Hydropower station operation data-oriented automatic analysis and report generation method, equipment and medium

Abstract

The invention discloses an automatic analysis and report generation method and device for hydropower station operation data and a medium, and the method comprises the steps: S1, collecting and sorting multiple types of operation data of a hydropower station, collecting various types of operation data, and carrying out the time sequence sorting and synchronous processing of the collected data; s2, operating state identification and reason tracing: distinguishing the current operating state of each device in the hydropower station, searching and sorting out various reasons causing the change according to the state change, and establishing a data-driven causal chain; s3, key index integration and feature discovery: performing integration analysis on each performance index of each device and system, and adaptively adjusting each parameter weight according to an actual operation condition; s4, dynamically generating a report template, automatically judging differentiated requirements of different users on report contents, and automatically selecting corresponding content modules; and S5, report content generation and accurate pushing: aiming at users of different roles, automatically pushing corresponding report contents to the users.

Description

Technical Field

[0001] This application belongs to the field of hydropower station data processing, and in particular relates to a method, equipment and medium for automatic analysis and report generation of hydropower station operation data. Background Technology

[0002] With the continuous improvement of the power industry's intelligence and informatization levels, hydropower stations, as important clean energy production units, have received widespread attention for their operational safety and economic efficiency. Existing hydropower stations are generally equipped with a large number of automation and monitoring devices, enabling real-time acquisition of various operational data such as flow rate, pressure, temperature, and electrical parameters. However, due to the diverse sources of various equipment, inconsistent data acquisition frequencies, and heterogeneous data formats, the time-series alignment and comprehensive analysis of operational data present certain challenges.

[0003] In practical applications, maintenance personnel often rely on manual data compilation and analysis, making it difficult to promptly and accurately identify equipment malfunctions and their causes, thus affecting the efficiency of fault response and the scientific nature of decision-making. Furthermore, operational reports for different users (such as maintenance personnel, managers, and experts) typically have a uniform format and fixed content, failing to meet the diverse needs of different roles for key indicators and depth of analysis.

[0004] To address the aforementioned issues, there is an urgent need for an advanced method that can automatically collect and organize multi-source heterogeneous operational data, intelligently identify operational status and trace the causes of anomalies, and simultaneously generate and push personalized reports automatically, in order to improve the level of intelligent operation and maintenance and scientific management of hydropower stations. Summary of the Invention

[0005] This invention aims to address the technical problems of existing hydropower station operation data, such as diverse data sources, inconsistent data types and frequencies, difficulties in information synchronization and fusion analysis, lack of intelligent identification of abnormal states and tracing of causes, and the single format of report content, which makes it difficult to meet the differentiated needs of different users. It proposes an automatic analysis and report generation method, equipment, and medium for hydropower station operation data. This method enables automated collection and synchronous processing of multiple types of operation data, intelligent identification of equipment operating status and causes of abnormalities, dynamic integration of key indicators and characteristics, and automatic generation and precise delivery of personalized reports based on user identity and needs, thereby improving the intelligence and scientific level of hydropower station operation management.

[0006] On the one hand, the objective of this application is achieved through the following technical solution: An automatic analysis and report generation method for hydropower station operation data, comprising: S1: Collection and processing of various types of operational data from hydropower stations. Utilize existing monitoring equipment at hydropower stations to collect various types of operational data; organize and synchronize the collected data in chronological order, and automatically adjust the frequency of different data collection to ensure the consistency of various information. S2: Operation status identification and cause tracing, identify the current operation status of each piece of equipment in the hydropower station, find and sort out the various causes of the changes in status, and establish a data-driven causal chain. S3: Key indicator integration and feature discovery. This involves integrating and analyzing various performance indicators of each device and system, and adaptively adjusting the weights of each parameter based on the actual operating conditions to make the analysis results more consistent with the current working conditions. S4: Report templates are dynamically generated, automatically determining the different needs of different users for report content and summarizing the key indicators that each user is concerned about; based on the actual results of each analysis, the corresponding content modules are automatically selected. S5: Report content generation and precise delivery. Based on industry experience and historical cases, combined with the results of this analysis, the system automatically generates report text conclusions and processing suggestions; and automatically pushes the corresponding report content to users with different roles.

