Multi-mode hybrid communication transmission system applied to underwater unmanned monitoring device

Abstract

The application discloses a multi-mode hybrid communication transmission system applied to an underwater unmanned monitoring device and relates to the technical field of underwater wireless communication.The underwater environmental parameters, the device operating state and the underwater sound ranging data are acquired through a data acquisition module, corresponding multi-source perception sequences are generated, the multi-source perception sequences are preprocessed through a data processing module, and standardized multi-source perception sequences are obtained; the standardized multi-source perception sequences are input into a constructed multi-mode evaluation model, and underwater sound channel quality factors and optical communication availability factors are output; an optimal communication control scheme is screened according to the underwater sound channel quality factors and the optical communication availability factors by a communication decision module, and communication control is implemented according to the optimal communication control scheme by a feedback module; the system realizes adaptive fusion and intelligent switching of underwater sound communication and underwater optical communication in a complex underwater environment, and effectively improves the communication reliability, the data transmission efficiency and the environmental adaptability of the underwater unmanned monitoring device in a variable water area.

Description

Technical Field

[0001] This invention relates to the field of underwater wireless communication technology, specifically a multi-mode hybrid communication transmission system applied to underwater unmanned monitoring equipment. Background Technology

[0002] Underwater unmanned monitoring equipment plays an increasingly important role in marine environmental monitoring, underwater resource exploration, national defense and security and underwater facility inspection. Such equipment usually needs to perform tasks autonomously in complex underwater environments for a long time and transmit the collected hydrological, biological, geological or acoustic images and other data back to the shore-based or ship-based control center. The performance of the communication link directly determines the real-time performance, reliability and operational efficiency of the monitoring system. Among existing underwater wireless communications, underwater acoustic communication has narrow bandwidth, low speed, large latency and is easily affected by the environment; underwater optical communication has high speed but extremely short transmission distance and strict alignment requirements; electromagnetic wave communication is not suitable for small devices due to power consumption and size limitations; a single communication mode cannot simultaneously achieve long-distance control and high-speed data transmission, and has obvious application shortcomings. How to meet the comprehensive requirements of underwater unmanned monitoring equipment for transmission distance, data rate, power consumption control and environmental adaptability is a problem we need to solve. To this end, we now provide a multi-mode hybrid communication transmission system for underwater unmanned monitoring equipment. Summary of the Invention

[0003] The purpose of this invention is to provide a multi-mode hybrid communication transmission system for use in underwater unmanned monitoring equipment.

[0004] The objective of this invention can be achieved through the following technical solution: a multi-mode hybrid communication transmission system applied to underwater unmanned monitoring equipment, comprising the following: The data acquisition module is used to acquire underwater environmental parameters, equipment operating status and underwater acoustic ranging data, and generate corresponding multi-source sensing sequences; The data processing module is used to process the multi-source sensing sequences to obtain standardized multi-source sensing sequences; The multi-mode evaluation module is used to input standardized multi-source sensing sequences into the constructed multi-mode evaluation model and output the underwater acoustic channel quality factor and optical communication availability factor. The communication decision module is used to generate multiple candidate communication schemes based on the underwater acoustic channel quality factor and the optical communication availability factor, and to screen the optimal communication control scheme. The execution feedback module is used to implement communication control according to the optimal communication control scheme.

[0005] Furthermore, the process by which the data acquisition module acquires underwater environmental parameters, equipment operating status, and underwater acoustic ranging data includes: The data acquisition module consists of several distributed sensing nodes, which are deployed at various key locations of the underwater unmanned monitoring device. Each distributed sensing node is equipped with different types of sensors, and a collection cycle is set to acquire the corresponding sensing data. The sensing data includes underwater environmental parameters, equipment operating status, and underwater acoustic ranging data. The underwater environmental parameters include current depth, water turbidity, water temperature, salinity, and underwater acoustic background noise level; The device operating status includes the current remaining power, the amount of data to be transmitted, the data urgency level, the device's travel speed, and attitude angle. The underwater acoustic ranging data includes the distance to the target receiving node and the azimuth angle of the target node.

[0006] Furthermore, the obtained underwater environmental parameters, equipment operating status, and underwater acoustic ranging data are integrated to obtain the corresponding multi-source sensing sequence.

