Hydropower station special inspection method, system and equipment based on intelligent management and medium

Abstract

The invention discloses a hydropower station special inspection method, system, equipment and medium based on intelligent management, and relates to the technical field of power station inspection, and the method comprises the steps: a platform accesses the multidimensional data of hydropower station equipment, intelligently assigns inspection tasks through risk assessment and topology construction, analyzes the task cycle similarity through a frequency matching engine, and carries out the classification processing; and the instruction is pushed to the mobile terminal, operation guidance is dynamically generated to guide execution, whole-process data is acquired, quality analysis and visual presentation are performed, and closed-loop management and improvement of the whole inspection process are realized. According to the invention, a data-driven unified platform is constructed, risk dynamic assessment and visualization are realized, a matching engine and a hypergraph algorithm are utilized to accurately plan a work order, a geofence and a CNN are combined to ensure operation specifications, and three closed-loop index visual analysis is carried out to form an intelligent management system, so that the safety, efficiency and economical efficiency of inspection are improved.

Description

Technical Field

[0001] This invention relates to the field of power plant inspection technology, specifically to a method, system, equipment, and medium for special inspections of hydropower stations based on intelligent management. Background Technology

[0002] In modern society, electricity is the core energy source supporting economic development and social operation, and a stable supply is crucial. Power plants are key hubs for electricity production, conversion, and transmission, and the operating status of their equipment directly affects the safety and efficiency of the power system. Whether it is the boilers and steam turbines of thermal power plants, the turbine units of hydropower plants, the wind turbines of wind power plants, or the photovoltaic arrays of photovoltaic power plants, any failure in any link can lead to power supply interruptions, causing significant economic losses and even affecting public safety. Therefore, the inspection of power plants has become a key link in ensuring the stable operation of the power system.

[0003] In existing special inspections, the special management tasks may have different inspection standards developed by different departments according to their own needs, lacking unified standards; different departments may use different inspection methods and judgment criteria, resulting in inconsistent assessments of equipment status, affecting timely maintenance and management of equipment. In addition, the special management tasks in the existing special inspections have the problem of different departments repeatedly inspecting the same equipment or areas, wasting manpower, material resources and time; the division of inspection areas by each department is not clear, resulting in double inspections, missed inspections, and multiple departments believing that an area is not within their inspection scope, resulting in no inspection, and some areas may have blind spots or over-inspections due to poor information flow. Summary of the Invention

[0004] In view of the above-mentioned existing problems, the present invention provides a method, system, equipment and medium for special inspection of hydropower stations based on intelligent management, in order to solve the problems of inconsistent assessment of equipment status in the prior art, and the existence of repeated inspections and missed inspections.

[0005] To address the aforementioned technical challenges, a specialized inspection method for hydropower stations based on intelligent management is proposed, including: A patrol task management platform is constructed, which accesses multi-dimensional source data of hydropower station equipment, performs standardized processing and integration to obtain integrated data, conducts equipment risk assessment through preset quantitative methods to generate equipment risk level data, and constructs a risk-oriented equipment topology network. Based on patrol specifications and equipment risk level data, patrol tasks are divided and assigned in the risk-oriented equipment topology network. Based on a frequency matching engine, special patrol tasks and daily patrol tasks are periodically analyzed to extract the regularity characteristics of task execution cycles, calculate the similarity of task cycles between tasks, compare the task cycle similarity with preset thresholds, output matching results, and classify and process them according to the matching results. After data standardization and safety processing, task instructions are imported into mobile terminals. The mobile terminals trigger preset execution environments based on task location information and dynamically generate operation instructions based on current equipment and environmental information. Patrol personnel execute according to the instructions and provide data feedback. The mobile terminals collect data throughout the process and perform multi-dimensional quality analysis to generate visualized analysis results.

[0006] As a preferred embodiment of the intelligent management-based hydropower station special inspection method described in this invention, the method of dividing and assigning inspection tasks includes, according to equipment risk level data, designating the tasks corresponding to high-risk equipment that require targeted in-depth testing as special inspection tasks; and designating the tasks corresponding to medium- and low-risk equipment that require routine periodic inspections as daily inspection tasks.

[0007] As a preferred embodiment of the intelligent management-based hydropower station special inspection method of the present invention, the extraction of task execution cycle regularity features includes setting the same analysis time window for special inspection tasks and daily inspection tasks respectively, and sliding the current window on the time axis according to a fixed step size. Within each window, the timestamp sequence of historical task execution is collected, and feature values ​​representing the stability and concentration of task execution intervals are statistically calculated based on the current sequence. Multiple feature values ​​together constitute the periodic regularity feature reflecting the inherent execution cycle of the task.

[0008] As a preferred embodiment of the intelligent management-based hydropower station special inspection method described in this invention, the classification process based on the matching results includes abstracting all equipment of special and daily inspection tasks with inconsistent frequencies, as well as the spatial area association, control logic dependency and energy flow transmission dependency between equipment, into an association network model. Inject historical collaborative operation and fault propagation knowledge into the current interconnected network model, and use network representation learning technology to train and optimize the model, quantify the correlation strength between devices, and identify the characteristics of tasks that can be executed in parallel. By combining task coordination constraints, the optimal task packaging and execution scheme is searched and determined in the associated network model through combinatorial optimization techniques, generating independent task instructions with a uniform format.

[0009] As a preferred embodiment of the intelligent management-based hydropower station special inspection method described in this invention, the calculation of task cycle similarity between tasks includes: using the entropy weight method to determine the weight of the extracted periodic regularity features, allocating weights according to the information entropy of each feature value in all analysis time windows, calculating the weighted Euclidean distance between the special inspection task and the daily inspection task on the corresponding feature value, and taking the reciprocal of the current weighted Euclidean distance as the task cycle similarity. The formula for calculating entropy weight is expressed as follows: in, For information entropy, Let be the probability distribution of the i-th feature. It is the natural logarithm. Let i be the weight of the i-th feature. The information entropy of the i-th feature; The formula for calculating task cycle similarity is expressed as: in, For task cycle similarity, This represents the mean characteristics of special inspection tasks. This represents the average characteristics of daily inspection tasks. The variance characteristics of the special inspection tasks. This represents the variance characteristics of daily inspection tasks. This represents the peak characteristics of special inspection tasks. This represents the peak characteristics of daily inspection tasks. The weights of the mean feature, The weights of the variance features, The weights are for the peak features.

