Operation and maintenance management method based on safe operation of hydropower station

Abstract

The invention discloses an operation and maintenance management method based on safe operation of a hydropower station, and the method comprises the steps: collecting operation environment and equipment state data, constructing a risk assessment model according to the equipment state and environment data, and carrying out the dynamic adjustment of a verification method based on a result outputted by the risk assessment model; the method comprises the following steps: acquiring operator information, classifying the levels of operators into primary operators, intermediate operators and advanced operators according to the operator information, implementing multiple verification based on the operators of different levels, constructing a networking relationship between the operators and equipment, and establishing a networking relationship between the operators and the equipment through a dynamic verification mechanism and the multiple verification. The verification intensity is flexibly adjusted according to different situations, the operation safety is ensured, the verification precision is adjusted in real time according to the personnel qualification and the equipment state, the safety is ensured, and the efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of operation and maintenance safety of hydropower stations, and particularly to an operation and maintenance management method based on safe operation of hydropower stations. Background Art

[0002] In the safe operation and maintenance management of hydropower stations, ensuring the safety of operations and the stable operation of equipment is of utmost importance. However, traditional operation and maintenance management methods often rely on static verification mechanisms and fixed verification processes, and it is difficult to adapt to the dynamic changes in the operating status of hydropower station equipment and the operating environment. With the continuous expansion of the scale of hydropower stations and the increasing complexity of equipment, such traditional operation and maintenance management methods have gradually revealed their limitations. Traditional verification mechanisms often lack flexibility and cannot be dynamically adjusted according to the risk levels of equipment status and operating environment. In the actual operation of hydropower stations, equipment status and environmental data are constantly changing. Therefore, fixed verification methods and intensities may not meet the safety requirements in different situations.

[0003] For example, Chinese Patent Application No. 202210796772.5 discloses a method and system for the operation and maintenance safety management of hydropower station equipment. The method specifically includes a stage of safe access of hydropower equipment and a stage of certification of operation and maintenance personnel. In the stage of safe access of hydropower equipment: before each hydropower equipment is ready to start, obtain the identification information of the equipment, and after the first verification and the second verification based on the calculated first output parameter and second output parameter, obtain the certification result; in the stage of certification of operation and maintenance personnel: authenticate the received certification label, mobile terminal identification, and basic information of the operation and maintenance personnel, and return the mobile terminal certification result. At the same time, the present invention provides a system for the operation and maintenance safety management of hydropower station equipment. Using the method and system of the present invention, the operation and maintenance safety system gradually develops towards the "intelligent" direction. While ensuring the reliable operation of equipment, the scientificity and rationality of anti-theft protection are realized, and the security of certification can also be further improved by combining the two methods.

[0004] In existing patent documents, traditional operation and maintenance management methods often neglect the impact of operator qualifications on operation safety. Operators at different levels possess different professional skills and experiences. Therefore, their operation permissions and verification requirements for equipment should also vary. However, traditional operation and maintenance management methods often adopt unified verification standards and processes, unable to conduct differential verification based on operator qualifications, which may lead to increased operation risks or reduced operation efficiency. In addition, traditional operation and maintenance management methods also lack the ability to monitor the real-time load status of equipment and dynamically adjust the verification accuracy. The equipment load status is an important indicator reflecting the operation status of the equipment, which has a direct impact on the stability and safety of the equipment. As a result, when the equipment load is high or changes violently, the verification accuracy is insufficient, and potential safety hazards cannot be detected in time; while when the equipment load is low or stable, over-verification may occur, causing resource waste. Summary of the Invention

[0005] This application provides an operation and maintenance management method based on safe operation of hydropower stations. Through a dynamic verification mechanism and multiple verifications, the verification intensity is flexibly adjusted according to different situations to ensure operation safety, and the verification accuracy is adjusted in real time according to personnel qualifications and equipment status, which not only ensures safety but also improves efficiency.

[0006] This application provides an operation and maintenance management method based on safe operation of hydropower stations, including:

[0007] S101, collect operation environment and equipment status data, construct a risk assessment model based on the equipment status and environment data, and dynamically adjust the verification method based on the results output by the risk assessment model;

[0008] S102, obtain operator information, classify the levels of operators into junior operators, intermediate operators, and senior operators according to the personnel information, conduct multiple verifications based on operators at different levels, and construct the networking relationship between personnel and equipment.

[0009] Preferably, the verification method further includes:

[0010] S201, collect operation data of hydropower station equipment, and divide the operation permissions of hydropower station equipment into several permission levels based on the collected operation data;

[0011] S202, collect the historical operation data of the equipment, calculate the load index according to the historical operation data, establish a load quantization index database according to the load index, set the threshold of the load index and the contact points for identifying the change of the load status according to the load quantization index database, divide the load quantity into levels, and dynamically adjust the verification accuracy according to the load level;

[0012] S203. Collect data information of different devices, formulate different verification precisions according to the collected information, establish a mapping model between device information and verification precision, and dynamically adjust the verification precision according to the mapping model.

[0013] Preferably, the several levels include viewing permission, parameter adjustment permission, start / stop permission, and emergency operation permission, and the permission levels are divided according to the responsibilities of the operators.

[0014] Preferably, the device load indicators include device load rate, equivalent operation duration, and device operation efficiency.

