CN116599602A

CN116599602A - Low-energy-consumption long-distance sonar communication system for ocean monitoring

Info

Publication number: CN116599602A
Application number: CN202310875797.9A
Authority: CN
Inventors: 薛钰飞; 佘炎; 杨耀明; 杨建鹏; 甘冰
Original assignee: Beijing Aht Technology Co ltd
Current assignee: Beijing Aht Technology Co ltd
Priority date: 2023-07-18
Filing date: 2023-07-18
Publication date: 2023-08-15
Anticipated expiration: 2043-07-18
Also published as: CN116599602B

Abstract

The invention discloses a low-energy-consumption long-distance sonar communication system for ocean monitoring, which relates to the field of radio measurement and solves the technical problem of improving the long-distance sonar communication capability; the output end of the sensor module is connected with the input end of the control calculation module; the output end of the control calculation module is connected with the input end of the acoustic wave communication module; the output end of the self-adaptive module is connected with the input end of the acoustic wave communication module; the output end of the auxiliary detection equipment is connected with the input end of the control calculation module; the invention greatly improves the ocean monitoring capability.

Description

Low-energy-consumption long-distance sonar communication system for ocean monitoring

Technical Field

The invention relates to the field of radio measurement, in particular to a low-energy-consumption long-distance sonar communication system for ocean monitoring.

Background

With the demands of global ocean resource development and ocean environmental protection, ocean monitoring using low-power long-distance communication systems has become increasingly important. The marine monitoring system needs to be able to stably and efficiently transmit signals to monitor changes in the marine environment and acquire real-time data. Traditional ocean monitoring adopts LoRa technology to realize data communication.

Low Power Long Range communication technology is a low-power-consumption broadband signal transmission system based on spread spectrum modulation, and utilizes a wireless network to realize long-distance transmission of information. The LoRa technology gradually becomes the main stream choice in the field of the Internet of things due to low energy consumption and long-distance conveying capability; the method has wide application in various fields of intelligent cities, agriculture, traffic management and the like; although this communication technology is advantageous in many aspects, as a low-power long-distance communication system for ocean monitoring, the LoRa technology still has some significant drawbacks; when long-distance data transmission is carried out, the LoRa technology has a limit on the signal transmission rate; while it may guarantee reliability of data, a lower data transmission rate may result in delays in remotely monitoring information and data; the marine environment is complex and changeable, and factors such as meridian lines, fishing boats and the like easily influence the transmission of wireless signals; this may lead to instability and packet loss of signal reception, affecting the accuracy and reliability of the overall marine monitoring system; while the LoRa technology has the advantage of low power consumption, deploying a large number of nodes in a wide range of marine monitoring scenarios can result in higher equipment investment costs. Meanwhile, due to the severe and unstable marine environment, additional cost and challenges are brought to maintenance work of equipment, and the LoRa technology has a good long-distance transmission effect, but the transmission speed of signals is sacrificed. If a marine monitoring task with intensive data updating is required, the LoRa technology may not meet the requirement of real-time monitoring.

Therefore, the invention discloses a low-energy-consumption long-distance sonar communication system for ocean monitoring.

Disclosure of Invention

Aiming at the defects of the prior art, the invention discloses a low-energy-consumption long-distance sonar communication system for ocean monitoring, which can realize efficient and stable communication and provide reliable ocean environment data; through the acoustic wave communication module, the underwater long-distance and low-power-consumption communication is realized, and meanwhile, the underwater long-distance transmission and the high-speed data transmission are realized; the problem of poor underwater propagation effect of the traditional radio communication is effectively solved; through the sensor module, high-precision monitoring and monitoring of various data are realized, and reliable data support is provided for ocean monitoring. Through the energy management module, the system power consumption is reduced, and the service life of the equipment is prolonged. The receiving analysis and the processing of the data are realized through controlling the calculation module, so that the system can operate more efficiently; through the self-adaptive adjusting module, the system can be automatically adjusted according to actual demands, so that the system can stably and efficiently work under different environments. By the aid of the auxiliary detection equipment, diversification of data provided by the system is achieved.

