An energy optimization method and system based on artificial intelligence

Abstract

The application discloses an energy optimization method and system based on artificial intelligence, and the method comprises the following steps: collecting energy operation data of an energy management system when a current energy parameter configuration scheme is adopted; according to the energy operation data, an energy state evaluation model based on artificial intelligence is used to evaluate the operation state of the energy management system and calculate an operation stability index of the energy management system; and according to the operation state and the operation stability index, the energy parameter configuration scheme of the energy management system is optimized. By using the embodiment of the application, the operation stability and energy efficiency of the energy management system can be effectively improved, thereby providing strong support for sustainable development.

Description

Technical Field

[0001] This invention belongs to the field of energy technology, specifically an energy optimization method and system based on artificial intelligence. Background Technology

[0002] Traditional energy management methods often rely on experience and static data analysis, making it difficult to adapt to the rapidly changing energy environment and complex user needs. Therefore, improving the intelligence and dynamism of energy management has become an urgent technical challenge. Summary of the Invention

[0003] The purpose of this invention is to provide an energy optimization method and system based on artificial intelligence to address the shortcomings of existing technologies, effectively improve the operational stability and energy efficiency of energy management systems, and provide strong support for sustainable development.

[0004] One embodiment of this application provides an artificial intelligence-based energy optimization method, the method comprising:

[0005] Collect energy operation data from the energy management system when the current energy parameter configuration scheme is used;

[0006] Based on the energy operation data, the operating status of the energy management system is assessed using a pre-trained AI-based energy status assessment model, and the operating stability index of the energy management system is calculated.

[0007] Based on the operating status and the operating stability index, optimize the energy parameter configuration scheme of the energy management system.

[0008] Optionally, the formula for calculating the operational stability index is:

[0009]

[0010] Among them, the To ensure operational stability, the aforementioned Let be the energy consumption rate at the t-th data collection time point, and the... Let be the energy consumption fluctuation frequency at the t-th data collection time point, and be... Let be the environmental disturbance index at the t-th collection time point, and the... Let be the system redundancy at the t-th data collection time point, and be... The above The above For the corresponding weighting coefficients, the This is the attenuation factor.

[0011] Optionally, the formula for calculating the energy consumption rate is:

[0012]

[0013] wherein, the E(t) is the total energy consumption at the tth collection time point, and the L(t) is the total load at the tth collection time point.

[0014] Optionally, the calculation formula of the energy consumption fluctuation frequency is:

[0015]

[0016] wherein, the k is a preset number of preceding collection time points, and k is less than t.

[0017] Optionally, the calculation formula of the environmental disturbance index is:

[0018]

[0019] wherein, the is the measurement value of the jth environmental index at the tth collection time point, the is the historical average value of the jth environmental index, the is the weight factor of the jth environmental index, and the n is the number of environmental indexes.

[0020] Optionally, the calculation formula of the system redundancy is:

[0021]

[0022] wherein, the is the connectivity of the mth device of the energy management system, and the M is the number of devices.

[0023] Optionally, the optimization of the energy parameter configuration scheme of the energy management system according to the running state and the running stability index comprises:

[0024] If the running state is an abnormal state and / or the running stability index exceeds a preset threshold, adjusting the energy parameter configuration scheme of the energy management system so that the running state is a normal state and the running stability index does not exceed the preset threshold.

[0025] Another embodiment of the present application provides an energy optimization system based on artificial intelligence, which comprises:

[0026] a collection module, configured to collect energy running data of an energy management system when a current energy parameter configuration scheme is adopted;

[0027] an evaluation module, configured to evaluate a running state of the energy management system and calculate a running stability index of the energy management system by using a pre-trained energy state evaluation model based on artificial intelligence according to the energy running data.

[0028] an optimization module configured to optimize an energy parameter configuration scheme of the energy management system according to the running state and the running stability index.

[0029] Yet another embodiment of the present application provides a storage medium having a computer program stored therein, wherein the computer program is configured to execute the method described in any one of the above embodiments when running.

[0030] Yet another embodiment of the present application provides an electronic device comprising a memory having a computer program stored therein and a processor configured to execute the computer program to perform the method described in any one of the above embodiments.

