An artificial intelligence-based emergency plan generation method and system

Abstract

The present application provides an emergency plan generation method and system based on artificial intelligence, belonging to the technical field of power systems. In the present application, the method includes: obtaining multi-source power grid condition data for the current data collection period, constructing multiple power grid condition sub-databases according to the categories of the multi-source power grid condition data, determining a target emergency scenario and a target emergency plan template matching the target emergency scenario according to the data sources and data characteristics of the multi-source power grid condition data, the target emergency plan template invoking the multi-source power grid condition data in the power grid condition sub-databases to generate an initial power grid emergency plan, verifying the initial power grid emergency plan according to the data quality evaluation report, outputting a target power grid emergency plan, and outputting the target power grid emergency plan as visual information and sending it to emergency response personnel. The present application aims to improve problems such as low response efficiency, insufficient accuracy of emergency plans, and difficulty in adapting to complex scenarios in existing solutions.

Description

Technical Field

[0001] This application relates to the technical field of power systems, and particularly to an emergency plan generation method and system based on artificial intelligence. Background Art

[0002] With the continuous expansion of the power grid scale and the increasing complexity of operation, the operation risks and fault types faced by the power grid show a trend of diversification and complexity. Common emergencies include equipment failures, load fluctuations, natural disasters, etc. These events pose great challenges to the safe and stable operation of the power grid.

[0003] Currently, the formulation and execution of emergency plans often rely on manual experience and existing rules, with problems such as low response efficiency, insufficient accuracy of plans, and difficulty in adapting to complex scenarios. Summary of the Invention

[0004] An embodiment of this application provides an emergency plan generation method and system based on artificial intelligence. This application adopts the following technical solutions:

[0005] In a first aspect, an emergency plan generation method based on artificial intelligence is provided. The method includes:

[0006] Obtain multi-source power grid operating condition data for the current data collection period, and construct multiple power grid operating condition sub-databases according to the categories of the multi-source power grid operating condition data;

[0007] Determine a target emergency scenario and a target emergency plan template matching the target emergency scenario according to the data sources and data characteristics of the multi-source power grid operating condition data;

[0008] The target emergency plan template calls the multi-source power grid operating condition data in the power grid operating condition sub-database to generate an initial power grid emergency plan;

[0009] Verify the initial power grid emergency plan according to the data quality evaluation report, and output a target power grid emergency plan;

[0010] Output the target power grid emergency plan as visual information and send it to emergency handlers.

[0011] In an embodiment of the first aspect of this application, obtaining multi-source power grid operating condition data for the current data collection period includes:

[0012] Import the initial multi-source power grid operating condition data, and select target multi-source power grid operating condition data from the initial multi-source power grid operating condition data by random selection;

[0013] Or, import the initial multi-source power grid operating condition data, and select target multi-source power grid operating condition data from the initial multi-source power grid operating condition data by means of weight information and sampling selection.

[0014] In an embodiment of the first aspect of the present application, according to the data sources and data characteristics of multi-source power grid operating conditions data, a target emergency scenario and a target emergency plan template matching the target emergency scenario are determined, including:

[0015] Obtain the data source identifier of the multi-source power grid operating conditions data and the data characteristics of the multi-source power grid operating conditions data;

[0016] Using the data source identifier as the first index and the data characteristics as the second index, determine the target emergency plan template from multiple alternative emergency plan templates.

[0017] In an embodiment of the first aspect of the present application, the target emergency plan template includes multiple sub-functional areas. The target emergency plan template calls the multi-source power grid operating conditions data in the power grid operating conditions sub-database to generate an initial power grid emergency plan, including:

[0018] The target emergency plan template imports the data in the power grid operating conditions sub-database into multiple sub-functional areas respectively according to the data categories of the multi-source power grid operating conditions data;

[0019] Each sub-functional area analyzes and automatically writes the fault problems according to its corresponding target task, and generates the corresponding initial power grid emergency plan.

[0020] In an embodiment of the first aspect of the present application, the sub-functional area analyzes and automatically writes the fault problems according to its corresponding target task, and generates the corresponding initial power grid emergency plan, including:

[0021] Perform feature analysis according to the target category and determine the target task value;

[0022] Locate the position corresponding to the target task value in the power grid operating conditions sub-database;

[0023] Based on the position corresponding to the target task value, automatically write the description text to generate the initial power grid emergency plan.

[0024] In an embodiment of the first aspect of the present application, performing feature analysis according to the target category and determining the target task value includes:

[0025] According to the target category, determine the corresponding first point estimate value and second point estimate value of the target category;

[0026] Generate a grouping table and a grouping description according to the first point estimate value;

[0027] Generate a trend chart and a trend description according to the second point estimate value.

[0028] In an embodiment of the first aspect of the present application, the initial power grid emergency plan is verified according to the data quality evaluation report, and the target power grid emergency plan is output, including:

[0029] Evaluate the initial power grid emergency plan in multiple dimensions according to the data quality evaluation report;

[0030] According to the results of the multi-dimensional evaluation, determine the evaluation score of the initial power grid emergency plan, and determine the initial power grid emergency plan with an evaluation score greater than the threshold as the target power grid emergency plan.

