Multi-dimensional energy intelligent management and control system and method based on adaptive optimization algorithm

Abstract

The invention relates to the field of smart energy management and optimal scheduling, in particular to a multi-dimensional energy smart management and control system and method based on a self-adaptive optimization algorithm. The method comprises the steps that multi-dimensional energy consumption data are monitored in real time and preprocessed, a multi-dimensional energy state model is constructed based on the preprocessed multi-dimensional energy consumption data, and an energy state vector is obtained; introducing an adaptive weighting mechanism, and dynamically adjusting the energy state vector to obtain an adjusted energy state vector; and performing transition adjustment on the adjusted energy state vector, and performing multi-stage path optimization based on the energy state vector after transition adjustment to obtain an optimal scheduling scheme. The problems that in the aspect of multi-dimensional energy management and control, a traditional method cannot respond to environment changes in real time, the energy distribution proportion is difficult to dynamically adjust, and energy waste or shortage is caused are solved; the problems of low energy utilization rate and poor system robustness caused by difficulty in balancing short-term and long-term scheduling targets in a complex energy system in a linear optimization method are solved.

Description

Technical Field

[0001] The present invention relates to the field of smart energy management and optimized scheduling, and in particular to a multi-dimensional energy smart management and control system and method based on an adaptive optimization algorithm. Background Art

[0002] With the continuous growth of global energy demand and the diversified development of energy structure, the intelligentization and optimization of energy management have become important issues that need to be solved urgently. Traditional energy management models mainly rely on fixed rules or static optimization methods, which are difficult to meet the dynamic and complexity of modern energy systems. At present, various energy forms such as electricity, heat, and gas are widely used in different application scenarios. However, due to the different supply and demand characteristics of various types of energy, how to achieve efficient coordination and optimal scheduling of energy has become an important research direction for improving energy utilization and reducing operating costs. At the same time, the impact of environmental factors (such as climate change and load fluctuations) on energy demand is becoming increasingly significant, requiring energy management systems to have stronger adaptability to maintain a stable and efficient balance of energy supply and demand under different conditions.

[0003] In recent years, the development of technologies such as smart sensors, big data analysis, and artificial intelligence has provided new possibilities for optimizing energy management. Through distributed sensor networks, real-time monitoring of various energy forms can be achieved, and dynamic data analysis can be performed in combination with environmental factors to provide reliable data support for intelligent scheduling. In addition, the application of adaptive optimization algorithms in complex system optimization is becoming more and more extensive. It can adjust the optimization strategy according to real-time data, so that the energy scheduling plan can be dynamically optimized as the system status and environment change, thereby avoiding the local optimal problem that may occur in traditional methods and improving the flexibility and overall benefits of energy management. Therefore, how to combine intelligent monitoring technology and adaptive optimization algorithms to build an efficient, intelligent, and flexible multi-dimensional energy intelligent management and control system has become an important direction for the development of energy management technology.

[0004] Traditional methods have the following technical problems in multi-dimensional energy management and control: insufficient optimization capabilities, poor adaptability, serious local optimal problems, inaccurate energy scheduling and other defects. They are unable to respond to environmental changes in real time and find it difficult to dynamically adjust the energy allocation ratio, resulting in energy waste or shortages. Linear optimization methods are prone to falling into local optimality in complex energy systems, ignoring the need for long-term energy efficiency optimization, lacking global search capabilities, and having difficulty balancing short-term and long-term scheduling goals. They are unable to effectively predict future changes in energy demand, resulting in low energy utilization, high operating costs, and poor system robustness. Summary of the invention

[0005] The present invention provides a multi-dimensional energy intelligent management and control system and method based on an adaptive optimization algorithm to solve the defects of traditional methods in multi-dimensional energy management and control, such as insufficient optimization ability, poor adaptability, serious local optimal problems, inaccurate energy scheduling, etc., and the inability to respond to environmental changes in real time and to dynamically adjust the energy allocation ratio, resulting in energy waste or shortage; linear optimization methods are prone to fall into local optimality in complex energy systems, ignoring the needs of long-term energy efficiency optimization, lacking global search capabilities, and having difficulty balancing short-term and long-term scheduling goals. It is impossible to effectively predict future changes in energy demand, resulting in low energy utilization, high operating costs, and poor system robustness.

