Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a cockpit multi-scene switching output control method based on artificial intelligence, which solves the problems of inflexible switching of cockpit display contents, low response speed, low decision efficiency and incapability of effectively coping with uncertainty factors in the prior art.
In order to achieve the purpose, the invention is realized by the following technical scheme that the cockpit multi-scene switching output control method based on artificial intelligence comprises the following steps:
S1, acquiring and fusing real-time data, namely acquiring power load, equipment state and environment data through a power load sensor, an equipment state sensor and an environment data sensor, fusing the acquired data and constructing a multidimensional state space to represent the current power system state;
S2, deep reinforcement learning and optimal decision path calculation, namely constructing a state space by utilizing collected real-time data, selecting the most appropriate display mode by using a reinforcement learning training model, and ensuring that the system can make optimal decisions in different scenes;
s3, game theory and collaborative decision-making, namely, under the participation of multiple operators, performing game theory optimization based on an optimal decision-making path, so as to ensure that the display modes selected by different operators can be coordinated;
s4, robust control and uncertainty optimization, namely processing uncertain factors of load fluctuation and equipment failure by a robust control method on the basis of game theory optimization, ensuring stable operation of the system and reducing decision errors;
s5, adjusting display contents and switching scenes, namely on the basis of a stable system, monitoring the state of the power system in real time, dynamically adjusting the display contents and switching display modes so as to adapt to the current power dispatching requirement;
And S6, performance evaluation and optimization, namely comparing the real-time data with the historical data, evaluating the system performance, performing self-tuning according to the evaluation result, and optimizing the display content and the decision path.
Preferably, the real-time data acquisition includes:
acquiring power load data at the current moment, including instantaneous load and load prediction;
monitoring the operation state of key equipment in real time, including temperature, load and equipment health state;
External environmental factors of weather changes and equipment failure rate are collected by sensors and monitoring equipment.
Preferably, the fusing step includes:
extracting characteristics of the collected load data, equipment state and environment data, and processing the load data, the equipment state and the environment data through a multi-sensor fusion technology;
and converting the fused data into a multidimensional state space, wherein the state space comprises load state, equipment state and environment data, and the three-dimensional state space is expressed as a three-dimensional data combination.
Preferably, the deep reinforcement learning model training includes:
mapping the state and action of the system to a Q value by using a deep Q network training model, and learning an optimal strategy;
the whole system is modeled as a Markov decision process, and actions under each state are selected through the MDP framework.
Preferably, the optimal decision path calculation includes:
calculating the maximum return value of each state through a Bellman optimality equation, and selecting an optimal display mode under each state;
And selecting the most suitable display mode under the current power load and equipment state according to the calculation result of the optimal decision path, and performing dynamic display content switching.
Preferably, the game theory optimization includes:
In a multi-operator scene, optimizing the decision of each operator by adopting a game theory model, and ensuring the consistency of the display content of each operator;
the decision among operators is optimized by solving Nash equilibrium, so that the display modes selected by the operators can be coordinated with each other and reach the optimal state.
Preferably, the robust control and uncertainty optimization includes:
through an H-infinity control method, a robust control strategy is designed, the influence of uncertainty in a system is reduced, and the stability of the system under load fluctuation and equipment failure is ensured;
And modeling and optimizing uncertainty factors of load fluctuation and equipment faults through a random control theory, and ensuring that the system runs stably in an uncertain environment.
Preferably, the display content adjustment includes:
Based on load data acquired in real time, the system dynamically judges the current load state and adjusts the priority of the display content of the cockpit according to the load;
According to the load and health condition of key equipment, adjusting the detail degree of the display content, including the system display equipment load condition and standby capacity when some equipment loads are higher;
and the displayed content is adjusted by combining the environment data, so that the displayed information is ensured to have timeliness and pertinence.
Preferably, the performance evaluation and tuning module includes:
based on the real-time data and the historical data, evaluating the performance of the system under different loads, equipment states and environmental conditions;
by analyzing the evaluation result, the system automatically selects and adjusts the optimization strategy so as to improve the accuracy and response efficiency of the system display content;
The system continuously optimizes the decision process through learning of historical data and real-time feedback, and improves the performance of the model and the accuracy of the display content switching strategy.
Preferably, the multi-scenario automatic switching mechanism includes:
According to the real-time power load, the equipment state and the external environment data, the system automatically identifies the current power scheduling scene and selects a proper display mode;
Based on the scene recognition result, the system automatically switches a cockpit display mode, wherein the cockpit display mode comprises displaying global equipment information in a low-load scene and mainly displaying the load condition and the standby capacity of key equipment in a high-load scene;
According to the model training result and real-time feedback, the multi-scene switching mechanism is continuously optimized, so that the display content can be adjusted in various complicated power scheduling scenes.
