Method and device for determining dam monitoring index scheme based on large language model, storage medium and electronic equipment

Abstract

The embodiment of the application provides a method and device for determining a dam monitoring index scheme based on a large language model, a storage medium and an electronic device, wherein the method comprises: analyzing a target database corresponding to a dam based on a large language model to extract key information in the target database, wherein the target database comprises: a first database for indicating operation parameters corresponding to the dam and a second database for indicating constraint conditions to be followed by the dam; determining target margin information corresponding to the dam according to the key information, and establishing a monitoring index framework of the dam according to a plurality of preset monitoring indexes corresponding to the dam; and determining a monitoring index scheme corresponding to the dam according to the monitoring index framework and the target margin information. According to the above method, the problem of high difficulty in manually preparing a monitoring index scheme of a dam in the related art can be solved.

Description

Technical Field

[0001] The present application relates to the field of safety monitoring, and more specifically, to a method and device for determining a dam monitoring indicator scheme based on a large language model, a storage medium, and an electronic device. Background Art

[0002] Reservoirs and dams are crucial components of river basin flood control systems, a vital part of the national water grid, and crucial instruments for ensuring national water security. They play an irreplaceable role in preventing floods and droughts, optimizing water resource allocation, restoring the ecological environment of rivers and lakes, and providing clean energy. my country currently has approximately 98,000 dams of various types, serving a wide range of functions, including power generation, flood control, navigation, and water supply. They are crucial comprehensive infrastructure for ensuring safety and promoting development.

[0003] With the advancement of science and technology, reservoir dam safety monitoring technology has been continuously innovated. Dam safety monitoring has evolved from traditional manual inspections to today's automated monitoring systems. The accuracy, efficiency and coverage of monitoring have been significantly improved, playing an important role in improving project performance, extending dam service life and enhancing emergency management capabilities.

[0004] However, since online monitoring of dam safety is highly professional and many power plants lack corresponding professionals, the difficulty and cost of compiling online monitoring plans for dam safety are further increased.

[0005] Regarding the problem that the safety monitoring of dams and gates requires strong professionalism in related technologies, which makes it difficult to formulate monitoring indicator plans for dams and gates, no effective solution has been proposed so far.

[0006] Therefore, it is necessary to improve the related technology to overcome the above-mentioned defects in the related technology. Summary of the Invention

[0007] The embodiments of the present application provide a method and device, a storage medium, and an electronic device for determining a dam monitoring indicator scheme based on a large language model, so as to at least solve the problem in the related art that it is difficult to manually compile a dam monitoring indicator scheme.

[0008] According to one embodiment of the present application, a method for determining a dam monitoring indicator scheme based on a large language model is provided, comprising: parsing a target database corresponding to the dam based on the large language model to extract key information in the target database, wherein the target database comprises: a first database for indicating operating parameters corresponding to the dam and a second database for indicating constraints to be followed by the dam; determining target margin information corresponding to the dam based on the key information, and establishing a monitoring indicator framework for the dam based on a plurality of preset monitoring indicators corresponding to the dam; and determining a monitoring indicator scheme corresponding to the dam based on the monitoring indicator framework and the target margin information.

[0009] In an exemplary embodiment, a target database corresponding to a sluice or dam is parsed based on a large language model to extract key information from the target database, including: performing format conversion on target data in the target database to convert the format of the target data into a target format; and performing semantic parsing on the target data in the target format based on the large language model to extract the key information from the target data.

[0010] In an exemplary embodiment, determining the target margin information corresponding to the gate and dam based on the key information includes: extracting the safety monitoring variables corresponding to the gate and dam from the key information, and obtaining historical data corresponding to the safety monitoring variables; determining the historical change trend corresponding to the safety monitoring variables based on the historical data, and calculating the first safety range of the safety monitoring variables based on the historical change trend and the design parameters corresponding to the gate and dam; determining the first margin information corresponding to the gate and dam based on the first safety range; performing safety verification on the first margin information, and determining the target margin information based on the verification result.

[0011] In an exemplary embodiment, security verification is performed on the first margin information, and the target margin information is determined based on the verification result, including: extracting a second safety range of the security monitoring variable from the key information, wherein the second safety range to be followed is used to indicate the safety range to be followed by the security monitoring variable; security verification is performed on the first margin information based on the second safety range to determine whether the first margin information complies with the second safety range based on the verification result, and if the verification result indicates that the first margin information complies with the second safety range, the first margin information is determined as the target margin information; if the verification result indicates that the first margin information does not comply with the second safety range, the target margin information is determined based on the first margin information and the second safety range.

[0012] In an exemplary embodiment, a monitoring indicator framework for the dam is established based on multiple preset monitoring indicators corresponding to the dam, including: determining the monitoring item corresponding to each preset monitoring indicator, and classifying each monitoring item to determine the monitoring level of each monitoring item; determining the monitoring method corresponding to each preset monitoring indicator based on the monitoring level and the monitoring item; and establishing the monitoring indicator framework based on the multiple preset monitoring indicators, the monitoring level, the monitoring item, and the monitoring method.

[0013] In an exemplary embodiment, a monitoring indicator scheme corresponding to the dam is determined based on the monitoring indicator framework and the target margin information, including: determining a safety monitoring variable corresponding to the target margin information, and determining a monitoring item having a corresponding relationship with the safety monitoring variable in the monitoring indicator framework; and determining the monitoring indicator scheme based on the corresponding relationship, the target margin information and the monitoring indicator framework.

[0014] In an exemplary embodiment, after determining the monitoring indicator scheme corresponding to the dam based on the monitoring indicator framework and the target margin information, the method further includes: extracting the safety monitoring variables corresponding to the dam from the key information, and obtaining historical data corresponding to the safety monitoring variables; determining the number of times the historical data complies with the monitoring indicator scheme; when the number of compliances is less than or equal to a preset threshold, performing a structural safety analysis on the monitoring indicator scheme based on the target database to determine whether the safety factor corresponding to the monitoring indicator scheme complies with the preset safety factor; when it is determined that the safety factor corresponding to the monitoring indicator scheme complies with the preset safety factor, determining that the monitoring indicator scheme has passed the verification.

