Digital intelligent analysis system based on hydropower station physical equipment

Abstract

The invention relates to the technical field of digital intelligence analysis, in particular to a digital intelligence analysis system based on hydropower station physical equipment, which comprises a state feature extraction module, a fault identification and early warning module, an equipment behavior deep analysis module and a collaborative learning and prediction module. According to the method, the deviation between the current operation data and the normal operation reference of the equipment is calculated, the cross-border data points are automatically identified and marked, and the time sequence analysis is combined, so that potential anomalies in the operation of the equipment can be timely identified in the physical equipment management of the hydropower station, and the behavior modes of the equipment under different operation conditions are deeply analyzed; according to the method, the change rule of behaviors and the prediction time of periodic faults can be revealed, non-planned shutdown is reduced, meanwhile, the performance degradation trend is recognized, the equipment health state is continuously updated, future potential risks are predicted and prevented in advance, the overall operation efficiency and economical efficiency of a hydropower station are optimized, and safe and long-term operation of equipment is ensured.

Description

Technical Field

[0001] The present invention relates to the field of digital intelligent analysis technology, and in particular to a digital intelligent analysis system based on physical equipment of a hydropower station. Background Art

[0002] Digital analysis technology is the integration of information technology and intelligent technology. It uses modern technologies such as big data, cloud computing, artificial intelligence and the Internet of Things to conduct in-depth analysis and intelligent processing of various types of data. The main purpose is to optimize business processes, improve operational efficiency and decision-making quality through efficient management and analysis of data. In the industrial field, digital analysis helps companies achieve real-time monitoring of equipment status, fault prediction, maintenance optimization and energy efficiency management, thereby enhancing the reliability and economy of production.

[0003] Among them, the digital analysis system based on the physical equipment of the hydropower station is a digital analysis system specifically for the physical equipment of the hydropower station. Its purpose is to achieve real-time monitoring and performance analysis of key equipment such as turbines, generators and sluices of the hydropower station by integrating sensor data and operation records. Using advanced data analysis tools, it is possible to predict equipment failures, optimize maintenance plans and improve energy efficiency. In addition, by analyzing the utilization of water resources, it can assist the hydropower station in more accurate water resource management and scheduling, enhance the operational efficiency and economic benefits of the hydropower station, and ensure the safety and long-term sustainability of equipment operation.

[0004] Although current technologies can achieve real-time monitoring and basic fault prediction in the monitoring and maintenance of hydropower station equipment, they often rely on traditional data processing technology, which is obviously insufficient in analyzing complex behavior patterns and long-term operating trends of equipment. They cannot effectively identify minor performance degradation or predict the exact time of periodic failures, resulting in delayed response, increased unexpected downtime and maintenance costs. In addition, the lack of efficient and comprehensive analysis of the performance data of hydropower station physical equipment has resulted in the inability of hydropower stations to maximize resource utilization efficiency, affecting economic and environmental sustainability. Summary of the invention

[0005] The present invention provides a digital intelligent analysis system based on physical equipment of a hydropower station.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: The digital analysis system based on the physical equipment of the hydropower station includes: The state feature extraction module collects temperature, pressure and vibration data of the hydropower station's physical equipment through sensors, filters out background noise, divides the data into time windows, captures the operating state characteristics of the hydropower station's physical equipment, and outputs the equipment state feature set; The fault identification and early warning module analyzes the operation data of the hydropower station physical equipment based on the equipment status feature set, calculates the deviation between the current operation data of the hydropower station physical equipment and the normal operation benchmark data, automatically marks the out-of-bounds data points according to the calculation results, and performs time series analysis on the out-of-bounds data points to identify potential abnormal operation of the hydropower station physical equipment and generate preliminary fault early warning indicators; The equipment behavior in-depth analysis module performs a retrospective analysis of the historical behavior of the hydropower station's physical equipment based on the preliminary fault warning indicators, analyzes and determines the abnormal behavior of the hydropower station's physical equipment before the failure by analyzing the behavior patterns of the hydropower station's physical equipment under different operating conditions, and performs a periodic analysis of the abnormal behavior based on the analysis results to reveal the changing laws of the hydropower station's physical equipment behavior, capture the time frame of periodic failures, and obtain equipment behavior analysis results; The collaborative learning and prediction module receives the equipment behavior analysis results, starts the data synchronization process between the cloud and edge computing resources, analyzes and compares the long-term data and short-term activities of the physical equipment behavior of the hydropower station, identifies the performance degradation trend of the physical equipment of the hydropower station, continuously monitors the physical equipment of the hydropower station based on the identification results, updates the health status record of the physical equipment of the hydropower station, predicts the potential risks of the physical equipment of the hydropower station in the future, and establishes a health management file for the physical equipment of the hydropower station.

[0007] As a further solution of the present invention, the step of acquiring the device status feature set is: Sensors are deployed around the physical equipment of the hydropower station to capture the temperature, pressure and vibration data of the equipment operation process in real time, and data points whose frequencies are not within the working frequency range are screened out to obtain the pure operation status data of the equipment; Based on the pure operation status data of the equipment, the time series is segmented according to the equipment operation cycle, each time segment represents a working cycle, and the data mean, standard deviation and range in each cycle are calculated to obtain a statistical description of the equipment cycle data; By using the statistical description of the equipment cycle data and combining it with the equipment operation rules, the relationship between the data fluctuation of each cycle and the equipment health status is evaluated, the current status of the equipment is revealed, and the equipment status feature set is obtained.

