Intelligent power management and energy efficiency optimization system of new energy ship

Abstract

The invention relates to the technical field of electric energy control of electric ships, in particular to an intelligent electric power management and energy efficiency optimization system of a new energy ship, which comprises a data acquisition module, a dynamic load management module, a self-adaptive energy distribution module, an electric power health detection module and a feedback and optimization module, the data acquisition module is used for monitoring parameters of each power utilization unit of a ship in real time, the dynamic load management module performs real-time priority adjustment on power supply of each power utilization unit according to data, and the self-adaptive energy distribution module predicts a power demand and dynamically adjusts a power distribution strategy according to an operation state, an environmental condition and historical data of the ship. The power health detection module monitors the health state of each power utilization unit in real time, the feedback and optimization module uploads operation data to a cloud end, an energy distribution scheme is continuously optimized through big data analysis, accurate prediction and dynamic allocation of power demands are achieved, and therefore the safety and economical efficiency of the pure electric ship are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy control of electric ships, and in particular to an intelligent power management and energy efficiency optimization system for new energy ships. Background Art

[0002] With the development of new energy and the popularity of electric vehicles, the use of electric motors as a power source is becoming more and more widespread. In shipbuilding, pure electric ships have also been interviewed. These pure electric ships are inland ships, generally used for patrol boats and official ships.

[0003] In order to ensure the power safety of ships, the applicant has published a publication number CN 218183075 U, which discloses a power monitoring system for an electric-driven ship. By placing detection units on each power unit on the ship, real-time detection of the working parameters of each unit can be achieved, and the control console can allocate the power consumption of the ship through real-time data. When sailing, the control center on land can formulate a more accurate sailing route based on the power data obtained.

[0004] However, in actual practice, the power demand of various power-consuming units on a ship, such as the power system, navigation equipment and communication equipment, varies depending on the navigation status, load and external environmental conditions.

[0005] Due to these factors, ships may face problems with insufficient power supply or uneven distribution, which will affect the safety and efficiency of the overall navigation. Existing power management systems are often unable to effectively adapt to such dynamic changes, resulting in the inability to adjust power distribution in time when the load fluctuates. This may not only affect the normal operation of key equipment, but also lead to a decline in the overall performance of the system. Therefore, it is particularly important to develop an intelligent system that can respond to changes in power demand in real time and optimize power distribution. Summary of the invention

[0006] In view of the shortcomings of the prior art, the purpose of the present invention is to provide an intelligent power management and energy efficiency optimization system for new energy ships that can achieve accurate prediction and dynamic allocation of power demand, thereby improving the safety and economy of pure electric ships.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] An intelligent power management and energy efficiency optimization system for new energy ships, comprising:

[0009] The data acquisition module is used to monitor the current, voltage, temperature, ambient humidity and other parameters of each power unit of the ship in real time, and upload the data to the control center;

[0010] A dynamic load management module adjusts the power supply priority of each power-consuming unit in real time according to the data to ensure that key equipment is given priority power supply;

[0011] Adaptive energy allocation module, which predicts power demand and dynamically adjusts power allocation strategy based on the ship’s operating status, environmental conditions and historical data;

[0012] The power health detection module monitors the health status of each power unit in real time, automatically alarms and adjusts power distribution when abnormalities are found, and provides predictive maintenance functions;

[0013] The feedback and optimization module uploads the operating data to the cloud and continuously optimizes the energy distribution plan through big data analysis.

[0014] The present invention is further configured as follows: the data acquisition module comprises:

[0015] Real-time monitoring unit, used to monitor the current, voltage, temperature and other power parameters of each power unit on board in real time, and upload the data to the control center;

[0016] Environmental monitoring unit, used to collect external environmental data, including weather conditions and ship load, to provide comprehensive operating data;

[0017] The data transmission unit transmits the real-time monitoring and environmental data to the control center via a wireless or wired network to achieve real-time updating and sharing of data.

