Method and system for establishing cascade power station real-time operation risk optimization guidance

Abstract

The invention discloses a guidance method and system for establishing cascade power station real-time operation risk optimization, and relates to the technical field of water conservancy scheduling and intelligent risk assessment, and the method comprises the steps: executing risk preliminary assessment based on collected water level and equipment data, and generating a state snapshot; performing compensation re-evaluation on the historical state snapshots based on the real meteorological data received in a delayed manner; and performing scheduling correction trigger judgment based on the risk assessment offset and the error function. According to the method, a complete and closed-loop dynamic risk scheduling decision chain is formed by constructing a risk snapshot mechanism, compensating historical errors and judging whether scheduling correction needs to be triggered or not, and the risk scheduling decision chain, the historical errors and the scheduling correction are closely matched. The scheduling system is endowed with self-adaptive analysis and intelligent judgment capability on various conditions such as prediction data delay, actual measurement deviation and risk evolution, and the safety, the real-time performance and the intelligent level of hydropower station scheduling are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy dispatching and intelligent risk assessment, and specifically to a method and system for establishing a real-time operation risk optimization guidance method for cascade power stations. Background Art

[0002] As an important infrastructure for the utilization of water resources in large and medium-sized river basins, the operation and dispatching of cascade hydropower stations directly affect the flood control capacity of the river basin, the utilization efficiency of water resources, and the stability of clean energy supply. With the improvement of the comprehensive management level of the river basin, more and more dispatching systems have begun to integrate multi-source data such as hydrological sensors, SCADA monitoring, remote sensing meteorology, etc., to achieve real-time perception and control of reservoirs and hydropower stations. At the same time, operation risk assessment has become a key content to ensure the stable operation of the system. Especially in the context of frequent floods and extreme weather, it is necessary to rely on higher-frequency and more accurate data processing technologies and decision support systems to assist dispatchers in formulating scientific operation strategies and improving the real-time, forward-looking, and safety of dispatching.

[0003] However, there are generally three technical limitations in existing cascade power station dispatching systems: risk assessment is often based on current instantaneous data for static determination, lacking a complete state history record and snapshot mechanism, and it is difficult to achieve dispatching prediction when information is incomplete; there is no unified compensation mechanism for the problem of delayed arrival of key prediction data such as remote sensing and meteorology. Currently, the systems generally use single static judgment and do not have the ability of automatic compensation and risk re-assessment after subsequent data updates; the system lacks an effective risk assessment deviation identification and trigger mechanism, and cannot dynamically adjust the dispatching plan according to the risk changes after the delayed data arrival, resulting in untimely response in high-risk scenarios and unable to optimize the dispatching strategy in a closed loop. Generally speaking, the existing technology has not established a "preliminary assessment - compensation - determination" closed-loop logic mechanism, and it is difficult to achieve dynamic perception, autonomous correction, and optimization guidance of the operation risk of cascade power stations. The present invention precisely proposes a systematic solution to these key problems. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: the existing operation risk assessment method for cascade power stations lacks a historical state snapshot mechanism, cannot compensate for the risk judgment deviation caused by delayed data, lacks a dispatching strategy trigger mechanism based on risk changes, and how to establish a real-time risk optimization guidance method with the ability of compensation correction and dispatching linkage.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: A method for establishing a real-time operation risk optimization guidance for cascade power stations, including performing a preliminary risk assessment based on the collected water level and equipment data and generating a status snapshot; compensating and re-evaluating the historical status snapshot based on the actually received meteorological data with a delay; and making a determination on triggering dispatching correction based on the risk assessment offset and the error function.

[0007] As a preferred embodiment of the method for establishing a real-time operation risk optimization guidance for cascade power stations according to the present invention, wherein: the performing of the preliminary risk assessment includes obtaining the real-time water level data of the current reservoir area through a hydrological monitoring device, collecting the current reservoir inflow velocity information through a flowmeter device, and at the same time collecting the operating temperature data of key power generation equipment by the power station monitoring system; obtaining the future rainfall prediction value and wind speed prediction value for auxiliary calculation within the current time period as risk assessment input factors; inputting all the collected and predicted data into a risk level calculation model including multiple non-linear transformations, and outputting the risk assessment value at the current moment, and the risk assessment value at the current moment is recorded as the original snapshot content of the current state.

