Emergency plan generation method and system based on artificial intelligence

Abstract

The invention provides an emergency plan generation method and system based on artificial intelligence, and belongs to the technical field of electric power systems.The method comprises the steps that multi-source power grid working condition data of a current data collection period is obtained, and a plurality of power grid working condition sub-databases are constructed according to the category of the multi-source power grid working condition data, according to data sources and data features of the multi-source power grid working condition data, determining a target emergency scene and a target emergency plan template matched with the target emergency scene, calling the multi-source power grid working condition data in the power grid working condition sub-database by the target emergency plan template, generating an initial power grid emergency plan, and sending the initial power grid emergency plan to the power grid working condition sub-database; and verifying the initial power grid emergency plan according to the data quality evaluation report, outputting a target power grid emergency plan, outputting the target power grid emergency plan as visual information, and sending the visual information to emergency processing personnel. The invention aims to improve the problems of low response efficiency, insufficient plan accuracy, difficulty in adapting to complex scenes and the like of the existing scheme.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to an artificial intelligence-based emergency plan generation method and system. Background Art

[0002] As the scale of power grids continues to expand and the complexity of their operation increases, the operational risks and types of failures faced by power grids are becoming more diverse and complex. Common emergencies include equipment failures, load fluctuations, natural disasters, etc. These events have brought huge challenges to the safe and stable operation of power grids.

[0003] At present, the process of emergency plan formulation and implementation often relies on manual experience and existing rules, resulting in problems such as low response efficiency, insufficient plan accuracy, and difficulty in adapting to complex scenarios. Summary of the invention

[0004] The embodiment of the present application provides an emergency plan generation method and system based on artificial intelligence. The present application adopts the following technical solutions: In a first aspect, a method for generating an emergency plan based on artificial intelligence is provided, the method comprising: Acquire multi-source power grid condition data of the current data collection cycle, and construct multiple power grid condition sub-databases according to the categories of the multi-source power grid condition data; According to the data sources and data characteristics of multi-source power grid operating condition data, determine the target emergency scenario and the target emergency plan template matching the target emergency scenario; The target emergency plan template calls the multi-source power grid condition data in the power grid condition sub-database to generate an initial power grid emergency plan; Verify the initial power grid emergency plan based on the data quality evaluation report and output the target power grid emergency plan; The target power grid emergency plan is output as visual information and sent to emergency response personnel.

[0005] In an embodiment of the first aspect of the present application, obtaining multi-source power grid operating condition data in a current data acquisition cycle includes: 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; Or, 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 weight information and sampling selection.

[0006] In an embodiment of the first aspect of the present application, a target emergency scenario and a target emergency plan template matching the target emergency scenario are determined according to data sources and data characteristics of multi-source power grid operating condition data, including: Obtaining data source identifiers of multi-source power grid operating condition data and data characteristics of the multi-source power grid operating condition data; A target emergency plan template is determined from a plurality of candidate emergency plan templates using a data source identifier as a first index and a data feature as a second index.

[0007] In an embodiment of the first aspect of the present application, the target emergency plan template includes multiple sub-functional areas, and the target emergency plan template calls multi-source power grid condition data in the power grid condition sub-database to generate an initial power grid emergency plan, including: The target emergency plan template imports the data in the power grid condition sub-database into multiple sub-functional areas according to the data categories of the multi-source power grid condition data; Each sub-functional area performs fault problem analysis and automatic writing according to its corresponding target tasks, and generates the corresponding initial power grid emergency plan.

[0008] In an embodiment of the first aspect of the present application, the sub-functional area performs analysis and automatic writing of fault problems according to its corresponding target tasks, and generates a corresponding initial power grid emergency plan, including: Conduct feature analysis based on target categories and determine target task values; In the power grid condition sub-database, locate the position corresponding to the target task value; Based on the position corresponding to the target task value, the descriptive text is automatically written to generate the initial power grid emergency plan.

[0009] 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: According to the target category, determining a first point estimate and a second point estimate of the corresponding target category; Generate a grouping table and grouping description based on the first point estimate; Generate a trend graph and trend description based on the second point estimate.

[0010] In an embodiment of the first aspect of the present application, the initial power grid emergency plan is verified according to the data quality report, and the target power grid emergency plan is output, including: Conduct a multi-dimensional evaluation of the initial power grid emergency plan based on the data quality evaluation report; According to the multi-dimensional evaluation results, the evaluation score of the initial power grid emergency plan is determined, and the initial power grid emergency plan with an evaluation score greater than a threshold is determined as the target power grid emergency plan.

