Device and method for adjusting audio distortion in ship communication equipment

Abstract

The invention discloses an audio distortion adjusting device and method in ship communication equipment, and relates to the technical field of signal processing, and the method comprises the steps: constructing a multi-dimensional environment sensing network, obtaining the positioning, environment and route information of a ship in real time, carrying out the environment trend prediction, and generating an environment change curve; based on an environment change curve, environment scene division and extreme environment extraction are carried out, multiple environment prediction combinations are generated, a signal prediction model is input for signal fluctuation prediction, signal distortion compensation is carried out according to a signal fluctuation prediction sequence, and dynamic adjustment of audio signals is carried out in the execution process through a self-adaptive signal compensation mechanism. The technical problem of poor real-time performance and stability of ship communication caused by diversity and complexity of marine environment change in the prior art is solved, and the technical effect of improving the real-time performance and stability of ship communication through real-time environment trend prediction and audio signal dynamic compensation is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of signal processing, and in particular to an audio distortion adjustment device and method in ship communication equipment. Background Art

[0002] In ship communications, the quality of audio signals directly affects communication efficiency and safety, especially in complex marine environments, where ship audio signals are often interfered with by various factors, such as signal attenuation, fluctuations, noise, etc., resulting in communication distortion. Existing ship communication equipment usually uses signal compensation technology based on static models. However, the marine environment is complex and changeable, and traditional audio distortion adjustment methods cannot respond to environmental changes and signal fluctuations in real time, resulting in unstable audio quality, affecting the real-time and stability of ship communications. Summary of the invention

[0003] The present application provides an audio distortion adjustment device and method in ship communication equipment, which is used to solve the technical problem in the prior art that the real-time and stability of ship communication are poor due to the diversity and complexity of changes in the marine environment.

[0004] The first aspect of the present application provides a method for adjusting audio distortion in ship communication equipment, the method comprising: constructing a multi-dimensional environmental perception network, obtaining real-time positioning information, real-time environmental information and target route information of a target ship, predicting environmental trends, and generating an environmental change curve; based on the environmental change curve, dividing environmental scenes and extracting extreme environments, generating multiple environmental prediction combinations; inputting the multiple environmental prediction combinations into a signal prediction model, matching corresponding signal fluctuation prediction units, predicting signal fluctuations, and obtaining a signal fluctuation prediction sequence; performing signal distortion compensation according to the signal fluctuation prediction sequence, generating an initial compensation scheme, and during the execution of the initial compensation scheme, dynamically adjusting the audio signal through an adaptive signal compensation mechanism.

[0005] The second aspect of the present application provides an audio distortion adjustment device in a ship communication device, the device comprising: an environmental trend prediction module, the environmental trend prediction module is used to build a multi-dimensional environmental perception network, obtain the real-time positioning information, real-time environmental information and target route information of the target ship, perform environmental trend prediction, and generate an environmental change curve; an environmental scene extraction module, the environmental scene extraction module is used to divide the environmental scene and extract the extreme environment based on the environmental change curve, and generate a plurality of environmental prediction combinations; a signal fluctuation prediction module, the signal fluctuation prediction module is used to input the plurality of environmental prediction combinations into a signal prediction model, match the corresponding signal fluctuation prediction units, perform signal fluctuation prediction, and obtain a signal fluctuation prediction sequence; an audio signal adjustment module, the audio signal adjustment module is used to perform signal distortion compensation according to the signal fluctuation prediction sequence, generate an initial compensation scheme, and during the execution of the initial compensation scheme, dynamically adjust the audio signal through an adaptive signal compensation mechanism.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The audio distortion adjustment device and method in the ship communication equipment provided by the present application relates to the field of signal processing technology. By constructing a multi-dimensional environment perception network, the ship positioning, environment and route information are obtained in real time, and environmental trend prediction and environmental scene division are performed to generate multiple environmental prediction combinations, and the signal prediction model is input to predict signal fluctuations, and signal distortion compensation is performed according to the prediction sequence, and the audio signal is dynamically adjusted through an adaptive mechanism. The technical problem of poor real-time and stability of ship communication due to the diversity and complexity of marine environmental changes in the prior art is solved, and the technical effect of improving the real-time and stability of ship communication through real-time environmental trend prediction and dynamic compensation of audio signals is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A schematic flow chart of a method for adjusting audio distortion in a ship communication device provided in an embodiment of the present application;

[0010] Figure 2 A schematic diagram of the structure of an audio distortion adjustment device in a ship communication device provided in an embodiment of the present application.

[0011] Explanation of the reference numerals: environmental trend prediction module 11 , environmental scene extraction module 12 , signal fluctuation prediction module 13 , audio signal adjustment module 14 . DETAILED DESCRIPTION

[0012] The present application provides an audio distortion adjustment device and method in ship communication equipment, which is used to solve the technical problem in the prior art that the real-time and stability of ship communication are poor due to the diversity and complexity of changes in the marine environment.