[0008] As a further solution of the present invention, the calculation steps of the deviation are: Based on the equipment status feature set, extract the real-time operation data of the physical equipment of the hydropower station, capture the current working status of the equipment, and generate a real-time data set; Based on the real-time data set, combined with the normal operation benchmark data of the equipment, the formula is applied:

[0009] Calculate the total deviation value Deviation to generate the total deviation result, where n represents the total number of data points, i represents the data point index, and D r represents the real-time dataset, D b Denotes the benchmark dataset, D ri represents the value of the i-th data point in the real-time data set, D bi represents the value of the i-th data point in the benchmark dataset; The total deviation result is compared with a preset abnormal threshold T. If Deviation>T, it is marked as abnormal and a device deviation analysis result is generated.

[0010] As a further solution of the present invention, the steps for obtaining the preliminary fault warning indicator are: According to the device deviation analysis result, all data points exceeding the preset abnormal threshold are automatically marked as out-of-bounds data points, the deviation size and timestamp of each out-of-bounds data point are recorded, and a set of out-of-bounds data points is output; Performing time series analysis based on the cross-border data point set, identifying periodic fluctuations and irregular changes in the data by examining the time dependency of the data points, revealing potential operating problems, and obtaining time series analysis results; Using the time series analysis results, the current analysis results are matched with the fault signatures stored in the database to determine the fault type corresponding to the current out-of-bounds data point and obtain preliminary fault warning indicators.

[0011] As a further solution of the present invention, the steps of analyzing and determining the abnormal behavior before the fault are: Calling the preliminary fault warning indicator, integrating the historical operation data of the hydropower station physical equipment, counting the operation time, load conditions and maintenance records, classifying the data according to the time axis, removing abnormal data points, and generating a historical behavior data set of the hydropower station physical equipment; Based on the historical behavior data set of the hydropower station physical equipment, the behavior change law of the equipment under different operating conditions is analyzed, and the formula is used:

[0012] Calculate the device behavior pattern score S and generate the behavior pattern determination result, where B i is the operating load under different operating conditions, is the average value of the operating load, C i is the average value of the operating load, is the average value of the maintenance frequency, and N is the total number of data points; According to the behavior pattern determination result, the abnormally increased part in the pattern score is identified, and the corresponding position of the fault warning indicator and the scored abnormal behavior is compared to obtain the abnormal behavior identification result before the fault.

[0013] As a further solution of the present invention, the steps of obtaining the device behavior analysis result are: Using the abnormal behavior recognition results before the fault, perform time series analysis on the abnormal behavior, determine the frequency and periodicity of occurrence, analyze the time point when the abnormal behavior occurs, and generate a periodic behavior pattern data set; Based on the periodic behavior pattern data set, the frequency components of the device behavior are analyzed, key periodic components are identified, frequency peaks in the statistical data set are counted, key change patterns of the device behavior are revealed, and change pattern analysis results are obtained; Based on the analysis results of the change rules, combined with the comparison of equipment maintenance records and fault history, the possible time frame for the occurrence of periodic faults is determined, the potential time points for the equipment to fail in the future are analyzed, and the equipment behavior analysis results are generated.

[0014] As a further solution of the present invention, the step of identifying the performance degradation trend is: Receive the equipment behavior analysis results, collect statistics on equipment operation data, performance indicators and abnormal event records, perform data integrity verification and timestamp verification, confirm data consistency and reliability, and generate equipment data verification results; By establishing a data channel, synchronizing the device data verification results between the cloud and edge computing resources, confirming that all computing nodes have a consistent data view, and generating a data synchronization completion confirmation result; The confirmation result is completed synchronously according to the data, the long-term and short-term equipment behavior data are compared, the degradation trend of equipment performance is identified, the equipment performance degradation mode is analyzed, and the equipment performance degradation analysis result is obtained.

[0015] As a further solution of the present invention, the steps for establishing the health management file of the physical equipment of the hydropower station are: According to the equipment performance degradation analysis results, monitor the equipment temperature, pressure and load, perform real-time data collection to obtain real-time operating data of key parameters, and generate a collated real-time monitoring data set; Based on the real-time monitoring data set, combined with the historical performance data of the equipment, the changing trend of the current health status of the equipment is analyzed item by item, using the formula:

[0016] Calculate the change range of the equipment health status H and generate the updated equipment health status record, where P i and P i-1 Represent the key performance parameter values ​​at adjacent time points, t i和 t i-1 are the corresponding time points respectively, and M is the total number of data points; According to the updated equipment health status record, combined with historical fault records, the changing trends of key parameters of equipment performance degradation are sorted out, the potential risk probability of the equipment in the future is analyzed, and the physical equipment health management file of the hydropower station is generated.