[0011] In the second aspect, based on the same inventive concept, an artificial intelligence-based emergency plan generation system is provided, the system comprising: An acquisition module is used to acquire multi-source power grid condition data in the current data acquisition cycle, and to construct multiple power grid condition sub-databases according to the categories of the multi-source power grid condition data; A matching module, used to determine a target emergency scenario and a target emergency plan template matching the target emergency scenario according to data sources and data characteristics of multi-source power grid operating condition data; A generation module, used for the target emergency plan template to call the multi-source power grid condition data in the power grid condition sub-database to generate an initial power grid emergency plan; A verification module is used to verify the initial power grid emergency plan according to the data quality evaluation report and output the target power grid emergency plan; The sending module is used to output the target power grid emergency plan as visual information and send it to emergency handling personnel.

[0012] In an embodiment of the second aspect of the present application, the acquisition module includes: A first acquisition submodule is used to import initial multi-source power grid operating data and select target multi-source power grid operating data from the initial multi-source power grid operating data by random selection; The second acquisition submodule is used to import initial multi-source power grid operating data, and select target multi-source power grid operating data from the initial multi-source power grid operating data by weight information and sampling selection.

[0013] In an embodiment of the second aspect of the present application, the matching module includes: A third acquisition submodule is used to obtain data source identifiers of multi-source power grid operating data and data features of multi-source power grid operating data; The matching submodule is used to determine a target emergency plan template from a plurality of candidate emergency plan templates using a data source identifier as a first index and a data feature as a second index.

[0014] In an embodiment of the second aspect of the present application, a generating module includes: The data import submodule is used for the target emergency plan template to import the data in the power grid condition subdatabase into multiple sub-functional areas according to the data categories of the multi-source power grid condition data; The analysis submodule is used for each sub-functional area to perform fault problem analysis and automatic writing according to its corresponding target tasks, and generate the corresponding initial power grid emergency plan.

[0015] In an embodiment of the second aspect of the present application, the analysis submodule includes: A feature analysis unit, used to perform feature analysis according to the target category and determine the target task value; A positioning analysis unit, used to locate the position corresponding to the target task value in the power grid condition sub-database; The writing unit is used to automatically write the description text based on the position corresponding to the target task value to generate an initial power grid emergency plan.

[0016] In an embodiment of the second aspect of the present application, the feature analysis unit includes: A determination subunit, configured to determine a first point estimate and a second point estimate of a corresponding target category according to the target category; A first description subunit, used for generating a grouping table and a grouping description according to the first point estimate; The second description subunit is used to generate a trend graph and a trend description according to the second point estimate.

[0017] In an embodiment of the second aspect of the present application, the verification module includes: The evaluation submodule is used to conduct a multi-dimensional evaluation of the initial power grid emergency plan based on the data quality evaluation report; The screening submodule is used to 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 a threshold as the target power grid emergency plan.

[0018] In summary, the above-mentioned artificial intelligence-based emergency plan generation method and system have the following technical effects: By automatically generating emergency plans for power grids, human intervention and response time can be significantly reduced, and the speed of emergency response can be improved. 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 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-making 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. By constructing a multi-dimensional data evaluation system, the method of this application can handle different types of power grid failures and emergencies, and flexibly adapt to 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.

[0019] The method of the present 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 capability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic diagram of the steps of an artificial intelligence-based emergency plan generation method provided in an embodiment of the present application; Figure 2 A schematic diagram of the functional modules of an emergency plan generation system based on artificial intelligence provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] The terms used in the following embodiments are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "", "above", "the" and "this" are intended to also include expressions such as "one or more", unless there is a clear contrary indication in the context. It should also be understood that in the following embodiments of the present application, "at least one", "one or more" refer to one or more than two (including two). The character " / " generally indicates that the objects associated before and after are in a "or" relationship.

[0022] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0023] In the following, the terms "first", "second", etc. are used only for convenience of description and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more. For example, a plurality of processing units refers to two or more processing units.

