Hydroelectric generating set fatigue accumulation prediction method and system based on holographic perception

Abstract

The invention discloses a hydroelectric generating set fatigue accumulation prediction method and system based on holographic sensing, and relates to the related technical field of hydroelectric generating set management, and the method comprises the steps: carrying out the real-time sensing of a target hydroelectric generating set through a holographic sensor array; performing data preprocessing; traversing the operation data processing information set, and extracting fatigue accumulation key features; digital twin modeling is carried out, and a fatigue accumulation prediction model of the target hydroelectric generating set is constructed; performing simulated operation of the target hydroelectric generating set, and performing fatigue evaluation according to a preset fatigue accumulation mechanism; and updating the first fatigue accumulation prediction result based on the second evaluation result, and obtaining a second fatigue accumulation prediction result. The technical problem that enough and accurate operation data of a giant hydroelectric generating set is difficult to obtain in existing hydroelectric generating set fatigue accumulation prediction, and consequently the fatigue accumulation prediction result has a large error is solved, and the technical effect of improving the accuracy and reliability of hydroelectric generating set fatigue accumulation prediction is achieved.

Description

Technical Field

[0001] The present application relates to the field related to hydropower unit management technology, and specifically to a method and system for predicting the fatigue accumulation of hydropower units based on holographic perception. Background Art

[0002] As a pillar industry of the national economy, the safety and stability of the power industry have received more and more attention. As the core equipment of hydropower generation, the operating status of hydropower units is directly related to the stable operation of the entire power system. With the long-term operation of giant hydropower stations, hydropower units will inevitably experience fatigue accumulation under the influence of multiple factors such as water flow impact, mechanical wear and electromagnetic force, which will lead to problems such as unit performance degradation and increased failure rate. Traditional fatigue accumulation prediction methods are often based on empirical formulas or simplified models, which are difficult to accurately reflect the actual operation of the unit and the fatigue accumulation process, and it is difficult to obtain sufficient and accurate data to support decision-making. In addition, the fatigue accumulation process of giant hydropower units is complex and involves the interaction of multiple factors.

[0003] Therefore, in the current technologies related to fatigue accumulation prediction of hydropower units, there is a technical problem that it is difficult to obtain sufficient and accurate operating data of giant hydropower units, which leads to large errors in fatigue accumulation prediction results. Summary of the invention

[0004] This application provides a method and system for predicting the fatigue accumulation of hydropower units based on holographic perception, and adopts technical means such as holographic perception, feature identification, and prediction model construction to solve the technical problem of difficulty in obtaining sufficient and accurate operating data of giant hydropower units in the existing fatigue accumulation prediction of hydropower units, which leads to large errors in the fatigue accumulation prediction results, thereby achieving the technical effect of improving the accuracy and reliability of the fatigue accumulation prediction of hydropower units.

[0005] The present application provides a fatigue accumulation prediction method for a hydropower unit based on holographic perception, the method comprising: using a holographic sensor array to perform real-time perception of a target hydropower unit to obtain a real-time operating status data set; performing data preprocessing on the real-time operating status data set to obtain an operating data processing information set; traversing the operating data processing information set to perform fatigue feature identification, and extracting fatigue accumulation key features based on the identification results; performing digital twin modeling based on the fatigue accumulation key features to construct a fatigue accumulation prediction model for the target hydropower unit; performing simulated operation of the target hydropower unit based on the fatigue accumulation prediction model, performing fatigue evaluation on the simulated operation results according to a preset fatigue accumulation mechanism, performing fatigue accumulation prediction on the target hydropower unit according to the first evaluation result, and outputting a first fatigue accumulation prediction result; performing a secondary fatigue evaluation on the simulated operation results through the preset fatigue accumulation mechanism to generate a second evaluation result, updating the first fatigue accumulation prediction result based on the second evaluation result, and obtaining a second fatigue accumulation prediction result.

[0006] In a possible implementation, a holographic sensor array is used to perform real-time perception of a target hydropower unit to obtain a real-time operating status data set, and the following processing is also performed: array layout parameters are determined based on the operating scenario of the target hydropower unit; the holographic sensor array is constructed, the holographic sensor array is laid out according to the array layout parameters, and a holographic perception test is performed to obtain a perception test result; the holographic sensor array is adjusted according to the perception test result, and multi-dimensional perception of the target hydropower unit is performed based on the adjustment information to obtain multi-dimensional operating perception information; the multi-dimensional operating perception information is integrated for real-time updating to generate the real-time operating status data set.

[0007] In a possible implementation, the operation data processing information set is traversed to identify fatigue characteristics, and fatigue accumulation key characteristics are extracted according to the identification results, and the following processing is performed: the historical operation fatigue data record archive is retrieved, the operation data processing information set is traversed to match the historical operation fatigue data record archive, and a matching data set is generated, wherein the matching data set includes fatigue characteristics; multiple fatigue degree data of the operation data processing information set are determined based on the fatigue characteristics; multiple fatigue levels are divided according to the multiple fatigue degree data; multiple fatigue characteristics of the operation data processing information set are identified based on the multiple fatigue levels to obtain the identification result.

[0008] In a possible implementation, digital twin modeling is performed according to the key characteristics of fatigue accumulation, a fatigue accumulation prediction model of the target hydropower unit is constructed, and the following processing is performed: based on the key characteristics of fatigue accumulation, the operation data processing information set is divided according to the prediction time scale to generate a training data set, a supervision data set, and a verification data set; the fatigue accumulation prediction model is trained based on the digital twin technology through the training data set and the supervision data set; the output result of the fatigue accumulation prediction model is verified and evaluated according to the verification data set, and the fatigue accumulation prediction model is iteratively optimized according to the evaluation result. When the number of consecutive iterations meets the preset requirements, it is regarded as convergence, and the fatigue accumulation prediction model is output.

