AU2023210611A1 - Method and apparatus for operating state analysis and early warning of auxiliary device of hydroelectric station, and decision support system for hydroelectric production - Google Patents

Abstract

A method for operating state analysis and early warning of an auxiliary device of a hydroelectric station includes: obtaining start-stop time data of a target auxiliary device of the hydroelectric station and working condition data of a generator unit in a preset time period; determining a plurality of operating time durations and/or a plurality of start-stop time intervals of the target auxiliary device under different working conditions according to the start-stop time data and the working condition data; determining a health value range of the target auxiliary device according to the plurality of operating time durations and/or the plurality of start-stop time intervals; and setting an early warning condition according to the health value range, so as to perform an early warning for a current operating situation of the target auxiliary device. obtaining start-stop time data of a target auxiliary device of the hydroelectric station and working condition data of a generator S101 unit in a preset time period determining a plurality of operating time durations and/or a plurality of start-stop time intervals of the target auxiliary device under different working conditions according to the start-stop time data and the working condition data determining a health value range of the target auxiliary device S103 according to the plurality of operating time durations and/or the plurality of start-stop time intervals setting an early warning condition according to the health value range, so as to perform an early warning for a current operating S104 situation of the target auxiliary device FIG. 1 distinguishin operating time duration generating data derived from operating rule of arstatefrom governor oil pump: dafptn mn shtdfown 1) operating time duration under power state quantity of -7generation state governor oil pump at 2) operating time duration under a testing point for. shutdown state operatmng start-3) start-stop time interval under power start-stoptime generationstate interval 4) start-stop time interval under shutdown state ---------------------------------------------- 1 health threshold state early warning model of automatic mining governor oil pump based on algorithm stat ealy arnig sste ofhealth threshold governor oil pump based on health threshold of state of governor oil pump health thresholdI FIG. 2

Description

obtaining start-stop time data of a target auxiliary device of the hydroelectric station and working condition data of a generator S101 unit in a preset time period

determining a plurality of operating time durations and/or a plurality of start-stop time intervals of the target auxiliary device under different working conditions according to the start-stop time data and the working condition data

determining a health value range of the target auxiliary device S103 according to the plurality of operating time durations and/or the plurality of start-stop time intervals

setting an early warning condition according to the health value range, so as to perform an early warning for a current operating S104 situation of the target auxiliary device

FIG. 1

distinguishin operating time duration generating data derived from operating rule of arstatefrom governor oil pump: dafptn mn shtdfown 1) operating time duration under power state quantity of -7generation state governor oil pump at 2) operating time duration under a testing point for. shutdown state operatmng start-3) start-stop time interval under power start-stoptime generationstate interval 4) start-stop time interval under shutdown state

---------------------------------------------- 1 health threshold state early warning model of automatic mining governor oil pump based on algorithm stat ealy arnig sste ofhealth threshold governor oil pump based on health threshold of state of governor oil pump health thresholdI

FIG. 2

METHOD AND APPARATUS FOR OPERATING STATE ANALYSIS AND EARLY WARNING OF AUXILIARY DEVICE OF HYDROELECTRIC STATION, AND DECISION SUPPORT SYSTEM FOR HYDROELECTRIC PRODUCTION

FIELD The present disclosure relates to the hydroelectric technical field, and more particularly to a method and apparatus for operating state analysis and early warning of an auxiliary device of a hydroelectric station, and a decision support system for hydroelectric production and a storage medium.

BACKGROUND Hydropower is a kind of clean, renewable, pollution-free energy with low operating costs, which is convenient for power peak shaving, and is conducive to improving resource utilization and comprehensive economic and social benefits. In the context of the increasing shortage of traditional energy on Earth, countries around the world generally give priority to the utilization of hydropower resources, resulting in rapid development of hydroelectric plants. During the operation of the hydroelectric plant, it is necessary to conduct inspection, economic operation optimization, and maintenance and other operations on hydroelectric device. However, the device inspection in the related art is mainly conducted manually, which will consume a lot of labor costs and the inspection accuracy is not high. In addition, most device defects and hidden dangers cannot be discovered in advance, and device maintenance decisions lack analysis and evaluation, which affects the service life of the device. In addition, the lack of optimization and adjustment for the economic operation of hydroelectric generating units also affects the economic benefits of hydroelectric plants. An auxiliary device in a hydroelectric station can be used for the control of an oil system, a water system and a gas system of the hydroelectric station, the operating rule of the auxiliary device can reflect operating states of devices and systems, and the operating reliability of the auxiliary device directly affects the operating safety and stability of the entire hydroelectric station. For the auxiliary device, its operating time duration and start-stop time interval under automatic control should not vary significantly. If the operating time duration is distinctly too long, or the start-stop time interval is distinctly too short, it indicates that there may be an abnormality, such as oil leakage in an oil tank or gas leakage in a gas tank. Therefore, the analysis of the operating time duration and the start-stop time interval of the auxiliary device is of great significance to early warning of the auxiliary device. In the related art, the statistical analysis of the operating rule of the auxiliary device is mainly performed manually, which is subjected to a certain degree of subjectivity, and costs a lot of manpower and time, thereby affecting the operating state analysis and early warning effects of the auxiliary device.