The invention provides a cockpit multi-scene switching output control method based on artificial intelligence. The beneficial effects are as follows:
1. According to the invention, by combining artificial intelligence with game theory, intelligent display content switching under a multi-operator scene is realized, and the coordination and consistency of display contents of operators are ensured. By solving Nash equilibrium, the decision path is optimized, the conflict of display content is avoided, and the overall decision efficiency is improved. Compared with the traditional scheme, the system greatly reduces the problems of repeated information display and coordination among operators, and optimizes the operation efficiency and the user experience.
2. The invention improves the stability under uncertain factors such as power load fluctuation, equipment failure and the like through robust control and uncertainty optimization technology. The response of the system to external disturbance is reduced to the greatest extent by utilizing H-infinity control and random control, and the continuous and stable operation of the system is ensured. The scheme overcomes the defect of lack of an uncertainty processing mechanism in the prior art, and enhances the robustness and reliability of the system.
3. According to the invention, through a self-adaptive performance evaluation and optimization mechanism, the system can adjust the decision path and the switching strategy of the display content in real time. According to the comparison of real-time and historical data, the system can continuously optimize decisions, ensure accurate matching of display contents and decision paths, and improve display efficiency and response speed. The feedback optimization-based mode avoids the problem of low efficiency of relying on manual adjustment in the traditional method, and realizes the effective combination of automation and intellectualization.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, an embodiment of the invention provides a cockpit multi-scene switching output control method based on artificial intelligence, which comprises the following steps:
S1, acquiring and fusing real-time data, namely acquiring power load, equipment state and environment data through a power load sensor, an equipment state sensor and an environment data sensor, fusing the acquired data and constructing a multidimensional state space to represent the current power system state;
the real-time data acquisition and fusion capturing system state provides key input data for subsequent deep learning, decision calculation, game optimization and other modules, so that the system can make a proper decision based on the latest system state.
In this embodiment, the core of step S1 is to collect multidimensional data such as power load, device status, external environment, etc., and convert the multidimensional data into a status space through a data fusion technique. The process not only aims at improving the accuracy of data acquisition, but also provides a reliable basis for subsequent decision making, scene switching and optimization.
First, the system acquires power load, device status, and external environment data in real time through a plurality of sensors. Typically, the main data collected by the system includes:
Electrical load data, such data mainly comprising instantaneous load and load prediction information. The instantaneous load reflects the instantaneous demand of the power system at the current moment, and the load prediction predicts the trend of the power demand for a certain period of time based on historical data and an algorithm model.
Instantaneous loadRepresenting the time of day of the systemIs a load of the vehicle.
Load predictionLoad prediction based on load history and external influencing factors (such as seasons, climates and the like) is used for judging load change of a future period.
Device status data-device status data includes the operational status of critical devices (e.g., substations, gensets, etc.), such as temperature, load, health, etc. The device status is important for the stability and emergency response of the real-time monitoring system.
Device loadRepresenting the load status of the device at a certain point in time, such as the device's workload, temperature, health, etc. The load data of the device may directly influence the selection of the presentation content, especially in high load situations, where the system needs to present the status information of the relevant device preferentially.
External environmental data including weather changes, equipment failure rates, etc. may have an impact on power load and equipment performance. Weather changes and equipment failure rates are important external factors that affect power system load fluctuations.
Environmental dataSuch data includes weather factors such as temperature, humidity, wind speed, etc., and information such as equipment failure rate, external disturbances, etc.
After the real-time data acquisition, the system converts the information into a multidimensional state space through a data fusion technology, and the current state of the power system is represented. In the process, feature extraction and data fusion are key steps, so that the information extracted from various data from different sensors can be ensured to be comprehensively utilized in subsequent decisions.
In the data fusion stage, the data of a plurality of sensors are integrated into a unified state representation through fusion algorithms (such as Kalman filtering, particle filtering and the like). For example, the system may utilize weighted averaging or Kalman filtering techniques to fuse data from different sources, eliminate noise and improve data accuracy.
By this fusion, the system can build a multidimensional state spaceThe state space represents the overall situation of the current power system. Specifically, the state spaceComprising the following dimensions:
;
Wherein: Is the state of the electrical load, including both instantaneous load data and load forecast data; the state of the equipment reflects the running load, health condition and the like of the equipment; Is environment data and covers external influencing factors such as weather change, equipment failure rate and the like.