The invention adopts the following technical scheme:

A low-energy long-range sonar communication system for marine monitoring, the system comprising:

the wireless navigation module is used for determining the position of sonar communication information in the ocean, and the wireless navigation module realizes wireless navigation by setting a differential global positioning system and receiving signals of the differential global positioning system; the differential global positioning system uses differential correction signals sent by a ground base station to improve the accuracy of GPS signals so as to improve the accuracy of position measurement and the accuracy of a radio navigation module; the radio navigation module comprises an information receiving module, an information transmitting module, a first long-distance sonar information identification module, a communication protocol selection module and an improved GPS positioning module;

the differential global positioning system comprises a second long-distance sonar information identification module, a navigation calculation module and a position selection module so as to improve the accuracy of GPS signals;

the sensor module is used for monitoring a complex and changeable ocean environment, and ocean data information monitored by the sensor module at least comprises ocean wind power, wind direction, weather temperature, salinity, water depth and flow rate;

the sound wave communication module is used for transmitting ocean monitoring data; the sound wave communication module adopts a high-speed data transmission network, an anti-multipath interference transmission channel and an array signal processing method to realize long-distance transmission of sound wave signals in the ocean;

The energy management module is used for collecting, distributing and managing the power consumption of the whole system, and ensuring that the energy consumption is always in the lowest state; the energy management module comprises an energy collection unit and an energy optimization unit so as to realize a low-power-consumption operation system, wherein the energy collection unit is used for collecting energy of the external environment and converting the energy into usable energy, and comprises a solar photovoltaic power generation device, a wind power generation device and a float type wave power generation device; the energy optimizing unit is used for optimizing and distributing the collected energy to ensure continuous low-energy consumption operation of the marine sonar communication system; the output end of the energy collecting unit is connected with the input end of the energy optimizing unit;

the control calculation module is used for receiving and processing the ocean monitoring data collected by the sensor module; the control calculation module comprises a data receiving module and a calculation module, wherein the data receiving module is used for receiving and sending ocean monitoring data, and the calculation module is used for preprocessing, decoding, demodulating, integrating and analyzing the data;

the self-adaptive adjusting module is used for adjusting the sound wave frequency, the wave beam width and the transmission power in real time under the conditions of different environmental noise and seawater, and the self-adaptive adjusting module uses a fuzzy logic controller and a high-efficiency self-adaptive algorithm to realize real-time adjustment of the sound wave frequency, the wave beam width and the transmission power;

The auxiliary detection equipment is used for more comprehensively carrying out ocean monitoring and comprises an underwater camera, a multi-beam echo depth sounder, a temperature sensor and a salinity sensor;

the output end of the radio navigation module is connected with the input end of the acoustic wave communication module; the output end of the sensor module is connected with the input end of the control calculation module; the output end of the control calculation module is connected with the input end of the acoustic wave communication module; the output end of the self-adaptive module is connected with the input end of the acoustic wave communication module; the output end of the auxiliary detection equipment is connected with the input end of the control calculation module; the energy management module works in the whole course.

As a further technical scheme of the invention, the first long-distance sonar information identification module comprises an information receiving module, a feature extraction module, a deep learning module, a matching tracking module and a space-time analysis module; the identification and tracking of the target signal are realized through preprocessing sonar information, extracting features and analyzing.

The working method of the first long-distance sonar information identification module is as follows:

the method comprises the steps that firstly, signal preprocessing is achieved through Fourier transformation, filtering and beam forming on sonar signals by the information receiving module, so that signal quality is improved, and noise is reduced;

Step two, extracting features, wherein the feature extraction module extracts instantaneous frequency, amplitude and purity features of the received sonar signals through a multi-feature acute neural network;

step three, deep learning, wherein the deep learning module trains mass sonar signal samples through a deep learning network so as to master the characteristics and rules of historical sonar signals;

step four, matching and tracking, wherein the matching and tracking module performs matching and identification on the extracted sonar signal characteristics and the historical sonar signal characteristics through big data comparison and analysis so as to improve the target positioning and tracking accuracy;

and fifthly, carrying out space-time analysis, wherein the space-time analysis module predicts the propagation paths, speeds and directions of the sonar signal time domain and space domain signals through cloud computing so as to assist in target positioning and tracking and improve the target positioning and tracking efficiency.

As a further technical scheme of the invention, the sensor module comprises a water depth monitoring unit, a temperature monitoring unit, a salinity monitoring unit and a flow rate monitoring unit, wherein the water depth monitoring unit monitors the sea water depth by adopting a piezoelectric pressure sensor, and the piezoelectric pressure sensor measures the water depth by using the pascal law between the pressure and the depth of a water column;

The temperature monitoring unit calculates the water temperature by adopting an NTC thermistor and a resistivity temperature coefficient function; the temperature coefficient of resistivity function output formula is:

（1）

in the case of the formula (1),the NTC thermistor has a temperature of +>Resistance value at time, < >>Is NTC thermistor temperatureResistance value at time, < >>Is->And->Is a difference in (2);

the salinity monitoring unit monitors the salinity of the seawater by adopting a conductivity sensor, and the flow rate monitoring unit monitors the flow rate of the seawater by adopting a Doppler sonar method DST; the Doppler sonar technology DST measures the speed and direction of ocean current by measuring the frequency variation of the received sound wave, the frequency output function of the received sound waveThe method comprises the following steps:

（2）

in formula (2), v and u are the speed of the observer relative to the medium and the speed of the wave source relative to the medium, respectively, v is the propagation speed of the sound wave, f is the frequency emitted by the wave source, v >0 or v <0 indicates that the observer approaches or deviates from the wave source, and u >0 or u <0 indicates that the wave source approaches or deviates from the observer, respectively.