[0008] As a preferred embodiment of the method for establishing a real-time operation risk optimization guidance for cascade power stations according to the present invention, wherein: the generating of the status snapshot includes storing the risk assessment value and feature input at the current moment as a timestamp snapshot, and marking the prediction data field as a to-be-completed state. When the delayed field arrives later, the snapshot content is retrieved again according to the time alignment index, and all snapshot states are arranged according to the time window.

[0009] As a preferred embodiment of the method for establishing a real-time operation risk optimization guidance for cascade power stations according to the present invention, wherein: the performing of the compensating and re-evaluating includes aligning the actually received real rainfall value and wind speed value with the stored predicted rainfall value and predicted wind speed value respectively according to the timestamp, and searching for the risk status snapshot corresponding to the time period; replacing the predicted rainfall value recorded in the snapshot with the currently actually collected rainfall value, and replacing the predicted wind speed value with the currently actually collected wind speed value; re-calculating the risk level at the snapshot moment, and the calculation process adopts a weighted combination model, and the inputs include water level, flow velocity, equipment temperature, real rainfall value and real wind speed value, and the output is the compensated risk assessment result.

[0010] As a preferred embodiment of the method for establishing an optimized guidance method for real-time operation risks of cascade power stations according to the present invention, wherein: the determination of triggering the scheduling correction includes receiving predicted rainfall data and predicted wind speed data, and obtaining the actual rainfall data and actual wind speed data corresponding to the predicted data after the arrival of the delayed data; calculating the difference between the predicted rainfall data and the actual rainfall data, and the difference between the predicted wind speed data and the actual wind speed data respectively, and taking the absolute value of the difference to reflect the error magnitude independent of direction; obtaining two standardized relative error ratios based on the difference between prediction and actuality to measure the error degree of prediction in the two dimensions of rainfall and wind speed; performing weighted average or directly taking the arithmetic average of the two relative error ratios to generate a normalized error index finally representing the uncertainty of the predicted data in the current evaluation window; constructing an uncertainty function and automatically executing it during each risk compensation evaluation.

[0011] As a preferred embodiment of the method for establishing an optimized guidance method for real-time operation risks of cascade power stations according to the present invention, wherein: the determination of triggering the scheduling correction further includes forming a comprehensive offset value based on the uncertainty function; comparing the comprehensive offset value with a preset risk response threshold.

[0012] As a preferred embodiment of the method for establishing an optimized guidance method for real-time operation risks of cascade power stations according to the present invention, wherein: the determination of triggering the scheduling correction further includes confirming to execute the scheduling plan or maintaining the existing strategy based on the scheduling response output.

[0013] Another object of the present invention is to provide a system for establishing an optimized guidance system for real-time operation risks of cascade power stations, which can re-evaluate the historical state snapshot by compensating based on the actually received meteorological data with a delay, and solves the problem of low reliability in the current water conservancy scheduling and intelligent risk assessment technologies.

[0014] As a preferred embodiment of the system for establishing an optimized guidance system for real-time operation risks of cascade power stations according to the present invention, wherein: it includes a data processing module, a compensation module, and a determination module; the data processing module is used to perform a preliminary risk assessment based on the collected water level and equipment data and generate a state snapshot; the compensation module is used to re-evaluate the historical state snapshot by compensating based on the actually received meteorological data with a delay; the determination module is used to determine the triggering of the scheduling correction based on the risk assessment offset and the error function.

[0015] A computer device includes a memory and a processor, the memory stores a computer program, and the processor executes the computer program to implement the steps of the method for establishing an optimized guidance method for real-time operation risks of cascade power stations.

[0016] A computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, it implements steps for establishing a real-time operation risk optimization guidance method for cascade power stations.

[0017] Advantages of the present invention: The method for establishing a real-time operation risk optimization guidance method for cascade power stations provided by the present invention forms a complete and closed-loop dynamic risk scheduling decision-making chain by constructing a risk snapshot mechanism, compensating for historical errors, and judging whether to trigger scheduling correction. The close cooperation of the three gives the scheduling system the ability of adaptive analysis and intelligent judgment for various situations such as prediction data delay, measured deviation, and risk evolution, greatly improving the safety, real-time performance, and intelligent level of hydropower station scheduling. The present invention has achieved better effects in terms of reliability, safety, and real-time performance. Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0019] Figure 1 It is the overall flowchart of a method for establishing a real-time operation risk optimization guidance method for cascade power stations provided by the first embodiment of the present invention.