[0006] The present invention provides a multi-dimensional energy intelligent management and control system and method based on an adaptive optimization algorithm, which specifically includes the following technical solutions: A multi-dimensional energy intelligent management and control method based on an adaptive optimization algorithm comprises the following steps: S1. Real-time monitoring of multi-dimensional energy consumption data and preprocessing to obtain preprocessed multi-dimensional energy consumption data; based on the preprocessed multi-dimensional energy consumption data, a multi-dimensional energy state model is constructed to obtain an energy state vector; an adaptive weighting mechanism is introduced to dynamically adjust the energy state vector to obtain an adjusted energy state vector; S2. Perform transition adjustment on the adjusted energy state vector to obtain a transition-adjusted energy state vector; perform multi-stage path optimization based on the transition-adjusted energy state vector to obtain an optimal scheduling solution.

[0007] Preferably, the S1 specifically includes: Based on the energy state vector, the instantaneous energy consumption rate is calculated and the supply and demand balance constraints are set.

[0008] Preferably, the S1 specifically includes: The adaptive weighting mechanism introduces a time adaptive weighting matrix and an energy state adjustment item based on an energy state vector, and calculates the expected state of energy at the next moment in combination with the instantaneous consumption rate of energy to obtain an adjusted energy state vector.

[0009] Preferably, the S1 specifically includes: By introducing the scheduling adaptability coefficient, the scheduling weights of different energy sources are calculated and the time-adaptive weighted matrix is ​​obtained.

[0010] Preferably, the S1 specifically includes: By introducing external environmental influencing factors and combining them with the total energy demand for calculation, the energy status adjustment item is obtained.

[0011] Preferably, the S2 specifically includes: A nonlinear state transition operator is introduced to perform transition adjustment on the adjusted energy state vector to obtain the energy state vector after transition adjustment. The specific formula is as follows: , in, yes Energy state vector after time transition adjustment; is a nonlinear state transition operator; and They represent the lowest and highest energy consumption states of the energy system respectively; yes Energy state vector at the moment; yes The energy state vector after adjustment at each moment.

[0012] Preferably, the S2 specifically includes: Based on the energy state vector after transition adjustment and combined with the target energy state vector, the energy scheduling deviation is calculated, and the weighting factor is introduced to construct the target optimization function.

[0013] Preferably, the S2 specifically includes: Solve the optimization objective function, obtain the optimal time-adaptive weighted matrix and the instantaneous energy consumption rate, and generate an energy scheduling plan; when the energy scheduling deviation exceeds the preset threshold, enter the secondary optimization mode and adjust the energy scheduling plan.

[0014] Preferably, the S2 specifically includes: In the process of implementing the secondary optimization mode, the energy state vector after transition adjustment is adjusted through the gradient descent strategy with adaptive step size to obtain the optimal energy scheduling plan; according to the optimal energy scheduling plan, the supply ratio of different energy forms is adjusted to ensure that the energy distribution reaches the optimal state.