[0017] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by calculating the deviation between the current operating data of the equipment and the normal operating benchmark, and automatically identifying and marking the out-of-bounds data points, combined with time series analysis, potential anomalies in the operation of the equipment can be timely identified in the physical equipment management of the hydropower station, and the behavior patterns of the equipment under different operating conditions can be deeply analyzed, which can reveal the changing laws of behavior and the predicted time of periodic failures, reduce unplanned downtime, identify performance degradation trends, continuously update the health status of the equipment, predict and prevent potential risks in the future in advance, optimize the overall operational efficiency and economy of the hydropower station, and ensure the safe and long-term operation of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. The drawings are only used to illustrate the implementation methods and are not to be considered as limitations of the present invention.

[0019] Figure 1 is a system flow chart of the present invention; Figure 2 A flowchart of obtaining a device status feature set according to the present invention; Figure 3 is a flow chart for calculating the deviation of the present invention; Figure 4 This is a flow chart for obtaining preliminary fault warning indicators of the present invention; Figure 5 Determine a flow chart for the analysis of abnormal behavior before failure of the present invention; Figure 6 A flowchart for obtaining the device behavior analysis results of the present invention; Figure 7 is a flow chart for identifying the performance degradation trend of the present invention; Figure 8 The present invention is a flow chart for establishing a health management file for physical equipment of a hydropower station. DETAILED DESCRIPTION

[0020] The technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all of the embodiments. Based on the embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly understood by technicians in the field of the present invention; the terms used in the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings, and are intended to cover non-exclusive inclusions.

[0022] In the description of the embodiments of the present invention, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present invention, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0023] In the description of the embodiments of the present invention, the term "and / or" is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0024] In the description of the embodiments of the present invention, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0025] In the description of the embodiments of the present invention, the technical terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the embodiments of the present invention.

[0026] In the description of the embodiments of the present invention, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the embodiments of the present invention can be understood according to the specific circumstances.

[0027] Embodiment 1, the embodiment of the present invention provides a digital intelligent analysis system based on the physical equipment of a hydropower station, such as Figure 1 As shown, including: The state feature extraction module collects temperature, pressure and vibration data of the hydropower station's physical equipment through sensors, filters out background noise, divides the data into time windows, captures the operating state characteristics of the hydropower station's physical equipment, and outputs the equipment state feature set; The fault identification and early warning module analyzes the operation data of the hydropower station's physical equipment based on the equipment status feature set, calculates the deviation between the current operation data of the hydropower station's physical equipment and the normal operation benchmark data, automatically marks the out-of-bounds data points based on the calculation results, and performs time series analysis on the out-of-bounds data points to identify potential abnormal operation of the hydropower station's physical equipment and generate preliminary fault early warning indicators; The equipment behavior in-depth analysis module conducts a retrospective analysis of the historical behavior of the hydropower station's physical equipment based on preliminary fault warning indicators. By analyzing the behavior patterns of the hydropower station's physical equipment under different operating conditions, the module determines the abnormal behavior of the hydropower station's physical equipment before the failure. Based on the analysis results, the module conducts a periodic analysis of the abnormal behavior, reveals the changing laws of the hydropower station's physical equipment behavior, captures the time frame of periodic failures, and obtains the equipment behavior analysis results. The collaborative learning and prediction module receives the results of equipment behavior analysis, initiates the data synchronization process between the cloud and edge computing resources, analyzes and compares the long-term data and short-term activities of the physical equipment behavior of the hydropower station, identifies the performance degradation trend of the physical equipment of the hydropower station, continuously monitors the physical equipment of the hydropower station based on the identification results, updates the health status records of the physical equipment of the hydropower station, predicts the potential risks of the physical equipment of the hydropower station in the future, and establishes a health management file for the physical equipment of the hydropower station.

[0028] The equipment status feature set includes temperature features, pressure features and vibration features; preliminary fault warning indicators include out-of-bounds data points, time series analysis results and potential abnormality marking records; equipment behavior analysis results include behavior pattern change analysis results, periodic analysis results and periodic failure time frame; the health management file of the hydropower station physical equipment includes performance degradation trend identification results, continuous monitoring records and potential risk prediction records.

[0029] See also Figure 2 , the steps to obtain the device status feature set are: Sensors are deployed around the physical equipment of the hydropower station to capture the temperature, pressure and vibration data of the equipment operation process in real time, and data points whose frequencies are not within the working frequency range are screened out to obtain the pure operation status data of the equipment; A variety of sensors, including temperature sensors, pressure sensors, and vibration sensors, are installed around the physical equipment of the hydropower station. These sensors are fixed to key parts of the equipment to monitor the physical parameters of the equipment in real time during operation. These sensors continuously collect data and transmit it to the monitoring center through wiring. First, the collected raw data is timestamped to ensure that each piece of data accurately reflects the physical state at the time of collection. Subsequently, all data that obviously does not conform to the operating frequency of the equipment, such as occasional noise or errors, is eliminated through the preset frequency filtering function. This process ensures the purity and availability of the data, making subsequent data analysis more accurate. In addition, the integrity of the data is checked to ensure that each data collection is complete and not interfered by any external factors.