[0018] The present invention is further configured as follows: the dynamic load management module comprises:

[0019] The priority rule setting unit is used to classify the various electrical equipment on board and set the power supply priority according to the importance of the equipment to ensure that key equipment has priority in power supply under different operating conditions;

[0020] The load adjustment decision unit automatically calculates the power distribution plan for each power consumption unit based on real-time monitoring data and priority rules to optimize overall energy efficiency and ensure the stable operation of high-priority equipment;

[0021] The control execution unit implements power output control based on load adjustment decisions to ensure that the system responds to changes in power demand in real time;

[0022] Fault response mechanism: when power anomalies or equipment failures are detected, the system can automatically reduce the power supply to low-priority equipment to protect critical equipment and maintain the normal operation of the ship.

[0023] The present invention is further configured as follows: the adaptive energy allocation module comprises:

[0024] A data analysis unit for real-time analysis of ship operating status, environmental conditions and historical power consumption data to predict future power demand;

[0025] Adaptive dispatch unit, which continuously optimizes power distribution strategy based on real-time and historical data through reinforcement learning algorithm to ensure efficient use of power;

[0026] The dynamic adjustment unit can automatically adjust the power supply of each power-consuming unit when the power demand changes, giving priority to the needs of key systems.

[0027] The present invention is further configured as follows: the power health detection module comprises:

[0028] Real-time health monitoring unit, used to monitor the operating status of each power unit, collect current, voltage, temperature and other parameters in real time to detect the health status of the equipment;

[0029] The abnormality detection and alarm unit automatically triggers the alarm mechanism and sends an alarm signal to the control center when it detects abnormal operation status of the power unit or equipment failure;

[0030] Automatic power adjustment unit: Based on the abnormal detection results, the system automatically adjusts the power distribution plan to reduce the power supply of faulty equipment and give priority to the normal operation of key equipment;

[0031] The predictive maintenance unit predicts potential failure risks based on historical operating data and equipment health status, and issues maintenance reminders in advance to reduce equipment downtime and optimize maintenance cycles.

[0032] The present invention is further configured as follows: the feedback and optimization module includes:

[0033] Data feedback unit, which uploads data during system operation to the cloud for long-term analysis and optimization;

[0034] Performance analysis mechanism, based on historical data and operational feedback, analyzes the system's operating efficiency and identifies improvement opportunities;

[0035] The optimization strategy generation unit generates an optimized power management strategy based on the performance analysis results and implements it in the system.

[0036] The present invention is further configured as follows: the calculation model formula in the load adjustment decision unit is as follows:

[0037]

[0038] Among them, P i (t) is the power distribution of power unit i at time t; w i is the priority weight of power consumption unit i; S iThe health status score of the power unit i reflects the equipment operation status (between 0 and 1, 1 means healthy); is the dynamic adjustment coefficient; P desires,i is the ideal power requirement of power consumption unit i;

[0039] is the environmental impact function; Z is the comprehensive normalization factor, which is used to ensure the rationality of power allocation;

[0040] Should,

[0041]

[0042] P available For the available power to the system, make sure the total allocation does not exceed this value.

[0043] The present invention is further configured as follows: the reinforcement learning algorithm formula of the adaptive scheduling is:

[0044]

[0045] Among them, argamx a To select the action a that can maximize the reward; R(a,s t ) is in state s t The immediate reward obtained by executing action a; is the discount factor, ranging from 0 to 1; is the state transition probability, indicating that from the current state s t After executing action a, transfer to the next state s t+1 The probability of V(s t+1 ) is the future state s t+1 The value function under , represents the potential benefit of this state.

[0046] The present invention is further configured that: the power health detection module uses a multi-objective optimization algorithm, and the power allocation formula is:

[0047]

[0048] Among them, Maximize J means that the goal is to make the value of objective function J as large as possible by optimizing power distribution; max,i is the maximum power capacity of power consumption unit i; (1-R i ) is the fault risk reversal value of device i; Penalty for the sum of equipment risks.

[0049] Compared with the shortcomings of the prior art, the beneficial effects of the present invention are:

[0050] The system installs detection units on each power unit to achieve real-time monitoring of power parameters, ensuring rapid response to dynamic changes, thereby ensuring the safety and reliability of the ship. The adaptive energy distribution module uses real-time data analysis and machine learning algorithms to intelligently predict power demand and dynamically adjust power distribution strategies based on navigation status and environmental conditions, improving the efficiency and flexibility of power use.