[0009] In a possible implementation, a simulated operation of the target hydropower unit is performed based on the fatigue accumulation prediction model, and a fatigue evaluation is performed on the simulated operation result according to a preset fatigue accumulation mechanism, and the following processing is also performed: simulated operation conditions are set for the target hydropower unit, and the simulated operation conditions include a simulated operation environment, a simulated workload, and a simulated operation time; based on the simulated operation environment, the fatigue accumulation prediction model is started to perform a simulated operation of the target hydropower unit according to the simulated workload, and it is determined whether the simulated operation time reaches the simulated operation time, and a stop instruction is generated when the simulated operation time is reached, and the simulated operation is stopped according to the stop instruction to generate a simulated operation result; a fatigue accumulation index is set based on the simulated operation result, and the preset fatigue accumulation mechanism is constructed according to the fatigue accumulation index, and a fatigue accumulation trend analysis is performed on the target hydropower unit through the preset fatigue accumulation mechanism to generate a fatigue accumulation rate; fatigue evaluation is performed according to the fatigue accumulation rate to generate the first evaluation result.

[0010] In a possible implementation, fatigue accumulation prediction is performed on the target hydropower unit based on the first evaluation result, and the first fatigue accumulation prediction result is output, and the following processing is also performed: the fatigue accumulation trend is extracted using the first evaluation result, and a fatigue accumulation trend chart is drawn according to the fatigue accumulation trend; the historical operation data of the target unit is retrieved and a fatigue prediction value is set based on the fatigue accumulation trend chart; fatigue accumulation prediction is performed based on the fatigue prediction value to obtain a fatigue accumulation prediction trend chart; and the first fatigue accumulation prediction result of the target hydropower unit is output according to the fatigue accumulation prediction trend chart.

[0011] In a possible implementation, a secondary fatigue assessment is performed on the simulation operation result through the preset fatigue accumulation mechanism to generate a second assessment result, and the first fatigue accumulation prediction result is updated based on the second assessment result to obtain a second fatigue accumulation prediction result, and the following processing is also performed: a dynamic impact monitoring and analysis is performed based on the target hydropower unit to obtain a dynamic impact data set, and the dynamic impact data set includes material change impact data and operating condition change impact data; a secondary fatigue assessment is performed on the simulation operation result based on the material change impact data and the operating condition change impact data through the preset fatigue accumulation mechanism to generate a second assessment result; the second assessment result is traversed and compared with the first assessment result for correction to obtain a fatigue accumulation update rate and fatigue accumulation potential risk points; the first fatigue accumulation prediction result is updated based on the fatigue accumulation update rate and the fatigue accumulation potential risk points to generate a second fatigue accumulation prediction result.

[0012] This application also provides a fatigue accumulation prediction system for hydropower units based on holographic perception, including:

[0013] A real-time sensing module for a hydropower unit, wherein the real-time sensing module for a hydropower unit is used to sense a target hydropower unit in real time by using a holographic sensor array to obtain a real-time operating status data set;

[0014] A key feature extraction module, the key feature extraction module is used to perform data preprocessing on the real-time operation status data set to obtain an operation data processing information set, traverse the operation data processing information set to perform fatigue feature identification, and extract fatigue accumulation key features according to the identification results;

[0015] A fatigue accumulation prediction model building module, wherein the fatigue accumulation prediction model building module is used to perform digital twin modeling according to the fatigue accumulation key features to build a fatigue accumulation prediction model of a target hydropower unit;

[0016] A hydropower unit fatigue assessment module, the hydropower unit fatigue assessment module is used to simulate the operation of the target hydropower unit based on the fatigue accumulation prediction model, perform fatigue assessment on the simulation operation result according to a preset fatigue accumulation mechanism, perform fatigue accumulation prediction on the target hydropower unit according to the first assessment result, and output a first fatigue accumulation prediction result;

[0017] The second fatigue accumulation prediction result acquisition module is used to perform a secondary fatigue evaluation on the simulation operation result through the preset fatigue accumulation mechanism, generate a second evaluation result, update the first fatigue accumulation prediction result based on the second evaluation result, and obtain a second fatigue accumulation prediction result.

[0018] The method and system for predicting fatigue accumulation of hydropower units based on holographic perception proposed in this application is intended to use a holographic sensor array to perceive the target hydropower unit in real time; perform data preprocessing; traverse the operation data processing information set to extract key features of fatigue accumulation; perform digital twin modeling to build a fatigue accumulation prediction model for the target hydropower unit; simulate the operation of the target hydropower unit and perform fatigue assessment according to the preset fatigue accumulation mechanism; update the first fatigue accumulation prediction result based on the second assessment result to obtain the second fatigue accumulation prediction result. This solves the technical problem that it is difficult to obtain sufficient and accurate operating data for giant hydropower units in the existing fatigue accumulation prediction of hydropower units, resulting in large errors in the fatigue accumulation prediction results, and achieves the technical effect of improving the accuracy of fatigue accumulation prediction of hydropower units. This solves the technical problem that it is difficult to obtain sufficient and accurate operating data for giant hydropower units in the existing fatigue accumulation prediction of hydropower units, resulting in large errors in the fatigue accumulation prediction results, and achieves the technical effect of improving the accuracy and reliability of fatigue accumulation prediction of hydropower units. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0020] Figure 1 A schematic flow chart of a method for predicting fatigue accumulation of a hydropower unit based on holographic perception provided in an embodiment of the present application;

[0021] Figure 2 A schematic diagram of the structure of a hydropower unit fatigue accumulation prediction system based on holographic perception provided in an embodiment of the present application.

