Intelligent power grid fault processing method based on multi-dimensional context awareness and dynamic risk assessment

Abstract

The invention relates to a smart grid fault processing method based on multi-dimensional context awareness and dynamic risk assessment. The method comprises the following steps: inputting multi-dimensional context awareness fusion data at the current time in a target power grid into a dynamic risk assessment model to obtain power grid dynamic risk assessment data; under the condition that any power grid risk data in the data exceeds the corresponding preset risk data, adjusting equipment parameters and power transmission parameters according to the power grid dynamic risk assessment data; further combining with multi-dimensional context awareness fusion data to optimize each risk assessment parameter of the dynamic risk assessment model; and inputting the multi-dimensional context awareness fusion data of the next time into the optimized dynamic risk assessment model until no power grid risk data exceeding the corresponding preset risk data exists in the power grid dynamic risk assessment data. By adopting the method, the probability of power grid accidents can be reduced, the safety and stability of power grid operation are improved, and economic loss is avoided.

Description

Technical Field

[0001] The present application relates to the field of smart grid technology, and in particular to a smart grid fault handling method with multi-dimensional situational awareness and dynamic risk assessment. Background Art

[0002] As the scale and complexity of power grids increase, traditional technologies for monitoring and troubleshooting power grids are no longer able to cope with the challenges of modern power grid operations. Traditional systems rely on fixed parameters and single-dimensional equipment status monitoring, which makes it difficult to fully consider external environments (such as climate change) and complex load fluctuations. This results in many potential faults that cannot be handled in advance and can only be handled after the event. This has led to a significant increase in the probability of power grid accidents and caused serious economic losses. Summary of the invention

[0003] Based on this, it is necessary to provide a smart grid fault handling method, device, computer equipment, computer-readable storage medium and computer program product with multi-dimensional situational awareness and dynamic risk assessment to reduce the probability of power grid accidents and thus avoid economic losses in response to the above-mentioned technical problems.

[0004] In a first aspect, the present application provides a smart grid fault handling method with multi-dimensional situational awareness and dynamic risk assessment. The method comprises:

[0005] Perform spatiotemporal correlation on the current environmental sensor data, equipment status data, and load monitoring data in the target power grid to obtain multi-dimensional situational awareness fusion data;

[0006] Inputting the multi-dimensional scenario-aware fusion data into the dynamic risk assessment model of the target power grid to obtain power grid dynamic risk assessment data;

[0007] When any grid risk data in the grid dynamic risk assessment data exceeds the corresponding preset risk data, adjusting the equipment parameters and transmission parameters of the target grid according to the grid dynamic risk assessment data;

[0008] Optimizing each risk assessment parameter of the dynamic risk assessment model according to the adjusted equipment parameters, the adjusted power transmission parameters and the multi-dimensional situational awareness fusion data;

[0009] The multi-dimensional situation-aware fusion data of the next time is input into the optimized dynamic risk assessment model until there is no grid risk data exceeding the corresponding preset risk data in the grid dynamic risk assessment data.

[0010] In a second aspect, the present application also provides a smart grid fault handling device with multi-dimensional situational awareness and dynamic risk assessment. The device comprises:

[0011] The data fusion module is used to perform spatiotemporal correlation on the current environmental sensor data, equipment status data, and load monitoring data in the target power grid to obtain multi-dimensional situational awareness fusion data;

[0012] A model processing module, used for inputting the multi-dimensional scenario-aware fusion data into the dynamic risk assessment model of the target power grid to obtain power grid dynamic risk assessment data;

[0013] A parameter adjustment module, configured to adjust the equipment parameters and transmission parameters of the target power grid according to the dynamic risk assessment data of the power grid when any power grid risk data in the dynamic risk assessment data of the power grid exceeds the corresponding preset risk data;

[0014] A model optimization module, configured to optimize each risk assessment parameter of the dynamic risk assessment model according to the adjusted equipment parameters, the adjusted power transmission parameters and the multi-dimensional situational awareness fusion data;

[0015] The parameter adjustment module is also used to input the multi-dimensional situation-aware fusion data of the next time into the optimized dynamic risk assessment model until there is no grid risk data in the grid dynamic risk assessment data that exceeds the corresponding preset risk data.

[0016] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0017] Perform spatiotemporal correlation on the current environmental sensor data, equipment status data, and load monitoring data in the target power grid to obtain multi-dimensional situational awareness fusion data;

[0018] Inputting the multi-dimensional scenario-aware fusion data into the dynamic risk assessment model of the target power grid to obtain power grid dynamic risk assessment data;

[0019] When any grid risk data in the grid dynamic risk assessment data exceeds the corresponding preset risk data, adjusting the equipment parameters and transmission parameters of the target grid according to the grid dynamic risk assessment data;

[0020] Optimizing each risk assessment parameter of the dynamic risk assessment model according to the adjusted equipment parameters, the adjusted power transmission parameters and the multi-dimensional situational awareness fusion data;

[0021] The multi-dimensional situation-aware fusion data of the next time is input into the optimized dynamic risk assessment model until there is no grid risk data exceeding the corresponding preset risk data in the grid dynamic risk assessment data.

[0022] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0023] Perform spatiotemporal correlation on the current environmental sensor data, equipment status data, and load monitoring data in the target power grid to obtain multi-dimensional situational awareness fusion data;

[0024] Inputting the multi-dimensional scenario-aware fusion data into the dynamic risk assessment model of the target power grid to obtain power grid dynamic risk assessment data;

[0025] When any grid risk data in the grid dynamic risk assessment data exceeds the corresponding preset risk data, adjusting the equipment parameters and transmission parameters of the target grid according to the grid dynamic risk assessment data;

[0026] Optimizing each risk assessment parameter of the dynamic risk assessment model according to the adjusted equipment parameters, the adjusted power transmission parameters and the multi-dimensional situational awareness fusion data;

[0027] The multi-dimensional situation-aware fusion data of the next time is input into the optimized dynamic risk assessment model until there is no grid risk data exceeding the corresponding preset risk data in the grid dynamic risk assessment data.