[0007] Furthermore, the data processing module is used to process the multi-source sensing sequences to obtain standardized multi-source sensing sequences. The process includes: For multi-source sensing sequences, sampling is performed within the acquisition period to obtain several sampling points, and each sampling point is labeled. The mean and standard deviation within the acquisition period are obtained based on the multi-source sensing sequence corresponding to each sampling point. Based on the corresponding mean and standard deviation, we iterate through the underwater environmental parameters, equipment operating status and underwater acoustic ranging data within the collection period to determine whether there are abnormal values. If there are, we remove and repair the abnormal values ​​to obtain a standardized multi-source sensing sequence.

[0008] Furthermore, the process of inputting standardized multi-source sensing sequences into the constructed multi-mode evaluation model and outputting the underwater acoustic channel quality factor and optical communication availability factor includes: A multi-modal evaluation model is constructed based on deep learning methods, and the constructed multi-modal evaluation model is initialized. Collect sample data, which includes historical operating parameters under different underwater environments and equipment conditions. The historical operating parameters include historical depth, historical water turbidity, historical water temperature, historical salinity, historical underwater acoustic background noise level, historical remaining power, historical amount of data to be transmitted, historical data urgency level, historical navigation speed, and historical attitude angle. The collected sample data is divided into training and test sets; The multi-mode evaluation model is trained using the training set to obtain training results. The training results are then validated using the test set. If the training results meet the preset requirements, the training of the multi-mode evaluation model is complete. If the training results do not meet the preset requirements, the model parameters are adjusted or the training data is expanded, and the training is repeated until the training results meet the expectations or the number of training iterations reaches the preset limit, thus completing the training of the model and obtaining the multi-mode evaluation model. The obtained standardized multi-source sensing sequence is input into the trained multi-mode evaluation model, and the multi-mode evaluation model outputs the underwater acoustic channel quality factor and optical communication availability factor corresponding to the standardized multi-source sensing sequence in the current environment.

[0009] Furthermore, the process of generating multiple candidate communication schemes based on the underwater acoustic channel quality factor and the optical communication availability factor includes: Based on the standardized multi-source sensing sequence and the underwater acoustic channel quality factor and optical communication availability factor output by the multi-mode evaluation model under the current environment, multiple candidate communication control schemes are generated. Each candidate scheme differs in control parameters such as the selected communication mode identifier, transmit power level, data rate, whether link aggregation is enabled, and whether low-power wake-up is enabled.

[0010] Furthermore, the process of selecting the optimal communication control scheme includes: The comprehensive transmission demand factor is obtained based on the amount of data to be transmitted, the urgency level of the data, and the remaining power. The comprehensive evaluation index of the corresponding scheme is obtained based on the underwater acoustic channel quality factor, optical communication availability factor, comprehensive transmission demand factor, and communication parameters of candidate communication control schemes. After obtaining the comprehensive evaluation index of all candidate communication control schemes, the comprehensive evaluation index of each candidate communication control scheme is sorted from largest to smallest, and the candidate communication control scheme with the largest comprehensive evaluation index is selected as the optimal communication control scheme.

[0011] Furthermore, the process of implementing communication control according to the optimal communication control scheme includes: The generated optimal communication control scheme is mapped to standardized digital instructions, which include: Based on the selected communication mode identifier, the corresponding underwater acoustic communication module or underwater optical communication module will be activated. Adjust the output level of the power amplifier of the corresponding module according to the transmission power level; Whether to enable load balancing and packet reordering at the data link layer depends on whether link aggregation is enabled.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention acquires underwater environmental parameters, equipment operating status and underwater acoustic ranging data through the data acquisition module, generates corresponding multi-source sensing sequences, realizes all-round three-dimensional sensing of the underwater environment, significantly improves the integrity and real-time performance of environmental parameter acquisition, and preprocesses the multi-source sensing sequences through the data processing module to obtain standardized multi-source sensing sequences, effectively eliminating the impact of sensor noise and sudden interference on data quality, and ensuring the reliability and consistency of input data; By inputting standardized multi-source sensing sequences into the constructed multi-mode evaluation model, the underwater acoustic channel quality factor and optical communication availability factor are output, overcoming the shortcomings of traditional fixed thresholds or empirical formulas that are difficult to adapt to variable water areas, and improving the evaluation accuracy and dynamic response capability. The communication decision module selects the optimal communication control scheme based on the underwater acoustic channel quality factor and the optical communication availability factor. The execution feedback module implements communication control based on the optimal communication control scheme. This comprehensively leverages the complementary advantages of the long-distance reliability of underwater acoustic communication and the high speed of underwater optical communication, significantly reducing the failure risk of a single communication mode under adverse conditions such as turbidity, depth, and dynamic disturbances, extending the effective operating time of underwater unmanned monitoring equipment, and ensuring the real-time transmission capability of key data. This system enables adaptive fusion and intelligent switching between underwater acoustic communication and underwater optical communication in complex underwater environments, effectively improving the communication reliability, data transmission efficiency, and environmental adaptability of underwater unmanned monitoring equipment in variable waters. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0014] Figure 1 This is a schematic diagram of the present invention. Detailed Implementation

