A dynamic energy efficiency management and control system

Abstract

The present invention relates to the technical field of program control, and specifically to a dynamic energy efficiency management and control system. The system includes: A data acquisition module collects real-time data from device power sensors based on device power and operation modes, records changes in operation modes, obtains device operation status parameters, marks them with timestamps, arranges them in chronological order, and integrates them into a real-time energy consumption data stream. In the present invention, by monitoring changes in device power and operation modes in real time and calculating the entropy change rate in real time, the system can effectively predict and adapt to energy consumption changes, achieve fine adjustment of device power and optimization of operation modes. Such measures can significantly improve energy efficiency, ensure the maximum benefit of energy use, and reduce unnecessary waste. The real-time feedback mechanism further enables the device adjustment measures to be optimized based on actual effects, ensuring the effectiveness of the adjustment measures, supporting continuous energy efficiency improvement, and having a more positive overall impact on the environment.

Description

Technical Field

[0001] The present invention relates to the technical field of program control, and particularly to a dynamic energy efficiency management and control system. Background Art

[0002] The technical field of program control involves technologies for automating the management and regulation of various mechanical or electronic systems using computer programs. This field mainly focuses on how to optimize system performance, improve efficiency, reduce costs, and prevent failures through preset programs or real-time algorithms. Program control can be applied to a variety of scenarios, including industrial production, environmental management, equipment maintenance, and energy management, which not only includes traditional control systems but also increasingly encompasses advanced control strategies based on artificial intelligence and machine learning to achieve more precise and flexible control.

[0003] Among them, a dynamic energy efficiency management and control system refers to a system that uses advanced program control technologies to monitor and optimize the energy consumption of equipment or systems in real time. The main purpose of such a system is to achieve energy conservation and emission reduction by dynamically adjusting and optimizing energy use, while maintaining or improving system performance. Dynamic energy efficiency management and control systems are commonly found in fields such as industrial automation, building energy management, and smart grids, and continuously adjust operation parameters through real-time data analysis and learning to adapt to changes in the environment or production requirements, thereby achieving efficient energy use.

[0004] Existing technologies often show insufficient adaptability when dealing with rapidly changing production requirements and environmental factors. Especially when the environment and production requirements change rapidly, they lack real-time data analysis and immediate adjustment capabilities. For example, in a highly variable industrial environment, traditional control systems, due to fixed parameter settings and response delays, cannot adjust the equipment status in a timely manner to match the latest operating requirements, resulting in low energy efficiency and excessive resource consumption. The slow response of such systems not only affects energy utilization efficiency but may also increase equipment wear and shorten equipment life, posing challenges to maintaining production efficiency and cost control. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art and propose a dynamic energy efficiency management and control system.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions: A dynamic energy efficiency management and control system includes:

[0007] The data acquisition module collects real-time data from the device power sensor based on the device power and operation mode, records the changes in the operation mode, obtains the device operation status parameters, marks them with timestamps, arranges them in chronological order, and integrates them into a real-time energy consumption data stream;

[0008] The entropy change rate calculation module extracts the time series data of the device power and operation mode based on the real-time energy consumption data stream, analyzes the fluctuation degree of the energy consumption data, calculates the energy consumption difference between adjacent time points, and calculates the entropy change rate of the device power and operation mode to obtain the entropy change rate dynamic index;

[0009] The dynamic adjustment strategy module compares the entropy change rate with the set threshold according to the entropy change rate dynamic index, judges whether the device power output and operation mode need to be adjusted, calculates the required device power adjustment amount, selects the operation mode, adjusts the device power output and operation mode, and generates optimized operation parameters;

[0010] The effect feedback and optimization module uses the optimized operation parameters to adjust the device power output and operation mode, collects the actual energy consumption data after adjustment, compares the actual energy consumption with the expected energy consumption data, calculates the difference between the two, and generates system optimization feedback.

[0011] As a further solution of the present invention, the real-time data of the device power sensor is collected, and the change of each operation mode is recorded in real time, marked by a time stamp to generate a power and operation mode record with a time stamp;

[0012] Using the power and operation mode record with a time stamp, it is integrated in chronological order, and at the same time, data cleaning is performed to remove duplicate and illogical time stamps to obtain the sorted real-time power and operation mode record;

[0013] For the sorted real-time power and operation mode record, the formula is used:

[0014] ;

[0015] Calculate the adjusted energy consumption to generate a real-time energy consumption data stream;

[0016] Among them, represents the power at the time point, is the time difference between adjacent time stamps, represents the 1.2th power of the time difference, represents the calculated total energy consumption.

