140 results about "Total energy" patented technology

A system and a method control the energy required from an internal combustion engine (ICE) of an all-terrain machine with terrain engagement elements for moving the machine and hydraulic pumps for supplying hydraulic flow to a material handling attachment. The ICE drives the terrain engagement elements and a first hydraulic pump. The ICE drives a generator to provide electrical energy, which is stored in a battery. The stored electrical energy is selectively supplied to an electric motor, which drives a second hydraulic pump. If the total energy supplied by the ICE exceeds a predetermined level, the hydraulic flow supplied by the first hydraulic pump is reduced, and the hydraulic flow supplied by the second hydraulic pump is increased. The system and method allow a less powerful ICE to drive the machine.

The application discloses a kind of multi-node server network topology and energy consumption perception's task scheduling method, belong to data center and high performance computing technical field.It includes: constructing the weighted directed graph topology model including server node and switch node, the dynamic energy consumption weight function of link is defined;Establish the three-dimensional energy consumption and thermal interference evaluation model of node, introduce space thermal interference factor;Analysis the characteristics of task to be scheduled;Based on energy consumption potential field screening candidate node;Build the global energy consumption cost function including computing energy consumption, network transmission energy consumption and thermal coupling penalty energy consumption;According to the principle of minimum total energy consumption, select target node to execute scheduling and update topology state.The application solves the problem of splitting computing and network energy consumption, ignoring the space coupling effect of heat dissipation in the prior art by the fusion of network topology perception and multi-dimensional energy consumption model, and realizes the optimization of global energy consumption of data center.

The downhole dynamic thermal field control method and apparatus provided in this application include: acquiring initial temperature field data of the target downhole wellbore and a pre-constructed formation fluid coupling model, and calculating the first total energy supply power of each heating section of the target downhole based on these data; segmenting and heating each heating section of the target downhole according to the first total energy supply power, and acquiring the first real-time temperature data of each heating section of the target downhole; determining the corresponding first downhole thermal field uniformity based on the first real-time temperature data, and determining the thermal field control state based on the uniformity and the first real-time temperature data; if the thermal field control state is a stable and compliant state, maintaining the first total energy supply power of each heating section of the target downhole; if the thermal field control state is a state to be adjusted, adjusting the first total energy supply power to obtain the adjusted second total energy supply power; thereby improving the accuracy of downhole dynamic thermal field control and enhancing the real-time matching capability between energy supply and formation heat demand.

This invention discloses a fuel cell hybrid propulsion system and its power fluctuation-consumption synergistic optimization method. The system includes a fuel cell power generation unit, a lithium-ion battery unit, an electric drive unit, a sensing and data acquisition unit, and an energy management controller. Based on near-end strategy optimization, an intelligent agent is constructed with system state and operating condition information as input and power source power allocation commands as output. A reward function integrating total energy consumption penalty and power fluctuation penalty is designed to drive the intelligent agent to learn the optimal energy management strategy in a continuous action space. This invention can suppress frequent and drastic fluctuations in lithium-ion battery power, extend battery life, ensure system safety, and minimize overall hydrogen fuel consumption while meeting dynamic power demand and safety constraints. It achieves synergistic optimization of long-term operational economy and system output stability, enabling efficient, smooth, and long-life operation of the hybrid propulsion system in dynamic environments.

This application relates to a method and device for determining the supply and return water temperature difference of a ground source heat pump system, comprising: obtaining the target operating parameters of the ground source heat pump system at a target time; based on the target operating parameters, a first linear regression equation, and a second linear regression equation, performing iterative calculations using a time-varying system online parameter identification algorithm to determine the target network impedance coefficient and the target heat transfer attenuation coefficient at the target time, wherein the first linear regression equation is obtained by transforming the pump energy consumption equation including the network impedance coefficient factor, and the second linear regression equation is obtained by transforming the host energy consumption equation including the heat transfer attenuation coefficient factor; based on the target network impedance coefficient and the target heat transfer attenuation coefficient, summing the pump energy consumption equation and the host energy consumption equation to construct a total energy consumption objective function; and optimizing the total energy consumption objective function using a nonlinear optimization algorithm to obtain the optimal supply and return water temperature difference when the function value of the total energy consumption objective function is minimized.