[0031] In a second aspect, based on the same inventive concept, an emergency plan generation system based on artificial intelligence is provided. The system includes:

[0032] An acquisition module, configured to acquire multi-source power grid condition data for the current data acquisition cycle, and construct multiple power grid condition sub-databases according to the categories of the multi-source power grid condition data;

[0033] A matching module, configured to determine a target emergency scenario and a target emergency plan template matching the target emergency scenario according to the data source and data characteristics of the multi-source power grid condition data;

[0034] A generation module, configured to call the multi-source power grid condition data in the power grid condition sub-database by the target emergency plan template to generate an initial power grid emergency plan;

[0035] A verification module, configured to verify the initial power grid emergency plan according to the data quality evaluation report and output a target power grid emergency plan;

[0036] A sending module, configured to output the target power grid emergency plan as visual information and send it to emergency handlers.

[0037] In an embodiment of the second aspect of the present application, the acquisition module includes:

[0038] A first acquisition sub-module, configured to import initial multi-source power grid condition data, and select target multi-source power grid condition data from the initial multi-source power grid condition data by random selection;

[0039] A second acquisition sub-module, configured to import initial multi-source power grid condition data, and select target multi-source power grid condition data from the initial multi-source power grid condition data by weight information and sampling selection.

[0040] In an embodiment of the second aspect of the present application, the matching module includes:

[0041] A third acquisition sub-module, configured to acquire the data source identifier of the multi-source power grid condition data and the data characteristics of the multi-source power grid condition data;

[0042] A matching sub-module, configured to determine the target emergency plan template from multiple alternative emergency plan templates with the data source identifier as the first index and the data characteristics as the second index.

[0043] In an embodiment of the second aspect of the present application, the generation module includes:

[0044] A data import sub-module, configured to import the data in the grid condition sub-database into multiple sub-functional areas respectively according to the data categories of the multi-source grid condition data for the target emergency plan template;

[0045] An analysis sub-module, configured to analyze and automatically write fault problems according to the corresponding target tasks of each sub-functional area, and generate corresponding initial grid emergency plans.

[0046] In an embodiment of the second aspect of the present application, the analysis sub-module includes:

[0047] A feature analysis unit, configured to perform feature analysis according to the target category and determine the target task value;

[0048] A location analysis unit, configured to locate the position corresponding to the target task value in the grid condition sub-database;

[0049] A writing unit, configured to automatically write description texts based on the position corresponding to the target task value to generate an initial grid emergency plan.

[0050] In an embodiment of the second aspect of the present application, the feature analysis unit includes:

[0051] A determination sub-unit, configured to determine the corresponding first point estimate value and second point estimate value of the target category according to the target category;

[0052] A first description sub-unit, configured to generate a grouping table and grouping description according to the first point estimate value;

[0053] A second description sub-unit, configured to generate a trend chart and trend description according to the second point estimate value.

[0054] In an embodiment of the second aspect of the present application, the verification module includes:

[0055] An evaluation sub-module, configured to perform multi-dimensional evaluation on the initial grid emergency plan according to the data quality evaluation report;

[0056] A screening sub-module, configured to determine the evaluation score of the initial grid emergency plan according to the multi-dimensional evaluation results, and determine the initial grid emergency plan with an evaluation score greater than the threshold as the target grid emergency plan.

[0057] In summary, the above-mentioned emergency plan generation method and system based on artificial intelligence have the following technical effects:

[0058] By automatically generating power grid emergency plans, it is possible to significantly reduce manual intervention and response time, and improve the speed of emergency response. Based on real-time and historical data analysis, the system can adjust and optimize emergency plans in real time to ensure that the power grid can respond quickly and effectively in emergencies. Using multi-source power grid operating condition data and artificial intelligence technology, the system can generate more accurate emergency plans based on accurate power grid data and operating condition analysis. Compared with the traditional experience-based plan formulation method, this method can dynamically adjust the plan according to the real-time changes of the power grid, improving the effectiveness and pertinence of emergency measures. The method of this application can handle different types of power grid failures and emergencies by constructing a multi-dimensional data evaluation system, and flexibly adapt to the complex situations in power grid operation. Through data analysis and template matching, the system can automatically generate emergency plans that meet actual needs in various complex power grid environments, ensuring the comprehensiveness and adaptability of emergency response.

[0059] The method of this application can not only effectively improve the response speed and accuracy of power grid emergency plans, but also automatically generate emergency plans with strong adaptability and high operability in complex and dynamic power grid environments, greatly improving the emergency management level and fault response ability of the power grid. Brief Description of the Drawings

[0060] Figure 1 It is a schematic diagram of the steps of a method for generating an emergency plan based on artificial intelligence provided by an embodiment of this application;

[0061] Figure 2 It is a schematic diagram of the functional modules of a system for generating an emergency plan based on artificial intelligence provided by an embodiment of this application. Detailed Embodiments

[0062] The terms used in the following embodiments are only for the purpose of describing specific embodiments and are not intended to limit this application. As used in the specification and appended claims of this application, the singular forms "a", "an", "", "the above", "the" and "this" are also intended to include expressions such as "one or more", unless the context clearly indicates otherwise. It should also be understood that in the following embodiments of this application, "at least one" and "one or more" mean one or more than two (including two). The character " / " generally indicates an "or" relationship between the associated objects before and after.