[0028] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0029] Perform spatiotemporal correlation on the current environmental sensor data, equipment status data, and load monitoring data in the target power grid to obtain multi-dimensional situational awareness fusion data;

[0030] Inputting the multi-dimensional scenario-aware fusion data into the dynamic risk assessment model of the target power grid to obtain power grid dynamic risk assessment data;

[0031] When any grid risk data in the grid dynamic risk assessment data exceeds the corresponding preset risk data, adjusting the equipment parameters and transmission parameters of the target grid according to the grid dynamic risk assessment data;

[0032] Optimizing each risk assessment parameter of the dynamic risk assessment model according to the adjusted equipment parameters, the adjusted power transmission parameters and the multi-dimensional situational awareness fusion data;

[0033] The multi-dimensional situation-aware fusion data of the next time is input into the optimized dynamic risk assessment model until there is no grid risk data exceeding the corresponding preset risk data in the grid dynamic risk assessment data.

[0034] The above-mentioned smart grid fault handling method, device, computer equipment, storage medium and computer program product with multi-dimensional situational awareness and dynamic risk assessment obtains multi-dimensional situational awareness fusion data by temporally and spatially correlating the environmental sensor data, equipment status data and load monitoring data in the target power grid at the current time; inputs the multi-dimensional situational awareness fusion data into the dynamic risk assessment model of the target power grid to obtain power grid dynamic risk assessment data; when there is any power grid risk data in the power grid dynamic risk assessment data that exceeds the corresponding preset risk data, adjusts the equipment parameters and transmission parameters of the target power grid according to the power grid dynamic risk assessment data; optimizes each risk assessment parameter of the dynamic risk assessment model according to the adjusted equipment parameters, the adjusted transmission parameters and the multi-dimensional situational awareness fusion data; inputs the multi-dimensional situational awareness fusion data at the next time into the optimized dynamic risk assessment model until there is no power grid risk data in the power grid dynamic risk assessment data that exceeds the corresponding preset risk data.

[0035] Through spatiotemporal correlation analysis, environmental sensor data, equipment status data and load monitoring data are integrated into multi-dimensional situational awareness data to comprehensively and in real time reflect the current operating status of the power grid. Based on the dynamic risk assessment model, the risk of the power grid can be accurately assessed, and when the risk exceeds the preset value, the power grid equipment parameters and transmission parameters are actively adjusted to timely avoid the occurrence of potential faults or safety hazards. At the same time, through real-time feedback optimization of various evaluation parameters of the model, it is adaptively adjusted to further ensure the continuous improvement of the accuracy of the assessment and ensure the elimination of potential faults or safety hazards. This process can not only reduce the probability of power grid accidents, improve the safety and stability of power grid operation and thus avoid economic losses, but also improve the intelligence level of risk management, reduce human intervention, significantly optimize the overall operation efficiency and risk control capabilities of the power grid, and effectively ensure the continuous and efficient operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] FIG1 is an application environment diagram of a smart grid fault handling method according to an embodiment;

[0037] FIG2 is a schematic diagram of a flow chart of a method for handling smart grid faults in one embodiment;

[0038] FIG3 is a schematic diagram of a flow chart of a method for adjusting device parameters and power transmission parameters in one embodiment;

[0039] FIG4 is a schematic diagram of a flow chart of a method for determining risk elimination strategy information in one embodiment;

[0040] FIG5 is a schematic flow chart of a method for adjusting device parameters and power transmission parameters in another embodiment;

[0041] FIG6 is a schematic diagram of a flow chart of a method for generating power grid dynamic risk assessment data in one embodiment;

[0042] FIG7 is a schematic diagram of a process flow of a risk assessment parameter optimization method in one embodiment;

[0043] FIG8 is a structural block diagram of a smart grid fault processing device in one embodiment;

[0044] FIG. 9 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0046] The embodiment of the present application provides a multi-dimensional situational awareness and dynamic risk assessment smart grid fault handling method, which can be applied in the application environment shown in FIG1. ​​The terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The server 104 obtains the environmental sensor data, equipment status data and load monitoring data of the terminal 102, and further performs spatiotemporal association on the environmental sensor data, equipment status data and load monitoring data of the current time in the target power grid to obtain multi-dimensional situation-aware fusion data; the multi-dimensional situation-aware fusion data is input into the dynamic risk assessment model of the target power grid to obtain the dynamic risk assessment data of the power grid; when there is any power grid risk data exceeding the corresponding preset risk data in the dynamic risk assessment data of the power grid, the equipment parameters and transmission parameters of the target power grid are adjusted according to the dynamic risk assessment data of the power grid; each risk assessment parameter of the dynamic risk assessment model is optimized according to the adjusted equipment parameters, the adjusted transmission parameters and the multi-dimensional situation-aware fusion data; the multi-dimensional situation-aware fusion data of the next time is input into the optimized dynamic risk assessment model until there is no power grid risk data exceeding the corresponding preset risk data in the dynamic risk assessment data of the power grid. Among them, the server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0047] In one embodiment, as shown in FIG2 , a smart grid fault handling method with multi-dimensional situational awareness and dynamic risk assessment is provided, which is described by taking the method applied to the server in FIG1 as an example, and includes the following steps:

[0048] Step 202 , performing spatiotemporal correlation on the current environmental sensing data, equipment status data, and load monitoring data in the target power grid to obtain multi-dimensional situational awareness fusion data.

[0049] Among them, environmental sensor data can be information related to the external environment of the power grid collected by various sensors, usually including meteorological data such as temperature, humidity, wind speed, and rainfall.

[0050] Among them, equipment status data can be real-time information reflecting the operating conditions of various equipment in the power grid (such as transformers, switches, cables, etc.). This type of data usually includes the voltage, current, temperature, operating time, load conditions, etc. of the equipment.

[0051] The load monitoring data may be real-time data for monitoring the power demand in different areas and time periods in the power grid. These data may reflect the load distribution, peak power consumption periods, load fluctuations, etc. of the power grid.

[0052] Among them, multi-dimensional situational awareness fusion data can be a data set generated by temporally and spatially correlating and comprehensively analyzing environmental sensor data, equipment status data and load monitoring data. This type of data comprehensively reflects the current operating status of the power grid from multiple dimensions.

[0053] Specifically, in the target power grid, through the sensor network deployed at various key locations, environmental sensor data (such as meteorological data such as temperature, humidity, wind speed), equipment status data (such as transformers, power lines, voltage, current, temperature, load rate and other equipment operating parameters) and load monitoring data (such as user power consumption, load peak distribution, power demand fluctuations, etc.) are obtained in real time. These data come from different sensors and monitoring systems, and there are temporal and spatial dependencies between the data. Through the spatiotemporal correlation analysis algorithm, the system fuses these multi-source heterogeneous data, considers factors such as the timestamps and collection locations of different data, analyzes the spatial and temporal relationships between these data, and generates a multi-dimensional context-aware fusion data that contains multiple dimensions and comprehensively reflects the current state of the power grid. Multi-dimensional context-aware fusion data can reflect the global operation of the power grid at the current point in time, including the impact of the external environment, the health status of the equipment, and the load pressure.