As a further technical scheme of the invention, the high-speed data transmission network reduces the data volume through data compression and sound velocity formulas so as to improve the transmission rate; the sound velocity formula is given by,

（3）

in formula (3), C represents the sound velocity, T represents the medium temperature, S represents the salinity, and D represents the depth.

As a further technical scheme of the invention, the energy optimizing unit adopts an energy management strategy to optimally allocate and store the collected energy; the energy optimization unit comprises a battery management tool, a charging controller, an energy scheduling network and an intelligent load management tool; the energy scheduling network dynamically allocates the parts according to the power requirement and the priority of the system so as to realize the optimal utilization of energy.

As a further technical scheme of the invention, the multipath interference resistant transmission channel adopts a self-adaptive protocol selection method, a multiprotocol concurrent transmission method, an advanced network topology structure, a distributed coordination strategy and a radio technology to realize flexible switching of sonar communication protocols: the self-adaptive protocol selection method adopts an optimal choice algorithm model to select an optimal communication protocol so as to improve communication efficiency and quality.

As a further technical scheme of the invention, the optimal choice algorithm model comprises an input layer, a data layer, a model layer, an algorithm layer, an optimization layer and an output layer, and the self-adaptive protocol selection method adopts the optimal choice algorithm model to select the optimal communication protocol and comprises the following steps:

S1, inputting data, performing format conversion on ocean monitoring data and communication protocol characteristic data, and inputting the ocean monitoring data and the communication protocol characteristic data into an optimal choice algorithm model through an input layer;

s2, determining calculated targets and basic parameters, and acquiring calculation parameters and limiting conditions from input data through a data layer, wherein the calculation parameters and the limiting conditions comprise calculation scale, an objective function, limiting conditions and variable ranges so as to ensure rationality and effectiveness of an optimal solution process;

s3, establishing an optimal choice mathematical model, wherein the model layer establishes an optimal choice mathematical model of the communication protocol based on the ocean monitoring data and the communication protocol characteristics;

s4, adopting an algorithm to solve the problem, adopting an optimal choice algorithm model to carry out iterative computation, parameter correction and comparison between a computation result and a true value by the algorithm layer, and acquiring a neighbor list of the computation node according to the distribution condition of the objective function and the computation node, wherein the optimal choice algorithm model optimizes the computation speed by maintaining the neighbor list of the computation node;

s5, carrying out fine control and optimization on the solving process, merging or splitting a measuring unit through an optimization layer, improving the calculation accuracy, setting a threshold value and iteration times through a self-adaptive parameter selection mode, and distributing calculation tasks to a plurality of processors or calculation nodes through the optimization layer by adopting a parallel calculation mode so as to improve the calculation speed;

S6, outputting a result, and outputting a calculation result through an output layer.

As a further technical scheme of the invention, the fuzzy logic controller realizes the self-adaptive adjustment of the system control by adjusting parameters and rules according to the surrounding real-time state and feedback information, and the working method of the fuzzy logic controller is as follows: :

step 1, defining input variables and output variables, wherein the input variables are environmental noise and sea water conditions, and the output variables are sound wave frequency, wave beam width and transmission power;

step 2, discretizing the input variable and the output variable into fuzzy sets;

step 3, combining the specific input variable value with rules in the rule base to obtain a fuzzy set of the output variable;

step 4, converting the fuzzy set into a specific numerical value by using a mean value maximum method;

step 5, optimizing parameters of the fuzzy logic controller by utilizing errors between the actual environment signals and the predicted signals;

and 6, adjusting the extracted sound wave frequency, the beam width and the transmission power in real time, and sending signals to sonar equipment so as to realize self-adaption under various environmental conditions.

As a further technical scheme of the invention, the high-efficiency self-adaptive algorithm adjusts the self-adaptive weight by calculating the minimum mean square error of the sound wave frequency, the wave beam width and the transmission power so as to reduce the error of the self-adaptive result; the efficient adaptive algorithm includes a least mean square algorithm that calculates a weight vector W by the following formula:

（4）

In formula (4), W (n) is a weight vector of the acoustic wave frequency, the beam width, and the transmission power at time n, μ is a learning rate parameter, e (n) is an error signal at time n, and x (n) is an input signal.