[0063] Next, the technical solutions in the embodiments of this application will be described in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments.

[0064] Hereinafter, the terms "first", "second", etc. are only used for convenience of description and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second", etc. may explicitly or implicitly include one or more such features. In the description of the present application, unless otherwise specified, the meaning of "a plurality of" is two or more. For example, a plurality of processing units means two or more processing units.

[0065] In addition, in the embodiments of the present application, "up", "down", "left", and "right" are not defined only by the orientation of the components in the relative drawings. It should be understood that these directional terms may be relative concepts, which are used for relative description and clarification, and may change accordingly with the change of the orientation of the components in the drawings. In the drawings, for clarity, the thickness of the layers and regions is exaggerated, and the dimensional proportional relationship between the various parts in the drawings does not reflect the actual dimensional proportional relationship.

[0066] In the embodiments of the present application, unless otherwise clearly specified and limited, the term "connection" should be understood in a broad sense. For example, "connection" may be a fixed connection, a detachable connection, or integrated; it may be directly connected or indirectly connected through an intermediate medium. In addition, the term "electrical connection" may be a direct electrical connection or an indirect electrical connection through an intermediate medium.

[0067] In the embodiments of the present application, the term "module" is usually a functional structure divided according to logic, and this "module" may be implemented by pure hardware, or implemented by combination of software and hardware. In the embodiments of the present application, "and / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, B exists alone, and A and B exist simultaneously, these three situations.

[0068] In the embodiments of the present application, words such as "exemplary" or "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as more preferred or more advantageous than other embodiments or design solutions. Exactly speaking, using words such as "exemplary" or "for example" aims to present relevant concepts in a specific way.

[0069] Hereinafter, the technical solutions in the present application will be described in conjunction with the drawings.

[0070] With the continuous expansion of the power grid scale and the increase in operation complexity, the operation risks and fault types faced by the power grid show a trend of diversification and complexity. Common emergencies include equipment failures, load fluctuations, natural disasters, etc. These events pose huge challenges to the safe and stable operation of the power grid. At present, the formulation and implementation of emergency plans often rely on manual experience and existing rules, resulting in problems such as low response efficiency, insufficient accuracy of the plans, and difficulty in adapting to complex scenarios. Traditional methods for formulating emergency plans cannot analyze a large amount of multi-source heterogeneous data in real time, nor can they adjust the plans in a timely manner to cope with the rapidly changing power grid operating conditions, leading to poor emergency response speed and accuracy when the power grid encounters emergencies. In addition, the emergency plans of traditional methods often lack comprehensive consideration of the integrity, chain effect, and complex factors of the power grid system, easily resulting in unreasonable resource allocation or unrealistic response measures, further increasing the vulnerability of the power grid in disasters or faults.

[0071] Based on this, the inventors proposed the inventive concept of this application: by constructing multiple sub-databases of power grid operating conditions, combining the sources and characteristics of the data, automatically identifying the target emergency scenarios and matching suitable plan templates, generating a preliminary emergency plan, and ensuring its accuracy and feasibility through data quality verification. Finally, the optimized power grid emergency plan will be output in a visual form, facilitating rapid decision-making and execution by emergency response personnel, effectively enhancing the intelligence and flexibility of power grid emergency management.

[0072] Refer to Figure 1 , an embodiment of the present invention provides an artificial intelligence-based emergency plan generation method, which is applied to a server and specifically may include the following steps:

[0073] S101: Obtain multi-source power grid operating condition data for the current data collection cycle, and construct multiple sub-databases of power grid operating conditions according to the categories of the multi-source power grid operating condition data.

[0074] In this embodiment, first, it is necessary to obtain real-time monitoring data from each data collection point in the power grid. These data include, but are not limited to, information such as voltage, current, frequency, load, temperature, equipment operating status, and environmental conditions. Through the data collection system, data from different devices and sensors are collected in real time to ensure the timeliness and accuracy of the data. Then, the collected power grid operating condition data is classified and processed according to its categories, and divided into multiple sub-databases of different types. Each sub-database is divided according to the characteristics and uses of the data. For example, it can be divided into substation data, transmission line data, distribution network data, etc. according to the equipment type; it can also be classified according to the power grid operating status, such as normal operating conditions, fault conditions, load fluctuations, etc. These sub-databases store different types of operating condition data respectively, and provide data support for subsequent emergency plan generation and analysis. Specifically, it includes:

[0075] S1011: Import the initial multi-source power grid operating condition data, and select the target multi-source power grid operating condition data from the initial multi-source power grid operating condition data by means of random selection;

[0076] S1012: Import the initial multi-source power grid operating condition data, and select the target multi-source power grid operating condition data from the initial multi-source power grid operating condition data by means of weight information and sampling selection.

