the channel adjustment coefficient based on the optimization parameter set to optimize the channel state and make it closer to the ideal channel characteristics. The system constructs a channel error minimization objective function, calculates the error between the current channel state and the ideal channel state, and takes minimizing the error as the optimization goal. The system introduces a channel adaptive weight matrix, which dynamically adjusts the weights of different channel paths by optimizing the parameter set to make the channel adjustment more targeted. During the optimization process, the system uses the gradient descent method to calculate the channel adjustment coefficient, and adopts an iterative optimization strategy when it cannot be solved directly. By gradually updating the optimization parameters, it ensures that the final channel state error is minimized. This method can effectively improve the stability and transmission efficiency of the channel state and enhance the data transmission capability of the underwater platform in complex environments.

[0042] S333, dynamic feedback adjustment unit, channel adjustment coefficient calculated based on the optimization objective function , combined with the channel error feedback mechanism, the channel state matrix Perform iterative adjustments and dynamically update the optimization adjustment coefficient , calculate the error correction : ; in, For the The error feedback value of the channel path, is the error adjustment coefficient; S334, optimize the channel state matrix calculation unit, based on the optimization adjustment coefficient , calculate the optimized channel state matrix .

[0043] This implementation constructs an optimized parameter set based on the joint calculation of the channel state matrix and the quantum measurement matrix. It then dynamically adjusts the channel weight matrix through an adaptive optimization strategy, optimizes the channel state error, and iteratively optimizes the channel adjustment coefficients using a gradient descent method, allowing the channel state matrix to gradually converge to its optimal state. Furthermore, an error feedback mechanism is used to dynamically correct the channel weights, improving the adaptive adjustment capabilities of the channel characteristics. This method can effectively reduce the impact of multipath fading in complex underwater environments, optimize signal gain, achieve real-time response to dynamic channel changes, and enhance the stability and reliability of underwater data transmission.

[0044] In this embodiment, the S4 specifically includes: S41, channel optimization parameter parsing unit, receives channel optimization parameters, parses channel correction factors, and extracts channel feature adjustment vectors : ; in, Indicates the Phase offset correction value of the channel path, is the amplitude compensation factor, is the propagation delay correction, is the total number of channel paths; S42, underwater acoustic-quantum joint feature matching network construction unit, based on channel feature adjustment vector , extract the underwater acoustic signal matrix and quantum state signal matrix Feature distribution, build a joint feature matching network, and calculate the feature mapping matrix : ; in, is the feature transformation function, is the feature mapping matrix, is the transpose of the underwater acoustic signal matrix; This embodiment is based on the channel characteristic adjustment vector, extracts the characteristic distribution of the underwater acoustic signal matrix and the quantum state signal matrix, constructs an underwater acoustic-quantum joint feature matching network, maps the feature space of the two signals through the feature transformation function, calculates the feature mapping matrix, and thus establishes a matching relationship between heterogeneous signals. The network can dynamically adjust the similarity measurement of the underwater acoustic signal and the quantum state signal, realize information fusion across physical domains, enable the system to adapt to changes in complex underwater channels, improve signal feature alignment capabilities, optimize the stability and reliability of data transmission, and enhance anti-interference capabilities, providing precise feature docking for subsequent channel optimization and data fusion, and improving the efficiency and accuracy of underwater data transmission.

[0045] S43, channel characteristic adaptive optimization unit, based on the feature mapping matrix and channel characteristic adjustment vector , adjust the channel adaptive weight matrix , calculate the optimized channel feature matrix : ; in, Optimize the weight coefficient for the channel, is the optimized channel feature matrix, is the channel feature matrix, is the channel adaptive weight matrix, is the channel characteristic correction factor; S44, signal fusion calculation unit, based on the optimized channel feature matrix , for the underwater acoustic signal matrix and quantum state signal matrix Perform weighted fusion and calculate the fusion signal matrix : ; in, are the fusion weight factors of the underwater acoustic signal matrix and the quantum state signal matrix, satisfying , Optimize impact factors for channels; S45, optimize the transmission parameter calculation unit, based on the fusion signal matrix , extract the channel adaptive transmission characteristics, calculate the optimized transmission parameter set, and output it to the data transmission optimization module.

[0046] This embodiment receives channel optimization parameters, analyzes channel correction factors, extracts channel characteristic adjustment vectors, conducts in-depth analysis of the characteristics of underwater acoustic signals and quantum state signals, constructs an underwater acoustic-quantum joint characteristic matching network, and calculates a characteristic mapping matrix to establish an optimal characteristic matching relationship between the signals. Based on the channel characteristic adjustment vector and the characteristic mapping matrix, the channel adaptive weight matrix is dynamically adjusted to optimize channel characteristics and improve signal stability. The optimized channel characteristic matrix is used for weighted fusion of underwater acoustic signals and quantum state signals to generate a fused signal matrix, ensuring the robustness and anti-interference capability of data transmission. Based on the fused signal matrix, the channel adaptive transmission characteristics are extracted, the optimized transmission parameters are calculated, and these parameters are input into the data transmission optimization module to achieve efficient and low-bit-error-rate underwater data transmission. Through the joint optimization of channel optimization and signal fusion, this method improves the reliability, anti-interference capability, and adaptive transmission capability of underwater signals, enabling the system to achieve stable and low-power data transmission in complex underwater environments.