[0022] Explanation of the reference numerals: hydropower unit real-time perception module 10 , key feature extraction module 20 , fatigue accumulation prediction model construction module 30 , hydropower unit fatigue assessment module 40 , second prediction result acquisition module 50 . DETAILED DESCRIPTION

[0023] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0024] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0025] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0026] The present application embodiment provides a method for predicting fatigue accumulation of hydropower units based on holographic perception, such as Figure 1 As shown, the method includes:

[0027] Step S100, using a holographic sensor array to sense the target hydropower unit in real time and obtain a real-time operating status data set. Using a holographic sensor array to sense the target hydropower unit in real time and obtain a real-time operating status data set mainly refers to the comprehensive and real-time data collection of the hydropower unit through a series of advanced holographic sensor devices. The holographic sensor array is a device network that integrates multiple sensors and can capture and record various operating parameters of the hydropower unit, including temperature sensors, pressure sensors, vibration sensors, flow sensors, etc. They can sense the temperature, pressure, vibration, flow and other key information of the hydropower unit in real time, and reflect the real-time operating status of the hydropower unit, including its operating efficiency, energy consumption, equipment health, etc.

[0028] In a possible implementation, step S100 further includes step S110, determining array layout parameters based on the operating scenario of the target hydropower unit. According to the actual operating environment and conditions of the hydropower unit, the layout and configuration parameters of the holographic sensor array are determined to ensure that the sensor array can effectively sense and collect the operating status data of the hydropower unit. Specifically, the operating scenario may include the location of the hydropower unit, the working environment, the climatic conditions, the unit type, the scale and the operating characteristics, etc. For example, the holographic sensor array may be a circular ring layout or a matrix layout. The circular ring layout is to evenly arrange the holographic sensors on the circumference of different radii, and the matrix arrangement layout is to arrange the holographic sensors in a rectangular shape. Different operating scenarios have different requirements for the layout and configuration of the sensor array. For example, in areas with harsh climatic conditions, it may be necessary to consider the waterproof, dustproof and high and low temperature resistance characteristics of the sensor; in the case of a large unit or a complex operating environment, it may be necessary to increase the number and density of sensors to improve the accuracy and comprehensiveness of perception. It also includes step S120, constructing the holographic sensor array, laying out the holographic sensor array according to the array layout parameters, and performing a holographic perception test to obtain the perception test results. According to the predetermined array layout parameters, the holographic sensor array is actually built and configured, and the array is actually tested for operation to verify its perception effect and data collection capability, and to obtain the perception test results. Specifically, the appropriate sensor type is selected, the sensor bracket or fixture is designed and manufactured, and the sensor is installed and connected according to the predetermined layout parameters, including determining the distance, angle and direction between the sensors, and fixing the sensor at the appropriate position of the hydropower unit according to the predetermined position and direction. In the actual operating environment, the holographic sensor array is started to collect data through the sensor. It also includes step S130, adjusting the holographic sensor array according to the perception test results, performing multi-dimensional perception of the target hydropower unit based on the adjustment information, and obtaining multi-dimensional operation perception information. After the preliminary holographic perception test, the holographic sensor array is optimized and adjusted according to the test results to improve the accuracy and comprehensiveness of perception, thereby achieving multi-dimensional perception of the target hydropower unit and obtaining more accurate multi-dimensional operating status perception information. Specifically, the process of adjusting the holographic sensor array according to the perception test results involves careful analysis of the test data. For example, if it is found that the data of some sensors are biased or unstable, the position, angle or calibration parameters of these sensors need to be adjusted; if some key parameters are not effectively perceived, new sensors need to be added. Based on the adjusted holographic sensor array, the target hydropower unit can be multi-dimensionally perceived to obtain more comprehensive and accurate multi-dimensional operating perception information. It also includes step S140, integrating the multi-dimensional operating perception information for real-time update to generate the real-time operating status data set.The multi-dimensional operation perception information obtained through multi-dimensional perception is collected, integrated and updated in real time, and finally a data set is generated that comprehensively reflects the multi-dimensional and real-time operation status of the target hydropower unit, including temperature, pressure, vibration, flow and other perception data. Specifically, the multi-dimensional perception information is integrated, and the latest perception information is continuously added to the data set through real-time updates to ensure the timeliness and accuracy of the data set.

[0029] Step S200, preprocess the real-time operation status data set to obtain an operation data processing information set. After obtaining the real-time operation status data set, a series of data preprocessing operations need to be performed on it, including cleaning data, eliminating noise, filling missing values, converting data formats, standardizing data, etc. Specifically, remove duplicate data, abnormal data or obviously erroneous data points. For example, some sensors may produce abnormal values ​​due to failure or interference; for missing values ​​in the data set, select an appropriate processing strategy according to the specific situation, for example, use the mean, median or interpolation method to fill the missing values; map the data to a specific range or distribution to eliminate the scale differences between different features and improve the accuracy of data analysis; thereby obtaining a more standardized, accurate and useful operation data processing information set.

