Real-time operation risk early warning method and system for establishing cascade power stations

Abstract

The invention discloses a method and a system for establishing a cascade power station real-time operation risk early warning, and relates to the technical field of power system operation management and monitoring, and the method comprises the steps: carrying out the real-time collection and preprocessing of multi-modal data; carrying out anomaly detection and carrying out adaptive risk identification; and generating optimization guidance based on the knowledge graph. According to the real-time operation risk early warning method for establishing the cascade power stations, potential complex abnormal modes are recognized through the multivariable abnormal detection step, rapid capture of potential risks is achieved, the breadth and accuracy of abnormal detection are enhanced, the false alarm rate and the missing report rate are effectively reduced, and it is ensured that the system responds to the abnormities in time. Through the weighted comprehensive risk scoring step, accurate grading early warning of the operation state of the power station is realized through comprehensive risk scoring, the distinguishing capability of the system for different risk grades is improved, and a scientific basis is provided for subsequent risk management and emergency response. According to the invention, better effects are achieved in the aspects of safety and intelligent level of operation management.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation management and monitoring, and in particular to a method and system for establishing real-time operation risk warning for cascade power stations. Background Art

[0002] In the operation and management of cascade power stations, real-time monitoring and risk warning technologies have undergone years of development. Traditional power station monitoring systems mainly rely on parameter monitoring and alarm functions of a single data source, and detect abnormalities in operating parameters through fixed thresholds. However, with the continuous expansion of the scale of cascade power stations and the increase in the complexity of monitoring data, a single data source and simple threshold settings can no longer meet the needs of high-precision risk identification. In recent years, multimodal data fusion and intelligent monitoring have gradually become research hotspots. By combining big data technology, machine learning algorithms and intelligent control methods, power station monitoring systems have gradually developed in the direction of multi-dimensional monitoring and intelligent early warning, in order to achieve all-round monitoring and anomaly detection of equipment status in a dynamic environment. However, in this field, there is still a lack of technical means that can quickly adapt to changes in diverse power station scenarios and generate accurate early warnings in real time.

[0003] The current risk warning methods for cascade power stations have many deficiencies. On the one hand, traditional methods are mainly based on a single data mode in data collection, with a single data type and failure to effectively integrate multiple data sources, resulting in insufficient monitoring reliability and data richness, which in turn affects the accurate judgment of potential risks. On the other hand, in terms of anomaly detection and risk identification, existing technologies are usually based on fixed threshold settings or static single variable monitoring, which is difficult to adapt to the dynamic and changeable operating environment of cascade power stations. For example, the operating parameters of power stations will fluctuate with changes in seasons, loads, and external environment. The alarm mechanism with fixed thresholds often has false alarms or missed alarms, making it difficult to ensure operating efficiency and risk control effects. In addition, the current early warning technology lacks adaptive capabilities and cannot dynamically adjust the early warning strategy according to different historical operating data and real-time scenarios. At the same time, the existing technology lacks intelligent means in terms of optimization guidance. The corresponding early warning only prompts abnormal conditions and fails to provide personalized operation suggestions for specific situations. This requires operators to rely on experience to judge and handle abnormalities, which poses a high operational risk. In contrast, the present invention can effectively overcome the above-mentioned defects of the prior art and improve the safety and intelligence level of cascade power station operation through multimodal data fusion, adaptive risk identification model and optimization guidance generation based on knowledge graph. Summary of the invention

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

[0005] Therefore, the technical problem solved by the present invention is: the existing risk warning method for cascade power stations has the shortcomings of a single data collection method, fixed threshold settings, and lack of intelligent optimization guidance, as well as the problem of how to achieve real-time, efficient, and intelligent risk warning.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a method for establishing real-time operation risk warning for cascade power stations, including real-time acquisition and preprocessing of multimodal data; anomaly detection and adaptive risk identification; and generation of optimization guidance based on knowledge graphs.

[0007] As a preferred solution of the method for establishing a real-time operation risk warning method for a cascade power station according to the present invention, the real-time acquisition and preprocessing of multimodal data includes real-time acquisition of multimodal data from various sensors, monitoring systems and dispatching platforms of the cascade power station, including voltage, current, temperature, water level, flow and equipment status operation information;

[0008] The collection module is connected to the existing scheduling system and supports dynamic adjustment of the collection frequency.