[0047] Example 1: In order to verify the feasibility of the present invention in practice, the present invention was applied to a deep-sea exploration project of a certain ocean research center. The underwater platform data acquisition and transmission system proposed in the present invention was used to conduct long-term monitoring and data transmission of the deep-sea environment. The project deployed a series of underwater sensor nodes and data relay stations to obtain deep-sea environmental parameters, including temperature, salinity, pressure, flow rate, and underwater biological signals. However, existing underwater communication systems face significant challenges in this environment, such as severe channel attenuation, strong multipath effects, low data transmission rates, and significant signal interference. These challenges result in unstable data transmission and high packet loss rates, affecting the continuity and reliability of deep-sea observation missions.

[0048] In this scenario, the underwater platform data acquisition and transmission system of the present invention is deployed between multiple deep-sea data acquisition nodes and relay stations to optimize data transmission paths and improve the stability of underwater acoustic communications. The system uses an underwater acoustic-quantum joint feature matching network, combining feature mapping of quantum state signals and underwater acoustic signals to achieve channel optimization and data fusion. Specifically, after the underwater sensor nodes collect environmental data, they transmit it through the underwater acoustic channel. The quantum measurement unit obtains channel state information in real time and combines sparse Bayesian modeling to perform channel prediction. At the receiving end, the system extracts the features of the underwater acoustic signal and the quantum state signal, and performs signal fusion through a joint feature matching network. The channel weights are dynamically adjusted to optimize channel characteristics and improve the stability and anti-interference capability of data transmission.

[0049] In actual application, this system uses multiple underwater sensing nodes to collect data at 5-minute intervals. The data is transmitted via underwater channels to a buoy station and a submarine relay station, and then to a ground-based data center. During data transmission, the channel optimization method of the present invention is used to perform real-time channel state assessment and adaptive adjustment to reduce multipath interference and signal attenuation. Experimental data compares the performance of a traditional underwater acoustic communication system with the system of the present invention in key indicators such as data transmission reliability, packet loss rate, channel estimation error, and energy consumption. The experimental results are shown in the table below.

[0050] Table 1 Performance comparison between traditional underwater acoustic communication system and the system of the present invention

[0051] It can be seen from the experimental data that the underwater data acquisition and transmission system of the present invention has significant improvements in many key indicators compared to traditional underwater acoustic communication systems. The average data transmission rate has increased from 2.4 kbps to 5.8 kbps, an increase of 141.7%; the data packet loss rate has been reduced to 5.6%, a reduction of 69.7% compared to traditional systems. The channel estimation error has been reduced to 3.9%, which has improved the accuracy of channel state prediction and effectively optimized the data transmission path. At the same time, the bit error rate has been reduced to 4.1%, which has significantly improved the data integrity at the receiving end. In addition, the energy consumption has been reduced to 0.67 J / KB, which has improved the endurance of underwater equipment and made the system suitable for long-term ocean observation missions.

[0052] During implementation, the system also conducted channel optimization tests for extreme underwater environments. For example, in the area of ocean hydrothermal vents, due to the dramatic changes in water flow, the data transmission rate of traditional underwater acoustic communication systems fluctuates greatly. However, through quantum measurement and adaptive channel optimization, this system improves the data transmission stability index from 65.4 to 92.8, effectively reducing the impact of environmental interference on data transmission. Furthermore, in deep sea areas below 4,000 meters, where water pressure and temperature changes affect channel attenuation characteristics, this system can dynamically adjust the signal modulation strategy based on channel feedback information, reducing the bit error rate of data transmission by 73.2%, ensuring the reliability of long-distance underwater communications.

[0053] In summary, the underwater platform data acquisition and transmission system of the present invention improves the stability, reliability and energy efficiency of data transmission through the underwater acoustic-quantum joint feature matching network and adaptive channel optimization technology, significantly reduces the data loss rate and bit error rate, and optimizes energy consumption, providing an efficient and low-energy solution for deep-sea observation, marine resource exploration and underwater communication.