[0077] In the implementation manners of S1011 to S1012, when determining the multi-source power grid operating condition data for the current data collection period, there are two implementation manners. One is to first import the initial multi-source power grid operating condition data, which can come from various monitoring points of the power grid, including multiple parameters such as load, voltage, current, frequency, etc., and may include data from different sources, such as real-time monitoring data, historical data, etc. Then, by means of random selection, a part of the data is randomly selected from these initial data as the target multi-source power grid operating condition data. The purpose of this manner is to obtain data samples in an unbiased manner for use in further analysis and emergency plan generation. This method is applicable to scenarios that require wide coverage of the data range and do not depend on specific data characteristics. The other is to introduce weight information for data selection in addition to importing the initial multi-source power grid operating condition data. The weight information is usually based on the importance of various power grid operating condition data, data quality, or the impact degree on emergency response. For example, some data may involve the operating status of key power grid equipment and have a higher weight; while other data may involve secondary equipment or external environmental factors and have a lower weight. In the data selection process, the target multi-source power grid operating condition data is selected from the initial data set by means of weighted sampling. This method can ensure that the selected data is more representative and has higher analysis value, thereby improving the accuracy and effectiveness of subsequent emergency plan generation.

[0078] S102: Determine the target emergency scenario and the target emergency plan template matching the target emergency scenario according to the data source and data characteristics of the multi-source power grid operating condition data.

[0079] In this embodiment, it is first necessary to conduct a detailed analysis of the imported multi-source power grid operating conditions data. According to the data sources (such as real-time monitoring data, historical operation data, meteorological warning data, etc.) and data characteristics (such as voltage, current, load fluctuations, equipment failures, etc.), the operating state of the current power grid is classified and evaluated. By analyzing the change trends, abnormal conditions and interrelationships of these data, potential target emergency scenarios are identified using algorithms or rules. For example, when the voltage fluctuates continuously and the load increases sharply, it may trigger emergency scenarios such as "power overload" or "equipment failure". Once the target emergency scenario is determined, the system selects one or more emergency plan templates that match this scenario from the preset emergency plan template library. Each template usually contains specific emergency response steps, resource allocation plans, time response requirements, etc., and is highly compatible with specific scenario types (such as equipment failures, load fluctuations, natural disasters, etc.). Template matching is not only based on the type of emergency scenario, but also takes into account the characteristics of the operating conditions data, such as the degree of data abnormality, equipment importance, fault impact scope, etc. Finally, the system generates a target emergency plan template suitable for the current power grid operating conditions based on the matching results, providing a basis for subsequent plan generation and implementation. The specific steps include:

[0080] S1021: Obtain the data source identifier of the multi-source power grid operating conditions data and the data characteristics of the multi-source power grid operating conditions data;

[0081] S1022: Determine the target emergency plan template from multiple alternative emergency plan templates with the data source identifier as the first index and the data characteristics as the second index.

[0082] In the implementation manners of S1021 to S1022, first, it is necessary to extract the data source identifier and data features from the collected multi-source power grid operating condition data. The data source identifier indicates the source of the data, such as a real-time monitoring system, meteorological data, external environmental conditions, etc. The data features refer to the key parameters or characteristic values extracted from the data, such as voltage, current, frequency, load fluctuation, etc. These features can reflect the operating state of the power grid. By analyzing the source and features of the data, the current operating condition of the power grid can be more comprehensively understood, providing an effective basis for subsequent emergency scenario recognition and template matching. Using the data source identifier as the first index and the data features as the second index, screening and matching are performed from multiple alternative emergency plan templates. Through this dual-index method, the system can more accurately select the most suitable emergency plan template according to the current operating condition of the power grid. Specifically, first, the applicable scope of the plan template is determined according to the data source identifier. For example, fault data from a substation may correspond to a certain type of plan, while external environmental data from a meteorological warning may correspond to another type of plan. Then, specific emergency response measures are further screened through the data features. For example, when voltage fluctuations and load imbalance are identified, the system will select an emergency plan template for dealing with power overload or voltage instability. In this way, it is ensured that the selected target emergency plan template can accurately match the specific operating condition of the current power grid, improving the practicality and accuracy of the plan.

[0083] As an example, assume that the current abnormal situation is that the equipment in the substation has an over-high temperature. The system first screens out the plan templates related to the substation equipment according to the data source identifier being "substation". Then, the system further matches according to the data features (such as abnormal temperature of substation equipment). If the equipment temperature is too high, the system will screen out the templates related to equipment overheating and fault response from the alternative emergency plan templates. Through this dual-index matching, the system finally selects an emergency plan template related to overheating of substation equipment. This template may include emergency cooling measures, equipment shutdown, activation of standby equipment and other emergency response plans to ensure that it can quickly respond and effectively solve the current power grid fault. Through this method, the system can automatically select the most suitable emergency plan template according to the actual operating condition and data characteristics of the power grid, improving the accuracy and timeliness of emergency response.

[0084] S103: The target emergency plan template calls the multi-source power grid operating condition data in the power grid operating condition sub-database to generate an initial power grid emergency plan.

[0085] In this embodiment, the system will call relevant multi-source power grid condition data from the power grid condition sub-database according to the selected target emergency plan template. The target emergency plan template defines the response measures and steps for specific emergency scenarios, while the multi-source power grid condition data provides detailed information on the actual state of the current power grid. The system calls the multi-source data stored in the power grid condition sub-database according to the requirements of the plan template. This data may cover key parameters such as the real-time voltage, current, load conditions, equipment status, and operation mode of the power grid. For example, when the emergency plan template needs to judge whether the voltage is too high or the load is overloaded, the system will extract the corresponding voltage and load data from the power grid condition sub-database.