[0015] A multi-dimensional energy intelligent management and control system based on adaptive optimization algorithm, including the following parts: Data acquisition and preprocessing module, energy state modeling and dynamic adjustment module, nonlinear transition adjustment module, optimization calculation and scheduling decision module, energy scheduling execution module; The data acquisition and preprocessing module monitors the multi-dimensional energy consumption data in real time and performs preprocessing, including time synchronization, outlier removal and missing data interpolation, to obtain the preprocessed multi-dimensional energy consumption data; the preprocessed multi-dimensional energy consumption data is transmitted to the energy state modeling and dynamic adjustment module; The energy state modeling and dynamic adjustment module analyzes the energy consumption state information based on the pre-processed multi-dimensional energy consumption data, builds a multi-dimensional energy state model, and calculates the energy state vectors at different time steps; introduces a time-adaptive weighting matrix to adjust the consumption ratio of different energy forms, and adjusts the energy state vector in combination with the energy state adjustment item to obtain the adjusted energy state vector; transmits the adjusted energy state vector to the nonlinear transition adjustment module; The nonlinear transition adjustment module randomly perturbs the energy state through the nonlinear state transition operator to obtain the energy state vector after the transition adjustment; the energy state vector after the transition adjustment is transmitted to the optimization calculation and scheduling decision module; The optimization calculation and scheduling decision module calculates the energy scheduling deviation based on the energy state vector after transition adjustment and the target energy state vector, introduces weighting factors, and constructs the optimization objective function; solves the optimization objective function and generates an energy scheduling plan; when the energy scheduling deviation exceeds the preset threshold, it enters the secondary optimization mode, adjusts the energy scheduling plan through the gradient descent strategy with adaptive step size, and obtains the optimal energy scheduling plan; transmits the optimal energy scheduling plan to the energy scheduling execution module, and feeds it back to the nonlinear transition adjustment module; The energy scheduling execution module adjusts the supply ratio of different energy forms according to the optimal energy scheduling plan, and feeds back the actual execution status to the data acquisition and preprocessing module.

[0016] The beneficial effects of the technical solution of the present invention are: 1. The present invention defines multi-dimensional energy state variables, calculates the instantaneous energy consumption rate, and sets supply and demand balance constraints to ensure that at any time, the total energy supply always meets the demand and avoids energy waste and shortage; by introducing a time-adaptive weighted matrix based on energy state modeling, the scheduling weights of different energy forms can be dynamically adjusted according to real-time energy consumption data, demand changes and environmental influencing factors, thereby optimizing the energy allocation ratio and improving the utilization efficiency of the overall energy system.

[0017] 2. In response to the dynamic changes of the energy system in a complex environment, the present invention introduces energy status adjustment items, so that the energy scheduling plan can adapt to changes in external conditions, optimize the adjustment of the energy consumption path, and dynamically correct the energy scheduling plan, so that the energy system has stronger environmental adaptability, thereby reducing unnecessary energy consumption and improving energy utilization.

[0018] 3. In order to avoid falling into local optimality, the present invention introduces a nonlinear state transition operator to make the energy state vector transition and adjust during each optimization process to explore a better energy scheduling trajectory; the transition operator can perform a global search between different energy scheduling schemes through a nonlinear jump mechanism to avoid long-term energy efficiency degradation due to short-term optimization, ensuring that the energy system remains flexible during the optimization process, making resource allocation more balanced, and further improving the global optimality of energy scheduling.

[0019] 4. In the process of energy scheduling, the present invention adopts a multi-stage path optimization method to ensure that the energy consumption path gradually approaches the optimal scheduling trajectory. By comprehensively considering the evolution of energy states in different time steps, energy scheduling can be smoothly adjusted, optimizing the trade-off between short-term and long-term scheduling goals, ensuring the stability of the energy system, and making the energy scheduling plan not only suitable for current energy demand, but also able to predict future energy usage trends based on historical data, so as to make optimization adjustments in advance and improve the sustainability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a structural diagram of a multi-dimensional energy intelligent management and control system based on an adaptive optimization algorithm according to the present invention; Figure 2 This is a flow chart of a multi-dimensional energy intelligent management and control method based on an adaptive optimization algorithm described in the present invention. DETAILED DESCRIPTION

[0021] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0023] The following is a detailed description of a multi-dimensional energy intelligent management and control system and method based on an adaptive optimization algorithm provided by the present invention with reference to the accompanying drawings.