[0030] Step S300, traverse the operation data processing information set to identify fatigue features, and extract fatigue accumulation key features according to the identification results. The pre-processed operation data processing information set is traversed to identify features related to fatigue and perform level identification, and further extract key features that can reflect fatigue accumulation. Specifically, features related to the fatigue state of the hydropower unit are identified according to the operation data processing information set, such as the temperature change trend, vibration frequency and amplitude, and operation stability of the hydropower unit. During the long-term operation of the hydropower unit, its vibration characteristics may change. The vibration amplitude and vibration frequency are important fatigue features. For example, if the vibration amplitude gradually increases, it may mean that some parts inside the unit are fatigued or damaged; if the temperature of the unit suddenly rises or fluctuates abnormally during normal operation, it may mean that some parts have accumulated heat due to friction, wear, etc., resulting in fatigue; operation stability includes pressure stability, speed stability, output power volatility, etc., and level identification is performed according to the fatigue degree. According to the identification results, the fatigue accumulation key features are extracted, that is, from multiple identified fatigue features, the key features that best reflect the fatigue accumulation are extracted.

[0031] In a possible implementation, step S300 further includes step S310, calling up the historical operation fatigue data record archive, traversing the operation data processing information set and matching it with the historical operation fatigue data record archive, generating a matching data set, and the matching data set includes fatigue features. The historical operation fatigue data record archive usually includes fatigue-related data recorded by the unit during past operation. These data may be collected and processed by sensors in real time, or may be obtained through regular inspection, maintenance or experiments. The data in the operation data processing information set are matched with the historical operation fatigue data record archive. By comparing the current data with the historical data, those features similar to the past fatigue state can be identified to generate a matching data set. The matching data set includes fatigue features, that is, it includes feature information extracted from the operation data processing information set and matched with the historical fatigue data. It also includes step S320, determining multiple fatigue degree data of the operation data processing information set based on the fatigue features. The fatigue features matched from the historical operation fatigue data record archive are used to analyze the current operation data processing information set, thereby obtaining multiple fatigue degree data, which may include fatigue degree values ​​at different time points, fatigue degree change trends, fatigue degree comparisons of different components or systems, etc. It also includes step S330, dividing multiple fatigue levels according to the multiple fatigue degree data. The multiple fatigue degree data are divided into multiple fatigue levels. For example, the data are divided into different levels such as mild fatigue, moderate fatigue, and severe fatigue according to the amplitude of abnormal temperature fluctuations, different ranges of vibration amplitude or vibration frequency, pressure stability changes, speed stability changes, and output power fluctuations. It also includes step S340, identifying multiple fatigue features of the operation data processing information set based on the multiple fatigue levels, and obtaining the identification results.

[0032] Step S400, digital twin modeling is performed according to the fatigue accumulation key features to construct a fatigue accumulation prediction model of the target hydropower unit. Using digital twin technology, combined with the obtained fatigue accumulation key features, a model that can predict the fatigue accumulation of the target hydropower unit is established. Specifically, the fatigue accumulation prediction model can accurately reflect the law of fatigue accumulation of the hydropower unit, predict the future fatigue accumulation state by inputting the operating data of the hydropower unit, and verify the model using historical data. According to the verification results, the model is adjusted and optimized as necessary to ensure the accuracy and reliability of its prediction results.

[0033] In a possible implementation, step S400 further includes step S410, dividing the operation data processing information set according to the prediction time scale based on the fatigue accumulation key features to generate a training data set, a supervision data set, and a verification data set. Using the known fatigue accumulation key features, the operation data processing information set is divided into the prediction time scale according to the prediction time scale to construct a data set for model training, supervision and verification, wherein the prediction time scale may include short-term prediction, medium-term prediction and long-term prediction, etc. Specifically, according to the operation mode and change trend of the fatigue accumulation key features, the operation data processing information set is reasonably divided according to the requirements of the prediction time scale to ensure that the divided data set can fully reflect the fatigue accumulation characteristics of the unit, and generate a training data set, a supervision data set and a verification data set. The training data set is used to train the prediction model; the supervision data set is used to provide real-time feedback and adjustment during the model training process to ensure that the model can accurately fit the data; the verification data set is used to evaluate the performance of the model and the fatigue accumulation prediction ability. It also includes step S420, using the training data set and the supervision data set to train the fatigue accumulation prediction model based on the digital twin technology. The method further includes step S430, wherein the output result of the fatigue accumulation prediction model is verified and evaluated according to the verification data set, and the fatigue accumulation prediction model is iteratively optimized according to the evaluation result. When the number of consecutive iterations meets the preset requirements, it is considered to be converged, and the fatigue accumulation prediction model is output. Specifically, the output result of the fatigue accumulation prediction model is verified and evaluated using the verification data set, and the fatigue accumulation prediction model is iteratively optimized according to the results of the verification and evaluation. For example, if the prediction error is large or the accuracy is low, the model needs to be adjusted, including adjusting the parameters of the model, changing the structure of the model, increasing or decreasing the input features, etc. When the number of consecutive iterations meets the preset requirements, the model is considered to have converged, and its output is used as the final fatigue accumulation prediction model.

[0034] Step S500, based on the fatigue accumulation prediction model, simulate the operation of the target hydropower unit, perform fatigue evaluation on the simulation operation results according to the preset fatigue accumulation mechanism, perform fatigue accumulation prediction on the target hydropower unit according to the first evaluation result, and output the first fatigue accumulation prediction result. Specifically, the target hydropower unit is simulated using the fatigue accumulation prediction model that has been constructed and optimized, that is, the operation status of the unit under specific conditions is simulated to obtain data and information related to fatigue accumulation. After the simulation operation is completed, the simulation operation results are fatigue evaluated according to the preset fatigue accumulation mechanism, wherein the preset fatigue accumulation mechanism is determined based on the design characteristics, operation experience and fatigue accumulation characteristics of the unit, and reflects the fatigue accumulation law during the operation of the target hydropower unit to obtain a first evaluation result. Based on the first evaluation result, the fatigue accumulation of the target hydropower unit is predicted using the fatigue accumulation prediction model, and the predicted fatigue accumulation is output as the first fatigue accumulation prediction result, including the fatigue accumulation degree information of the unit at a certain time point or time period in the future.