By fusing these data, the system is able to obtain a real-time, accurate representation of the state, thereby providing a powerful support for subsequent decision making, control and presentation switching.
Feature extraction is a very critical step in order to ensure that the system is able to more efficiently use the acquired data. In this embodiment, the system performs feature extraction on the load data, the device state data, and the environment data, and screens out the information most important for the current decision.
Load data feature extraction, e.g. transient load dataAnd removing high-frequency noise through a filtering algorithm, and extracting a long-term trend. Therefore, the system can be helped to accurately identify the load change trend, and inaccurate judgment caused by short-term fluctuation is avoided.
And extracting the state characteristics of the equipment, namely extracting the change mode of the state of the equipment through time sequence analysis on the temperature, the load and the health condition of the equipment. For example, when the equipment load is high, the system can judge whether the equipment needs emergency maintenance according to the historical data and the equipment health condition.
Environmental data feature extraction, namely weather changes and changes of equipment failure rate can be extracted as features, and particularly in the case of extreme weather or equipment failure, the system can automatically adjust the display content through learning of historical data so as to facilitate an operator to respond emergently.
Alternatively, a Kalman filtering algorithm is used in this embodiment for data fusion. Kalman filtering is a common recursive data processing method, and can effectively reduce sensor noise and improve data accuracy. Specifically, the kalman filtering method realizes the estimation update of the state through the following recursive formula:
;
Wherein: is an estimate of the state, representing the system at that timeIs a predicted state of (1); is a sensor measurement value, representing real-time data acquired by the sensor; The Kalman gain determines the weight between the sensor data and the system state estimation value; Is an observation matrix describing the relationship between the sensor measurement and the system state; Indicating that the system is at timeIs used to estimate the state of the object.
Through Kalman filtering, the system can adjust state estimation in real time, eliminate noise and ensure that subsequent decisions are made based on accurate data.
In one possible implementation, particle filtering techniques may also be used as an alternative. Particle filtering can handle the case of non-linear and non-gaussian noise, and is suitable for more complex system state estimation, especially when processing multidimensional sensor data, the particle filtering can better capture fine changes in the data
Through a data fusion algorithm (such as Kalman filtering or particle filtering), the system can eliminate noise and ensure the accuracy of information. The method provides accurate input data for the following steps of deep reinforcement learning model, optimal decision path calculation, game theory optimization and the like, and ensures that the system can make timely and accurate decisions according to real-time power load and equipment state.
S2, deep reinforcement learning and optimal decision path calculation, namely constructing a state space by utilizing collected real-time data, selecting the most appropriate display mode by using a reinforcement learning training model, and ensuring that the system can make optimal decisions in different scenes;
And processing the state space by using a deep reinforcement learning model, particularly a Deep Q Network (DQN), calculating an optimal decision path, and finally selecting the most suitable display mode. Through the process, the system can dynamically adjust the display content, and the display requirements of the power system under different loads and equipment states are met.
In this embodiment, the system first loads the power load dataDevice status dataAnd environmental dataConverts to a multidimensional state space and trains through a Deep Q Network (DQN). The deep Q network evaluates the rewards of each state-action pair with a Q value to select the optimal presentation mode. The updating of the Q value and the calculation of the optimal path are based on the Q-learning algorithm and the Belman optimality equation, and the decision is optimized in a recursion mode.
In general, the system is based on the current state at each instantSelecting an actionThe actions correspond to a presentation mode (e.g., presentation device load, standby capacity, etc.). The Q value calculation formula is:
;
Wherein: Indicating that the system is in stateDown selection actionThe expected return obtained. This Q value is used to evaluate the long-term return for the current action; indicating immediate rewards, i.e. system in stateExecute action downwardsThe return obtained thereafter. The reward is typically related to the relevance of the presentation and the validity of the presentation mode; A discount factor, a larger discount factor (near 1) indicates that the system is more concerned with future rewards, and a smaller discount factor indicates that the system is more concerned with current rewards; Indicating that the system is in the next stateNext, selecting an optimal actionThe maximum Q value obtained.
Through the Q-learning algorithm, the system optimizes the decision path by continuously updating the Q value, and finally selects the optimal display mode, so that the power system can make optimal display decisions under different load and equipment states.
To calculate the optimal decision path for each state, the system employs a Markov Decision Process (MDP) model. MDP is a mathematical model for describing a decision making process, which is composed of a state spaceSpace of actionProbability of state transitionReward functionAnd discount factorFive elements constitute. Through MDP, the system can select the optimal display mode under each state, so that the optimal switching of display contents is realized.