[0007] According to a preferred embodiment, step S1, which involves time-sequentially organizing and synchronizing the collected data, includes: calculating the original sampling frequency for each type of device as... The expected target uniform sampling frequency is For all i, satisfy The data stream is interpolated using the Lagrange interpolation method; for In this case, the sliding window averaging method is used; For missing data, a dual-domain autoregressive completion method is proposed: that is, an autoregressive model is constructed in both the original time domain and the feature transformation domain of the collected data; for any missing position... The optimal value was determined by combining least squares fitting and error minimization methods. For outlier detection and correction, the mean and standard deviation of each data set are calculated under a unified window. For the identified outliers, the neighborhood sliding median method is used for correction.

[0008] According to a preferred embodiment, the various types of operational data collected in step S1 include: operational data collected from flow meters, pressure gauges, thermometers, and video surveillance.

[0009] According to a preferred embodiment, step S2, operation status identification and cause tracing, includes: For preset core performance parameters, calculate their moving average and moving standard deviation. Combining the equipment operation manual and historical thresholds, set the status judgment threshold ranges for each indicator, including normal operation range, alarm range, and fault range, and define the current operating status. ; A smoothing mechanism based on historical state sequences is introduced. Once a change in equipment state from "normal" to "alarm" or "fault" is detected, an automatic cause tracing process is initiated. For the state change segment, the change curves of relevant indicators are back-analyzed.

[0010] According to a preferred embodiment, the process of backtracking and analyzing the change curves of relevant indicators in step S2 includes: First, perform mutual information analysis on all preset core parameters to measure each variable. The impact of equipment status changes was determined using time-lag cross-correlation analysis. Does the change precede the change in state in time? , By iterating through different time lags If in and If a significant peak is reached, then it is considered... Changes It has a causal leading role.

[0011] According to a preferred embodiment, step S3, the key indicator integration and feature discovery process, includes: Configure each device D j It has m traditional performance indicators. By using the max-min normalization method, the indicators are standardized to a uniform scale, and an adaptive weight adjustment algorithm based on working condition clustering is introduced: First, the historical data is classified into different operating conditions using the K-means clustering method. Within each category, the volatility and fault contribution of each indicator are statistically analyzed, and the importance weight of the k-th indicator under a given operating condition is denoted as... ; The equipment's current operating data is dynamically assigned to the closest operating condition category based on cluster center distance or Bayesian probability. And use the indicator weights corresponding to the current category. Perform weighted consolidation; The design is based on a historical-real-time hybrid feature generation mechanism. Principal component analysis or variational autoencoder dimensionality reduction method is used to map multidimensional original indicators to a new low-dimensional feature space to obtain a set of innovative health features that take into account both relevance and information content. A novel indicator based on dynamic statistics, slope, and volatility under a sliding window is adopted.

[0012] According to a preferred embodiment, step S4, the dynamic generation process of the report template, includes: Based on different user identity information and their historical usage habits, the system automatically determines the user's focus, establishes a dedicated topic library for each user category, and automatically matches relevant content modules for each analysis result. It dynamically adjusts the report structure and display order, flexibly inserts charts, text descriptions, and key data, and generates modular and reconfigurable report templates to meet the differentiated needs of different users for report content.

[0013] According to a preferred embodiment, step S5, the report content generation and precise push process, includes: Based on industry knowledge bases and historical operation and maintenance cases, the analysis results are summarized and the text conclusions and handling suggestions are automatically generated. According to user roles, contact information and configuration preferences, the corresponding report content is pushed to users accurately and timely through email, App notification or information system integration, so as to realize personalized information delivery and task response.

[0014] On the other hand, this application also discloses: An electronic device includes: at least one processor; and a memory communicatively connected to said at least one processor; The memory stores instructions that can be executed by the at least one processor, and the at least one processor executes the instructions stored in the memory to perform the aforementioned method.

[0015] On the other hand, this application also discloses: A computer-readable storage medium for storing instructions that, when executed, cause the aforementioned method to be implemented.

[0016] The aforementioned main solution and its various further alternative solutions can be freely combined to form multiple solutions, all of which are solutions that can be adopted and are claimed in this application. Those skilled in the art, after understanding the solution of this application, will realize that there are many combinations based on the prior art and common general knowledge, all of which are technical solutions to be protected in this application, and will not be exhaustively listed here.