[0086] The system combines the multi-source data obtained from the power grid condition sub-database with the response measures in the target emergency plan template to generate an initial power grid emergency plan. This preliminary plan will customize specific emergency operation steps according to the current condition of the power grid, such as adjusting the load, enabling standby power sources, switching the power grid routing, or shutting down faulty equipment. The initial power grid emergency plan takes into account the specific operating state of the power grid and the current type of fault or risk, ensuring that the implementation of the plan can mitigate power grid faults to the greatest extent or avoid system collapse.

[0087] The specific steps include:

[0088] S1031: According to the data categories of the multi-source power grid condition data, the target emergency plan template imports the data in the power grid condition sub-database into multiple sub-functional areas respectively;

[0089] S1032: Each sub-functional area analyzes and automatically writes the fault problems according to its corresponding target task, and generates the corresponding initial power grid emergency plan.

[0090] In the implementation manners of S1031 to S1032, the target emergency plan template distributes the data extracted from the power grid condition sub-database to multiple sub-functional areas for processing according to different categories of the multi-source power grid condition data. These sub-functional areas are respectively responsible for processing different types of tasks, such as load management, equipment fault analysis, power dispatching, environmental impact assessment, etc. The data types and functional modules processed by each sub-functional area are different, enabling it to focus on specific power grid condition characteristics and perform efficient analysis according to its target task. For example:

[0091] Load management sub-functional area: Receives data related to the power grid load, such as current load, load fluctuation, load prediction, etc., and is responsible for analyzing whether the power grid load exceeds the load capacity and whether load switching or load reduction is required.

[0092] Equipment Fault Analysis Sub - function Area: Receive data related to the status of power grid equipment, such as equipment operation status, current, voltage fluctuations, etc., conduct equipment fault analysis, and determine whether there are problems such as equipment overload and equipment faults.

[0093] Power Dispatching Sub - function Area: Analyze the dispatching requirements of the power grid, including the activation of standby power supplies, adjustment of power flow directions, etc., to ensure the stability and security of the power grid.

[0094] Environmental Impact Assessment Sub - function Area: Receive data related to meteorological and environmental factors, such as wind speed, rainfall, etc., evaluate their potential impact on the power grid, and determine whether external environmental emergency responses are required.

[0095] In this way, data is reasonably allocated and processed, ensuring that each sub - function area focuses on handling specific types of problems and providing strong data support for generating accurate emergency plans.

[0096] Each sub - function area conducts fault analysis and automatically generates an initial power grid emergency plan according to its responsible tasks. These tasks include identifying anomalies from data, analyzing the causes of faults, evaluating the effectiveness of emergency measures, etc. The specific operation process is as follows:

[0097] Each sub - function area will first analyze the fault problems according to the input data (such as load, equipment status, environmental conditions, etc.). For example, the load management area may analyze whether there is an overload risk in the power grid, the equipment fault analysis area may analyze whether there are faults or anomalies in the power grid equipment, and the power dispatching area may evaluate the effectiveness of the current power flow direction and dispatching strategy.

[0098] After the analysis is completed, the sub - function area will automatically generate a preliminary emergency plan according to the preset templates and rules. These plans will include specific operation steps required to solve the faults, such as load adjustment, power source switching, equipment repair, activation of standby systems, etc. At the same time, the system will also consider the safety and stability of the power grid to ensure that the generated emergency plan can effectively respond to the current power grid risks. Finally, each sub - function area generates the corresponding initial power grid emergency plan according to its analysis results and the automatically written content. These plans will cover all aspects of power grid restoration and provide operational plans and strategies for subsequent emergency responses.

[0099] In a feasible implementation manner, the sub - function area conducts the analysis of fault problems and automatic writing according to its corresponding target tasks, and generates the corresponding initial power grid emergency plan, including:

[0100] S10321: Conduct feature analysis according to the target category and determine the target task value;

[0101] S10322: Locate the position corresponding to the target task value in the power grid operating condition sub - database;

[0102] S10323: Automatically compile descriptive text based on the position corresponding to the target task value to generate an initial power grid emergency plan.

[0103] In the implementation manners of S10321 to S10323, the system first performs feature analysis on relevant data according to the category of the target emergency task. The target task value refers to the key numerical value or index related to a specific emergency task, and these values are used to evaluate the current power grid condition and guide the emergency response. For example, if the target task is a load overload problem, the target task value can be the ratio of the current power grid load to the rated load; if it is an equipment failure problem, the target task values can be parameters such as the temperature, current, and vibration of the equipment. Through the feature analysis of the power grid condition data, the system identifies the key data values related to the emergency task and determines these task values as the basis for subsequent steps. Then, based on the target task value determined in the previous step, the system locates the corresponding data position in the power grid condition sub-database. Since the power grid condition data is usually stored in multiple different data sub-databases, the system needs to accurately find the specific data entries or records related to the target task value. For example, if the target task value is the ratio of power grid load overload, the system will search for the historical and real-time load data related to this ratio in the load monitoring sub-database, or search for the records of the current load status in the equipment status database. Through accurate positioning, the system can extract the data closely related to the target task value, providing a basis for the generation of the next emergency plan. Finally, based on the previously located target task value and its corresponding power grid condition data, the system automatically generates descriptive text related to this emergency task. These texts will detail the contents and specific implementation measures of the emergency plan. For example, if the target task is to solve the power grid overload problem, the system will generate descriptive text based on the load data, such as: "The current power grid load exceeds the rated value by 90%, and it is recommended to take measures such as load shunting, enabling standby power sources, or dispatching standby lines." The system will combine the context of the data and automatically generate actionable emergency plan content, including fault analysis, affected areas, and recommended emergency steps. Finally, the automatically compiled descriptive text will be combined with the target task value to generate a preliminary power grid emergency plan.