[0030] Based on the pure operation status data of the equipment, the time series is segmented according to the equipment operation cycle. Each time segment represents a working cycle. The mean, standard deviation and range of the data in each cycle are calculated to obtain the statistical description of the equipment cycle data. After data cleaning, the purified data is segmented into time series, and the continuous data stream is segmented into multiple independent time periods, each of which corresponds to a complete operation cycle of the device. In this process, a time window is first defined, usually with a complete cycle of device operation as the boundary. The data is then cut according to this time window and divided into multiple independent data blocks, each of which contains a complete cycle of operation data. For each data block, its basic statistical indicators are calculated, including the maximum value, minimum value, mean and standard deviation in the data block. Statistical indicators can describe the operating status of the device in each cycle. For example, the mean and standard deviation can reflect the average level and fluctuation of the device operation, and the maximum and minimum values ​​provide intuitive data on the extreme status of the device operation.

[0031] By using the statistical description of equipment cycle data and combining it with the equipment operation rules, the relationship between the data fluctuation of each cycle and the health status of the equipment is evaluated to reveal the current status of the equipment and obtain the equipment status feature set. By analyzing the statistical data of each time window in detail, we can further explore the deep characteristics of the equipment's operating status and evaluate how each statistical indicator reflects the equipment's operating efficiency and possible functional abnormalities individually and collectively. First, based on the design and functional requirements of the equipment, key operating indicators are identified, such as temperature stability, normal fluctuation range of pressure, and frequency characteristics of vibration. Then, for the data in each time window, specific values ​​and changing trends of key indicators are extracted. For example, the frequency distribution in vibration data is analyzed to identify whether there are unusual high-frequency components, indicating wear or failure of certain components. Further combined with key indicators, a comprehensive equipment status feature set is constructed, covering the data extracted from each time window, reflecting the overall operating quality of the equipment during the observation period, and intuitively understanding the performance of the equipment in each detection cycle, which improves the immediate understanding of the equipment status and enhances the ability of preventive maintenance.

[0032] See also Figure 3 , the calculation steps of the deviation are: Based on the equipment status feature set, the real-time operation data of the hydropower station's physical equipment is extracted to capture the current working status of the equipment and generate a real-time data set; Acquiring real-time operating data based on the equipment status feature set first involves confirming data access rights to ensure that operators have sufficient permissions to obtain information from the server. Next, it is necessary to cooperate with the database administrator to ensure the security and integrity of the data. Specific operations include querying the database, writing SQL query statements, and selecting and filtering specific data tables and fields, such as temperature, pressure, and vibration, to ensure that the acquired data set contains all the required data points. In addition, the query operation also needs to consider the timestamp of the data to ensure that the extracted data is in real-time or near real-time time sequence. Convert any non-standard data formats for the queried data so that the output of the data preparation stage is a structured and standardized data set.

[0033] Based on the real-time data set, combined with the normal operation benchmark data of the equipment, the formula is applied:

[0034] Calculate the total deviation value Deviation to generate the total deviation result, where n represents the total number of data points, i represents the data point index, and D r represents the real-time dataset, D b Denotes the benchmark dataset, D ri represents the value of the i-th data point in the real-time data set, D bi represents the value of the i-th data point in the benchmark dataset; The following temperature data (in °C) were collected over a period of time: Real-time operating temperature D ri=[65,68,67,70,72] Reference temperature D bi =[60,60,60,60,60] The number of data points n=5; Compute the square of the difference for each data point:

[0035] Compute the sum of squared differences: 25+64+49+100+144=382 Calculate the square root of the deviation:

[0036] The calculated deviation is approximately 8.74°C, which represents the average deviation between the actual measured temperature and the normal operating temperature of the device. This large deviation indicates that the device is overheating and needs further inspection to see if there is a fault or external factor causing the device to run at a higher temperature.

[0037] The total deviation result is compared with the preset abnormal threshold T. If Deviation>T, it is marked as abnormal and the equipment deviation analysis result is generated.

[0038] After the deviation calculation is completed, the deviation value is compared with the preset abnormal threshold. For each equipment type, the normal range of the deviation is determined based on historical maintenance data and the manufacturer's instruction manual. Once the threshold definition is completed, a logical judgment statement is written to compare whether the deviation value of each data point exceeds its corresponding threshold, such as whether the deviation value is greater than the threshold T. The threshold is usually preset during the data monitoring process or adjusted by the operator according to actual conditions. Data points that exceed the threshold will be marked as abnormal for subsequent analysis and investigation to ensure that each abnormality is properly recorded and responded to. The generated results will reflect the operating status of the equipment and indicate the need for further technical evaluation or direct maintenance intervention.

[0039] See also Figure 4 ,The steps to obtain the preliminary fault warning indicators are: According to the equipment deviation analysis results, all data points that exceed the preset abnormal threshold are automatically marked as out-of-bounds data points, the deviation size and timestamp of each out-of-bounds data point are recorded, and the out-of-bounds data point set is output; In the process of marking out-of-bounds data points, each data point is first obtained from the real-time data stream, and each data point is compared with the preset threshold. When the data point value exceeds the threshold, the point is marked as out-of-bounds, and the specific value of the data point and its corresponding timestamp are recorded. The recording method is to create a record item for each out-of-bounds point in the database, including the data point's identifier, timestamp and value. The records are then integrated into a set of out-of-bounds data points, which will be used for further analysis.