[0024] In addition, in the embodiments of the present application, "upper", "lower", "left" and "right" are not limited to being defined relative to the orientation of the components schematically placed in the drawings. It should be understood that these directional terms can be relative concepts, which are used for description and clarification relative to the components, and can change accordingly according to the change in the orientation of the components placed in the drawings. In the drawings, for the sake of clarity, the thickness of the layers and regions is exaggerated, and the size ratio relationship between the parts in the drawings does not reflect the actual size ratio relationship.

[0025] 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" can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection or an indirect connection through an intermediate medium. In addition, the term "electrical connection" can be a direct electrical connection or an indirect electrical connection through an intermediate medium.

[0026] In the embodiments of the present application, the term "module" is generally a functional structure divided according to logic, and the "module" can be implemented by pure hardware, or by a 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 can exist. For example, A and / or B can represent: A exists alone, B exists alone, and A and B exist at the same time.

[0027] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0028] The technical solution in this application will be described below in conjunction with the accompanying drawings.

[0029] With the continuous expansion of the scale of power grids and the increase in operational complexity, the operational risks and fault types faced by power grids are becoming more diversified and complex. Common emergencies include equipment failures, load fluctuations, natural disasters, etc. These events have brought huge challenges to the safe and stable operation of power grids. At present, the formulation and implementation of emergency plans often rely on manual experience and existing rules, and there are problems such as low response efficiency, insufficient plan accuracy, and difficulty in adapting to complex scenarios. Traditional emergency plan formulation methods cannot analyze a large amount of multi-source heterogeneous data in real time, and it is difficult to adjust the plan in time to cope with rapidly changing power grid conditions, resulting in poor emergency response speed and accuracy of the power grid when encountering emergencies. In addition, the plans of traditional methods often lack comprehensive consideration of the integrity, chain effects and complex factors of the power grid system, which can easily lead to unreasonable resource allocation or unrealistic response measures, further increasing the vulnerability of the power grid in disasters or failures.

[0030] Based on this, the inventor proposed the inventive concept of this application: by constructing multiple power grid operating sub-databases, combining the source and characteristics of the data, automatically identifying the target emergency scenario and matching the appropriate plan template, generating a preliminary emergency plan, and ensuring its accuracy and feasibility through data quality verification. Ultimately, the optimized power grid emergency plan will be output in a visual form, facilitating emergency response personnel to make quick decisions and execute, effectively improving the intelligence and flexibility of power grid emergency management.

[0031] Reference Figure 1 The embodiment of the present invention provides an artificial intelligence-based emergency plan generation method, which is applied to a server and may specifically include the following steps: S101: Acquire multi-source power grid operating condition data in a current data collection cycle, and construct multiple power grid operating condition sub-databases according to the categories of the multi-source power grid operating condition data.

[0032] In this embodiment, it is first necessary to obtain real-time monitoring data from various data collection points in the power grid. These data include but are not limited to information such as voltage, current, frequency, load, temperature, equipment operating status, environmental conditions, etc. Through the data acquisition 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 data is classified and processed according to the category, and divided into multiple different types of sub-databases. 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 data respectively, and provide data support for subsequent emergency plan generation and analysis. It specifically includes: S1011: 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; S1012: 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 means of weight information and sampling selection.

[0033] In the implementation of S1011 to S1012, when determining the multi-source power grid condition data of the current data acquisition cycle, there can be two implementation methods. One is to first import the initial multi-source power grid 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 contain data from different sources, such as real-time monitoring data, historical data, etc. Then, a random selection method is adopted to randomly select a part of the data from these initial data as the target multi-source power grid condition data. The purpose of this method is to obtain data samples in an unbiased manner for use in further analysis and emergency plan generation. This method is suitable for scenarios that require a wide range of data coverage and do not rely on specific data features. Another method is to introduce weight information for data selection in addition to importing the initial multi-source power grid condition data. Weight information is usually based on the importance of various types of power grid condition data, data quality, or the degree of impact 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 condition data is selected from the initial data set through weighted sampling. This method can ensure that the selected data is more representative and has higher analytical value, thereby improving the accuracy and effectiveness of subsequent emergency plan generation.

[0034] S102: Determine a target emergency scenario and a target emergency plan template matching the target emergency scenario according to data sources and data characteristics of the multi-source power grid operating condition data.