[0054] Step 204 , input the multi-dimensional scenario-aware fusion data into the dynamic risk assessment model of the target power grid to obtain the power grid dynamic risk assessment data.

[0055] Among them, the dynamic risk assessment model can be a mathematical or algorithmic model based on historical data, current status and environmental information, which is used to evaluate power grid risks in real time. The model analyzes the input multi-dimensional situational awareness data, evaluates the safety of power grid operation, identifies possible failure risks, and provides corresponding risk prediction results.

[0056] The dynamic risk assessment data of the power grid can be the assessment results of the current and future potential risks of the power grid calculated by the dynamic risk assessment model. These data can include specific quantitative values ​​of various risks in the operation of the power grid, such as equipment overload risk, line failure possibility, etc.

[0057] Specifically, the generated multi-dimensional situational awareness data is input into the dynamic risk assessment model of the target power grid. The dynamic risk assessment model is usually based on the historical operation data of the power system, equipment aging, environmental change patterns and load characteristics, and uses machine learning or physical mechanism-based simulation technology to dynamically calculate the potential risks in the operation of the power grid. In addition, the dynamic risk assessment model will evaluate various risks, such as equipment overload risk, line heating or voltage instability, and assign a quantitative risk value to each risk. This risk value is compared with the preset safety threshold to determine whether the power grid is in a safe operating state. The model can also predict the changing trend of risks in the short term in the future and perceive the possible risk nodes in the power grid in advance.

[0058] Step 206: When any grid risk data in the grid dynamic risk assessment data exceeds the corresponding preset risk data, adjust the equipment parameters and transmission parameters of the target grid according to the grid dynamic risk assessment data.

[0059] Among them, the power grid risk data can be a specific risk value output by the dynamic risk assessment model, which indicates the degree of risk that may occur in certain equipment or areas in the power grid. These risk data cover a variety of potential problems in the power grid, such as equipment damage, line failure, and overload operation.

[0060] Among them, the preset risk data can be a risk safety threshold set according to power grid design standards, historical operation data and industry specifications, which is used to evaluate the safety status of power grid operation.

[0061] Among them, the equipment parameters can be the setting values ​​or operating states of various types of equipment during operation, including the output power of the transformer, the voltage, current, temperature, operating mode, etc. of the equipment.

[0062] Among them, transmission parameters can be control variables related to power transmission in the power grid, such as power flow direction, load distribution, voltage level, etc. of the transmission line.

[0063] Specifically, if any grid risk data in the dynamic risk assessment data of the power grid exceeds the corresponding preset risk data, the system will automatically trigger the intelligent control device to remotely issue a dispatch command, or adjust the parameters of the grid-related equipment and the transmission network by enabling backup equipment, switching the operating mode, etc., to achieve the effect of reducing risks. The parameter adjustment includes adjusting the output power of the transformer, switching the gear of the voltage regulator, redistributing the load, or adjusting the power flow distribution of the transmission line. This adjustment process is automated, and it optimizes the electrical parameters in the power grid, alleviates equipment overload or line pressure, and ensures that the power grid operates within a controllable range.

[0064] Step 208 , optimizing each risk assessment parameter of the dynamic risk assessment model according to the adjusted device parameters, the adjusted power transmission parameters and the multi-dimensional situational awareness fusion data.

[0065] Among them, risk assessment parameters can be variables used in dynamic risk assessment models to calculate grid operation risks. These parameters are usually related to data such as equipment status, environmental conditions, and load changes.

[0066] Specifically, as the equipment parameters and transmission parameters of the target power grid are adjusted, the system will again apply these updated equipment parameters and transmission parameters to the dynamic risk assessment model as bias data, and re-input the multi-dimensional situational awareness fusion data of the current time into the dynamic risk assessment model. The dynamic risk assessment model will analyze the input data again, and gradually optimize its own risk assessment parameters based on the new power grid dynamic risk assessment data. This means that the dynamic risk assessment model can adaptively learn according to the actual operation of the power grid, continuously improve its risk identification ability and assessment accuracy, and thus improve its adaptability to different operating scenarios. This step ensures that the risk assessment model can dynamically respond to changes in the power grid and continue to maintain efficient and accurate risk assessment capabilities.

[0067] Step 210, input the multi-dimensional situation-aware fusion data of the next time into the optimized dynamic risk assessment model until there is no grid risk data exceeding the corresponding preset risk data in the grid dynamic risk assessment data.

[0068] Specifically, since the operating environment of the power grid is dynamic, the system will input new environmental sensor data, equipment status data, and load monitoring data at each time step for spatiotemporal association, generate new multi-dimensional situational perception fusion data, and continue to execute the cycle of risk assessment, parameter adjustment, and model optimization. Each cycle gradually optimizes the equipment parameters and transmission parameters to ensure that the system remains in the optimal state. When the output data of the evaluation model shows that all power grid risk assessment data do not exceed the preset safety threshold, it means that the power grid is in a stable state, and the optimization process enters the convergence state. The system will continue to monitor the dynamics of the power grid, but no further adjustment is required when there is no risk exceeding the limit. This cyclic mechanism can quickly respond to potential risks in the power grid and ultimately achieve long-term safe, stable, and efficient operation of the power grid.

[0069] In the above-mentioned smart grid fault handling method with multi-dimensional situational awareness and dynamic risk assessment, multi-dimensional situational awareness fusion data is obtained by temporally and spatially correlating the environmental sensor data, equipment status data and load monitoring data in the target power grid at the current time; the multi-dimensional situational awareness fusion data is input into the dynamic risk assessment model of the target power grid to obtain the power grid dynamic risk assessment data; when there is any power grid risk data in the power grid dynamic risk assessment data that exceeds the corresponding preset risk data, the equipment parameters and transmission parameters of the target power grid are adjusted according to the power grid dynamic risk assessment data; according to the adjusted equipment parameters, the adjusted transmission parameters and the multi-dimensional situational awareness fusion data, each risk assessment parameter of the dynamic risk assessment model is optimized; the multi-dimensional situational awareness fusion data at the next time is input into the optimized dynamic risk assessment model until there is no power grid risk data in the power grid dynamic risk assessment data that exceeds the corresponding preset risk data.