[0054] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. An underwater platform data acquisition and transmission system, characterized in that: The steps include: S1, underwater data acquisition module, uses heterogeneous sensor networks to monitor underwater environmental parameters in real time, collect underwater environmental information data and perform standardized processing to form multimodal underwater data; S2, channel modeling module, based on multimodal underwater data, uses sparse Bayesian method to extract channel features, build an adaptive channel model, and generate channel estimation parameters; S3, channel estimation module, based on channel estimation parameters, combines quantum state interferometry measurement method to perform channel detection, correct the adaptive channel model, and generate channel optimization parameters; S4, channel optimization and data fusion module, builds a joint feature matching network of underwater acoustic signals and quantum state signals based on channel optimization parameters, dynamically optimizes channel characteristics, fuses underwater acoustic signals and quantum state signals, and generates optimized transmission parameters; S5, data transmission optimization module, based on the optimized transmission parameters, uses dynamic error correction and adaptive modulation strategy to optimize the data transmission path and generate transmission control instructions; S6, the energy management module, dynamically adjusts data acquisition and transmission power based on transmission control instructions, optimizes energy consumption distribution, and achieves low-power operation.

2. The underwater platform data acquisition and transmission system according to claim 1, characterized in that: The S2 specifically includes: S21, channel data preprocessing unit, receives multimodal underwater data, performs time synchronization, denoising and standardization conversion, removes acquisition errors and environmental interference, and generates channel input data matrix , and extract the signal frequency; S22, channel feature extraction unit, based on the channel input data matrix , using sparse Bayesian learning method to construct channel feature vector ,in, Indicates the The channel gain parameters of each path are calculated, the main characteristics and time variables of the multipath channel are extracted, and sparse modeling optimization is performed: ； in, Indicates the The channel gain parameters of the paths, is the measurement signal vector, is the channel measurement matrix, is the regularization factor, is the sparse Bayesian prior parameter, is the total number of effective multipath propagation paths in the underwater channel, that is, the channel eigenvector Dimensions; S23, adaptive channel modeling unit, based on the channel feature vector Building an adaptive channel model , establish a time-varying feature map of the channel dynamics: ； in, is the signal frequency, is the time variable, For the The propagation delay of each path, For the The signal attenuation factor of each path, is an imaginary unit, satisfying , is the total number of effective multipath propagation paths in the underwater channel, is pi, is an exponential function; S24, channel parameter optimization unit, based on adaptive channel model , an adaptive optimization strategy is used to update the channel eigenvector to correct the effects of multipath fading and time-varying noise: ； in, For the The channel gain parameter during the round iteration, For the The updated value of the channel gain parameter after round iteration, is the step size factor, is the channel estimation error, defined as: ； in, is the channel estimation error, are the elements of the channel measurement matrix, For the A measurement signal, is the total number of effective multipath propagation paths in the underwater channel, is the number of measurement signals; S25, channel estimation parameter output unit receives the updated channel gain parameter , calculate the channel estimation parameters and transmit them to the channel estimation module.

3. The underwater platform data acquisition and transmission system according to claim 2, characterized in that: The S22 specifically includes: S221, channel data analysis unit, receiving channel input data matrix , analyze its time domain and frequency domain characteristics, perform preliminary statistics on the channel state of the channel input data matrix, and generate the measurement signal vector and channel measurement matrix ; S222, sparse Bayesian modeling unit, based on the measurement signal vector and channel measurement matrix , establish a sparse channel feature estimation model, use the variational Bayesian inference method to sparsely represent the channel features, and solve the channel feature vector ; S223, channel feature iterative optimization unit, using iterative optimization strategy to update the channel feature vector , optimize the channel sparsity according to the maximum a posteriori estimation principle and calculate the posterior probability: ； in, represents the likelihood probability of the measured signal, is the prior distribution of the channel feature vector, is the posterior probability of the channel feature; S224, the measurement matrix adaptive adjustment unit adjusts the channel measurement matrix according to the channel characteristic estimation result. Adjust to minimize the channel estimation error so that the measurement matrix meets the optimal estimation criterion: ； in, is the optimized channel measurement matrix, are the elements of the channel measurement matrix, For the A measurement signal, Indicates the The channel gain parameters of the paths, is the total number of effective multipath propagation paths in the underwater channel, is the number of measurement signals; S225, channel feature output unit, based on the optimized channel measurement matrix , adjust the channel eigenvector , and output it to the adaptive channel modeling unit.