[0020] Figure 2 It is the overall flowchart of a system for establishing a real-time operation risk optimization guidance method for cascade power stations provided by the third embodiment of the present invention. Detailed Embodiments

[0021] To make the above objects, features, and advantages of the present invention more obvious and understandable, the detailed embodiments of the present invention will be described below in conjunction with the drawings of the specification. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0022] Embodiment 1, referring to Figure 1 , which is an embodiment of the present invention, provides a method for establishing a real-time operation risk optimization guidance method for cascade power stations, including:

[0023] S1: Perform a preliminary risk assessment based on the collected water level and equipment data and generate a status snapshot.

[0024] Furthermore, performing the initial risk assessment includes obtaining real-time water level data of the current reservoir area through a hydrological monitoring device, collecting the current reservoir inflow velocity information through a flowmeter device, and collecting the operating temperature data of key power generation equipment by the power station monitoring system; obtaining the future rainfall prediction value and wind speed prediction value for auxiliary calculation within the current time period as risk assessment input factors; inputting all the collected and predicted data into a risk level calculation model containing multiple non-linear transformations, outputting the risk assessment value at the current moment, and recording the risk assessment value at the current moment as the original snapshot content of the current state.

[0025] It should also be noted that a preferred solution for performing the initial risk assessment specifically includes, in the case of real-time data input, the system collecting key features and performing preliminary risk calculation, and the result is saved as the original state snapshot, expressed as:

[0026]

[0027] Among them, R0 is the risk level value estimated based on available data during the operation of the cascade power station at the current moment, H is the real-time water level of the current reservoir area (unit: meter), obtained from the dam front monitoring sensor, and it is the core variable of cascade dispatching control. Q is the current inflow velocity (unit: cubic meters per second), collected in real-time by the upstream hydrological station, directly reflecting the water body input intensity. T d is the temperature of the key components of the main engine or auxiliary engine (unit: degree Celsius), obtained from the power station SCADA system. is the future rainfall prediction value corresponding to the current time period (unit: millimeter), obtained from the meteorological model prediction result, placeholder value before delay arrival. is the current predicted wind speed value, α1, α2, α3, α4, α5 are the corresponding weighting coefficients respectively, H safe is the set threshold of the power station's safe operating water level. Exceeding this value will significantly increase the flood risk. δ is the water level regulation response coefficient, used to adjust the amplification effect on the overall risk assessment when the water level approaches or exceeds the safety line, reflecting the risk non-linear response intensity.

[0028] It should be noted that generating the state snapshot includes storing the risk assessment value at the current moment and the feature input as a timestamp snapshot, and marking the predicted data field as the to-be-completed state. When the delayed field arrives later, the snapshot content is retrieved again according to the time alignment index, and all snapshot states are arranged according to the time window.

[0029] It should also be noted that a preferred solution for generating the state snapshot specifically includes the current risk assessment value R0 and the feature input it depends on. Stored as a timestamp snapshot, and mark the predicted data fields in it as the status to be completed. When the delay fields arrive subsequently, retrieve the snapshot content according to the time alignment index for subsequent calculation, and arrange all snapshot states in a time window for subsequent scheduling rollback or risk reconstruction.

[0030] It should also be noted that by generating a "status snapshot", the risk assessment value and all its input data are saved together according to the timestamp, and the predicted fields are clearly marked as the status to be completed, establishing a data basis for subsequent error compensation and risk reconstruction, establishing a traceable, reconstructable, and extensible scheduling risk modeling mechanism, improving the prediction ability and response robustness of the scheduling system in the face of incomplete data, and reducing mis-scheduling and potential safety hazards.

[0031] S2: Based on the real meteorological data received with delay, conduct a compensation re-evaluation of the historical state snapshot.