[0040] Time series analysis is performed based on the out-of-bounds data point set. By examining the time dependency of the data points, periodic fluctuations and irregular changes in the data are identified, potential operational problems are revealed, and time series analysis results are obtained. First, the out-of-bounds data points are arranged in chronological order. Then, the data is analyzed in detail to identify potential trends and patterns. This is mainly done by observing the distribution of data points on the time axis to identify periodicity and random fluctuations in the data. For example, if multiple consecutive data points show the same out-of-bounds behavior, it may be a sign of a systematic failure. During the analysis process, the time interval and degree of violation between data points are carefully considered to determine whether there are any identifiable patterns or trends. This is done by checking the data point set one by one. Each data point is evaluated to determine its impact on the overall trend, helping to understand the abnormal operating mode of the equipment and providing support for further fault diagnosis.

[0041] Using the time series analysis results, the current analysis results are matched with the fault signatures stored in the database to determine the fault type corresponding to the current out-of-bounds data point and obtain preliminary fault warning indicators; In the process of comparing the results of time series analysis with historical fault data, the analysis results are first matched with a database containing historical fault patterns. The matching process involves checking whether the new abnormal pattern is similar to the known patterns in the database. If a match is found, the matched fault type and the corresponding warning level are recorded. This is done by comparing each newly identified pattern with each pattern in the database one by one. The comparison benchmarks include the duration of the abnormality, the affected equipment components, and the conditions under which the fault occurred. Each successfully matched event will be recorded in detail, including the confidence of the match and the potential impact assessment, which can quickly identify possible equipment problems and warn before the problem becomes a larger failure, thereby ensuring the stable operation and safety of the equipment.

[0042] See also Figure 5 , the analysis and determination steps of abnormal behavior before failure are: Call the preliminary fault warning indicators, integrate the historical operation data of the hydropower station's physical equipment, count the operation time, load conditions and maintenance records, classify the data according to the time axis, remove abnormal data points, and generate a historical behavior data set of the hydropower station's physical equipment; The preliminary fault warning indicators are called up, and the historical operating data of the physical equipment of the hydropower station are integrated. During the data integration process, the required data types are first defined, including operating time, load conditions, maintenance records, etc., and then directly exported from the monitoring data according to the actual operating conditions of the hydropower station. In order to ensure the accuracy and availability of the data, the data is cleaned and verified, and obvious errors or abnormal data are eliminated, such as data points with abnormally long operating time or sudden load changes. The data is manually verified and automatically detected by comparing daily maintenance logs and operation logs to ensure that the data used can truly reflect the historical operating status of the equipment. This process involves data preprocessing techniques, such as null value processing, outlier detection, etc. The processed data is stored in a central database for subsequent behavioral analysis.

[0043] Based on the historical behavior data set of the hydropower station physical equipment, the behavior change law of the equipment under different operating conditions is analyzed, and the formula is used:

[0044] Calculate the device behavior pattern score S and generate the behavior pattern determination result, where B i is the operating load under different operating conditions, is the average value of the operating load, C i is the average value of the operating load, is the average value of the maintenance frequency, and N is the total number of data points; Setting B i is the operating load parameter of the equipment under different operating conditions. The specific data is [100, 105, 98, 103, 107]. The average value is =102.6; set C i is the maintenance frequency parameter under the same operating conditions. The specific data is [3, 3, 2, 3, 4]. The average value is =3.0; Set N to the total number of data points, which is 5.

[0045] First calculate

[0046]

[0047] Then calculate

[0048]

[0049] Then substitute the above results into the formula:

[0050] The results show that the average deviation score of the device behavior patterns in the given data set is 3.1628. The score reflects the degree of variation in the device's behavior under different operating conditions. A high S value indicates that the device exhibits unusual behavior under certain operating conditions, which is related to potential device problems.

[0051] Determine the result based on the behavior pattern, identify the abnormally increased part of the pattern score, compare the corresponding position of the fault warning indicator and the scored abnormal behavior, and obtain the abnormal behavior identification result before the fault; The results are determined through behavioral patterns, and abnormal behavioral patterns associated with failures are identified. First, the threshold for abnormally increased scores in the behavioral pattern analysis results is determined. This threshold is obtained by statistically analyzing the behavioral scores of past failure cases. The current analysis results are then compared with the threshold to identify behavioral patterns that exceed the threshold. These are often closely related to potential equipment failures. In this way, potential problems can be warned before significant equipment failures occur, providing data support for the technical team so that they can perform targeted maintenance or adjustments, thereby improving the reliability and safety of the equipment. The final result is the identification of abnormal behavior before equipment failure, which is crucial to improving the predictability of equipment operation and reducing unexpected downtime.