[0015] According to another embodiment of the present application, a device for determining a dam monitoring indicator scheme based on a large language model is provided, including: a parsing module, used to parse a target database corresponding to the dam based on a large language model to extract key information in the target database, wherein the target database includes: a first database for indicating operating parameters corresponding to the dam and a second database for indicating constraints to be followed by the dam; a first determination module, used to determine target margin information corresponding to the dam based on the key information, and establish a monitoring indicator framework for the dam based on multiple preset monitoring indicators corresponding to the dam; a second determination module, used to determine the monitoring indicator scheme corresponding to the dam based on the monitoring indicator framework and the target margin information.

[0016] According to another embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored. The computer program is configured to execute the steps of any one of the above method embodiments when run.

[0017] According to another embodiment of the present application, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0018] According to another embodiment of the present application, a computer program product is provided, including a computer program, which implements the steps of any of the above method embodiments when executed by a processor.

[0019] Through the embodiments of the present application, key information in a target database corresponding to a dam, including a first database for indicating operating parameters corresponding to the dam and a second database for indicating constraints to be followed by the dam, is parsed based on a large language model to extract key information from the target database; target margin information corresponding to the dam is determined based on the key information, and a monitoring indicator framework for the dam is established based on multiple preset monitoring indicators corresponding to the dam; and a monitoring indicator scheme corresponding to the dam is determined based on the monitoring indicator framework and the target margin information. In other words, the embodiments of the present application can parse the key information in the target database, and then determine the target margin information corresponding to the dam based on the key information, and establish a monitoring indicator framework based on the preset monitoring indicators, and then determine a monitoring indicator scheme for the dam based on the target margin information and the monitoring indicator framework. According to the embodiments of the present application, the problem of the difficulty of manually compiling monitoring indicator schemes for dams in related technologies can be solved. This reduces the difficulty of compiling monitoring indicator schemes for dams. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0022] Figure 1 This is a hardware structure block diagram of a computer terminal device for a method for determining a dam monitoring indicator solution based on a large language model according to an embodiment of the present application;

[0023] Figure 2 is a flow chart of a method for determining a dam monitoring indicator solution based on a large language model according to an embodiment of the present application;

[0024] Figure 3 This is a flowchart of a method for constructing online monitoring indicators for dam safety based on a large language model according to an optional embodiment of the present application.

[0025] Figure 4 is a schematic diagram of a monitoring indicator architecture according to an optional embodiment of the present application;

[0026] Figure 5 is a schematic diagram of an indicator scheme according to an optional embodiment of the present application;

[0027] Figure 6 is a schematic diagram of a system for constructing online monitoring indicators for dam safety based on a large language model according to an optional embodiment of the present application;

[0028] Figure 7 It is a structural block diagram of a device for determining a dam monitoring indicator solution based on a large language model according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] The embodiments of the present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0030] It should be noted that the terms "first", "second", etc. in the description and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0031] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal device or a similar computing device. Taking running on a computer terminal device as an example, Figure 1 This is a hardware structure block diagram of a computer terminal device for a method for determining a dam monitoring indicator solution based on a large language model in an embodiment of the present application. Figure 1 As shown, the computer terminal device may include one or more ( Figure 1 Only one is shown) a processor 102 (the processor 102 may include but is not limited to a microprocessor MCU or a programmable logic device FPGA and other processing devices) and a memory 104 for storing data, wherein the above-mentioned computer terminal device may also include a transmission device 106 for communication functions and an input and output device 108. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal device. For example, the computer terminal device may also include Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0032] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for determining the monitoring indicator scheme in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implementing the above method. The memory 104 may include a high-speed random access memory and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the computer terminal device via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0033] The transmission device 106 is used to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by a communication provider of a computer terminal device. In one embodiment, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0034] In this embodiment, a method for determining a dam monitoring indicator scheme based on a large language model is provided. Figure 2 is a flow chart of a method for determining a dam monitoring indicator solution based on a large language model according to an embodiment of the present application, such as Figure 2 As shown, the process includes the following steps:

[0035] Step S202: parsing a target database corresponding to the dam based on the large language model to extract key information from the target database, wherein the target database includes: a first database for indicating operating parameters corresponding to the dam and a second database for indicating constraints to be followed by the dam;

[0036] Step S204: determining target margin information corresponding to the dam based on the key information, and establishing a monitoring indicator framework for the dam based on a plurality of preset monitoring indicators corresponding to the dam;

[0037] Step S206: determining a monitoring indicator solution corresponding to the dam according to the monitoring indicator framework and the target margin information.

[0038] Through the above steps, the key information in the target database corresponding to the dam, including: a first database for indicating the operating parameters corresponding to the dam and a second database for indicating the constraints to be followed by the dam, is parsed based on the large language model to extract the key information in the target database; the target margin information corresponding to the dam is determined based on the key information, and a monitoring indicator framework for the dam is established based on multiple preset monitoring indicators corresponding to the dam; and the monitoring indicator scheme corresponding to the dam is determined based on the monitoring indicator framework and the target margin information. In other words, the embodiment of the present application can parse the key information in the target database, and then determine the target margin information corresponding to the dam based on the key information, and establish a monitoring indicator framework based on the preset monitoring indicators, and then determine the monitoring indicator scheme for the dam based on the target margin information and the monitoring indicator framework. According to the embodiment of the present application, the problem of the difficulty of manually compiling monitoring indicator schemes for dams in related technologies can be solved. This reduces the difficulty of compiling monitoring indicator schemes for dams.

[0039] Optionally, the above-mentioned step S202 of parsing the target database corresponding to the sluice dam based on the large language model to extract key information in the target database includes: performing format conversion on the target data in the target database to convert the format of the target data into the target format; performing semantic parsing on the target data in the target format based on the large language model to extract the key information from the target data.