[0024] See attached Figure 1 , which shows a structure diagram of a multi-dimensional energy intelligent management and control system based on an adaptive optimization algorithm provided by an embodiment of the present invention. The system includes the following parts: Data acquisition and preprocessing module, energy state modeling and dynamic adjustment module, nonlinear transition adjustment module, optimization calculation and scheduling decision module, energy scheduling execution module; The data acquisition and preprocessing module monitors the multi-dimensional energy consumption in real time through a distributed sensor network, comprehensively acquires the current energy consumption data, obtains the multi-dimensional energy consumption data, and performs preprocessing, including time synchronization, outlier removal and missing data interpolation, to establish a complete energy consumption data stream, and obtain the preprocessed multi-dimensional energy consumption data; the preprocessed multi-dimensional energy consumption data is transmitted to the energy state modeling and dynamic adjustment module; The energy state modeling and dynamic adjustment module, based on the pre-processed multi-dimensional energy consumption data, analyzes the energy consumption state information, builds a multi-dimensional energy state model, calculates the energy state vectors at different time steps, and ensures that the energy system meets the supply and demand balance constraints; introduces a time-adaptive weighted matrix to adjust the consumption ratio of different energy forms, and adjusts the energy state vector in combination with the energy state adjustment item to adapt to changes in the external environment, improve energy utilization efficiency, and obtain the adjusted energy state vector; transmits the adjusted energy consumption vector to the nonlinear transition adjustment module; The nonlinear transition adjustment module randomly perturbs the adjusted energy state vector through the nonlinear state transition operator to prevent the optimization from falling into the local optimum, and obtains the energy state vector after the transition adjustment; the energy state vector after the transition adjustment is transmitted to the optimization calculation and scheduling decision module; The optimization calculation and scheduling decision module calculates the energy scheduling deviation based on the energy state vector after transition adjustment and the target energy state vector, introduces weighting factors, and constructs the optimization objective function; solves the optimization objective function and generates an energy scheduling plan; determines whether the current energy scheduling plan meets the optimization goal by calculating the energy scheduling deviation. If not, enters the secondary optimization mode, optimizes the energy scheduling trajectory through the gradient descent strategy with adaptive step size, adjusts the energy scheduling plan, obtains the optimal energy scheduling plan, and realizes reasonable energy distribution; transmits the optimal energy scheduling plan to the energy scheduling execution module, and feeds it back to the nonlinear transition adjustment module; The energy scheduling execution module adjusts the supply ratio of different energy forms according to the optimal energy scheduling plan to ensure that energy distribution reaches the optimal state, and feeds back the actual execution status to the data acquisition and preprocessing module for real-time monitoring and optimization adjustment.

[0025] See attached Figure 2 , which shows a flow chart of a multi-dimensional energy intelligent management and control method based on an adaptive optimization algorithm provided by an embodiment of the present invention, the method comprising the following steps: S1. Real-time monitoring of multi-dimensional energy consumption data and preprocessing to obtain preprocessed multi-dimensional energy consumption data; based on the preprocessed multi-dimensional energy consumption data, a multi-dimensional energy state model is constructed to obtain an energy state vector; an adaptive weighting mechanism is introduced to dynamically adjust the energy state vector to obtain an adjusted energy state vector; The consumption of various forms of energy is monitored in real time through a distributed sensor network to ensure that the acquired data can fully reflect the current operating status of the energy system. Various sensors are installed on energy supply equipment, user load side and environmental monitoring points to obtain key data such as energy consumption, supply capacity and external environmental parameters.

[0026] For electric energy, the acquisition equipment records instantaneous power, voltage, current and equipment operating status, and stores the power curve for subsequent analysis; for thermal energy, the sensor monitors the heat load demand, transmission temperature and heat exchange efficiency to ensure the rationality of energy distribution; for gas energy, the gas flow, pressure change and operating status of the transmission pipeline are obtained to evaluate the supply and demand balance in real time. In addition, environmental parameters (such as temperature, humidity, and wind speed) are obtained by independent sensor modules and synchronized with energy consumption data to support environmental adaptability calculations. During the data transmission process, all multidimensional energy consumption data are preprocessed, including time synchronization, outlier removal and missing data interpolation processing, to obtain preprocessed multidimensional energy consumption data, to ensure that the input data received in the subsequent optimization and scheduling process is stable and reliable, and to provide accurate real-time information for energy optimization and scheduling.