Specifically, under the MDP framework, the system recursively calculates an optimal value function for each state by the Bellman optimality equationThe optimal action is then selected according to the function, ensuring that the system is able to make optimal decisions as the power load and equipment state change.
The bellman optimality equation is as follows:
;
Wherein: as a function of the optimum value, indicating that the system is in stateThe maximum length return that can be obtained. By recursively calculating the value, the system can identify the optimal decision path for each state; Is shown in the stateNext, the system may select an action, i.e., a presentation mode. For example, in a low load scenario, the system may choose to show the overall distribution of equipment resources, while in a high load scenario, the load situation of the equipment may be chosen; for immediate rewards, indicating that the system is in stateExecute action downwardsAnd awards obtained later. The instant rewards reflect the influence of the current decision on the overall performance of the system; Representing the influence degree of future rewards as a discount factor; For the probability of state transition, the system is represented in stateExecute action downwardsPost transition to StateIs a probability of (2). In power dispatching systems, this typically reflects the pattern of changes in load and device status; Is in state ofThe next optimum function represents the maximum return that the system can obtain in the next state.
Through the Belman optimality equation, the system can calculate the optimal display mode under each state. At each moment, according to the current stateThe system selects the optimal actionI.e. the most suitable presentation mode. Specifically, when the power load is high, the system preferentially selects information such as the load, the standby capacity and the like of the key equipment, and when the load is low, the system selects global information showing the resource distribution and the equipment state.
Alternatively, when the system detects load fluctuations, the system can adjust the priority of the display content based on the trained Q value, preferentially display the device information with higher load, and provide a standby scheme or an emergency plan. This switching mechanism ensures that the cockpit is able to respond in real time to changes in power load and equipment status.
Through the Q-learning algorithm, the system can select an optimal display mode according to the current power load, equipment state and external environment data, and continuously calculate an optimal decision path through a Bellman optimality equation. The decision path of the system is recursively calculated, so that the most suitable reaction can be made according to the latest power system state every time the display content is switched.
S3, game theory and collaborative decision-making, namely, under the participation of multiple operators, performing game theory optimization based on an optimal decision-making path, so as to ensure that the display modes selected by different operators can be coordinated;
When multiple operators use the cockpit together, how to ensure that the selection of the display content is not in conflict and can effectively cooperate is a key problem for further improving the system performance. Thus, step S3 introduces a game theory and collaborative decision mechanism to optimize decision coordination in a multi-operator environment.
In this embodiment, the objective of step S3 is to optimize the decision of each operator through the game theory model, so that multiple operators can coordinate and agree when selecting the display mode, avoid decision conflicts, and optimize the overall system performance. This process ensures that each operator's decision is optimal under the policies of the other operators by solving for Nash equilibrium, thereby achieving overall optimization of the system.
In a multi-operator environment, each operator has a different decision requirement and they choose the presentation mode based on the current state. However, since multiple operators may choose different presentation modes, the system needs to optimize these choices so that presentation is as little repeatable and consistent as possible. The heart of this process is a game theory model by which the system optimizes the decisions of each operator.
Typically, the game theory model is used to measure how good each operator chooses the presentation mode by a utility function. Each operator selects an action (presentation mode) at a certain time, and its utility (satisfaction) is affected by the selection of other operators. Thus, the system needs to find a stable combination of strategies through game optimization, ensuring that each operator's decision is both optimal and compatible with the choices of other operators.
The goal in gaming is to ensure that the operator's presentation selections do not conflict by solving for Nash equilibrium. In game theory, nash equilibrium means that under one policy combination, all operators have made the best choice, i.e. no operator can obtain greater benefit by unilaterally changing his own policy. By solving for Nash equalization, the system can find a balance point so that each operator's decision can be optimized under the decision framework of the other operators.
Alternatively, in this embodiment, the game theory model optimizes the presentation mode decisions for each operator by solving for Nash equilibrium. Through the model, each operator can select a display mode according to own requirements, and finally, decisions of all operators are kept consistent through game solving, no conflict occurs, and the overall performance of the system is maximized.
In the present invention, the core part of the game theory model is a utility function, and each operator's utility functionFor indicating that in a certain state, the operator selects a certain display modeThe latter utility (degree of satisfaction). The utility function reflects whether the operator's need for presentation content is sufficiently satisfied, and the operator's goal is to select a presentation mode that maximizes the utility function.