[0040] It is understandable that the target database may include: a first database and a second database, wherein:

[0041] The first database may be an original database, which may include: hydropower station design reports, monitoring data comprehensive analysis reports, operation performance analysis and diagnosis reports, comprehensive evaluation reports and other information;

[0042] The second database may be a legal and standard database, which may include: regulations on reservoir and dam safety management, technical specifications for online monitoring systems for hydropower station dam operation safety, and other information.

[0043] The data in the first database and the second database may be stored in different file formats, such as PDF, Word documents, Excel spreadsheets, image files (drawings), etc., which contain a large amount of text descriptions, data tables and engineering drawing information.

[0044] At this time, it is necessary to convert the target data in different formats. For example, PDF and Word documents can be converted into plain text format through optical character recognition (OCR) technology, Excel tables can be converted into structured data formats such as CSV or JSON, and engineering drawings or image files may need to be converted into text information describing their characteristics, or key information may need to be pre-marked before being converted into text format.

[0045] For example, suppose the target database contains a PDF report on operational performance analysis. This report details the displacement, strain, and stress data of a dam under specific water levels. The format conversion step converts this PDF report into plain text so that the large language model can read and analyze the text and data.

[0046] To gain a deeper understanding of the converted target data, a large language model can be used to identify and extract key information from the target data. Key information includes, but is not limited to, the dam's physical parameters (such as displacement, stress, seepage, etc.), design standards, operating status descriptions, abnormal event records, and technical details in engineering drawings.

[0047] For example, a large language model will perform semantic analysis on text, identifying keywords such as "displacement," "stress," and "seepage." Through contextual understanding, it can further extract information related to these keywords, including specific numerical values, measurement time and location, descriptions of abnormal events, and comparative analysis results with design standards. For example, the model might identify a description such as "In the summer of 2022, when the upstream water level reached 845.0 meters, the displacement of the northeast corner of the dam crest was 5.2 mm." It would then extract "upstream water level 845.0 meters," "displacement of the northeast corner of the dam crest 5.2 mm," and "summer 2022" as key information.

[0048] The extracted key information will be used to build an online monitoring indicator framework for dam and sluice gate safety. This includes automatically generating margin settings for variables such as displacement, deformation, uplift pressure, and seepage around the dam, as well as developing customized monitoring indicator solutions. These solutions ensure the rationality and professionalism of dam monitoring indicators, improving monitoring efficiency and early warning accuracy.

[0049] Optionally, the above-mentioned step S204 of determining the target margin information corresponding to the gate and dam based on the key information includes: extracting the safety monitoring variables corresponding to the gate and dam from the key information, and obtaining historical data corresponding to the safety monitoring variables; determining the historical change trend corresponding to the safety monitoring variables based on the historical data, and calculating the first safety range of the safety monitoring variables based on the historical change trend and the design parameters corresponding to the gate and dam; determining the first margin information corresponding to the gate and dam based on the first safety range; performing safety verification on the first margin information, and determining the target margin information based on the verification result.

[0050] Among them, the first margin information is security verified and the target margin information is determined according to the verification result, including: extracting the second safety range of the safety monitoring variable from the key information, wherein the second safety range of the safety range to be followed is used to indicate the safety range to be followed by the safety monitoring variable; security verification is performed on the first margin information according to the second safety range to determine whether the first margin information complies with the second safety range according to the verification result, and when the verification result indicates that the first margin information complies with the second safety range, the first margin information is determined as the target margin information; when the verification result indicates that the first margin information does not comply with the second safety range, the target margin information is determined according to the first margin information and the second safety range.

[0051] It is understood that the safety monitoring variables (such as dam crest horizontal displacement, dam body stress, seepage, etc.) of a dam (e.g., a concrete dam) and their historical data can be determined based on the above key information. This historical data includes the actual monitored values ​​of the variables under different operating conditions (such as water level changes, temperature changes, etc.), as well as related abnormal event records and expert evaluation reports.

[0052] For example, the target database contains historical monitoring data on the horizontal displacement of a dam crest. By reading and understanding this data, the large language model extracts the specific values ​​of the dam crest displacement under different water levels and temperatures, as well as records of displacement changes during specific events (such as floods and earthquakes).

[0053] Furthermore, historical data can be used to determine the historical trends of safety monitoring variables. Based on these trends and the design parameters of the dam, a first safety range for the safety monitoring variables can be calculated. Design parameters include material properties, structural design, and safety factors of the dam. These design parameters are combined with historical data to determine the safety margin of the safety monitoring variables under normal operating conditions through model prediction or engineering calculations.

[0054] For example, based on historical displacement data and design parameters, the calculated first safety range for the horizontal displacement of the dam crest under normal operating conditions might be ±5 mm. This means that under normal operating conditions, the displacement value should remain within this range; exceeding this range could indicate a safety hazard.

[0055] Furthermore, based on the first safety range, first margin information of the dam is determined. The first margin information is an additional safety buffer within the safety range, and is intended to prevent false alarms caused by accidental fluctuations or external factors.

[0056] For example, within the first safety range of ±5 mm, a margin of ±2 mm can be set to form the first margin information. That is, during normal operation, the displacement value should be kept within the range of ±7 mm.

[0057] After determining the primary margin information, the second safety range within the key information can be extracted. This is the safety range for safety monitoring variables, as determined by laws, regulations, and industry standards. The second safety range is typically more stringent than the first and is an absolute requirement for ensuring the safe operation of the dam. The first margin information is compared with the second safety range to verify whether it meets the more stringent safety standards. If so, the first margin information is used directly as the target margin information. If not, the margin value is readjusted based on the difference between the two ranges.

[0058] For example, according to the technical specifications for the online monitoring system for hydropower station dam operation safety, the second safety margin for dam crest horizontal displacement may be ±3 mm. The large language model compares the first margin information (±7 mm) with the second safety margin and finds that it exceeds the second safety margin requirement. Therefore, the margin value needs to be adjusted to ensure that the monitoring indicators meet regulatory requirements. Through recalculation, the margin may be adjusted to ±2 mm, thus forming the target margin information. Under normal operating conditions, the displacement value should remain within the range of ±5 mm to meet the requirements of both the first and second safety margins.