[0017] The beneficial effects of this application are: This application enables the automatic collection and unified processing of various types of operational data from hydropower stations, improving the standardization and timeliness of data management. By intelligently identifying the operating status and root causes of each piece of equipment, it can promptly detect operational anomalies and trace their causes, contributing to improved accuracy and response speed in fault diagnosis. Employing key indicator integration and adaptive feature discovery methods, it can dynamically reflect the true health status of equipment and systems, supporting intelligent decision-making. Report templates and content are dynamically generated according to the needs of different users, providing more targeted and practical analysis results. The automated generation and precise delivery of report content significantly reduces the workload of maintenance personnel, improves the efficiency of information transmission, and enhances the overall level of intelligent operation and maintenance of hydropower stations. Overall, this invention helps promote the informatization, intelligentization, and personalization of hydropower station operation and management, improving the safety, reliability, and economic benefits of hydropower stations. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the method of this application; Figure 2 This is a schematic diagram of the equipment structure in this application. Detailed Implementation

[0019] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0020] It should be noted that similar reference numerals and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, this application should indicate that unless otherwise specified, the structures, connections, positions, power sources, etc., involved in this application are all things that a person skilled in the art could discover without inventive effort based on existing technology.

[0021] Example 1 refer to Figure 1 As shown in the figure, this embodiment discloses an automatic analysis and report generation method for hydropower station operation data, which specifically includes the following steps.

[0022] Step 1: Collection and processing of various types of operational data from hydropower stations By utilizing various existing monitoring devices at the hydropower station (such as flow meters, pressure gauges, thermometers, and video surveillance), a variety of operational data are collected. The collected data is then organized and synchronized in chronological order, and the frequency of different data collections is automatically adjusted to ensure that all types of information can be analyzed uniformly. For missing, abnormal, or erroneous data, an innovative synchronous correction and intelligent compensation mechanism is adopted to effectively repair incomplete data and improve data quality.

[0023] In the process of collecting and processing various types of operational data at hydropower stations, real-time data collection is first performed using various monitoring devices deployed at the hydropower station (such as flow meters, pressure gauges, thermometers, video surveillance, etc.). Let the types of these devices be... Each device corresponds to a set of sampling signals, denoted as ,in This represents the sampled value of the i-th type of device at time t. Due to differences in physical properties and sampling mechanisms, the actual sampling frequency or sampling time may vary between different devices. To ensure consistent analysis later, all data streams are first time-aligned.

[0024] Therefore, an adaptive sampling frequency adjustment mechanism is introduced: the original sampling frequency for each type of device is calculated as follows: The expected target uniform sampling frequency is For all i, the following condition is satisfied: The data stream is interpolated using Lagrange interpolation, with the following formula:

[0025] in, Represents the Lagrange basis functions. This refers to the actual time point of data collection. For In this case, the sliding window averaging method is used, i.e.

[0026] in .

[0027] After time alignment and unified sampling frequency processing, to ensure data integrity and high quality, it is necessary to address missing, outlier, or error issues in the data. For missing data, a dual-domain autoregressive completion method is proposed: that is, an autoregressive model is constructed in both the original time domain and the feature transformation domain (such as the wavelet domain) of the acquired data. For any missing location... ,use:

[0028] in, , For model weights, These are the wavelet transform coefficients for this type of data. They are determined using a combined least-squares fitting and error minimization method. , The optimal value.

[0029] For outlier detection and correction, an adaptive anomaly scoring mechanism is introduced. The mean of each data set is calculated within a uniform window. and standard deviation Define anomaly score:

[0030] when ( If a threshold value (2-3) is used, it is considered an outlier. For identified outliers, a neighborhood sliding median method is used for correction, i.e.:

[0031] Where h is the half-width of the sliding window.

[0032] Through the above mechanism, the timing of hydropower station operation data from different sources, frequencies, and formats is unified, missing data is filled in, anomalies are corrected, and data integrity is improved, providing a high-quality input data foundation for subsequent automatic analysis and reporting.

[0033] Step 2: Identifying Operational Status and Tracing the Root Cause Through statistical analysis and rule detection, combined with historical and recent data, the system automatically identifies the current operating status of the equipment (such as normal, alarm, fault, maintenance, etc.); for changes in status, it automatically finds and sorts out various possible causes of the changes, establishes a data-driven causal chain, helps to locate the root cause of the problem, and provides early warning of potential risks.