[0009] As a preferred solution of the method for establishing a real-time operation risk warning method for a cascade power station according to the present invention, wherein: the real-time acquisition and preprocessing of multimodal data includes preprocessing the collected data, including data cleaning, format standardization, denoising and completion;

[0010] Preprocessing removes outliers and invalid data from the data.

[0011] As a preferred solution of the method for establishing a real-time operation risk warning method for a cascade power station according to the present invention, the abnormality detection and adaptive risk identification includes building an abnormality detection model based on historical data and equipment characteristics. For water level, current and voltage information, a multivariate abnormality detection formula based on Mahalanobis distance is used to determine whether the real-time data deviates from the historical mean, which is expressed as:

[0012]

[0013] Among them, X(t) represents the real-time data vector at time t, μ represents the mean vector of historical data, Σ -1 Represents the inverse covariance matrix of historical data;

[0014] D M (X(t)) exceeds the set threshold D t , determined as abnormal and triggering primary warning;

[0015] D t Make dynamic adjustments based on seasonal, load, and environmental changes.

[0016] As a preferred solution for establishing a real-time operation risk warning method for cascade power stations according to the present invention, the abnormal detection and adaptive risk identification includes adopting a weighted comprehensive risk scoring method for water level and load, and generating an overall risk score according to the real-time deviation degree of each parameter, which is expressed as:

[0017]

[0018] Among them, X i represents the real-time value of the i-th parameter, X i,target represents its target value, σ i represents the standard deviation of the i-th parameter, w i represents the weight of the i-th parameter, δ i Represents a binary variable, if X i Beyond the threshold range, δ i =1, otherwise δ i =0, β represents the adjustment factor to amplify the contribution of outliers;

[0019] When R exceeds the predetermined threshold R t When an emergency occurs, the system triggers an early warning according to the risk level and reminds the operator through images and sounds.

[0020] As a preferred solution of the method for establishing a real-time operation risk warning method for a cascade power station according to the present invention, the abnormality detection and adaptive risk identification includes using an adaptive weighted sliding window time series model to predict key parameters at a future moment, which is expressed as:

[0021]

[0022] Among them, α represents the model constant term, p and q represent the order of autoregressive term and moving average term, φ j and θ k represents the coefficients of the autoregressive and moving average models, ∈ t-k represents the error term, γ represents the sliding window adjustment factor, M represents the sliding window size, and w m Represents the weight coefficient of the window historical data, Represents historical data at time tm;

[0023] Adapt to the fluctuation of real-time data through adaptive sliding window adjustment. t+1 When the preset safety threshold is exceeded, the system will generate an early warning.

[0024] As a preferred solution of the method for establishing a real-time operation risk warning method for cascade power plants described in the present invention, wherein: the optimization guidance generation based on the knowledge graph includes organizing and structuring expert experience, equipment operation manuals, and dispatching procedures information to form a knowledge graph for cascade power plants, covering operating specifications, risk management, and emergency plans;

[0025] Optimization guidance generation: When an early warning occurs, the system automatically calls the knowledge graph and generates optimization suggestions based on the risk score;

[0026] Using reinforcement learning algorithms, we simulate the impact of different operation strategies on the system and dynamically select the optimal response plan;

[0027] The system records the dispatcher's operational feedback and uses it to optimize the reinforcement learning model.

[0028] Another object of the present invention is to provide a system for establishing a real-time operation risk warning system for cascade power plants, which can detect anomalies and perform adaptive risk identification, thereby solving the problem that the current risk warning method for cascade power plants contains fixed threshold settings.

[0029] As a preferred solution for establishing a real-time operation risk warning system for cascade power stations as described in the present invention, it includes: a multimodal data real-time acquisition and preprocessing module, an anomaly detection and adaptive risk identification module, and an optimization guidance generation module based on a knowledge graph; the multimodal data real-time acquisition and preprocessing module is used to collect and organize parameter data in cascade power stations in real time; the anomaly detection and adaptive risk identification module is used to detect potential abnormal conditions in the operating status of the power station, and dynamically evaluate the overall risk level according to the degree of data deviation, and generate a real-time warning; the optimization guidance generation module based on the knowledge graph is used to provide dispatching personnel with countermeasures and optimization guidance.