[0070] Through spatiotemporal correlation analysis, environmental sensor data, equipment status data and load monitoring data are integrated into multi-dimensional situational awareness data to comprehensively and in real time reflect the current operating status of the power grid. Based on the dynamic risk assessment model, the risk of the power grid can be accurately assessed, and when the risk exceeds the preset value, the power grid equipment parameters and transmission parameters are actively adjusted to timely avoid the occurrence of potential faults or safety hazards. At the same time, through real-time feedback optimization of various evaluation parameters of the model, it is adaptively adjusted to further ensure the continuous improvement of the accuracy of the assessment and ensure the elimination of potential faults or safety hazards. This process can not only reduce the probability of power grid accidents, improve the safety and stability of power grid operation and thus avoid economic losses, but also improve the intelligence level of risk management, reduce human intervention, significantly optimize the overall operation efficiency and risk control capabilities of the power grid, and effectively ensure the continuous and efficient operation of the power system.

[0071] In one embodiment, as shown in FIG3 , adjusting the equipment parameters and transmission parameters of the target power grid according to the power grid dynamic risk assessment data includes:

[0072] Step 302: identifying each grid operation risk point of the target grid according to the grid dynamic risk assessment data.

[0073] Among them, the grid operation risk point may be that during the grid operation process, due to temporarily unknown reasons such as equipment overload, line failure, equipment aging or external environment changes, the operating status of certain equipment clusters or areas exceeds the safe operating range, which may cause grid failure or affect key locations of power supply.

[0074] Specifically, when the system identifies that any grid risk data in the grid dynamic risk assessment data exceeds the corresponding preset risk data, it automatically identifies the area or equipment cluster that exceeds the threshold and marks it as a "grid operation risk point". For example, when the load rate of a certain section of the transmission line continues to exceed the rated load, the line will be marked as a potential overload risk point; or when the transformer temperature is too high and close to the safe operation limit of the equipment, the equipment will be identified as a faulty grid operation risk point.

[0075] Step 304, determining risk elimination strategy information of the target power grid according to the risk points of each power grid operation.

[0076] Among them, the risk elimination strategy information can be a specific response plan formulated for the identified power grid operation risk points, including how to eliminate or mitigate potential risks.

[0077] Specifically, after clarifying the various risk points in the operation of the power grid, the system needs to rely on the system's built-in expert knowledge base and historical data, the specific situation of each power grid operation risk point, such as comprehensively considering the type of risk, the cause of occurrence, the scope of impact, and the current operating conditions of the power grid (such as the load conditions of other equipment, the availability of backup equipment, etc.), so as to formulate a suitable elimination plan, that is, risk elimination strategy information. For example, for the identified overload risk point, the system may suggest diverting part of the load to adjacent lines, or temporarily switching to backup equipment with lower load. For risks caused by equipment aging, it may be recommended to reduce the pressure by lowering the output power of the equipment or increasing the cooling measures of the equipment. Each strategy needs to be concretized into a set of executable operations, such as adjusting equipment parameters, redistributing loads, etc., and setting priorities and time windows for operations to ensure that risks can be effectively controlled and eliminated.

[0078] Step 306: Adjust the equipment parameters and transmission parameters of the target power grid according to each risk elimination strategy information.

[0079] Specifically, according to the determined risk elimination strategy, the system will automatically adjust the operating parameters of relevant equipment in the power grid and the load distribution of the transmission line. This process is executed through an automated control system to ensure rapid response and precise operation. For example, for a transmission line with an overload risk, the system will automatically send a control command to transfer part of the load to the backup line through the dispatch center or reduce the load by changing the transmission direction. At the same time, for risk points where the equipment status is abnormal, the system may adjust the output power of the equipment, change the operating mode of the cooling device, or reduce the operating load of the equipment to ensure that the equipment operates within a safe range. The entire parameter adjustment process is fed back to the risk assessment model in real time to evaluate the effect of the adjustment, to ensure that the risk is quickly reduced or eliminated while minimizing the impact on the operation of the power grid, and to restore the safe and stable operation of the power grid; the system will also continue to monitor the adjusted power grid status to prevent new risks from occurring.

[0080] In this embodiment, the risk points in the operation of the power grid are accurately identified through the dynamic risk assessment data of the power grid, and targeted risk elimination strategies are formulated based on these risk points to ensure that the power grid can respond in a timely manner when facing potential problems. The system adjusts the equipment parameters and transmission parameters according to the strategy, automatically optimizes the operating status of the power grid, and effectively reduces the risk of equipment failure or overload. In this way, the power grid can improve operating efficiency and safety, reduce the possibility of accidents, and maintain continuous stability and reliability during operation.

[0081] In one embodiment, as shown in FIG4 , the risk elimination strategy information of the target power grid is determined according to the risk points of each power grid operation, including:

[0082] Step 402, for any grid operation risk point, identifying various grid operation abnormal parameters of the grid operation risk point according to the environmental sensor data, equipment status data and load monitoring data of the grid operation risk point.

[0083] The abnormal power grid operation parameters may refer to indicators of various hardware or software in the power grid that exceed the normal range during the power grid operation.

[0084] Specifically, when the system detects one or more grid operation risk points, the same steps are performed for each grid operation risk point, that is, the system will call the data of the environmental sensors, equipment status monitors and load monitoring systems related to the grid operation risk point for detailed analysis. These data usually cover the external environment of the area (such as temperature, humidity, wind speed, etc.), the specific status of the equipment (such as the voltage, current, power factor, temperature, etc. of the transformer or transmission line), and the distribution of the load (such as real-time load value, power consumption fluctuation trend, etc.). By performing time series and spatial analysis on these data, the system will identify the abnormal grid operation parameters directly related to the grid operation risk point. For example, it may be detected that the equipment current rises abnormally, the load is overloaded, the equipment temperature exceeds the safety threshold, or the environmental conditions (such as extreme high temperature) affect the normal operation of the equipment. All these abnormal parameters will serve as the basis for diagnosing potential problems at the risk point.

[0085] Step 404, determining the cause of the abnormal operation of the power grid at the power grid operation risk point according to each abnormal operation parameter of the power grid.