[0015] like Figure 1 As shown, a multi-mode hybrid communication transmission system applied to underwater unmanned monitoring equipment includes a data acquisition module, a data processing module, a multi-mode evaluation module, a communication decision module, and an execution feedback module. The data acquisition module is used to acquire underwater environmental parameters, equipment operating status and underwater acoustic ranging data, and generate corresponding multi-source sensing sequences. The data processing module is used to process the multi-source sensing sequence to obtain a standardized multi-source sensing sequence; The multi-mode evaluation module is used to input standardized multi-source sensing sequences into the constructed multi-mode evaluation model and output underwater acoustic channel quality factor and optical communication availability factor. The communication decision module is used to generate multiple candidate communication schemes based on the underwater acoustic channel quality factor and the optical communication availability factor, and to screen the optimal communication control scheme. The execution feedback module is used to implement communication control according to the optimal communication control scheme.

[0016] It should be further explained that, in the specific implementation process, the data acquisition module acquires underwater environmental parameters, equipment operating status, and underwater acoustic ranging data, including: The data acquisition module consists of several distributed sensing nodes, which are deployed at various key locations of the underwater unmanned monitoring device. Each distributed sensing node is equipped with different types of sensors, and a collection cycle is set to acquire the corresponding sensing data. The sensing data includes underwater environmental parameters, equipment operating status, and underwater acoustic ranging data. The underwater environmental parameters include current depth, water turbidity, water temperature, salinity, and underwater acoustic background noise level; The device operating status includes the current remaining power, the amount of data to be transmitted, the data urgency level, the device's travel speed, and attitude angle. The underwater acoustic ranging data includes the distance to the target receiving node and the azimuth angle of the target node.

[0017] It should be further explained that, in the specific implementation process, the data acquisition module generates the corresponding multi-source sensing sequence based on underwater environmental parameters, equipment operating status, and underwater acoustic ranging data, including: The underwater environmental parameters, equipment operating status and underwater acoustic ranging data are integrated to obtain the corresponding multi-source sensing sequence; Specifically: Each distributed sensing node is labeled as i, where i = 1, 2, ..., n; The depth, water turbidity, water temperature, salinity, underwater background noise level, remaining battery power, amount of data to be transmitted, data urgency level, navigation speed, attitude angle, distance to the target receiving node, and azimuth angle of the target node obtained by the distributed sensing node labeled i are respectively marked as follows: , , , , , , , , , , , Obtain the multi-source sensing sequence corresponding to the distributed sensing node, denoted as . ,Right now: .

[0018] It should be further explained that, in the specific implementation process, the data processing module is used to process the multi-source sensing sequences to obtain standardized multi-source sensing sequences. The process includes: For multi-source sensing sequences, sampling is performed within the acquisition period to obtain several sampling points. Each sampling point is labeled and denoted as... ,in ; The mean and standard deviation of the multi-source sensing sequences within the acquisition period are obtained, denoted as . and ,in: ; ; in, This indicates that the multi-source sensing sequence is in the first... The value at each sampling time, This represents the total number of sampling points within the period. Iterate through the underwater environmental parameters, equipment operating status, and underwater acoustic ranging data within the acquisition period. If the data meets the requirements... If the corresponding data is an anomaly, it is marked as such and removed. The removed data is then interpolated and repaired based on the mean of adjacent time windows to obtain a standardized multi-source sensing sequence. .

[0019] It should be further explained that, in the specific implementation process, the process of inputting the standardized multi-source sensing sequence into the constructed multi-mode evaluation model and outputting the underwater acoustic channel quality factor and optical communication availability factor includes: A multi-modal evaluation model is constructed based on deep learning methods, and the constructed multi-modal evaluation model is initialized. Collect sample data, which includes historical operating parameters under different underwater environments and equipment conditions. The historical operating parameters include historical depth, historical water turbidity, historical water temperature, historical salinity, historical underwater acoustic background noise level, historical remaining power, historical amount of data to be transmitted, historical data urgency level, historical navigation speed, and historical attitude angle. The collected sample data is divided into training and test sets; The multi-mode evaluation model is trained using the training set to obtain training results. The training results are then validated using the test set. If the training results meet the preset requirements, the training of the multi-mode evaluation model is complete. If the training results do not meet the preset requirements, the model parameters are adjusted or the training data is expanded, and the training is repeated until the training results meet the expectations or the number of training iterations reaches the preset limit, thus completing the training of the model and obtaining the multi-mode evaluation model. The obtained standardized multi-source sensing sequence is input into the trained multi-mode evaluation model. The multi-mode evaluation model outputs the underwater acoustic channel quality factor and optical communication availability factor corresponding to the standardized multi-source sensing sequence in the current environment, denoted as [reference 1]. .