[0059] Optionally, the above-mentioned step S204 establishes a monitoring indicator framework for the dam based on multiple preset monitoring indicators corresponding to the dam, including: determining the monitoring item corresponding to each preset monitoring indicator, and classifying each monitoring item to determine the monitoring level of each monitoring item; determining the monitoring method corresponding to each preset monitoring indicator based on the monitoring level and the monitoring item; and establishing the monitoring indicator framework based on the multiple preset monitoring indicators, the monitoring level, the monitoring item and the monitoring method.

[0060] It is understandable that in addition to determining the target margin information, it is also necessary to determine the monitoring indicator framework, specifically:

[0061] First, it's necessary to analyze the dam's multiple preset monitoring indicators and determine the corresponding monitoring items for each indicator, such as displacement, deformation, uplift pressure, and seepage. Then, the monitoring items are categorized and assigned a monitoring level based on their importance and impact on dam safety. Monitoring levels are typically divided into primary, secondary, and tertiary levels. Primary indicators refer to key indicators that directly impact dam safety, secondary indicators address long-term operational performance and maintenance, and tertiary indicators may include auxiliary monitoring or lower-priority indicators.

[0062] For example, for a concrete dam, crest displacement and foundation uplift pressure might be identified as primary monitoring indicators because they are directly related to the dam's stability and safety. However, crack width in the dam body might be classified as a secondary monitoring indicator because it relates to the long-term maintenance and performance assessment of the dam.

[0063] Secondly, select an appropriate monitoring method based on the monitoring level and characteristics of each monitoring project. Monitoring methods can include fixed limit methods, historical extreme value methods, trend methods, and other methods. The specific method should be selected based on the project's real-time requirements, data fluctuation characteristics, and engineering practice experience.

[0064] For example, the first-level monitoring indicator, "horizontal displacement of the dam crest," might use a fixed limit method combined with a trend method, as this requires real-time monitoring to determine whether it exceeds safety thresholds while also focusing on long-term trends to prevent potential structural stability issues. Meanwhile, the second-level monitoring indicator, "dam crack width," might primarily use a historical extreme value method, analyzing crack width trends across different seasons and water levels to set appropriate monitoring thresholds.

[0065] The identified monitoring items, monitoring levels, and monitoring methods are then integrated into a monitoring indicator framework. This monitoring indicator framework should comprehensively cover the safety monitoring needs of the dam and sluice gate, while also considering the priorities of different monitoring items and the effectiveness of monitoring methods to ensure the overall performance of the monitoring system and the rationality of resource allocation.

[0066] For example, you can build the following monitoring indicator framework:

[0067] Level 1 monitoring indicator: Horizontal displacement of the dam crest: This monitoring method uses a fixed limit method combined with a trend method, monitoring the displacement in real time to see if it exceeds ±5 mm, while also analyzing long-term trends. Uplift pressure on the dam foundation: This monitoring method uses a fixed limit method combined with a historical extreme value method, monitoring the uplift pressure in real time to see if it exceeds a preset safety value. Historical data is analyzed to set reasonable alarm thresholds.

[0068] Secondary monitoring indicators: Dam crack width: The monitoring method is the historical extreme value method, which regularly analyzes changes in crack width and sets reasonable alarm thresholds.

[0069] Level 3 monitoring indicators: Working environment temperature: The monitoring method is the fixed limit method, which monitors whether the temperature exceeds the normal range, but its alarm priority is low.

[0070] Through the monitoring indicator framework, the dam safety monitoring system can implement targeted monitoring of monitoring items at different levels. The first-level indicators require real-time attention and rapid response, the second-level indicators require regular inspections, and the third-level indicators are monitored when necessary, thereby balancing the system's monitoring efficiency and resource consumption.

[0071] The embodiment of the present application uses the natural language understanding and data analysis capabilities of a large language model to effectively extract key information from massive databases, and then automatically generates a monitoring indicator framework through professional engineering knowledge and algorithms, which not only improves the efficiency of monitoring indicator formulation, but also ensures its professionalism and rationality.

[0072] Optionally, the above-mentioned step S206 of determining the monitoring indicator scheme corresponding to the dam based on the monitoring indicator framework and the target margin information includes: determining the safety monitoring variables corresponding to the target margin information, and determining the monitoring items that have a corresponding relationship with the safety monitoring variables in the monitoring indicator framework; and determining the monitoring indicator scheme based on the corresponding relationship, the target margin information and the monitoring indicator framework.

[0073] It is understandable that after determining the target margin information and the monitoring indicator framework, a monitoring indicator plan can be determined based on the target margin information and the monitoring indicator framework. Specifically:

[0074] Based on the target margin information, variables requiring monitoring are identified. These variables are typically defined as key indicators of interest within the monitoring indicator framework. Furthermore, monitoring items corresponding to these variables are identified within the monitoring indicator framework. Monitoring items are specific physical quantities to be monitored, such as displacement, stress, and seepage, while safety monitoring variables are the numerical representations of these physical quantities under specific operating conditions.

[0075] For example, if the target margin information indicates that the safety margin for "dam crest horizontal displacement" is ±5 mm, then "dam crest horizontal displacement" becomes a variable that needs to be monitored. In the monitoring indicator framework, you can find monitoring items related to "dam crest horizontal displacement", including the location of the displacement sensor, monitoring frequency, and data processing method.

[0076] Then, based on the corresponding relationships among monitoring items, target margin information, and the monitoring indicator framework, specific monitoring methods are selected and the level of monitoring indicators is determined. Monitoring methods may include the use of sensors, visual monitoring, and manual inspections. The level of indicators is determined based on the importance of the variables and margin information, and is generally divided into primary, secondary, and tertiary levels.

[0077] For example, within the monitoring indicator framework, the first-level monitoring indicator, "horizontal displacement of the dam crest," might employ a combination of fixed limits and trend analysis. The fixed limits method triggers an alarm if the displacement exceeds a ±5 mm margin. The trend analysis method monitors long-term displacement trends to identify potential structural issues.

