Power grid situation deduction risk assessment method based on reinforcement learning

Abstract

The invention discloses a power grid situation deduction risk assessment method based on reinforcement learning, and relates to the technical field of power grid operation and risk assessment, and the method comprises the steps: S1, building a power grid situation model based on an equipment health state assessment result and historical operation data, and S2, predicting power grid operation states under different maintenance plans through the power grid situation model, s3, determining risk factors based on the predicted operation state of the power grid and calculating an unplanned power failure risk probability; S4, regulating and controlling a maintenance plan according to the risk probability to optimize the operation safety of the power grid; according to the power grid situation deduction risk assessment method based on reinforcement learning, the arrangement of the maintenance plan is regulated and controlled according to the equipment health state assessment result so as to solve the problem of unplanned power failure risk.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid operation and maintenance and risk assessment, and in particular to a power grid situation deduction risk assessment method based on reinforcement learning. Background Art

[0002] In modern power grid operation and management, the assessment of equipment health status is crucial to ensuring the safety and stability of the power grid. Traditional power grid maintenance strategies usually rely on fixed-cycle maintenance plans or manual experience judgment. However, this approach has many shortcomings. On the one hand, fixed-cycle maintenance fails to fully consider the actual operating status of the equipment, which may lead to waste of resources. At the same time, it is impossible to identify potential faults in a timely manner, causing the equipment to malfunction without maintenance, increasing the risk of unplanned power outages. On the other hand, although manual experience can assist in judging the status of equipment to a certain extent, due to the complex and changing operating environment of the power grid, it is difficult to comprehensively and accurately formulate a scientific and reasonable maintenance plan based on experience alone, which affects the overall safety and economy of the power grid.

[0003] In recent years, equipment health assessment methods based on big data and machine learning have been introduced into power grid operations and maintenance management. These methods can predict equipment status through historical data analysis and pattern recognition. However, these methods still have certain limitations when it comes to optimizing maintenance plans. For example, they lack autonomous decision-making capabilities, making it difficult to quickly adjust maintenance plans in emergencies. Furthermore, existing methods often fail to fully consider maintenance resource constraints, such as manpower, materials, and time, making it difficult to achieve global optimization of maintenance plans. Summary of the Invention

[0004] The purpose of the present invention is to provide a power grid situation deduction risk assessment method based on reinforcement learning, which regulates the arrangement of maintenance plans according to the equipment health status assessment results to solve the problem of unplanned power outage risks.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a power grid situation deduction risk assessment method based on reinforcement learning, the method comprising:

[0006] S1. Build a power grid situation model based on equipment health status assessment results and historical operation data;

[0007] S2. Predict the grid operation status under different maintenance plans through the grid situation model;

[0008] S3. Determine risk factors based on the predicted grid operation status and calculate the probability of unplanned power outage risk;

[0009] S4. Adjust maintenance plans based on risk probability to optimize grid operation safety.

[0010] Preferably, said S1 includes constructing a power grid situation model based on the equipment health status assessment results and historical operation data, and the calculation formula thereof is: M(t)=H(t)+D(t-1);

[0011] Where M(t) represents the overall operating status of the power grid at time t, t represents time, H(t) represents the health status of the equipment at time t, and D(t-1) represents the power grid operation-related data at time t-1.

[0012] Preferably, the S2 includes using the power grid situation model to predict the power grid operation status under different maintenance plans, and the calculation formula is: S i (t+1)=M(t)-P i ;

[0013] Among them, S i (t+1) represents the grid operation status at time t+1 under the i-th maintenance plan, i represents the maintenance plan number, M(t) represents the overall grid operation status at time t, P i represents the maintenance plan in the ith order.

[0014] Preferably, the S3 includes estimating the risk probability of unplanned power outage by calculating the risk factor and combining historical data, and the calculation formula is: P outage，i =R i (t)+X;

[0015] Among them, P outage，i represents the risk probability of unplanned power outage under the i-th maintenance plan, R i (t) represents the risk factor calculated at time t under the i-th maintenance plan, and X represents the historical impact factor.