[0017] As a further solution of the present invention, the steps for obtaining the entropy change rate dynamic index are specifically as follows:

[0018] Extract the time series data of the device power and operation mode in the real-time energy consumption data stream, reflect the energy consumption situation of the device at different time points, and generate a time series data set;

[0019] Based on the time series data set, calculate the energy consumption difference between every two adjacent points to reveal the energy consumption fluctuation in the short term and obtain an energy consumption difference sequence;

[0020] Adopt the formula:

[0021] ;

[0022] Calculate the entropy change rate and generate a dynamic entropy change rate index;

[0023] Among them, represents the entropy change rate, indicating the uncertainty and variability of energy consumption, represents the proportion of energy consumption at each time point, is the conventional calculation part of information entropy, used to measure the influence of the energy consumption contribution at the time point on the total entropy value, represents the weight coefficient, used to enhance the sensitivity of the entropy value to extreme fluctuations in energy consumption.

[0024] As a further solution of the present invention, the specific steps for obtaining the optimized operation parameters are as follows:

[0025] Compare the dynamic entropy change rate index with a preset threshold to determine whether there is a need to adjust the power output and operation mode. If the entropy change rate exceeds the threshold, it is marked as needing adjustment, and an adjustment demand signal is generated;

[0026] According to the adjustment demand signal, calculate the amount of power that needs to be adjusted, analyze the deviation between the current power and the ideal power, calculate the power adjustment value, and generate power adjustment data;

[0027] Select an appropriate operation mode and apply the power adjustment data. Adopt the formula:

[0028] ;

[0029] Adjust the device power output to the current set value and generate optimized operation parameters;

[0030] Among them, represents the optimized operation parameter, represents the current power setting, represents the calculated power adjustment value, is an adjustment coefficient determined according to the device performance and response characteristics.

[0031] As a further solution of the present invention, the specific steps for obtaining the system optimization feedback are as follows:

[0032] Use the optimized operation parameters to adjust the power output and operation mode of the device, ensure that the device operates according to the current configuration, and record and generate an adjusted device status record;

[0033] Based on the adjusted device status record, collect the adjusted actual energy consumption data to ensure that the energy consumption data reflects the operating status of the device, and obtain the actual energy consumption data record;

[0034] Call the actual energy consumption data record and the expected energy consumption data, and use the formula:

[0035] ;

[0036] Calculate the difference between the actual energy consumption and the expected energy consumption, and generate system optimization feedback;

[0037] Wherein, represents the energy consumption difference, represents the actual energy consumption data, represents the expected energy consumption data, represents the adjustment coefficient, which is used to balance the influence of relative difference and absolute difference, represents the smoothing parameter, which is used to reduce the influence of extreme values on the difference calculation.

[0038] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0039] In the present invention, by real-time monitoring the changes in device power and operation mode and performing real-time calculation of the entropy change rate, the system can effectively predict and adapt to energy consumption changes, realize fine adjustment of device power and optimization of operation mode. Such measures can significantly improve energy efficiency, ensure the maximum benefit of energy use, and reduce unnecessary waste. The real-time feedback mechanism further enables the device adjustment measures to be optimized based on the actual effects, ensures the effectiveness of the adjustment measures, supports continuous energy efficiency improvement, and has a more positive overall impact on the environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is the system flow chart of the present invention;

[0041] Figure 2 is the flow chart of the acquisition steps of the real-time energy consumption data stream of the present invention;

[0042] Figure 3 is the flow chart of the acquisition steps of the entropy change rate dynamic index of the present invention;

[0043] Figure 4 is the flow chart of the acquisition steps of the optimized operation parameters of the present invention;

[0044] Figure 5 is the flow chart of the acquisition steps of the system optimization feedback of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0045] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0046] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more unless otherwise specifically defined.