[0030] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of establishing a real-time operation risk early warning method for a cascade power station.

[0031] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements steps for establishing a real-time operation risk early warning method for a cascade power station.

[0032] Beneficial effects of the present invention: The method for establishing a real-time operation risk warning for a cascade power station provided by the present invention realizes the real-time acquisition of multimodal data through the data collection step, realizes all-round real-time monitoring of the power station operation status, enhances the comprehensiveness of the data and the response speed of the system, and provides a high-quality data foundation for anomaly detection and risk warning. Through the data preprocessing step, the accuracy and reliability of the data are ensured, the reliability of the data and the accuracy of the analysis are improved, the influence of noise interference on the anomaly detection results is avoided, and the accuracy of risk identification and the overall stability of the system are enhanced. Through the multivariate anomaly detection step, potential complex abnormal patterns are identified, the rapid capture of potential risks is achieved, the breadth and accuracy of anomaly detection are enhanced, the false alarm and missed alarm rates are effectively reduced, and the system's timely response to anomalies is ensured. Through the weighted comprehensive risk scoring step, a comprehensive risk assessment is achieved, and the comprehensive risk scoring realizes accurate graded warning of the power station operation status, improves the system's ability to distinguish different risk levels, and provides a scientific basis for subsequent risk management and emergency response. Through the adaptive trend prediction and early warning steps, potential risk trends are identified in advance, predictive monitoring of future risks is achieved, passive response due to sudden abnormalities is avoided, the system's early warning foresight and stability are improved, and the power station operation is made safer and more controllable. The present invention achieves better results in terms of the safety and intelligence level of operation management. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0034] Figure 1 An overall flow chart of a method for establishing real-time operation risk warning of cascade power plants provided in the first embodiment of the present invention.

[0035] Figure 2 An overall flow chart for establishing a real-time operation risk warning system for cascade power plants is provided for the third embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0037] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides a method for establishing a real-time operation risk warning method for a cascade power station, comprising:

[0038] S1: Real-time acquisition and preprocessing of multimodal data.

[0039] Furthermore, multimodal data is acquired in real time from various sensors, monitoring systems and dispatching platforms of the cascade power stations, including operating information such as voltage, current, temperature, water level, flow and equipment status.

[0040] The collection module is seamlessly connected with the existing scheduling system, supports dynamic adjustment of collection frequency, and ensures the comprehensiveness and timeliness of data.

[0041] It should be noted that the collected data is preprocessed, including data cleaning, format standardization, denoising and completion. The preprocessing module ensures the quality of the data and provides accurate data support for subsequent analysis.

[0042] Furthermore, multimodal data includes information such as voltage, current, temperature, water level, flow rate and equipment status, aiming to reflect the multi-dimensional operating status of cascade power stations. The acquisition module is not only responsible for the extraction of real-time data, but also seamlessly connects to the existing dispatching system to ensure the coverage of data sources and system compatibility. The rationality of the acquisition module lies in that its design adapts to the diversity of multi-source sensors in power stations and meets the efficient operation requirements of power station dispatching and real-time monitoring.

[0043] It should be noted that dynamic adjustment of the acquisition frequency means that the acquisition module adaptively adjusts the sampling frequency according to the real-time monitoring needs to maintain the timeliness of data updates when the data changes suddenly or changes at a high frequency. Dynamic frequency adjustment helps to ensure the comprehensiveness and timeliness of data, especially to provide accurate monitoring when the power station's operating status changes rapidly.

[0044] S2: Anomaly detection and adaptive risk identification.

[0045] Furthermore, an anomaly detection model based on historical data and equipment characteristics is constructed. For information such as water level, current, and voltage, a multivariate anomaly detection formula based on Mahalanobis distance is used to determine whether the real-time data deviates from the historical mean, which is expressed as:

[0046]

[0047] Among them, X(t) represents the real-time data vector at time t, μ represents the mean vector of historical data, Σ -1 Represents the inverse covariance matrix of historical data;

[0048] D M (X(t)) exceeds the set threshold D t, determined as abnormal and triggering primary warning;

[0049] D t Make dynamic adjustments based on seasonal, load, and environmental changes.