[0051] The power health detection module monitors the health of the equipment in real time and provides predictive maintenance functions, which can identify potential faults in advance, thereby reducing the risk of equipment failure and ensuring navigation safety. At the same time, the system comprehensively considers the priority and health status of the equipment through a multi-objective optimization algorithm to comprehensively improve the overall energy efficiency, ensure that the ship achieves higher energy utilization efficiency during operation, save resources and reduce operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic diagram of a module of the present invention;

[0053] Figure 2 It is a schematic diagram of information interaction between modules of the present invention. DETAILED DESCRIPTION

[0054] Reference Figure 1 to Figure 2 The embodiments of the present invention are further described.

[0055] The system framework of this application consists of: data acquisition module, dynamic load management module, adaptive energy allocation module, power health detection module, feedback and optimization module.

[0056] These modules are connected to the central control module (which receives data, processes data, and outputs execution commands) through the ship's internal network (such as CAN bus or Ethernet) to achieve real-time data transmission, monitoring and control, thus forming an efficient and intelligent power management system. Information sharing and collaborative work between modules ensure rapid response to various changes in complex navigation environments and optimize the ship's power use.

[0057] The data acquisition module includes a real-time monitoring unit, an environmental monitoring unit and a data transmission unit, which specifically include:

[0058] Real-time monitoring unit: responsible for real-time collection of power parameters such as current, voltage, power and temperature of each power unit. These data are directly fed back to the control center through built-in sensors to ensure that the power usage conditions are monitored.

[0059] Environmental monitoring unit: collects external environmental data such as weather conditions, wind speed and water flow through sensors to optimize power distribution strategies. Environmental data provides an important basis for decision-making, especially in severe weather conditions.

[0060] Data transmission unit: Use wireless or wired networks to upload collected data to the control center in real time to ensure timely updating and sharing of information, and provide a basis for subsequent data analysis and decision support.

[0061] The dynamic load management module makes real-time adjustments to the power supply based on the information provided by the data acquisition module. Specifically:

[0062] Priority rule setting unit: Classify the various electrical equipment on board and set the power supply priority according to the mission requirements to ensure that key equipment has priority in obtaining power during operation. This function is particularly suitable for core systems that ensure safe and efficient operation.

[0063] Load adjustment decision unit: Based on real-time monitoring data and priority rules, it automatically calculates the power distribution plan for each power consumption unit to optimize overall energy efficiency and ensure that high-priority equipment is always met when the load fluctuates.

[0064] The formula used to calculate the model is as follows:

[0065]

[0066] Among them, P i (t) is the power distribution of power unit i at time t; w i is the priority weight of power consumption unit i; S i The health status score of the power unit i reflects the equipment operation status (between 0 and 1, 1 means healthy); is the dynamic adjustment coefficient; P desires,i is the ideal power requirement of power consumption unit i;

[0067] is the environmental impact function; Z is the comprehensive normalization factor, which is used to ensure the rationality of power allocation;

[0068] Should,

[0069]

[0070] P available For the available power to the system, make sure the total allocation does not exceed this value.

[0071] Control execution unit: According to the load adjustment decision, control the power output of each power-consuming unit, respond to the change of power demand in time, ensure the flexibility and real-time performance of power distribution, and improve the adaptability of the system.

[0072] The adaptive energy distribution module uses machine learning algorithms to predict and dynamically adjust power demand, including:

[0073] Data Analysis Unit: Analyzes ship operating status, environmental conditions and historical electricity consumption data in real time, and uses statistical methods and machine learning models (such as linear regression, random forest, etc.) to predict future power demand and ensure the accuracy of power supply.

[0074] Adaptive scheduling algorithm: Based on deep learning of real-time and historical data, it dynamically optimizes power distribution strategies to ensure efficient use of power. This algorithm can flexibly adjust power output according to changing navigation status and equipment health.

[0075] The reinforcement learning algorithm formula for adaptive scheduling is:

[0076]

[0077] Among them, argamx a To select the action a that can maximize the reward; R(a,s t ) is in state s t The immediate reward obtained by executing action a; is the discount factor, ranging from 0 to 1; is the state transition probability, indicating that from the current state s t After executing action a, transfer to the next state s t+1 The probability of V(s t+1 ) is the future state s t+1 The value function under , represents the potential benefit of this state.