Specifically, the goal of game theory is to optimize the overall utility of all operators, i.e., find the optimal strategy for each operator by the following equation:
;
Wherein: Indicating that the system is in stateThe optimal strategy under, i.e., the optimal presentation mode selected by each operator. The strategy can ensure that the decisions of all operators are consistent with the overall target of the system, so that the repetition or conflict of the display content is avoided; Represent the firstThe action selected by the individual operator, i.e. the presentation mode. Each operator makes his own decision according to the current state, selecting the appropriate presentation mode. Different display modes bring different effects; Represent the firstThe individual operators are selecting the display modeThe utility obtained later. The utility function is used for measuring the satisfaction degree of the requirement of the operator on the display content and determining the decision preference of the operator; Representing the total number of operators participating in the game. In a multi-operator environment, the system needs to consider the needs of multiple operators at the same time and optimize the decisions of each operator; representing a policy space, i.e., a set of all possible presentation mode selections; Representing a strategy for solving the maximized utility function, namely, the system finds out a strategy combination capable of maximizing the total utility by solving a game theory model; The sum of all operator utilities is represented as a total utility function.
In a multi-operator environment, selection of presentation content requires optimization according to the needs of multiple operators and the overall goals of the system. By solving for Nash equalization, the system can find a stable policy combination, ensuring that all operators' decisions are consistent and coordinated, thereby avoiding redundancy or conflict of the presentation content.
In particular, application of the gaming model in a multi-operator environment can optimize the policies for each operator to select presentation content such that the presentation content of each operator can be coordinated with the presentation content of other operators. For example, when one operator chooses to display the equipment load, the system will optimize the choices of the other operators through game theory to avoid repeated display of the same information and to ensure that each operator obtains the most efficient information needed.
In one possible implementation, the game theory model may be solved by genetic algorithm or dynamic programming method of game theory. The system finally finds out a display content selection which can be accepted by all operators through iterative optimization of strategy combinations of each operator, so that the decision efficiency of the system is improved.
By solving for Nash equilibrium, the system is able to coordinate presentation content selection among multiple operators so that decisions of all operators can be coordinated with each other. In a multi-operator scenario, the operator's presentation may have intersections, so the system needs to avoid duplication of presentation information through game optimization.
For example, in situations where the electrical load is high, multiple operators may all need to view load information for critical equipment. Through game theory optimization, the system can ensure that when each operator selects to display information such as load conditions, spare capacity and the like, other operators can not repeatedly display the same information, but select to display complementary information, so that the system efficiency is improved.
By introducing game theory optimization, the system can ensure that decisions of multiple operators on the presentation content do not conflict, but can coordinate. The display mode of each operator is selected to meet the requirements of the operator, meanwhile, the requirements of other operators are considered, and finally, the global optimal decision is realized. In this way, the system can provide timely and accurate information presentation under the complex power dispatching and equipment management scenarios, and help operators to make effective decisions.
Alternatively, game theory optimization may also improve the response speed of the system. When operators choose to display information, the system can quickly optimize the display content according to the real-time load and the equipment state, and ensure that each operator can obtain the required information. The process not only improves the decision efficiency, but also reduces information overload.
By solving for Nash equilibrium, the system can ensure consistent presentation content selection among multiple operators, avoid conflicts, and maximize system utility. The application of the game theory effectively improves the decision-making efficiency in a multi-operator environment, ensures that each operator selects the display content according to the actual needs, and simultaneously maintains the overall coordination of the system.
S4, robust control and uncertainty optimization, namely processing uncertain factors of load fluctuation and equipment failure by a robust control method on the basis of game theory optimization, ensuring stable operation of the system and reducing decision errors;
In power systems, there are often several unpredictable factors and uncertainties, such as fluctuations in power load, changes in equipment failure rates, and external environmental effects, which may have an impact on the stability of the system and the accuracy of decisions. Thus, step S4 aims to enhance the adaptability of the system to these uncertainty factors by robust control and uncertainty optimization, thereby ensuring that the system can continue to operate stably in an uncertain environment, maintaining efficient decisions and switching of presentation content.
In this embodiment, step S4 helps the system reduce the impact caused by external disturbance in the decision process in power scheduling by applying a robust control method (e.g., H-infinity control) and an uncertainty optimization method (e.g., random control theory). By the optimization technology, the system can process uncertainty factors such as power load fluctuation, equipment faults and the like, ensure that the power system can still keep stable when facing different disturbance and uncertain environments, and make efficient and accurate decisions in the environments.