[0086] Among them, the cause of abnormal grid operation can be the fundamental problem that leads to abnormal grid operation parameters. By analyzing the abnormal parameters and related data, the system can determine the cause of the abnormality, such as equipment aging, overload operation, environmental factors (such as high temperature or storms), sudden increase in load, etc.

[0087] Specifically, after identifying the abnormal operation parameters of each power grid, the system needs to conduct an in-depth analysis of the abnormal operation parameters of each power grid to determine the root cause of these abnormal parameters. The analysis can be done by comparing the historical operation data of the equipment, the change information of the external environment and the change trend of the load, and the system can gradually lock in the cause of the abnormal operation of the power grid. For example, if the current and temperature of the equipment exceed the standard at the same time, and the load monitoring data shows that the load in the area has suddenly increased, the system may determine that this is due to equipment overload caused by overload; if the equipment temperature is too high but the load is stable and the environmental sensor data shows that the external temperature is abnormally high, the system may believe that the high ambient temperature causes poor heat dissipation of the equipment. In addition, the system may also combine the service life and maintenance records of the equipment to infer whether there is equipment aging or component failure.

[0088] Step 406, determining risk elimination strategy information for eliminating the cause of abnormal power grid operation.

[0089] Specifically, after clarifying the cause of the abnormality, the system tailors a method for eliminating risks based on the system's built-in knowledge base and historical experience, combined with the specific operating conditions of the current power grid. For example, if the system determines that the risk point is caused by overload, it may recommend redistributing the load of the power grid and transferring part of the load on the overloaded line to other backup lines; if the problem is that the equipment temperature is too high and is related to the high ambient temperature, the system may recommend increasing the cooling capacity of the equipment or alleviating the temperature rise by lowering the output power of the equipment. At the same time, the system may also propose methods for inspecting and maintaining overloaded equipment to prevent further failures. Combining the above-mentioned methods for eliminating abnormalities and inspection and maintenance methods, a corresponding risk elimination strategy is generated. The risk elimination strategy not only includes specific parameter adjustment steps, but may also involve backup plans in emergency situations. Each operation requires a clear execution order and time node to ensure that the abnormality is eliminated quickly and effectively, prevent the risk from further expanding, and minimize the impact on the overall operation of the power grid.

[0090] In this embodiment, by targeting the risk points of power grid operation, in-depth analysis of the relevant environmental sensor data, equipment status data and load monitoring data, the abnormal parameters in the power grid operation are accurately identified, and the abnormal causes are quickly located through these parameters. This process ensures that the power grid management system can accurately determine the root cause of the fault or risk, so as to formulate targeted risk elimination strategy information. By effectively eliminating the abnormal causes, the failure risk of the power grid is greatly reduced, the operational stability and efficiency are improved, and the continuity and safety of power supply are ensured.

[0091] In one embodiment, as shown in FIG5 , adjusting the equipment parameters and transmission parameters of the target power grid according to each risk elimination strategy information includes:

[0092] Step 502: input the environmental sensor data, the equipment status data and the load monitoring data into the power grid operation prediction model to obtain the power grid operation prediction data.

[0093] The grid operation prediction data may be a simulation result of the grid operation status in a future time period generated by a grid operation prediction model based on the current environment, equipment status and load monitoring data.

[0094] Specifically, the system will input various data sources in the power grid at the current time point, including environmental sensor data (such as meteorological conditions such as temperature, wind speed, humidity, etc.), equipment status data (such as real-time operating parameters such as current, voltage, power, temperature, etc. of transformers, cables and switchgear), and load monitoring data (such as user power demand, regional load distribution, load peak and valley changes) into the power grid operation prediction model. The power grid operation prediction model simulates and predicts the operation of the power grid in the future time period based on physical mechanisms, statistical methods or machine learning technology. In the prediction process, factors such as equipment aging, climate change and load fluctuations are fully considered to generate operation prediction data for various equipment and transmission lines in the power grid in the future period, including possible load pressure, equipment operating temperature, transmission line load, etc.

[0095] Step 504: Determine future operation disaster information of the target power grid based on the power grid operation prediction data and various risk elimination strategy information.

[0096] Among them, future operational disaster information can be based on the power grid operation forecast data, combined with the existing risk elimination strategies, and analyzed to obtain specific data on risk events or failure conditions that may occur in the future in the power grid.

[0097] Specifically, based on the grid operation prediction data generated by the grid operation prediction model and the existing risk elimination strategy information, the system can identify upcoming risk events by analyzing future environmental conditions, equipment loads, and historical operation data. For example, it is predicted that the continued rise in temperature in a certain period of time in the future may cause the equipment temperature to be too high, or the load pressure of a certain transmission line to be too high during the peak load period. The system will further evaluate each potential risk point in combination with the risk elimination strategy, generate detailed future operation disaster information, clarify the time when the disaster may occur, the scope of impact, the equipment or lines affected, and the possible severity, and finally determine the future operation disaster information corresponding to the disaster risks that the power grid may face in the future operation.

[0098] Step 506, calculating the equipment parameter adjustment amount and the transmission parameter adjustment amount of the target power grid according to the future operation disaster information.

[0099] Specifically, when the future operational disaster information corresponding to the future disaster risk point is clearly identified, the system will calculate a specific response plan based on the future operational disaster information. For each possible disaster in the future, the system will calculate the specific values ​​of the equipment parameter adjustment and the transmission parameter adjustment based on the physical characteristics of the equipment and lines, the operation history, and the system's built-in optimization algorithm. For example, if a transmission line is predicted to be overloaded due to excessive load, the system will calculate the amount of power load that needs to be redistributed; if a transformer is predicted to overheat due to rising ambient temperature, the system will calculate how much its output power needs to be reduced, or how much cooling capacity the cooling system needs to increase, to ensure that each adjustment can effectively mitigate or eliminate future disaster risks.

[0100] Step 508: Use the device parameter adjustment amount to adjust the device parameter, and use the power transmission parameter adjustment amount to adjust the power transmission parameter.

[0101] Specifically, the system sends control instructions to adjust the power grid in real time based on the calculated equipment parameter adjustments and transmission parameter adjustments. Specific operations include adjusting the operating parameters of the equipment, such as reducing the output power of the transformer and increasing the operation of the cooling system to cope with the risk of rising temperatures; or redistributing the load on the transmission line to reduce the pressure on a certain line and avoid the risk of overload. These adjustments are precisely performed by the automated power grid control system to ensure that the adjustment amount is consistent with the value calculated by the system. After the operation is completed, the system will continue to monitor the adjustment effect and further optimize it based on real-time data to ensure that the power grid is restored to a safe operating state before the risk of disaster occurs.