[0016] Preferably, the S4 includes adjusting the maintenance plan according to the calculated unplanned power outage risk probability, and the calculation formula of the optimized maintenance plan is: P opt =min(P i +C);

[0017] Among them, P out represents the optimized maintenance plan, P i represents the maintenance plan in the ith order, and C represents the maintenance cost.

[0018] Preferably, in said S1, the construction of the power grid situation model combines multi-source data, including equipment operating parameters, environmental variables, power grid load conditions and historical fault records, so as to improve the accuracy and adaptability of the model.

[0019] Preferably, in said S2, when predicting the operating status of the power grid under different maintenance plans, a reinforcement learning algorithm is used to train an intelligent decision-making model based on historical data to improve the accuracy of the prediction and the adaptability to different maintenance strategies.

[0020] Preferably, in S3, the determination of risk factors not only considers the health status of the equipment and the load level, but also combines the grid topology to identify potential risks of key nodes, so as to improve the comprehensiveness of the unplanned power outage risk assessment.

[0021] Preferably, in S4, the regulation of the maintenance plan is not only based on the probability of unplanned power outage risk, but also combined with the availability, economic cost and scheduling constraints of maintenance resources to ensure the feasibility and economy of the optimization plan.

[0022] Preferably, in said S3, an adaptive weight adjustment mechanism is introduced into the calculation of the risk probability of unplanned power outage, so that the risk assessment can dynamically adjust the calculation parameters according to the real-time operating conditions to improve the prediction accuracy.

[0023] It can be seen from the above technical solution that the present invention has the following beneficial effects:

[0024] This reinforcement learning-based power grid situation deduction risk assessment method constructs a power grid situation model based on equipment health status assessment results and historical operating data. The power grid operation status under different maintenance plans is predicted using the power grid situation model. Based on the predicted power grid operation status, risk factors are determined and the probability of unplanned power outage risk is calculated. The maintenance plan is adjusted according to the risk probability to optimize power grid operation safety. The method can autonomously learn the optimal matching strategy between equipment health status and maintenance plan, achieve accurate decision-making, and reduce reliance on manual experience. It can monitor the health status of power grid equipment in real time and dynamically adjust the maintenance plan based on the latest data, thereby effectively responding to sudden failures and reducing the risk of unplanned power outages. By comprehensively considering the constraints of maintenance resources such as manpower, materials, and time, the maintenance plan can ensure power grid safety while reducing unnecessary maintenance costs and improving the economic efficiency of operation and maintenance management. The method can more accurately calculate the probability of unplanned power outages, providing a reliable basis for optimizing maintenance plans, reducing safety hazards caused by untimely or excessive maintenance of power grid equipment, and thus improving the reliability and stability of the overall power grid operation. The maintenance plan is adjusted according to the equipment health status assessment results to address the risk of unplanned power outages. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0027] like Figure 1 As shown, the present invention provides a technical solution: a power grid situation deduction risk assessment method based on reinforcement learning, the method comprising:

[0028] S1. Build a power grid situation model based on equipment health status assessment results and historical operation data;

[0029] S2. Predict the grid operation status under different maintenance plans through the grid situation model;

[0030] S3. Determine risk factors based on the predicted grid operation status and calculate the probability of unplanned power outage risk;

[0031] S4. Adjust maintenance plans based on risk probability to optimize grid operation safety.