[0104] In a feasible implementation manner, performing feature analysis according to the target category and determining the target task value includes:

[0105] Determine the corresponding first-point estimate value and second-point estimate value of the target category according to the target category;

[0106] Generate a grouping table and grouping description according to the first-point estimate value;

[0107] Generate a trend chart and trend description according to the second-point estimate value.

[0108] In this embodiment, first, the system selects corresponding estimated values according to the category of the target task. These estimated values are used to quantify the current operating conditions and potential risks of the power grid. For example, if the target task is to handle the problem of power grid load overload, the first point estimate of the target category can be the "current load value", and the second point estimate can be the "load increase rate" or "load trend". By determining these estimated values, the system can obtain the basic information of the power grid operating state, which serves as the basis for subsequent analysis and decision-making. Then, based on the first point estimate (such as the "current load value"), the system divides the data into several categories or groups. Each group represents a different power grid load state or risk level, such as "low load", "normal load", and "overload". The system will automatically generate a grouping table, which lists the power grid data intervals under different load states and generates corresponding grouping descriptions for each group to explain the power grid operating conditions under different load conditions. For example, the system may generate the following description: "When the load value is less than 80%, the power grid is in a low load state with high operating safety; when the load value exceeds 100%, the power grid is in an overload state and may cause failures." Based on the second point estimate (such as the "load increase rate" or "load trend"), the system analyzes the change trend of the power grid load over time and generates a trend chart. The trend chart can visually reflect the change of the power grid load, such as the growth, decrease, or fluctuation trend of the load. According to the trend chart, the system also generates a trend description to describe the dynamic situation of the load change and its possible impacts. For example, the system may generate the description text: "The load growth rate is 5% per hour, and it is expected that the load will reach 95% within the next 3 hours. Load balancing or starting standby equipment is required to avoid overloading." Through these steps, the system can deeply analyze the current state and trend of the power grid based on the estimated values of the target task values and generate intuitive and detailed analysis results.

[0109] S104: Check the initial power grid emergency plan according to the data quality evaluation report and output the target power grid emergency plan.

[0110] In this embodiment, first, the system comprehensively evaluates the quality of the data extracted from the power grid operating condition sub-database according to the data quality evaluation report, including the accuracy, integrity, consistency, timeliness, etc. of the data. The data quality evaluation report may point out outliers, missing values or errors in the data, thus helping the system identify and correct potential problems. Next, the system will modify and optimize the analysis results and recommended measures of the initial power grid emergency plan according to the data quality evaluation report to ensure the accuracy and operability of the plan. If loopholes or inadaptability to the current power grid state are found in the preliminarily generated emergency plan during the verification process, the system will adjust the plan according to the feedback provided by the data quality evaluation report. Finally, after the data quality verification is completed, the system will output the target power grid emergency plan after verification and modification. Specifically, it includes:

[0111] S1041: Evaluate the initial power grid emergency plan in multiple dimensions according to the data quality evaluation report;

[0112] S1042: Determine the evaluation score of the initial power grid emergency plan according to the multi-dimensional evaluation results, and determine the initial power grid emergency plan with an evaluation score greater than the threshold as the target power grid emergency plan.

[0113] In the implementation manners of S1041 to S1042, the system evaluates the initial power grid emergency plan in multiple dimensions according to the data quality evaluation report. The purpose of this process is to comprehensively evaluate the effectiveness and feasibility of the plan from different perspectives. The multi-dimensional evaluation includes but is not limited to the following aspects: data accuracy, data integrity, timeliness of plan response, executability of various measures in the plan, matching degree between the plan and the actual power grid operating conditions, etc. Through the multi-dimensional evaluation, the system can identify potential problems or optimization spaces in the plan, ensure that the plan can cover all key areas during execution, and effectively respond to various complex situations in the power grid. Then, the system quantitatively evaluates the initial power grid emergency plan according to the multi-dimensional evaluation results in the previous step and generates an evaluation score. The evaluation score is comprehensively calculated based on multiple indicators (such as accuracy, timeliness, execution difficulty, etc.) and can reflect the overall quality and actual feasibility of the initial plan. The system compares the evaluation score according to the set threshold standard. If the evaluation score is greater than the predetermined threshold, it means that the initial power grid emergency plan meets the requirements and can be used as the final target power grid emergency plan. If the evaluation score is lower than the threshold, it indicates that there are potential problems in the initial emergency plan and it may need to be further optimized or modified. After being determined as the target power grid emergency plan, the plan will enter the subsequent execution stage to ensure effective implementation in case of power grid failures or emergencies.

[0114] S105: Output the target power grid emergency plan as visual information and send it to the emergency handlers.