4. The underwater platform data acquisition and transmission system according to claim 1, characterized in that: The S3 specifically includes: S31, channel sounding data acquisition unit, receives channel estimation parameters, extracts time-varying state information of the channel, obtains channel attenuation, phase offset, delay distribution and noise characteristics, and generates a channel state matrix ; S32, quantum state interference measurement unit, based on the channel state matrix , using quantum interferometry to obtain the interferometry phase matrix and channel amplitude adjustment factor , and construct the quantum measurement matrix : ； in, represents the interferometric phase matrix, is the channel amplitude adjustment factor, is an imaginary unit; S33, channel state optimization calculation unit, based on quantum measurement matrix and the channel state matrix , using adaptive optimization strategy to adjust the channel state, combined with dynamic feedback mechanism to calculate the optimized channel state matrix : ； in, To optimize the adjustment coefficient, is the optimized channel state matrix; S34, adaptive channel correction unit, based on the optimized channel state matrix , adjust the adaptive channel model , correct the channel gain parameters and use the phase compensation strategy to optimize the interference error caused by phase mismatch: ； in, is the modified adaptive channel model, For the The propagation delay of each path, For the The signal attenuation factor of each path, is the signal frequency, is the time variable; S35, channel optimization parameter output unit, receives the modified adaptive channel model , generate channel optimization parameters and output them to the channel optimization and data fusion module.

5. The underwater platform data acquisition and transmission system according to claim 4, characterized in that: The S32 specifically includes: S321, quantum state channel analysis unit, receiving channel state matrix , analyze the time-varying characteristic parameters of the channel state matrix, including path loss, phase offset, amplitude variation and noise interference, and convert them into quantum state input signals; S322, single photon interferometry unit, based on the quantum state input signal, uses the single photon interferometry method to perform interference measurement on the coherence characteristics of the channel and obtain the interference measurement phase matrix and channel amplitude adjustment factor ; S323, quantum state phase compensation unit, receiving interference measurement phase matrix , calculate the phase offset based on the channel path characteristics , correct the channel phase error and obtain the compensated phase matrix: ； in, is the phase offset, is the phase matrix after compensation; S324, quantum measurement matrix generation unit, based on the compensated phase matrix and channel amplitude adjustment factor , construct the final quantum measurement matrix .

6. The underwater platform data acquisition and transmission system according to claim 5, characterized in that: The S33 specifically includes: S331, channel state data calculation unit, receiving channel state matrix and the quantum measurement matrix , based on the joint calculation of the channel state matrix and the quantum measurement matrix to optimize the parameter set : ； in, is the initial estimation function optimized for the channel state, To optimize the parameter set, including channel state adjustment factor, noise estimation and channel adaptive weight matrix ; S332, adaptive optimization parameter calculation unit, based on the optimization parameter set , using adaptive optimization strategy to calculate channel adjustment coefficient , the optimization goal is to minimize the channel state error: ； in, is the ideal channel state matrix, To optimize the objective function, is the channel adaptive weight matrix, which comes from the optimized parameter set , used to adjust the weights of different channel paths, channel adjustment coefficient Obtained by solving the following equation: ； in, is the gradient of the channel state error; When direct solution is not feasible, an iterative optimization method can be used: ； in, For the The optimized channel adjustment coefficient of the round iteration, is the step size factor, For the Optimized channel adjustment coefficients for rounds of iterations; S333, dynamic feedback adjustment unit, channel adjustment coefficient calculated based on the optimization objective function , combined with the channel error feedback mechanism, the channel state matrix Perform iterative adjustments and dynamically update the optimization adjustment coefficient , calculate the error correction : ； in, For the The error feedback value of the channel path, is the error adjustment coefficient; S334, optimize the channel state matrix calculation unit, based on the optimization adjustment coefficient , calculate the optimized channel state matrix .

7. The underwater platform data acquisition and transmission system according to claim 1, characterized in that: The S4 specifically includes: S41, channel optimization parameter parsing unit, receives channel optimization parameters, parses channel correction factors, and extracts channel feature adjustment vectors : ； in, Indicates the Phase offset correction value of the channel path, is the amplitude compensation factor, is the propagation delay correction, is the total number of channel paths; S42, underwater acoustic-quantum joint feature matching network construction unit, based on channel feature adjustment vector , extract the underwater acoustic signal matrix and quantum state signal matrix Feature distribution, build a joint feature matching network, and calculate the feature mapping matrix : ； in, is the feature transformation function, is the feature mapping matrix, is the transpose of the underwater acoustic signal matrix; S43, channel characteristic adaptive optimization unit, based on the feature mapping matrix and channel characteristic adjustment vector , adjust the channel adaptive weight matrix , calculate the optimized channel feature matrix : ； in, Optimize the weight coefficient for the channel, is the optimized channel feature matrix, is the channel feature matrix, is the channel adaptive weight matrix, is the channel characteristic correction factor; S44, signal fusion calculation unit, based on the optimized channel feature matrix , for the underwater acoustic signal matrix and quantum state signal matrix Perform weighted fusion and calculate the fusion signal matrix : ； in, are the fusion weight factors of the underwater acoustic signal matrix and the quantum state signal matrix, satisfying , Optimize impact factors for channels; S45, optimize the transmission parameter calculation unit, based on the fusion signal matrix , extract the channel adaptive transmission characteristics, calculate the optimized transmission parameter set, and output it to the data transmission optimization module.

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