[0034] After the initial data collection and processing, all hydropower station operational data were standardized and stored with high quality, providing a reliable foundation for subsequent equipment operation status identification and cause tracing. For any given piece of equipment... Time series data First, statistical analysis and rule detection are used to determine its operating status.

[0035] Specifically, for core performance parameters (such as water flow rate, pressure, temperature, vibration, etc.), their moving average values ​​are calculated. and sliding standard deviation ,in:

[0036] w represents the width of the sliding window. Based on the equipment operation manual and historical thresholds, set the threshold ranges for judging the status of various indicators, such as the normal operation range. Alarm range and fault range Based on this, the current running state is defined. :

[0037] To enhance the accuracy of identification, historical state sequences are also introduced. Smooth discrimination mechanisms, such as Markov transition probabilities This filters out occasional false alarms. Once a device status change from "normal" to "alarm" or "fault" is detected, an automatic cause-finding process is initiated. For each status change segment, the change curves of key relevant indicators are analyzed retrospectively. First, mutual information analysis is performed on all core parameters to measure each variable. The impact of changes in equipment status, calculated.

[0038] in For joint probability distribution, and Marginal probabilities are used. Variables with significant influence are selected as candidate causal parameters, and further time-lag cross-correlation analysis is employed to determine the variables. Does the change precede the change in state in time? Its related function is expressed as:

[0039] By iterating through different time lags If in and If a significant peak is reached, then it is considered... Changes It has a causal leading role.

[0040] Finally, by combining the above causal parameters and their order of action, the "causal chain" of state changes is automatically drawn, i.e., using a directed graph. This means that node V represents the key causal parameters, and edge E represents the direct impact of the parameters on the state. The report clearly identifies the primary cause and several secondary influencing factors. In this way, the root cause of the problem can be quickly located, and risk warnings and response suggestions can be provided to management and maintenance personnel in advance, thereby achieving the goal of automatic state identification and intelligent cause tracing.

[0041] Step 3: Key Indicator Integration and Feature Discovery The performance indicators of various equipment and systems in the hydropower station are integrated and analyzed, and the weights of various parameters are adaptively adjusted according to the actual operating conditions to make the analysis results more consistent with the current operating conditions. Based on traditional performance indicators, and combined with various historical and real-time data, new features and indicators reflecting the health status of equipment operation are automatically extracted and generated.

[0042] In the key indicator integration and feature discovery phase, to comprehensively analyze the operational health status of different equipment and systems in the hydropower station, the historical and current observation data of all performance parameters were first normalized. A D value was set for each piece of equipment. j It has m traditional performance indicators By using the min-max normalization method, all indicators are standardized to a uniform scale: ,in and These are the historical minimum and maximum values ​​for this parameter, respectively.

[0043] To ensure the flexibility and dynamic adaptability of indicator integration, an adaptive weight adjustment algorithm based on operating condition clustering is further introduced. First, methods such as K-means clustering are used to classify historical data into different operating condition categories. The volatility (variance) and fault contribution of each indicator within each category are statistically analyzed, and the importance weight of the k-th indicator under a certain operating condition is denoted as... The current equipment operating data is dynamically assigned to the closest operating condition category based on cluster center distance or Bayesian probability. And use the indicator weights corresponding to the current category. Perform weighted consolidation:

[0044] in, For equipment The comprehensive operational status score at time t.

[0045] Building upon this foundation, to enhance the depth of equipment health analysis, an innovative mechanism based on historical-real-time hybrid feature generation is designed. Specifically, principal component analysis (PCA) or variational autoencoders are used to map the multidimensional original indicators to a new low-dimensional feature space, obtaining a set of innovative health features that balance relevance and information content. For example, PCA features are expressed as:

[0046] in, denoted as the weight corresponding to the u-th principal component.

[0047] Furthermore, to detect "early signs of abnormality" or "degradation trends" in equipment operation, novel indicators such as dynamic statistics, slope, and volatility under a sliding window are employed. For example, health degradation characteristics can be represented as:

[0048] Where w is the length of the sliding time window. Used to reflect the rate at which the condition of the equipment deteriorates.

[0049] By normalizing and integrating traditional indicators, dynamically adjusting weights based on real-time operating conditions, and automatically extracting innovative indicators based on feature transformation and trend analysis, a set of core features that can highly reflect the operational health and risk status of hydropower station equipment is ultimately formed, providing a solid data foundation for intelligent analysis and subsequent decision-making.