[0032] The core of this approach is to leverage reinforcement learning technology to improve the accuracy of grid situation predictions and optimize risk assessment and maintenance strategies. During training, the reinforcement learning agent continuously learns optimal strategies through interaction with the simulation environment, improving the accuracy of grid operational predictions. During the situation modeling phase, the system combines equipment health assessment data with historical operational data to construct a highly accurate grid operation model. This model can simulate different maintenance plans, predict potential grid operational states, and identify key factors that may lead to unplanned power outages, such as line overloads, equipment aging and failure, and voltage instability. During risk assessment, the system extracts key risk factors based on the prediction results and calculates the risk of unplanned power outages using probabilistic statistics or data-driven approaches. Finally, the reinforcement learning agent dynamically adjusts maintenance plans based on these assessment results to ensure the stability and safety of grid operations. This approach significantly improves the accuracy of grid situation predictions and risk assessments, making maintenance plans more targeted and reducing the occurrence of unplanned power outages. Through reinforcement learning optimization strategies, the system can continuously adjust and optimize risk assessment methods in a dynamic environment to ensure grid stability. This approach also leverages historical grid data and equipment health assessment data to improve the accuracy of the prediction model. Furthermore, compared to traditional rule-based or empirical approaches, this solution can automatically adapt to different grid operation scenarios, enabling precise and intelligent risk assessment and maintenance optimization.

[0033] S1 includes building a power grid situation model based on the equipment health status assessment results and historical operation data, and its calculation formula is: M(t) = H(t) + D(t-1);

[0034] Where M(t) represents the overall operating status of the power grid at time t, t represents time, H(t) represents the health status of the equipment at time t, and D(t-1) represents the power grid operation-related data at time t-1.

[0035] The core principle of this method lies in leveraging historical data and equipment health assessment information to construct a highly accurate grid status model that accurately describes the grid's operating status. Equipment health assessment can be based on sensor data, historical fault records, load changes, and other factors, using data fusion technology to generate comprehensive health assessment indicators. Furthermore, by combining previous grid operating data, dynamic grid characteristics can be extracted and used to predict current and future operating conditions. This method enables dynamic monitoring of grid status and provides a reliable basis for subsequent risk assessment and maintenance planning. The model can be adaptively optimized using continuously updated real-time data to ensure its adaptability to diverse grid operating conditions. By integrating equipment health data and historical operating data, real-time monitoring of grid operating status is achieved, improving situational awareness accuracy. This method can proactively identify potential abnormal grid conditions and provide data support for unplanned outage risk assessment. The dynamic predictive capabilities of the grid status model can provide more accurate operational decision support to the dispatching center, improving grid efficiency and safety. The constructed grid status model can be used to assess the impact of different maintenance plans on the grid, assist in optimizing maintenance strategies, and reduce unnecessary outage losses.

[0036] S2 includes using the power grid situation model to predict the power grid operation status under different maintenance plans. The calculation formula is: S i (t+1)=M(t)-P i ;

[0037] Among them, S i (t+1) represents the grid operation status at time t+1 under the i-th maintenance plan, i represents the maintenance plan number, M(t) represents the overall grid operation status at time t, P i represents the maintenance plan in the ith order.

[0038] The core principle of this method is to use the power grid situation model to simulate and deduce to evaluate the impact of different maintenance plans on the power grid operation status. In the situation prediction process, the system will be based on the current state of the power grid M(t) and combine different maintenance plans P i , calculate the grid operation status S at the future time point t+1 i(t+1). Each maintenance plan corresponds to a different equipment maintenance and outage plan, affecting the grid's load distribution, power flow, and overall stability. The system uses a reinforcement learning agent to simulate various maintenance plans and dynamically learn how different maintenance plans affect grid operation. By continuously optimizing strategies, the reinforcement learning model can identify maintenance plans with lower risks and less impact, assisting decision-makers in selecting the optimal maintenance plan and ensuring stable grid operation. Situational simulations can predict the grid's operating status under different maintenance plans in advance, mitigating unexpected risks. Assessing the impact of different maintenance plans on the grid's status helps grid operators develop more optimal maintenance plans and improve dispatch efficiency. By optimizing maintenance strategies, unplanned outages caused by inappropriate maintenance plans can be reduced, improving power supply reliability. The reinforcement learning agent can autonomously learn from various maintenance plans, optimizing grid operation and management, reducing manual intervention, and enhancing intelligent decision-making.