[0027] Based on the pre-processed multi-dimensional energy consumption data, define multi-dimensional energy state variables, including real-time data of multiple dimensions such as electricity, heat energy, and gas energy, build a multi-dimensional energy state model, and obtain the energy state vector of the energy system : , in, represent Moment The consumption status of the energy source; is the total number of energy types; the instantaneous rate of energy consumption It is expressed as: , in, yes The energy state change at each moment, i.e. the instantaneous consumption rate; for Moment The instantaneous consumption rate of energy is affected by the total demand of the energy system and needs to meet the energy supply and demand balance constraint: , in, yes The total energy demand at any moment. The energy supply and demand balance constraint ensures that at any moment, the total energy supply of the energy system is equal to the demand, avoiding energy waste or shortage.

[0028] After the multidimensional energy state model is completed, an adaptive weighting mechanism is introduced to dynamically adjust the consumption ratio of different energy forms according to the current energy consumption status, so as to ensure that the utilization efficiency of various energy sources is optimized as much as possible while meeting the overall energy demand; specifically, at each time step, the expected state of energy at the next moment is calculated based on the current energy consumption status and the instantaneous energy consumption rate. Due to the different conversion efficiencies, usage patterns and environmental influencing factors of different energies, direct linear adjustments will lead to local optimality or waste of resources. Therefore, in the process of energy state adjustment, a time-adaptive weighting matrix is ​​introduced to optimize the energy allocation ratio. The energy state is The adjustment formula for the time is as follows: , in, yes The energy state vector at the moment, i.e. the adjusted energy state vector; yes Energy status adjustment items at the moment; yes The time-adaptive weighted matrix at each moment is used to determine the dispatch weights of different energy sources. The calculation method is as follows: , in, represent Moment The consumption status of the energy source; express Moment Energy and The state differences between energy types; is the scheduling adaptability coefficient, obtained through experiments; In the process of energy state adjustment, in order to adapt to changes in the external environment, energy state adjustment items are introduced. : , in, It is an external environmental influencing factor, which is calculated by empirical regression method based on real-time environmental data (such as temperature and wind speed); Reflects the rate of change of energy demand and indicates future trends; yes The environmental impact matrix at a certain moment is used to describe the impact of environmental factors on different energy sources and is obtained through experiments.

[0029] S2. Perform transition adjustment on the adjusted energy state vector to obtain a transition-adjusted energy state vector; perform multi-stage path optimization based on the transition-adjusted energy state vector to obtain an optimal scheduling solution.

[0030] In order to prevent the energy system from falling into a local optimal state and enhance its optimization ability in a complex dynamic environment, a nonlinear state transition operator is introduced on the basis of energy state adjustment, so that the energy state can be adjusted during the optimization process, and the energy state vector after the transition adjustment can be obtained, so as to explore a better energy scheduling trajectory; through the nonlinear state transition operator, the energy state is adjusted in each iterative process, not only according to the traditional gradient optimization to update the energy consumption distribution, but also introduces nonlinear transitions in the optimization process, so as to have adaptive trajectory correction capabilities, thereby avoiding the imbalance of energy resource allocation caused by local extreme value traps. Specifically, the mathematical expression of energy state transition adjustment is as follows: , in, yes The energy state vector after the moment transition adjustment is the predicted energy state vector; It is a nonlinear state transition operator, which is used to control the nonlinear degree of energy trajectory adjustment and is obtained through experiments; and Represent the lowest and highest energy consumption states of the energy system, respectively, and are obtained based on historical energy consumption data. Energy state transition can ensure that energy scheduling does not fall into a fixed mode, but continuously and randomly transitions within the optimal scheduling area to ensure the diversity of optimization effects.