[0013] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0014] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0015] Embodiment 1, as Figure 1 As shown, the present application provides a method for adjusting audio distortion in ship communication equipment, the method comprising:

[0016] P10: Build a multi-dimensional environmental perception network to obtain the real-time positioning information, real-time environmental information and target route information of the target ship, make environmental trend predictions, and generate environmental change curves.

[0017] Furthermore, step P10 of the embodiment of the present application further includes:

[0018] P11: Construct a multi-dimensional environmental perception network, which includes a multi-sensor module and an environmental trend prediction model, and the multi-dimensional environmental perception network executes a dynamic progressive prediction mechanism; P12: Based on the multi-sensor module, multi-data collection is performed to obtain the real-time positioning information, real-time environmental information and target route information of the target ship; P13: The real-time positioning information, real-time environmental information and target route information are input into the environmental trend prediction model to perform environmental trend prediction and generate an environmental change curve; P14: According to the dynamic progressive prediction mechanism, rolling environmental data collection and environmental change prediction are performed according to a preset period, and the environmental change curve is dynamically corrected.

[0019] It should be understood that by building a multi-dimensional environmental perception network, comprehensive monitoring and dynamic prediction of the environment in which the target ship is located can be achieved, providing an accurate environmental basis for audio distortion adjustment.

[0020] First, a multi-dimensional environmental perception network is constructed, which consists of a multi-sensor module and an environmental trend prediction model. The multi-sensor module is responsible for collecting different types of environmental data, such as the real-time positioning of the ship, environmental meteorological parameters (wind speed, temperature, air pressure, etc.) and route information. The environmental trend prediction model generates prediction information of environmental changes by processing these data. The network implements a dynamic progressive prediction mechanism, that is, it continuously updates and predicts the changing trend of the environment based on the real-time collected data to ensure a high degree of consistency between the prediction results and the actual environmental status.

[0021] Specifically, multi-dimensional data collection is performed through a multi-sensor module. These data include real-time positioning information of the target ship (such as longitude and latitude, speed, heading, etc.), real-time environmental information (such as weather conditions, ocean conditions, etc.) and target route information (including voyage, estimated arrival time, etc.). The role of the multi-sensor module is to collect data in multiple dimensions, provide comprehensive information, and provide a basis for environmental trend prediction.

[0022] The collected real-time positioning information, environmental information and route data are input into the environmental trend prediction model. The environmental trend prediction model is based on machine learning and data analysis algorithms, which can identify patterns of environmental changes and make trend predictions. The prediction results are presented in the form of environmental change curves, which intuitively show the trend of environmental changes in the future. These change curves can reflect meteorological conditions, ocean dynamics and other environmental factors that may affect ship communications, thereby providing key information for subsequent audio distortion adjustment.

[0023] A dynamic progressive prediction mechanism is adopted to re-collect environmental data and predict environmental changes every predetermined period (such as every minute, every hour, etc.). This rolling update mechanism ensures the system's high sensitivity to environmental changes and can dynamically correct the environmental change curve. Through dynamic correction, the model can reflect slight changes in the environment in real time, making the prediction results more realistic, thereby improving compensation accuracy. The dynamic progressive prediction mechanism not only improves the real-time nature of the prediction, but also responds quickly to sudden environmental changes, providing more reliable real-time data support, enabling the system to continuously adjust itself, enhance its ability to respond to emergencies, and further improve the stability and reliability of ship communication equipment in a dynamic environment.

[0024] P20: Based on the environmental change curve, environmental scene division and extreme environment extraction are performed to generate multiple environmental prediction combinations.

[0025] Furthermore, step P20 of the embodiment of the present application further includes:

[0026] P21: Perform trend analysis on the environmental change curve to identify and obtain multiple environmental feature sets; P22: Perform cluster analysis on the multiple environmental feature sets to divide them into multiple weather scenes; P23: Based on the environmental change curve, extract extreme environmental nodes that exceed the environmental threshold and mark them as extreme scenes; P24: Combine the multiple weather scenes and extreme environmental nodes to generate multiple environmental prediction combinations, and the multiple environmental prediction combinations cover conventional and extreme environmental conditions.

[0027] Optionally, based on the environmental change curve generated above, the environmental data is deeply analyzed and divided to identify different environmental scenarios and extract extreme environmental conditions, so as to provide a basis for generating a more accurate and comprehensive prediction combination.

[0028] First, a trend analysis is performed on the generated environmental change curve. Regular changes in the curve can be extracted through trend analysis algorithms (such as regression analysis or sliding average method). These changes include information such as short-term fluctuations and long-term trends in the climate, from which multiple environmental feature sets are identified. These environmental feature sets represent the change patterns of environmental variables (such as temperature, humidity, wind speed, etc.) in different time periods. For example, a rapid rise in temperature over a period of time may mean that hot weather is about to begin, while an increase in wind speed may indicate the arrival of a storm.