[0035] In a possible implementation, step S500 further includes step S510, setting simulated operating conditions for the target hydropower unit, wherein the simulated operating conditions include simulated operating environment, simulated workload, and simulated operating time. Before performing simulated operation of the target hydropower unit, a series of conditions need to be set to simulate various situations that the unit may encounter in actual operation, and reflect the operating environment and load characteristics of the unit as realistically as possible, so as to more accurately predict its fatigue accumulation, wherein the simulated operating environment refers to the external conditions of the simulated target hydropower unit during operation, such as water temperature, water quality, atmospheric pressure, humidity, temperature and other environmental factors; the simulated workload refers to the tasks and workload that the simulated target hydropower unit needs to undertake during operation, for example, different modes such as high load, low load, and periodic load can be set to observe the fatigue performance of the unit under different loads; the simulated operating time refers to the length of time that the simulated unit continuously runs, which can be several hours, several days or even longer, to observe the fatigue accumulation trend and change law of the unit in different time periods. It also includes step S520, based on the simulated operation environment, starting the fatigue accumulation prediction model to simulate the operation of the target hydropower unit according to the simulated workload, judging whether the simulated operation time reaches the simulated operation time, generating a stop instruction when the simulated operation time is reached, stopping the simulated operation according to the stop instruction, and generating a simulated operation result. It also includes step S530, setting a fatigue accumulation index based on the simulated operation result, constructing the preset fatigue accumulation mechanism according to the fatigue accumulation index, performing fatigue accumulation trend analysis on the target hydropower unit through the preset fatigue accumulation mechanism, and generating a fatigue accumulation rate. Among them, the fatigue accumulation index is a parameter or standard used to quantitatively evaluate the fatigue accumulation of the target hydropower unit, reflecting the fatigue accumulation state of the target hydropower unit during the simulated operation. After setting the fatigue accumulation index, a preset fatigue accumulation mechanism is constructed according to the fatigue accumulation index, and the fatigue accumulation trend analysis is performed on the simulated operation results of the target hydropower unit, and the fatigue accumulation law and rate of the target hydropower unit during the simulated operation are identified, and the fatigue accumulation rate is generated. The fatigue accumulation rate is a quantitative indicator that describes the speed of fatigue accumulation of the target hydropower unit. By analyzing the simulated operation results, the fatigue accumulation rate of the unit in different time periods can be calculated, and the severity of the fatigue accumulation of the unit can be judged, thereby predicting the future fatigue accumulation. It also includes step S540, performing fatigue evaluation according to the fatigue accumulation rate to generate the first evaluation result. Using the obtained fatigue accumulation rate, the fatigue state of the target hydropower unit is evaluated and judged, and the first evaluation result is generated accordingly.

[0036] In a possible implementation, step S500 further includes step S550, extracting fatigue accumulation trend using the first evaluation result, and drawing fatigue accumulation trend graph according to the fatigue accumulation trend. Based on the first evaluation result, the fatigue accumulation trend of the target hydropower unit is extracted, and a fatigue accumulation trend graph is drawn to intuitively display the change of fatigue accumulation over time or other variables, for example, using a line graph or a bar graph. It also includes step S560, retrieving the historical operation data of the target unit and setting the fatigue prediction value based on the fatigue accumulation trend graph. Retrieving the historical operation data of the target unit, and setting the fatigue prediction value in combination with the fatigue accumulation trend graph, the fatigue prediction value is an estimate of the fatigue accumulation of the target hydropower unit in the future. It also includes step S570, performing fatigue accumulation prediction according to the fatigue prediction value, and obtaining a fatigue accumulation prediction trend graph. Using the set fatigue prediction value, the fatigue accumulation prediction of the target hydropower unit is performed in combination with the operating characteristics, environmental factors, etc. of the unit, and the prediction result is graphically displayed to form a fatigue accumulation prediction trend graph. It also includes step S580, outputting the first fatigue accumulation prediction result of the target hydropower unit according to the fatigue accumulation prediction trend graph. Based on the generated fatigue accumulation prediction trend chart, the future fatigue accumulation status of the target hydropower unit is predicted, and the prediction result is output to form the first fatigue accumulation prediction result. For example, a concise text description is given, "It is predicted that the fatigue accumulation of the unit will show an upward trend in the next three months." By outputting the prediction results in a timely manner, the future fatigue accumulation status of the target hydropower unit can be understood in a timely manner, and corresponding measures and strategies can be formulated to ensure the safe and stable operation of the unit.

[0037] Step S600, perform a secondary fatigue assessment on the simulation operation result through the preset fatigue accumulation mechanism to generate a second assessment result, update the first fatigue accumulation prediction result based on the second assessment result, and obtain a second fatigue accumulation prediction result. Using the preset fatigue accumulation mechanism, combined with the simulation operation data, a secondary fatigue assessment is performed on the simulation operation result, that is, the fatigue accumulation of each component of the target hydropower unit is divided and quantified more finely, and more factors that may affect fatigue accumulation are considered to more accurately capture the fatigue accumulation characteristics of the unit in the simulation operation, and generate a second assessment result, such as the material properties, fatigue state and fatigue accumulation distribution of different components. Based on the second assessment result, the first fatigue accumulation prediction result is updated and optimized to generate a second fatigue accumulation prediction result, which is more accurate than the first prediction result.