[0010] As a preferred embodiment of the intelligent management-based hydropower station special inspection method described in this invention, the associated network model includes mapping each device to a super vertex and binding attribute information including device ID, risk level, installation location, device type, operating parameter range, and historical fault records to each super vertex; mapping the task association, control logic dependency between devices, and energy transmission link dependency in the same inspection area to different super edges, with each super edge connecting multiple associated super vertices and labeling the relationship type; The method of training and optimizing the model using network representation learning technology includes processing with a hypergraph convolutional neural network, inputting the constructed initial hypergraph model into the hypergraph convolutional neural network, performing aggregation calculations on the neighborhood hyperedges and associated hypervertices of each hypervertices through a neighborhood aggregation operator, and outputting the optimized hypergraph topology, aggregated hypervertices feature vectors, optimized hyperedge weights, and extracted features of task parallel execution feasibility and equipment fault propagation probability through iterative learning of multiple convolutional layers. The update formula for a hypergraph convolutional neural network is expressed as: in, Let v be the feature vector of the supervertex v at the l-th layer. For activation function, For super-edge, Let v be the set of all superedges containing a supervertex v. Adjacent supervertices Let be the learnable weight matrix of the l-th layer. Let u be the feature vector of the adjacent supervertex u at the (l-1)th layer. For the bias term of the l-th layer, For supervertices, It is a convolutional layer; The formula for updating the superedge weight is expressed as: in, Let e ​​be the weight of the hyperedge at layer l. It is the Sigmoid activation function. Let l be the learnable parameter vector of the l-th layer; The combined optimization technique includes using a Monte Carlo random algorithm for simulation, iterating simulation based on task collaboration constraints and equipment association weights and task collaboration features obtained from the optimized hypergraph model, generating a scheme that combines special and routine inspection tasks into a work order in each simulation, and calculating the comprehensive cost of the current scheme based on factors such as execution time, personnel allocation difficulty, and fault risk transmission coefficient. After all simulations are completed, the scheme with the lowest comprehensive cost is selected as the optimal work order combination, and an independent work order is generated. The formula for calculating the overall cost of the current solution is as follows: in, The total cost of work order combinations. For execution time weighting coefficient, For the estimated execution time, Weighting coefficients for the difficulty of personnel allocation. Rate the difficulty of personnel deployment. This is the weighting coefficient for the failure risk coefficient. This is the fault risk transmission coefficient.

[0011] As a preferred embodiment of the intelligent management-based hydropower station special inspection method described in this invention, the data standardization and security processing includes: converting the task data to be imported into a JSON format compatible with mobile terminal applications; the JSON structure includes task type, task list, and execution constraint fields; the task list contains task objects for specific inspection tasks; and using the AES-256 symmetric encryption algorithm to encrypt sensitive data such as task ID, inspection location coordinates, and equipment core parameter ranges in the JSON data. During encryption, a key is generated to group and encrypt sensitive data to generate ciphertext, and the key is stored in the local secure area of ​​the mobile terminal through a secure channel. The preset execution environment includes a mobile terminal application constructing a circular virtual geofence centered on the current coordinates based on the inspection location coordinates extracted from the task data, and dividing the area within the fence into grids of preset size, with each grid having a unique identifier. When the inspection personnel are detected entering the grid where the target device is located, the fence entry signal is automatically triggered and a task reminder pops up. The dynamically generated work instructions include inputting the current grid identifier and equipment type information into the trained convolutional neural network model, extracting scene features through the convolutional layer, and matching and generating a standard work process, detailed operating specifications and a list of safety precautions in the output layer, which are then displayed on the application interface and provided with a data entry point. The formula for extracting scene features using convolutional layers is expressed as follows: in, Let be the feature value of the l-th convolutional layer at position (x, y). and This refers to the position index within the convolution kernel. The radius of the convolution kernel is 1. For the l-th layer convolution kernel at position The weight of the position, For input feature values.

[0012] The beneficial effects of this preferred technical solution are as follows: by integrating multi-dimensional data, risk-oriented and accurate assignment is achieved; frequency matching and hypergraph neural network optimization are used to solve the problems of task coordination and cost control; data encryption and geofencing are combined to ensure security and accurate triggering; and intelligent guidance and full-process closed-loop analysis are relied upon to comprehensively improve the pertinence, scientificity, safety and standardization of inspections.

[0013] As a preferred embodiment of the intelligent management-based hydropower station special inspection system described in this invention, it is characterized by including an inspection task management module, a frequency matching engine module, a task processing and optimization module, a mobile terminal support and execution module, and a quality management and analysis module.

[0014] The inspection task management module is used to access and integrate multi-dimensional data of hydropower station equipment through a standardized interface to form a unified database. It uses the combination of entropy weight method and genetic algorithm to evaluate the risk level of equipment and construct an inspection topology map. Based on the risk level, it intelligently divides special and daily inspection tasks.

[0015] The frequency matching engine block is used to extract task cycle features through a sliding time window and calculate similarity using the entropy weight method. The comparison results are automatically matched with the threshold to determine frequency consistency, thus transforming subjective experience into objective quantitative evidence.

[0016] The task processing and optimization module is used to generate a comprehensive inspection list for merged execution for tasks with consistent frequencies using the particle swarm optimization algorithm, and for tasks with inconsistent frequencies, it uses a hypergraph convolutional neural network to mine related features and combines the Monte Carlo algorithm to select the optimal solution at the lowest cost and generate an independent work order.

[0017] The mobile terminal support and execution module is used to verify and push task data to the APP, dynamically generate personalized work instructions using virtual geofences and convolutional neural networks, and provide data entry and anomaly reporting functions, execute task issuance and intelligently guide inspection personnel to standardize on-site operations.

[0018] The quality management and analysis module is used to automatically calculate core indicators and generate interactive dashboards based on the full-process data collected by the mobile terminal, and synchronize the analysis results with the management platform in real time to realize closed-loop management of inspection work.