[0031] Compared with the prior art, the energy optimization method based on artificial intelligence provided by the present application can collect energy running data of an energy management system when the current energy parameter configuration scheme is adopted, evaluate the running state of the energy management system by using a pre-trained energy state evaluation model based on artificial intelligence according to the energy running data, calculate a running stability index of the energy management system, and optimize the energy parameter configuration scheme of the energy management system according to the running state and the running stability index, thereby effectively improving the running stability and energy efficiency of the energy management system and providing strong support for sustainable development. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 A hardware structure block diagram of a computer terminal of the energy optimization method based on artificial intelligence provided by the embodiment of the present application is shown in the figure.

[0033] Figure 2 A flowchart of the energy optimization method based on artificial intelligence provided by the embodiment of the present application is shown in the figure.

[0034] Figure 3 A structure diagram of the energy optimization system based on artificial intelligence provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0035] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be explained as a limitation of the present application.

[0036] The embodiment of the present application first provides an energy optimization method based on artificial intelligence, which can be applied to an electronic device such as a computer terminal, specifically, a general computer, etc.

[0037] The following will be described in detail taking a computer terminal as an example. Figure 1 A hardware structure block diagram of a computer terminal of the energy optimization method based on artificial intelligence provided by the embodiment of the present application is shown in the figure. Figure 1As shown, a computer terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. Optionally, the computer terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the computer terminal described above. For example, the computer terminal may also include components that are more complex than those described above. Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0038] The memory 104 can be used to store software programs and modules for application software, such as the program instructions / modules corresponding to the artificial intelligence-based energy optimization method in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0039] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider for the computer terminal. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0040] See Figure 2 The present invention provides an energy optimization method based on artificial intelligence, which may include the following steps:

[0041] S201, Collect energy operation data of the energy management system when the current energy parameter configuration scheme is adopted;

[0042] The process involves collecting energy operation data from the energy management system under specific energy parameter configurations. The core of this process lies in systematically recording and monitoring the real-time operating indicators of the energy management system through various sensors and data acquisition devices, including but not limited to energy consumption, load, equipment status, and external environmental factors. Accurate data collection is a crucial foundation for subsequent analysis and optimization, ensuring that the evaluation model can make scientifically sound judgments based on real data. Significance:

[0043] 1. Data-driven decision-making foundation: Reliable energy operation data sources make the energy management decision-making process more transparent and efficient, avoiding subjective judgments based on experience or guesswork.

[0044] 2. Real-time response capability: By continuously collecting real-time data, the system can identify and respond to instantaneous changes in energy demand, improving the system's flexibility and responsiveness.

[0045] 3. Comprehensive system performance assessment: Detailed operational data helps identify potential inefficiencies and failures in the energy management system, supporting the development of improvement measures. One implementation approach may include:

[0046] Step 1: System Architecture Design. Design a multi-layered data acquisition system, consisting of a data acquisition layer, a data transmission layer, and a data processing layer. Each layer adopts a modular design for easy expansion and maintenance.

[0047] Step 2: Select Sensors and Data Acquisition Equipment. Choose sensors with high precision and high sensitivity, capable of real-time monitoring of energy consumption, load, environmental factors, etc. Examples include power sensors, temperature sensors, and humidity sensors, and install them on critical equipment and energy transmission channels.

[0048] Step 3: Set the data acquisition frequency. Determine a reasonable data acquisition frequency. Based on the actual usage scenario, it is recommended to set it to once per minute to ensure real-time performance and data integrity.

[0049] Step 4: Intelligent Data Integration. At the data transmission layer, edge computing technology is used to perform preliminary processing and integration of the collected data, calculating local energy consumption and load indicators, etc. For example, by calculating the real-time energy consumption change rate, immediate feedback is provided for subsequent data analysis.

[0050] Step 5: Build a data protocol. Design an effective data transmission protocol to ensure that the collected data can be seamlessly transmitted between different devices and systems. Use low-latency network technologies (such as LoRa, NB-IoT, etc.) to improve the reliability and security of data transmission.

[0051] Step 6: Real-time monitoring and alarm mechanism implementation The system sets thresholds for energy consumption and load and establishes an alarm mechanism. When the collected data exceeds or falls below the set threshold, the system must immediately issue an alarm to facilitate timely inspection and maintenance by technical personnel.

[0052] Step 7: Data storage and backup strategy The system adopts a combination of cloud storage and local storage to ensure high availability and security of data. At the same time, regular backups are made to prevent system failure or data loss.

[0053] Step 8: Data visualization platform A centralized data visualization platform is created to display the running status and historical data of the energy management system through real-time data charts and dashboards, facilitating intuitive judgment and decision-making by management personnel.