SUMMARY Embodiments of the present disclosure provide a method and apparatus for operating state analysis and early warning of an auxiliary device of a hydroelectric station, a decision support system for hydroelectric production and a storage medium, aiming to solve at least one of the problems existing in the related art to at D least some extent. According to embodiments of a first aspect of the present disclosure, there is provided a method for operating state analysis and early warning of an auxiliary device of a hydroelectric station. The method includes: obtaining start-stop time data of a target auxiliary device of the hydroelectric station and working condition data of a generator unit in a preset time period; determining a plurality of operating time durations and/or a plurality of start-stop time intervals of the target auxiliary device under different working conditions according to the start-stop time data and the working condition data; determining a health value range of the target auxiliary device according to the plurality of operating time durations and/or the plurality of start-stop time intervals; and setting an early warning condition according to the health value range, so as to perform an early warning for a current operating situation of the target auxiliary device. D According to embodiments of a second aspect of the present disclosure, there is provided an apparatus for operating state analysis and early warning of an auxiliary device of a hydroelectric station. The apparatus includes: an obtaining module configured to obtain start-stop time data of a target auxiliary device of the hydroelectric station and working condition data of a generator unit in a preset time period; a first determining module configured to determine a plurality of operating time durations and/or a plurality of start-stop time intervals of the target auxiliary device under different working conditions according to the start-stop time data and the working condition data; a second determining module configured to determine a health value range of the target auxiliary device according to the plurality of operating time durations and/or the plurality of start-stop time intervals; and an early warning module configured to set an early warning condition according to the health value range, so as to perform an early warning for a current operating situation of the target auxiliary device.

According to embodiments of a third aspect of the present disclosure, there is provided an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor and having stored therein instructions executable by the at least one processor. The instructions, when executed by the at least one processor, cause the at least one processor to perform the method as described in embodiments of the second aspect of the present disclosure. According to embodiments of a fourth aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium having stored therein computer instructions. The computer instructions are configured to cause a computer to perform the method as described in embodiments of the second aspect of the present disclosure. In embodiments of the present disclosure, by obtaining the start-stop time data of the target auxiliary device of the hydroelectric station and the working condition data of the generator unit in the preset time period; determining the plurality of operating time durations and/or the plurality of start-stop time intervals of the target auxiliary device under different working conditions according to the start-stop time data and the working condition data; determining a health value range of the target auxiliary device according to the plurality of operating time durations and/or the plurality of start-stop time intervals; and setting the early warning condition according to the health value range so as to perform the early warning to the current operating situation of the target auxiliary device, analysis can be made in combination with different working conditions of the generator unit and the operating rule of the target auxiliary device to reasonably and accurately determine the health value range of the target auxiliary device, thereby improving the early warning effect on the operation of the target auxiliary device. According to embodiments of a fifth aspect of the present disclosure, there is provided a decision support system for hydroelectric production, which includes: a data acquiring subsystem, connected with each of a plurality of hydroelectric devices, and configured to acquire operating data of each of the plurality of hydroelectric devices; an inspection subsystem, connected with the data acquiring subsystem, and configured to acquire the operating data of each of the plurality of hydroelectric devices from the data acquiring subsystem, and determine whether an abnormal event occurs in an observation item of a hydroelectric plant inspection scenario according to the operating data; an operation optimizing subsystem, connected with the data acquiring subsystem, and configured to acquire the operating data of each of the plurality of hydroelectric devices from the data acquiring subsystem, and determine a startup sequence of hydroelectric generating units under different working conditions according to the operating data; and a condition based maintenance (CBM) support subsystem, connected with the data acquiring subsystem, and configured to acquire the operating data of each of the plurality of hydroelectric devices from the data acquiring subsystem, determine a current operating state of an electrical device according to the operating data, and support condition based maintenance of the electrical device. In embodiments of the present disclosure, the data acquiring subsystem can collect the operating data of each hydroelectric device in the hydroelectric plant. The inspection subsystem determines whether an abnormal event occurs in the observation item of the hydroelectric plant inspection scenario according to the operating data. The operation optimizing subsystem determines the startup sequence of the hydroelectric generating units under different working conditions according to the operating data. The CBM support subsystem determines the D current operating state of the electrical device based on the operating data. Therefore, in the operation and maintenance process of the hydroelectric plant, the remote intelligent inspection on the hydroelectric device can be carried out through the inspection subsystem, which improves the inspection accuracy and reduces labor costs; the startup sequence of the hydroelectric generating units may be optimized through the operation optimizing subsystem, which improves the economic benefits of the hydroelectric plant; and the current operating state of the electrical device can be determined through the CBM support subsystem, which makes it possible to evaluate the operation of the electrical device, so as to ensure the service life of the electrical device. As a result, intelligent operation and maintenance of the hydroelectric plants is realized, and the economic efficiency and service life of the hydroelectric plant are improved. Additional aspects and advantages of embodiments of present disclosure will be given in part in the D following descriptions, become apparent in part from the following descriptions, or be learned from the practice of the embodiments of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS The above and/or additional aspects and advantages of embodiments of the present disclosure will become apparent and more readily appreciated from the following descriptions made with reference to the drawings, in which: FIG. 1 is a schematic flowchart of a method for operating state analysis and early warning of an auxiliary device of a hydroelectric station according to an embodiment of the present disclosure; FIG. 2 is a schematic flowchart for early warning of the operation of a governor oil pump according to an embodiment of the present disclosure; FIG. 3 is a schematic diagram showing a time period of manual control according to an embodiment of the present disclosure; FIG. 4 is a schematic flowchart of a method for operating state analysis and early warning of an auxiliary device of a hydroelectric station according to an embodiment of the present disclosure; FIG. 5A is a schematic diagram showing a functional structure of an auxiliary device state intelligent analysis system according to an embodiment of the present disclosure; FIG. 5B is a schematic structure diagram showing operating rule analysis functions of an auxiliary device state intelligent analysis system according to an embodiment of the present disclosure; FIG. 5C is a schematic diagram showing an analysis list structure of an auxiliary device state intelligent analysis system according to an embodiment of the present disclosure; FIG. 6 is a schematic block diagram of an apparatus for operating state analysis and early warning of an auxiliary device of a hydroelectric station according to an embodiment of the present disclosure; and FIG. 7 is a schematic block diagram of an illustrative computer device suitable for implementing embodiments of the present disclosure. FIG. 8 is a schematic block diagram of a decision support system for hydroelectric production according to embodiments of the present disclosure; FIG. 9 is a schematic flowchart of a method for extracting and constructing electromagnetic vibration state samples of a hydroelectric generating unit according to embodiments of the present disclosure; FIG. 10 is a schematic diagram of an electromagnetic vibration characteristic of a hydroelectric generating unit according to embodiments of the present disclosure; FIG. 11 is a schematic flowchart of an operating method of a knowledge center subsystem according to embodiments of the present disclosure; FIG. 12 is a schematic flowchart of an operating method of an inspection subsystem according to embodiments of the present disclosure; FIG. 13 is a schematic flowchart of an operating method of an operation optimizing subsystem according to embodiments of the present disclosure; FIG. 14a is a two-dimensional distribution diagram of guide vane opening data according to embodiments of the present disclosure; FIG. 14b is a schematic diagram showing a first load interval according to embodiments of the present disclosure; FIG. 14c is a schematic diagram showing a step length of the first load interval according to embodiments of the present disclosure; FIG. 15 shows a load distribution table according to embodiments of the present disclosure; and FIG. 16 is a schematic flowchart of an operating method of a CBM support subsystem according to embodiments of the present disclosure.