[0050] It should be noted that for water level and load, a weighted comprehensive risk scoring method is used to generate an overall risk score based on the real-time deviation of each parameter, expressed as:

[0051]

[0052] Among them, X i represents the real-time value of the i-th parameter, X i,target represents its target value, σ i represents the standard deviation of the i-th parameter, w i represents the weight of the i-th parameter, δ i Represents a binary variable, if X i Beyond the threshold range, δ i =1, otherwise δ i =0, β represents the adjustment factor to amplify the contribution of outliers;

[0053] When R exceeds the predetermined threshold R t When an emergency occurs, the system triggers an early warning according to the risk level and reminds the operator through images and sounds.

[0054] Furthermore, an adaptive weighted sliding window time series model is used to predict the key parameters at the next moment, which can be expressed as:

[0055]

[0056] Among them, α represents the model constant term, p and q represent the order of autoregressive term and moving average term, φ j and θ k represents the coefficients of the autoregressive and moving average models, ∈ t-k represents the error term, γ represents the sliding window adjustment factor, M represents the sliding window size, and w m Represents the weight coefficient of the window historical data, Represents historical data at time tm;

[0057] Adapt to the fluctuation of real-time data through adaptive sliding window adjustment. t+1 When the preset safety threshold is exceeded, the system will generate an early warning.

[0058] It should be noted that the multivariate anomaly detection step measures the deviation of multidimensional data from the normal operating state through the Mahalanobis distance formula, realizing the correlation anomaly detection of multimodal data. The design of this step is based on the complex correlation between multiple parameters (such as voltage, temperature, and flow) in the operation of cascade power stations. The inverse matrix of the covariance matrix is ​​used to reflect the synergistic relationship between the parameters, so as to more comprehensively judge the overall state abnormality. This correlation detection not only considers a single parameter, but also makes full use of the inherent connection between multiple variables, solving the technical problem that a single parameter anomaly may not be able to identify the overall anomaly.

[0059] Traditional single variable detection methods are usually unable to identify correlated anomalies in power plant operation, while multivariate anomaly detection provides a method that considers the coordinated changes of parameters through the Mahalanobis distance formula. M (X(t)) exceeds D t When the system triggers a primary warning, the method performs well in reducing the false alarm rate and ensures sensitivity to abnormal conditions. By introducing multivariate anomaly detection, the present invention can timely identify complex potential risks in cascade power plants and provide accurate preliminary judgments for subsequent comprehensive evaluations.

[0060] The weighted comprehensive risk score generates a quantitative score for the overall risk by combining the real-time deviation degree and weight of each key parameter. The risk contribution of each parameter is accurately allocated through the weighting coefficient, and the abnormal flag and adjustment factor enable the risk scoring system to significantly increase the risk value of the abnormal item when the parameter is detected to be out of the normal range. In this way, the system can focus on the parameter combinations with higher current risks, effectively avoiding the averaging error of traditional scoring methods.

[0061] In traditional risk scoring systems, the superposition of multiple anomalies often leads to a lack of focus in risk assessment. However, the present invention uses weighted comprehensive risk scoring to significantly amplify the risk of serious deviations from the parameters, thereby ensuring the accuracy and rationality of abnormal alarms. t This step significantly improves the system's ability to identify sudden multi-parameter deviations and can effectively support real-time risk assessment in the complex operating environment of cascade power plants.

[0062] Adaptive trend forecasting and early warning uses the AARMA model to dynamically adjust the parameter change trend using a weighted sliding window. The model uses a combination of autoregression and moving average to predict the key parameter X at a certain moment in the future. t+1 The prediction is made, and the window weight and window size are adjusted dynamically to adapt to the changing operating conditions of the power station. The introduction of adaptive windows makes the prediction more accurate when fluctuations are frequent, and can identify risks in advance when trend changes intensify.

[0063] From the perspective of solving technical problems: Traditional fixed window prediction is often difficult to adapt to frequent power station load fluctuations, while the adaptive prediction and early warning of the present invention can be dynamically adjusted according to the trend, so that the prediction error is small. t+1 When the safety threshold is exceeded, the system can issue an early warning, significantly improving the foresight of power plant risk management. By introducing an adaptive sliding window model, the present invention effectively solves the problem of delayed warning in traditional prediction methods in high-frequency dynamic scenarios, providing more accurate protection for the safe operation of power plants.