[0115] In this embodiment, the system converts the confirmed target power grid emergency plan into a visual format so that emergency response personnel can quickly understand and take corresponding emergency measures. The visual information includes but is not limited to forms such as charts, flowcharts, alarm information, task lists, etc. These contents can intuitively display the current operating conditions of the power grid, the key steps of emergency response, the priority of resource scheduling, etc. The system will automatically generate an easy-to-understand graphical interface or dashboard to display the operations in the emergency plan, ensuring that emergency response personnel can obtain key information in the shortest time and execute corresponding operations according to the instructions in the plan. The generated visual information will be sent to relevant emergency response personnel through appropriate channels (such as emails, text messages, dedicated emergency platforms, etc.) to ensure that they can respond in a timely manner and take necessary measures to avoid larger-scale failures or damages to the power grid.

[0116] The method for generating an emergency plan based on artificial intelligence provided by this application can significantly reduce manual intervention and response time and improve the speed of emergency response by automatically generating a power grid emergency plan. Based on real-time and historical data analysis, the system can adjust and optimize the emergency plan in real time to ensure that the power grid can respond quickly and effectively in case of emergencies. Using multi-source power grid operating condition data and artificial intelligence technology, the system can generate a more accurate emergency plan based on accurate power grid data and operating condition analysis. Compared with the traditional experience-based plan formulation method, this method can dynamically adjust the plan according to the real-time changes of the power grid, improving the effectiveness and pertinence of emergency measures. The method of this application can handle different types of power grid faults and emergencies and flexibly adapt to the complex situations in power grid operation by constructing a multi-dimensional data evaluation system. Through data analysis and template matching, the system can automatically generate an emergency plan that meets the actual needs in various complex power grid environments, ensuring the comprehensiveness and adaptability of emergency response.

[0117] The method of this application can not only effectively improve the response speed and accuracy of the power grid emergency plan, but also automatically generate an emergency plan with strong adaptability and high operability in a complex and dynamic power grid environment, greatly improving the emergency management level and fault response ability of the power grid.

[0118] In a second aspect, based on the same inventive concept, referring to Figure 2 , there is shown an emergency plan generation system 200 based on artificial intelligence provided by an embodiment of this application. The system includes:

[0119] An acquisition module 201, configured to acquire multi-source power grid operating condition data of the current data acquisition cycle and construct a plurality of power grid operating condition sub-databases according to the categories of the multi-source power grid operating condition data;

[0120] A matching module 202, configured to determine a target emergency scenario and a target emergency plan template matching the target emergency scenario according to the data source and data characteristics of the multi-source power grid condition data;

[0121] A generating module 203, configured to call the multi-source power grid condition data in the power grid condition sub-database by using the target emergency plan template to generate an initial power grid emergency plan;

[0122] A verification module 204, configured to verify the initial power grid emergency plan according to the data quality evaluation report and output a target power grid emergency plan;

[0123] A sending module 205, configured to output the target power grid emergency plan as visual information and send it to emergency handlers.

[0124] In an embodiment of the second aspect of the present application, the obtaining module includes:

[0125] A first obtaining sub-module, configured to import initial multi-source power grid condition data and randomly select target multi-source power grid condition data from the initial multi-source power grid condition data;

[0126] A second obtaining sub-module, configured to import initial multi-source power grid condition data and select target multi-source power grid condition data from the initial multi-source power grid condition data by using the weight information and sampling selection.

[0127] In an embodiment of the second aspect of the present application, the matching module includes:

[0128] A third obtaining sub-module, configured to obtain the data source identifier of the multi-source power grid condition data and the data characteristics of the multi-source power grid condition data;

[0129] A matching sub-module, configured to determine the target emergency plan template from multiple alternative emergency plan templates by using the data source identifier as the first index and the data characteristics as the second index.

[0130] In an embodiment of the second aspect of the present application, the generating module includes:

[0131] A data import sub-module, configured to import the data in the power grid condition sub-database into the multiple sub-functional areas respectively according to the data category of the multi-source power grid condition data by using the target emergency plan template;

[0132] An analysis sub-module, configured to perform analysis and automatic writing of fault problems in each sub-functional area according to its corresponding target task and generate a corresponding initial power grid emergency plan.

[0133] In an embodiment of the second aspect of the present application, the analysis sub-module includes:

[0134] A feature analysis unit, configured to perform feature analysis according to a target category and determine a target task value;

[0135] A location analysis unit, configured to locate a position corresponding to the target task value in the power grid condition sub-database;

[0136] A compilation unit, configured to automatically compile descriptive text based on the position corresponding to the target task value and generate an initial power grid emergency plan.

[0137] In an embodiment of the second aspect of the present application, the feature analysis unit includes:

[0138] A determination subunit, configured to determine a corresponding first point estimate value and second point estimate value of the target category according to the target category;

[0139] A first description subunit, configured to generate a grouping table and a grouping description according to the first point estimate value;

[0140] A second description subunit, configured to generate a trend chart and a trend description according to the second point estimate value.

[0141] In an embodiment of the second aspect of the present application, the verification module includes:

[0142] An evaluation sub-module, configured to perform multi-dimensional evaluation on the initial power grid emergency plan according to a data quality evaluation report;

[0143] A screening sub-module, configured to determine an evaluation score of the initial power grid emergency plan according to the multi-dimensional evaluation result, and determine the initial power grid emergency plan with the evaluation score greater than a threshold as the target power grid emergency plan.