[0015] Preferably, the formula for the device load rate is: where η L represents the load rate of the device, P avg represents the actual average active power of the device, P rated represents the rated power of the device. When P avg >P rated , η L takes 100%; the formula for the equivalent operation duration is: where T eq represents the equivalent operation duration, t i represents the duration of the i-th time period, η L,i represents the load rate of the i-th time period, and n represents the total number of time periods; the formula for the device operation efficiency is: where η E is the device operation efficiency, Q represents the actual output, T std represents the standard working hours per unit product, T total represents the total working time. If Q = 0, then η E = 0%.

[0016] Preferably, calculate the comprehensive load index according to the device load rate, equivalent operation duration, and device operation efficiency: where CLI represents the comprehensive load index; α, β, and γ represent weight coefficients, and α + β + γ = 1; E act represents the actual energy consumption, and E std represents the standard energy consumption.

[0017] Preferably, divide the load amount into levels, including low load, medium load, and high load. For low load, the device load rate is less than 30%, the equivalent operation duration is less than 4 hours, and the device operation efficiency is less than 60%; for medium load, the device load rate is between 30% and 70%, the equivalent operation duration is between 4 and 12 hours, and the device operation efficiency is between 60% and 90%; for high load, the device load rate is greater than 70%, the equivalent operation duration is greater than 12 hours, and the device operation efficiency is greater than 90%.

[0018] Preferably, the method for identifying the contact points of equipment state transition is as follows:

[0019] S301. Set up a load monitoring module in the hydropower station management system, collect equipment operation data in real time through the load monitoring module, and identify load contact points using the load contact point recognition algorithm;

[0020] S302. Use historical operation data and real-time monitoring data to establish a load contact point correlation conduction model between equipment, and calculate the load transfer coefficient according to the load contact point correlation conduction model;

[0021] S303. Collect data on the current load status, historical failure rate, and operation risk level of equipment, establish a prediction model based on the collected data, and dynamically adjust the verification accuracy based on the prediction model.

[0022] Preferably, the formula for the load transfer coefficient is: where L i and L j are the load values of equipment i and equipment j at a certain moment, μ Li and μ Lj are the load means of equipment i and equipment j, is the mathematical expectation operator, and the load transfer coefficient β i→j represents the degree of load change transferred from one equipment to another.

[0023] Preferably, the steps for constructing the networking relationship between personnel and equipment are as follows:

[0024] S401. Collect information on personnel and equipment, and use an algorithm to construct a personnel networking topology structure based on the collected information;

[0025] S402. Establish a load contact point vector model based on historical load data, and identify the direction and intensity of load changes through the model;

[0026] S403. Identify the level of the load segment at the initial stage of the equipment, set preset parameters according to the level of the load segment at the initial stage, and adjust the load segment and verification accuracy of the next verification according to the prediction parameters when the load change during a single-person operation exceeds the current verified load segment through real-time monitoring of the load change.

[0027] One or more technical solutions provided in this application have at least the following technical effects or advantages: Through the dynamic verification mechanism and multiple verifications, the verification intensity can be flexibly adjusted according to different situations to ensure operation safety. The verification accuracy can be adjusted in real time according to personnel qualifications and equipment status, which not only ensures safety but also improves efficiency. By setting clear thresholds and parameter ranges, the system can more accurately judge when to increase the verification intensity, thus effectively preventing unauthorized operations and safety accidents. Under normal working conditions, standard-precision verification is adopted to reduce unnecessary verification steps; when personnel qualifications are high or the equipment status is normal, the verification process is simplified to improve operation efficiency. The verification intensity and method are dynamically adjusted according to situational factors to meet the requirements of different operation scenarios;

[0028] Achieve precise control of the verification process of hydropower station operating equipment, ensure that operators can only perform operations that match their responsibilities and qualifications, and at the same time dynamically adjust the verification accuracy according to the equipment load status to improve the accuracy and efficiency of verification. Establish a verification accuracy improvement plan, including refining verification granularity, load quantification index system, load level classification, differential verification accuracy standards, and mapping relationship between equipment information and verification accuracy, etc., effectively avoiding problems of excessive or insufficient permissions, and improving verification accuracy and safety. By refining verification granularity, achieve precise control of equipment operation permissions, ensure that operators can only perform operations that match their responsibilities and qualifications, dynamically adjust verification accuracy according to the equipment load status, ensure that the verification process matches the actual situation of the equipment, and improve the accuracy and efficiency of verification. Develop differential verification accuracy standards for different equipment and different operation types to ensure that the verification process is neither too strict nor too loose. Through strict identity authentication methods, operation confirmation steps, and permission verification depth, effectively prevent misoperations and illegal operations, and improve the overall safety of the hydropower station;

[0029] By introducing new concepts such as equipment load connection points, equipment - to - equipment load connection point correlation conduction models, equipment load and verification accuracy demand prediction models, and intelligent hydropower station equipment load perception systems, the dynamic adjustment of verification accuracy is realized, which can more precisely control the verification process, improve the accuracy and efficiency of verification. By intelligently monitoring the equipment load status and dynamically adjusting the verification accuracy, improve the safety and accuracy of hydropower station operations, realize the real - time matching of equipment load status and verification accuracy, ensure that when the equipment load changes greatly or the operation risk is high, the verification process is more strict and accurate, reduce the occurrence of equipment failures and operation mistakes, and improve the overall operation efficiency and safety of the hydropower station;