[0078] Dynamic adjustment unit: It can automatically adjust the power supply of each power-consuming unit when the power demand changes, give priority to the needs of key systems, ensure that the system is always in the best operating state, and improve overall energy efficiency.

[0079] The power health detection module ensures the safe operation of equipment through real-time monitoring and predictive maintenance. Specifically:

[0080] Real-time health monitoring unit: monitors the operating status of each power-consuming unit, collects current, voltage, temperature and other parameters in real time to detect the health of the equipment, and promptly discovers and handles potential faults through monitoring data.

[0081] Abnormal detection and alarm unit: When the operating status of the power unit is detected to be abnormal, the alarm mechanism is automatically triggered, an alarm signal is sent to the control center, and the fault event is recorded to facilitate subsequent analysis and processing.

[0082] Predictive maintenance unit: Based on historical operating data and equipment health status, it uses predictive algorithms (such as time series analysis and machine learning models) to predict potential failure risks and issue maintenance reminders in advance to reduce equipment downtime and optimize maintenance cycles.

[0083] The power health detection module uses a multi-objective optimization algorithm, and the power allocation formula is:

[0084]

[0085] Among them, Maximize J means that the goal is to make the value of objective function J as large as possible by optimizing power distribution; max,i is the maximum power capacity of power consumption unit i; (1-R i ) is the fault risk reversal value of device i; Penalty for the sum of equipment risks.

[0086] The feedback and optimization module is used to continuously improve system performance, including:

[0087] Data feedback unit: uploads data from the operation process to the cloud for long-term analysis and optimization, which enables the system to accumulate data and provide support for subsequent decision-making.

[0088] Performance analysis mechanism: Based on historical data and operational feedback, analyze the system's operating efficiency, identify improvement opportunities, and ensure continuous system optimization.

[0089] Optimization strategy generation unit: Combines the performance analysis results to generate an optimized power management strategy and implements it in the system. Through continuous iteration, it ensures that the system is always in the best state.

[0090] In the operation of inland official vessels, this system can monitor the power demand and environmental changes of the ship in real time. For example, when encountering sudden weather conditions, the system can automatically adjust the power distribution to ensure the normal operation of the navigation system and power system, thereby significantly improving the safety of navigation. For ships performing patrol missions, this system can dynamically optimize the power supply according to the navigation status and mission priority to ensure that key equipment (such as monitoring and communication equipment) is always in good working condition. Especially in complex environments, the system's intelligent deployment capabilities can enhance the ship's response speed and processing capabilities.

[0091] During long voyages, the system continuously optimizes the power distribution plan through the analysis of power usage data, improves the energy efficiency of the ship, and reduces energy consumption. For example, the system can predict the energy consumption of a specific section based on historical operation data, and then adjust the power output strategy to maximize energy efficiency.

[0092] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Common changes and substitutions made by those skilled in the art within the scope of the technical solution of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent power management and energy efficiency optimization system for new energy ships, characterized in that: include: The data acquisition module is used to monitor the current, voltage, temperature, ambient humidity and other parameters of each power unit of the ship in real time, and upload the data to the control center; A dynamic load management module adjusts the power supply priority of each power-consuming unit in real time according to the data to ensure that key equipment is given priority power supply; Adaptive energy allocation module, which predicts power demand and dynamically adjusts power allocation strategy based on the ship’s operating status, environmental conditions and historical data; The power health detection module monitors the health status of each power unit in real time, automatically alarms and adjusts power distribution when abnormalities are found, and provides predictive maintenance functions; The feedback and optimization module uploads the operating data to the cloud and continuously optimizes the energy distribution plan through big data analysis.

2. According to claim 1, the intelligent power management and energy efficiency optimization system for new energy ships is characterized in that: The data acquisition module comprises: Real-time monitoring unit, used to monitor the current, voltage, temperature and other power parameters of each power unit on board in real time, and upload the data to the control center; Environmental monitoring unit, used to collect external environmental data, including weather conditions and ship load, to provide comprehensive operating data; The data transmission unit transmits the real-time monitoring and environmental data to the control center via a wireless or wired network to achieve real-time updating and sharing of data.