[0032] Furthermore, the compensation re-evaluation includes aligning the received real rainfall value and wind speed value with the stored predicted rainfall value and predicted wind speed value respectively according to the timestamp, and finding the risk state snapshot for the corresponding time period; replacing the predicted rainfall value recorded in the snapshot with the currently actually collected rainfall value, and replacing the predicted wind speed value with the currently actually collected wind speed value; recalculating the risk level at the snapshot moment, and the calculation process uses a weighted combination model, with the input including water level, flow velocity, equipment temperature, real rainfall value and real wind speed value, and the output is the compensated risk assessment result.

[0033] It should be noted that a preferred specific solution for the compensation re-evaluation includes when the delayed data (real rainfall R p and real wind speed W) arrives, the system re-evaluates the previous snapshot, expressed as:

[0034]

[0035] where R c is the risk level recalculated based on the complete features after the compensation data arrives, is the actual rainfall value (millimeters), obtained by the delayed access of remote sensing, radar or meteorological station data, W is the actual wind speed (meters per second), sourced from the measured wind field data with delayed access. In this step, only the original predicted items are replaced with the real observed data, enabling the system to re-output the risk level based on the complete features.

[0036] It should also be noted that a consistent mathematical evaluation model is used to ensure the coherence of the prediction and measured data in the calculation logic, avoiding evaluation biases caused by model switching. Implement a lag correction for the risk assessment results to provide a more realistic and reliable risk judgment for the system; implement a data-driven model self-supervised correction mechanism to facilitate the evaluation of the reliability of the prediction model; support the construction of a "closed-loop feedback chain of data integrity and decision-making bias" to promote the continuous optimization of the model. Through the "prediction-observation comparison + snapshot re-evaluation" mechanism, the time fault tolerance ability and risk perception posterior enhancement of the scheduling system are realized, ensuring that the scheduling strategy can be automatically corrected after new data arrives, thus significantly enhancing the system's dynamic response to sudden weather and boundary events.

[0037] S3: Determine the trigger for scheduling correction based on the risk assessment offset and the error function.

[0038] Furthermore, determining the trigger for scheduling correction includes receiving predicted rainfall data and predicted wind speed data, and obtaining the corresponding actual rainfall data and actual wind speed data after the delayed data arrives; calculating the differences between the predicted rainfall data and the actual rainfall data, and between the predicted wind speed data and the actual wind speed data respectively, and taking the absolute values of the differences to reflect the error magnitude regardless of direction; obtaining two normalized relative error ratios based on the differences between prediction and actual to measure the error degrees of prediction in the two dimensions of rainfall and wind speed; performing a weighted average or directly taking the arithmetic average of the two relative error ratios to generate a final normalized error index representing the uncertainty of the prediction data in the current evaluation window; constructing an uncertainty function, which is automatically executed during each risk compensation assessment.

[0039] It should also be noted that a preferred scheme for constructing the uncertainty function specifically includes that the uncertainty function caused by the delayed data error is expressed as:

[0040]

[0041] Among them, U is the uncertainty coefficient based on the error between the predicted value and the actual value, which is used to adjust the risk correction amplitude and is an index for measuring the information reliability in the cascade scheduling system. ε is a very small positive number used to prevent division-by-zero errors.

[0042] It should be noted that determining the trigger for scheduling correction also includes forming a comprehensive offset value based on the uncertainty function; comparing the comprehensive offset value with a preset risk response threshold.

[0043] It should also be noted that a preferred scheme for forming a comprehensive offset value specifically includes multiplying the risk difference by an uncertainty adjustment factor to obtain the true "impact degree" of the risk, performing risk offset calculation and constructing a scheduling discrimination function expressed as:

[0044] ΔR = |R c - R0|·(1 + U)

[0045]

[0046] Wherein, Δ R represents the comprehensive offset value of the risk level caused by the delayed data compensation, which is the core judgment basis for the scheduling update. D is a judgment variable used to determine whether scheduling correction is required (1 means yes, 0 means no), and it is the output of the instruction control logic module.

[0047] It should also be noted that the determination of triggering the scheduling correction also includes confirming the execution of the scheduling plan or maintaining the existing strategy based on the scheduling response output.