[0047] Embodiment 1:

[0048] Please refer to Figure 1 , a dynamic energy efficiency management and control system includes:

[0049] The data acquisition module collects real-time data of the device power sensor based on the device power and operation mode, records the changes in the operation mode, obtains the device operation status parameters, marks them with timestamps, arranges them in chronological order, and integrates them into a real-time energy consumption data stream.

[0050] The entropy change rate calculation module extracts the time series data of the device power and operation mode based on the real-time energy consumption data stream, analyzes the fluctuation degree of the energy consumption data, calculates the energy consumption difference between adjacent time points, and calculates the entropy change rate of the device power and operation mode to obtain the entropy change rate dynamic index.

[0051] The dynamic adjustment strategy module compares the entropy change rate with the set threshold according to the entropy change rate dynamic index, determines whether the device power output and operation mode need to be adjusted, calculates the required device power adjustment amount, selects the operation mode, adjusts the device power output and operation mode, and generates optimized operation parameters.

[0052] The effect feedback and optimization module uses the optimized operation parameters to adjust the device power output and operation mode, collects the actual energy consumption data after adjustment, compares the actual energy consumption with the expected energy consumption data, calculates the difference between the two, and generates a system optimization feedback.

[0053] The real-time energy consumption data stream includes device power data, operation mode change data, device operation status parameters and timestamps. The entropy change rate dynamic index includes the device power entropy change rate index and the operation mode entropy change rate index. The optimized operation parameters include device power adjustment parameters and operation mode parameters. The system optimization feedback includes the energy consumption difference analysis result and the optimization effect evaluation.

[0054] Please refer to Figure 2 , the steps for obtaining the real-time energy consumption data stream are specifically as follows:

[0055] Collect the real-time data of the device power sensor, and record the change of each operation mode in real time. Mark the change with a timestamp to generate a timestamped power and operation mode record;

[0056] Collect the real-time data of the device power sensor and record the change of each operation mode in real time. This process involves the configuration of data acquisition hardware and the setting of a real-time data processing system. The data is marked with timestamps to ensure the time accuracy of the data. At the same time, each change in the operation mode is recorded to ensure that all operation data is captured and correctly associated with the power readings. Mark the change with a timestamp to generate a timestamped power and operation mode record. This record is crucial for data analysis and ensures the integrity and traceability of the data.

[0057] Use the timestamped power and operation mode records, integrate them in chronological order, and at the same time perform data cleaning to remove duplicate and non-logical timestamps to obtain the sorted real-time power and operation mode records;

[0058] Use the generated timestamped power and operation mode records. The records are first integrated in chronological order. The integration process involves verifying the timestamps of the data to exclude records with incorrect or discontinuous times. Data cleaning includes removing records with duplicate and non-logical timestamps, such as abnormal data caused by equipment failures or data transmission errors. The sorted real-time power and operation mode records obtained provide the basic data for energy consumption calculation and ensure the accuracy of subsequent calculations and the usability of the data.

[0059] For the sorted real-time power and operation mode records, use the formula:

[0060] ;

[0061] Calculate the adjusted energy consumption to generate a real-time energy consumption data stream;

[0062] where, represents the power at the time point, is the time difference between adjacent timestamps, represents the 1.2th power of the time difference, represents the total calculated energy consumption.

[0063] Formula:

[0064] ;

[0065] The advantage of the formula is that by introducing the 1.2 power of the time period, the sensitivity of the model to power changes within a short period is enhanced, which is applicable to changing operation modes and devices with large power fluctuations, improving the adaptability and accuracy of energy consumption calculation;

[0066] Detailed explanation of the formula and the derivation process of formula calculation:

[0067] Set data points, where the unit is kilowatt (kW), corresponding to the time difference the unit is hour, apply the 1.2 power processing to each time period, and get .

[0068] The calculation process is as follows:

[0069] ;

[0070] The results show that the total energy consumption is 34.9 kilowatt-hours. This value reflects the total energy consumption of the device within the given time. Based on this model, the energy cost budget and the analysis of energy use efficiency can be carried out more accurately.