[0030] Through the mapping and matching of the personnel networking topology and the device networking topology, it is possible to quickly locate the personnel with specific device operation qualifications, improve the emergency response speed, realize the intelligent networking of personnel and devices, optimize the man-machine collaboration efficiency. The establishment of the load contact point vector model can more accurately predict the direction and intensity of the device load change, providing a more accurate verification basis for the operator. The setting of the prediction parameters enables the operator to make preparations in advance when the load change exceeds the current verified load section, ensuring the continuity and accuracy of the verification process. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a schematic flow chart of an operation and maintenance management method based on the safe operation of a hydropower station according to the present invention;

[0032] Figure 2 It is a schematic flow chart of the verification method of an embodiment of the present invention;

[0033] Figure 3 It is a schematic flow chart of the process for identifying the contact points of the device state transition in an embodiment of the present invention;

[0034] Figure 4 It is a schematic flow chart of the process for constructing the networking relationship between personnel and devices in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] To facilitate the understanding of the present invention, the present application will be described more comprehensively with reference to the relevant drawings; the preferred embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein; on the contrary, these embodiments are provided to make the disclosure of the present invention more thorough and comprehensive.

[0036] It should be noted that the terms "vertical", "horizontal", "upper", "lower", "left", "right" and similar expressions used herein are for illustrative purposes only and do not represent the only embodiments.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs; the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention; the term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0038] Embodiment 1: Figure 1 It is a schematic flow chart of an operation and maintenance management method based on the safe operation of a hydropower station according to an embodiment of the present invention, including:

[0039] S101. Collect operation environment and equipment status data, construct a risk assessment model based on the equipment status and environmental data, and dynamically adjust the verification method based on the results output by the risk assessment model;

[0040] Further, use a thermometer, a hygrometer, a power meter, a pressure sensor, and a flow meter to collect data such as temperature, humidity, equipment power, water pressure, and water flow rate, and collect operation environment and equipment status data in real time through monitoring devices (thermometer, hygrometer, power meter, pressure sensor, and flow meter).

[0041] The verification method includes verification intensity and verification method. According to the operation status of hydropower station equipment and environmental data, construct a risk assessment model, clean the collected equipment status and environmental data to remove duplicate, incorrect, or invalid data, extract selected features from the original data to form a feature vector or feature matrix, select a neural network machine learning model, use historical data to train the selected model, adjust the parameters and structure of the model to enable it to accurately predict the risk level of equipment operation status and environmental data, use methods such as cross-validation and leave-one-out method to verify the model, evaluate the performance and stability of the model, define different risk levels according to the actual needs of the hydropower station and industry standards, set thresholds for different risk levels according to the output of the model and the definition of risk levels. The model output value (such as failure probability, safety risk index, etc.) is between 0 and 20, and the hydropower station is at low risk; the model output value is between 21 and 50, and the hydropower station is at medium risk; the model output value is between 51 and 100, and the hydropower station is at high risk; adjust and optimize the thresholds through actual operation data and expert experience. As the operation environment of hydropower station equipment changes continuously, the system will continuously update the risk level of operation or equipment status. The hydropower station equipment management system automatically adjusts the verification intensity and method according to the risk level of the equipment status output by the current risk assessment model. When the risk level increases, the system will automatically strengthen the verification measures to ensure the safety of operation and the stable operation of equipment; when the risk level decreases, the system will simplify the verification process to improve operation efficiency and user experience.

[0042] S102. Obtain operator information, classify the operator level into junior operator, intermediate operator, and senior operator based on the personnel information, and implement multiple verifications based on operators of different levels;

[0043] Furthermore, the system accesses the operator information management system of the hydropower station through a preset interface, extracts the identity information of the operators from the system, including basic information such as name, job number, position, and professional level. At the same time, it extracts the historical operation records of the operators, including operation time, operation type, operation result, and any relevant remarks or evaluations. The extracted information is sorted to form a detailed file of the operators, and the file is stored in the secure database of the system. For junior operators, they have basic operation skills and can complete simple tasks under supervision. They need certain guidance and supervision to ensure the safety and accuracy of operations. Junior operators complete identity verification through two or more verification methods. In addition to the traditional password verification, fingerprint recognition or facial recognition is also required to increase the accuracy and reliability of identity verification. For intermediate operators, they are proficient in multiple operation skills, can independently handle common problems, have certain troubleshooting abilities, and can solve most operation problems without direct supervision. For senior operators, they have profound professional knowledge and rich practical experience, can handle complex faults, can guide junior and intermediate operators to help them improve their skills, and participate in formulating operation procedures to provide institutional guarantee for the safe and efficient operation of the hydropower station. For intermediate and senior operators, the system simplifies the verification process. Intermediate and senior operators only need to pass a relatively convenient verification method, such as password verification or fingerprint recognition, to complete identity verification.