[0038] In a possible implementation, step S600 further includes step S610, performing dynamic impact monitoring and analysis based on the target hydropower unit to obtain a dynamic impact data set, wherein the dynamic impact data set includes material change impact data and operating condition change impact data. Various dynamic factors of the target hydropower unit during operation are monitored and analyzed in real time to obtain a data set reflecting the change in the unit's operating state, forming a dynamic impact data set, including changes in material properties, changes in operating conditions, etc., wherein the material change impact data reflects the change in material properties of the target hydropower unit during operation, including material aging, material crack extension, and changes in material hardness, etc.; the operating condition change impact data reflects the operating state of the target hydropower unit under different operating conditions, including vibration characteristics under different loads, the impact of flow changes on unit performance, and the impact of temperature fluctuations on unit stability, etc. It also includes step S620, performing a secondary fatigue assessment on the simulation operation result based on the material change impact data and the operating condition change impact data through the preset fatigue accumulation mechanism to generate a second assessment result. It also includes step S630, traversing the second evaluation result and comparing and correcting it with the first evaluation result to obtain the fatigue accumulation update rate and fatigue accumulation potential risk points. The second evaluation result is compared and corrected with the first evaluation result, including comparing the evaluation indicators, numerical values, change trends, etc., eliminating or reducing the error between the two evaluation results, improving the accuracy of the evaluation, and further obtaining the fatigue accumulation update rate and fatigue accumulation potential risk points. The fatigue accumulation update rate refers to the rate of change of the fatigue accumulation between different time points, so as to judge whether the fatigue state of the unit is accelerating or slowing down; the fatigue accumulation potential risk point refers to the part or condition where the fatigue accumulation may accelerate or suddenly increase during the operation of the unit, such as the weak links in the unit structure, the unreasonable setting of the operating parameters, the negative impact of environmental factors, etc. It also includes step S640, updating the first fatigue accumulation prediction result based on the fatigue accumulation update rate and the fatigue accumulation potential risk point, and generating the second fatigue accumulation prediction result. The first fatigue accumulation prediction result is updated and optimized according to the fatigue accumulation update rate and the fatigue accumulation potential risk points to generate the second fatigue accumulation prediction result, thereby improving the accuracy and reliability of the future fatigue accumulation prediction of the target hydropower unit and ensuring the safe and stable operation of the target hydropower unit.

[0039] In the above, refer to Figure 1 The fatigue accumulation prediction method of a hydropower unit based on holographic perception according to an embodiment of the present invention is described in detail. Figure 2 A fatigue accumulation prediction system for a hydropower unit based on holographic perception according to an embodiment of the present invention is described.

[0040] The fatigue accumulation prediction system of a hydropower unit based on holographic perception according to an embodiment of the present invention is used to solve the technical problem that it is difficult to obtain sufficient and accurate operating data of giant hydropower units in the existing fatigue accumulation prediction of hydropower units, resulting in large errors in the fatigue accumulation prediction results, thereby achieving the technical effect of improving the accuracy and reliability of fatigue accumulation prediction of hydropower units. The fatigue accumulation prediction system of a hydropower unit based on holographic perception includes: a real-time perception module 10 for a hydropower unit, a key feature extraction module 20, a fatigue accumulation prediction model construction module 30, a fatigue evaluation module 40 for a hydropower unit, and a second prediction result acquisition module 50.

[0041] A hydropower unit real-time sensing module 10, which is used to sense the target hydropower unit in real time using a holographic sensor array to obtain a real-time operating status data set;

[0042] A key feature extraction module 20, the key feature extraction module 20 is used to perform data preprocessing on the real-time operation status data set, obtain an operation data processing information set, traverse the operation data processing information set to perform fatigue feature identification, and extract fatigue accumulation key features according to the identification results;

[0043] A fatigue accumulation prediction model building module 30, wherein the fatigue accumulation prediction model building module 30 is used to perform digital twin modeling according to the fatigue accumulation key features to build a fatigue accumulation prediction model of a target hydropower unit;

[0044] A hydropower unit fatigue assessment module 40, the hydropower unit fatigue assessment module 40 is used to simulate the operation of the target hydropower unit based on the fatigue accumulation prediction model, perform fatigue assessment on the simulation operation result according to a preset fatigue accumulation mechanism, perform fatigue accumulation prediction on the target hydropower unit according to the first assessment result, and output a first fatigue accumulation prediction result;

[0045] The second prediction result acquisition module 50 is used to perform a secondary fatigue evaluation on the simulation operation result through the preset fatigue accumulation mechanism, generate a second evaluation result, update the first fatigue accumulation prediction result based on the second evaluation result, and obtain a second fatigue accumulation prediction result.

[0046] The specific configuration of the real-time perception module 10 of the hydropower unit will be described in detail below. The real-time perception module 10 of the hydropower unit may further include: determining the array layout parameters based on the operation scenario of the target hydropower unit; constructing the holographic sensor array, laying out the holographic sensor array according to the array layout parameters, and performing a holographic perception test to obtain the perception test results; adjusting the holographic sensor array according to the perception test results, performing multi-dimensional perception of the target hydropower unit based on the adjustment information, and obtaining multi-dimensional operation perception information; integrating the multi-dimensional operation perception information for real-time update to generate the real-time operation status data set.