[0102] In this embodiment, by inputting environmental sensor data, equipment status data and load monitoring data into the power grid operation prediction model, the future operation status of the power grid is predicted in advance. Combined with the risk elimination strategy, the system can identify the disaster risks that may occur in the future, and actively optimize the parameters by calculating the adjustment amount of equipment parameters and transmission parameters. This method of early prevention and automated adjustment can effectively reduce the risk of equipment failure and transmission overload, improve the safety, stability and efficiency of power grid operation, ensure the reliability of power supply and reduce the occurrence of potential accidents.

[0103] In one embodiment, as shown in FIG6 , the multi-dimensional scenario-aware fusion data is input into the dynamic risk assessment model of the target power grid to obtain the power grid dynamic risk assessment data, including:

[0104] Step 602: extract local features from the multi-dimensional context-aware fusion data to identify the perception data feature change points of the multi-dimensional context-aware fusion data.

[0105] Among them, the perception data feature change point can be the moment when sudden or significant changes occur in the multi-dimensional situational perception data of the power grid. These changes usually reflect abnormal fluctuations in the power grid equipment, environment or load status.

[0106] Specifically, the system extracts local features from the multi-dimensional situational perception data of the target power grid. That is, by using data mining and signal processing algorithms, the system identifies key change points or local anomalies in the multi-dimensional situational perception data. These perception data feature change points represent sudden changes or trend turning points in the operation status of the power grid. For example, when the temperature of a device suddenly rises or the load suddenly increases, the system will mark these changes as feature change points. Perception data feature change points are crucial indicators in the dynamic risk assessment of power grids, reflecting the potential operating risks of the power grid at the current moment.

[0107] Step 604 , generating a historical operation data change curve of the target power grid according to the multi-dimensional scenario-aware fusion data at each historical moment and the multi-dimensional scenario-aware fusion data at the current time.

[0108] The historical operation data change curve can be a curve drawn based on the change trend of the multi-dimensional situational awareness data of the power grid in different time periods. This curve shows the past operation mode of the power grid, including factors such as equipment performance, load fluctuations and environmental impacts.

[0109] Specifically, while extracting the characteristic change points of the perception data at the current moment, the system also needs to refer to the multi-dimensional situational perception fusion data of each historical moment and the multi-dimensional situational perception fusion data of the current time to generate a historical operation data change curve. This process involves collecting multi-dimensional situational perception data over a period of time and comparing these data with the data at the current time point. The historical data change curve reflects the operation mode and trend of the power grid in different time periods, such as the gradual change of equipment status, the trend of load fluctuation over time, and the long-term impact of the external environment on the equipment. This curve shows the normal fluctuation range of the power grid and reveals the long-term operation status of the power grid, helping to analyze whether the abnormality of the current data exceeds the normal operation trajectory of the power grid.

[0110] Step 606, generating power grid dynamic risk assessment data based on change information of the characteristic change points of the sensed data relative to the change curve of the historical operating data.

[0111] Specifically, the system combines the extracted perception data feature change points with the historical operation data change curve for dynamic risk assessment. By comparing the perception data feature change points of the current data with the fluctuation trend in the historical change curve, the system can determine whether these changes are beyond the normal range. If the amplitude, frequency or trend of the current feature change points are significantly different from the historical changes, the system will identify the potential risks of the power grid and quantify these risks. For example, if the load rises much faster than the historical average, or the equipment temperature rises sharply and exceeds the historical threshold, the system will mark these situations as potential high-risk areas. Finally, the system will generate dynamic risk assessment data for the power grid, and at the same time give the current and possible future risk levels, providing a basis for decision makers to take corresponding preventive and adjustment measures.

[0112] In this embodiment, local features are extracted from multi-dimensional scenario-aware fusion data to accurately identify key feature change points, and the abnormality of the current change point is analyzed in combination with the historical operation data change curve. This process can effectively reflect the abnormal trends and potential risks in power grid operation, and generate more accurate dynamic risk assessment data based on historical data comparison. In this way, the power grid management system can detect potential faults or operational anomalies earlier, take timely countermeasures, improve the predictability and safety of power grid operation, and ensure the continuous and stable operation of the power grid.

[0113] In one embodiment, as shown in FIG. 7 , each risk assessment parameter of the dynamic risk assessment model is optimized according to the adjusted device parameters, the adjusted power transmission parameters, and the multi-dimensional situational awareness fusion data, including:

[0114] Step 702: According to the adjusted equipment parameters, the adjusted power transmission parameters and the multi-dimensional situational awareness fusion data, the weights of the risk assessment parameters are adjusted to obtain the weight-adjusted risk assessment parameters.

[0115] Among them, the weight-adjusted risk assessment parameter may refer to a parameter generated by dynamically adjusting the importance weights of different risk assessment parameters in the risk assessment model according to changes in equipment parameters, transmission parameters and multi-dimensional situational awareness data of the power grid.

[0116] Specifically, the system determines the current multi-dimensional situational awareness fusion data and evaluates the impact of the adjusted equipment parameters and the adjusted transmission parameters on the safety and stability of the power grid. That is, the isolation analysis method is used to simply evaluate whether the adjusted equipment parameters and the adjusted transmission parameters have brought changes to the safety and stability of the power grid. For example, whether the load reduction of a certain transmission line reduces the potential overload risk, or whether the temperature drop of the equipment reduces the possibility of failure. Then, the system will adjust the weights of each risk assessment parameter in the dynamic risk assessment model according to these impacts, giving higher weights to those parameters that have a greater impact on the safety of the power grid and reducing the focus on minor parameters. This process ensures that the assessment model can accurately reflect the impact of various factors under the current operating state of the power grid and generate more realistic weight adjustment risk assessment parameters.

[0117] Step 704, based on the multi-dimensional scenario-aware fusion data of each historical moment and the multi-dimensional scenario-aware fusion data of the current time, the equipment degradation mode parameters and the load fluctuation mode parameters of each weight adjustment risk assessment parameter are optimized to obtain a mode adjustment risk assessment model.

[0118] The equipment degradation mode parameter may be a key parameter for describing the change trend of power grid equipment due to aging or performance degradation during long-term operation.