[0052] See also Figure 6 , the steps to obtain the device behavior analysis results are: Using the abnormal behavior identification results before the fault, perform time series analysis on the abnormal behavior, determine the frequency and periodicity of occurrence, analyze the time point of abnormal behavior, and generate a periodic behavior pattern data set; Using the results of abnormal behavior identification before the failure, time series analysis is performed to determine the periodicity of the abnormal behavior. First, detailed logs of equipment operation are collected, including the specific date and time of each operation. The log data is filtered to exclude any records of non-operating time, such as maintenance or downtime. Next, the filtered data is sorted and organized in chronological order to ensure the continuity of the analysis. Possible periodic patterns are identified by looking at changes in operation frequency and duration. The identification process involves examining each time point of the log data, comparing the previous and subsequent data points, and finding common patterns in the time intervals. For example, if the time interval is found to be consistent in multiple consecutive operation cycles, this will be marked as a potential periodic behavior. Ultimately, these marked periodic behaviors will be further analyzed to determine whether they are associated with known equipment problems, and the generated periodic behavior data set provides the basis for subsequent in-depth analysis.

[0053] Based on the periodic behavior pattern data set, the frequency components of the equipment behavior are analyzed, the key periodic components are identified, the frequency peaks in the statistical data set are analyzed, the key change patterns of the equipment behavior are revealed, and the change pattern analysis results are obtained; By performing frequency analysis on the marked periodic behavior data, the main changing patterns of equipment behavior can be further revealed. The analysis first involves converting time data into frequency data, that is, looking at the speed and pattern of changes in equipment behavior over time, calculating the average time interval between each periodic behavior, and monitoring whether these intervals remain consistent over time or show a certain trend. By drawing a scatter plot of time intervals and observing whether the data points are clustered around a certain average line, it is helpful to identify frequently occurring behavior patterns. For example, if most time intervals are clustered around a few specific values, this indicates that the equipment behavior has strong periodicity. In addition, statistical analysis of frequency data, such as calculating its standard deviation and variance, is performed to evaluate the consistency and predictability of periodicity, helping to determine which periodic behaviors are associated with equipment performance degradation or potential failures, thereby providing specific operational recommendations for the maintenance team.

[0054] Based on the change pattern analysis results, combined with the equipment maintenance records and fault history comparison, determine the possible time frame for periodic faults, analyze the potential time points for future equipment failures, and generate equipment behavior analysis results; After confirming the periodic behavior pattern of the equipment, compare the information with the maintenance records and historical failure data of the equipment. First, collect the maintenance and failure records of the equipment, and record in detail the time when each failure occurred and the maintenance measures taken. Classify and code the records to facilitate comparative analysis. For each recorded failure, analyze the correspondence between its occurrence time and the periodic behavior pattern to see if the failure occurs after certain specific periodic behaviors. This requires a detailed comparison of the behavior patterns before and after each failure, looking for any possible similarities or patterns. Detailed comparative analysis can reveal which periodic behaviors may cause equipment performance problems or failures. In addition, by calculating the time difference between periodic behavior and failure, the warning time of these behavior patterns is evaluated, thus providing a basis for preventive maintenance of equipment.

[0055] See also Figure 7 , the steps to identify the performance degradation trend are: Receive equipment behavior analysis results, collect statistics on equipment operation data, performance indicators and abnormal event records, perform data integrity verification and timestamp verification, confirm data consistency and reliability, and generate equipment data verification results; After receiving the results of the device behavior analysis, the format and structure of the incoming data stream are first checked to ensure that the received information is consistent with the data reception standard. Then, the timestamp and data integrity of each record are checked by reading the data records line by line, which is completed by comparing the data length of the record with the preset data structure. If the data is found to be damaged or missing during the transmission process, the batch of data is marked as incomplete and a retransmission is requested. In addition, the received data will be preliminarily sorted and arranged in chronological order to lay the foundation for subsequent data processing and analysis.

[0056] By establishing a data channel, the device data verification results are synchronized between the cloud and edge computing resources, confirming that all computing nodes have a consistent data view and generating a data synchronization completion confirmation result; Once the data is received and initially processed, the data synchronization process with the cloud is initiated to ensure that all operational data is updated to the cloud server in real time. By establishing a scheduled task, new data is automatically pushed from the local server to the cloud at regular intervals. Before the data is pushed, an integrity check is performed to compare the latest record of local data with the difference in data after the last synchronization to ensure that all new data is included. Data transmission uses an encrypted communication protocol to protect the security of data transmission over the Internet. Once the data is successfully uploaded to the cloud, a synchronization log is automatically recorded locally, including the time and amount of data synchronized, for future auditing and troubleshooting.

[0057] According to the confirmation results of data synchronization, long-term and short-term equipment behavior data are compared to identify the degradation trend of equipment performance, analyze the equipment performance degradation mode, and obtain the equipment performance degradation analysis results; After completing data synchronization, a detailed comparative analysis of long-term and short-term data is conducted to identify and quantify the performance degradation trend of the equipment. First, the historical performance data of the equipment, including past operating efficiency, maintenance records, and failure rates, is extracted from the database and compared with the latest current data. By calculating the percentage change of equipment performance indicators, such as the reduction in operating efficiency or the growth trend of failure rates, the degradation rate of the equipment can be quantitatively evaluated. Through detailed calculations and comparisons, a detailed report on equipment performance degradation can be obtained to provide decision support for the maintenance team.