[0019] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of a method for special inspection of hydropower stations based on intelligent management.

[0020] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a method for special inspection of hydropower stations based on intelligent management.

[0021] The beneficial effects of this invention are as follows: By constructing a unified inspection task management platform, this invention achieves standardized integration of multi-dimensional source data and visualization of equipment topology networks based on risk levels, solving the problems of inconsistent standards and fragmented management in traditional inspections and laying a standardized foundation for intelligent management. Through a frequency matching engine, it uses a sliding window and entropy weighting method to quantify and analyze the task cycle pattern, achieving objective and accurate matching of special and daily inspection tasks, effectively avoiding duplicate and missed inspections. For complex task scenarios, it introduces hypergraph models and hypergraph convolutional neural networks to automatically mine deep correlations and fault propagation characteristics between devices, and optimizes multi-constraint work order combinations based on Monte Carlo algorithms, realizing global intelligent planning of inspection resources and paths. At the task execution layer, through encrypted transmission, geofencing triggering, and scenario-based operation guidance based on convolutional neural networks, it ensures the safety, accuracy, and standardization of on-site operations. At the same time, the structured collection and analysis dashboard generation of the entire process data on the mobile terminal forms a quality closed loop, achieving a synergistic improvement in inspection safety management and operational efficiency. Furthermore, through full-process digitalization, it provides reliable data-driven capabilities for lean operation and maintenance and continuous improvement of power plant equipment. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 The overall flowchart of a special inspection method for hydropower stations based on intelligent management, provided as an embodiment of the present invention.

[0024] Figure 2 A system flowchart of a hydropower station special inspection system based on intelligent management, provided as an embodiment of the present invention. Detailed Implementation

[0025] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0026] Example 1, referring to Figure 1 As an embodiment of the present invention, a special inspection method for hydropower stations based on intelligent management is provided, including: S100: Construct an inspection task management platform, access multi-dimensional source data of hydropower station equipment, perform standardized processing and integration to obtain integrated data, conduct equipment risk assessment through preset quantitative methods, generate equipment risk level data, and construct a risk-oriented equipment topology network. Based on inspection specifications and equipment risk level data, divide and assign inspection tasks in the risk-oriented equipment topology network.

[0027] S200: Based on the frequency matching engine, it performs periodic analysis on special inspection tasks and daily inspection tasks, extracts the regularity features of task execution cycles, calculates the task cycle similarity between tasks, compares the task cycle similarity with a preset threshold, outputs the matching results, and performs classification processing based on the matching results.

[0028] S300: After standardizing and securing the data, the task instructions are imported into the mobile terminal. The mobile terminal triggers the preset execution environment based on the task location information and dynamically generates operation instructions based on the current equipment and environment information. The inspection personnel execute the instructions and provide feedback data. The mobile terminal collects data throughout the process and performs multi-dimensional quality analysis to generate visualized analysis results.

[0029] It should be noted that this invention achieves standardized management and dynamic risk adaptation of inspection tasks by constructing a data-driven unified inspection platform and risk topology network. It avoids duplicate and missed inspections by using a frequency matching engine and a periodic quantification model. Combined with hypergraph convolutional networks and Monte Carlo optimization, it intelligently generates optimal work orders that balance safety and efficiency. Through geofencing triggering, scenario-based operation guidance, and full-process data closure, it ensures accurate and standardized on-site execution and traceability of quality, thereby improving the safety, efficiency, and economy of hydropower station inspections.

[0030] Example 2, refer to Figure 1 This is a second embodiment of the present invention, which provides a special inspection method for hydropower stations based on intelligent management, including: In step S100, obtaining the integrated data includes steps S101 to S103: S101: Obtain hydropower station equipment ledger data, historical fault data, equipment runtime data, and environmental impact factor data; The equipment ledger data includes equipment model, installation date, design parameters, and rated load basic information; historical fault data includes fault occurrence time, fault type, fault location, affected area, repair time and solution record; equipment running time data is the cumulative running hours of the equipment; environmental impact factor data includes humidity, temperature fluctuation range, vibration intensity, and dust concentration monitoring data of the area where the equipment is located.

[0031] Four types of data are accessed through a standardized RESTful interface. The interface supports automatic data format adaptation to ensure data compatibility and interoperability from different sources. An association system with device ID as the unique identifier is established to bind various types of data with corresponding devices, thus building a unified inspection task management platform.

[0032] S102: The inspection task management platform starts the data processing process, identifies and removes abnormal data such as duplicate entries and missing key fields through preset data verification rules, and uses normalization processing to convert the indicator data of different formats into dimensionless data in the 0-1 range. The normalization formula is expressed as: in, The original data, It is the minimum value in the indicator data set. The maximum value in the indicator data set. This is the dimensionless value after normalization.

[0033] S103: Convert device runtime and failure frequency into corresponding dimensionless values, and classify and integrate all processed data according to device ID to generate a complete basic dataset for a single device. Store the dataset in the platform's distributed database to ensure data security and fast retrieval.

[0034] Furthermore, in this embodiment of the application, step S100, the equipment risk assessment includes steps S111~S113: S111: The inspection task management platform extracts five core risk assessment indicators from the equipment's basic data: fault frequency, fault impact range, runtime, maintenance difficulty, and degree of environmental interference. It then constructs an assessment matrix and performs normalization processing. S112: The entropy weight method is used to calculate the entropy value and difference coefficient of each indicator, and the objective weight is determined according to the dispersion of the data, so that the indicator with a higher degree of dispersion has a greater weight. The formula for calculating entropy is expressed as: in, Let the entropy value of the j-th risk assessment indicator be . For device serial number index, The total number of devices. Let m be the characteristic weight of the m-th device on the j-th risk assessment indicator.

[0035] S113: Using a genetic algorithm with the prediction accuracy of historical risk events as the objective function, iterative optimization is performed to eliminate information redundancy between indicators and obtain the comprehensive weight data of each indicator. The formula is expressed as: in, Let be the coefficient of variation for the j-th risk assessment indicator. The comprehensive weight of the j-th risk assessment indicator is... This represents the total number of risk assessment indicators. This serves as an index for risk assessment indicators.