This invention discloses a high-fidelity cooperative jamming method and system for UAV swarms targeting networked radar. The method includes: constructing a UAV dynamics model; constructing a UAV energy consumption model, including UAV propulsion energy consumption and DRFM relay energy consumption; designing false flight paths based on the dynamic characteristics, RCS characteristics, and scene adaptability factors of the false target; calculating the required relay power to simulate RCS fluctuations by calculating the target attitude angle and radar observation angle, combined with three-dimensional RCS data; establishing an optimization model containing UAV dynamics constraints with the goal of minimizing the total energy consumption of the UAV swarm, using the flight heading angle as the optimization variable, and solving the optimization model using a genetic algorithm. This invention improves the credibility of false flight paths, increases the duration of flight path deception, ensures the stability of deception in complex battlefield environments, and enhances the practical adaptability of cooperative jamming.

This invention discloses a ship cloud computing load balancing method based on a hybrid intelligent algorithm, relating to the field of ship intelligent computing and cloud computing integration technology. The method includes the following steps: task classification modeling, edge-cloud resource status monitoring, multi-objective optimization decision-making, and dynamic load adjustment. This invention reduces the load standard deviation of ship edge-cloud nodes through a multi-objective optimization algorithm and a dynamic adjustment mechanism. It optimizes task processing efficiency, reducing the average latency of real-time tasks and shortening the total processing time of non-real-time tasks. It reduces system energy consumption by optimizing the energy consumption coefficient, thereby reducing the total energy consumption of edge nodes and cloud servers. It adapts to ship scenarios, improving the robustness and adaptability of scheduling strategies to address the heterogeneity of ship tasks and network dynamics.

This invention discloses a dynamic control method and system for data center cooling systems based on a large model. The method involves preprocessing the collected data to obtain preprocessed data; using a graph convolutional network (GCN) to model the thermal environment of the data center to obtain spatial features; and using a gated recurrent unit (GRU) to receive the data and spatial features, capturing the dynamic dependencies in time to obtain temporal features. A spatiotemporal attention mechanism is introduced to evaluate the weights of different sensor nodes in predicting future thermal states at different time points. Based on these weights, the spatial and temporal features are adjusted to output predicted temperature values. A deep reinforcement learning agent (DRL) is constructed, and based on the predicted temperature values, a cooperative control strategy is obtained through proximal policy optimization (PPO) to reduce the total energy consumption of the cooling system and is then executed. This achieves a dynamic balance between data center energy consumption and heat dissipation.

The application discloses a multi-wall surface operation path planning method and system for an underwater wall-climbing cleaning robot, and belongs to the technical field of special robots. The method comprises the following steps: constructing a three-dimensional motion model of the underwater wall-climbing cleaning robot according to a basic motion form of the underwater wall-climbing cleaning robot; performing stress analysis on the three-dimensional motion model to obtain an energy efficiency coefficient of the underwater wall-climbing cleaning robot; constructing an underwater three-dimensional multi-wall surface operation target model; establishing a target function with the purpose of minimizing the total energy consumption generated by the underwater wall-climbing cleaning robot during the motion process; obtaining the motion speed of the underwater robot; and solving the target function by using a simulated annealing algorithm according to the motion speed and the corresponding energy efficiency coefficient to obtain an optimal energy consumption operation path of the underwater wall-climbing cleaning robot. The method can improve the efficiency and accuracy of multi-wall surface operation path planning, and solves the problem that the existing technology has a method for evaluating the power consumption of an underwater robot which is too complex and not universal.

The present application relates to intelligent manufacturing and production scheduling technical field, specifically for turning and milling machining center production scheduling management system;Contain data acquisition, particle mapping, potential field construction, evolution calculation and scheduling generation module;System obtains order and resource state, maps the order into virtual particle flow with spin attribute, and converts the resource into gravitational potential well;Its core is based on the principle of free energy minimization, driving particles in multidimensional space-time potential field to carry out gradient evolution;When the total energy of the system converges, the particle steady-state coordinates are locked to generate the schedule;The present application converts discrete scheduling into physical field energy minimization problem, uses gradient guidance instead of blind search, and solves the problem that static algorithm is difficult to balance calculation depth and response speed.