[0050] Step 4: Dynamically generate report template It automatically identifies the different needs of different users (such as operations and maintenance personnel, management, and decision-makers) for report content, and sorts out the key indicators they focus on. Based on the actual results of each analysis, it automatically selects the corresponding content modules, dynamically assembles or adjusts the report structure and presentation, and flexibly inserts charts, text descriptions, and key data to generate professional reports with clear structure and rich content.

[0051] During the dynamic generation phase of the report template, the system first automatically summarizes and organizes the core indicators and key analysis points that each type of user is most concerned about, based on user identity information (such as maintenance personnel, management, or decision-makers) and historical usage habits. For users with different roles, the system establishes a dedicated library of topics of interest. For example, maintenance personnel focus on equipment operating status and details of abnormal alarms, management emphasizes overall energy efficiency, reliability, and trends in operating indicators, while decision-makers pay more attention to macro-level aspects such as cost-effectiveness, risk warnings, and long-term maintenance strategies.

[0052] After identifying the personalized needs described above, the system will automatically associate template content modules highly relevant to the results of this round of data analysis, including but not limited to status overview, key indicator changes, anomaly tracking, root cause visualization, performance trend charts, health assessment scores, and early warning suggestions. For each module, the system will intelligently adjust the display order and level of detail based on the quantity and importance of the current analysis output. For example, if the analysis detects a critical fault in a device, the report will automatically prioritize displaying the device's individual health score, problem tracing path, and visualization charts, and provide targeted maintenance suggestions; while if the overall operating status is stable, it will highlight more global information such as operational efficiency, energy saving and emission reduction data, and uptime of important equipment.

[0053] Furthermore, the report structure adopts a modular and reconfigurable design. Each module can automatically expand or contract in length based on content availability. Relevant charts (such as operational trend diagrams, fault distribution diagrams, and equipment network diagrams) are dynamically generated and inserted into their corresponding positions. Key data is updated in real time as the analysis progresses, and each report is accompanied by concise text descriptions and conclusions. All generated reports support export in multiple formats, such as PDF, Word, and web pages, facilitating user access and archiving. Through this process, each report can best meet the actual needs of different users, combining professionalism, readability, and operability, thereby improving the efficiency and scientific rigor of hydropower station management and decision-making.

[0054] Step 5: Report Content Generation and Targeted Delivery Based on industry experience and historical cases, combined with the results of this analysis, the system automatically generates easy-to-understand, key-point reports with conclusions and recommendations. For users in different roles, the system automatically pushes the corresponding report content to users via email, app notifications, or information system integration, ensuring timely delivery and effective implementation of information.

[0055] During the report generation and targeted delivery phase, the system prioritizes accessing the industry knowledge base and historical operation and maintenance case library to compare and summarize the main conclusions, risk factors, and health assessment results obtained from this analysis. Combining automatically extracted key data and analytical findings, the system can automatically generate report conclusions that are relevant to industry practice and highlight key points, using concise, clear, and professional language. The report not only describes the current operating status and major changes of the equipment but also automatically generates handling suggestions for different scenarios based on experience from similar past cases, including emergency measures, subsequent maintenance plans, and optimization suggestions, ensuring that the conclusions are practical and instructive.

[0056] The generated text content and conclusions are flexibly adjusted in expression based on the reading habits and focus of the target users. For example, for operations and maintenance personnel, a detailed list of faulty equipment, reasons for status changes, and recommended operating procedures will be provided; for management, the overall operational efficiency, risk level, and improvement suggestions will be highlighted; while information for decision-makers will focus more on trend predictions and strategic recommendations.

[0057] Once the content is ready, the system can select the most suitable push channel based on the user's contact information and configuration preferences, including email, WeChat Work, dedicated app message notifications, or automatic push notifications through integration with the internal management information system. Each user will receive precisely the report content relevant to their responsibilities, along with key reminders, ensuring that important information is delivered promptly and attracts attention. For major faults or emergency warnings, the system will also enable simultaneous push notifications through multiple channels, enhancing the timeliness of information delivery and the efficiency of event handling. This design significantly improves the speed at which report content is implemented and its practical value, helping hydropower station teams and management make timely and informed decisions and respond effectively.