[0029] Next, the identified multiple sets of environmental features are clustered and analyzed, and clustering algorithms (such as K-means clustering or hierarchical clustering) are used to divide the environmental features into multiple weather scenarios according to their similarities. These scenarios can include normal weather conditions (such as clear, showers, etc.) and abnormal weather conditions (such as strong winds, thunderstorms, etc.). Through cluster analysis, the system can automatically group different environmental patterns to facilitate subsequent environmental combination generation and prediction.

[0030] After analyzing the environmental change curve, the system will further extract extreme environmental nodes that exceed the preset environmental threshold. These nodes represent extreme weather conditions that may have a significant impact on ship communications, such as extremely high temperatures, high wind speeds, lightning, etc. By setting environmental thresholds, these extreme environmental nodes can be automatically identified and marked, and classified as extreme scenarios. Extreme scenarios usually require special processing because they may have a significant impact on the performance, stability and signal quality of communication equipment.

[0031] Finally, the divided multiple weather scenarios are combined with the marked extreme environment nodes to generate multiple environmental prediction combinations. These combinations not only cover conventional weather changes (such as sunny days, showers, etc.), but also include extreme weather scenarios (such as typhoons, blizzards, etc.). Through such environmental combinations, the system can comprehensively predict the performance of communication signals under different environmental conditions, and provide a multi-dimensional basis for subsequent signal fluctuation prediction and distortion compensation. Combining conventional and extreme environmental scenarios to generate multiple environmental prediction combinations can ensure that the communication system can adapt to various complex and changing environmental conditions, thereby enhancing the robustness and flexibility of the system.

[0032] P30: Input the multiple environmental prediction combinations into a signal prediction model, match corresponding signal fluctuation prediction units, perform signal fluctuation prediction, and obtain a signal fluctuation prediction sequence.

[0033] It should be understood that based on multiple environmental prediction combinations, the signal fluctuation is predicted through the signal prediction model, and then the predicted signal fluctuation sequence is obtained, which provides a basis for subsequent signal distortion compensation. The core of this process is to use historical data and environmental change predictions, combined with machine learning or deep learning models, to model and predict the impact of environmental changes on signal fluctuations.

[0034] Specifically, first, multiple environmental prediction combinations are used as input data and input into the signal prediction model. These environmental prediction combinations contain a variety of environmental scenarios, including regular and extreme weather conditions, such as sunny days, showers, strong winds, thunderstorms, etc. The signal prediction model can identify the signal fluctuation pattern under each environmental condition according to different environmental changes. The input data usually includes a set of environmental features (such as temperature, humidity, wind speed, etc.), and the corresponding environmental scenarios (such as strong winds, thunderstorms, etc.), which constitute the multi-dimensional information of the model input.

[0035] In the signal prediction model, according to the input environmental prediction combination, the system will automatically match the corresponding signal fluctuation prediction unit. Each prediction unit is responsible for modeling the signal fluctuation under a specific environmental scenario, usually using time series prediction methods (such as ARIMA model, long short-term memory network LSTM, etc.) for modeling. Different signal fluctuation prediction units generate corresponding signal fluctuation prediction models based on different environmental characteristics and historical data. For example, in strong wind weather, the signal may be more disturbed, so the fluctuation amplitude is larger, while in stable weather conditions, the signal fluctuation is smaller. By matching specific environmental scenarios and corresponding fluctuation prediction units, the system can more accurately simulate the changing trend of the signal under different environmental conditions.

[0036] After the prediction unit matching is completed, the signal prediction model will predict the signal fluctuation according to the selected prediction unit. The model will use regression analysis, deep neural network or other machine learning algorithms to predict the trend of signal fluctuation in the future time period based on the combination of historical data and input environmental prediction. Through the time series analysis of signal fluctuations, the signal strength changes at multiple time points and the possible fluctuation range can be obtained. For example, the model may predict that in the next 10 minutes, the signal will have 2 obvious fluctuations, each with a fluctuation range of 10%. These prediction information provides a real-time basis for subsequent signal compensation.

[0037] Finally, the signal prediction model will output a complete signal fluctuation prediction sequence, which represents the expected change in signal strength over a period of time in the future. The signal fluctuation prediction sequence contains not only the time nodes of signal changes, but also the predicted value and fluctuation amplitude at each time node. This information can help the communication system identify and process signal changes in advance, and provide accurate reference data for subsequent signal distortion compensation.

[0038] Through the above steps, the signal fluctuation sequence can be quickly predicted according to real-time environmental changes, providing high-precision data support for signal distortion compensation. This process will improve the signal reliability and stability of ship communication equipment under complex environmental conditions and effectively avoid communication failures caused by signal fluctuations.