[0047] The specific configuration of the key feature extraction module 20 will be described in detail below. The key feature extraction module 20 further includes: retrieving the historical operation fatigue data record archive, traversing the operation data processing information set and matching it with the historical operation fatigue data record archive to generate a matching data set, wherein the matching data set includes fatigue features; determining multiple fatigue degree data of the operation data processing information set based on the fatigue features; dividing multiple fatigue levels according to the multiple fatigue degree data; and identifying multiple fatigue features of the operation data processing information set based on the multiple fatigue levels to obtain the identification results.

[0048] The specific configuration of the fatigue accumulation prediction model construction module 30 will be described in detail below. The fatigue accumulation prediction model construction module 30 may further include: based on the fatigue accumulation key features, the operation data processing information set is divided according to the prediction time scale to generate a training data set, a supervision data set, and a verification data set; through the training data set and the supervision data set, the fatigue accumulation prediction model is trained based on the digital twin technology; the output result of the fatigue accumulation prediction model is verified and evaluated according to the verification data set, and the fatigue accumulation prediction model is iteratively optimized according to the evaluation result. When the number of consecutive iterations meets the preset requirements, it is considered to be converged, and the fatigue accumulation prediction model is output.

[0049] The specific configuration of the fatigue assessment module 40 of the hydropower unit will be described in detail below. The fatigue assessment module 40 of the hydropower unit may further include: setting simulated operating conditions for the target hydropower unit, the simulated operating conditions including simulated operating environment, simulated workload, and simulated operating time; based on the simulated operating environment, starting the fatigue accumulation prediction model to perform simulated operation of the target hydropower unit according to the simulated workload, judging whether the simulated operating time reaches the simulated operating time, generating a stop instruction when the simulated operating time is reached, stopping the simulated operation according to the stop instruction, and generating a simulated operation result; setting a fatigue accumulation index based on the simulated operating result, constructing the preset fatigue accumulation mechanism according to the fatigue accumulation index, performing fatigue accumulation trend analysis on the target hydropower unit through the preset fatigue accumulation mechanism, and generating a fatigue accumulation rate; performing fatigue assessment according to the fatigue accumulation rate, and generating the first assessment result.

[0050] The specific configuration of the fatigue assessment module 40 of the hydropower unit will be described in detail below. The fatigue assessment module 40 of the hydropower unit may further include: extracting the fatigue accumulation trend using the first assessment result, and drawing a fatigue accumulation trend graph according to the fatigue accumulation trend; retrieving the historical operation data of the target unit, and setting the fatigue prediction value based on the fatigue accumulation trend graph; performing fatigue accumulation prediction according to the fatigue prediction value, and obtaining the fatigue accumulation prediction trend graph; and outputting the first fatigue accumulation prediction result of the target hydropower unit according to the fatigue accumulation prediction trend graph.

[0051] The specific configuration of the second prediction result acquisition module 50 will be described in detail below. The second prediction result acquisition module 50 may further include: performing dynamic impact monitoring and analysis based on the target hydropower unit to obtain a dynamic impact data set, wherein the dynamic impact data set includes material change impact data and operating condition change impact data; performing a secondary fatigue assessment on the simulation operation result based on the material change impact data and the operating condition change impact data through the preset fatigue accumulation mechanism to generate a second assessment result; traversing the second assessment result and comparing and correcting it with the first assessment result to obtain a fatigue accumulation update rate and fatigue accumulation potential risk points; updating the first fatigue accumulation prediction result based on the fatigue accumulation update rate and the fatigue accumulation potential risk points to generate the second fatigue accumulation prediction result.

[0052] The hydropower unit fatigue accumulation prediction system based on holographic perception provided by the embodiment of the present invention can execute the hydropower unit fatigue accumulation prediction method based on holographic perception provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0053] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0054] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A fatigue accumulation prediction method for hydropower units based on holographic perception, characterized in that: The method comprises: Use holographic sensor arrays to perceive the target hydropower units in real time and obtain real-time operating status data sets; Performing data preprocessing on the real-time operation status data set to obtain an operation data processing information set; Traversing the operation data processing information set to identify fatigue features, and extracting fatigue accumulation key features according to the identification results; Digital twin modeling is performed based on the key characteristics of fatigue accumulation to build a fatigue accumulation prediction model for the target hydropower unit; Performing a simulated operation of a target hydropower unit based on the fatigue accumulation prediction model, performing fatigue evaluation on the simulated operation result according to a preset fatigue accumulation mechanism, performing fatigue accumulation prediction on the target hydropower unit according to a first evaluation result, and outputting a first fatigue accumulation prediction result; A secondary fatigue evaluation is performed on the simulation operation result through the preset fatigue accumulation mechanism to generate a second evaluation result, and the first fatigue accumulation prediction result is updated based on the second evaluation result to obtain a second fatigue accumulation prediction result.

2. The method for predicting fatigue accumulation of a hydropower unit based on holographic perception according to claim 1 is characterized in that: The holographic sensor array is used to sense the target hydropower unit in real time and obtain the real-time operation status data set. The method includes: Determine array layout parameters based on the operating scenario of the target hydropower unit; Constructing the holographic sensor array, laying out the holographic sensor array according to the array layout parameters, and performing a holographic perception test to obtain a perception test result; Adjusting the holographic sensor array according to the perception test result, performing multi-dimensional perception on the target hydropower unit based on the adjustment information, and acquiring multi-dimensional operation perception information; The multi-dimensional operation perception information is integrated for real-time updating to generate the real-time operation status data set.