[0035] In this embodiment, it is first necessary to conduct a detailed analysis of the imported multi-source power grid condition data. According to the data source (such as real-time monitoring data, historical operation data, meteorological warning data, etc.) and data characteristics (such as voltage, current, load fluctuation, equipment failure, etc.), the current power grid operation status is classified and evaluated. By analyzing the change trend, abnormal conditions and mutual relationship of these data, the potential target emergency scenario is identified by using algorithms or rules. For example, when the voltage continues to fluctuate and the load increases sharply, the emergency scenario of "power overload" or "equipment failure" may be triggered. Once the target emergency scenario is determined, the system will select one or more emergency plan templates matching the scenario from the preset emergency plan template library. Each template usually contains specific emergency response steps, resource allocation schemes, time response requirements, etc., and is highly consistent with specific scenario types (such as equipment failure, load fluctuation, natural disasters, etc.). Template matching is not only based on the type of emergency scenario, but also considers the characteristics of the operating condition data, such as the degree of abnormality of the data, the importance of the equipment, the scope of the fault impact, etc. Finally, the system generates a target emergency plan template suitable for the current power grid condition based on the matching results, providing a basis for subsequent plan generation and implementation. The specific steps include: S1021: Obtain data source identifiers of multi-source power grid operating condition data and data characteristics of the multi-source power grid operating condition data; S1022: Using the data source identifier as the first index and the data feature as the second index, determine a target emergency plan template from a plurality of candidate emergency plan templates.

[0036] In the implementation of S1021 to S1022, it is first necessary to extract the data source identifier and data features from the collected multi-source power grid operating 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 key parameters or characteristic values ​​extracted from the data, such as voltage, current, frequency, load fluctuations, etc. These features can reflect the operating status of the power grid. By analyzing the source and characteristics of the data, the current power grid operating conditions can be more comprehensively understood, providing an effective basis for subsequent emergency scene identification and template matching. Using the data source identifier as the first index and the data feature as the second index, multiple alternative emergency plan templates are screened and matched. Through this dual indexing method, the system can more accurately select the most suitable emergency plan template according to the current working conditions of the power grid. Specifically, the scope of application of the plan template is first determined according to the data source identifier. For example, the fault data from the substation may correspond to a certain type of plan, while the external environment data from the meteorological warning may correspond to another type of plan. Then, the specific emergency response measures are further screened out through data features. For example, when voltage fluctuations and load imbalances are identified, the system will select a 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 working conditions of the current power grid, improving the practicality and accuracy of the plan.

[0037] As an example, assuming that the current abnormal situation is that the temperature of the substation equipment is too high, the system first identifies the data source as "substation" and selects the emergency plan template related to the substation equipment. Then, the system further matches according to the data characteristics (such as abnormal temperature of substation equipment). If the equipment temperature is too high, the system will select templates related to equipment overheating and fault response from the alternative emergency plan templates. Through this double index (matching), the system finally selects an emergency plan template related to overheating of substation equipment. The template may include emergency response plans such as emergency cooling measures, equipment shutdown, and activation of standby equipment to ensure rapid response and effective resolution of current power grid failures. In this way, the system can automatically select the most suitable emergency plan template based on the actual working conditions and data characteristics of the power grid, thereby improving the accuracy and timeliness of emergency response.

[0038] S103: The target emergency plan template calls the multi-source power grid condition data in the power grid condition sub-database to generate an initial power grid emergency plan.

[0039] 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 status 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 operating mode of the power grid. For example, when the emergency plan template needs to determine 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.

[0040] 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 power grid conditions, such as adjusting loads, enabling backup power sources, switching power grid routes, or shutting down faulty equipment. The initial power grid emergency plan will take into account the specific operating status of the power grid and the current fault or risk type to ensure that the execution of the plan can minimize power grid failures or avoid system crashes.

[0041] The specific steps include: S1031: The target emergency plan template imports the data in the power grid condition sub-database into multiple sub-functional areas according to the data categories of the multi-source power grid condition data; S1032: Each 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.

[0042] In the implementation 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 multi-source power grid condition data. These sub-functional areas are responsible for processing different types of tasks, such as load management, equipment failure analysis, power dispatching, environmental impact assessment, etc. Each sub-functional area processes different data types and functional modules, and can focus on specific power grid condition characteristics and perform efficient analysis according to its target tasks. For example: Load management sub-functional area: Receives data related to the grid load, such as current load, load fluctuation, load forecast, etc., and is responsible for analyzing whether the grid load exceeds the load capacity and whether load switching or load reduction is required.