[0054] Through the above steps, the energy management system can efficiently and stably collect energy operation data, providing accurate basis for subsequent state evaluation and optimization decision-making. The implementation of this method not only improves the intelligent level of the system, but also creates conditions for enhancing the flexibility and adaptability of energy management.

[0055] S202, according to the energy operation data, using a pre-trained artificial intelligence-based energy state evaluation model, evaluating the running state of the energy management system, and calculating the running stability index of the energy management system;

[0056] Step is to use a pre-trained artificial intelligence-based energy state evaluation model to analyze and evaluate the collected energy operation data. The purpose of this process is to accurately evaluate the state of the energy management system by comprehensively analyzing various influencing factors, so as to effectively control and optimize the system. Specifically, the model inputs multi-dimensional energy operation data, processes complex nonlinear relationships, and outputs the running state and running stability index (ESI) of the system. Significance:

[0057] 1. Intelligent evaluation: Using artificial intelligence models instead of traditional rule-based algorithms can more intelligently process complex data sets, have stronger adaptability, and help to evaluate system status in real time.

[0058] 2. Improve accuracy: By training the model, the system can identify key factors affecting the running state based on historical data, improving the accuracy of evaluation results and reducing human error.

[0059] 3. Dynamic adjustment: Through dynamic evaluation of the running state and stability index, the system can adjust strategies in a timely manner to respond to changing environments and load demands, improving operational efficiency.

[0060] 4. Decision Support: The calculation of the stability index provides clear decision-making basis for subsequent energy parameter optimization, enabling managers to make adjustments and improvements based on scientific evaluation results. One implementation can include:

[0061] Step 1: Data Preprocessing Before inputting into the artificial intelligence model, the collected energy operation data is preprocessed, including removing outliers, filling missing data and standardization. Z-score standardization method is adopted to convert all data into standard normal distribution, so as to eliminate the influence of different dimensions on the model.

[0062] Step 2: Feature Engineering Useful features are extracted from the original data to enhance the accuracy and robustness of the model. For example, based on energy consumption rate, load historical trend, environmental disturbance factors, etc., a more representative feature vector is constructed. These features include but are not limited to: sliding average of energy consumption, standard deviation of energy consumption fluctuation, frequency of device working state, etc.

[0063] Step 3: Model Selection and Optimization Use multiple artificial intelligence algorithms (such as decision tree, random forest, support vector machine or deep learning model) for preliminary training and cross-validation to select the best model. When training the model, use K-fold cross-validation method to evaluate the performance of the model, and continuously optimize the model performance through hyperparameter adjustment (such as learning rate, tree depth, etc.).

[0064] Step 4: Model Training The processed training data is input into the selected artificial intelligence model for training. During the training process, a labeled data set is used, and the label content includes the normal state and abnormal state (such as shutdown, overload, etc.) of the system. Use the training set to learn the model, and generate a state evaluation model that can be generalized.

[0065] Step 5: Real-time Evaluation During system operation, new energy operation data is input into the trained model for real-time state evaluation. The model will generate a prediction of the running state according to the real-time data, and the evaluation output should include the classification of the current running state (such as normal, abnormal, etc.) and the corresponding running stability index (ESI).

[0066] Step 6: Stability Index Calculation According to the state evaluation output by the model, combined with specific calculation formulas, the running stability index (ESI) is calculated. The calculation of ESI can combine multiple input factors such as energy consumption rate, fluctuation frequency and environmental disturbance, and use weighted average method to generate a comprehensive stability index.

[0067] Specifically, a formula for calculating the running stability index is:

[0068]

[0069] The formula is designed to comprehensively evaluate the operational stability of the energy management system, and the operational stability index (ESI) is an important indicator for quantifying system performance and stability. By combining key factors such as energy consumption rate, fluctuation frequency, and environmental disturbance, the overall stability of the system within a specific time period can be comprehensively reflected.