[0044] The system presents a verification interface to the operator. The required verification methods and steps are clearly listed on the interface. The operator completes the required verification methods in sequence according to the prompts on the interface. For example, first enter the password, and then perform fingerprint recognition or facial recognition. The system provides real-time feedback and guidance during the operation process. After the operator completes the verification method, the system immediately conducts real-time identity verification. The system compares the verification information provided by the operator with the information stored in the system to ensure that their identity matches the operation authority. If the operator successfully passes the verification, the system immediately allows them to perform subsequent operations and displays a successful verification prompt message. If the operator fails to pass the verification, the system rejects their operation request, displays a rejection prompt message, and informs the operator to re-perform identity verification or contact the management for assistance.

[0045] The technical solutions in the embodiments of the present application at least have the following technical effects or advantages: Through the dynamic verification mechanism and multiple verifications, the verification intensity is flexibly adjusted according to different situations to ensure operation safety. The verification accuracy is adjusted in real time according to personnel qualifications and equipment status, which not only ensures safety but also improves efficiency. By setting clear thresholds and parameter ranges, the system can more accurately determine when to increase the verification intensity, thereby effectively preventing unauthorized operations and safety accidents. Under normal working conditions, standard-precision verification is adopted to reduce unnecessary verification steps; when personnel qualifications are high or the equipment status is normal, the verification process is simplified to improve operation efficiency. The verification intensity and method are dynamically adjusted according to situational factors to meet the requirements of different operation scenarios.

[0046] Embodiment 2: Based on the verification of the hydropower station equipment in Embodiment 1, the verification process mainly verifies whether the equipment can be operated. In this embodiment, the verification method is refined by adding what degree of operation can be achieved during the verification process to avoid problems caused by excessive or insufficient permissions.

[0047] As Figure 2 shown, the verification method further includes:

[0048] S201, collect the operation data of the hydropower station equipment, and divide the operation permissions of the hydropower station equipment into several levels based on the collected operation data. The several levels include viewing permission, parameter adjustment permission, start / stop permission, and emergency operation permission.

[0049] Specifically, conduct an in-depth analysis of the entire process of hydropower station equipment operation, collect operation data of hydropower station equipment, clarify the impact degree of different operations on the operation safety of the hydropower station, and determine the divided authority levels and their specific contents in combination with industry standards and actual operation experience. The authority levels include viewing authority, parameter adjustment authority, start / stop authority, and emergency operation authority. For the viewing authority, operators are allowed to view information such as the operation status, parameter settings, and historical data of hydropower station equipment, but are not allowed to make any modifications or operations. The applicable personnel for the viewing authority are all operators, especially newly recruited or interns, as well as those who need to understand the equipment operation status but do not need to operate. For the parameter adjustment authority, operators are allowed to adjust the parameter settings of hydropower station equipment within a certain range, such as adjusting the water turbine speed, generator voltage, etc. The applicable personnel for the parameter adjustment authority are operators with certain professional skills and experience (junior operators), and usually need to obtain this authority after training and assessment. For the start / stop authority, operators are allowed to start or stop hydropower station equipment, such as turning on or off the water turbine, generator, etc. The applicable personnel for the start / stop authority are senior operators or management personnel who need to have an in-depth understanding and control ability of the overall operation of the hydropower station. For the emergency operation authority, in case of an emergency or failure in the hydropower station, operators are allowed to take emergency measures, such as cutting off the power supply, starting standby equipment, etc. The applicable personnel for the emergency operation authority are operators or management personnel (senior operators) who have received special training and have the ability to handle emergencies. They need to make decisions and take measures quickly in case of an emergency.

[0050] Design an authority control mechanism in the hydropower station management system to ensure that different levels of operation authorities can be clearly distinguished and effectively controlled. Adopt the Role-Based Access Control (RBAC) model to achieve flexible allocation and management of authorities. According to the result of the authority level division, configure corresponding authority settings in the system to ensure that operators can only perform operations that match their authorities. Conduct a comprehensive test of the system to verify the effectiveness and reliability of the authority control mechanism and ensure that there will be no authority confusion or abuse in actual operation.

[0051] S202, collect the historical operation data of the equipment, calculate the load index according to the historical operation data, establish a load quantification index database according to the load index, set the threshold of the load index according to the load quantification index system, divide the load quantity into levels, and dynamically adjust the verification accuracy according to the load level;

[0052] Furthermore, use the automatic data acquisition system SCADA system to collect the historical operation data of the equipment in real time, store the collected data in a temporary database, clean the collected data, and remove outliers, missing values or duplicate values. The equipment load index includes equipment load rate, equivalent operation duration, and equipment operation efficiency.

[0053] The formula for calculating the device load rate is as follows:

[0054]

[0055] , where η L represents the load rate of the device, P avg represents the actual average active power of the device, P rated represents the rated power of the device. When P avg >P rated , η L is forced to take 100% (overload state). At this time, the power data during non-production periods such as device standby and idling are excluded;

[0056] The formula for the equivalent operating duration is as follows:

[0057]

[0058] , where T eq represents the equivalent operating duration, t i represents the duration of the i-th time period, η L,i represents the load rate of the i-th time period, and n represents the total number of time periods;

[0059] The calculation formula for the device operating efficiency is as follows:

[0060]

[0061] , where η E is the device operating efficiency, Q represents the actual output, T std represents the standard man-hours per unit product, T total represents the total working time. When the device is in the maintenance or debugging state, T total needs to deduct the corresponding time period. If Q = 0 (such as idling), then η E = 0%. According to the above device load rate, equivalent operating duration, and device operating efficiency, calculate the comprehensive load index:

[0062]

[0063] Among them, CLI represents the comprehensive load index; α, β, and γ represent the weight coefficients, and α + β + γ = 1; E act represents the actual energy consumption, and E std represents the standard energy consumption.