[0064] S3: Optimization guidance generation based on knowledge graph.

[0065] Furthermore, information such as expert experience, equipment operation manuals, dispatching procedures, etc. is collated and structured to form a knowledge map of cascade power stations, covering operating specifications, risk management, and emergency plans.

[0066] It should be noted that when an early warning occurs, the system automatically calls the knowledge graph and generates optimization suggestions based on the risk score. For example, it provides specific operational guidance such as load regulation, flow control, and equipment adjustment under different risk sources to help dispatchers respond to abnormalities efficiently.

[0067] Furthermore, the knowledge graph is a graphical representation of information structured into nodes and relationships, covering operating specifications, risk management, and emergency plans. In the present invention, the construction of the knowledge graph includes key contents such as power station expert experience, equipment manuals, and dispatching procedures. Its rationality lies in the efficient association and structuring of a large amount of information in the form of a graph, so that the system can quickly extract response strategies.

[0068] The risk score measures the risk level of the current power plant operation. In the invention, the risk score is used as input to call the corresponding part of the knowledge graph to generate optimization guidance. The rationality of this score lies in its ability to quantify and reflect the operating status, ensuring the pertinence and accuracy of the optimization suggestions.

[0069] Optimization guidance is a specific operational suggestion retrieved from the knowledge graph based on risk scores, including load regulation, flow control, equipment adjustment, etc. The rationality of optimization guidance lies in that it ensures that dispatchers can quickly obtain effective response measures in the early warning state of the power station to reduce judgment time and operation errors.

[0070] From the perspective of invention conception, the core idea of ​​knowledge graph construction is to systematize and structure the knowledge and experience related to power plant operation, and to associate the knowledge nodes of power plant operation and emergency management into a knowledge network. This design helps to achieve efficient retrieval and rapid response of knowledge in power plant management with huge data volumes and complex relationships, and solves the problem of slow information extraction and difficult retrieval in traditional paper manuals or scattered documents. In daily power plant operation and sudden emergency situations, the knowledge graph can help the system to instantly call operating specifications and emergency plans, thereby realizing the function of rapid guidance.

[0071] From the perspective of solving technical problems, traditional power plant knowledge and experience are mostly scattered in different documents and lack systematic associations. Dispatchers are often unable to quickly access relevant information when needed, delaying the response time. Through the knowledge graph, the complex relationships and emergency knowledge in power plant management can be systematically represented and can be called up with one click when needed, effectively shortening the response time. In addition, the various types of information associated with the knowledge graph can ensure the efficient sharing and inheritance of knowledge, allowing dispatchers to respond quickly according to standardized emergency plans even in emergencies. This knowledge graph-based solution optimizes the efficiency of knowledge extraction and lays the foundation for the system to intelligently handle power plant operation problems.

[0072] Example 2, an embodiment of the present invention, provides a method for establishing a real-time operation risk warning method for a cascade power station. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0073] First, this embodiment establishes a complete experimental process based on multimodal data collection and processing, anomaly detection and risk identification, and knowledge graph optimization guidance for a cascade power station operation system, aiming to verify the innovation and advantages of the invention in practical applications.

[0074] In the experiment, the system is connected to the power station monitoring platform through the acquisition module to extract real-time data including voltage (V), current (A), temperature (℃), water level (m), flow (m 3 / s) and equipment status (such as start and stop status). The acquisition frequency is dynamically adjusted according to real-time monitoring needs. Specifically, when the system detects that the load change of the power station is intensified, the sampling frequency is increased to once a minute to ensure the timeliness of data changes. Under stable load, the acquisition frequency is reduced to once every 5 minutes to optimize resource consumption. All collected data are cleaned, formatted, denoised and completed by the system's preprocessing module to ensure stable data quality and consistent structure, providing reliable input for anomaly detection and risk identification.