[0039] S3 includes estimating the risk probability of unplanned power outage by calculating risk factors and combining historical data. The calculation formula is: P outage，i =R i (t)+X;

[0040] Among them, P outage，i represents the risk probability of unplanned power outage under the i-th maintenance plan, R i (t) represents the risk factor calculated at time t under the i-th maintenance plan, and X represents the historical impact factor.

[0041] The core principle of this method lies in using a data-driven approach to assess the risk of unplanned power outages and incorporating historical influencing factors to enhance the accuracy of risk assessment. During the risk factor calculation process, the system analyzes data such as the current grid health, equipment failure rates, and load levels to determine the potential failure risks associated with different maintenance plans. Furthermore, the system incorporates historical data to analyze unplanned power outages caused by past maintenance plans to refine the current risk assessment. During the continuous optimization process, the reinforcement learning agent calculates risk probabilities based on different maintenance strategies and adaptively adjusts the risk model to improve prediction accuracy. For example, if a maintenance plan has historically resulted in a high probability of unplanned power outages, the system will adjust the risk value of that plan to more accurately reflect its potential impact. Ultimately, this method provides grid operators with a sound basis for maintenance decision-making, reducing the occurrence of unplanned power outages and improving grid stability. The integration of current risk factors and historical influencing factors improves the accuracy of risk calculations. Based on the risk probability assessment results, grid operators can select low-risk maintenance plans, thereby reducing unplanned power outages caused by maintenance. By reducing the number of unplanned outages, fault repair costs can be reduced and the overall operational efficiency of the power grid can be improved. Using reinforcement learning technology, the risk assessment model can be dynamically adjusted to adapt it to different operating environments, improving the intelligent operation and maintenance capabilities of the power grid.

[0042] S4 includes adjusting the maintenance plan based on the calculated unplanned power outage risk probability. The calculation formula for the optimized maintenance plan is: P opt =min(P i +C);

[0043] Among them, P opt represents the optimized maintenance plan, P i represents the maintenance plan in the ith order, and C represents the maintenance cost.

[0044] The core principle of this method is to optimize the maintenance plan by calculating the risk probability of unplanned power outages and combining it with the maintenance cost to select the plan with the least impact and the lowest cost. First, in the previous step, the system has calculated the risk probability of unplanned power outages for different maintenance plans. Then, in this step, these risk probabilities are weighted with the maintenance cost to form the optimization objective function. During the training process, the reinforcement learning agent will iteratively optimize different maintenance plans and finally select the plan P with the lowest risk of unplanned power outages and the lowest maintenance cost. opt During the optimization process, the system can dynamically adjust the maintenance time window, resource allocation strategy and load migration plan to reduce the risk of maintenance. For example, if a maintenance plan P iThe system prioritizes nighttime maintenance plans, which can effectively reduce the impact of unplanned power outages without increasing maintenance costs. By combining risk probability with maintenance costs, maintenance plans are optimized to reduce unplanned power outages caused by maintenance. A reinforcement learning-based intelligent optimization model makes maintenance decisions more intelligent, reducing human error. While ensuring grid safety, the lowest-cost maintenance plan is selected to improve resource utilization. By rationally scheduling maintenance times and plans, the overall grid load is balanced, minimizing the impact on power supply.