[0078] Furthermore, the monitoring indicator plan should also include the design of data collection and processing mechanisms to ensure that monitoring data can be accurately and timely collected and processed for use by the monitoring system. This may involve the deployment of sensors, the establishment of data transmission networks, and the development of data analysis algorithms.

[0079] For example, to monitor the horizontal displacement of the dam crest, displacement sensors must be deployed at the top and key locations of the dam, ensuring smooth data transmission between the sensors and the monitoring center. Simultaneously, data processing algorithms must be developed to analyze displacement data in real time and determine whether it exceeds a predefined safety margin.

[0080] By integrating the monitoring items, monitoring methods, indicator levels and data processing mechanisms determined in the above steps, a comprehensive monitoring indicator plan can be formed.

[0081] For example, the monitoring indicator plan describes in detail the monitoring process for the first-level monitoring indicator of "horizontal displacement of the dam crest": using high-precision displacement sensors to collect data in a 15-minute cycle; after the data is transmitted back to the monitoring center, it is analyzed in real time using the fixed limit method + trend analysis method. Once the displacement value exceeds the margin of ±5 mm, a first-level alarm is immediately issued and the emergency response procedure is initiated; at the same time, the trend of the displacement data is regularly analyzed to evaluate the stability of the dam structure.

[0082] Optionally, after determining the monitoring indicator scheme corresponding to the dam based on the monitoring indicator framework and the target margin information, the method further includes: extracting the safety monitoring variables corresponding to the dam from the key information, and obtaining historical data corresponding to the safety monitoring variables; determining the number of times the historical data complies with the monitoring indicator scheme; when the number of compliances is less than or equal to a preset threshold, performing a structural safety analysis on the monitoring indicator scheme based on the target database to determine whether the safety factor corresponding to the monitoring indicator scheme complies with the preset safety factor; when it is determined that the safety factor corresponding to the monitoring indicator scheme complies with the preset safety factor, determining that the monitoring indicator scheme has passed the verification.

[0083] It is understandable that in the process of verifying the monitoring indicator plan, data comparison and verification are first required. Specifically, the historical monitoring data in the first database is used to compare these data with the thresholds and margins in the customized monitoring indicator plan. Check whether the monitoring data has ever exceeded or approached the alarm thresholds in the plan under historical operating conditions. If the monitoring data frequently touches the alarm threshold during normal operation, it may mean that the indicator setting is too strict and needs to be relaxed appropriately; conversely, if the monitoring data fails to trigger an alarm in an abnormal event, it may indicate that the indicator setting is too wide and the margin needs to be narrowed.

[0084] Secondly, a structural safety analysis can be conducted. Specifically, a customized monitoring indicator scheme is analyzed based on the design reports, construction records, and completion acceptance reports in the original database. The scheme's monitoring point settings, indicator selection, and thresholds are verified to ensure they match the dam's structural design and safety factors, and fully reflect the dam's operating status and potential risks.

[0085] Then, a compliance check for engineering specifications can be performed: by comparing relevant data in the second database to ensure that all indicators in the customized monitoring indicator solution meet the requirements of these specifications. This includes indicator type, measurement method, alarm mechanism, and data processing flow.

[0086] Finally, risk assessment and scenario simulation can be conducted. Specifically, using the risk assessment reports and emergency response plans in the original database, scenario simulations can be conducted to test the performance of the indicator plan under various hypothetical abnormal or extreme conditions. For example, simulations of emergencies such as earthquakes and floods can be used to check whether the indicators in the plan can promptly reflect the abnormal status of the dam and provide accurate information for emergency decision-making.

[0087] In order to better understand the process of the method for determining the above-mentioned dam monitoring indicator scheme based on a large language model, the following describes the implementation method flow of the above-mentioned dam monitoring indicator scheme based on a large language model in combination with an optional embodiment, but it is not used to limit the technical solution of the embodiment of this application.

[0088] Online monitoring of dam safety can play an important role in many aspects:

[0089] (1) Timely detection of safety hazards: By real-time monitoring of the dam's deformation, seepage, stress and other parameters, potential problems such as dam cracks, increased leakage, structural deformation, etc. can be detected in a timely manner, thus buying valuable time for taking repair and reinforcement measures.

[0090] (2) Ensuring the safe operation of the dam: It helps to understand the working status of the dam under various working conditions, ensure its normal operation within the design range, and avoid catastrophic consequences caused by dam failure, such as flooding, casualties and property losses.

[0091] (3) Optimizing engineering design and construction: Long-term monitoring data can verify the rationality of dam design, provide reference for subsequent similar projects, improve design and construction methods, and enhance the quality and safety of dam construction.

[0092] (4) Extend the service life of the dam: Based on the monitoring results, targeted maintenance and management are carried out to delay the aging and damage of the dam and give full play to its economic and social benefits.

[0093] (5) Improve emergency management capabilities: When emergencies (such as earthquakes, floods, etc.) occur, real-time monitoring data can provide important basis for emergency decision-making, help formulate reasonable response plans, and reduce disaster losses.

[0094] However, due to the diverse nature of key dam safety indicators, such as water level, flow, seepage, stress, and strain, and the diverse monitoring methods, the dam design and construction process generates a massive volume of documents in diverse formats and types. Furthermore, for older hydropower stations, relevant documents are often outdated or even missing, making the development of online dam safety monitoring plans a significant challenge. Furthermore, the highly specialized nature of online dam safety monitoring, coupled with the lack of specialized personnel at many power stations, further increases the difficulty and cost of developing such plans.

[0095] In response to the above problems, the optional embodiment of the present application proposes a model, method and system for constructing online monitoring indicators for dam safety based on a large language model. The optional embodiment of the present application adopts a large language model to analyze and extract data from massive dam documents and related policy standards, and constructs a monitoring framework that adapts to different dam conditions; a margin analysis model is designed to automatically generate customized margin values ​​for each monitoring variable; on the above basis, a dam safety monitoring indicator scheme is formed and verified, ensuring the standardization and professionalism of the dam monitoring indicator scheme and improving the efficiency of indicator scheme formulation.