[0119] The load fluctuation pattern parameter may be a parameter used to describe how the grid load fluctuates at different times and under different conditions.

[0120] The mode adjustment risk assessment model may refer to an assessment model that can more accurately assess the dynamic risk of the power grid after optimizing the equipment degradation mode parameters and the load fluctuation mode parameters.

[0121] Specifically, the system will analyze the aging and degradation trends of power grid equipment in historical operation, such as the gradually increasing temperature of the equipment, the gradually decreasing power efficiency, etc., and combine the current data to find out the actual degradation rate of the equipment; at the same time, the system will also analyze the load fluctuation pattern of the power grid, and optimize the predicted data of load fluctuation through historical load data and real-time monitoring data. In this way, the system can identify the aging status of the equipment and the law of load changes. Through the above calculation results, the system will further optimize the equipment degradation mode parameters and load fluctuation mode parameters in the weight adjustment risk assessment parameters; the basis of the optimization is the comparison of historical data with current operation data. The risk assessment model can better reflect the actual operation of the equipment and possible load changes in the future, and the optimized model can more accurately predict potential risks.

[0122] Step 706 , performing generalization training on the mode adjustment risk assessment model according to the associated parameters of the equipment degradation mode parameters and the associated parameters of the load fluctuation mode parameters.

[0123] Specifically, the system will perform correlation analysis on the correlation parameters of the optimized equipment degradation mode parameters and the correlation parameters of the load fluctuation mode parameters to find out the relationship between them and other risk assessment parameters. For example, whether the degradation rate of the equipment is affected by load fluctuations, or whether load fluctuations have an accelerating effect on the operating life of a specific device. Through this analysis, the system uses these correlations as the basis for generalization training and trains the risk assessment model using a variety of historical operating scenario data and operating scenario simulation data. The purpose of generalization training is to enhance the performance of the model in various complex situations, so that it can maintain a stable risk assessment capability when facing different environments, equipment states or load conditions. After generalization training, the assessment model can adapt to a variety of uncertainties, improve the early identification and early warning capabilities of abnormal power grid operation, and thus more effectively predict potential risks of the power grid.

[0124] In this embodiment, the weights of risk assessment parameters are dynamically adjusted by adjusting the adjusted equipment parameters, transmission parameters and multi-dimensional situational awareness fusion data, so that the model can more accurately reflect the current operating status of the power grid. Combining historical data with current data, the equipment degradation mode and load fluctuation mode are optimized so that the model can better predict the risks of equipment aging and load changes. Through generalization training, the adaptability of the model is further improved, so that it can maintain efficient risk prediction capabilities under different power grid operating conditions. This optimization and training process enhances the accuracy, flexibility and reliability of power grid risk assessment and improves the safety and operating efficiency of the power grid.

[0125] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0126] Based on the same inventive concept, the embodiment of the present application also provides a multi-dimensional situational awareness and dynamic risk assessment smart grid fault handling device for implementing the multi-dimensional situational awareness and dynamic risk assessment smart grid fault handling method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more multi-dimensional situational awareness and dynamic risk assessment smart grid fault handling device embodiments provided below can refer to the above limitations on a multi-dimensional situational awareness and dynamic risk assessment smart grid fault handling method, which will not be repeated here.

[0127] In one embodiment, as shown in FIG8 , a multi-dimensional situational awareness and dynamic risk assessment smart grid fault processing device is provided, including: a data fusion module 802, a model processing module 804, a parameter adjustment module 806 and a model optimization module 808, wherein:

[0128] The data fusion module 802 is used to perform spatiotemporal correlation on the current environmental sensor data, equipment status data and load monitoring data in the target power grid to obtain multi-dimensional situation-aware fusion data;

[0129] The model processing module 804 is used to input the multi-dimensional scenario-aware fusion data into the dynamic risk assessment model of the target power grid to obtain the dynamic risk assessment data of the power grid;

[0130] The parameter adjustment module 806 is used to adjust the equipment parameters and transmission parameters of the target power grid according to the power grid dynamic risk assessment data when any power grid risk data in the power grid dynamic risk assessment data exceeds the corresponding preset risk data;

[0131] A model optimization module 808, for optimizing each risk assessment parameter of the dynamic risk assessment model according to the adjusted equipment parameters, the adjusted transmission parameters and the multi-dimensional situational awareness fusion data;

[0132] The parameter adjustment module 806 is also used to input the multi-dimensional situation-aware fusion data of the next time into the optimized dynamic risk assessment model until there is no power grid risk data in the power grid dynamic risk assessment data that exceeds the corresponding preset risk data.

[0133] In one embodiment, the parameter adjustment module 806 is also used to identify various grid operation risk points of the target grid based on the grid dynamic risk assessment data; determine various risk elimination strategy information of the target grid based on each grid operation risk point; and adjust the equipment parameters and transmission parameters of the target grid based on each risk elimination strategy information.

[0134] In one embodiment, the parameter adjustment module 806 is also used to identify various abnormal power grid operation parameters of any power grid operation risk point based on the environmental sensor data, equipment status data and load monitoring data of the power grid operation risk point; determine the cause of the abnormal power grid operation at the power grid operation risk point based on each abnormal power grid operation parameter; and determine risk elimination strategy information for eliminating the cause of the abnormal power grid operation.

[0135] In one embodiment, the parameter adjustment module 806 is also used to input environmental sensor data, equipment status data and load monitoring data into the power grid operation prediction model to obtain power grid operation prediction data; determine the future operation disaster information of the target power grid based on the power grid operation prediction data and various risk elimination strategy information; calculate the equipment parameter adjustment amount and the transmission parameter adjustment amount of the target power grid based on the future operation disaster information; use the equipment parameter adjustment amount to adjust the equipment parameters, and use the transmission parameter adjustment amount to adjust the transmission parameters.

[0136] In one embodiment, the model processing module 804 is also used to extract local features of the multi-dimensional scenario-aware fusion data, identify the perception data feature change points of the multi-dimensional scenario-aware fusion data; generate a historical operation data change curve of the target power grid based on the multi-dimensional scenario-aware fusion data of each historical moment and the multi-dimensional scenario-aware fusion data of the current time; generate power grid dynamic risk assessment data based on the change information of the perception data feature change points relative to the historical operation data change curve.