[0043] Equipment failure analysis sub-functional area: Receive data related to the status of power grid equipment, such as equipment operating conditions, current, voltage fluctuations, etc., conduct equipment failure analysis, and determine whether there are problems such as equipment overload and equipment failure.

[0044] Power dispatch sub-functional area: Analyze the dispatch needs of the power grid, including the activation of backup power sources, adjustment of power flow, etc., to ensure the stability and security of the power grid.

[0045] Environmental impact assessment sub-functional 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 an external environmental emergency response is required.

[0046] In this way, data is reasonably distributed and processed, ensuring that each sub-functional area focuses on dealing with specific types of problems and providing strong data support for the generation of accurate emergency plans.

[0047] Each sub-functional area performs fault analysis and automatically generates an initial power grid emergency plan based on its tasks. These tasks include identifying anomalies from data, analyzing fault causes, and evaluating the effectiveness of emergency measures. The specific operation process is as follows: Each sub-functional area will first analyze the fault problem based on its input data (such as load, equipment status, environmental conditions, etc.). For example, the load management area may analyze whether the power grid is at risk of overload, the equipment failure analysis area may analyze whether the power grid equipment is faulty or abnormal, and the power dispatching area may evaluate the effectiveness of the current power flow and dispatching strategy.

[0048] After the analysis is completed, the sub-functional area will automatically generate preliminary emergency plans based on preset templates and rules. These plans will include specific operational steps required to resolve the fault, such as load adjustment, power switching, equipment maintenance, and activation of backup systems. At the same time, the system will also take into account the security and stability of the power grid to ensure that the generated emergency plans can effectively respond to current power grid risks. Finally, each sub-functional area generates a corresponding initial power grid emergency plan based on its analysis results and automatically written content. These plans will cover all aspects of power grid restoration and provide actionable solutions and strategies for subsequent emergency responses.

[0049] In a feasible implementation, 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: S10321: Perform feature analysis according to the target category and determine the target task value; S10322: Locate the position corresponding to the target task value in the power grid operating condition sub-database; S10323: Based on the position corresponding to the target task value, automatically write the descriptive text to generate an initial power grid emergency plan.

[0050] In the implementation of S10321 to S10323, the system first performs feature analysis on the relevant data according to the category of the target emergency task. The target task value refers to the key numerical value or indicator related to the specific emergency task, which is 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 value can be the temperature, current, vibration and other parameters of the equipment. The system identifies the key data values ​​related to the emergency task through feature analysis of the power grid condition data, and determines these task values ​​as the basis for subsequent steps. Then, the system locates the corresponding data position in the power grid condition sub-database according to the target task value determined in the previous step. 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 historical and real-time load data related to the ratio in the load monitoring sub-database, or search for records of the current load status in the equipment status database. Through accurate positioning, the system can extract data closely related to the target task value, providing a basis for the next step of emergency plan generation. Finally, the system automatically generates descriptive text related to the emergency task based on the previously located target task value and its corresponding power grid condition data. These texts will describe in detail the contents of the emergency plan and specific implementation measures. For example, if the target task is to solve the problem of power grid overload, the system will generate descriptive text based on the load data, such as: "The current power grid load exceeds 90% of the rated value. It is recommended to take measures such as load diversion, enabling backup power supply or dispatching backup lines". The system will automatically generate actionable emergency plan content based on the context of the data, including fault analysis, possible affected areas, recommended emergency steps, etc. Finally, the automatically written descriptive text will be combined with the target task value to generate a preliminary power grid emergency plan.

[0051] In a feasible implementation, feature analysis is performed according to the target category, and the target task value is determined, including: According to the target category, determining a first point estimate and a second point estimate of the corresponding target category; Generate a grouping table and grouping description based on the first point estimate; Generate a trend graph and trend description based on the second point estimate.

[0052] In this embodiment, first, the system selects corresponding estimates according to the category of the target task. These estimates are used to quantify the current working conditions and potential risks of the power grid. For example, if the target task is to deal with the problem of power grid overload, the first point estimate of the target category can be "current load value", and the second point estimate can be "rate of load increase" or "load trend". By determining these estimates, the system can obtain basic information about the operating status of the power grid as a basis for subsequent analysis and decision-making. Then, based on the first point estimate (such as "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 load". The system automatically generates a group table, which lists the power grid data intervals under different load states, and generates corresponding group 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 and has high operational 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 "load increase rate" or "load trend"), the system will analyze the change trend of the power grid load over time and generate a trend chart. The trend chart can intuitively reflect the changes in the power grid load, such as the growth, decrease or fluctuation trend of the load. Based on the trend chart, the system will also generate a trend description to describe the dynamic situation of the load change and the possible impact. For example, the system may generate a description text: "The load growth rate is 5% per hour, and the load is expected to reach 95% in the next 3 hours. Load balancing or starting backup equipment is required to avoid overload." Through these steps, the system can conduct an in-depth analysis of the current state and trend of the power grid based on the estimated value of the target task value, and generate intuitive and detailed analysis results.