The present application relates to a kind of mine earthquake internal seismic source power similarity simulation test system and method based on metal wire electric explosion, pre-embedded metal wire electric explosion is used to form transient volume expansion seismic source in similar material model inside, it is realized by the conversion of the accurate release energy source from the external monotone extensive mechanical disturbance to model inside;Through the division of labour design of charging branch, main discharge branch, discharge branch and detection branch, make circuit organization relationship clear, protection chain is complete, it is convenient to stable implementation high voltage pulse discharge test;Through the current limiting inductance to the discharge current front and pulse width are adjusted, so that mine earthquake simulation is no longer only pay attention to total energy similarity, also consider time process similarity;The corresponding relationship between the energy of field mine earthquake event and the input electric energy of similar material model is established, the scientificity of test parameter setting is improved;Through cabinet type layered assembly, host computer software control and standardization experimental process organization, the repeatability, traceability and safety of device operation are improved.

The application discloses a kind of highway service area energy consumption prediction systems, including multi-source heterogeneous data acquisition module, space-time feature extraction module, dynamic fusion module, energy consumption prediction module, prediction effect verification module and model self-optimization module.System is integrated by service area dynamic external and internal operation data, extracts multidimensional space-time characteristics, and is weighted and fused based on the fusion weight of dynamic allocation, generates space-time fusion feature set.The feature set is input to machine learning prediction model, and the total energy consumption of service area and the refined energy consumption prediction curve of each target partition in future preset period can be output simultaneously.System compares predicted and actual energy consumption data, automatically generates deviation analysis results, and accordingly realizes the self-optimization of model parameters and feature weights, thereby improving the prediction accuracy and the adaptive ability of the system, and providing effective support for realizing energy-saving operation and intelligent management of service area.

This invention discloses a laser burn-in system, relating to the field of laser burn-in technology. The laser burn-in system includes a burn-in component, a beam-uniforming and expanding module, a photoelectric conversion component, and a power processing module. The burn-in component is connected to the laser. The beam-uniforming and expanding module receives the laser beam and outputs a uniformly diffused beam. The photoelectric conversion component receives the uniformly diffused beam and outputs power to be processed. The power processing module is electrically connected to the photoelectric conversion component and receives the power to be processed and outputs usable power. The technical solution provided by this invention aims to reduce the total energy consumption of laser burn-in by converting the light energy during burn-in into reusable electrical energy, thereby reducing the total energy consumption and cost of burn-in.

The present application relates to the technical field of agricultural robot path planning, and particularly relates to a water product breeding feeding robot path planning method and system based on energy consumption and safety joint constraint, the steps of the method comprising: constructing an environment grid map and calculating a terrain gradient vector field; constructing a single-step multi-dimensional comprehensive cost model; establishing a safety constraint judgment mechanism, including longitudinal slope constraint judgment and transverse stability constraint judgment; performing global search on the environment grid map, always preferentially expanding the candidate grid node with the minimum comprehensive evaluation cost in the search process, and searching for a target discrete path of the water product breeding feeding robot under the premise of meeting the safety constraint; performing parameterized smoothing processing and secondary safety verification process on the key points of the target discrete path, calculating the linear velocity and angular velocity reference control instructions of the water product breeding feeding robot at each time, and performing feeding operation along the smoothed path trajectory. The present application can reduce the total energy consumption of operation and improve driving safety.

This invention discloses a rapid evaluation method for the enhancement factor of the dynamic coefficient by the high-frequency component of a strong impact load, belonging to the field of structural dynamic response analysis technology. This method performs time-frequency domain energy accumulation analysis on the strong impact load signal, extracts the concentrated frequency of load energy and its proportion in the total energy, and then, based on this, combines the dynamic amplification factor under the triangular time history assumption to quantitatively calculate the enhancement factor of the dynamic coefficient by the high-frequency component of the strong impact load. This method is further extended to arbitrary frequency components and the entire frequency band to obtain the comprehensive enhancement result of the dynamic amplification factor by the high-frequency effect of the strong impact load. Compared with traditional dynamic analysis methods based solely on idealized time history assumptions, this invention can introduce the real time-frequency characteristics of strong impact loads while maintaining computational efficiency, effectively compensating for the insufficient consideration of high-frequency excitation effects. It is applicable to rapid dynamic response evaluation in engineering fields such as aerospace structures, high-frequency components, and precision equipment.