[0058] See also Figure 8 , the steps to establish the health management file of the physical equipment of the hydropower station are: Based on the results of equipment performance degradation analysis, monitor equipment temperature, pressure and load, conduct real-time data collection to obtain real-time operating data of key parameters, and generate a collated real-time monitoring data set; Before starting data monitoring, first set key monitoring parameters such as temperature, pressure, and load based on historical performance analysis results. Parameters are critical to assessing the health of the equipment. Next, continue to collect operating data. After data collection, perform preliminary screening processing, including verifying the timestamp of the data and checking the integrity and consistency of the data to confirm that all data conform to the preset standard format. For incomplete or abnormal data points, remove them from the data set. The process is implemented through automated scripts to ensure that only valid and complete data is used for subsequent analysis, generate a collated real-time monitoring data set, and provide an accurate data basis for continuous monitoring of equipment status and future analysis work.

[0059] Based on the collated real-time monitoring data set and combined with the historical performance data of the equipment, the changing trend of the current health status of the equipment is analyzed item by item, using the formula:

[0060] Calculate the change range of the equipment health status H and generate the updated equipment health status record, where P i and P i-1 Represent the key performance parameter values ​​at adjacent time points, t i和 t i-1 are the corresponding time points respectively, and M is the total number of data points; The time points t1, t2, and t3 correspond to the 1st, 2nd, and 3rd days.

[0061] The values ​​of the performance parameter P at t1, t2, and t3 are 100, 105, and 95, respectively.

[0062] Calculate the rate of change of the performance parameter between each two consecutive time points and square it:

[0063] Accumulate square values: 25+100=125 Calculate the value of H:

[0064] The calculated H value is 11.18, which reflects the overall change in the performance parameters of the equipment during the monitoring period. A higher H value indicates that the performance parameters of the equipment have changed significantly during the observation period, indicating that the performance of the equipment is degrading or the equipment status is unstable, requiring further inspection and possible maintenance.

[0065] Based on the updated equipment health status records and historical fault records, the changing trends of key parameters of equipment performance degradation are sorted out, the potential risk probability of the equipment in the future is analyzed, and the physical equipment health management file of the hydropower station is generated; After updating the equipment health record, the data is further aggregated and analyzed to identify any possible early signs of performance degradation or potential failure. In this process, data analysis does not rely on complex external tools, but is completed through built-in data processing functions, including trend analysis and periodic inspection of data, which helps to reveal any atypical patterns in equipment operation. The analysis results are then used to update the equipment's health record, which includes not only updating the numerical data, but also supplementing the equipment operation log, such as changes in operating conditions, additions to maintenance history, and replacement records of any key components. Ultimately, all this information is integrated into the equipment health management file, which is dynamically updated to reflect the latest status of the equipment at any time and provide data support for the formulation of future maintenance and monitoring strategies.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, not to limit them; although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention. In particular, as long as there is no structural conflict, the various technical features mentioned in each embodiment can be combined in any way. The present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions that fall within the scope of the claims.

Claims

1. A digital intelligent analysis system based on the physical equipment of a hydropower station, characterized by: The system comprises: The state feature extraction module collects temperature, pressure and vibration data of the hydropower station's physical equipment through sensors, filters out background noise, divides the data into time windows, captures the operating state characteristics of the hydropower station's physical equipment, and outputs the equipment state feature set; The fault identification and early warning module analyzes the operation data of the hydropower station physical equipment based on the equipment status feature set, calculates the deviation between the current operation data of the hydropower station physical equipment and the normal operation benchmark data, automatically marks the out-of-bounds data points according to the calculation results, and performs time series analysis on the out-of-bounds data points to identify potential abnormal operation of the hydropower station physical equipment and generate preliminary fault early warning indicators; The equipment behavior in-depth analysis module performs a retrospective analysis of the historical behavior of the hydropower station's physical equipment based on the preliminary fault warning indicators, analyzes and determines the abnormal behavior of the hydropower station's physical equipment before the failure by analyzing the behavior patterns of the hydropower station's physical equipment under different operating conditions, and performs a periodic analysis of the abnormal behavior based on the analysis results to reveal the changing laws of the hydropower station's physical equipment behavior, capture the time frame of periodic failures, and obtain equipment behavior analysis results; The collaborative learning and prediction module receives the equipment behavior analysis results, starts the data synchronization process between the cloud and edge computing resources, analyzes and compares the long-term data and short-term activities of the physical equipment behavior of the hydropower station, identifies the performance degradation trend of the physical equipment of the hydropower station, continuously monitors the physical equipment of the hydropower station based on the identification results, updates the health status record of the physical equipment of the hydropower station, predicts the potential risks of the physical equipment of the hydropower station in the future, and establishes a health management file for the physical equipment of the hydropower station.

2. The digital intelligent analysis system based on the physical equipment of the hydropower station according to claim 1 is characterized in that: The steps of obtaining the device status feature set are: Sensors are deployed around the physical equipment of the hydropower station to capture the temperature, pressure and vibration data of the equipment operation process in real time, and data points whose frequencies are not within the working frequency range are screened out to obtain the pure operation status data of the equipment; Based on the pure operation status data of the equipment, the time series is segmented according to the equipment operation cycle, each time segment represents a working cycle, and the data mean, standard deviation and range in each cycle are calculated to obtain a statistical description of the equipment cycle data; By using the statistical description of the equipment cycle data and combining it with the equipment operation rules, the relationship between the data fluctuation of each cycle and the equipment health status is evaluated, the current status of the equipment is revealed, and the equipment status feature set is obtained.