Typically, the operation of an electrical power system is affected by a variety of uncertainty factors. These factors come not only from the system itself, such as aging of the equipment, fluctuation of the load, etc., but also from external environmental factors, such as weather changes, equipment failure, etc. These uncertainty factors often lead to increased difficulty in predicting and controlling the system. Therefore, the invention solves the problems of uncertainty by introducing a robust control method, and ensures that the system can still maintain the stability of the system and make optimal decisions under the action of the uncertainty factors.
Alternatively, the system uses an H-infinity control method for robust control. The goal of the H-infinity control is to design a controller such that the performance of the system is optimized in the face of external disturbances and internal uncertainties, and the impact of the uncertainties on the system output can be minimized. The H-infinity control method is not only suitable for processing noise in a system, but also can cope with external interference and unknown uncertainty.
The goal of the H-infinity control problem is to make the output response of the system as immune as possible to uncertainty factors by controlling the design. The H-infinity control achieves this goal by minimizing the H-infinity norm. The H-infinity norm represents the maximum gain of the system to the disturbance, and the H-infinity control method requires that this norm be less than a given thresholdThereby ensuring that the system remains stable in the face of external disturbances. The specific mathematical expression is:
;
Wherein: Representing the transfer function of the system, i.e. the relation function between input and output. In a power system, a transfer function describes the response capability of the system to factors such as load fluctuation, equipment state change and the like; the H-infinity norm of the system, i.e., the maximum gain of the system, is represented. The system measures the response capability of the system to external disturbance, and the larger the norm is, the stronger the response of the system to disturbance is, and the worse the robustness of the system is; is an optimization objective that represents the maximum degree of uncertainty that the system tolerates. By designing the control strategy, the system expects that willIs controlled at a minimum to ensure system stability and minimize the risk of uncertainty.
By adjusting the parameters of the controller, the H-infinity control method can ensure that the system can still maintain the stability of the system with smaller disturbance influence under the uncertainty conditions of load fluctuation, equipment failure and the like, and ensure the precision of power dispatching.
In this embodiment, in addition to robust control, random control theory is used to further optimize the decision process of the system. The stochastic control theory mainly solves the problem that the system has randomness and uncertainty factors, wherein the factors comprise equipment faults, uncertainty of power requirements and the like.
Specifically, the system models external disturbances (such as load fluctuations and equipment failures) using a stochastic process modeling method and optimizes the control strategy by a stochastic optimization algorithm. In power systems, load fluctuations and equipment failures often occur with randomness, so that by means of stochastic control theory, this uncertainty can be handled accurately and optimal countermeasures can be designed.
The system models uncertainty factors by adopting a random process, so that the system can adaptively adjust a decision strategy under random change conditions such as power load fluctuation or equipment failure. The goal of the system is to enable the system to maintain stable and efficient operating conditions in an uncertain environment by optimizing control strategies.
In a randomly controlled optimization process, the system is typically optimized by the following formula:
;
Wherein: Representing the control inputs of the system, i.e. the adjustment of the presentation content or the control strategy of the system. The control input will be over timeTo adjust for changes in the system; Representing a loss function of a system that measures the system in a given stateAnd control inputThe following. The loss function reflects the operational efficiency of the system and the quality of the decision, the goal of the system being to minimize the loss function; The square of the control input is represented, the control input is usually limited in the optimal control problem, and the square penalty term of the control input is used for controlling the control input of the system and is not excessively large, so that the stability and the efficiency of the system are ensured; is a weight coefficient used to balance the relationship between the magnitude of the control input and the system loss; Representing expected values for calculating the expectations of the loss function, typically representing the average performance behavior of the system in the face of random disturbances; Representing a time window, typically representing a period of time from the beginning of the system operation to the end; Indicating that the system is at timeThe state variables of the power system at a certain moment, the power equipment state, etc.
By minimizing the loss function, the system is able to make optimal decisions in the face of external disturbances and uncertainties. The optimization process ensures that the system can continuously and stably run under the uncertainty conditions such as power load fluctuation, equipment failure and the like.
Alternatively, by robust control and stochastic control optimization, the system can remain stable in the face of multiple uncertainty factors and make each decision as efficient as possible. Through H-infinity control and random control theory, the system can predict and adaptively adjust factors such as load fluctuation, equipment faults and the like, so that the reliability and stability of the power dispatching system are improved.
By such optimization, the system can reduce errors and system performance degradation due to external disturbances. For example, when the load of the system suddenly fluctuates, the system can timely adjust the display content through a robust control strategy so as to ensure the real-time performance and accuracy of the display content.