[0036] In an optional implementation, step S100, the equipment risk assessment further includes: constructing a hierarchical structure model of equipment risk influencing factors; forming a judgment matrix through pairwise comparisons; obtaining the subjective weights of each risk indicator through calculation of feature vectors and consistency tests; determining the evaluation level and establishing a membership function; converting the actual equipment data into a fuzzy relation matrix; performing a synthesis operation between the weight vector and the fuzzy relation matrix; and determining the equipment risk level based on the maximum membership principle or the weighted scoring method.

[0037] In another optional implementation, in step S100, the equipment risk assessment may further include standardizing the original risk index data of the equipment, using principal component analysis to extract uncorrelated principal components that can represent most of the information, using historical failure data as labels, using principal component scores as feature variables to train a logistic regression model, and inputting the principal component scores of the equipment to be assessed into the model to obtain the predicted probability value of a serious failure, and mapping the probability value range to the corresponding risk level.

[0038] Furthermore, in this embodiment of the application, in step S100, the division and assignment of inspection tasks includes steps S121~S123: S121: Based on the extracted risk assessment indicators, optimized indicator weight data, and actual indicator values ​​in the equipment basic data, a risk quantification score for each piece of equipment is calculated using a weighted summation formula, expressed as follows: in, The risk quantification score for the m-th device. The comprehensive weight of the j-th risk assessment indicator is... Let be the normalized value of the m-th device on the j-th index.

[0039] Obtain equipment risk level data, classify levels according to risk quantification score ranges, when When, it is judged as Level 1 risk (extremely high risk); when When, it is judged as Level 2 risk (high risk); when When, it is judged as a level three risk (medium risk); when When, it is judged as a level four risk (low risk); when At that time, it was determined to be a level 5 risk (extremely low risk).

[0040] S122: Obtain equipment relationship data from the hydropower station equipment management system, including hydropower transmission links and control logic dependencies between equipment; based on equipment risk level data and equipment relationship data, with equipment ID as nodes and hydropower transmission links and control logic dependencies between equipment as edges, embed equipment risk level data, specific installation location X coordinate, Y coordinate, Z coordinate, and equipment type information into the nodes to automatically generate a visualized equipment inspection topology map, which intuitively presents the distribution location, relationship status, and risk level of the equipment.

[0041] S123: Obtain pre-set inspection specification data from industry inspection standard documents and internal operation and maintenance procedures of hydropower stations, and clarify the inspection requirements for equipment with different risk levels; Based on the equipment risk level data in the equipment inspection topology diagram, the targeted inspection tasks corresponding to Level 1 and Level 2 risk equipment are divided into special inspection tasks. These tasks include accurate inspection of high-risk components, real-time monitoring of key operating parameters, and multi-dimensional status assessment. The periodic inspection tasks corresponding to conventional equipment at risk levels three, four, and five are divided into daily inspection tasks. These tasks include regular visual inspections, reading basic operating parameters, and simple status confirmations. By dividing these tasks into two categories, inspection resources are concentrated on high-risk equipment while also covering the basic maintenance needs of conventional equipment.

[0042] In an optional implementation, step S100, the division and assignment of inspection tasks further includes selecting multiple core risk indicators as features to perform K-means clustering analysis on all devices, and dividing the devices into different feature groups based on the clustering results; operation and maintenance experts customize and define the corresponding inspection task types, contents and frequencies based on the core characteristics of each group; when assigning tasks, the corresponding task template is called according to the group to which the device belongs to generate a more targeted dynamic inspection work order.

[0043] In another optional implementation, in step S100, the division and assignment of inspection tasks may further include: constructing training samples using historical inspection data; using multi-dimensional indicators of equipment as features and task type as labels; training a decision tree model using the CART algorithm to learn classification rules from equipment features to inspection task types; automatically outputting suggested inspection task types after real-time indicators are input into the model for new cycles or new equipment; and scheduling resources according to task complexity and resource requirements.

[0044] In this embodiment of the application, step S200, the extraction of task execution cycle regularity features, includes steps S201 to S203: S201: Based on the special inspection tasks and daily inspection task data assigned by the inspection task management platform, combined with the regular cycle characteristics of hydropower station inspection and the historical task execution rules, a fixed time window length is set, with a step of 1 day, and continuous sliding interception is performed from the earliest execution time of the two types of tasks to the current time. Each window corresponds to a continuous time interval. The length of the time window is determined based on the coverage requirements of most daily inspection tasks and special inspection tasks.

[0045] S202: By directly reading the task execution records stored in the database of the inspection task management platform, extract the task execution timestamp, execution frequency and interval duration in each window to form special inspection window data and daily inspection window data.

[0046] Both types of data are stored in tabular form, with each row corresponding to a window. The columns include window start time, end time, execution timestamp list, execution frequency, average interval duration, maximum interval duration, and minimum interval duration information to ensure data integrity and traceability.

[0047] S203: Based on special inspection window data and daily inspection window data, a sliding window model is used for feature extraction and similarity calculation; The task interval duration within each window is preprocessed to remove temporary inspection intervals caused by sudden equipment failures, and the mean, variance, and peak value are calculated for the processed interval duration. The formula for calculating entropy weight is expressed as follows: in, For information entropy, Let be the probability distribution of the i-th feature. It is the natural logarithm. Let i be the weight of the i-th feature. Let be the information entropy of the i-th feature.

[0048] In an optional implementation, step S200 further includes extracting the periodic characteristics of task execution, dividing the long time period into time slices in units of days, constructing a binary or counting time series based on the execution records, performing a Fast Fourier Transform (FFT) to convert it to the frequency domain to obtain a spectrum, and extracting frequency domain characteristics such as the main frequency, the concentration of spectral energy, and the presence of harmonics from the spectrum to reflect the periodicity of the task.

[0049] In another optional implementation, in step S200, the extraction of the regularity features of the task execution cycle may further include constructing a binary time series and performing zero-mean standardization, calculating the autocorrelation coefficient under different time lags, and evaluating the existence, persistence and average length of the task cycle by analyzing the periodic peaks, peak decay patterns and first-crossing-zero lags in the autocorrelation function graph.