[0053] S104: Verify the initial power grid emergency plan according to the data quality evaluation report and output the target power grid emergency plan.

[0054] In this embodiment, first, the system conducts a comprehensive quality assessment of the data extracted from the power grid operating condition sub-database based on the data quality evaluation report, including the accuracy, completeness, consistency and timeliness of the data. The data quality evaluation report may point out outliers, missing values ​​or errors in the data, thereby helping the system to identify and correct potential problems. Next, the system will revise and optimize the various analysis results and recommended measures of the initial power grid emergency plan based on the data quality report to ensure the accuracy and operability of the plan. If the preliminary generated emergency plan is found to have loopholes or is not suitable for the current power grid status during the verification process, the system will adjust the plan based on the feedback provided by the data quality report. Finally, after the data quality verification is completed, the system will output the verified and corrected target power grid emergency plan. It specifically includes: S1041: Conduct a multi-dimensional evaluation of the initial power grid emergency plan based on the data quality evaluation report; S1042: Determine an evaluation score of the initial power grid emergency plan based on the multi-dimensional evaluation results, and determine the initial power grid emergency plan with an evaluation score greater than a threshold as the target power grid emergency plan.

[0055] In the implementation of S1041 to S1042, the system performs a multi-dimensional evaluation of the initial power grid emergency plan based on the data quality evaluation report. The purpose of this process is to comprehensively evaluate the effectiveness and feasibility of the plan from different angles. 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, and matching degree between the plan and actual power grid conditions. Through multi-dimensional evaluation, the system can identify potential problems or optimization space in the plan, ensure that the plan can cover all key areas when it is executed, and effectively respond to various complex conditions in the power grid. Then, the system quantitatively evaluates the initial power grid emergency plan based on the multi-dimensional evaluation results in the previous step and generates an evaluation score. The evaluation score is calculated based on multiple indicators (such as accuracy, timeliness, execution difficulty, etc.), which can reflect the overall quality and practical feasibility of the initial plan. The system compares the evaluation scores 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 below the threshold, it means that there are potential problems with the initial emergency plan and it may need to be further optimized or revised. After being determined as the target grid emergency plan, the plan will enter the subsequent execution phase to ensure that it can be effectively implemented in the event of a grid failure or emergency.

[0056] S105: Output the target power grid emergency plan as visual information and send it to emergency response personnel.

[0057] 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 appropriate emergency measures. Visual information includes but is not limited to charts, flow charts, alarm information, task lists, etc., which can intuitively display the current working 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 various operations in the emergency plan, ensuring that emergency response personnel can obtain key information in the shortest time and perform 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 email, text messages, dedicated emergency platforms, etc.) to ensure that they can respond in time and take necessary measures to avoid larger-scale failures or damage to the power grid.

[0058] The artificial intelligence-based emergency plan generation method provided in this application can significantly reduce manual intervention and response time and improve the speed of emergency response by automatically generating power grid emergency plans. 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 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, and improve the effectiveness and pertinence of emergency measures. By constructing a multi-dimensional data evaluation system, the method of this application can handle different types of power grid failures and emergencies, and flexibly adapt to 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 the present 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 capability of the power grid.

[0060] In the second aspect, based on the same inventive concept, refer to Figure 2 , shows an artificial intelligence-based emergency plan generation system 200 provided in an embodiment of the present application, the system comprising: An acquisition module 201 is used to 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 the categories of the multi-source power grid operating condition data; A matching module 202, configured to determine a target emergency scenario and a target emergency plan template matching the target emergency scenario according to data sources and data characteristics of the multi-source power grid operating condition data; A generation module 203 is used for the target emergency plan template to call the multi-source power grid condition data in the power grid condition sub-database to generate an initial power grid emergency plan; Verification module 204, used to verify the initial power grid emergency plan according to the data quality evaluation report and output a target power grid emergency plan; The sending module 205 is used to output the target power grid emergency plan as visual information and send it to emergency handling personnel.