Has the positive beneficial effects that:

Drawings

FIG. 1 is a schematic diagram of the overall architecture of a low-energy-consumption long-distance sonar communication system for marine monitoring;

FIG. 2 is a diagram of an acoustic communication module architecture of a low-energy-consumption long-distance sonar communication system for marine monitoring;

fig. 3 is a diagram showing the refractive error correction of radio-electric distance measurement electric waves of the low-energy-consumption long-distance sonar communication system for ocean monitoring.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

the energy management module is used for collecting, distributing and managing the power consumption of the whole system, and ensuring that the energy consumption is always in the lowest state; the energy management module comprises an energy collection unit and an energy optimization unit so as to realize a low-power-consumption operation system, wherein the energy collection unit is used for collecting energy of the external environment and converting the energy into usable energy, and comprises a solar power generation device, a wind power generation device and a float-type wave energy generation device; the energy optimizing unit is used for optimizing and distributing the collected energy to ensure continuous low-energy consumption operation of the marine sonar communication system; the output end of the energy collecting unit is connected with the input end of the energy optimizing unit;

In a specific embodiment, the low-energy consumption long-distance sonar communication system for ocean monitoring comprises the following characteristics:

low energy consumption: by reducing the signal transmission power and adopting an efficient modulation and demodulation technology, the energy required by the system operation is reduced, and the endurance time of the sensor and the equipment is prolonged.

Long-distance transmission: on the basis of optimizing signal processing and modulation-demodulation technology, the transmission distance of sonar communication is improved so as to meet the monitoring requirement in wide sea areas.

The anti-interference performance is strong: the advanced signal processing technology and the multipath compensation algorithm are adopted, so that the transmission stability of signals in a complex marine environment is improved, and acoustic interference of submarine topography, water flow, marine organisms and the like is effectively resisted.

The time delay is small: by adopting high-efficiency encoding, decoding and data compression technology, the time delay of data transmission is reduced, and quick response is realized.

Ad hoc networking capability: the system has the functions of autonomous configuration and automatic addressing, and can realize wireless communication among nodes, network relay and adjustment of dynamic topological structure.

The fault tolerance is high: by utilizing the redundancy design and the self-adaptive adjustment strategy, the usability and stability of the system can be ensured even under the condition of local faults.

Positioning and navigation functions: high-precision positioning and guiding of the marine monitoring device are realized through time difference measurement among multiple nodes and sonar communication technology.

In the above embodiment, the first long-distance sonar information identification module includes an information receiving module, a feature extraction module, a deep learning module, a matching tracking module, and a space-time analysis module; the identification and tracking of the target signal are realized through preprocessing sonar information, extracting features and analyzing.

In a specific embodiment, the information receiving module may be a terminal with wireless data communication, such as a mobile phone, a tablet computer or a computer, and these modules are usually integrated in a chip or a memory in the device, and are connected to the wireless communication module by radio or other manners so as to receive information from other devices. The information receiving module can realize signal preprocessing by carrying out Fourier transform, filtering and beam forming on sonar signals so as to improve the signal quality and reduce noise, and can also carry out data information processing.

The feature extraction module is a module capable of extracting features from the raw data in a specific operation. Feature extraction is a very important step in machine learning and data mining because algorithmic analysis and prediction is better only if meaningful features are extracted from the raw data. When the characteristic extraction module extracts instantaneous frequency, amplitude and purity characteristics of the received sonar signals through the multi-characteristic acute neural network, useful characteristics in data can be extracted through the neural network, a support vector machine, a decision tree, a random forest and the like. In particular embodiments, by statistical analysis, feature selection, feature transformation, and the like. The raw data is mapped into the new space for better feature analysis and modeling. The feature extraction module also requires the use of data preprocessing techniques and algorithms, such as data cleansing, normalization, etc., to help ensure the quality and accuracy of the extracted features in such a way as to increase data information processing and computing power.

In a specific embodiment, the deep learning module trains mass sonar signal samples through a deep learning network so as to master the characteristics and rules of historical sonar signals; in a specific application, a learning network is firstly constructed, in the learning network, a deep learning model is a machine learning algorithm, and the neuron structure of the human brain is simulated by constructing a multi-layer neural network, so that automatic learning and classification of data are realized. Deep learning models typically include one or more neural network layers, each layer containing a number of neurons through which data can be feature extracted and abstracted. The problems of data quantity training, data information calculation, model training time and the like can be carried out in specific application. In further embodiments, models such as GPU acceleration, neural network structure optimization, etc. may be provided to improve the data information computing capability.