[0096] Figure 3 is a flow chart of a method for constructing online monitoring indicators for dam safety based on a large language model according to an optional embodiment of the present application, such as Figure 3 As shown:

[0097] Step S301: Establish two key databases (ie, target databases).

[0098] The key databases include the original database (i.e. the first database) and the law and standards database (i.e. the second database).

[0099] The original database mainly contains data during the design and operation of the power station, which is used to extract key parameters; the legal and standard database mainly contains laws and relevant standards formulated by the state and relevant units.

[0100] like Figure 3 As shown, the original data library includes: hydropower station design reports, comprehensive monitoring data analysis reports, operational performance analysis and diagnosis reports, comprehensive evaluation reports, evaluation and appraisal reports, completion acceptance reports, inspection reports, drawings and other materials, and is as complete as possible. The legal and standards library includes: Reservoir and Dam Safety Management Regulations, Technical Specifications for Online Monitoring Systems for Hydropower Station Dam Operation Safety, Technical Specifications for Concrete Dam Safety Monitoring, Regulations for Supervision and Management of Hydropower Station Dam Operation Safety, and Guidelines for Hydropower Station Dam Operation Safety Evaluation, and is as complete as possible.

[0101] Step S302: data processing and learning.

[0102] Process files of different formats and types to extract key information from the files.

[0103] Step S303: margin setting.

[0104] Based on the database learning results (i.e. key information), the margins (i.e. target margin information) of variables such as displacement, deformation, uplift pressure, and seepage around the dam (i.e. safety monitoring variables) are automatically generated.

[0105] Step S304: Building a monitoring framework.

[0106] Based on the existing monitoring indicators (i.e., preset monitoring indicators) of the dam (the dam and sluice dam in the optional embodiments of this application can both be the dam of this application), a customized dam monitoring indicator framework (i.e., dam monitoring indicator framework) is formed for a specific power station.

[0107] Figure 4 This is a schematic diagram of the monitoring indicator architecture according to an optional embodiment of the present application. Figure 4 As shown, the monitoring indicator architecture includes options such as monitoring objects, monitoring parts, monitoring items, monitoring methods, margin analysis and remarks, among which, in the optional embodiments of the present application, the monitoring objects may include: concrete dams, water diversion systems and powerhouses, etc. Taking the concrete dam as an example, the monitoring parts corresponding to the concrete system may be the dam body and key focus areas; the monitoring items corresponding to the dam body may be: deflection deformation, deformation of the joint between the dam foundation and the bedrock, etc., and the monitoring items corresponding to the key focus areas may be: horizontal displacement of the dam top, vertical displacement of the dam top, uplift pressure of the dam foundation and seepage of the dam, etc.; taking deflection deformation as an example, the monitoring methods corresponding to deflection deformation may be: fixed limit method, historical extreme value method and trend method, etc. Margin analysis can be performed according to different monitoring methods, and corresponding remarks can be filled in, wherein the remarks may be: basis for selection of monitoring method.

[0108] Step S305: Generate indicator plan.

[0109] Based on the constructed monitoring framework (i.e., the monitoring indicator framework of the dam), margin settings (i.e., target margin information), and data learning results (i.e., key information), an online monitoring indicator scheme for dam safety (i.e., monitoring indicator scheme) is generated.

[0110] Figure 5 is a schematic diagram of an indicator scheme according to an optional embodiment of the present application, such as Figure 5As shown, the indicator scheme can include options such as monitoring items, single-point monitoring methods, specific methods, specific indicator settings, and remarks. Monitoring items can include upstream water level and dam crest horizontal displacement. The single-point monitoring method and specific method for the upstream water level can be the fixed limit method, with specific indicator settings as follows: the first-level upper limit indicator is the normal water level of 845.5m, the second-level upper limit indicator is the check water level of 848.01m, and the third-level upper limit indicator is the dead water level of 842.5m. The single-point monitoring method for dam crest horizontal displacement can be the fixed limit method + trend method + historical extreme value method. The specific indicator settings for the fixed limit method can be: the first-level indicator: the extreme value (fixed) of each measuring point over the past three years ± a margin, with a margin of 3mm; the specific indicator settings for the historical extreme value method can be: the first-level indicator: the extreme value (dynamic) of each measuring point over the past five years ± a margin, with a margin of 3mm; and the specific indicator settings for the trend method can be: time-dependent, bidirectional, uniform, or divergent. The notes for the fixed limit method could be: "The measured value varies between -7.66mm and 10.53mm, with a small fluctuation within a reasonable range." The indicator is determined by the characteristic value ± a margin of 3mm. The margin is based on the "Concrete Dam Monitoring Accuracy" in the "Technical Specifications for Concrete Dam Safety Monitoring" and the measured accuracy. The notes for the historical extreme value method could be: "The margin value is the same as above." The notes for the trend method could be: "The measured value is generally stable and shows no trend."

[0111] Step S306: Verify the indicator plan.

[0112] Use key databases to verify the rationality of monitoring indicator plans.

[0113] Figure 6 is a schematic diagram of a system for constructing online monitoring indicators for dam safety based on a large language model according to an optional embodiment of the present application, such as Figure 6 As shown, the system for constructing online monitoring indicators for dam safety based on a large language model may include: a database, a data learning module based on a large language model, a monitoring scheme generation module and a monitoring scheme verification module, wherein the database is used to store the key database in step S301; the data learning module based on the large language model is used to indicate the above step S302, the monitoring scheme generation module is used to execute the above steps S303-S305, the key of which is used for framework construction and margin setting, and the monitoring scheme verification module is used to execute the above step S306.

[0114] An optional embodiment of this application applies a large language model to the construction of dam safety monitoring indicators, reducing the professional requirements and labor costs for those compiling dam safety monitoring indicator plans, and improving plan preparation efficiency. Furthermore, an optional embodiment of this application proposes a model, method, and system for constructing dam safety online monitoring indicators, achieving standardization and specialization in the construction of dam safety online monitoring indicator plans, while ensuring customization of indicator plans for specific power plants.