[0039] P40: Perform signal distortion compensation according to the signal fluctuation prediction sequence to generate an initial compensation scheme, and during the execution of the initial compensation scheme, dynamically adjust the audio signal through an adaptive signal compensation mechanism.

[0040] Furthermore, step P40 of the embodiment of the present application also includes:

[0041] P41: Extract key fluctuation features based on the signal fluctuation prediction sequence, and determine the signal distortion type based on the key fluctuation features, and the signal distortion type includes at least one type; P42: Based on the signal distortion type, match the target signal compensation calculation unit, and generate a corresponding signal compensation scheme in combination with the predicted signal distortion information; P43: Integrate various types of predicted signal distortion information and signal compensation schemes to generate the initial compensation scheme.

[0042] In a possible embodiment of the present application, possible signal distortion is compensated according to the signal fluctuation prediction sequence, and an initial compensation scheme is generated. To ensure the accuracy and real-time performance of the compensation process, this step extracts key features from the signal fluctuation, identifies the type of signal distortion, and dynamically generates and adjusts the compensation strategy for the audio signal according to the prediction results.

[0043] First, based on the signal fluctuation prediction sequence, the key fluctuation features are extracted. These key features usually include signal amplitude fluctuations, frequency changes, and phase shifts. By analyzing the characteristics of signal fluctuations, it is possible to identify which fluctuations are caused by environmental factors and which fluctuations are abnormal or distorted. Common types of signal distortion include amplitude distortion, frequency distortion, and phase distortion. Specifically, amplitude distortion usually manifests as a sudden increase or decrease in signal strength, and frequency distortion manifests as a shift in signal frequency. This distortion may affect communication quality and cause signal reception errors.

[0044] By analyzing the characteristics of the signal fluctuation prediction sequence, the type of signal distortion can be clearly determined. For example, in severe weather conditions, strong winds may cause amplitude distortion of the signal, while thunderstorms may cause frequency distortion of the signal. The determination of this type of distortion is the basis for the formulation of subsequent compensation plans.

[0045] Next, according to the identified signal distortion type, the appropriate signal compensation calculation unit is automatically selected. For example, in the case of amplitude distortion, a gain control algorithm may need to be applied to restore the signal strength; in the case of frequency distortion, a frequency correction algorithm may be required to correct the frequency offset of the signal. The signal compensation calculation unit can be based on traditional digital signal processing (DSP) technology, or optimized in combination with modern machine learning methods. The unit will combine the predicted signal distortion information to generate a preliminary signal compensation scheme to ensure that the compensation strategy is adjusted for the specific distortion type and distortion amplitude. Moreover, the generation of the compensation scheme depends not only on the distortion type of the signal, but also needs to consider the impact of other environmental factors (such as ship movement, weather changes, etc.) on the signal. Therefore, the system needs to continuously receive real-time data and dynamically adjust the compensation strategy to cope with signal fluctuations in different scenarios.

[0046] After the initial compensation scheme is generated, all generated compensation strategies will be integrated with different types of signal distortion information to form an initial compensation scheme. This scheme is a basic scheme for correcting signal distortion, which usually includes signal amplitude adjustment, frequency offset repair, and noise suppression. In order to ensure the optimization of the compensation effect, the system will dynamically adjust the execution process of the compensation scheme. Through the adaptive signal compensation mechanism, the compensation effect is monitored in real time, and the compensation strategy is automatically adjusted according to the actual signal quality feedback. For example, if the signal distortion is not fully compensated, the system will increase the amplitude of signal enhancement or adjust the accuracy of frequency compensation. The adaptive mechanism ensures that the compensation process can flexibly respond to changes in signal quality under complex environmental conditions.

[0047] Through the above steps, the application can quickly generate targeted signal compensation solutions based on real-time signal fluctuation prediction and signal distortion type identification, and ensure the optimization of compensation effect through dynamic adjustment. This process significantly improves the signal stability of ship communication equipment in complex environments, effectively reduces communication interference caused by signal distortion, and ensures the improvement of communication quality.

[0048] Furthermore, the embodiment of the present application further includes step P50, and step P50 further includes:

[0049] P51: Connect to the distributed computing platform to split the computing tasks of signal fluctuation prediction and signal distortion compensation into multiple parallel subtasks respectively; P52: Process the multiple parallel subtasks in parallel to quickly generate a signal compensation solution.

[0050] Optionally, in this embodiment, in order to meet the requirements of real-time and high efficiency in ship communications, a distributed computing platform is used to accelerate the calculation process of signal fluctuation prediction and distortion compensation. The distributed computing platform realizes the rapid processing of computing tasks by splitting the signal prediction and compensation tasks into multiple parallel subtasks, and greatly reduces the computing time and delay, ensuring timely response to environmental changes and signal distortion.