[0020] It should be further explained that, in the specific implementation process, the process of generating multiple candidate communication schemes based on the underwater acoustic channel quality factor and the optical communication availability factor includes: Based on the standardized multi-source sensing sequence and the underwater acoustic channel quality factor and optical communication availability factor output by the multi-mode evaluation model under the current environment, the communication decision module generates multiple candidate communication control schemes. Each candidate scheme differs in control parameters such as the selected communication mode identifier, transmit power level, data rate, whether link aggregation is enabled, and whether low-power wake-up is enabled, which facilitates the calculation of comprehensive evaluation indicators and the selection of the optimal scheme.

[0021] It should be further explained that, in the specific implementation process, the process of selecting the optimal communication control scheme includes: The generated candidate communication control schemes are labeled and denoted as k, where k = 1, 2, ..., m; Each candidate communication control scheme includes specific communication parameters, such as the selected communication mode identifier, transmit power level, data rate, whether link aggregation is enabled, and whether low-power wake-up is enabled, denoted as follows: ; The communication mode identifier has the following values: 1 represents pure underwater acoustic communication, 2 represents pure underwater optical communication, and 3 represents the convergence mode of underwater acoustic and optical communication. The comprehensive transmission demand factor is obtained based on the amount of data to be transmitted, the urgency level of the data, and the remaining battery power. ,Right now: ; in, Data urgency level, The amount of data to be transmitted. Remaining battery power , Preset weighting coefficients; The comprehensive evaluation index of the corresponding scheme is calculated based on the underwater acoustic channel quality factor, optical communication availability factor, integrated transmission demand factor, and communication parameters of candidate communication control schemes, denoted as . ,Right now: ; Among them, if ,but ,like ,but ,like ,but , Indicates the target data rate. Indicates the maximum permissible transmission power level; After obtaining the comprehensive evaluation index of all candidate communication control schemes, the comprehensive evaluation index of each candidate communication control scheme is sorted from largest to smallest, and the candidate communication control scheme with the largest comprehensive evaluation index is selected as the optimal communication control scheme.

[0022] It should be further explained that, in the specific implementation process, the process of implementing communication control according to the optimal communication control scheme includes: The generated optimal communication control scheme is mapped into standardized digital instructions, which include three parts; Specifically: Based on the selected communication mode identifier, the underwater acoustic communication module or underwater optical communication module is activated via the power management circuit. Adjust the output level of the power amplifier of the corresponding module according to the transmission power level; Whether to enable load balancing and packet reordering at the data link layer depends on whether link aggregation is enabled.

[0023] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications or equivalent substitutions made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A multi-mode hybrid communication transmission system for underwater unmanned monitoring equipment, characterized in that, include: The data acquisition module is used to acquire underwater environmental parameters, equipment operating status and underwater acoustic ranging data, and generate corresponding multi-source sensing sequences; The data processing module is used to process the multi-source sensing sequences to obtain standardized multi-source sensing sequences; The multi-mode evaluation module is used to input standardized multi-source sensing sequences into the constructed multi-mode evaluation model and output the underwater acoustic channel quality factor and optical communication availability factor. The communication decision module is used to generate multiple candidate communication schemes based on the underwater acoustic channel quality factor and the optical communication availability factor, and to screen the optimal communication control scheme. The execution feedback module is used to implement communication control according to the optimal communication control scheme.

2. The multi-mode hybrid communication transmission system for underwater unmanned monitoring equipment according to claim 1, characterized in that, The process by which the data acquisition module acquires underwater environmental parameters, equipment operating status, and underwater acoustic ranging data includes: The data acquisition module consists of several distributed sensing nodes, which are deployed at various key locations of the underwater unmanned monitoring device. Each distributed sensing node is equipped with different types of sensors, and a collection cycle is set to acquire the corresponding sensing data. The sensing data includes underwater environmental parameters, equipment operating status, and underwater acoustic ranging data. The underwater environmental parameters include current depth, water turbidity, water temperature, salinity, and underwater acoustic background noise level; The device operating status includes the current remaining power, the amount of data to be transmitted, the data urgency level, the device's travel speed, and attitude angle. The underwater acoustic ranging data includes the distance to the target receiving node and the azimuth angle of the target node.