[0144] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable systems. The computer program or instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer program or instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0145] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. Additionally, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood by referring to the context.

[0146] In this application, "at least one" means one or more, and "multiple" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0147] It should be understood that in various embodiments of this application, the magnitudes of the serial numbers of the above processes do not imply the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of this application.

[0148] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0149] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, systems, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0150] In the several embodiments provided in this application, it should be understood that the disclosed systems, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of systems or units can be electrical, mechanical, or other forms.

[0151] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units. That is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0152] In addition, the functional units in each embodiment of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0153] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art or part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of this application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes. As described above, this is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. An artificial intelligence-based emergency plan generation method, characterized in that: The method comprises: Acquire multi-source power grid operating condition data in a current data acquisition cycle, and construct multiple power grid operating condition sub-databases according to categories of the multi-source power grid operating condition data; Determining a target emergency scenario and a target emergency plan template matching the target emergency scenario based on data sources and data characteristics of the multi-source power grid operating condition data; The target emergency plan template calls the multi-source power grid operating condition data in the power grid operating condition sub-database to generate an initial power grid emergency plan; Verify the initial power grid emergency plan according to the data quality evaluation report, and output a target power grid emergency plan, where the target power grid emergency plan is an unexecuted plan; Outputting the target power grid emergency plan as visual information to emergency response personnel; The target emergency plan template includes multiple sub-functional areas. The target emergency plan template calls the multi-source power grid operating condition data in the power grid operating condition sub-database to generate an initial power grid emergency plan, including: The target emergency plan template imports the data in the power grid operating condition sub-database into the multiple sub-functional areas respectively according to the data categories of the multi-source power grid operating condition data; Each of the sub-functional areas performs fault problem analysis and automatic writing according to its corresponding target task, and generates a corresponding initial power grid emergency plan; The sub-functional area performs fault problem analysis and automatic writing according to its corresponding target task, and generates a corresponding initial power grid emergency plan, including: Perform feature analysis based on target categories and determine target task values; Locating a position corresponding to the target task value in the power grid operating condition sub-database; Based on the position corresponding to the target task value, automatically write the descriptive text to generate an initial power grid emergency plan; Perform feature analysis based on target categories and determine target task values, including: Determining, according to the target category, a first point estimate and a second point estimate of the corresponding target category; generating a grouping table and a grouping description according to the first point estimate; generating a trend graph and a trend description based on the second point estimate; The verifying the initial power grid emergency plan according to the data quality evaluation report and outputting a target power grid emergency plan includes: Conduct a multi-dimensional evaluation of the initial power grid emergency plan based on the data quality evaluation report; An evaluation score of the initial power grid emergency plan is determined according to the multi-dimensional evaluation result, and the initial power grid emergency plan having the evaluation score greater than a threshold is determined as the target power grid emergency plan.

2. The method according to claim 1, characterized in that Obtain multi-source power grid operating data for the current data collection cycle, including: Importing initial multi-source power grid operating condition data, and selecting target multi-source power grid operating condition data from the initial multi-source power grid operating condition data by random selection; Alternatively, initial multi-source power grid operating condition data is imported, and target multi-source power grid operating condition data is selected from the initial multi-source power grid operating condition data by using weight information and sampling selection.

3. The method according to claim 1, characterized in that The determining, based on the data sources and data characteristics of the multi-source power grid operating condition data, a target emergency scenario and a target emergency plan template matching the target emergency scenario includes: Obtaining a data source identifier of the multi-source power grid operating condition data and a data feature of the multi-source power grid operating condition data; The target emergency plan template is determined from a plurality of candidate emergency plan templates using the data source identifier as a first index and the data feature as a second index.

4. An artificial intelligence-based emergency plan generation system, characterized in that: For implementing the method of claims 1-3, the system comprises: an acquisition module, configured to acquire multi-source power grid operating condition data of a current data acquisition period, and construct a plurality of power grid operating condition sub-databases according to the categories of the multi-source power grid operating condition data; A matching module, configured to determine a target emergency scenario and a target emergency plan template matching the target emergency scenario based on data sources and data characteristics of the multi-source power grid operating condition data; A generation module, configured for the target emergency plan template to call the multi-source power grid operating condition data in the power grid operating condition sub-database to generate an initial power grid emergency plan; a verification module, configured to verify the initial power grid emergency plan according to the data quality evaluation report and output a target power grid emergency plan, wherein the target power grid emergency plan is an unexecuted plan; The sending module is used to output the target power grid emergency plan as visual information and send it to emergency handling personnel.

5. The system according to claim 4, characterized in that The acquisition module includes: A first acquisition submodule is configured to import initial multi-source power grid operating condition data and select target multi-source power grid operating condition data from the initial multi-source power grid operating condition data by random selection; The second acquisition submodule is configured to import initial multi-source power grid operating condition data and select target multi-source power grid operating condition data from the initial multi-source power grid operating condition data by using weight information and sampling selection.

6. The system according to claim 4, characterized in that The matching module includes: The third acquisition submodule is used to obtain the data source identifier of the multi-source power grid operating condition data and the data characteristics of the multi-source power grid operating condition data; the matching submodule is used to use the data source identifier as the first index and the data characteristics as the second index to determine the target emergency plan template from multiple alternative emergency plan templates.

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