The matching tracking module can accurately predict the running path of the marine monitoring sonar communication system in data communication in specific application, so that the marine monitoring sonar communication system can better plan a route and provide a safer data communication environment. The sonar signal features extracted through big data comparison analysis are matched with the historical sonar signal features to realize information identification, so that the target positioning and tracking accuracy is improved;

The space-time analysis module is used for analyzing grammar and rules of time and space in a specific working process. Since this module can help the program or robot better understand the temporal and spatial concepts in natural language, the task can be performed more accurately. The spatio-temporal parsing module may be implemented using natural language processing techniques, for example, translating data information in a data communication into coordinate values in a computer program, or drawing a point on a time axis representing the current time.

In a further embodiment, the spatio-temporal parsing module refers to components for processing spatio-temporal data in a deep learning model. The structural package can comprise:

convolutional Neural Network (CNN) layer: convolutional neural networks are a common tool for processing image and video data, and can extract spatial features; in the space-time analysis module, a one-dimensional or two-dimensional convolution layer is used for capturing spatial information in input data;

a timing modeling layer: for time series data, such as video or voice, temporal continuity needs to be considered. The timing modeling layer may be a Recurrent Neural Network (RNN) or a long short term memory network (LSTM), etc., for capturing characteristics and dependencies of the input data in the time dimension.

Attention mechanism (Attention): the attention mechanism may help the model focus on critical areas or time periods in the spatiotemporal data. By learning the weight assignments, the model may selectively focus on certain portions of the input data to improve the performance and efficiency of the model.

Space-time encoder: the space-time encoder may map the input spatio-temporal data into a low dimensional space and preserve critical spatio-temporal information. This helps to reduce the complexity of the model and extract meaningful representations.

Space-time decoder: the space-time decoder maps the low-dimensional space-time representation back to the original space-time data. This step can be used to reconstruct or generate new spatiotemporal data.

The above is a structural feature of one embodiment of the space-time analysis module, but the specific design and parameter set may vary from task to task. The modules can be stacked, connected and combined as required to adapt to various space-time data processing tasks, such as offshore communication characteristics, communication attribute identification, communication direction prediction and the like.

In a specific embodiment, the main characteristics and functions of the first long-distance sonar information identification module in the marine monitoring sonar communication system are as follows:

Long distance transmission capability: the module has the capability of transmitting and receiving sonar signals underwater at long distances. By using low frequency sound waves, it is possible to accurately identify and locate objects over a wide range.

Environmental suitability: in various marine environments, such as under the conditions of different depths, temperatures, salinity and the like, the first long-distance sonar information identification module can ensure stable and reliable transmission and receiving functions.

High resolution: advanced signal processing technology is adopted to improve resolution, so that the module can accurately distinguish and identify different types of targets at a long distance.

Noise immunity: the module has the capability of resisting ocean background noise and interference, and the reliability of sonar signals is enhanced by effectively reducing the influence of noise.

Adaptivity: the module can automatically adjust sonar parameters according to the current ocean environment and the target characteristics so as to improve the accuracy of target detection and positioning.

Real-time performance: the first long-distance sonar information identification module has quick response and processing capacity, can identify, position and track targets in real time, and is convenient for executing marine monitoring tasks.

In a word, the first long-distance sonar information identification module plays a key role in the marine monitoring sonar communication system, improves the capability of marine target detection, positioning and identification, and provides important support for the fields of marine scientific research, national defense safety, environmental protection and the like.

In the above embodiment, the sensor module includes a water depth monitoring unit, a temperature monitoring unit, a salinity monitoring unit, and a flow rate monitoring unit, the water depth monitoring unit monitors the sea water depth using a piezoelectric pressure sensor that measures the water depth using pascal's law between the water column pressure and depth;

（1）

（2）

In a specific embodiment, the sensor module has the following features:

high sensitivity: the sensor needs to have high sensitivity so as to be able to detect weak acoustic signals, improving the accuracy of signal detection and capture.

Low power consumption: the sensor should be designed to consume minimal electrical energy in order to reduce the battery size or extend its useful life over long periods of use and away from the power source.

High signal-to-noise ratio: the sensor should have a high signal-to-noise ratio performance to reduce the impact of ambient noise on signal transmission and detection.

Anti-interference performance: the sensor needs to have the capability of resisting electromagnetic interference and other environmental interference, and reduces the false alarm rate and the signal loss rate.

High stability and reliability: in severe marine environments (e.g., high salinity, high temperature, high pressure, storms, etc.), such sensors need to ensure good performance and can be adapted to a variety of environmental conditions.

Multichannel processing capability: the sensor should be capable of supporting simultaneous signature transmission to multiple receiving stations to improve communication efficiency.

Function expandability: the sensor has expandability, and functions or parameters can be easily added or switched according to different application scenes or requirements.

Network optimization: the sensor is capable of optimizing through self-organizing network technology to realize automatic adjustment of parameters, path selection and node positioning.