[0031] After the energy state transition adjustment, multi-stage path optimization is performed to ensure that the energy consumption path gradually approaches the optimal scheduling trajectory within multiple time steps to avoid the local optimal problem caused by short-term optimization, while ensuring the dynamic adaptability of energy distribution. The optimal scheduling trajectory represents the time series of the target energy state vector; due to the volatility of energy demand and environmental conditions, a single-step adjustment may cause the optimal state in the short term to be mismatched with the long-term global optimal state. Therefore, it is necessary to comprehensively consider the evolution of energy states in different time steps so that the energy scheduling plan can be optimized and adjusted. Defining the future The optimization goal within a time step is to make the energy state at the current moment gradually approach the optimal energy state obtained by historical prediction; the optimization objective function is constructed as follows: , in, is the optimization objective function, which is used to measure The energy state vector after time With the target energy state vector The final optimization goal is to optimize the objective function Minimum, thus achieving optimal energy scheduling; Represents from the current moment Start forecasting future time steps; It is The weighting factor of each time step is used to control the trade-off between short-term and long-term optimization objectives, which can be obtained through experiments; From the current moment Start forecasting The target energy state vector expected in each time step is calculated by regression analysis of historical energy consumption data and combining the impact of environmental factors on energy demand. Optimizing the objective function enables energy scheduling to have a smooth adjustment trajectory in multiple time steps.

[0032] After solving the optimization objective function, the optimal time-adaptive weighting matrix is ​​obtained. and the instantaneous rate of energy consumption , and form an energy scheduling plan; if the optimization target fails to meet the scheduling requirements, the predicted energy state vector With the target energy state vector If the energy scheduling deviation between exceeds the allowed threshold, it will enter the secondary optimization mode to further adjust the energy scheduling plan to make it more in line with the dynamic characteristics of actual energy demand and environmental changes; the energy scheduling trajectory is adjusted using the gradient descent method to ensure that the system gradually converges to the optimal scheduling trajectory and obtains the optimal energy scheduling plan. The energy scheduling trajectory represents the time series of the energy state vector. Specifically, the optimization objective function is first calculated The energy state vector is then corrected based on the gradient to reduce the energy scheduling deviation. The correction method uses an adaptive step-size gradient descent strategy to make the energy state vector closer to the global optimal solution each time, while avoiding the oscillation problem caused by large step-size updates. The correction process is as follows: , in, To adjust the step size, used to control the amplitude of the energy state vector update, it is obtained experimentally; To optimize the gradient of the objective function, it represents the sensitivity of the energy state vector to the optimized objective function.

[0033] During the optimization process, the corrected energy state vector is continuously evaluated to see whether it is closer to the target energy state vector, and based on the optimization convergence, it is decided whether to further adjust the step size to improve the convergence stability, thereby ensuring the rationality of energy distribution among various energy forms and maximizing the overall energy efficiency.

[0034] In summary, a multi-dimensional energy intelligent management and control system and method based on adaptive optimization algorithm has been completed.

[0035] The order of the embodiments of the invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0036] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0037] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A multi-dimensional energy intelligent management and control method based on an adaptive optimization algorithm, characterized in that: The following steps are involved: S1. Real-time monitoring of multi-dimensional energy consumption data and pre-processing to obtain pre-processed multi-dimensional energy consumption data; Based on the pre-processed multi-dimensional energy consumption data, a multi-dimensional energy state model is constructed to obtain an energy state vector; an adaptive weighting mechanism is introduced to dynamically adjust the energy state vector to obtain an adjusted energy state vector; S2. Perform transition adjustment on the adjusted energy state vector to obtain a transition-adjusted energy state vector; perform multi-stage path optimization based on the transition-adjusted energy state vector to obtain an optimal scheduling solution.

2. According to claim 1, a multi-dimensional energy intelligent management and control method based on an adaptive optimization algorithm is characterized in that: The S1 specifically includes: Based on the energy state vector, the instantaneous energy consumption rate is calculated and the supply and demand balance constraints are set.

3. According to claim 2, a multi-dimensional energy intelligent management and control method based on an adaptive optimization algorithm is characterized in that: The S1 specifically includes: The adaptive weighting mechanism introduces a time adaptive weighting matrix and an energy state adjustment item based on an energy state vector, and calculates the expected state of energy at the next moment in combination with the instantaneous consumption rate of energy to obtain an adjusted energy state vector.

4. According to claim 3, a multi-dimensional energy intelligent management and control method based on an adaptive optimization algorithm is characterized in that: The S1 specifically includes: By introducing the scheduling adaptability coefficient, the scheduling weights of different energy sources are calculated and the time-adaptive weighted matrix is ​​obtained.