[0075] In the anomaly detection phase of the experiment, multivariate anomaly detection is first used to monitor real-time data. The deviation between the current data vector and the historical mean is measured based on the Mahalanobis distance to identify the abnormal state of a single parameter or a combination of multiple parameters. When the Mahalanobis distance calculation result at a certain point in time exceeds the set threshold, the system triggers a primary warning and records the abnormal data to enter the comprehensive risk scoring phase. The scoring process is based on the real-time deviation value of the key parameters. The deviation of each parameter is digitized through a weighted scoring model to generate a comprehensive risk score; if the score exceeds the preset safety threshold, the system triggers an advanced warning according to the risk level.

[0076] After abnormal event identification and early warning response, the system predicts the future trend of key parameters based on the adaptive weighted sliding window model (AARMA). Through the adaptive adjustment of the model, the system generates trend prediction values ​​of key parameters in real time and monitors whether these trend values ​​exceed the threshold range. If the trend prediction shows that there may be an abnormality at a certain moment in the future, the system will issue an early warning.

[0077] In terms of optimization guidance, when a risk warning is triggered, the system automatically extracts the operating specifications, risk handling solutions and emergency plans that match the current risk situation from the knowledge graph. For example, when the system identifies that the load is too high and the equipment temperature exceeds the limit, the knowledge graph provides guidance on load adjustment and equipment cooling, and the power station dispatcher can refer to the operation. This process ensures that in the event of an emergency, the system can instantly generate personalized response plans to support dispatchers in efficient operations.

[0078] Experimental data recording

[0079] The experimental data are recorded in Table 1 below:

[0080] Table 1 Experimental data table

[0081]

[0082] From the experimental data, we can observe that the system collects multimodal data at different time points, and enters the anomaly detection and risk assessment process after data preprocessing. At time points 4 and 6, due to the over-limit deviation of voltage and temperature, the risk score increased significantly, reaching 1.1 and 1.3 respectively, exceeding the safety threshold set by the system (assuming the threshold is 1.0). At this time, the system automatically triggered an advanced warning, further called the knowledge graph, and the system provided optimization guidance for adjusting the load and reducing the temperature of the equipment. The real-time response of these steps verifies the system's ability to provide timely warnings under complex abnormal conditions.

[0083] In this experiment, the present invention significantly improved the response and accuracy of the system compared to the traditional single-parameter anomaly detection technology. Multivariate detection combined with Mahalanobis distance effectively identifies the abnormal state of multi-parameter coordination, avoiding the false alarms or omissions that may be caused by single-parameter monitoring. At time point 6, through the correlation analysis of multimodal data, the system not only detected the abnormal values ​​of voltage, current, and temperature, but also identified the increase in risk level through risk scoring, and generated early warnings in real time. This early warning capability enables the system to respond to changes more quickly and avoid damage to equipment due to delayed processing.

[0084] In addition, through the optimization guidance generated by the knowledge graph, the system can provide dispatchers with fast and targeted operational suggestions under different risk conditions, thereby reducing the time for dispatchers to make judgments in emergency situations and improving the accuracy and safety of operations. The knowledge graph associates a variety of actual operation scenarios and plans, so that the system can efficiently and accurately guide response operations in any risk situation, and has extremely high intelligent management advantages. This embodiment shows that the present invention has significant innovations in real-time multimodal data acquisition, risk identification and optimization guidance, and can provide systematic intelligent support that is difficult to achieve with traditional power station monitoring technology.

[0085] Example 3, reference Figure 2 , which is an embodiment of the present invention, provides a method for establishing a real-time operation risk warning system for cascade power stations, including a multimodal data real-time acquisition and preprocessing module, an anomaly detection and adaptive risk identification module, and an optimization guidance generation module based on a knowledge graph.

[0086] The multimodal data real-time acquisition and preprocessing module is used to collect and organize parameter data in cascade power stations in real time. The anomaly detection and adaptive risk identification module is used to detect potential abnormal situations in the operation status of the power station, and dynamically evaluate the overall risk level according to the degree of data deviation, and generate real-time warnings. The optimization guidance generation module based on the knowledge graph is used to provide dispatchers with response measures and optimization guidance.

[0087] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0088] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0089] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0090] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned 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, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0091] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for establishing a real-time operation risk warning method for cascade power plants, characterized in that: include: Real-time acquisition and preprocessing of multimodal data; Anomaly detection and adaptive risk identification; Optimization guidance generation based on knowledge graph.