3. The digital intelligent analysis system based on the physical equipment of the hydropower station according to claim 2 is characterized in that: The calculation steps of the deviation are: Based on the equipment status feature set, extract the real-time operation data of the physical equipment of the hydropower station, capture the current working status of the equipment, and generate a real-time data set; Based on the real-time data set, combined with the normal operation benchmark data of the equipment, the formula is applied: Calculate the total deviation value Deviation to generate the total deviation result, where n represents the total number of data points, i represents the data point index, and D r represents the real-time dataset, D b Denotes the benchmark dataset, D ri represents the value of the i-th data point in the real-time data set, D bi represents the value of the i-th data point in the benchmark dataset; The total deviation result is compared with a preset abnormal threshold T. If Deviation>T, it is marked as abnormal and a device deviation analysis result is generated.

4. The digital intelligent analysis system based on the physical equipment of the hydropower station according to claim 3 is characterized in that: The steps for obtaining the preliminary fault warning indicator are: According to the device deviation analysis result, all data points exceeding the preset abnormal threshold are automatically marked as out-of-bounds data points, the deviation size and timestamp of each out-of-bounds data point are recorded, and a set of out-of-bounds data points is output; Performing time series analysis based on the cross-border data point set, identifying periodic fluctuations and irregular changes in the data by examining the time dependency of the data points, revealing potential operating problems, and obtaining time series analysis results; Using the time series analysis results, the current analysis results are matched with the fault signatures stored in the database to determine the fault type corresponding to the current out-of-bounds data point and obtain preliminary fault warning indicators.

5. The digital intelligent analysis system based on the physical equipment of the hydropower station according to claim 4 is characterized in that: The analysis and determination steps of the abnormal behavior before the fault are as follows: Calling the preliminary fault warning indicator, integrating the historical operation data of the hydropower station physical equipment, counting the operation time, load conditions and maintenance records, classifying the data according to the time axis, removing abnormal data points, and generating a historical behavior data set of the hydropower station physical equipment; Based on the historical behavior data set of the hydropower station physical equipment, the behavior change law of the equipment under different operating conditions is analyzed, and the formula is used: Calculate the device behavior pattern score S and generate the behavior pattern determination result, where B i is the operating load under different operating conditions, is the average value of the operating load, C i is the average value of the operating load, is the average value of the maintenance frequency, and N is the total number of data points; According to the behavior pattern determination result, the abnormally increased part in the pattern score is identified, and the corresponding position of the fault warning indicator and the scored abnormal behavior is compared to obtain the abnormal behavior identification result before the fault.

6. The digital intelligent analysis system based on the physical equipment of the hydropower station according to claim 5 is characterized in that: The steps for obtaining the device behavior analysis result are as follows: Using the abnormal behavior recognition results before the fault, perform time series analysis on the abnormal behavior, determine the frequency and periodicity of occurrence, analyze the time point when the abnormal behavior occurs, and generate a periodic behavior pattern data set; Based on the periodic behavior pattern data set, the frequency components of the device behavior are analyzed, key periodic components are identified, frequency peaks in the statistical data set are counted, key change patterns of the device behavior are revealed, and change pattern analysis results are obtained; Based on the analysis results of the change rules, combined with the comparison of equipment maintenance records and fault history, the possible time frame for the occurrence of periodic faults is determined, the potential time points for the equipment to fail in the future are analyzed, and the equipment behavior analysis results are generated.

7. The digital intelligent analysis system based on the physical equipment of the hydropower station according to claim 6 is characterized in that: The steps for identifying the performance degradation trend are: Receive the equipment behavior analysis results, collect statistics on equipment operation data, performance indicators and abnormal event records, perform data integrity verification and timestamp verification, confirm data consistency and reliability, and generate equipment data verification results; By establishing a data channel, synchronizing the device data verification results between the cloud and edge computing resources, confirming that all computing nodes have a consistent data view, and generating a data synchronization completion confirmation result; The confirmation result is completed synchronously according to the data, the long-term and short-term equipment behavior data are compared, the degradation trend of equipment performance is identified, the equipment performance degradation mode is analyzed, and the equipment performance degradation analysis result is obtained.

8. The digital intelligent analysis system based on the physical equipment of the hydropower station according to claim 7 is characterized in that: The steps for establishing the health management file of the hydropower station physical equipment are as follows: According to the equipment performance degradation analysis results, monitor the equipment temperature, pressure and load, perform real-time data collection to obtain real-time operating data of key parameters, and generate a collated real-time monitoring data set; Based on the real-time monitoring data set, combined with the historical performance data of the equipment, the changing trend of the current health status of the equipment is analyzed item by item, using the formula: Calculate the change range of the equipment health status H and generate the updated equipment health status record, where P i and P i-1 Represent the key performance parameter values ​​at adjacent time points, t i和 t i-1 are the corresponding time points respectively, and M is the total number of data points; According to the updated equipment health status record, combined with historical fault records, the changing trends of key parameters of equipment performance degradation are sorted out, the potential risk probability of the equipment in the future is analyzed, and the physical equipment health management file of the hydropower station is generated.

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