[0050] It should be noted that in step S200, the output matching result includes steps S211 to S212: S211: Calculate the mean, variance, and peak characteristic values ​​of the special inspection window data and the daily inspection window data respectively, set the weight coefficient of each characteristic, and use the reciprocal of the weighted Euclidean distance to calculate the periodic similarity of the two types of task data. The formula for calculating task cycle similarity is expressed as: in, For task cycle similarity, This represents the mean characteristics of special inspection tasks. This represents the average characteristics of daily inspection tasks. The variance characteristics of the special inspection tasks. This represents the variance characteristics of daily inspection tasks. This represents the peak characteristics of special inspection tasks. This represents the peak characteristics of daily inspection tasks. The weights of the mean feature, The weights of the variance features, The weights for peak features; The weighted Euclidean distance is calculated based on the difference between the corresponding feature values ​​of the two objects and their weights. The final similarity result ranges from 0 to 1. The closer the value is to 1, the more similar the periodic patterns of the two types of tasks are.

[0051] S212: Obtain a preset configurable threshold determined based on statistical analysis of historical inspection data and inspection accuracy requirements. Compare the calculated task cycle similarity S with the current threshold. If S exceeds the threshold, it is determined that the frequency is inconsistent. If S is less than or equal to the threshold, it is determined that the frequency is consistent.

[0052] Furthermore, in step S200, the classification process based on the matching results includes steps S221-S222: S221: When the matching results are inconsistent in frequency, perform constraint parameter extraction, construct a hypergraph model, process the hypergraph convolutional neural network, and generate an independent work order.

[0053] A1: Constraint parameter extraction includes obtaining hydropower station inspection specifications and equipment maintenance priority data from the hydropower station operation and maintenance management system. The hydropower station inspection specifications include inspection items, operating standards, and safety requirements for various types of equipment. The equipment maintenance priority data is formulated based on the role of the equipment in the hydropower production chain, the scope of fault impact, and the difficulty of repair. Based on the data, the core parameters required for work order generation are extracted, the execution time limit is set according to the equipment risk level, the personnel qualification requirements are clarified to specify the professional qualifications required for the inspection of different types of equipment, and the collaborative operation rule parameters clarify the execution order and personnel cooperation requirements when multiple equipment are inspected together. The parameters are integrated to form a special task generator to provide standardized constraints for the generation of independent work orders.

[0054] A2: Constructing the hypergraph model involves mapping each device involved to a hypervertices of the hypergraph based on data from special inspection tasks and daily inspection tasks with inconsistent frequencies. Each hypervertices is then bound with complete attribute information, including device ID, risk level, inspection location, device type, operating parameter range, and historical fault records. Task associations, inter-device control logic dependencies, and energy transmission link dependencies within the same inspection area are mapped to hyperedges of the hypergraph, and each hyperedge is labeled with a corresponding relationship type label. Based on the mapped hypervertices and hyperedges, an initial hypergraph model is constructed.

[0055] A3: The hypergraph convolutional neural network processing includes retrieving historical collaborative operation data and fault propagation records from the hydropower station equipment management system, assigning an initial feature vector containing runtime and fault frequency indicators to each hypervertex, and assigning an initial weight to each hyperedge; inputting the constructed hypergraph model into the hypergraph convolutional neural network, performing aggregation calculation and updating of the features of the neighboring hyperedges and associated vertices of the hypervertex through the neighborhood aggregation operator, optimizing the hyperedge weights based on the aggregation results, so that the weights of hyperedges with high correlation strength are close to 1 and those with low correlation strength are close to 0; and through iterative learning of three convolutional layers, mining the deep correlation strength between equipment and extracting core features such as the parallel execution feasibility and equipment fault propagation probability at the task level, and integrating and constructing a task collaboration feature set; The update formula for a hypergraph convolutional neural network is expressed as: in, Let v be the feature vector of the supervertex v at the l-th layer. For activation function, For superedge, Let v be the set of all superedges containing a supervertex v. Adjacent supervertices Let be the learnable weight matrix of the l-th layer. Let u be the feature vector of the adjacent supervertex u at the (l-1)th layer. For the bias term of the l-th layer, For supervertices, It is a convolutional layer; The formula for updating the superedge weight is expressed as: in, Let e ​​be the weight of the hyperedge at layer l. It is the Sigmoid activation function. Let be the learnable parameter vector of the l-th layer.

[0056] S222: When the matching results are consistent in frequency, perform TviteT tube tag generation and binding, as well as task fusion and optimized sorting.

[0057] B1: The generation and binding of Twete ​​tube tags includes generating Twete ​​tube tags based on data of special inspection tasks and daily inspection tasks with consistent frequency. The tags contain task ID, matching window start time, matching window end time, merging identifier, and associated daily task ID. The tags are in string format. Furthermore, by adding a tag field to the database of the inspection task management platform for the corresponding special inspection task, the Twete ​​pipe tag is bound to the corresponding special inspection task, and the inspection task with the Twete ​​pipe tag is obtained.

[0058] B2: Task fusion and optimization sorting includes initiating task fusion, aiming at the shortest inspection path and the least execution time, and using the particle swarm optimization algorithm to optimize and sort special and daily inspection tasks with the TVETT tag; Each task is treated as a particle. A fitness function is constructed using the weighted sum of the total execution path length and total time. The optimal sorting scheme is found by iteratively updating the position and velocity of the particles. Duplicate inspections are merged to generate a comprehensive inspection task list containing information such as task number, type, association ID, inspection content, location, execution requirements and precautions, so that inspection personnel can execute them in sequence. The fitness function is expressed as: in, Let be the fitness value of particle a. Let a be the position vector of particle a. This is the path length weighting coefficient. This represents the total execution path length. For execution time weighting coefficient, Total execution time; The formulas for particle velocity update and particle position update are expressed as follows: in, Let be the velocity vector of particle a at the (t+1)th iteration. For inertial weights, Let be the velocity vector of particle a at the t-th iteration. For individual learning factors, and A random number within the interval [0, 1] This represents the historical best position of particle a. Let be the current position vector of particle a at the t-th iteration. As a social learning factor, This represents the group's historically optimal position. Let be the position vector of particle a at the (t+1)th iteration.