3. The method for predicting fatigue accumulation of a hydropower unit based on holographic perception according to claim 1 is characterized in that: Traversing the operation data processing information set to identify fatigue features, and extracting fatigue accumulation key features according to the identification results, the method includes: Retrieving a historical operation fatigue data record file, traversing the operation data processing information set and matching it with the historical operation fatigue data record file to generate a matching data set, wherein the matching data set includes fatigue characteristics; determining a plurality of fatigue degree data of the operation data processing information set based on the fatigue characteristics; Dividing a plurality of fatigue levels according to the plurality of fatigue degree data; A plurality of fatigue features of the operation data processing information set are identified based on the plurality of fatigue levels to obtain the identification result.

4. The method for predicting fatigue accumulation of a hydropower unit based on holographic perception according to claim 1 is characterized in that: Digital twin modeling is performed according to the key characteristics of fatigue accumulation to build a fatigue accumulation prediction model for the target hydropower unit, and the method includes: Based on the fatigue accumulation key features, the operation data processing information set is divided according to the prediction time scale to generate a training data set, a supervision data set, and a verification data set; Using the training data set and the supervision data set, the fatigue accumulation prediction model is trained based on the digital twin technology; The output result of the fatigue accumulation prediction model is verified and evaluated according to the verification data set, and the fatigue accumulation prediction model is iteratively optimized according to the evaluation result. When the number of consecutive iterations meets the preset requirements, it is regarded as convergence and the fatigue accumulation prediction model is output.

5. The method for predicting fatigue accumulation of a hydropower unit based on holographic perception according to claim 1 is characterized in that: Based on the fatigue accumulation prediction model, simulated operation of the target hydropower unit is performed, and fatigue evaluation is performed on the simulated operation result according to the preset fatigue accumulation mechanism, the method comprising: Setting simulated operating conditions for the target hydropower unit, wherein the simulated operating conditions include a simulated operating environment, a simulated workload, and a simulated operating time; Based on the simulated operation environment, the fatigue accumulation prediction model is started to perform simulated operation of the target hydropower unit according to the simulated workload, and it is determined whether the simulated operation time reaches the simulated operation time. When the simulated operation time is reached, a stop instruction is generated, and the simulated operation is stopped according to the stop instruction, and a simulated operation result is generated; Based on the simulation operation result, a fatigue accumulation index is set, and the preset fatigue accumulation mechanism is constructed according to the fatigue accumulation index, and fatigue accumulation trend analysis is performed on the target hydropower unit through the preset fatigue accumulation mechanism to generate a fatigue accumulation rate; Fatigue assessment is performed according to the fatigue accumulation rate to generate the first assessment result.

6. The method for predicting fatigue accumulation of a hydropower unit based on holographic perception according to claim 1 is characterized in that: According to the first evaluation result, fatigue accumulation prediction is performed on the target hydropower unit, and a first fatigue accumulation prediction result is output. The method includes: extracting a fatigue accumulation trend using the first evaluation result, and drawing a fatigue accumulation trend graph according to the fatigue accumulation trend; Retrieving historical operating data of the target unit and setting a fatigue prediction value based on the fatigue accumulation trend graph; Perform fatigue accumulation prediction according to the fatigue prediction value, and obtain a fatigue accumulation prediction trend graph; The first fatigue accumulation prediction result of the target hydropower unit is output according to the fatigue accumulation prediction trend diagram.

7. The method for predicting fatigue accumulation of a hydropower unit based on holographic perception according to claim 1 is characterized in that: The method includes: performing a secondary fatigue evaluation on the simulation operation result by using the preset fatigue accumulation mechanism to generate a second evaluation result; updating the first fatigue accumulation prediction result based on the second evaluation result; and obtaining a second fatigue accumulation prediction result. Perform dynamic impact monitoring and analysis based on the target hydropower unit to obtain a dynamic impact data set, wherein the dynamic impact data set includes material change impact data and operating condition change impact data; Performing a secondary fatigue evaluation on the simulation operation result based on the material change impact data and the operating condition change impact data through the preset fatigue accumulation mechanism to generate a second evaluation result; Traversing the second evaluation result and comparing and correcting it with the first evaluation result to obtain fatigue accumulation update rate and fatigue accumulation potential risk points; The first fatigue accumulation prediction result is updated based on the fatigue accumulation update rate and the fatigue accumulation potential risk point to generate the second fatigue accumulation prediction result.

8. The fatigue accumulation prediction system of hydropower units based on holographic perception is characterized by: The system is used to implement the fatigue accumulation prediction method of a hydropower unit based on holographic perception according to any one of claims 1 to 7, and the system comprises: A real-time sensing module for a hydropower unit, wherein the real-time sensing module for a hydropower unit is used to sense a target hydropower unit in real time by using a holographic sensor array to obtain a real-time operating status data set; A key feature extraction module, the key feature extraction module is used to perform data preprocessing on the real-time operation status data set to obtain an operation data processing information set, traverse the operation data processing information set to perform fatigue feature identification, and extract fatigue accumulation key features according to the identification results; A fatigue accumulation prediction model building module, wherein the fatigue accumulation prediction model building module is used to perform digital twin modeling according to the fatigue accumulation key features to build a fatigue accumulation prediction model of a target hydropower unit; A hydropower unit fatigue assessment module, the hydropower unit fatigue assessment module is used to simulate the operation of the target hydropower unit based on the fatigue accumulation prediction model, perform fatigue assessment on the simulation operation result according to a preset fatigue accumulation mechanism, perform fatigue accumulation prediction on the target hydropower unit according to the first assessment result, and output a first fatigue accumulation prediction result; The second fatigue accumulation prediction result acquisition module is used to perform a secondary fatigue evaluation on the simulation operation result through the preset fatigue accumulation mechanism, generate a second evaluation result, update the first fatigue accumulation prediction result based on the second evaluation result, and obtain a second fatigue accumulation prediction result.

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