The application provides a wind turbine load control method based on wind wheel thrust, relates to the wind turbine regulation and control field, and obtains wind wheel thrust load; the wind wheel thrust load is low-pass filtered to obtain expected wind wheel thrust load; the cut-off frequency Wc of the filter is selected by performing spectrum analysis on a wind speed signal; the cut-off frequency selection method is that the cut-off frequency Wc of the thrust load low-pass filter is selected as a frequency threshold value lower than a preset frequency threshold value and a wind energy ratio reaching 90% of total wind speed spectrum energy; the expected wind wheel thrust load is taken as a control target, feedback control is performed on actual wind wheel thrust, a nonlinear feedback controller is established, and the pitch angle is dynamically adjusted according to the amplitude of the deviation of the actual thrust load from the expected wind wheel thrust load. The method can dynamically adjust the pitch angle through the nonlinear feedback controller, and the purpose of optimizing the fatigue and limit load related to the wind wheel thrust of the wind turbine is achieved.

This invention discloses a joint optimization method for mobile edge computing and service caching assisted by multiple UAVs, comprising: constructing a multi-UAV-assisted edge computing system model; constructing a communication model; dividing the system decision time into a set of large-timescale periodic sequences and a set of small-timescale time slot sequences, each large-timescale periodic sequence containing multiple consecutive small-timescale time slot sequences; deriving expressions for the total task processing latency and total energy consumption of the system in any time slot; establishing a joint optimization objective function with the goal of minimizing the total system latency and total energy consumption; constructing a Markov decision mechanism; employing the SAC deep reinforcement learning algorithm to implement the dual-timescale decision-making of the system; and solving the joint optimization objective function to obtain the optimal joint optimization scheme. This invention can significantly improve the edge service cache hit rate, optimize UAV load balancing, and reduce task processing latency and overall system energy consumption.

The application discloses an energy consumption prediction and optimization system and method based on artificial intelligence, relates to the technical field of energy consumption prediction and optimization, and comprises the following steps: calculating the energy consumption intensity indexes of each device in different time periods to form a time period set of historical energy consumption abnormalities of the device; generating a candidate scheduling strategy, obtaining the device calling sequence and the energy consumption switching time between devices for any strategy, calculating the actual operation energy consumption of the device in the abnormal time period when the scheduling time of the device falls into the historical energy consumption abnormal period set; traversing the device calling time in the strategy to calculate the actual total energy consumption and the number of times of exceeding the predicted energy consumption, calculating the energy efficiency matching value of each candidate strategy, and generating a strategy recommendation sequence; selecting the strategy with the highest energy efficiency matching value from the strategy recommendation sequence as the preferred execution scheme, collecting real-time energy consumption data after execution to calculate the energy consumption deviation rate, and comparing the energy consumption deviation rate with a threshold to determine the effectiveness of the strategy, so that the energy consumption prediction and optimization are realized.

The application relates to the technical field of energy efficiency simulation, and particularly discloses a central air conditioner operation energy efficiency simulation system and method based on machine learning, which comprises the following steps: S1: acquiring annual hourly meteorological data, auxiliary equipment comprehensive power coefficient and cooling equipment performance parameters, and fitting a load rate polynomial; S2: for each hour, inputting a total cooling load, estimating a cooling equipment average energy efficiency coefficient through the load rate polynomial, calculating a condensing heat load, and combining meteorological data and equipment inherent parameters to solve a cooling equipment outlet water temperature; S3: enumerating all cooling equipment start-stop combinations; for each hour, establishing a nonlinear objective function with the maximum instantaneous system total energy efficiency as an objective, and solving an optimal load distribution scheme under each start-stop combination; S4: calculating the instantaneous total energy efficiency of each start-stop combination under the optimal load distribution, selecting a combination corresponding to a maximum value as an optimal combination, and finally calculating the system total energy efficiency in a whole year.

This invention discloses an energy-saving pump system based on adaptive flow regulation, relating to the field of energy-saving control technology for fluid transport equipment. The system comprises the following components: constructing a distributed control network, establishing a distributed multi-objective optimization model based on load forecasting, an improved intelligent optimization algorithm, executing distributed collaborative optimization, and outputting optimized control commands. By constructing a multi-objective optimization model that integrates operating energy consumption costs, equipment wear costs, and start-stop penalties, combined with a dynamic adaptive penalty function and an intelligent optimization algorithm based on the optimal efficiency point, this invention significantly reduces the total energy consumption of the system while ensuring accurate fulfillment of flow requirements. Furthermore, by dynamically correcting the pump performance model and adaptively adjusting the pump combination strategy, it avoids long-term deviation of the pump from the optimal efficiency point, reduces equipment wear index, extends expected service life, and significantly reduces the total life-cycle maintenance cost.