[0064] According to the calculated equipment load indicators, a load quantification index database is established. The index database includes an equipment information table and a load data table. The load levels are divided according to the equipment load rate, equivalent operation duration, and equipment operation efficiency, which are low load, medium load, and high load respectively. For the low load, the equipment load rate is less than 30%, the equivalent operation duration is less than 4 hours, and the equipment operation efficiency is less than 60%; for the medium load, the equipment load rate is between 30% and 70%, the equivalent operation duration is between 4 and 12 hours, and the equipment operation efficiency is between 60% and 90%; for the high load, the equipment load rate is greater than 70%, the equivalent operation duration is greater than 12 hours, and the equipment operation efficiency is greater than 90%. For the low load level, the verification accuracy is reduced; for the medium load level, the verification accuracy is maintained at a moderate level to ensure the reliability of the equipment and avoid resource waste caused by over-verification; for the high load level, the verification accuracy is increased to ensure the stable operation of the equipment under high load.

[0065] S203, collect the data information of different equipment, formulate differentiated verification accuracies according to the collected information, establish a mapping model between equipment information and verification accuracy, and dynamically adjust the verification accuracy according to the mapping model;

[0066] Specifically, the data information of each device is collected in real time by sensors, and the collected information is stored in a database. The verification accuracy is divided into four levels: basic verification, regular verification, enhanced verification, and strict verification. Basic verification uses the most basic identity authentication methods, such as user names and passwords. The operation confirmation steps are simplified, only basic operation confirmation is required, and the depth of permission verification is relatively shallow, mainly checking the basic roles of users. Regular verification adds more secure identity authentication methods, such as SMS or email verification codes, on the basis of basic verification. The operation confirmation steps are slightly increased, and the depth of permission verification is moderate, checking the roles and specific permissions of users. Enhanced verification further strengthens identity authentication on the basis of regular verification, using biometric technology. The operation confirmation steps are more strict, and the depth of permission verification is increased, and the operation history and permission matching of users will be carefully reviewed. Strict verification implements the highest level of verification, using multi-factor identity authentication (such as biometrics + hardware tokens). The operation confirmation steps include clear confirmation of operation intentions and possible expert review. The depth of permission verification is the deepest, and all relevant permissions and operation history of users will be comprehensively reviewed. Different devices have different requirements for verification accuracy. Key devices (main control system of the host group) require higher verification accuracy to ensure the safety and accuracy of operations. High-risk operations (such as changing the device operation status and modifying key parameters) require more strict verification, while low-risk operations (such as viewing device status and log records) can use more basic verification. The current state of the device (such as full-load operation, maintenance, shutdown status) may also affect the requirements for verification accuracy. For example, the main control system of the host group in full-load operation requires more strict verification to prevent system failures caused by misoperations. Specific verification accuracy rules are formulated according to the importance of the device, the risk level of the operation, and the current state of the device. For the main control system of the host group in full-load operation, due to the high operation risk and the high importance of the device, strict verification is required, including multi-factor identity authentication, operation intention confirmation, and expert review. For low-risk operations of the auxiliary system, such as viewing the status or log of auxiliary devices, since the operation risk is low and the importance of the device is relatively small, basic verification can be used to simplify the verification process and only perform basic identity authentication and operation confirmation.

[0067] A mapping model is constructed, with different device information as input and the verification accuracy level as output. The verification accuracy rules are transformed into mapping relationships in the mapping model. The device information monitored in real time is input into the mapping model to calculate the current verification accuracy level. According to the calculated verification accuracy level, the system automatically adjusts the parameters and settings of the verification process. For device operations that require strict verification, the system starts multi-factor identity authentication, operation intention confirmation, and expert review. For device operations with basic verification, the system simplifies the verification process and only performs basic identity authentication and operation confirmation.

[0068] The technical solutions in the embodiments of the present application at least have the following technical effects or advantages: achieving precise control of the verification process of hydropower station operating equipment, ensuring that operators can only perform operations that match their responsibilities and qualifications, dynamically adjusting the verification accuracy according to the equipment load status, improving the accuracy and efficiency of verification, establishing a verification accuracy improvement plan, including refining the verification granularity, load quantification index system, load level classification, differential verification accuracy standard, and mapping relationship between equipment information and verification accuracy, etc. By implementing this plan, the problems of excessive or insufficient permissions can be effectively avoided, and the verification accuracy and security can be improved. By refining the verification granularity, precise control of equipment operation permissions can be achieved, ensuring that operators can only perform operations that match their responsibilities and qualifications. Dynamically adjusting the verification accuracy according to the equipment load status to ensure that the verification process matches the actual situation of the equipment, improving the accuracy and efficiency of verification. Formulating differential verification accuracy standards for different equipment and different operation types to ensure that the verification process is neither too strict nor too loose. Through strict identity authentication methods, operation confirmation steps, and permission verification depth, misoperations and illegal operations can be effectively prevented, improving the overall security of the hydropower station.