[0051] First, the entire signal fluctuation prediction and distortion compensation calculation process is split into multiple parallel subtasks by connecting to a distributed computing platform. Distributed computing platforms are usually based on cloud computing architecture or high-performance computing clusters, which can process large amounts of data and perform distributed computing. In this process, the signal fluctuation prediction and distortion compensation tasks are decomposed into independent modules, each of which is responsible for different computing tasks. For example:

[0052] For signal fluctuation prediction, the fluctuation prediction process can be divided into signal fluctuation calculation tasks in multiple regions, and each subtask is responsible for processing signal predictions at different ship positions and under different environmental conditions. For signal distortion compensation, the compensation task can be split into multiple subtasks according to the distortion type and compensation strategy, and each subtask performs compensation calculations for a specific type of distortion (such as frequency distortion, amplitude distortion, etc.). This task splitting method can improve the efficiency of parallel computing and accelerate the entire signal processing process.

[0053] After splitting the tasks, the distributed computing platform will execute these parallel subtasks simultaneously. Each subtask runs independently, using the resources of the computing platform to process data in parallel and complete its own computing tasks. Through parallel processing, the system can obtain the calculation results of each subtask in a shorter time, thereby quickly generating a signal compensation solution.

[0054] The key advantage of parallel computing is that it significantly reduces the total computing time. For example, in traditional serial computing, all tasks must be executed in sequence, while parallel computing allocates computing resources to allow multiple tasks to be performed simultaneously, greatly shortening the time. The resource scheduling and task allocation mechanism of the distributed platform ensures the efficiency and stability of the computing process.

[0055] By splitting the signal fluctuation prediction and distortion compensation tasks in parallel, the calculation process is made more efficient. Traditional signal processing methods usually use serial calculations, which are prone to delays. This solution greatly speeds up the processing speed through parallel calculations, ensuring that the compensation scheme can be adjusted in real time in a dynamic environment, and improving the response speed and stability of ship communications.

[0056] In addition, the introduction of a distributed computing platform makes the system more scalable, able to cope with more complex and dynamic environmental data and communication tasks, and ensure that accurate signal compensation solutions can be quickly generated under different environmental changes and large-scale data flow conditions.

[0057] Furthermore, step P51 of the embodiment of the present application further includes:

[0058] P51-1: Adopt a distributed computing architecture and build a distributed computing platform, which is configured with multiple independent computing nodes and corresponding storage space; P51-2: Based on the distributed computing platform, divide the signal fluctuation prediction task according to time windows or geographical areas, generate multiple fluctuation prediction sub-tasks, divide the distortion compensation task according to the signal distortion type, and obtain multiple distortion compensation sub-tasks; P51-3: Associate the multiple fluctuation prediction sub-tasks and the multiple distortion compensation sub-tasks to generate the multiple parallel sub-tasks.

[0059] It should be understood that the parallel processing of signal fluctuation prediction and signal distortion compensation is further optimized through a more precise distributed computing architecture configuration.

[0060] First, a computing platform is built by adopting a distributed computing architecture. The platform consists of multiple independent computing nodes, each of which is equipped with independent storage space and can independently process computing tasks. Each computing node has a certain computing power and storage capacity, and can independently process the tasks assigned to it, and can also work with other nodes to complete large-scale computing tasks together.

[0061] This distributed architecture design ensures that the platform is highly scalable and can dynamically expand computing resources according to task load. For example, when the computing requirements for signal prediction and compensation increase, the system can automatically add computing nodes to cope with higher computing requirements. At the same time, the independent storage space of each node ensures distributed storage and fast access to data, avoiding the problem of single-node storage bottleneck.

[0062] After the distributed computing platform is built, the system divides the computing tasks according to specific needs. For the signal fluctuation prediction task, the fluctuation prediction task can be divided into multiple subtasks according to the time window (for example, prediction in hours) or geographical area (such as the location of different ships). This can localize the prediction task so that each computing node can independently perform signal fluctuation calculations in different areas or time periods, thereby improving computing efficiency.

[0063] For signal distortion compensation tasks, tasks can be divided according to different signal distortion types (such as amplitude distortion, frequency distortion, etc.). Each subtask is responsible for a specific type of distortion compensation, ensuring that the most suitable compensation strategy is adopted for different distortion problems. This division method can maximize the use of computing resources and realize parallel processing of tasks.

[0064] After completing the task division, the system will reasonably associate multiple fluctuation prediction subtasks and distortion compensation subtasks according to the correlation between the tasks. This step identifies which fluctuation prediction tasks and distortion compensation tasks are temporally or spatially related, and combines them into parallel subtasks so that related computing tasks can be executed at the same time. For example, when the signal fluctuation prediction of a certain area is completed, the related distortion compensation task can be started immediately to avoid delays caused by the sequential relationship between tasks. Through task association, various types of data can be processed more efficiently, and the generation of compensation plans can be more compact and timely.