[0058] Example 2 like Figure 2 As shown, based on Embodiment 1, this embodiment also discloses an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; the specific connection medium between the processor and the memory is not limited in this embodiment of the invention.

[0059] Figure 2 The example used is the connection between the processor and memory via a bus. The bus... Figure 2 The connections between other components are indicated by thick lines and are for illustrative purposes only, not as limiting information. Buses can be divided into address buses, data buses, control buses, etc., but for ease of representation, [the specific bus type is not shown here]. Figure 2 The processor is represented by a single thick line, but this does not imply that there is only one bus or one type of bus. Alternatively, a processor can also be called a controller; there are no restrictions on the name.

[0060] In this embodiment, the memory stores instructions executable by the at least one processor. By executing the instructions stored in the memory, the at least one processor performs the method described in Embodiment 1. The processor can implement... Figure 2 The functions of each module in the device shown.

[0061] The processor is the control center of the device. It can connect to various parts of the control device through various interfaces and lines. By running or executing instructions stored in memory and calling data stored in memory, it can monitor the device's various functions and process data, thereby enabling overall monitoring of the device.

[0062] In an alternative design, the processor may include one or more processing units. The processor may integrate an application processor and a modem processor, wherein the application processor primarily handles the operating system, user interface, and applications, while the modem processor primarily handles wireless communication. It is understood that the modem processor may also not be integrated into the processor. In some embodiments, the processor and memory may be implemented on the same chip; in some embodiments, they may also be implemented separately on separate chips.

[0063] The processor can be a general-purpose processor, such as a CPU, digital signal processor, application-specific integrated circuit, field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the automatic analysis and report generation method for hydropower station operation data disclosed in the embodiments of this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0064] Memory, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory can include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), and electrically erasable programmable read-only memory (EPROM). Only memory (EEPROM), magnetic storage, magnetic disks, optical disks, etc. A memory is any other medium capable of carrying or storing desired program code in the form of instructions or data structures, and accessible by a computer, but is not limited thereto. The memory in embodiments of this invention can also be a circuit or any other device capable of performing storage functions for storing program instructions and / or data.

[0065] By designing and programming the processor, the code corresponding to the automatic analysis and report generation method for hydropower station operation data described in the foregoing embodiments can be embedded into the chip, enabling the chip to execute the steps of the methods described in the foregoing embodiments during operation. How to design and program the processor is a technique well-known to those skilled in the art and will not be elaborated upon here.

[0066] Example 3 Based on Embodiment 1, this embodiment also discloses: a computer-readable storage medium for storing instructions that, when executed, cause the method described in Embodiment 1 to be implemented.

[0067] In some alternative embodiments, the present invention also provides that various aspects of the method for automatic analysis and report generation of hydropower station operation data can also be implemented in the form of a program product, which includes program code that, when the program product is run on a device, causes the control device to perform the steps in the method for automatic analysis and report generation of hydropower station operation data according to various exemplary embodiments of the present invention described above.

[0068] It should be noted that although several units or sub-units of the apparatus have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the invention, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units. Furthermore, although the operation of the method of the invention is described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0069] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can be implemented in one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs) containing computer-usable program code. The form of a computer program product implemented on ROM, optical memory, etc.

[0070] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a server, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0071] Program code for performing the operations of this invention can be written using any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0072] In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0073] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0074] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the functions specified in one or more boxes. The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for automatic analysis and report generation of hydropower station operation data, characterized in that, The method for automatic analysis and report generation of hydropower station operation data includes: S1: Collection and processing of various types of operational data from hydropower stations. Utilize existing monitoring equipment at hydropower stations to collect various types of operational data; organize and synchronize the collected data in chronological order, and automatically adjust the frequency of different data collection to ensure the consistency of various information. S2: Operation status identification and cause tracing, identify the current operation status of each piece of equipment in the hydropower station, find and sort out the various causes of the changes in status, and establish a data-driven causal chain. S3: Key indicator integration and feature discovery. This involves integrating and analyzing various performance indicators of each device and system, and adaptively adjusting the weights of each parameter based on the actual operating conditions to make the analysis results more consistent with the current working conditions. S4: Report templates are dynamically generated, automatically determining the different needs of different users for report content and summarizing the key indicators that each user is concerned about; based on the actual results of each analysis, the corresponding content modules are automatically selected. S5: Report content generation and precise delivery. Based on industry experience and historical cases, combined with the results of this analysis, the system automatically generates report text conclusions and processing suggestions; and automatically pushes the corresponding report content to users with different roles.