2. The method for establishing a real-time operation risk early warning method for a cascade power station according to claim 1, characterized in that: The real-time acquisition and preprocessing of multimodal data includes real-time acquisition of multimodal data from various sensors, monitoring systems and dispatching platforms of the cascade power station, including voltage, current, temperature, water level, flow and equipment status operation information; The collection module is connected to the existing scheduling system and supports dynamic adjustment of the collection frequency.

3. The method for establishing a real-time operation risk warning method for a cascade power station according to claim 2, characterized in that: The real-time acquisition and preprocessing of multimodal data includes preprocessing the acquired data, including data cleaning, format standardization, denoising and completion; Preprocessing removes outliers and invalid data from the data.

4. The method for establishing a real-time operation risk warning method for a cascade power station according to claim 3, characterized in that: The anomaly detection and adaptive risk identification includes building an anomaly detection model based on historical data and equipment characteristics. For water level, current, and voltage information, a multivariate anomaly detection formula based on Mahalanobis distance is used to determine whether the real-time data deviates from the historical mean, which is expressed as: Among them, X(t) represents the real-time data vector at time t, μ represents the mean vector of historical data, Σ -1 Represents the inverse covariance matrix of historical data; D M (X(t)) exceeds the set threshold D t , determined as abnormal and triggering primary warning; D t Make dynamic adjustments based on seasonal, load, and environmental changes.

5. The method for establishing a real-time operation risk warning method for a cascade power station according to claim 4, characterized in that: The abnormal detection and adaptive risk identification includes using a weighted comprehensive risk scoring method for water level and load, and generating an overall risk score according to the real-time deviation degree of each parameter, which is expressed as: Among them, X i represents the real-time value of the i-th parameter, X i,target represents its target value, σ i represents the standard deviation of the i-th parameter, w i represents the weight of the i-th parameter, δ i Represents a binary variable, if X i Beyond the threshold range, δ i =1, otherwise δ i =0, β represents the adjustment factor to amplify the contribution of outliers; When R exceeds the predetermined threshold R t When an emergency occurs, the system triggers an early warning according to the risk level and reminds the operator through images and sounds.

6. The method for establishing a real-time operation risk warning method for a cascade power station according to claim 5, characterized in that: The anomaly detection and adaptive risk identification includes using an adaptive weighted sliding window time series model to predict key parameters at a future moment, which is expressed as: Among them, α represents the model constant term, p and q represent the order of autoregressive term and moving average term, φ j and θ k represents the coefficients of the autoregressive and moving average models, ∈ t-k represents the error term, γ represents the sliding window adjustment factor, M represents the sliding window size, and w m Represents the weight coefficient of the window historical data, Represents historical data at time tm; Adapt to the fluctuation of real-time data through adaptive sliding window adjustment. t+1 When the preset safety threshold is exceeded, the system will generate an early warning.

7. The method for establishing a real-time operation risk warning method for a cascade power station according to claim 6, characterized in that: The optimization guidance generation based on the knowledge graph includes organizing and structuring expert experience, equipment operation manuals, and dispatching procedures to form a knowledge graph for cascade power plants, covering operating specifications, risk management, and emergency plans; Optimization guidance generation: When an early warning occurs, the system automatically calls the knowledge graph and generates optimization suggestions based on the risk score; Using reinforcement learning algorithms, we simulate the impact of different operation strategies on the system and dynamically select the optimal response plan; The system records the dispatcher's operational feedback and uses it to optimize the reinforcement learning model.

8. A system for establishing a real-time operation risk early warning method for cascade power plants according to any one of claims 1 to 7, characterized in that: It includes multimodal data real-time acquisition and preprocessing module, anomaly detection and adaptive risk identification module, and knowledge graph-based optimization guidance generation module; The multi-modal data real-time acquisition and pre-processing module is used to collect and organize parameter data in cascade power stations in real time; The anomaly detection and adaptive risk identification module is used to detect potential anomalies in the power plant operation status, and dynamically evaluate the overall risk level according to the degree of data deviation to generate real-time warnings; The knowledge graph-based optimization guidance generation module is used to provide dispatchers with response measures and optimization guidance.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for establishing a real-time operation risk warning method for a cascade power station according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for establishing a real-time operation risk warning method for a cascade power station according to any one of claims 1 to 7 are implemented.

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