[0059] It should be further noted that, in the embodiments of this application, the generation of independent work orders in step S221 includes steps C1 to C3: C1: Integrates the hypergraph topology, hypervertex features, hyperedge weights, and task collaboration features optimized through convolutional learning to form a complete device-associated hypergraph data.

[0060] C2: Based on the constraint parameters of the special task generator, combined with the optimized hyperedge weights and task collaboration features, the Monte Carlo random algorithm is used for simulation iteration. The comprehensive cost is calculated by comprehensively considering the execution time, personnel allocation difficulty and failure risk coefficient, and the combination with the lowest cost is selected as the optimal solution. The formula for calculating the overall cost of the current solution is as follows: in, The total cost of work order combinations. For execution time weighting coefficient, For the estimated execution time, Weighting coefficients for the difficulty of personnel allocation. Rate the difficulty of personnel deployment. This is the weighting coefficient for the failure risk coefficient. This is the fault risk transmission coefficient.

[0061] C3: Generates independent work orders containing complete information such as task ID, equipment ID, inspection location, project, time limit, personnel qualifications, collaborative operation instructions, and risk warnings, ensuring that inspection personnel can execute them directly.

[0062] In an optional implementation, step S221, generating an independent work order further includes modeling the work order generation process as a Markov decision process containing state, action, and reward, training a reinforcement learning agent using a deep Q-network algorithm, learning the optimal policy in a simulated environment, and after training, the agent directly outputs a work order generation action sequence based on the current state to form an independent task instruction.

[0063] In another optional implementation, in step S221, generating an independent work order may further include: establishing an integer programming model, defining decision variables, objective function, and constraints including task allocation, time limits, personnel qualifications, and task association, solving the model using a CPLEX solver, and directly mapping the optimal solution of the output decision variables to generate an independent task instruction containing a task list, execution order, and resource requirements.

[0064] In step S300, importing the mobile terminal includes steps S301 to S303: S301: Based on the inspection task with the TVIT tag, the optimization goal is to minimize the inspection path and maximize the task execution efficiency. The particle swarm optimization algorithm is used to optimize and sort the execution order of special inspection tasks and daily inspection tasks.

[0065] The particle population size is set to 30, the number of iterations is 50, and the convergence condition is that the fitness value changes by less than 0.01 for 5 consecutive iterations. The optimal execution order is found by updating the particle positions.

[0066] Repeated inspection steps are merged. By comparing the equipment ID and the inspection item name in the task, the same inspection item for the same equipment is retained only once. The priority of the inspection items is supplemented, and a comprehensive inspection task list is generated. The list includes task number, task ID, label, equipment ID, inspection location, inspection item, execution standard, and estimated time information, and is stored in a structured table format.

[0067] S302: Based on comprehensive inspection tasks or independent work order data, perform data format conversion operations to convert tabular data into JSON format compatible with mobile apps; the JSON structure contains three first-level fields: task type, task list, and execution constraints. The task list contains multiple task objects, and each task object corresponds to complete information of an inspection task.

[0068] Sensitive data is encrypted using the AES-256 symmetric encryption algorithm. Sensitive data includes task ID, inspection location coordinates, and equipment core parameter ranges. The encryption process involves the system automatically generating a 128-bit random key, which is used to encrypt the sensitive data in groups. The key is stored in the local security module of the mobile APP through a secure channel and is only used for data decryption. The encrypted data is then combined with non-sensitive data to form standardized import data.

[0069] S303: Standardized imported data is transmitted to the relay area of ​​the preset MQTT transmission protocol via a wired network. The integrity of the data is verified by calculating the SHA-256 checksum and comparing it with the sending end. After removing abnormal data, the valid data is associated with the inspection personnel account by device ID using the wireless communication module that supports 4G, Wi-Fi and Bluetooth, and pushed to the corresponding mobile APP terminal in real time with high accuracy.

[0070] Furthermore, in step S300, generating the visualization analysis results includes steps S311 to S315: S311: The mobile app parses the received standardized imported data and extracts the topology for equipment inspection. Figure 1 The system retrieves the inspection location coordinates and equipment type information, constructs a circular virtual geofence with a radius of 20 meters centered on the current coordinates, and evenly divides the internal area into 10m x 10m grids, generating inspection area grid data containing unique identifiers and equipment information. When inspection personnel enter the target grid with their mobile terminals, the APP uses high-precision GPS to locate and refresh the position in real time. Once the location data coincides with the boundary of the target grid, the geofence signal is automatically triggered, and a task reminder pop-up window informs the inspection personnel that they have arrived at the designated area.

[0071] S312: Input the grid data of the inspection area and the equipment type data into the convolutional neural network pre-trained based on historical data. The model extracts scene features through multi-layer convolution operations and matches the standard operating procedures, operating specifications and safety precautions in the current scene in the output layer to generate targeted operation guidelines. The formula for extracting scene features using convolutional layers is expressed as follows: in, Let be the feature value of the l-th convolutional layer at position (x, y). and This refers to the position index within the convolution kernel. The radius of the convolution kernel is 1. For the l-th layer convolution kernel at position The weight of the position, For input feature values.

[0072] Meanwhile, the mobile app provides access for data entry, result uploading, and anomaly reporting, making it convenient for inspection personnel to follow instructions and record information.

[0073] S313: Inspection personnel follow the standard operating instructions generated by the APP to perform tasks step by step and record inspection data of equipment appearance and operating parameters in real time. The APP automatically records the task start time, execution duration, timestamp, geographical location and operator account process data. After the inspection is completed, the personnel submit the task completion status, reasons for non-completion and additional on-site conditions through the feedback portal. The APP integrates the inspection data, feedback data and process data to form a complete dataset, which is automatically synchronized to the APP local storage and cloud server to ensure that the data is not lost.

[0074] S314: Conduct closed-loop quality analysis based on feedback data, task execution process data, and inspection data, and calculate three core indicators: the task completion rate is calculated by statistically analyzing the ratio of the actual number of completed tasks to the number of planned tasks, and partially completed tasks are counted as 0.5 items. The formula for calculating task completion rate is as follows: in, For task completion rate, This represents the actual number of tasks completed. This represents the number of planned tasks.