[0069] Embodiment 3: Based on the equipment load status in Embodiment 2, in this embodiment, by finding the critical points and high-risk points of the equipment load status change, and analyzing how the load change of one equipment affects other equipment through system transmission, more precise verification control can be carried out at the source, such as Figure 3 shown.

[0070] S301, set a load monitoring module in the hydropower station management system, collect equipment operation data in real time through the load monitoring module, and use the load contact point recognition algorithm to identify the load contact points;

[0071] Further, a load monitoring module is set in the hydropower station management system. The load monitoring module is a sensor that uses the sensor to collect equipment operation data in real time, cleans and preprocesses the collected equipment operation data, removes outliers, fills in missing values, ensures the accuracy and integrity of the data, sorts the data in chronological order to form an equipment operation curve. The principle of the load connection point recognition algorithm is to calculate the load change rate at each time point in the equipment operation data, that is, the difference between the current moment load value and the previous moment load value divided by the time interval. Traverse the equipment operation data, calculate the load change rate at each time point, set the change rate threshold, and when the load change rate exceeds the threshold, record the change point at that moment. Set the sliding window size, calculate the statistical indicators of the average value and standard deviation of the load within the sliding window, set the threshold of the statistical indicators, and when the statistical indicators within the sliding window exceed the threshold, record the fluctuation point at the start time of the window. Combining the results of the change rate detection and the sliding window statistics, when both the change rate exceeds the threshold and the average value and standard deviation within the sliding window exceed the threshold, mark that moment as the load connection point.

[0072] S302. Use the historical operation data and real-time monitoring data to establish a load connection point association conduction model between equipment, and calculate the load transfer coefficient according to the load connection point association conduction model.

[0073] Specifically, extract the load data of each equipment from the historical operation database. The data includes the load change conditions under different time periods and different working conditions. At the same time, use the real-time monitoring system to obtain the real-time load data of the current equipment, clean the collected data, remove outliers and missing values, construct an association network of load connection points between equipment according to the physical connection conditions between equipment. The association network is represented by a graph in graph theory, where the equipment is used as nodes and the connections and relationships between equipment are used as edges. Use a machine learning algorithm to establish a conduction model of load change between equipment. After the model is established, use simulation data for operation testing. The simulation data includes the load change conditions under different time periods and different working conditions to comprehensively test the performance of the model. Based on the results of the model, calculate the load transfer coefficient. The formula is: where, L i and L j are the load values of equipment i and equipment j at the moment, μ Li and μ Lj are the load means of equipment i and equipment j, is the mathematical expectation operator, and the load transfer coefficient β i→jIt represents the degree to which the load change is transferred from one device to another. According to business requirements and device characteristics, the threshold range of the load transfer coefficient is set to 0.4 - 0.7. A load transfer coefficient greater than 0.7 is of high grade, a load transfer coefficient greater than or equal to 0.4 and less than or equal to 0.7 is of medium grade, and a load transfer coefficient less than 0.4 is of low grade. If the load transfer coefficient of a device is of high grade, it indicates that its load change is easily affected by other devices, so its impact degree is large. If the load transfer coefficient of a device is of low grade, it indicates that its load change is relatively independent and less affected by other devices.

[0074] S303, collect the current load status, historical failure rate, and operation risk level data of the device, establish a prediction model based on the collected data, and dynamically adjust the verification accuracy based on the prediction model;

[0075] Furthermore, the load status of the device is monitored in real time through sensors, and the data is recorded in the database. The device maintenance records are queried, the historical failure rate is statistically analyzed, a prediction model is constructed using regression analysis. The load status of the device, historical failure rate, and operation risk level are used as input variables to output the predicted value of the verification accuracy. The load connection point identification result, load transfer coefficient between devices, and predicted value of the verification accuracy requirement are combined to dynamically adjust the verification accuracy. When the device load exceeds the threshold, the verification accuracy is increased; when the device load is low and stable, the verification accuracy is decreased to save costs, and higher verification accuracy requirements are set for high-risk operations.

[0076] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: By introducing new concepts such as device load connection points, load connection point correlation conduction models between devices, prediction models of device load and verification accuracy requirements, and intelligent load perception systems for hydropower station devices, the dynamic adjustment of verification accuracy is achieved, which can more precisely control the verification process, improve the accuracy and efficiency of verification. By intelligently monitoring the load status of devices and dynamically adjusting the verification accuracy, the safety and accuracy of hydropower station operations are improved, the real-time matching of device load status and verification accuracy is realized, ensuring that the verification process is more strict and accurate when the device load changes greatly or the operation risk is high, reducing the occurrence of device failures and operation errors, and improving the overall operation efficiency and safety of the hydropower station.

[0077] Embodiment 4: Based on the above Embodiment 1 to Embodiment 3, in this embodiment, by constructing the networking relationship between personnel and devices, personnel with different positions and different skill levels are intelligently grouped according to device type, complexity, and risk level to form a personnel networking topology structure, as Figure 4 shown.