[0071] Please refer to Figure 3 , and the specific steps for obtaining the dynamic index of entropy change rate are as follows:

[0072] Extract the time series data of the device power and operation mode from the real-time energy consumption data stream, which reflects the energy consumption of the device at different time points, and generate a time series data set;

[0073] When extracting real-time energy consumption data, first collect the data of the device power and operation mode based on sensor input. The data comes from the device power sensor and the operation mode monitoring system. The real-time power and operation mode changes during each device operation are recorded through the time series method, and the time stamp is used to mark the data to ensure that the time point of each record can be traced. The data collection is the key to ensuring the effectiveness of subsequent analysis. Through the data collection module, various operating states of the device are monitored in real time, including power peaks, valleys, and any changes in the operation mode. This process involves batch data screening and preliminary processing, such as removing abnormal data caused by sensor errors or external interference, thus laying a solid foundation for the next energy consumption analysis and ensuring that the obtained time series data set can truly reflect the operating state of the device.

[0074] Based on the time series data set, calculate the energy consumption difference between every two adjacent points to reveal the energy consumption fluctuations in the short term, and obtain an energy consumption difference sequence;

[0075] After obtaining the time series dataset, the next step is to calculate the energy consumption difference between adjacent points in the data. This process is completed by performing difference operations on every two consecutive data points, aiming to reveal the short-term fluctuations in the device's energy consumption. Fluctuation analysis is a key indicator for evaluating the performance stability of the device, assisting engineers in analyzing the energy consumption performance of the device under different operating modes. In addition, by comparing the energy consumption differences at different time points, signs of device failures or performance degradation can be identified. This analysis process not only includes numerical calculations but also involves the graphical representation of data, facilitating technicians to monitor the trend of energy consumption fluctuations. By setting thresholds, energy consumption fluctuations beyond the normal range can be automatically identified, triggering alarms or prompts to provide decision-making support for device maintenance.

[0076] Use the formula:

[0077] ;

[0078] Calculate the entropy change rate to generate a dynamic entropy change rate indicator;

[0079] Among them, represents the entropy change rate, indicating the uncertainty and variability of energy consumption, represents the proportion of energy consumption at each time point, is the conventional calculation part of the information entropy, used to measure the impact of the energy consumption contribution at the time point on the total entropy value, represents the weight coefficient, used to enhance the sensitivity of the entropy value to extreme fluctuations in energy consumption.

[0080] Formula:

[0081] ;

[0082] The advantage of the formula is that by introducing the proportion of energy consumption and the adjustment coefficient, the formula can measure and analyze the performance of the entropy change rate of energy consumption fluctuations under extreme change conditions, which helps to dynamically evaluate the energy efficiency performance of the device.

[0083] Detailed explanation of the formula and the derivation process of formula calculation:

[0084] Set that within a monitoring period, the total energy consumption of the device is 10,000 kWh, and the energy consumptions at 5 recorded time points are 2,000, 2,500, 1,500, 3,000, and 1,000 kWh respectively. First, calculate the proportion of energy consumption at each time point , for example, at the first time point .

[0085] Then, substitute into the formula to calculate the entropy contribution of each point. Assuming , then the entropy contribution of the first point is . Add up the entropy contributions of all points to obtain the total entropy change rate 。

[0086] The results show that by comprehensively referring to the energy consumption ratio and energy consumption fluctuation, a quantitative entropy change rate is obtained, which provides a scientific basis for iteratively optimizing the device operation parameters and improving energy efficiency.

[0087] Please refer to Figure 4 , and the specific steps for obtaining the optimized operation parameters are as follows:

[0088] Compare the dynamic index of the entropy change rate with a preset threshold to determine whether there is a need to adjust the power output and operation mode. If the entropy change rate exceeds the threshold, it is marked as needing adjustment, and an adjustment demand signal is generated;

[0089] The process of comparing the entropy change rate with the preset threshold involves real-time monitoring of the device's power output and operation mode. After the real-time data is obtained, it is processed by a data analysis system to obtain the real-time value of the entropy change rate. This value will be compared with the previously set threshold. The entropy change rate is obtained by collecting the device's operation data and using statistical methods. After comparing with the preset threshold, the system will automatically judge whether it exceeds the threshold. If it exceeds, an adjustment signal is triggered. This process ensures that the device operates in an optimal state, improves energy efficiency, and ensures the safety of the device.