[0045] In S1, the grid state model is constructed by integrating data from multiple sources, including equipment operating parameters, environmental variables, grid load conditions, and historical fault records, to improve the model's accuracy and adaptability. The core of this method lies in improving the accuracy of grid state modeling by integrating multi-source data, enabling it to accurately depict the grid's operating status and potential risk factors. Equipment operating parameters, including key indicators such as voltage, current, power factor, and temperature, reflect the health of the equipment. Environmental variables (such as temperature, humidity, and wind speed) affect equipment operating conditions and are crucial to grid stability, especially under extreme weather conditions. Grid load conditions reflect fluctuations in electricity demand over time and can be used to analyze power flow distribution and load balancing. Historical fault records provide historical fault patterns and frequencies, which can be used to train machine learning models and enhance their ability to predict potential faults. By integrating this data, the system can use deep learning or reinforcement learning methods to train the grid state model, enabling it to adapt and dynamically update its operating state predictions based on real-time input data. Combined with historical fault records, the system can identify potentially high-risk conditions and continuously optimize the model's prediction accuracy during the training of the reinforcement learning agent. Integrating multi-source data enables the model to more comprehensively reflect the grid's operating status and improve situational awareness. Incorporating historical fault data and environmental variables enhances grid risk prediction capabilities and reduces misjudgment rates. Incorporating grid load information optimizes power dispatch strategies, improves load balancing, and reduces overload risks. Leveraging reinforcement learning technology, the model adapts to diverse grid operating environments, ensuring accurate predictions even under complex conditions.

[0046] In S2, a reinforcement learning algorithm is used to predict the grid's operating status under different maintenance plans. This algorithm trains an intelligent decision-making model based on historical data to improve prediction accuracy and adaptability to different maintenance strategies. The core of this approach is to use reinforcement learning algorithms to establish an intelligent decision-making model capable of predicting the grid's operating status under different maintenance strategies. First, the system extracts grid operating data from a historical database, including equipment health status, maintenance records, load distribution, and historical fault conditions, and constructs a reinforcement learning environment. A reinforcement learning agent is trained within this environment and continuously adjusts its decision-making strategy to maximize grid stability and security. The reinforcement learning agent learns through a "state-action-reward" mechanism. The current grid operating status (such as load distribution and equipment health) is defined as the "state" in the reinforcement learning model, while different maintenance plans are considered "actions." The system determines the "reward value" through simulation calculations or real-world feedback. By continuously adjusting its strategy, the agent selects the maintenance plan that minimizes the risk of unplanned outages, optimizes power flow distribution, and reduces operating costs. In addition, reinforcement learning algorithms can employ techniques such as deep Q-networks (DQNs), proximal policy optimization (PPO), or deep deterministic policy gradients (DDPGs) to enhance decision-making stability and generalization capabilities. Reinforcement learning algorithms automatically learn from historical data to improve the accuracy of grid operating status predictions under different maintenance plans. Reinforcement learning agents can dynamically adjust decision-making strategies based on different grid operating environments, load conditions, and equipment status. Intelligent decision-making models can weigh the effectiveness of maintenance plan execution and select the optimal solution to reduce the risk of unplanned power outages. This approach reduces the need to rely on manual experience to develop maintenance plans and enhances the intelligence level of grid dispatching.

[0047] In S3, risk factor determination not only considers equipment health and load levels but also incorporates grid topology to identify potential risks at key nodes, thereby enhancing the comprehensiveness of unplanned outage risk assessments. The core of this method lies in incorporating grid topology information to improve the accuracy of unplanned outage risk assessments. Traditional risk assessment methods primarily rely on equipment health and load levels, ignoring the complexity of the grid structure. However, in actual operation, failures at certain key nodes (such as main substations, important transmission lines, or areas with concentrated loads) can trigger widespread cascading effects. Therefore, this method identifies key nodes through topological analysis and, combined with reinforcement learning techniques, focuses on assessing the operational risks of these nodes. The risk factor calculation process first obtains equipment health data, including historical fault records and real-time monitoring data for transformers, switchgear, and transmission lines. Secondly, the grid load level is analyzed to identify high-load areas and calculate their impact on overall power supply stability. Finally, based on the grid topology, complex network analysis methods (such as degree centrality, betweenness centrality, and network connectivity analysis) are used to identify key nodes and assess their impact on overall grid reliability. Ultimately, by combining the optimization capabilities of reinforcement learning agents, risk weights for different regions are dynamically adjusted, improving the comprehensiveness and accuracy of unplanned power outage risk assessments. By incorporating grid topology analysis, risk assessments are not limited to individual devices but also identify global, systemic risks. Complex network analysis methods are used to accurately locate nodes critical to grid stability and implement targeted protection. By integrating device health, load levels, and topology, the accuracy of unplanned power outage risk predictions is improved, reducing both misjudgments and missed detections. Reinforcement learning agents can adjust their decisions based on topological characteristics, improving the grid's ability to respond to emergencies and reducing the probability of large-scale power outages.