[0078] S401, collect the information of personnel and devices, and use algorithms to construct a personnel networking topology structure based on the collected information;

[0079] Further, collect information on personnel and equipment. The personnel information includes name, position, skills, qualification certificates, and contact information. The equipment information includes equipment name, type, operation difficulty, required qualifications, and location. Use the KM algorithm in graph theory to construct a personnel networking topology structure. According to the personnel information, create personnel nodes for each person with specific skills or qualifications. According to the equipment information, create equipment nodes for each piece of equipment that needs to be operated. Initialize an empty graph, add the personnel nodes and equipment nodes to the graph to form a node set. Traverse the personnel nodes and equipment nodes, and check whether there is a matching relationship according to the qualifications of the personnel and the operation requirements of the equipment. If a person has the qualification to operate a piece of equipment, establish an edge between the corresponding personnel node and equipment node. For each established edge, assign a weight to the edge according to factors such as the proficiency, response time, or priority of the person operating the equipment. The weight represents the operation priority or proficiency of the person for the equipment. Take the constructed graph as input and pass it to the KM algorithm. After the algorithm finishes executing, it will output a matching result indicating which personnel and which equipment are successfully matched. The matching result is a list that records the matching relationship between each personnel node and the corresponding equipment node. According to the matching result, an association table of personnel and equipment can be generated to facilitate quickly locating personnel with specific equipment operation qualifications in case of an emergency. According to the generated association table, group the personnel with the same equipment operation qualifications into one group. On the basis of the preliminary grouping, further consider the cooperation efficiency between personnel by checking historical cooperation records, team project experience, or evaluations among colleagues, identify personnel who cooperate well with each other, and group them into one group. Verify the optimized grouping to ensure that each group has the required equipment operation qualifications, and the cooperation efficiency and geographical location between personnel meet the expectations. If any problems or deficiencies are found during the verification process, adjust the grouping in a timely manner, including adding or reducing group members, and adjusting the selection of group leaders.

[0080] S402. Establish a load connection point vector model based on historical load data, and identify the direction and intensity of load changes through the model;

[0081] Specifically, a load connection point vector model is established using time series analysis based on historical load data. Each load connection point in the model represents the load status of the device during a specific time period, and the vector represents the changing trend of the load over time. The positive and negative directions of the vector are used to indicate whether the load is increasing or decreasing: the positive direction indicates an increasing load, and the negative direction indicates a decreasing load. Determine the time interval for analysis, expressed in hours, days, or weeks. For each load connection point, calculate the load change within the time interval, that is, the load value at the end time minus the load value at the start time. Determine the vector direction based on the positive or negative of the load change. If the change is positive, the vector direction is positive, indicating an increasing load; if the change is negative, the vector direction is negative, indicating a decreasing load. The absolute value of the load value at the end time minus the load value at the start time represents the magnitude of the vector. The longer the magnitude, the more drastic the load change.

[0082] S403. Identify the level of the load segment in the initial stage of the device, set preset parameters according to the level of the load segment in the initial stage, and adjust the load segment and verification accuracy for the next verification according to the prediction parameters by monitoring the load change in real time when the load change during single-person operation exceeds the current verified load segment.

[0083] Further, historical load data at the initial stage is collected, and the load segment at the initial stage is determined to be which level of low load, medium load or high load according to the historical load data. The preset parameters include the load change rate and the change time. For the low load level, the load change rate is less than or equal to 5%. In the low load state, the equipment runs relatively stably and the load fluctuation is small. Therefore, the preset load change rate is relatively small, 5% / hour or less, to reflect this stability. The load change time is greater than or equal to twelve hours. In the low load state, the equipment may maintain a stable load level for a long time. Therefore, the preset load change time is relatively long, such as 12 hours or more, to reflect this stability. For the medium load level, the load change rate is greater than 5% and less than 15%. In the medium load state, the equipment experiences a moderate increase or decrease in load. Therefore, the preset load change rate is moderate, such as 5%-15% / hour, to cover the possible load fluctuation range. The load change time is greater than four hours and less than twelve hours. In the medium load state, the equipment experiences a moderate load change, and these changes may occur over a relatively long period of time. Therefore, the preset load change time is moderate, such as 4-12 hours, to cover the possible load change time range. For the high load level, the load change rate is greater than or equal to 15%. In the high load state, the equipment is more vulnerable to external factors, resulting in large load fluctuations. Therefore, the preset load change rate may be relatively large, such as 15% / hour or more, to cope with this uncertainty. The load change time is less than or equal to four hours. In the high load state, the equipment reaches the critical point of load change faster. Therefore, the preset load change time is relatively short, such as 4 hours or less, to cope with this rapidly changing load situation.

[0084] During the single-person operation process, the change of the load is monitored in real time, and the real-time load data is compared with the level data of the load segment at the initial stage. If the real-time load data is within the verified load segment, it indicates that the equipment is operating normally and no measures need to be taken. If the load change exceeds the current verified load segment, the warning mechanism is triggered, and the load segment and verification accuracy of the next verification are adjusted according to the prediction parameters. Combining the prediction parameters and the real-time load data, a prediction model is used to predict the range of the next load. The prediction model is a prior art and will not be elaborated here.