[0061] In an embodiment of the second aspect of the present application, the acquisition module includes: A first acquisition submodule is used 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 used 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 the weight information and sampling selection.

[0062] In an embodiment of the second aspect of the present application, the matching module includes: A third acquisition submodule is used to obtain 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 matching submodule is used to determine the target emergency plan template from multiple candidate emergency plan templates using the data source identifier as the first index and the data feature as the second index.

[0063] In an embodiment of the second aspect of the present application, a generating module includes: A data import submodule, used for the target emergency plan template to import the data in the power grid condition subdatabase into the multiple sub-functional areas respectively according to the data categories of the multi-source power grid condition data; The analysis submodule is used for each of the sub-functional areas to perform analysis and automatic writing of fault problems according to its corresponding target tasks, and to generate a corresponding initial power grid emergency plan.

[0064] In an embodiment of the second aspect of the present application, the analysis submodule includes: A feature analysis unit, used to perform feature analysis according to the target category and determine the target task value; A positioning analysis unit, used to locate the position corresponding to the target task value in the power grid operating condition sub-database; The writing unit is used to automatically write the descriptive text based on the position corresponding to the target task value to generate an initial power grid emergency plan.

[0065] In an embodiment of the second aspect of the present application, the feature analysis unit includes: A determination subunit, configured to determine a first point estimate and a second point estimate of a corresponding target category according to the target category; A first description subunit, configured to generate a grouping table and a grouping description according to the first point estimation value; The second description subunit is used to generate a trend graph and a trend description according to the second point estimation value.

[0066] In an embodiment of the second aspect of the present application, the verification module includes: An evaluation submodule, used to perform a multi-dimensional evaluation on the initial power grid emergency plan according to the data quality evaluation report; The screening submodule is used to determine the 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.

[0067] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. 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 a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable system. The computer program or instruction can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer program or instruction can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0068] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0069] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0070] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0071] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0072] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0073] In the several embodiments provided in the present 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 only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the system or unit can be electrical, mechanical or other forms.

[0074] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0075] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0076] If the 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 the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0077] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for generating emergency plans based on artificial intelligence, characterized in that: The method comprises: Acquire multi-source power grid operating condition data of a current data collection 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 according to 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 the target power grid emergency plan; The target power grid emergency plan is output as visual information and sent to emergency handling personnel.

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; Or, 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 weight information and sampling selection.

3. The method according to claim 1, characterized in that The determining, according to 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 comprises: Acquire 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 the first index and the data feature as the second index.

4. The method according to claim 1, characterized in that: The target emergency plan template includes a plurality of sub-functional areas. The target emergency plan template calls the multi-source power grid condition data in the power grid condition sub-database to generate an initial power grid emergency plan, including: The target emergency plan template imports the data in the power grid condition sub-database into the multiple sub-functional areas respectively according to the data categories of the multi-source power grid condition data; Each of the sub-functional areas performs analysis and automatic writing of fault problems according to its corresponding target tasks, and generates a corresponding initial power grid emergency plan.

5. The method according to claim 4, characterized in that 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: Conduct feature analysis based on target categories and determine target task values; In the power grid operating condition sub-database, locating a position corresponding to the target task value; Based on the position corresponding to the target task value, the descriptive text is automatically written to generate an initial power grid emergency plan.

6. The method according to claim 5, characterized in that Perform feature analysis based on target categories and determine target task values, including: According to the target category, determining 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; A trend graph and a trend description are generated based on the second point estimate.

7. The method according to claim 1, characterized in that The verifying the initial power grid emergency plan according to the data quality report and outputting the 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; According to the multi-dimensional evaluation result, an evaluation score of the initial power grid emergency plan is determined, and the initial power grid emergency plan with the evaluation score greater than a threshold is determined as the target power grid emergency plan.

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

9. The system according to claim 8, characterized in that The acquisition module comprises: A first acquisition submodule is used 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 used 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 weight information and sampling selection.

10. The system according to claim 8, characterized in that The matching module comprises: A third acquisition submodule is used to obtain 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 matching submodule is used to determine the target emergency plan template from multiple candidate emergency plan templates using the data source identifier as the first index and the data feature as the second index.

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