[0065] By accurately dividing the signal fluctuation prediction task and the distortion compensation task, and utilizing the parallel processing capability of the distributed computing platform, this embodiment can effectively improve computing efficiency, reduce processing delays, and meet the strict requirements of the ship communication system for real-time and efficiency. The distributed computing architecture not only ensures the scalability of the platform, but also provides technical support for the efficient processing of complex computing tasks.

[0066] Furthermore, step P52 of the embodiment of the present application also includes:

[0067] P52-1: Prioritize the multiple parallel subtasks, and based on the priority sorting results, extract high priority subtasks, optimize the splitting granularity, and generate a priority processing task subset; P52-2: Use the priority processing task subset to update and replace the multiple parallel subtasks to generate a refined parallel subtask set; P52-3: Based on the multiple independent computing nodes and the corresponding storage space, the refined parallel subtask set is evenly distributed through a load balancing mechanism to form a plurality of task processing units; P52-4: Perform parallel signal distortion compensation analysis using the multiple task processing units to generate a signal compensation solution.

[0068] Specifically, multiple parallel subtasks are processed in detail to ensure that the tasks can be executed efficiently, prioritized, and balanced.

[0069] First, prioritize the multiple parallel subtasks. Prioritization is based on the importance and urgency of the task and its impact on the final signal compensation effect. For example, the signal fluctuation prediction subtask may need to be prioritized according to the urgency of the actual environmental changes, while the distortion compensation subtask needs to be prioritized according to the severity of the distortion type.

[0070] After sorting, the system will extract high-priority subtasks and optimize the split granularity of these high-priority tasks. The purpose of granularity optimization is to reduce the management burden caused by too many small-granularity tasks while ensuring the accuracy of the tasks. By adjusting the task granularity, the optimal use of computing resources can be achieved, excessive splitting or overly coarse task division can be avoided, and the system processing efficiency can be improved. Finally, these optimized high-priority tasks will be formed into a priority task subset for further processing.

[0071] After generating the priority task subset, the system will update and replace the original parallel subtasks. This update and replacement is not just a simple task replacement, but adjusts the execution order and processing strategy of tasks according to the priority of tasks, optimized granularity, and real-time changes in the execution environment. In this way, it can be ensured that during the task execution process, high-priority tasks are given priority, while low-priority tasks can be postponed or processed in parallel if resources allow. This process will generate a refined parallel subtask set, that is, a set of optimized and adjusted parallel tasks that can match computing resources more efficiently and minimize the processing bottleneck of the system.

[0072] After the refined parallel subtask set is generated, the system will evenly distribute the task set to multiple independent computing nodes through the load balancing mechanism. The load balancing mechanism is designed to ensure that the load of each computing node remains within a reasonable range to avoid overloading of a single node or waste of idle resources. By evenly distributing tasks, the computing resources of the system can be used most effectively to avoid task backlogs or waste of resources. After the tasks of each computing node are assigned, multiple task processing units are formed, and each unit independently executes the computing tasks it is responsible for, ensuring efficient parallel processing of tasks.

[0073] Finally, after the task processing unit is formed, the system will start these parallel processing units to analyze the signal distortion and perform specific parallel signal distortion compensation analysis. Through parallel processing, multiple task processing units simultaneously analyze different signal fluctuation characteristics, distortion types and environmental conditions to quickly generate signal compensation solutions. This parallel analysis greatly improves the response speed of signal distortion compensation, allowing ship communication equipment to accurately compensate for signal distortion in real time.

[0074] In summary, the embodiments of the present application have at least the following technical effects:

[0075] The present application constructs a multi-dimensional environmental perception network to obtain the ship's positioning, environment and route information in real time, predict environmental trends, generate environmental change curves, divide environmental scenes and extract extreme environments based on the environmental change curves, generate multiple environmental prediction combinations, input signal prediction models to predict signal fluctuations, compensate for signal distortion based on the signal fluctuation prediction sequence, and dynamically adjust the audio signal during execution through an adaptive signal compensation mechanism.

[0076] The technical effect of improving the real-time and stability of ship communications is achieved through real-time environmental trend prediction and audio signal dynamic compensation.

[0077] Embodiment 2, based on the same inventive concept as the method for adjusting audio distortion in ship communication equipment in the aforementioned embodiment, Figure 2As shown, the present application provides an audio distortion adjustment device in a ship communication device, and the device and method embodiments in the present application are based on the same inventive concept. The device includes:

[0078] The environmental trend prediction module 11 is used to build a multi-dimensional environmental perception network, obtain the real-time positioning information, real-time environmental information and target route information of the target ship, perform environmental trend prediction, and generate an environmental change curve.