[0048] In S4, maintenance plans are adjusted not only based on the probability of unplanned outages but also on the availability of maintenance resources, economic costs, and scheduling constraints to ensure the feasibility and cost-effectiveness of the optimized solution. The core of this approach lies in the development of a multi-factor optimization mechanism that ensures that maintenance plans not only reduce the probability of unplanned outages but also balance resource allocation and cost control. First, the system calculates the probability of unplanned outages based on grid situation simulation results and assesses the potential impact of different maintenance plans. Second, the system incorporates a maintenance resource management module to assess the current availability of manpower, equipment, and supplies to ensure that the optimized solution meets real-world conditions. Furthermore, the system analyzes maintenance costs, including equipment maintenance costs, labor costs, and power loss costs, to calculate the economic feasibility of different solutions. Furthermore, the scheduling constraint analysis module considers grid load conditions to ensure that the maintenance plan does not affect the power supply to critical loads and optimizes the maintenance time window to minimize user impact. During the optimization process, a reinforcement learning agent trains its strategy based on these multiple factors, ultimately selecting the optimal maintenance solution that strikes a balance between risk minimization, resource allocation, and cost optimization. Comprehensive consideration of maintenance resource availability ensures that the optimized solution is feasible under real-world conditions. Integrate cost analysis to reduce unnecessary expenditures and improve the economic benefits of maintenance plans. Consider scheduling constraints during the optimization process to ensure that maintenance does not affect the normal operation of the power grid and improve grid reliability. Leverage reinforcement learning technology to optimize under multiple influencing factors, reduce the subjectivity of human decision-making, and improve the intelligence of maintenance plans.

[0049] In S3, an adaptive weight adjustment mechanism is introduced into the calculation of the unplanned power outage risk probability. This allows the risk assessment to dynamically adjust calculation parameters based on real-time operating conditions, thereby improving prediction accuracy. The core of this method lies in the use of an adaptive weight adjustment mechanism, which optimizes the unplanned power outage risk probability calculation as real-time operating conditions change. Traditional risk assessments are typically based on fixed historical weights and are unable to adapt to dynamic changes in the power grid. This method utilizes a reinforcement learning agent to dynamically adjust calculation parameters based on real-time data to improve prediction accuracy. First, the system collects current equipment status, load levels, and historical failure modes based on sensor data and inputs this data into a reinforcement learning model. Then, by constructing a risk calculation model based on weighted functions, the system weights various risk factors (such as equipment aging, power flow distribution, and failure history) according to the current power grid operating status. The adaptive weight adjustment mechanism can utilize methods such as gradient descent optimization, Bayesian updating, or adaptive fuzzy inference to dynamically adjust weights during operation, ensuring that the assessment results are more consistent with the actual conditions of the current power grid. The reinforcement learning agent continuously optimizes its weight allocation strategy by interacting with its environment, ensuring accurate prediction of the probability of unplanned power outages under varying load levels, seasonal fluctuations, and special operating conditions. Ultimately, this approach improves the real-time and accuracy of grid risk predictions, providing more accurate data support for grid operation and maintenance planning. An adaptive weight adjustment mechanism enables dynamic optimization of risk calculations based on real-time operating data. Reinforcement learning continuously optimizes risk weight allocation, reducing misjudgments and missed detections and improving prediction accuracy. Based on more accurate risk assessment results, maintenance strategies are optimized, reducing the incidence of unplanned power outages and improving grid reliability. Combining real-time monitoring data with machine learning techniques enhances the intelligence of grid control, enabling it to automatically adapt to changes in the operating environment.