[0048] It should also be noted that a specific scheme for confirming the execution of the scheduling plan or maintaining the existing strategy includes:

[0049] Scenario A: The deviation of the delayed data is small, the risk change is not significant, |R c - R0| = 0.3, the prediction error is also small, U = 0.1, then Δ R = 0.33. If θ = 0.8, then D = 0. Result: The system maintains the original judgment and there is no need to update the scheduling plan. Scenario B: The delayed data has obvious changes but is not extreme, |R c - R0| = 0.7, U = 0.3, Δ R = 0.7·(1 + 0.3) = 0.91. If θ = 0.8, then D = 1. Result: The system judges that the risk has been significantly upgraded or downgraded, triggering the scheduling optimization process. Scenario C: The prediction error is very large, but the difference in risk values is not significant, |R c - R0| = 0.4, but U = 0.9, Δ R = 0.4·1.9 = 0.76. If θ = 0.8, D = 0. The system believes that the numerical offset is not sufficient to trigger a scheduling recalculation, but a confidence reminder mechanism can be added.

[0050] It should also be noted that by dynamically evaluating whether the scheduling plan needs to be updated, over - response or delayed adjustment is avoided; the adaptive ability of the system in complex weather scenarios is enhanced; a risk prompt with a confidence interval is provided for the scheduling control center. This enables the scheduling system to have the ability of active adjustment and precise triggering. By fusing the changes in prediction error and actual risk, the sensitivity and robustness of the scheduling decision - making are improved, effectively preventing mis - scheduling or reaction lag caused by prediction errors.

[0051] Embodiment 2 is an embodiment of the present invention, which provides a method for establishing a real - time operation risk optimization guidance for cascade power stations. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0052] First, in this embodiment, a certain cascade hydropower station is selected as the risk assessment scenario. By deploying a water level sensor in front of the dam, a meteorological prediction interface, and a simplified data acquisition framework, the effectiveness of the present invention in judging the risk level and triggering scheduling under simplified parameter conditions is verified. In the experiment, data is collected once every 2 hours, and a total of 6 groups of samples are collected. The collection indicators include three variables: the current water level, predicted rainfall, and predicted wind speed. Combining with the risk assessment model, the preliminary risk value is output in real time; then, after a 1-hour delay, the measured rainfall and wind speed data are accessed, and the compensated risk value is recalculated. The difference between the preliminary evaluation value and the compensated value is recorded during the experiment, and it is judged whether the threshold for triggering scheduling correction is reached. Whether the scheduling scheme needs to be updated is output through the scheduling trigger flag, so as to verify the fault tolerance, discriminability, and scheduling response rationality of the proposed method under incomplete information conditions. Refer to Table 1 for recording and analyzing part of the experimental data.

[0053] Table 1 Experimental Data Record Table

[0054]

[0055] Through the analysis of the experimental data, it can be seen that the risk values before and after compensation show different trends in different samples. Among them, in sample points 4 and 6, due to the measured values of rainfall and wind speed being significantly higher than the predicted values, the risk value rises above the triggering scheduling threshold, and the system successfully identifies the scenarios that require adjustment of the scheduling strategy; in other samples, although there are certain prediction deviations, the risk changes are not sufficient to have an effective impact, and the system maintains the original strategy unchanged, reflecting the selectivity and accuracy of the judgment. By constructing a three-step collaborative mechanism of state snapshot, compensation re-evaluation, and risk offset trigger, the present invention not only improves the dynamic adaptation ability of risk assessment, but also effectively enhances the robustness and fault tolerance of scheduling judgment by introducing a regulation factor in the presence of prediction errors, overcomes the deficiencies of "single threshold drive and rigid response" in traditional methods, and fully demonstrates the practicality and innovation of this method in the real hydropower scheduling system.

[0056] Example 3, referring to Figure 2 , is an embodiment of the present invention, which provides a real-time operation risk optimization guidance system for cascade power stations, including a data processing module, a compensation module, and a determination module.

[0057] Among them, the data processing module is used to perform preliminary risk assessment based on the collected water level and equipment data and generate a state snapshot; the compensation module is used to perform compensation re-evaluation on the historical state snapshot based on the real meteorological data received with a delay; the determination module is used to perform scheduling correction trigger determination based on the risk assessment offset and the error function.

[0058] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, etc., which can store program codes of various types.

[0059] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or used in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0060] More specific examples (nonexhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), optical fiber devices, and portable compact disc read-only memories (CDROMs). Additionally, a computer-readable medium can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or otherwise processing it as appropriate, and then storing it in a computer memory.