2. The automatic analysis and report generation method for hydropower station operation data as described in claim 1, characterized in that, Step S1 involves processing the collected data in chronological order and synchronizing it. This includes calculating the original sampling frequency for each type of device as follows: The expected target uniform sampling frequency is For all i, satisfy The data stream is interpolated using the Lagrange interpolation method; for In this case, the sliding window averaging method is used; For missing data, a dual-domain autoregressive completion method is proposed: that is, an autoregressive model is constructed in both the original time domain and the feature transformation domain of the collected data; for any missing position... The optimal value was determined by combining least squares fitting and error minimization methods. For outlier detection and correction, the mean and standard deviation of each data set are calculated under a unified window. For the identified outliers, the neighborhood sliding median method is used for correction.

3. The automatic analysis and report generation method for hydropower station operation data as described in claim 2, characterized in that, The various operational data collected in step S1 include: operational data collected from flow meters, pressure gauges, thermometers, and video surveillance.

4. The automatic analysis and report generation method for hydropower station operation data as described in claim 2, characterized in that, Step S2, operation status identification and cause tracing, includes: For preset core performance parameters, calculate their moving average and moving standard deviation. Combining the equipment operation manual and historical thresholds, set the status judgment threshold ranges for each indicator, including normal operation range, alarm range, and fault range, and define the current operating status. ; A smoothing mechanism based on historical state sequences is introduced. Once a change in equipment state from "normal" to "alarm" or "fault" is detected, an automatic cause tracing process is initiated. For the state change segment, the change curves of relevant indicators are back-analyzed.

5. The automatic analysis and report generation method for hydropower station operation data as described in claim 4, characterized in that, Step S2 involves backtracking and analyzing the change curves of relevant indicators, including: First, perform mutual information analysis on all preset core parameters to measure each variable. The impact of equipment status changes was determined using time-lag cross-correlation analysis. Does the change precede the change in state in time? , By iterating through different time lags If in and If a significant peak is reached, then it is considered... Changes It has a causal leading role.

6. The automatic analysis and report generation method for hydropower station operation data as described in claim 5, characterized in that, Step S3, the key indicator integration and feature discovery process, includes: Configure each device D j It has m traditional performance indicators. By using the max-min normalization method, the indicators are standardized to a uniform scale, and an adaptive weight adjustment algorithm based on working condition clustering is introduced: First, the historical data is classified into different operating conditions using the K-means clustering method. Within each category, the volatility and fault contribution of each indicator are statistically analyzed, and the importance weight of the k-th indicator under a given operating condition is denoted as... ; The equipment's current operating data is dynamically assigned to the closest operating condition category based on cluster center distance or Bayesian probability. And use the indicator weights corresponding to the current category. Perform weighted consolidation; The design is based on a historical-real-time hybrid feature generation mechanism. Principal component analysis or variational autoencoder dimensionality reduction method is used to map multidimensional original indicators to a new low-dimensional feature space to obtain a set of innovative health features that take into account both relevance and information content. A novel indicator based on dynamic statistics, slope, and volatility under a sliding window is adopted.

7. The automatic analysis and report generation method for hydropower station operation data as described in claim 1, characterized in that, Step S4, the dynamic generation process of the report template, includes: Based on different user identity information and their historical usage habits, the system automatically determines the user's focus, establishes a dedicated topic library for each user category, and automatically matches relevant content modules for each analysis result. It dynamically adjusts the report structure and display order, flexibly inserts charts, text descriptions, and key data, and generates modular and reconfigurable report templates to meet the differentiated needs of different users for report content.

8. The automatic analysis and report generation method for hydropower station operation data as described in claim 1, characterized in that, Step S5, the process of generating and precisely pushing report content, includes: Based on industry knowledge bases and historical operation and maintenance cases, the analysis results are summarized and the text conclusions and handling suggestions are automatically generated. According to user roles, contact information and configuration preferences, the corresponding report content is pushed to users accurately and timely through email, App notification or information system integration, so as to realize personalized information delivery and task response.

9. An electronic device, characterized in that, include: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, which executes the instructions stored in the memory to perform the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store instructions that, when executed, cause the method as described in any one of claims 1 to 8 to be implemented.

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