Through H-infinity control and random control theory, the system can be kept stable under the uncertainty factors such as load fluctuation, equipment failure and the like, and the influence of uncertainty on decision making is reduced through optimizing a control strategy.
S5, adjusting display contents and switching scenes, namely on the basis of a stable system, monitoring the state of the power system in real time, dynamically adjusting the display contents and switching display modes so as to adapt to the current power dispatching requirement;
The dynamic nature of the power system means that the priority of the display content, the display mode and the information switching must be precisely adjusted according to the real-time power scheduling requirements. The objective of step S5 is to dynamically adjust the display content and switch the display modes according to the current load state of the power system, the health condition of the equipment, and external environment data.
In this embodiment, step S5 mainly includes adjusting the priority and display mode of the display content by monitoring the system state in real time and combining with an optimization algorithm, so as to ensure that the operator can receive the most relevant information at the key moment and make an accurate decision. The system can flexibly switch the display content according to different scenes (such as low load, high load, fault early warning and the like), and the response efficiency of an operator to power dispatching is ensured.
In general, in a power scheduling system, adjustment of presentation content is closely related to switching of scenes. The display content of the power system can be dynamically adjusted according to the change of the power load, the different health conditions of equipment and the influence of external environments. For example, when the electrical load is low, the operator may be more concerned with the overall operating state of the equipment, while when the load is high, it is necessary to pay attention to the load, spare capacity and fault warning information of the critical equipment.
Alternatively, in this embodiment, the system classifies and identifies the current scenario by integrating information such as real-time power load data, device status, and environmental data. Based on the identified scene type, the system automatically selects the appropriate presentation mode to provide the operator with the most relevant information in any event.
In particular, scene recognition is performed by real-time load data, device health status, and external environment data. When the power demand is low, the system can automatically switch to display the states of all the devices, including the information of resource distribution, device load and the like, and when the power demand is high, the system can automatically highlight the information of the load, standby capacity and the like of the key devices, and decision support is provided through an emergency plan or fault early warning.
Adjustment of the presentation content requires determining the priority of the presentation mode according to the real-time power system status. To ensure that the presentation content can be dynamically adjusted, the system calculates the priority of the presentation content based on multiple dimensions such as load, device status, and environmental factors. The priority calculation model typically combines a plurality of factors and calculates the final priority by a weighted sum. The calculation formula is as follows:
;
Wherein: the higher the priority of the display content, the more important the display content is in the current scene, and the display content should be preferentially displayed;,, the weight coefficients are the power load, the device state, and the external environment data, respectively. The weight coefficients of different factors can influence the importance of the factors in priority calculation; Indicating the power load, i.e. the system is at timeIs a power requirement of (1); representing the state of equipment, including information such as the load, health condition, temperature and the like of the equipment; External environmental data such as weather, equipment failure rate, etc. are represented.
Alternatively, the presentation mode will automatically switch when the system recognizes a load fluctuation or a change in the status of the device. For example, when the load is low, the system displays the information of the overall load condition, resource distribution and the like of the equipment, and when the load is high, the system changes to display the load, standby capacity and fault early warning information of the key equipment. In these cases, the system will preferentially present the relevant data and ensure that the operator is able to receive the critical decision information.
Specifically, when the system finds that a sudden change occurs in the power load or an abnormality occurs in the operating state of the device, the exhibition mode will be switched according to the following rules:
when the power demand is low, the system displays the running states and resource distribution of all the devices, and helps operators to manage the resources.
When the power load is high, the system will highlight the status of the heavy-load equipment, the situation of spare capacity, and potential fault early warning, helping operators to quickly cope with emergency.
Priority adjustment for presentation content switching is based on scene recognition and real-time data analysis. The system dynamically adjusts the priority of the display content according to the current power load, equipment state and environmental data. For some emergency scenarios, the system may automatically increase the priority of the content displayed, for example, during peak load hours, the system may increase the load, standby capacity, etc. information of the key device to content with higher priority for the operator to react the first time.
The presentation mode switching in this embodiment is not only dependent on the power load and the device status, but also incorporates external environmental factors. By comprehensively analyzing the factors, the system can accurately identify the current power scheduling scene and automatically adjust the display content according to the requirements of different scenes. Through the intelligent display content switching, the system can ensure that an operator can always obtain the most relevant information under various complex conditions, so that the decision efficiency and accuracy of power dispatching are improved.