[0075] The data anomaly rate is derived by analyzing the percentage of records that exceed the normal range. The normal range is determined based on equipment parameters, industry standards, and historical data. The formula for calculating the data anomaly rate is as follows: in, For data anomaly rate, The number of records exceeding the normal range. This represents the total number of inspection data entries.

[0076] The standard execution rate is determined by comparing the actual operation with the standard operating procedure guidelines. The indicators comprehensively evaluate the completeness of the execution steps, the correctness of the operation sequence, and the standardization of parameter recording. The formula for calculating the standard compliance rate is expressed as follows: in, For standard compliance, To score the completeness of the execution steps, Score is awarded based on the correctness of the operation sequence. Record the standardization score for the parameters.

[0077] S315: Visualize the calculated task completion rate, data anomaly rate, and standard execution rate data using bar charts, pie charts, and line charts, respectively. Mark the inspection location, equipment type, and data details corresponding to the abnormal indicators in the charts, and generate an interactive analysis dashboard that supports multi-dimensional filtering. The dashboard data is synchronized to the inspection task management platform in real time, helping managers to keep track of inspection quality, equipment operating status and personnel work status, and providing data support for subsequent inspection plan adjustments, equipment maintenance arrangements and personnel training optimization.

[0078] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0079] Example 3, referring to Figure 2 The third embodiment of the present invention provides a special inspection system for hydropower stations based on intelligent management, including an inspection task management module, a frequency matching engine module, a task processing and optimization module, a mobile terminal support and execution module, and a quality management and analysis module.

[0080] The inspection task management module is used to access and integrate multi-dimensional data of hydropower station equipment through a standardized interface to form a unified database. It uses the combination of entropy weight method and genetic algorithm to evaluate the risk level of equipment and construct an inspection topology map. Based on the risk level, it intelligently divides special and daily inspection tasks.

[0081] The frequency matching engine block is used to extract task cycle features through a sliding time window and calculate similarity using the entropy weight method. The comparison results are automatically matched with the threshold to determine frequency consistency, thus transforming subjective experience into objective quantitative evidence.

[0082] The task processing and optimization module is used to generate a comprehensive inspection list for merged execution for tasks with consistent frequencies using the particle swarm optimization algorithm, and for tasks with inconsistent frequencies, it uses a hypergraph convolutional neural network to mine related features and combines the Monte Carlo algorithm to select the optimal solution at the lowest cost and generate an independent work order.

[0083] The mobile terminal support and execution module is used to verify and push task data to the APP, dynamically generate personalized work instructions using virtual geofences and convolutional neural networks, and provide data entry and anomaly reporting functions, execute task issuance and intelligently guide inspection personnel to standardize on-site operations.

[0084] The quality management and analysis module is used to automatically calculate core indicators and generate interactive dashboards based on the full-process data collected by the mobile terminal, and synchronize the analysis results with the management platform in real time to realize closed-loop management of inspection work.

[0085] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0086] Example 4, the fourth embodiment of the present invention, differs from the previous three embodiments in that: If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0087] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0088] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0089] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

Claims

1. A specialized inspection method for hydropower stations based on intelligent management, characterized by: include, A patrol task management platform is built, which accesses multi-dimensional source data of hydropower station equipment, performs standardized processing and integration to obtain integrated data, conducts equipment risk assessment through preset quantitative methods, generates equipment risk level data, and constructs a risk-oriented equipment topology network. Based on patrol specifications and equipment risk level data, patrol tasks are divided and assigned in the risk-oriented equipment topology network. Based on the frequency matching engine, the special inspection tasks and daily inspection tasks are periodically analyzed to extract the regularity features of the task execution cycle, calculate the task cycle similarity between tasks, compare the task cycle similarity with the preset threshold, output the matching results, and classify the processing according to the matching results. After the task instructions are standardized and secured, they are imported into the mobile terminal. The mobile terminal triggers the preset execution environment based on the task location information and dynamically generates operation instructions based on the current equipment and environment information. The inspection personnel execute the instructions and provide feedback data. The mobile terminal collects data throughout the process and performs multi-dimensional quality analysis to generate visualized analysis results.

2. The method for specialized inspection of hydropower stations based on intelligent management as described in claim 1, characterized in that: The process of dividing and assigning inspection tasks includes, based on equipment risk level data, designating tasks that require targeted in-depth testing for high-risk equipment as special inspection tasks. The tasks of routine periodic inspections of equipment at medium and low risk levels are designated as daily inspection tasks.

3. The special inspection method for hydropower stations based on intelligent management as described in claim 2, characterized in that: The extracted task execution cycle regularity features include setting the same analysis time window for special inspection tasks and daily inspection tasks, and sliding the current window on the time axis according to a fixed step size; Within each window, the timestamp sequence of historical task execution is collected, and feature values ​​representing the stability and concentration of task execution intervals are statistically calculated based on the current sequence. Multiple feature values ​​together constitute the periodic regularity feature reflecting the inherent execution cycle of the task.

4. The special inspection method for hydropower stations based on intelligent management as described in claim 3, characterized in that: The classification process based on the matching results includes abstracting all equipment in the special inspection tasks with inconsistent frequencies and daily inspection tasks, as well as the spatial area association, control logic dependency and energy flow transmission dependency between equipment, into an association network model. Inject historical collaborative operation and fault propagation knowledge into the current interconnected network model, and use network representation learning technology to train and optimize the model, quantify the correlation strength between devices, and identify the characteristics of tasks that can be executed in parallel. By combining task coordination constraints, the optimal task packaging and execution scheme is searched and determined in the associated network model through combinatorial optimization techniques, generating independent task instructions with a uniform format.