[0070] wherein the is the operational stability index, the is the energy consumption rate at the tth collection time point, which measures the energy efficiency performance of the system. When energy consumption is high, it may indicate that the system is under pressure and has high operating intensity, with potential instability. The is the energy consumption fluctuation frequency at the tth collection time point, which indicates the stability of energy consumption. The greater the fluctuation, the greater the instability of the operation. The is the environmental disturbance index at the tth collection time point, reflecting the influence of external environment on the system. The greater the disturbance of environmental factors, the more likely it is to lead to a decrease in stability. The is the system redundancy at the tth collection time point, describing the fault tolerance capability of the system. The higher the redundancy, the better the system stability is generally. The , the , the are the corresponding weight coefficients, ensuring that the contribution of each factor to the stability index is reasonably reflected. Expert evaluation setting or feature importance analysis based on machine learning may be needed to optimize these coefficients. The is the decay factor, which controls the speed of the influence of redundancy on the stability index, ensuring the real-time performance and dynamic response of the system. It can be optimized through experiments to analyze the impact of different proportions of redundancy on system stability to set the optimal value.

[0071] Specifically, an energy consumption rate calculation formula is as follows:

[0072]

[0073] wherein the E(t) is the total energy consumption at the tth collection time point, reflecting the actual energy consumption of the energy management system at a specific time. The L(t) is the total load at the tth collection time point, indicating the total burden that all devices and tasks need to bear at that time. This formula is used to calculate the energy consumption rate at a specific time, which is a basic indicator for evaluating energy management efficiency. Its purpose is to quantify the energy consumption performance under unit load in order to track energy efficiency changes.

[0074] Specifically, an energy consumption fluctuation frequency calculation formula is as follows:

[0075]

[0076] where k is a preset number of previous collection time points, and k is less than t. This formula is used to measure the fluctuation degree of energy consumption, which is another key indicator for evaluating system stability. The higher the fluctuation frequency, the more unstable the system's energy consumption changes, which may affect management decisions. The number of previous collection time points k determines the width of the fluctuation band and affects the sensitivity of energy consumption changes.

[0077] Specifically, a formula for calculating an environmental disturbance index is:

[0078]

[0079] wherein is the measured value of the jth environmental indicator at the tth collection time point, which affects the system's operation (such as temperature, humidity, etc.). The is the historical average value of the jth environmental indicator, which provides a benchmark to judge the current environmental changes. The is the weight factor of the jth environmental indicator, which defines the contribution of each indicator to the overall disturbance effect. The n is the number of environmental indicators.

[0080] This formula aims to comprehensively consider the impact of external environment on the operation of energy management system, helping to evaluate the stability and reliability of the system under different environmental conditions.

[0081] Specifically, a formula for calculating system redundancy is:

[0082]

[0083] wherein is the connectivity of the mth device of the energy management system, which measures the availability and redundancy of the device in the entire system. The M is the number of devices. This formula is used to evaluate the redundancy of devices in the system, which is an important indicator for evaluating system reliability. The higher the redundancy, the stronger the system's fault tolerance, which helps to maintain stable operation. The connectivity between devices can be calculated by network topology analysis tools, or manually confirmed based on logical design diagrams and actual connection conditions.

[0084] Through the above steps, the entire energy state evaluation model based on artificial intelligence can effectively process complex data of energy management system, providing accurate operation state evaluation and stability index calculation. The implementation of this method not only improves the intelligent management level of the system, but also provides a solid technical foundation for realizing efficient energy optimization.

[0085] S203, according to the running state and the running stability index, optimizing the energy parameter configuration scheme of the energy management system.

[0086] In an energy management system, the operating state refers to the system's working condition at a specific moment, including normal operation and abnormal state, while the energy stability index (ESI) is an important indicator reflecting the stability of the system's operation. Through comprehensive analysis of these two aspects, more accurate energy management can be achieved, thereby optimizing the system's energy parameter configuration scheme, improving overall operational efficiency, saving energy consumption, and reducing operating costs.

[0087] - Enhancing system reliability: By timely adjusting energy management parameters, ensure the system is in normal state, avoid equipment failure due to overload or improper operation, prolong the service life of the system.

[0088] - Optimizing energy utilization: According to the change of ESI value, dynamically adjust energy distribution, make resource utilization optimal, improve overall operational efficiency.

[0089] - Enhancing anti-interference ability: In the case of strong environmental disturbance, timely adjust parameters to maintain the stability and reliability of the system.

[0090] - Saving energy costs: Reduce unnecessary energy consumption, reduce operating costs, and improve economic benefits.