Self-adaptive zooming: according to sonar monitoring distance and target size, the sensor has a self-adaptive zooming function, and accuracy and efficiency of target detection are improved.

Bio-friendliness: the sensor material should be selected, if possible, to be environmentally friendly to marine organisms in order to reduce the impact on the ecosystem.

These features help ensure that the embodiment sensor exhibits superior performance in marine monitoring applications, providing more reliable and accurate data.

The relationship between the resistance value and the actual temperature is as follows:

TABLE 1 relation table of resistance value and actual temperature

From this table, the relationship between resistance and temperature can be derived.

In the above embodiment, the high-speed data transmission network reduces the data amount through the data compression and sound velocity formula to increase the transmission rate; the sound velocity formula is given by,

（3）

In a specific embodiment, the acoustic wave communication module includes the following features:

low power consumption design: in order to meet the long-distance ocean monitoring requirement, the acoustic wave communication module needs to have the characteristic of low power consumption so as to reduce energy consumption and prolong the service life of equipment. The method of low power consumption electronic elements, effective management of the working state of the module, reasonable energy distribution and the like is adopted to realize low power consumption.

Long-distance communication: by optimizing the acoustic wave transmission technology, adopting a high-sensitivity receiver, reducing signal attenuation and other means, the sonar communication system can maintain good communication effect in a longer distance range.

Anti-interference performance: the anti-interference capability of the acoustic wave communication module in a complex marine environment is improved through a digital signal processing technology, a self-adaptive noise suppression algorithm, a channel estimation method and the like.

Data compression and fault tolerance techniques: by using an effective data pressure test and an error detection and repair algorithm, the data transmission quantity is reduced, and the reliability of data transmission is improved.

Multifunction: besides the sonar communication function, the data acquisition, processing and storage functions can be realized, and the comprehensive performance of the ocean monitoring system is improved.

In the above embodiment, the energy optimizing unit adopts an energy management policy to optimally allocate and store the collected energy; the energy optimization unit comprises a battery management tool, a charging controller, an energy scheduling network and an intelligent load management tool; the energy scheduling network dynamically allocates the parts according to the power requirement and the priority of the system so as to realize the optimal utilization of energy.

In a specific embodiment, the energy management module can effectively provide stable and reliable energy support for the long-distance low-power consumption sonar communication system for ocean monitoring, reduce the energy consumption of the system and improve the energy utilization efficiency, and the energy management system is characterized by comprising the following aspects:

optimizing energy management: the energy management module optimizes energy supply and improves energy utilization efficiency by monitoring and evaluating energy demand and consumption of the system in real time.

Highly integrated design: the key functions of power consumption control, energy collection, energy storage, system monitoring and the like are integrated into one module, so that the system is convenient to install, maintain and upgrade.

Adaptive energy consumption strategy: according to the real-time requirements of ocean monitoring tasks and environmental conditions, the working mode and power consumption of the system are automatically adjusted, for example, the system enters a low-power-consumption sleep mode or different sensor sampling frequencies are selected, and the system is kept to work continuously for a long time.

Real-time remote monitoring and adjustment: the energy management module can transmit the energy state information and working parameters of the system to the remote monitoring center in real time, and remote adjustment and equipment maintenance are carried out through a wireless communication technology.

Modularization and scalability: the modularized design is convenient for realizing rapid customization and deployment in different sonar communication systems. Meanwhile, the system has good expandability, and energy supply modes and modules are supported to be added or reduced according to the actual requirements of the system.

In the above embodiment, the multipath interference resistant transmission channel adopts an adaptive protocol selection method, a multiprotocol concurrent transmission method, an advanced network topology structure, a distributed coordination strategy and a radio technology to realize flexible switching of sonar communication protocols: the adaptive protocol selection method adopts an optimal choice algorithm model to select an optimal communication protocol so as to improve communication efficiency and quality

In particular embodiments, the communication protocol selection module generally employs the following techniques:

adaptive protocol selection: in a complex marine environment, the adaptive protocol selection technology can select an optimal communication protocol according to the current propagation condition, so that the communication efficiency and quality are improved.

Multiprotocol coexistence: a system capable of supporting multiple communication protocols simultaneously is designed, and the communication protocols are dynamically adjusted according to different tasks, distances and underwater sound environments.

Advanced network topology design: in the ocean monitoring sonar communication system, advanced communication topology can be designed by utilizing network structures such as clusters, grids and the like so as to improve communication capacity and fault tolerance.

Distributed coordination policy: by deploying multiple protocol selection modules in the network, channel utilization, protocol selection, and node resource allocation can be optimized throughout the network using a distributed coordination strategy.