[0115] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0116] This embodiment also provides a device for determining a dam monitoring indicator scheme based on a large language model. This device is used to implement the above-mentioned embodiments and preferred implementations, and details already described will not be repeated. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0117] Figure 7 is a structural block diagram of a device for determining a dam monitoring indicator solution based on a large language model according to an embodiment of the present application, such as Figure 7 As shown, the device includes:

[0118] a parsing module 72 for parsing a target database corresponding to the dam based on a large language model to extract key information from the target database, wherein the target database includes: a first database for indicating operating parameters corresponding to the dam and a second database for indicating constraints to be followed by the dam;

[0119] A first determining module 74 is configured to determine target margin information corresponding to the dam based on the key information, and to establish a monitoring indicator framework for the dam based on a plurality of preset monitoring indicators corresponding to the dam;

[0120] The second determining module 76 is configured to determine a monitoring indicator solution corresponding to the dam according to the monitoring indicator framework and the target margin information.

[0121] Through the above-mentioned device, based on the large language model, the key information in the target database corresponding to the dam, including: a first database for indicating the operating parameters corresponding to the dam and a second database for indicating the constraints to be followed by the dam, is parsed to extract the key information in the target database; the target margin information corresponding to the dam is determined based on the key information, and a monitoring indicator framework for the dam is established based on multiple preset monitoring indicators corresponding to the dam; and the monitoring indicator scheme corresponding to the dam is determined based on the monitoring indicator framework and the target margin information. In other words, the embodiment of the present application can parse the key information in the target database, and then determine the target margin information corresponding to the dam based on the key information, and establish a monitoring indicator framework based on the preset monitoring indicators, and then determine the monitoring indicator scheme for the dam based on the target margin information and the monitoring indicator framework. According to the embodiment of the present application, the problem of the difficulty of manually compiling monitoring indicator schemes for dams in the related art can be solved. This reduces the difficulty of compiling monitoring indicator schemes for dams.

[0122] In an exemplary embodiment, the parsing module 72 is further used to perform format conversion on the target data in the target database to convert the format of the target data into a target format; and perform semantic parsing on the target data in the target format based on the large language model to extract the key information from the target data.

[0123] In an exemplary embodiment, the first determination module 74 is further used to extract the safety monitoring variables corresponding to the gate and dam from the key information, and obtain historical data corresponding to the safety monitoring variables; determine the historical change trend corresponding to the safety monitoring variables based on the historical data, and calculate the first safety range of the safety monitoring variables based on the historical change trend and the design parameters corresponding to the gate and dam; determine the first margin information corresponding to the gate and dam based on the first safety range; perform safety verification on the first margin information, and determine the target margin information based on the verification result.

[0124] In an exemplary embodiment, the first determination module 74 is further used to extract a second safety range of the safety monitoring variable from the key information, wherein the second safety range to be followed is used to indicate the safety range to be followed by the safety monitoring variable; the first margin information is security verified according to the second safety range to determine whether the first margin information complies with the second safety range according to the verification result, and when the verification result indicates that the first margin information complies with the second safety range, the first margin information is determined as the target margin information; when the verification result indicates that the first margin information does not comply with the second safety range, the target margin information is determined according to the first margin information and the second safety range.

[0125] In an example embodiment, the first determining module 74 is further configured to determine a monitoring item corresponding to each preset monitoring indicator, and classify each monitoring item to determine a monitoring level of the monitoring item; determine a monitoring method corresponding to each preset monitoring indicator according to the monitoring level and the monitoring item; and establish the monitoring indicator framework according to the plurality of preset monitoring indicators, the monitoring level, the monitoring item and the monitoring method.

[0126] In an example embodiment, the second determining module 76 is further configured to determine a safety monitoring variable corresponding to the target margin information, and determine a monitoring item having a corresponding relationship with the safety monitoring variable in the monitoring indicator framework; and determine the monitoring indicator scheme according to the corresponding relationship, the target margin information and the monitoring indicator framework.

[0127] In an example embodiment, the second determining module 76 is further configured to extract a safety monitoring variable corresponding to the dam from the key information, and obtain historical data corresponding to the safety monitoring variable; determine a number of times that the historical data conforms to the monitoring indicator scheme; and in a case where the number of times is less than or equal to a preset threshold, perform structural safety analysis on the monitoring indicator scheme according to the target database to determine whether a safety coefficient corresponding to the monitoring indicator scheme conforms to a preset safety coefficient; and in a case where it is determined that the safety coefficient corresponding to the monitoring indicator scheme conforms to the preset safety coefficient, determine that the monitoring indicator scheme passes the check.

[0128] It should be noted that each of the above modules can be implemented by software or hardware, and for the latter, the following implementation manners can be used, but are not limited thereto: all of the above modules are located in the same processor; or the above modules are located in different processors in any combination.

[0129] Embodiments of the present application also provide a computer readable storage medium, which stores a computer program, wherein the computer program is configured to execute the steps in any of the above method embodiments when running.

[0130] Optionally, in the present embodiment, the above storage medium can be configured to store program code for executing the following steps:

[0131] S1, analyzing a target database corresponding to a dam based on a large language model to extract key information in the target database, wherein the target database includes a first database for indicating operating parameters corresponding to the dam and a second database for indicating constraint conditions to be followed by the dam;

[0132] S2, determining target margin information corresponding to the dam based on the key information, and establishing a monitoring indicator framework for the dam based on a plurality of preset monitoring indicators corresponding to the dam;

[0133] S3: Determine a monitoring indicator solution corresponding to the dam according to the monitoring indicator framework and the target margin information.

[0134] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disk.

[0135] An embodiment of the present application further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0136] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0137] Optionally, in this embodiment, the processor may be configured to execute the following steps through a computer program:

[0138] S1, parsing a target database corresponding to the sluice dam based on a large language model to extract key information from the target database, wherein the target database includes: a first database for indicating operating parameters corresponding to the sluice dam and a second database for indicating constraints to be followed by the sluice dam;

[0139] S2, determining target margin information corresponding to the dam based on the key information, and establishing a monitoring indicator framework for the dam based on a plurality of preset monitoring indicators corresponding to the dam;

[0140] S3: Determine a monitoring indicator solution corresponding to the dam according to the monitoring indicator framework and the target margin information.