[0085] The technical solutions in the embodiments of the present application have at least the following technical effects or advantages: Through the mapping and matching of the personnel networking topology and the device networking topology, it is possible to quickly locate the personnel with specific device operation qualifications, improve the emergency response speed, realize the intelligent networking of personnel and devices, optimize the man-machine cooperation efficiency. The establishment of the load contact point vector model can more accurately predict the direction and intensity of the device load change, providing a more accurate verification basis for the operator. The setting of the prediction parameters enables the operator to make preparations in advance when the load change exceeds the current verification load section, ensuring the continuity and accuracy of the verification process.

[0086] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An operation and maintenance management method based on safe operation of a hydropower station, characterized in that: include: S101, collect operating environment and equipment status data, build a risk assessment model based on the equipment status and environment data, and dynamically adjust the verification method based on the results output by the risk assessment model; S102, obtaining operator information, classifying the operator levels into primary operators, intermediate operators and senior operators according to the operator information, implementing multiple verifications based on operators of different levels, and establishing a networking relationship between personnel and equipment.

2. The operation and maintenance management method based on safe operation of a hydropower station according to claim 1, characterized in that: The verification method further comprises: S201, collecting operation data of hydropower station equipment, and dividing the operation authority of the hydropower station equipment into several authority levels based on the collected operation data; S202, collecting historical operation data of the equipment, calculating the load index according to the historical operation data, establishing a load quantification index database according to the load index, setting the threshold of the load index and the contact point for identifying the load state transition according to the load quantification index database, classifying the load into grades, and dynamically adjusting the verification accuracy according to the load grade; S203, collecting data information of different devices, formulating different verification accuracies according to the collected information, establishing a mapping model between device information and verification accuracy, and dynamically adjusting the verification accuracy according to the mapping model.

3. The operation and maintenance management method based on the safe operation of a hydropower station as claimed in claim 2, characterized in that: The several levels include viewing authority, parameter adjustment authority, start / stop authority and emergency operation authority, and the authority levels are divided according to the responsibilities of the operator.

4. The operation and maintenance management method based on safe operation of a hydropower station as claimed in claim 2, characterized in that: The equipment load indicators include equipment load rate, equivalent operating time and equipment operating efficiency.

5. The operation and maintenance management method based on safe operation of a hydropower station as claimed in claim 4, characterized in that: The formula for the equipment load rate is: Among them, η L Indicates the load rate of the equipment, P avg Indicates the actual average active power of the device, P rated Indicates the rated power of the equipment. avg >P rated When η L Take 100%; the formula for equivalent operating hours is: Among them, T eq represents the equivalent running time, t i represents the duration of the i-th time period, η L,i represents the load rate of the i-th time period, and n represents the total number of time periods; the equipment operation efficiency formula is: Among them, η E is the equipment operating efficiency, Q represents the actual output, T std Indicates the standard working hours per unit product, T total Represents the total working time. If Q = 0, then η E =0%.

6. The operation and maintenance management method based on safe operation of a hydropower station as claimed in claim 5, characterized in that: The comprehensive load index is calculated based on the equipment load rate, equivalent operating time and equipment operating efficiency: Wherein, CLI represents the comprehensive load index; α, β and γ represent weight coefficients, α+β+γ=1; E act Indicates the actual energy consumption, E std Indicates standard energy consumption.

7. The operation and maintenance management method based on safe operation of a hydropower station as claimed in claim 2, characterized in that: The load is divided into low load, medium load and high load, and the equipment load rate of low load is less than 30%, the equivalent operation time is less than 4 hours, and the equipment operation efficiency is less than 60%; The load rate of medium-load equipment is between 30% and 70%, the equivalent operating time is between 4 and 12 hours, and the equipment operating efficiency is between 60% and 90%; the load rate of high-load equipment is greater than 70%, the equivalent operating time is greater than 12 hours, and the equipment operating efficiency is greater than 90%.

8. The operation and maintenance management method based on safe operation of a hydropower station as claimed in claim 2, characterized in that: The method for identifying the contact point of device state transition is as follows: S301, setting a load monitoring module in the hydropower station management system, collecting equipment operation data in real time through the load monitoring module, and identifying load contacts using a load contact identification algorithm; S302, using historical operation data and real-time monitoring data, establishing a load contact correlation conduction model between devices, and calculating a load transfer coefficient according to the load contact correlation conduction model; S303, collect the current load status, historical failure rate and operation risk level data of the equipment, establish a prediction model based on the collected data, and dynamically adjust the verification accuracy based on the prediction model.

9. The operation and maintenance management method based on safe operation of a hydropower station as claimed in claim 8, characterized in that: The formula for the load transfer coefficient is: Among them, L i and L j is the load value of equipment i and equipment j at the time, and is the load mean of equipment i and equipment j, is the mathematical expectation operator, the load transfer coefficient β i→j Indicates the degree to which load changes are transferred from one device to another.

10. The operation and maintenance management method based on safe operation of a hydropower station according to claim 1, characterized in that: The steps to build a networking relationship between personnel and devices are as follows: S401, collecting information of personnel and equipment, and constructing a personnel network topology structure using an algorithm based on the collected information; S402, establishing a load contact vector model based on historical load data, and identifying the direction and intensity of load change through the model; S403, identify the level of the load segment in the initial stage of the equipment, set preset parameters according to the level of the load segment in the initial stage, and monitor the load changes in real time. When the load changes exceed the current verification load segment during single-person operation, adjust the load segment and verification accuracy of the next verification according to the predicted parameters.

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