Software Defined Radio (SDR) technology: by using the software defined radio technology, the switching of various communication protocols can be flexibly completed at the hardware level, and the flexibility and adaptability of the sonar communication system are further improved.

In the above embodiment, the optimal choice algorithm model includes an input layer, a data layer, a model layer, an algorithm layer, an optimization layer, and an output layer, and the adaptive protocol selection method adopts the optimal choice algorithm model to select an optimal communication protocol, and includes the following steps:

The statistics of the transmission speed after adding the optimal choice algorithm model compared with the transmission speed without adding the optimal choice algorithm model are shown in table 2.

Table 2 transmission speed versus statistics table

The transmission speed after adding the optimal choice algorithm model is larger than that without adding the optimal choice algorithm model, and the transmission speed can be improved by adding the optimal choice algorithm model.

In the above embodiment, the fuzzy logic controller adjusts its parameters and rules to realize the adaptive adjustment of the system control according to the surrounding real-time state and feedback information, and the working method of the fuzzy logic controller is as follows: the working method of the fuzzy logic controller is as follows: :

In a specific embodiment, the fuzzy logic controller is a controller based on the fuzzy logic theory, and the main purpose of the fuzzy logic controller is to realize dynamic control of a complex system by introducing a control concept of the fuzzy logic theory.

In yet further embodiments, the fuzzy logic controller generally comprises the following structure:

input layer: the input layer receives input signals such as sensor data and input instructions, and converts the input signals into digital signals for processing.

Representation layer: the representation layer represents the input signal through a fuzzy logic theory, and generates a fuzzy logic expression.

Decision layer: and the decision layer makes a decision according to the result of the fuzzy logic expression and outputs a control signal.

The execution layer: the execution layer controls the system according to the output control signal of the decision layer, and realizes the dynamic control of the system.

Feedback layer: the feedback layer feeds back the decision layer and the execution layer by observing the output result of the system so as to improve the performance of the control system.

In still further embodiments, an accelerator and an encoder may be further added to the fuzzy logic controller to improve the computing capability of the data information, or the data information may be encoded to increase the data encoding capability, thereby improving the processing efficiency of the data information.

In particular embodiments, the use of fuzzy logic controllers may improve overall system performance. The characteristics are as follows:

the fuzzy logic controller can adapt to various uncertainties, including parameter changes, environmental noise and the like, so that automatic adjustment of a communication system is realized.

The fuzzy logic controller has stronger robustness, can cope with uncertainty factors in the marine environment, and reduces the fault risk of the system.

In the long-distance low-power consumption communication system for ocean monitoring, the fuzzy logic controller can effectively reduce energy consumption. By adaptively adjusting parameters such as transmission power, transmission rate and the like, the communication quality is ensured and the energy consumption performance is optimized.

The fuzzy logic controller is capable of rapidly processing the input information, providing a real-time control strategy to cope with changing conditions in the marine surveillance environment.

In the ocean monitoring long-distance low-power consumption communication system, the fuzzy logic controller can cope with complex road conditions, multipath propagation and other phenomena, so that the quality of a communication link is maintained, and the consistency and reliability of data transmission are improved. In a word, the fuzzy logic controller can improve the self-adaptability, stability and reliability of the system in the long-distance low-power-consumption communication system for ocean monitoring, thereby realizing efficient and effective ocean monitoring tasks.

In the above embodiment, the efficient adaptive algorithm adjusts the adaptive weights by calculating the minimum mean square error of the acoustic wave frequency, the beam width and the transmission power to reduce the adaptive result error; the efficient adaptive algorithm includes a least mean square algorithm that calculates a weight vector W by the following formula:

（4）

In a specific embodiment, an efficient adaptive algorithm is generally used to optimize communication performance in a low-power long-distance sonar communication system, so as to improve robustness and reliability. Such systems typically employ the following methods and techniques:

An adaptive filter: the signal is processed using adaptive filters, such as a minimum Mean Square Error (MSE) filter and a mean square error derivation (LMS) filter, and the filter parameters are dynamically adjusted according to channel conditions.

RAKE receiver: RAKE receivers are a common receiving technique in spread spectrum communications that collect and combine multiple copies of a signal in multipath propagation to improve the signal-to-noise ratio (SNR) of the received signal.

Parity and forward error correction coding (FEC): the reliability and anti-interference capability of the transmitted data can be improved by using the parity check code and the forward error correction coding technology.

Polymerization technology: in the data transmission process, a data aggregation technology is used, and a plurality of data packets are combined into one large data packet for transmission, so that energy is saved.

Multiple input multiple output technique: by using a plurality of transmitting and receiving antennas, the MIMO technology can improve the transmission rate and signal reception quality of a communication system.