[0141] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0142] An embodiment of the present application further provides another computer program product, comprising a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any of the above method embodiments are implemented.

[0143] An embodiment of the present application also provides a computer program, which includes computer instructions, which are stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps of any of the above method embodiments.

[0144] Optionally, in this embodiment, the processor may be configured to execute the following steps through a computer program:

[0145] S1, parsing a target database corresponding to the sluice dam based on a large language model to extract key information from the target database, wherein the target database includes: a first database for indicating operating parameters corresponding to the sluice dam and a second database for indicating constraints to be followed by the sluice dam;

[0146] S2, determining target margin information corresponding to the dam based on the key information, and establishing a monitoring indicator framework for the dam based on a plurality of preset monitoring indicators corresponding to the dam;

[0147] S3: Determine a monitoring indicator solution corresponding to the dam according to the monitoring indicator framework and the target margin information.

[0148] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail here.

[0149] Obviously, those skilled in the art should understand that the modules or steps of the present application described above can be implemented using a general-purpose computing device, they can be concentrated on a single computing device, or distributed across a network composed of multiple computing devices, they can be implemented using program code executable by the computing device, and thus, they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be performed in a different order than herein, or they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0150] The above description is merely a preferred embodiment of the present application and is not intended to limit the present application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A method for determining a dam monitoring indicator scheme based on a large language model, characterized in that: include: Parsing a target database corresponding to the dam based on a large language model to extract key information from the target database, wherein the target database includes: a first database for indicating operating parameters corresponding to the dam and a second database for indicating constraints to be followed by the dam; Determining target margin information corresponding to the dam based on the key information, and establishing a monitoring indicator framework for the dam based on a plurality of preset monitoring indicators corresponding to the dam; Determining a monitoring indicator scheme corresponding to the dam according to the monitoring indicator framework and the target margin information; Wherein, determining the target margin information corresponding to the dam according to the key information includes: Extracting safety monitoring variables corresponding to the dam from the key information, and obtaining historical data corresponding to the safety monitoring variables; Determining a historical change trend corresponding to the safety monitoring variable based on the historical data, and calculating a first safety range of the safety monitoring variable based on the historical change trend and design parameters corresponding to the dam; determining first margin information corresponding to the dam according to the first safety range; A security verification is performed on the first margin information, and the target margin information is determined according to a verification result.

2. The method according to claim 1, characterized in that The target database corresponding to the dam is parsed based on the large language model to extract key information from the target database, including: Performing format conversion on the target data in the target database to convert the format of the target data into a target format; The target data in the target format is semantically parsed based on the large language model to extract the key information from the target data.

3. The method according to claim 1, characterized in that Performing security verification on the first margin information and determining the target margin information according to the verification result, including: Extracting a second safety range of the safety monitoring variable from the key information, wherein the second safety range to be followed is used to indicate a safety range to be followed by the safety monitoring variable; performing security verification on the first margin information according to the second safety range, so as to determine whether the first margin information complies with the second safety range according to the verification result, If the verification result indicates that the first margin information complies with the second safety range, determining the first margin information as the target margin information; In a case where the verification result indicates that the first margin information does not comply with the second safety range, the target margin information is determined according to the first margin information and the second safety range.

4. The method according to claim 1, wherein Establishing a monitoring indicator framework for the dam based on a plurality of preset monitoring indicators corresponding to the dam includes: Determine the monitoring items corresponding to each preset monitoring indicator, and classify each monitoring item to determine the monitoring level of each monitoring item; Determine the monitoring method corresponding to each preset monitoring indicator according to the monitoring level and the monitoring item; The monitoring indicator framework is established according to the multiple preset monitoring indicators, the monitoring levels, the monitoring items and the monitoring methods.

5. The method according to claim 1, wherein Determining a monitoring indicator scheme corresponding to the dam according to the monitoring indicator framework and the target margin information includes: Determining a safety monitoring variable corresponding to the target margin information, and determining a monitoring item having a corresponding relationship with the safety monitoring variable in the monitoring indicator framework; The monitoring indicator scheme is determined according to the corresponding relationship, the target margin information and the monitoring indicator framework.

6. The method according to claim 1, characterized in that After determining a monitoring indicator scheme corresponding to the dam according to the monitoring indicator framework and the target margin information, the method further includes: Extracting safety monitoring variables corresponding to the dam from the key information, and obtaining historical data corresponding to the safety monitoring variables; Determining the number of times the historical data complies with the monitoring indicator scheme; When the number of compliance times is less than or equal to a preset threshold, performing a structural safety analysis on the monitoring indicator scheme according to the target database to determine whether the safety factor corresponding to the monitoring indicator scheme meets the preset safety factor; When it is determined that the safety factor corresponding to the monitoring indicator scheme meets the preset safety factor, it is determined that the monitoring indicator scheme passes the verification.

7. A device for determining a dam monitoring indicator scheme based on a large language model, characterized in that: include: a parsing module, configured to parse a target database corresponding to the dam based on a large language model to extract key information from the target database, wherein the target database includes: a first database for indicating operating parameters corresponding to the dam and a second database for indicating constraints to be followed by the dam; a first determining module, configured to determine target margin information corresponding to the sluice dam according to the key information, and to establish a monitoring indicator framework for the sluice dam according to a plurality of preset monitoring indicators corresponding to the sluice dam; A second determining module is configured to determine a monitoring indicator scheme corresponding to the dam according to the monitoring indicator framework and the target margin information; Among them, the first determination module is also used to extract the safety monitoring variables corresponding to the gate and dam from the key information, and obtain historical data corresponding to the safety monitoring variables; determine the historical change trend corresponding to the safety monitoring variables based on the historical data, and calculate the first safety range of the safety monitoring variables based on the historical change trend and the design parameters corresponding to the gate and dam; determine the first margin information corresponding to the gate and dam based on the first safety range; perform safety verification on the first margin information, and determine the target margin information based on the verification result.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 6 when executed.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

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