[0091] Specifically, if the operating state is abnormal and / or the energy stability index exceeds the preset threshold, adjust the energy parameter configuration scheme of the energy management system to make the operating state normal and the energy stability index not exceed the preset threshold. One implementation can include:

[0092] Adopting an adaptive genetic algorithm, based on the current operating state and stability index, a set of energy parameter adjustment strategies is generated. The key of this algorithm lies in formulating the fitness function, considering the operating state, ESI value, and user-set target sustainable energy efficiency. In each generation of the genetic algorithm, multiple candidate solutions are generated, and through selection, crossover, mutation, etc. mechanism, the optimal energy parameter configuration scheme is constantly optimized.

[0093] For the generated best parameter configuration, system dynamic simulation tools (such as MATLAB / Simulink) can be used for pre-implementation simulation to evaluate the expected performance under different interference conditions and load conditions. By inputting different environmental disturbances and load fluctuations, the system stability and energy consumption performance under the new parameter configuration are tested.

[0094] During implementation, a feedback loop mechanism is established to monitor the changes in operating state and ESI in real time to form data feedback. When the current system operating state or ESI fails to meet the requirements, through analysis of feedback data, adjust the fitness function weight of the genetic algorithm, regenerate the parameter optimization scheme, and implement it again.

[0095] Design a decision support system that utilizes artificial intelligence algorithms, such as reinforcement learning, to provide real-time optimization suggestions based on current operating conditions and historical data. This system can adjust system operation strategies in coordination with market energy price fluctuations, real-time load demand, and environmental conditions, achieving both economic and ecological benefits.

[0096] Through the above steps, a real-time dynamic optimization system is constructed with intelligent algorithms as the core, which can flexibly adapt to the changing energy use environment and ensure the efficiency and stability of the energy management system under various operating conditions.

[0097] As can be seen, the energy operation data of the energy management system when using the current energy parameter configuration scheme is collected; based on the energy operation data, an energy state evaluation model based on artificial intelligence is used to evaluate the operating state of the energy management system and calculate the operating stability index of the energy management system; and the energy parameter configuration scheme of the energy management system is optimized according to the operating state and the operating stability index, thereby effectively improving the operating stability and energy efficiency of the energy management system and providing strong support for sustainable development.

[0098] Another embodiment of the present application provides an artificial intelligence-based energy optimization system, as shown in Figure 3 , which can include:

[0099] The acquisition module 301 is used to collect the energy operation data of the energy management system when using the current energy parameter configuration scheme;

[0100] The evaluation module 302 is used to evaluate the operating state of the energy management system and calculate the operating stability index of the energy management system based on the energy operation data using a pre-trained energy state evaluation model based on artificial intelligence;

[0101] The optimization module 303 is used to optimize the energy parameter configuration scheme of the energy management system according to the operating state and the operating stability index.

[0102] As can be seen, the energy operation data of the energy management system when using the current energy parameter configuration scheme is collected; based on the energy operation data, an energy state evaluation model based on artificial intelligence is used to evaluate the operating state of the energy management system and calculate the operating stability index of the energy management system; and the energy parameter configuration scheme of the energy management system is optimized according to the operating state and the operating stability index, thereby effectively improving the operating stability and energy efficiency of the energy management system and providing strong support for sustainable development.

[0103] The embodiment of the present application also provides a storage medium, wherein the storage medium stores a computer program, and the computer program is arranged to execute the steps in any one of the method embodiments.

[0104] Specifically, in the embodiment, the storage medium can be arranged to store a computer program for executing the following steps:

[0105] S201, collecting energy running data of an energy management system when a current energy parameter configuration scheme is adopted;

[0106] S202, according to the energy running data, evaluating an operation state of the energy management system by using a pre-trained energy state evaluation model based on artificial intelligence, and calculating an operation stability index of the energy management system;

[0107] S203, according to the operation state and the operation stability index, optimizing the energy parameter configuration scheme of the energy management system.

[0108] It can be seen that the energy running data of the energy management system when the current energy parameter configuration scheme is adopted is collected, the operation state of the energy management system is evaluated by using the pre-trained energy state evaluation model based on artificial intelligence according to the energy running data, the operation stability index of the energy management system is calculated, and the energy parameter configuration scheme of the energy management system is optimized according to the operation state and the operation stability index, so that the operation stability and energy efficiency of the energy management system can be effectively improved, and strong support can be provided for sustainable development.

[0109] The embodiment of the present application also provides an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor is arranged to execute the computer program to execute the steps in any one of the method embodiments.

[0110] Specifically, the electronic device can further comprise a transmission device and an input and output device, wherein the transmission device is connected with the processor, and the input and output device is connected with the processor.