[0079] The environmental scene extraction module 12 is used to perform environmental scene division and extreme environment extraction based on the environmental change curve to generate multiple environmental prediction combinations.

[0080] The signal fluctuation prediction module 13 is used to input the plurality of environmental prediction combinations into a signal prediction model, match corresponding signal fluctuation prediction units, perform signal fluctuation prediction, and obtain a signal fluctuation prediction sequence.

[0081] The audio signal adjustment module 14 is used to perform signal distortion compensation according to the signal fluctuation prediction sequence, generate an initial compensation scheme, and during the execution of the initial compensation scheme, dynamically adjust the audio signal through an adaptive signal compensation mechanism.

[0082] Furthermore, the environmental trend prediction module 11 is also used to perform the following steps:

[0083] A multi-dimensional environmental perception network is constructed, the multi-dimensional environmental perception network includes a multi-sensor module and an environmental trend prediction model, and the multi-dimensional environmental perception network executes a dynamic progressive prediction mechanism; based on the multi-sensor module, multi-data collection is performed to obtain the real-time positioning information, real-time environmental information and target route information of the target ship; the real-time positioning information, real-time environmental information and target route information are input into the environmental trend prediction model to perform environmental trend prediction and generate an environmental change curve; according to the dynamic progressive prediction mechanism, rolling environmental data collection and environmental change prediction are performed according to a preset period, and the environmental change curve is dynamically corrected.

[0084] Furthermore, the environment scene extraction module 12 is further configured to perform the following steps:

[0085] Perform trend analysis on the environmental change curve to identify and obtain multiple environmental feature sets; perform cluster analysis on the multiple environmental feature sets to divide them into multiple weather scenes; based on the environmental change curve, extract extreme environmental nodes that exceed environmental thresholds and mark them as extreme scenes; combine the multiple weather scenes and extreme environmental nodes to generate multiple environmental prediction combinations, and the multiple environmental prediction combinations cover conventional and extreme environmental conditions.

[0086] Furthermore, the audio signal adjustment module 14 is further configured to perform the following steps:

[0087] According to the signal fluctuation prediction sequence, key fluctuation features are extracted, and the signal distortion type is determined according to the key fluctuation features, and the signal distortion type includes at least one type; based on the signal distortion type, a target signal compensation calculation unit is matched, and a corresponding signal compensation scheme is generated in combination with the predicted signal distortion information; various types of predicted signal distortion information and signal compensation schemes are integrated to generate the initial compensation scheme.

[0088] Furthermore, the device also includes:

[0089] A computing task splitting module, which is used to connect to a distributed computing platform to split the computing tasks of signal fluctuation prediction and signal distortion compensation into multiple parallel subtasks respectively; a parallel processing module, which is used to perform parallel processing on the multiple parallel subtasks to quickly generate a signal compensation solution.

[0090] Furthermore, the computing task splitting module is also used to perform the following steps:

[0091] A distributed computing architecture is adopted to build a distributed computing platform, which is configured with multiple independent computing nodes and corresponding storage space; based on the distributed computing platform, the signal fluctuation prediction task is divided according to time windows or geographical areas to generate multiple fluctuation prediction subtasks, and the distortion compensation task is divided according to the signal distortion type to obtain multiple distortion compensation subtasks; the multiple fluctuation prediction subtasks and the multiple distortion compensation subtasks are associated and corresponded to generate the multiple parallel subtasks.

[0092] Furthermore, the parallel processing module is also used to perform the following steps:

[0093] The multiple parallel subtasks are prioritized, and based on the priority sorting results, high priority subtasks are extracted, the split granularity is optimized, and a priority processing task subset is generated; the priority processing task subset is used to update and replace the multiple parallel subtasks to generate a refined parallel subtask set; based on the multiple independent computing nodes and the corresponding storage space, the refined parallel subtask set is evenly distributed through a load balancing mechanism to form a plurality of task processing units; parallel signal distortion compensation analysis is performed using the multiple task processing units to generate a signal compensation solution.

[0094] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0095] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0096] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A method for adjusting audio distortion in ship communication equipment, characterized in that: The method comprises: Build a multi-dimensional environmental perception network to obtain the real-time positioning information, real-time environmental information and target route information of the target ship, make environmental trend predictions and generate environmental change curves; Based on the environmental change curve, environmental scene division and extreme environment extraction are performed to generate multiple environmental prediction combinations; Input the plurality of environmental prediction combinations into a signal prediction model, match corresponding signal fluctuation prediction units, perform signal fluctuation prediction, and obtain a signal fluctuation prediction sequence; Signal distortion compensation is performed according to the signal fluctuation prediction sequence to generate an initial compensation scheme, and during the execution of the initial compensation scheme, the audio signal is dynamically adjusted through an adaptive signal compensation mechanism.