[0090] According to the adjustment demand signal, calculate the amount of power to be adjusted, analyze the deviation between the current power and the ideal power, calculate the power adjustment value, and generate power adjustment data;

[0091] According to the adjustment demand signal, the system automatically calculates the required power adjustment amount, which involves calculating the deviation between the current power setting and the target power. Real-time power data is obtained through a data acquisition system and compared with the set target power. The difference data is processed by an algorithm to generate an adjustment value, which indicates how much power needs to be increased or decreased to reach the ideal state. During the adjustment process, the system continuously monitors and adjusts until the preset target power is reached, ensuring the efficient operation of the device.

[0092] Select an appropriate operation mode and apply the power adjustment data, using the formula:

[0093] ;

[0094] Adjust the device power output to the current set value to generate optimized operation parameters;

[0095] Among them, represents the optimized operation parameter, represents the current power setting, represents the calculated power adjustment value, is an adjustment coefficient determined according to the device performance and response characteristics.

[0096] Formula:

[0097] ;

[0098] The benefit of the formula is to provide a method to directly adjust the device power to the ideal state, by adjusting the coefficient the adjustment amplitude can be flexibly controlled to match the characteristics and operation requirements of different devices.

[0099] Detailed explanation of the formula and the derivation process of formula calculation:

[0100] Set the current power to 500 kW, the power to be adjusted is 50 kW, and the adjustment coefficient is set to 0.8, substitute into the formula for calculation:

[0101] ;

[0102] ;

[0103] ;

[0104] The result shows that the new power after adjustment is set to 540 kW, which means that the power output of the device will increase, matching the change of the working load, and ensuring the continuity and stability of the device operation.

[0105] Please refer to Figure 5 , the specific steps to obtain the system optimization feedback are as follows:

[0106] Use the optimized operation parameters to adjust the power output and operation mode of the device, ensure that the device runs according to the current configuration, and record the adjusted device status record;

[0107] Adjust the power output and operation mode of the device to ensure that the device runs according to the current configuration. This step involves the evaluation of the existing settings of the device and the adjustment of the device status according to the optimized parameters, ensuring that all adjustment measures are directly applied to the device control system, and these measures should be based on the actual operation data and the preset performance goals. The adjusted device status record is automatically recorded by the real-time monitoring system to ensure the accuracy and real-time update of the recorded data, which is crucial for subsequent performance evaluation and energy consumption optimization.

[0108] Based on the adjusted device status record, collect the actual energy consumption data after adjustment to ensure that the energy consumption data reflects the operation status of the device, and obtain the actual energy consumption data record;

[0109] Collect the actual energy consumption data after adjustment. The data can intuitively reflect the operating efficiency of the equipment after adjustment. The data collection should be completed through sensors and a data acquisition system. The system must be calibrated regularly to ensure the accuracy of the data. The actual energy consumption data record should include not only the total power consumption, but also the time distribution and peak information of the power consumption. These data are crucial for comparing the actual energy consumption with the expected energy consumption and can directly affect the accuracy of the energy consumption difference analysis.

[0110] Call the actual energy consumption data record and the expected energy consumption data, and use the formula:

[0111] ;

[0112] Calculate the difference between the actual energy consumption and the expected energy consumption, and generate system optimization feedback;

[0113] Among them, represents the energy consumption difference, represents the actual energy consumption data, represents the expected energy consumption data, represents the adjustment coefficient, which is used to balance the influence of relative difference and absolute difference, represents the smoothing parameter, which is used to reduce the influence of extreme values on the difference calculation.

[0114] Formula:

[0115] ;

[0116] The advantage of the formula is that it not only calculates the absolute difference of energy consumption, but also increases the flexibility and accuracy of the calculation by introducing the adjustment coefficient and the smoothing parameter , making it more adaptable to the changes in differential operating conditions and environments, and helping to accurately evaluate the effect of adjustment measures.

[0117] Detailed explanation of the formula and the derivation process of formula calculation:

[0118] Set , , , , then:

[0119] ;

[0120] The result shows that there is a difference of 5.02666 kWh between the actual energy consumption after adjustment and the expected energy consumption, which indicates that the adjustment measures have achieved the expected effect, but there is still room for iterative optimization.