[0111] Specifically, in the embodiment, the processor can be arranged to execute the following steps by the computer program:

[0112] S201, collecting energy running data of an energy management system when a current energy parameter configuration scheme is adopted;

[0113] S202, according to the energy running data, evaluating an operation state of the energy management system by using a pre-trained energy state evaluation model based on artificial intelligence, and calculating an operation stability index of the energy management system;

[0114] S203, optimizing the energy parameter configuration scheme of the energy management system according to the running state and the running stability index.

[0115] It can be seen that the energy running data of the energy management system when the current energy parameter configuration scheme is adopted is collected; the running state of the energy management system is evaluated and the running stability index of the energy management system is calculated by using the pre-trained energy state evaluation model based on artificial intelligence according to the energy running data; and the energy parameter configuration scheme of the energy management system is optimized according to the running state and the running stability index, so that the running stability and energy efficiency of the energy management system can be effectively improved, thereby providing strong support for sustainable development.

[0116] The above describes the structure, features and effects of the present application in detail according to the embodiments shown in the drawings. The above description is only the preferred embodiments of the present application, but the present application is not limited by the drawings. Any changes or modifications made in accordance with the concept of the present application, or equivalent embodiments with equivalent changes, are still within the scope of the present application.

Claims

1. An energy optimization method based on artificial intelligence, characterized in that, The method includes: Collect energy operation data from the energy management system when the current energy parameter configuration scheme is used; Based on the energy operation data, a pre-trained AI-based energy status assessment model is used to evaluate the operational status of the energy management system and calculate its operational stability index; wherein the formula for calculating the operational stability index is: Wherein, ESI is the operational stability index, R(t) is the energy consumption rate at the t-th data collection time point, S(t) is the energy consumption fluctuation frequency at the t-th data collection time point, D(t) is the environmental disturbance index at the t-th data collection time point, V(t) is the system redundancy at the t-th data collection time point, alpha, beta, and gamma are the corresponding weighting coefficients, and lambda is the attenuation factor; Based on the operating status and the operating stability index, optimize the energy parameter configuration scheme of the energy management system.

2. The method according to claim 1, characterized in that, The formula for calculating the energy consumption rate is as follows: Wherein, E(t) is the total energy consumption at the t-th sampling time point, and L(t) is the total load at the t-th sampling time point.

3. The method according to claim 1, characterized in that, The formula for calculating the frequency of energy consumption fluctuations is as follows: Wherein, k is the preset number of previous data collection time points, and k is less than t.

4. The method according to claim 1, characterized in that, The formula for calculating the environmental disturbance index is as follows: Wherein, G_j(t) is the measured value of the j-th environmental indicator at the t-th collection time point, bar{G}_j is the historical average value of the j-th environmental indicator, P_j is the weighting factor of the j-th environmental indicator, and n is the number of environmental indicators.

5. The method according to claim 1, characterized in that, The formula for calculating the system redundancy is: Where deg(m) is the connectivity of the m-th device in the energy management system, and M is the number of devices.

6. The method according to any one of claims 2-5, characterized in that, The step of optimizing the energy parameter configuration scheme of the energy management system based on the operating status and the operating stability index includes: If the operating status is abnormal and / or the operating stability index exceeds the preset threshold, adjust the energy parameter configuration scheme of the energy management system to make the operating status normal and the operating stability index not exceed the preset threshold.

7. An energy optimization system based on artificial intelligence, characterized in that, The system includes: The data acquisition module is used to collect energy operation data of the energy management system when the current energy parameter configuration scheme is adopted; The evaluation module is used to evaluate the operating status of the energy management system based on the energy operation data, using a pre-trained AI-based energy status evaluation model, and to calculate the operating stability index of the energy management system; wherein the formula for calculating the operating stability index is: Wherein, ESI is the operational stability index, R(t) is the energy consumption rate at the t-th data collection time point, S(t) is the energy consumption fluctuation frequency at the t-th data collection time point, D(t) is the environmental disturbance index at the t-th data collection time point, V(t) is the system redundancy at the t-th data collection time point, alpha, beta, and gamma are the corresponding weighting coefficients, and lambda is the attenuation factor; The optimization module is used to optimize the energy parameter configuration scheme of the energy management system based on the operating status and the operating stability index.

8. A storage medium, characterized in that, The storage medium stores a computer program, wherein the computer program is configured to execute the method of any one of claims 1-6 when it is run.

9. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the method of any one of claims 1-6.

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