5. The special inspection method for hydropower stations based on intelligent management as described in claim 4, characterized in that: The calculation of task cycle similarity between tasks includes: using the entropy weight method to determine the weight of the extracted cycle pattern features; assigning weights according to the information entropy of each feature value in all analysis time windows; calculating the weighted Euclidean distance between special inspection tasks and daily inspection tasks on the corresponding feature values; and taking the reciprocal of the current weighted Euclidean distance as the task cycle similarity. The formula for calculating entropy weight is expressed as follows: in, For information entropy, Let be the probability distribution of the i-th feature. It is the natural logarithm. Let i be the weight of the i-th feature. The information entropy of the i-th feature; The formula for calculating task cycle similarity is expressed as: in, For task cycle similarity, This represents the mean characteristics of special inspection tasks. This represents the average characteristics of daily inspection tasks. The variance characteristics of the special inspection tasks. This represents the variance characteristics of daily inspection tasks. This represents the peak characteristics of special inspection tasks. This represents the peak characteristics of daily inspection tasks. The weights of the mean feature, The weights of the variance features, The weights are for the peak features.

6. The method for specialized inspection of hydropower stations based on intelligent management as described in claim 5, characterized in that: The associated network model includes mapping each device to a super vertex and binding attribute information, including device ID, risk level, installation location, device type, operating parameter range, and historical fault records, to each super vertex; mapping the task association, control logic dependency between devices, and energy transmission link dependency in the same inspection area to different super edges, with each super edge connecting multiple associated super vertices and labeling the relationship type. The method of training and optimizing the model using network representation learning technology includes processing with a hypergraph convolutional neural network, inputting the constructed initial hypergraph model into the hypergraph convolutional neural network, performing aggregation calculations on the neighborhood hyperedges and associated hypervertices of each hypervertices through a neighborhood aggregation operator, and outputting the optimized hypergraph topology, aggregated hypervertices feature vectors, optimized hyperedge weights, and extracted features of task parallel execution feasibility and equipment fault propagation probability through iterative learning of multiple convolutional layers. The update formula for a hypergraph convolutional neural network is expressed as: in, Let v be the feature vector of the supervertex v at the l-th layer. For activation function, For superedge, Let v be the set of all superedges containing a supervertex v. Adjacent supervertices Let be the learnable weight matrix of the l-th layer. Let u be the feature vector of the adjacent supervertex u at the (l-1)th layer. For the bias term of the l-th layer, For supervertices, It is a convolutional layer; The formula for updating the superedge weight is expressed as: in, Let e ​​be the weight of the hyperedge at layer l. It is the Sigmoid activation function. Let l be the learnable parameter vector of the l-th layer; The combined optimization technique includes using a Monte Carlo random algorithm for simulation, iterating simulation based on task collaboration constraints and equipment association weights and task collaboration features obtained from the optimized hypergraph model, generating a scheme that combines special and routine inspection tasks into a work order in each simulation, and calculating the comprehensive cost of the current scheme based on factors such as execution time, personnel allocation difficulty, and fault risk transmission coefficient. After all simulations are completed, the scheme with the lowest comprehensive cost is selected as the optimal work order combination, and an independent work order is generated. The formula for calculating the overall cost of the current solution is as follows: in, The total cost of work order combinations. For execution time weighting coefficient, For the estimated execution time, Weighting coefficients for the difficulty of personnel allocation. Rate the difficulty of personnel deployment. This is the weighting coefficient for the failure risk coefficient. This is the fault risk transmission coefficient.

7. The method for specialized inspection of hydropower stations based on intelligent management as described in claim 6, characterized in that: The data standardization and security processing includes converting the task data to be imported into a JSON format compatible with mobile terminal applications. The JSON structure includes task type, task list, and execution constraint fields. The task list contains task objects for specific inspection tasks. The AES-256 symmetric encryption algorithm is used to encrypt sensitive data such as task ID, inspection location coordinates, and core equipment parameter range in JSON data. During encryption, a key is generated to encrypt the sensitive data in groups to generate ciphertext. The key is stored in the local secure area of ​​the mobile terminal through a secure channel. The preset execution environment includes a mobile terminal application constructing a circular virtual geofence centered on the current coordinates based on the inspection location coordinates extracted from the task data, and dividing the area within the fence into grids of preset size, with each grid having a unique identifier. When the inspection personnel are detected entering the grid where the target device is located, the fence entry signal is automatically triggered and a task reminder pops up. The dynamically generated work instructions include inputting the current grid identifier and equipment type information into the trained convolutional neural network model, extracting scene features through the convolutional layer, and matching and generating a standard work process, detailed operating specifications and a list of safety precautions in the output layer, which are then displayed on the application interface and provided with a data entry point. The formula for extracting scene features using convolutional layers is expressed as follows: in, Let be the feature value of the l-th convolutional layer at position (x, y). and This refers to the position index within the convolution kernel. The radius of the convolution kernel is 1. For the l-th layer convolution kernel at position The weight of the position, For input feature values.

8. A hydropower station special inspection system based on intelligent management, employing the hydropower station special inspection method based on intelligent management as described in any one of claims 1 to 7, characterized in that, It includes an inspection task management module, a frequency matching engine module, a task processing and optimization module, a mobile terminal support and execution module, and a quality management and analysis module; The inspection task management module is used to access and integrate multi-dimensional data of hydropower station equipment through a standardized interface to form a unified database, use the entropy weight method and genetic algorithm to evaluate the risk level of equipment and construct an inspection topology map, and intelligently divide special and daily inspection tasks based on the risk level. The frequency matching engine block is used to extract task cycle features through a sliding time window and calculate similarity using the entropy weight method. It automatically judges the frequency consistency by matching the comparison results with the threshold, and transforms subjective experience into objective quantitative evidence. The task processing and optimization module is used to generate a comprehensive inspection list for merged execution for tasks with consistent frequencies using the particle swarm optimization algorithm, and for tasks with inconsistent frequencies, it uses a hypergraph convolutional neural network to mine related features and combines the Monte Carlo algorithm to select the optimal solution at the lowest cost and generate an independent work order. The mobile terminal support and execution module is used to verify and push task data to the APP, dynamically generate personalized work instructions using virtual geofences and convolutional neural networks, and provide data entry and anomaly reporting functions, execute task issuance and intelligently guide inspection personnel to standardize on-site operations. The quality management and analysis module is used to automatically calculate core indicators and generate interactive dashboards based on the full-process data collected by the mobile terminal, and synchronize the analysis results with the management platform in real time to realize closed-loop management of inspection work.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the special inspection method for hydropower stations based on intelligent management as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the special inspection method for hydropower stations based on intelligent management as described in any one of claims 1 to 7.

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