The embodiment of the application provides a kind of energy consumption evaluation method, device, electronic equipment and storage medium, belong to energy storage technical field.The method comprises: obtaining the operating parameter and the environmental parameter of energy storage system to select the cooling component of constructing energy storage cooling system;Equivalent model of cooling component is used to establish the total energy consumption model of energy storage cooling system;The operating parameter and the environmental parameter are input to total energy consumption model, to obtain the energy consumption result of energy storage cooling system by evaluation.The above technical scheme, according to the operating parameter and the environmental parameter of energy storage system, selects suitable cooling component, and according to the equivalent model of cooling component, the total energy consumption model of cooling system is constructed, in the early stage of cooling system design, the operating parameter and the environmental parameter of energy storage system can be input, the energy consumption of cooling system is evaluated by total energy consumption model, improve the accuracy of cooling system energy consumption evaluation, provide scientific basis for early design and later management of cooling system.

The application discloses a data computing method and system for cooperation of air aggregation and edge unloading, and belongs to the technical field of communication signal processing. The method comprises the following steps: constructing a UAV-assisted computing integration scene, the ground node has edge computing and AirComp heterogeneous requirements, the UAV has edge server and AirComp aggregation center functions; establishing an air-ground communication link model and system constraints; constructing a joint optimization problem with the minimum system total energy consumption as the target; adopting energy-aware cooperative scheduling and BCD iterative solution until the optimal strategy is obtained through energy consumption convergence. The system comprises a ground node, a UAV, communication modeling, constraint construction, optimization construction, cooperative scheduling, iterative solution, and convergence judgment modules. The application realizes global cooperation of computing unloading and air aggregation, reduces system energy consumption under the premise of ensuring computing accuracy, improves the energy efficiency and coverage capability of the UAV-assisted computing system, and is suitable for 6G space-air-ground integrated network, wide-area perception, emergency communication and other scenes.

This invention specifically relates to an adaptive compensation method for the wiring direction of a rail meter based on current flow direction detection, belonging to the field of energy metering technology. It includes: calculating the total energy based on the corrected active power through time integration; and converting this into a pulse output representing a fixed energy increment using a metering chip. In this invention, the original power value is low-pass filtered to remove high-frequency interference signals such as harmonics and instantaneous pulses from the power grid, ensuring the stability of the power detection value. Combined with a verification mechanism of three consecutive samples, it avoids the problem of misjudgment of flow direction caused by single instantaneous interference. Simultaneously, the meter program repeatedly executes the flow direction detection and wiring determination process at a fixed cycle, enabling timely response to mid-term changes in wiring methods during construction adjustments and other scenarios, adapting to the complexity of actual installation and usage scenarios.

This invention belongs to the field of electrical engineering technology, specifically relating to a comprehensive planning method and system based on mobile hydrogen energy storage vehicles and energy storage power stations. The method first constructs a spatiotemporal interaction model—an electro-hydrogen energy model. Then, based on this model, a two-stage comprehensive capacity planning model is built. In the two-stage comprehensive capacity planning, the planning stage model is constructed with the objective of minimizing the sum of system operating costs and energy storage construction costs, while the operation stage model is constructed with the objective of minimizing system operating costs. Finally, the two-stage comprehensive capacity planning model is solved to obtain the optimal energy storage planning scheme, which includes the number of mobile hydrogen energy storage vehicles, the construction location and capacity of the energy storage power station. This invention, through the spatiotemporal interaction model—an electro-hydrogen energy model, enables more accurate comprehensive planning of mobile hydrogen energy storage vehicles and energy storage power stations, reducing the total energy storage construction cost while ensuring system operational reliability.

This invention discloses a refrigeration cycle method with liquid refrigerant compression, relating to the fields of refrigeration and gas liquefaction. The method includes a liquid tank, a liquid compression pump fixedly connected to one side of the liquid tank, and a main heat exchanger fixedly connected to the side of the liquid compression pump away from the liquid tank. The main heat exchanger is connected to an expander unit, a circulating gas compressor unit, and the liquid tank. The circulating gas compressor unit includes a circulating compressor and a circulating compression cooler. A cold box is provided outside the liquid tank, the liquid compression pump, the main heat exchanger, and the first and second expansion ends of the expander unit. This invention adds a liquid compression process to the refrigeration cycle, increasing the refrigerant pressure in both the liquid and gaseous states. Based on thermodynamics, a new refrigeration cycle method is created, reducing energy consumption during circulating compressed gaseous refrigerant, while simultaneously increasing energy recovery and utilization in multiple ways, improving the coefficient of performance (COP), and thus achieving the goal of reducing the total energy consumption of the refrigeration cycle.