[0137] In one embodiment, the model optimization module 808 is also used to adjust the weights of each risk assessment parameter according to the adjusted equipment parameters, the adjusted transmission parameters and the multi-dimensional situational awareness fusion data to obtain each weight-adjusted risk assessment parameter; optimize the equipment degradation pattern parameters and the load fluctuation pattern parameters of each weight-adjusted risk assessment parameter according to the multi-dimensional situational awareness fusion data of each historical moment and the multi-dimensional situational awareness fusion data of the current time to obtain a pattern adjustment risk assessment model; and perform generalization training on the pattern adjustment risk assessment model according to the associated parameters of the equipment degradation pattern parameters and the associated parameters of the load fluctuation pattern parameters.

[0138] Each module in the above-mentioned multi-dimensional situational awareness and dynamic risk assessment smart grid fault handling device can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of the above modules.

[0139] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in FIG9 . The computer device includes a processor, a memory, and a network interface connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store server data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a smart grid fault handling method with multi-dimensional situational awareness and dynamic risk assessment is implemented.

[0140] Those skilled in the art will understand that the structure shown in FIG. 9 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0141] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0142] In one embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0143] In one embodiment, a computer program product or computer program is provided, the computer program product or computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in the above-mentioned method embodiments.

[0144] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0145] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0146] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0147] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the present application. It should be noted that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A smart grid fault handling method with multi-dimensional situational awareness and dynamic risk assessment, characterized in that: The method comprises: Perform spatiotemporal correlation on the current environmental sensor data, equipment status data, and load monitoring data in the target power grid to obtain multi-dimensional situational awareness fusion data; Inputting the multi-dimensional scenario-aware fusion data into the dynamic risk assessment model of the target power grid to obtain power grid dynamic risk assessment data; When any grid risk data in the grid dynamic risk assessment data exceeds the corresponding preset risk data, adjusting the equipment parameters and transmission parameters of the target grid according to the grid dynamic risk assessment data; Optimizing each risk assessment parameter of the dynamic risk assessment model according to the adjusted equipment parameters, the adjusted power transmission parameters and the multi-dimensional situational awareness fusion data; The multi-dimensional situation-aware fusion data of the next time is input into the optimized dynamic risk assessment model until there is no grid risk data exceeding the corresponding preset risk data in the grid dynamic risk assessment data.

2. The method according to claim 1, characterized in that The adjusting the equipment parameters and transmission parameters of the target power grid according to the power grid dynamic risk assessment data includes: Identifying each grid operation risk point of the target grid according to the grid dynamic risk assessment data; Determining risk elimination strategy information of the target power grid according to each of the power grid operation risk points; According to each of the risk elimination strategy information, the equipment parameters and the transmission parameters of the target power grid are adjusted.

3. The method according to claim 2, characterized in that Determining the risk elimination strategy information of the target power grid according to each of the power grid operation risk points includes: For any of the grid operation risk points, identifying each grid operation abnormality parameter of the grid operation risk point according to the environmental sensor data, equipment status data and load monitoring data of the grid operation risk point; Determining the cause of the abnormal operation of the power grid at the risk point of the power grid operation according to each of the abnormal operation parameters of the power grid; Determine risk elimination strategy information for eliminating the cause of abnormal operation of the power grid.

4. The method according to claim 2, characterized in that The step of adjusting the equipment parameters and transmission parameters of the target power grid according to each of the risk elimination strategy information includes: Inputting the environmental sensing data, the equipment status data and the load monitoring data into a power grid operation prediction model to obtain power grid operation prediction data; Determining future operation disaster information of the target power grid according to the power grid operation prediction data and each of the risk elimination strategy information; Calculating the equipment parameter adjustment amount and the transmission parameter adjustment amount of the target power grid according to the future operation disaster information; The device parameter is adjusted using the device parameter adjustment amount, and the power transmission parameter is adjusted using the power transmission parameter adjustment amount.

5. The method according to claim 1, characterized in that: The step of inputting the multi-dimensional scenario-aware fusion data into the dynamic risk assessment model of the target power grid to obtain the power grid dynamic risk assessment data includes: Extracting local features of the multi-dimensional context-aware fusion data to identify perception data feature change points of the multi-dimensional context-aware fusion data; Generate a historical operation data change curve of the target power grid according to the multi-dimensional scenario-aware fusion data of each historical moment and the multi-dimensional scenario-aware fusion data of the current time; The power grid dynamic risk assessment data is generated according to the change information of the characteristic change point of the perception data relative to the historical operation data change curve.

6. The method according to claim 1, characterized in that The optimizing the risk assessment parameters of the dynamic risk assessment model according to the adjusted equipment parameters, the adjusted power transmission parameters and the multi-dimensional situational awareness fusion data includes: According to the adjusted equipment parameters, the adjusted power transmission parameters and the multi-dimensional situational awareness fusion data, the weights of the risk assessment parameters are adjusted to obtain the weight-adjusted risk assessment parameters; According to the multi-dimensional situation-aware fusion data of each historical moment and the multi-dimensional situation-aware fusion data of the current time, the equipment degradation mode parameters and the load fluctuation mode parameters of each weight adjustment risk assessment parameter are optimized to obtain a mode adjustment risk assessment model; The mode adjustment risk assessment model is generalized and trained according to the associated parameters of the equipment degradation mode parameters and the associated parameters of the load fluctuation mode parameters.

7. A smart grid fault handling device with multi-dimensional situational awareness and dynamic risk assessment, characterized in that: The device comprises: The data fusion module is used to perform spatiotemporal correlation on the current environmental sensor data, equipment status data, and load monitoring data in the target power grid to obtain multi-dimensional situational awareness fusion data; A model processing module, used for inputting the multi-dimensional scenario-aware fusion data into the dynamic risk assessment model of the target power grid to obtain power grid dynamic risk assessment data; A parameter adjustment module, configured to adjust the equipment parameters and transmission parameters of the target power grid according to the dynamic risk assessment data of the power grid when any power grid risk data in the dynamic risk assessment data of the power grid exceeds the corresponding preset risk data; A model optimization module, configured to optimize each risk assessment parameter of the dynamic risk assessment model according to the adjusted equipment parameters, the adjusted power transmission parameters and the multi-dimensional situational awareness fusion data; The parameter adjustment module is also used to input the multi-dimensional situation-aware fusion data of the next time into the optimized dynamic risk assessment model until there is no grid risk data in the grid dynamic risk assessment data that exceeds the corresponding preset risk data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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