[0050] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A power grid situation deduction risk assessment method based on reinforcement learning, characterized by: The method comprises: S1. Build a power grid situation model based on equipment health status assessment results and historical operation data; S2. Predict the grid operation status under different maintenance plans through the grid situation model; S3. Determine risk factors based on the predicted grid operation status and calculate the probability of unplanned power outage risk; S4. Adjust maintenance plans based on risk probability to optimize grid operation safety.

2. The power grid situation deduction risk assessment method based on reinforcement learning according to claim 1 is characterized by: Said S1 includes constructing a power grid situation model based on the equipment health status assessment results and historical operation data, and its calculation formula is: M(t)=H(t)+D(t-1); Where M(t) represents the overall operating status of the power grid at time t, t represents time, H(t) represents the health status of the equipment at time t, and D(t-1) represents the power grid operation-related data at time t-1.

3. The power grid situation deduction risk assessment method based on reinforcement learning according to claim 2 is characterized by: The S2 includes using the power grid situation model to predict the power grid operation status under different maintenance plans, and its calculation formula is: S i (t+1)=M(t)-P i ; Among them, S i (t+1) represents the grid operation status at time t+1 under the i-th maintenance plan, i represents the maintenance plan number, M(t) represents the overall grid operation status at time t, P i represents the maintenance plan in the ith order.

4. The power grid situation deduction risk assessment method based on reinforcement learning according to claim 1 is characterized by: S3 includes estimating the risk probability of unplanned power outage by calculating risk factors and combining historical data. The calculation formula is: outage，i =R i (t)+X; Among them, P outage，i represents the risk probability of unplanned power outage under the i-th maintenance plan, R i (t) represents the risk factor calculated at time t under the i-th maintenance plan, and X represents the historical impact factor.

5. The power grid situation deduction risk assessment method based on reinforcement learning according to claim 4 is characterized in that: Said S4 includes adjusting the maintenance plan according to the calculated unplanned power outage risk probability. The calculation formula of the optimized maintenance plan is: P out =min(P i +C); Among them, P opt represents the optimized maintenance plan, P i represents the maintenance plan in the ith order, and C represents the maintenance cost.

6. The power grid situation deduction risk assessment method based on reinforcement learning according to claim 1 is characterized by: In S1, the construction of the power grid situation model combines multi-source data, including equipment operating parameters, environmental variables, power grid load conditions and historical fault records, to improve the accuracy and adaptability of the model.

7. The power grid situation deduction risk assessment method based on reinforcement learning according to claim 1 is characterized by: In S2, when predicting the grid operation status under different maintenance plans, a reinforcement learning algorithm is used to train an intelligent decision-making model based on historical data to improve the accuracy of the prediction and the adaptability to different maintenance strategies.

8. The power grid situation deduction risk assessment method based on reinforcement learning according to claim 1 is characterized by: In S3, the determination of risk factors not only considers the health status of equipment and load level, but also combines the grid topology to identify potential risks of key nodes, so as to improve the comprehensiveness of the unplanned power outage risk assessment.

9. The power grid situation deduction risk assessment method based on reinforcement learning according to claim 1 is characterized by: In S4, the maintenance plan is regulated not only based on the probability of unplanned power outage risk, but also in combination with the availability, economic cost and scheduling constraints of maintenance resources to ensure the feasibility and economy of the optimization plan.

10. The power grid situation deduction risk assessment method based on reinforcement learning according to claim 1 is characterized in that: In S3, an adaptive weight adjustment mechanism is introduced into the calculation of the unplanned power outage risk probability, so that the risk assessment can dynamically adjust the calculation parameters according to the real-time operating conditions to improve the prediction accuracy.

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