2. The method for adjusting audio distortion in ship communication equipment according to claim 1, characterized in that: The method further comprises: Connect to the distributed computing platform and split the computing tasks of signal fluctuation prediction and signal distortion compensation into multiple parallel subtasks; The multiple parallel subtasks are processed in parallel to quickly generate a signal compensation solution.

3. The method for adjusting audio distortion in ship communication equipment according to claim 2, characterized in that: Connect to the distributed computing platform and split the signal fluctuation prediction and signal distortion compensation computing tasks into multiple parallel subtasks, including: A distributed computing architecture is adopted to build a distributed computing platform, wherein the distributed computing platform is configured with multiple independent computing nodes and corresponding storage spaces; Based on the distributed computing platform, the signal fluctuation prediction task is divided according to the time window or geographical area to generate multiple fluctuation prediction subtasks, and the distortion compensation task is divided according to the signal distortion type to obtain multiple distortion compensation subtasks; The multiple fluctuation prediction subtasks and the multiple distortion compensation subtasks are associated with each other to generate the multiple parallel subtasks.

4. The method for adjusting audio distortion in ship communication equipment according to claim 3, characterized in that: The plurality of parallel subtasks are processed in parallel to quickly generate a signal compensation solution, including: Prioritize the multiple parallel subtasks, and extract high-priority subtasks according to the priority sorting results, optimize the splitting granularity, and generate a priority task subset; Using the priority processing task subset, updating and replacing the multiple parallel subtasks to generate a refined parallel subtask set; According to the multiple independent computing nodes and the corresponding storage spaces, the refined parallel subtask sets are evenly distributed through a load balancing mechanism to form multiple task processing units; The plurality of task processing units are used to perform parallel signal distortion compensation analysis to generate a signal compensation solution.

5. The method for adjusting audio distortion in ship communication equipment according to claim 1, characterized in that: Build a multi-dimensional environmental perception network to obtain the real-time positioning information, real-time environmental information and target route information of the target ship, make environmental trend predictions, and generate environmental change curves, including: Constructing a multi-dimensional environment perception network, the multi-dimensional environment perception network includes a multi-sensor module and an environmental trend prediction model, and the multi-dimensional environment perception network implements a dynamic progressive prediction mechanism; Based on the multi-sensor module, multi-data collection is performed to obtain real-time positioning information, real-time environmental information and target route information of the target ship; Inputting the real-time positioning information, real-time environmental information and target route information into the environmental trend prediction model to perform environmental trend prediction and generate an environmental change curve; According to the dynamic progressive prediction mechanism, rolling environmental data collection and environmental change prediction are carried out according to a preset period, and the environmental change curve is dynamically corrected.

6. The method for adjusting audio distortion in ship communication equipment according to claim 1, characterized in that: Based on the environmental change curve, environmental scene division and extreme environment extraction are performed to generate multiple environmental prediction combinations, including: Performing trend analysis on the environmental change curve to identify and obtain multiple environmental feature sets; Performing cluster analysis on the multiple environmental feature sets to divide the multiple weather scenes; Based on the environmental change curve, extreme environmental nodes exceeding environmental thresholds are extracted and marked as extreme scenarios; The multiple weather scenarios and extreme environment nodes are combined to generate multiple environmental prediction combinations, where the multiple environmental prediction combinations cover conventional and extreme environmental conditions.

7. The method for adjusting audio distortion in ship communication equipment according to claim 1, characterized in that: Performing signal distortion compensation according to the signal fluctuation prediction sequence to generate an initial compensation scheme includes: Extracting key fluctuation features according to the signal fluctuation prediction sequence, and determining a signal distortion type according to the key fluctuation features, wherein the signal distortion type includes at least one type; Based on the signal distortion type, matching the target signal compensation calculation unit, and generating a corresponding signal compensation scheme in combination with the predicted signal distortion information; The initial compensation scheme is generated by integrating various types of predicted signal distortion information and signal compensation schemes.

8. An audio distortion adjustment device in ship communication equipment, characterized in that: The device comprises: An environmental trend prediction module, which is used to build a multi-dimensional environmental perception network, obtain real-time positioning information, real-time environmental information and target route information of the target ship, perform environmental trend prediction, and generate an environmental change curve; An environmental scene extraction module, the environmental scene extraction module is used to perform environmental scene division and extreme environment extraction based on the environmental change curve, and generate multiple environmental prediction combinations; A signal fluctuation prediction module, the signal fluctuation prediction module is used to input the plurality of environmental prediction combinations into a signal prediction model, match corresponding signal fluctuation prediction units, perform signal fluctuation prediction, and obtain a signal fluctuation prediction sequence; The audio signal adjustment module is used to perform signal distortion compensation according to the signal fluctuation prediction sequence, generate an initial compensation scheme, and during the execution of the initial compensation scheme, dynamically adjust the audio signal through an adaptive signal compensation mechanism.

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