Alternatively, the system may provide more targeted decision support based on the priority of the presentation. For example, when a device fails, the system will preferentially display information such as the status, failure cause, standby capacity, emergency plan, etc. of the device, so as to help the operator take measures quickly. The adaptive display content switching mechanism ensures that operators can make the most timely response under any environment.
The system calculates the priority of the display content according to the power load, the equipment state and the external environment factors, and automatically decides the switching of the display mode. Through the intelligent display content adjustment and scene switching, the system can ensure that an operator always acquires the most relevant information, and the response efficiency and accuracy of decision making are enhanced, so that the performance of the power dispatching system is optimized.
S6, performance evaluation and optimization, namely comparing real-time data with historical data, evaluating system performance, performing self-tuning according to an evaluation result, and optimizing display content and decision paths;
The main task of the step S6 is to perform comparison and evaluation according to the real-time data and the historical data, analyze the current performance of the system, and further perform self-optimization adjustment on the display content and the decision path, so that the response speed, the decision accuracy and the relevance of the display content of the system are improved.
In general, performance assessment of a system typically focuses on several aspects:
the accuracy of the display content switching, namely whether the system can accurately adjust the display content according to the load change and the real-time change of the equipment state.
And the decision efficiency is that whether the system can rapidly select and switch the most suitable display mode or not, and redundant information and unnecessary switching are avoided.
And the response speed of the system is that the system can respond and adjust the display content in time under the conditions of abrupt load change, equipment failure and the like.
Alternatively, the performance assessment and optimization process in this embodiment analyzes the performance of the system in each scenario by comparing real-time data with historical data. For example, in the case of higher load, the display mode can display the equipment with higher load preferentially, respond to the power dispatching requirement timely, and quickly call out the status information and emergency plan of the failed equipment when the equipment fails. Through the evaluation, the system can optimize the decision path, the display mode and the response strategy according to the evaluation result, so that the overall performance of the system is improved.
In this embodiment, the performance evaluation of the system is to calculate the overall performance of the system by comprehensively considering three aspects of accuracy, response time and decision efficiency. The performance evaluation formula is as follows:
;
Wherein: Representing an overall performance score of the system; weight coefficients representing accuracy in overall performance; indicating the accuracy of the presentation. In particular, it measures whether the system is able to present the correct information at the right moment; Representing the weight coefficient of the response time in the overall performance. The response time refers to the response speed of the system to sudden events such as power load change, equipment failure and the like; Indicating the response time for the presentation mode switch. The method measures the time interval from the detection of the load change or equipment failure to the switching of the display content; Weight coefficients representing decision efficiency in overall performance; The decision efficiency is represented, i.e. whether the decision made by the system when the presentation content is switched is efficient.
Specifically, by evaluating the performance of the system, the system can adjust the decision path and expose the content switching policy according to the evaluation result. The goal of optimizing the decision path is to reduce unnecessary presentation mode switching and to ensure that each presentation content switching can maximally assist the operator in making an effective decision. For example, during peak load times, the system may oversubscribe the status of some devices, resulting in an operator not being able to quickly obtain critical information. In this case, the system can identify redundant presentations through performance evaluation and reduce the switching frequency and number of presentations through optimization algorithms, thereby improving the efficiency of presentation content switching.
Alternatively, the system may also self-adjust based on the performance evaluation results. For example, the system may find that some of the presentation content is overstretched at low load, resulting in reduced decision efficiency for the operator. At this time, the system can automatically adjust the display priority, so as to reduce unnecessary information display when the load is low, thereby reducing information overload and improving decision efficiency.
In one possible implementation, the system learns the historical evaluation data through a deep reinforcement learning (DQN) model, constantly optimizing presentation mode selection and decision paths. With continuous feedback and self-adjustment, the system can better adapt to dynamic changes of power load and equipment state, and continuously improves the accuracy of decision making and the efficiency of display content switching.
Through performance evaluation, the system can not only optimize switching of the display content, but also adjust the priority of the display content according to the change of the power load and the abnormality of the equipment state. For example, when the load is high, the system can automatically adjust the priority of the display content according to the load condition, so as to ensure that the display mode can effectively support the decision of an operator. When equipment is in fault or abnormal, the system can quickly adjust the display mode, and the load, the health condition and the fault early warning of the equipment are displayed preferentially, so that an operator can take measures timely.
By introducing indexes such as accuracy, response time, decision efficiency and the like, the system can optimize and display a mode switching strategy under different power loads, equipment states and environmental changes, and the performance of the system is continuously improved. The performance evaluation and optimization mechanism not only enhances the adaptability of the system, but also helps the system to realize efficient decision making and display content switching in a complex environment.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.