[0061] It should be understood that each part of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following technologies well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

[0062] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A real-time operation risk optimization guidance method for cascade power stations, characterized in that including: performing an initial risk assessment based on the collected water level and equipment data and generating a status snapshot; performing a compensated re-evaluation on the historical status snapshot based on the real-time meteorological data received with a delay; performing a scheduling correction trigger determination based on the risk assessment offset and the error function.

2. The real-time operation risk optimization guidance method for cascade power stations according to claim 1, characterized in that: The performing of the initial risk assessment includes obtaining the real-time water level data of the current reservoir area through a hydrological monitoring device, collecting the current inflow velocity information of the reservoir through a flowmeter device, and collecting the operating temperature data of key power generation equipment by the power station monitoring system; obtaining the future rainfall prediction value and wind speed prediction value for auxiliary calculation within the current time period as risk assessment input factors; inputting all the collected and predicted data into a risk level calculation model containing multiple non-linear transformations, outputting the risk assessment value at the current moment, and recording the risk assessment value at the current moment as the original snapshot content of the current state.

3. The real-time operation risk optimization guidance method for cascade power stations according to claim 2, characterized in that: The generating of the status snapshot includes storing the risk assessment value and feature input at the current moment as a timestamp snapshot, marking the predicted data field as a to-be-completed state, retrieving the snapshot content again according to the time alignment index when the delayed field arrives later, and arranging all the snapshot states according to the time window.

4. The real-time operation risk optimization guidance method for cascade power stations according to claim 3, characterized in that: The performing of the compensated re-evaluation includes aligning the received real rainfall value and wind speed value with the stored predicted rainfall value and predicted wind speed value respectively by timestamp, and searching for the risk status snapshot in the corresponding time period; replacing the predicted rainfall value recorded in the snapshot with the currently actually collected rainfall value, and replacing the predicted wind speed value with the currently actually collected wind speed value; recalculating the risk level at the snapshot moment, and the calculation process uses a weighted combination model, the input includes water level, flow velocity, equipment temperature, real rainfall value and real wind speed value, and the output is the compensated risk assessment result.

5. The real-time operation risk optimization guidance method for cascade power stations as claimed in claim 4, wherein: The performing of the scheduling correction trigger determination includes receiving the predicted rainfall data and predicted wind speed data, and obtaining the actual rainfall data and actual wind speed data corresponding to the predicted data after the delayed data arrives; calculating the difference between the predicted rainfall data and the actual rainfall data, and the difference between the predicted wind speed data and the actual wind speed data respectively, and taking the absolute value of the difference to reflect the error magnitude regardless of direction; obtaining two standardized relative error ratios based on the difference between the prediction and the actual to measure the error degree of the prediction in the two dimensions of rainfall and wind speed; performing a weighted average or directly taking an arithmetic average of the two relative error ratios to generate a final normalized error index representing the uncertainty of the predicted data in the current evaluation window; constructing an uncertainty function, which is automatically executed during each risk compensation evaluation.

6. The real-time operation risk optimization guidance method for cascade power stations according to claim 5, characterized in that: The performing of the scheduling correction trigger determination also includes forming a comprehensive offset value based on the uncertainty function; comparing the comprehensive offset value with a preset risk response threshold.

7. The real-time operation risk optimization guidance method for cascade power stations according to claim 6, characterized in that: The performing of the scheduling correction trigger determination also includes confirming to execute the scheduling scheme or maintaining the existing strategy based on the scheduling response output.

8. A system for adopting the method for establishing an optimized guidance for real-time operation risks of cascade power stations as described in any one of claims 1 to 7, characterized in that: including a data processing module, a compensation module, and a determination module; The data processing module is used to perform an initial risk assessment based on the collected water level and equipment data and generate a status snapshot; The compensation module is used to perform a compensated re-evaluation on the historical status snapshot based on the real-time meteorological data received with a delay; The determination module is used to perform a scheduling correction trigger determination based on the risk assessment offset and the error function.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for establishing a real-time operation risk optimization guidance method for cascade power stations described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for establishing a real-time operation risk optimization guidance method for cascade power stations described in any one of claims 1 to 7.

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