This invention provides a method, system, electronic device, and storage medium for analyzing the energy consumption of neural networks based on ion channel regulation. It constructs a neural network dynamics model containing excitatory and inhibitory neurons. The neuronal membrane potential dynamics equation is defined by a set of differential equations for various voltage-gated ion currents (fast sodium ion current, continuous sodium ion current, slow potassium ion current, etc.) and leakage current. By adjusting the unit area conductivity parameter and / or inactivation time constant parameter of the voltage-gated ion channels in the model, the external intervention effect is simulated. After obtaining membrane potential sequence data through simulation, the total energy consumption, synaptic energy consumption ratio, average discharge rate, and average synchronization rate between neurons of the model are calculated. Finally, a quantitative correlation model between parameter changes and the above indicators is established, clarifying the coupling effect of the target ion channel dynamics characteristics on the neural network discharge behavior pattern and energy metabolism.

This application discloses a multi-source energy management system. The multi-source energy management system includes: a data access terminal for receiving or collecting multi-source energy data, converting the multi-source energy data according to a preset format to obtain target energy data; a data processing terminal for calculating indicators based on the target energy data, obtaining several types of indicator data, and storing these indicator data in a database; the indicator types include at least two of the following: total energy consumption, carbon emissions, year-on-year comparison, and month-on-month comparison; and a data display terminal for receiving multi-dimensional search conditions input by the user, retrieving data from the database based on these conditions, obtaining corresponding search results, and displaying them on the data display terminal; wherein the search results include at least the corresponding indicator data. Through the above method, the problems of difficult multi-source data fusion, inaccurate traceability, and unintuitive interaction in related energy monitoring technologies can be solved.

The application provides a millimeter wave radar-based anti-crossing human body static trigger detection method and system, comprising the following steps: S1, transmitting and receiving radar signals by a radar, and preprocessing the received radar echo signals to generate a range-Doppler matrix reflecting target distance and Doppler information; S2, extracting region of interest data within a preset distance range from the range-Doppler matrix; S3, respectively performing energy accumulation on positive Doppler units and negative Doppler units in the region of interest data to obtain positive Doppler total energy and negative Doppler total energy, and calculating an energy proportion coefficient for representing a target motion state; S4, performing stationarity statistics on the energy proportion coefficients obtained by continuous multiple frames by using a sliding window to obtain a stationarity statistical result; and S5, judging the existence state of a target by a three-level state machine based on the stationarity statistical result. The application realizes accurate distinction between "crossing through" and "static staying".

The application discloses a distributed energy cluster operation method and device, equipment and medium, and belongs to the field of distributed energy cluster operation. The method is as follows: obtaining a power source energy benefit function, a power source energy strategy space, a load energy benefit function and a load energy strategy subspace set; obtaining a total load energy strategy space based on a load aggregation direction vector and the load energy strategy subspace set; constructing a total energy constraint condition based on a preset power grid game sequence, a preset target total energy power, the power source energy strategy space and the total load energy strategy space; constructing a total energy target subspace function set based on a benefit penalty function, a benefit penalty coefficient, the power source energy benefit function and the load energy benefit function; and obtaining a to-be-implemented energy object power set based on the total energy constraint condition and the total energy target subspace function set, so as to operate a power source energy set and a load energy set, thereby improving the reliability of distributed energy cluster operation.

The application discloses an underwater sensor network cross-layer MAC protocol implementation method and system, and belongs to the technical field of underwater acoustic wireless sensor networks. The protocol constructs a clustering network structure, combines OFDM physical layer technology and TDMA medium access control, dynamically allocates transmission modes, subcarrier numbers, transmission powers and symbol numbers, so as to meet node differentiated rate requirements and adapt to underwater acoustic channel characteristics. The NSGA-OT algorithm is adopted to solve a multi-objective optimization problem, and the network throughput and total energy consumption are simultaneously optimized under the guarantee of the bit error rate constraint. The application overcomes the problems that the existing MAC protocol ignores node differentiated rate requirements and wastes resources, and improves data transmission reliability, network life cycle and real-time performance.