[0121] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the relevant art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A dynamic energy efficiency management and control system, characterized in that: The system comprises: The data acquisition module collects real-time data from the device power sensor based on the device power and operation mode, records changes in the operation mode, obtains device operating status parameters, marks them with timestamps, arranges them in chronological order, and integrates them into a real-time energy consumption data stream; The entropy change rate calculation module extracts the time series data of the device power and the operation mode based on the real-time energy consumption data stream, analyzes the fluctuation degree of the energy consumption data, calculates the energy consumption difference at adjacent time points, and calculates the entropy change rate of the device power and the operation mode to obtain the entropy change rate dynamic index; The steps for obtaining the entropy change rate dynamic index are specifically as follows: Extracting time series data of device power and operation mode from the real-time energy consumption data stream to reflect the energy consumption of the device at differentiated time points and generate a time series data set; Based on the time series data set, the energy consumption difference between each two adjacent points is calculated to reveal the energy consumption fluctuation in the short term and obtain the energy consumption difference sequence; Using the formula: ； Calculate the entropy change rate and generate a dynamic index of entropy change rate; in, represents the entropy change rate, indicating the uncertainty and variability of energy consumption, Represents the energy consumption ratio at each time point, It is the conventional calculation part of information entropy, which is used to measure the The impact of energy consumption contribution at a given time point on the total entropy value, represents the weight coefficient used to enhance the sensitivity of entropy value to extreme fluctuations in energy consumption; The dynamic adjustment strategy module compares the entropy change rate with the set threshold value according to the entropy change rate dynamic indicator, determines whether the device power output and the operation mode need to be adjusted, calculates the required device power adjustment amount, selects the operation mode, adjusts the device power output and the operation mode, and generates optimized operation parameters; The effect feedback and optimization module uses the optimized operating parameters to adjust the equipment power output and operating mode, collects the adjusted actual energy consumption data, compares the actual energy consumption with the expected energy consumption data, calculates the difference between the two, and generates system optimization feedback.

2. The dynamic energy efficiency management and control system according to claim 1, characterized in that: Collect real-time data from the device power sensor and record each change in operation mode in real time, mark the change with a timestamp, and generate a timestamped record of power and operation mode; Using the power and operation mode records with timestamps, integrating them in chronological order, and performing data cleaning to remove duplicate and non-logical timestamps, to obtain sorted real-time power and operation mode records; For the collated real-time power and operation mode records, the formula is used: ； Calculate the adjusted energy consumption and generate real-time energy consumption data stream; in, Representative The power at a point in time, is the time difference between adjacent timestamps, represents the time difference to the power of 1.2, Represents the calculated total energy consumption.

3. The dynamic energy efficiency management and control system according to claim 1, characterized in that: The steps of obtaining the optimization operation parameters are specifically as follows: Comparing the entropy change rate dynamic index with a preset threshold value to determine whether there is a need to adjust the power output and the operation mode, if the entropy change rate exceeds the threshold value, it is marked as requiring adjustment, and an adjustment demand signal is generated; According to the adjustment demand signal, the amount of power to be adjusted is calculated, the deviation between the current power and the ideal power is analyzed, the power adjustment value is calculated, and the power adjustment data is generated; Select the appropriate operating mode and apply the power adjustment data using the formula: ； Adjust the equipment power output to the current set value and generate optimized operating parameters; in, represents the optimized operating parameters, Represents the current power setting, represents the calculated power adjustment value, It is an adjustment factor determined based on equipment performance and response characteristics.

4. The dynamic energy efficiency management and control system according to claim 3 is characterized in that: The steps for obtaining the system optimization feedback are specifically as follows: Using the optimized operating parameters, adjusting the power output and operating mode of the device to ensure that the device operates according to the current configuration, and recording and generating a record of the adjusted device status; Based on the adjusted equipment status record, collect the adjusted actual energy consumption data to ensure that the energy consumption data reflects the operating status of the equipment and obtain the actual energy consumption data record; The actual energy consumption data record and the expected energy consumption data are called, and the formula is used: ； Calculate the difference between actual and expected energy consumption and generate system optimization feedback; in, Represents the difference in energy consumption, Represents actual energy consumption data, Represents the expected energy consumption data, Represents the adjustment factor, which is used to balance the impact of relative differences and absolute differences. Represents a smoothing parameter that is used to reduce the impact of extreme values ​​on the difference calculation.

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