3. According to claim 2, the intelligent power management and energy efficiency optimization system for new energy ships is characterized by: The dynamic load management module comprises: The priority rule setting unit is used to classify the various electrical equipment on board and set the power supply priority according to the importance of the equipment to ensure that key equipment has priority in power supply under different operating conditions; The load adjustment decision unit automatically calculates the power distribution plan for each power consumption unit based on real-time monitoring data and priority rules to optimize overall energy efficiency and ensure the stable operation of high-priority equipment; The control execution unit implements power output control based on load adjustment decisions to ensure that the system responds to changes in power demand in real time; Fault response mechanism: when power anomalies or equipment failures are detected, the system can automatically reduce the power supply to low-priority equipment to protect critical equipment and maintain the normal operation of the ship.

4. According to claim 3, the intelligent power management and energy efficiency optimization system for new energy ships is characterized in that: The adaptive energy allocation module includes: A data analysis unit for real-time analysis of ship operating status, environmental conditions and historical power consumption data to predict future power demand; Adaptive dispatch unit, which continuously optimizes power distribution strategy based on real-time and historical data through reinforcement learning algorithm to ensure efficient use of power; The dynamic adjustment unit can automatically adjust the power supply of each power-consuming unit when the power demand changes, giving priority to the needs of key systems.

5. According to claim 4, the intelligent power management and energy efficiency optimization system for new energy ships is characterized in that: The power health detection module includes: Real-time health monitoring unit, used to monitor the operating status of each power unit, collect current, voltage, temperature and other parameters in real time to detect the health status of the equipment; The abnormality detection and alarm unit automatically triggers the alarm mechanism and sends an alarm signal to the control center when it detects abnormal operation status of the power unit or equipment failure; Automatic power adjustment unit: Based on the abnormal detection results, the system automatically adjusts the power distribution plan to reduce the power supply of faulty equipment and give priority to the normal operation of key equipment; The predictive maintenance unit predicts potential failure risks based on historical operating data and equipment health status, and issues maintenance reminders in advance to reduce equipment downtime and optimize maintenance cycles.

6. According to claim 5, the intelligent power management and energy efficiency optimization system for new energy ships is characterized in that: The feedback and optimization module includes: Data feedback unit, which uploads data during system operation to the cloud for long-term analysis and optimization; Performance analysis mechanism, based on historical data and operational feedback, analyzes the system's operating efficiency and identifies improvement opportunities; The optimization strategy generation unit generates an optimized power management strategy based on the performance analysis results and implements it in the system.

7. According to claim 2, the intelligent power management and energy efficiency optimization system for new energy ships is characterized in that: The calculation model formula in the load adjustment decision unit is as follows: Among them, P i (t) is the power distribution of power unit i at time t; w i is the priority weight of power consumption unit i; S i The health status score of the power unit i reflects the equipment operation status (between 0 and 1, 1 means healthy); is the dynamic adjustment coefficient; P desires,i is the ideal power requirement of power consumption unit i; is the environmental impact function; Z is the comprehensive normalization factor, which is used to ensure the rationality of power allocation; Should, P available For the available power to the system, make sure the total allocation does not exceed this value.

8. According to claim 5, the intelligent power management and energy efficiency optimization system for new energy ships is characterized by: The reinforcement learning algorithm formula for the adaptive scheduling is: Among them, argamx a To select the action a that can maximize the reward; R(a,s t ) is in state s t The immediate reward obtained by executing action a; is the discount factor, ranging from 0 to 1; is the state transition probability, indicating that from the current state s t After executing action a, transfer to the next state s t+1 The probability of V(s t+1 ) is the future state s t+1 The value function under , represents the potential benefit of this state.

9. According to claim 3, the intelligent power management and energy efficiency optimization system for new energy ships is characterized by: The power health detection module uses a multi-objective optimization algorithm, and the power allocation formula is: Among them, Maximize J means that the goal is to make the value of objective function J as large as possible by optimizing power distribution; max,i is the maximum power capacity of power consumption unit i; (1-R i ) is the fault risk reversal value of device i; Penalty for the sum of equipment risks.

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