Cross-layer design: by optimizing and coordinating among the physical layer, the data link layer, and the network layer, cross-layer design may improve system performance and improve energy efficiency.

In summary, the use of these efficient adaptive algorithms and techniques in low-power long-range sonar communication systems enables higher energy efficiency, transmission rates, and reliability.

While specific embodiments of the present invention have been described above, it will be understood by those skilled in the art that these specific embodiments are by way of example only, and that various omissions, substitutions, and changes in the form and details of the methods and systems described above may be made by those skilled in the art without departing from the spirit and scope of the invention. For example, it is within the scope of the present invention to combine the above-described method steps to perform substantially the same function in substantially the same way to achieve substantially the same result. Accordingly, the scope of the invention is limited only by the following claims.

Claims

1. A low-energy consumption long-distance sonar communication system for ocean monitoring is characterized in that;

the system comprises:

the wireless navigation module is used for determining the position of sonar communication information in the ocean, and the wireless navigation module realizes wireless navigation by setting a differential global positioning system and receiving signals of the differential global positioning system; the differential global positioning system uses differential correction signals sent by a ground base station to improve the accuracy of GPS signals so as to improve the accuracy of position measurement and the accuracy of a radio navigation module; the radio navigation module comprises an information receiving module, an information transmitting module, a first long-distance sonar information identification module, a communication protocol selection module and an improved GPS positioning module; the differential global positioning system comprises a second long-distance sonar information identification module, a navigation calculation module and a position selection module so as to improve the accuracy of GPS signals; the sensor module is used for monitoring a complex and changeable ocean environment, and ocean data information monitored by the sensor module at least comprises ocean wind power, wind direction, weather temperature, salinity, water depth and flow rate;

2. The low-power long-range sonar communication system for marine monitoring of claim 1, wherein: the first long-distance sonar information identification module comprises an information receiving module, a feature extraction module, a deep learning module, a matching tracking module and a space-time analysis module; the target signal is identified and tracked through preprocessing sonar information, extracting features and analyzing; the working method of the first long-distance sonar information identification module is as follows:

3. The low-power long-range sonar communication system for marine monitoring of claim 1, wherein: the sensor module comprises a water depth monitoring unit, a temperature monitoring unit, a salinity monitoring unit and a flow rate monitoring unit, wherein the water depth monitoring unit monitors the sea water depth by adopting a piezoelectric pressure sensor, and the piezoelectric pressure sensor measures the water depth by using the Pascal law between the pressure and the depth of a water column;

（1）

in the case of the formula (1),the NTC thermistor has a temperature of +>Resistance value at time, < >>The NTC thermistor has a temperature of +>Resistance value at time, < >>Is->And->Is a difference in (2);

（2）

4. The low-power long-range sonar communication system for marine monitoring of claim 1, wherein: the high-speed data transmission network reduces the data volume through a data compression and sound velocity formula so as to improve the transmission rate; the sound velocity formula is given by,

（3）

5. The low-power long-range sonar communication system for marine monitoring of claim 1, wherein: the energy optimizing unit adopts an energy management strategy to optimally allocate and store the collected energy; the energy optimization unit comprises a battery management tool, a charging controller, an energy scheduling network and an intelligent load management tool; the energy scheduling network dynamically allocates the parts according to the power requirements and the priority of the system.

6. The low-power long-range sonar communication system for marine monitoring of claim 1, wherein: the multipath interference resistant transmission channel adopts a self-adaptive protocol selection method, a multiprotocol concurrent transmission method, an advanced network topology structure, a distributed coordination strategy and a radio technology to realize flexible switching of sonar communication protocols: the adaptive protocol selection method adopts an optimal choice algorithm model to select an optimal communication protocol.

7. The low-energy-consumption long-distance sonar communication system for marine monitoring of claim 6, wherein: the optimal choice algorithm model comprises an input layer, a data layer, a model layer, an algorithm layer, an optimization layer and an output layer, and the self-adaptive protocol selection method adopts the optimal choice algorithm model to select the optimal communication protocol and comprises the following steps:

8. The low-power long-range sonar communication system for marine monitoring of claim 1, wherein: the fuzzy logic controller realizes the self-adaptive adjustment of the system control by adjusting parameters and rules according to the surrounding real-time state and feedback information, and the control method of the fuzzy logic controller comprises the following steps:

9. The low-power long-range sonar communication system for marine monitoring of claim 1, wherein: the high-efficiency self-adaptive algorithm adjusts self-adaptive weights through calculating the minimum mean square error of the sound wave frequency, the wave beam width and the transmission power so as to reduce the error of self-adaptive results; the efficient adaptive algorithm includes a least mean square algorithm that calculates a weight vector W by the following formula:

（4）