3. The multi-mode hybrid communication transmission system for underwater unmanned monitoring equipment according to claim 2, characterized in that, The underwater environmental parameters, equipment operating status, and underwater acoustic ranging data are integrated to obtain the corresponding multi-source sensing sequence.

4. The multi-mode hybrid communication transmission system for underwater unmanned monitoring equipment according to claim 3, characterized in that, The data processing module is used to process multi-source sensing sequences. The process of obtaining standardized multi-source sensing sequences includes: For multi-source sensing sequences, sampling is performed within the acquisition period to obtain several sampling points, and each sampling point is labeled. The mean and standard deviation within the acquisition period are obtained based on the multi-source sensing sequence corresponding to each sampling point. Based on the corresponding mean and standard deviation, we iterate through the underwater environmental parameters, equipment operating status and underwater acoustic ranging data within the collection period to determine whether there are abnormal values. If there are, we remove and repair the abnormal values ​​to obtain a standardized multi-source sensing sequence.

5. The multi-mode hybrid communication transmission system for underwater unmanned monitoring equipment according to claim 4, characterized in that, The process of inputting standardized multi-source sensing sequences into a constructed multi-mode evaluation model and outputting underwater acoustic channel quality factors and optical communication availability factors includes: A multi-modal evaluation model is constructed based on deep learning methods, and the constructed multi-modal evaluation model is initialized. Collect sample data, which includes historical operating parameters under different underwater environments and equipment conditions. The historical operating parameters include historical depth, historical water turbidity, historical water temperature, historical salinity, historical underwater acoustic background noise level, historical remaining power, historical amount of data to be transmitted, historical data urgency level, historical navigation speed, and historical attitude angle. The collected sample data is divided into training and test sets; The multi-mode evaluation model is trained using the training set to obtain training results. The training results are then validated using the test set. If the training results meet the preset requirements, the training of the multi-mode evaluation model is complete. If the training results do not meet the preset requirements, the model parameters are adjusted or the training data is expanded, and the training is repeated until the training results meet the expectations or the number of training iterations reaches the preset limit, thus completing the training of the model and obtaining the multi-mode evaluation model. The obtained standardized multi-source sensing sequence is input into the trained multi-mode evaluation model, and the multi-mode evaluation model outputs the underwater acoustic channel quality factor and optical communication availability factor corresponding to the standardized multi-source sensing sequence in the current environment.

6. The multi-mode hybrid communication transmission system for underwater unmanned monitoring equipment according to claim 5, characterized in that, The process of generating multiple candidate communication schemes based on the underwater acoustic channel quality factor and the optical communication availability factor includes: Based on the standardized multi-source sensing sequence and the underwater acoustic channel quality factor and optical communication availability factor output by the multi-mode evaluation model under the current environment, multiple candidate communication control schemes are generated. Each candidate scheme differs in control parameters such as the selected communication mode identifier, transmit power level, data rate, whether link aggregation is enabled, and whether low-power wake-up is enabled.

7. The multi-mode hybrid communication transmission system for underwater unmanned monitoring equipment according to claim 6, characterized in that, The process of selecting the optimal communication control scheme includes: The comprehensive transmission demand factor is obtained based on the amount of data to be transmitted, the urgency level of the data, and the remaining power. The comprehensive evaluation index of the corresponding scheme is obtained based on the underwater acoustic channel quality factor, optical communication availability factor, comprehensive transmission demand factor, and communication parameters of candidate communication control schemes. After obtaining the comprehensive evaluation index of all candidate communication control schemes, the comprehensive evaluation index of each candidate communication control scheme is sorted from largest to smallest, and the candidate communication control scheme with the largest comprehensive evaluation index is selected as the optimal communication control scheme.

8. The multi-mode hybrid communication transmission system for underwater unmanned monitoring equipment according to claim 7, characterized in that, The process of implementing communication control according to the optimal communication control scheme includes: The generated optimal communication control scheme is mapped to standardized digital instructions, which include: Based on the selected communication mode identifier, the corresponding underwater acoustic communication module or underwater optical communication module will be activated. Adjust the output level of the power amplifier of the corresponding module according to the transmission power level; Whether to enable load balancing and packet reordering at the data link layer depends on whether link aggregation is enabled.

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