5. The multi-dimensional energy intelligent management and control method based on the adaptive optimization algorithm according to claim 4 is characterized in that: The S1 specifically includes: By introducing external environmental influencing factors and combining them with the total energy demand for calculation, the energy status adjustment item is obtained.

6. The multi-dimensional energy intelligent management and control method based on the adaptive optimization algorithm according to claim 3 is characterized in that: The S2 specifically includes: A nonlinear state transition operator is introduced to perform transition adjustment on the adjusted energy state vector to obtain the energy state vector after transition adjustment. The specific formula is as follows: ， in, yes Energy state vector after time transition adjustment; is a nonlinear state transition operator; and They represent the lowest and highest energy consumption states of the energy system respectively; yes Energy state vector at the moment; yes The energy state vector after adjustment at each moment.

7. The multi-dimensional energy intelligent management and control method based on the adaptive optimization algorithm according to claim 6 is characterized in that: The S2 specifically includes: Based on the energy state vector after transition adjustment and combined with the target energy state vector, the energy scheduling deviation is calculated, and the weighting factor is introduced to construct the target optimization function.

8. The multi-dimensional energy intelligent management and control method based on the adaptive optimization algorithm according to claim 7 is characterized in that: The S2 specifically includes: Solve the optimization objective function, obtain the optimal time-adaptive weighted matrix and the instantaneous energy consumption rate, and generate an energy scheduling plan; when the energy scheduling deviation exceeds the preset threshold, enter the secondary optimization mode and adjust the energy scheduling plan.

9. The multi-dimensional energy intelligent management and control method based on the adaptive optimization algorithm according to claim 8 is characterized in that: The S2 specifically includes: In the process of implementing the secondary optimization mode, the energy state vector after transition adjustment is adjusted through the gradient descent strategy with adaptive step size to obtain the optimal energy scheduling plan; according to the optimal energy scheduling plan, the supply ratio of different energy forms is adjusted to ensure that the energy distribution reaches the optimal state.

10. A multi-dimensional energy intelligent management and control system based on an adaptive optimization algorithm, applied to a multi-dimensional energy intelligent management and control method based on an adaptive optimization algorithm as claimed in claim 1, characterized in that: Includes the following parts: Data acquisition and preprocessing module, energy state modeling and dynamic adjustment module, nonlinear transition adjustment module, optimization calculation and scheduling decision module, energy scheduling execution module; The data acquisition and preprocessing module monitors multi-dimensional energy consumption data in real time and performs preprocessing, including time synchronization, outlier removal, and missing data interpolation, to obtain preprocessed multi-dimensional energy consumption data; The pre-processed multi-dimensional energy consumption data is transmitted to the energy state modeling and dynamic adjustment module; The energy state modeling and dynamic adjustment module analyzes the energy consumption state information based on the pre-processed multi-dimensional energy consumption data, builds a multi-dimensional energy state model, and calculates the energy state vectors at different time steps; introduces a time-adaptive weighting matrix to adjust the consumption ratio of different energy forms, and adjusts the energy state vector in combination with the energy state adjustment item to obtain the adjusted energy state vector; transmits the adjusted energy state vector to the nonlinear transition adjustment module; The nonlinear transition adjustment module randomly perturbs the energy state through the nonlinear state transition operator to obtain the energy state vector after the transition adjustment; the energy state vector after the transition adjustment is transmitted to the optimization calculation and scheduling decision module; The optimization calculation and scheduling decision module calculates the energy scheduling deviation based on the energy state vector after transition adjustment and the target energy state vector, introduces weighting factors, and constructs the optimization objective function; solves the optimization objective function and generates an energy scheduling plan; when the energy scheduling deviation exceeds the preset threshold, it enters the secondary optimization mode, adjusts the energy scheduling plan through the gradient descent strategy with adaptive step size, and obtains the optimal energy scheduling plan; transmits the optimal energy scheduling plan to the energy scheduling execution module, and feeds it back to the nonlinear transition adjustment module; The energy scheduling execution module adjusts the supply ratio of different energy forms according to the optimal energy scheduling plan, and feeds back the actual execution status to the data acquisition and preprocessing module.

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