DETAILED DESCRIPTION Embodiments of the present disclosure will be described in detail below, examples of which are shown in D the accompanying drawings, in which the same or similar elements and elements having same or similar functions are denoted by like reference numerals throughout the descriptions. The embodiments described herein with reference to the accompanying drawings are explanatory and illustrative, which are used to generally understand the present disclosure, and shall not be construed to limit the present disclosure. In addition, for ease of description, the accompanying drawings only show components related to the present disclosure, not all the structure. On the contrary, embodiments of the present disclosure include all changes, modifications and equivalents falling within the spirit and scope of the appended claims. It should be noted that an executive subject of a method for operating state analysis and early warning of an auxiliary device of a hydroelectric station according to embodiments of the present disclosure may be an apparatus for operating state analysis and early warning of an auxiliary device of a hydroelectric station, and the D apparatus may be implemented in the form of software and/or hardware, and the apparatus may be configured in an electronic device, which may include, but is not limited to, a terminal, a server, or the like. As described in the background part, the statistical analysis of the operating rule of the auxiliary device is mainly performed manually, which is subjected to a certain degree of subjectivity, and costs a lot of manpower and time. In addition, the actual study found that for the auxiliary device, its operating time duration and start-stop time interval under automatic control should not vary significantly, if the operating time duration is distinctly too long, or the start-stop time interval is distinctly too short, it indicates that there may be an abnormality, such as oil leakage in an oil tank or gas leakage in a gas tank. Therefore, the analysis of the operating time duration and the start-stop time interval of the auxiliary device is of great significance to early warning of the auxiliary device. However, the related art only involves the analysis of abnormal operations of the device, but does not study the abnormal situation of the start-stop time of the auxiliary device under different working conditions. In view of this, embodiments of the present disclosure provide a method for operating state analysis and early warning of an auxiliary device of a hydroelectric station. FIG. 1 is a schematic flowchart of a method for operating state analysis and early warning of an auxiliary device of a hydroelectric station according to an embodiment of the present disclosure. Referring to FIG. 1, the method includes the following actions. In S101, start-stop time data of a target auxiliary device of the hydroelectric station and working condition data of a generator unit in a preset time period are obtained. A device in the hydroelectric station for ensuing the safe and economical operation of the hydroelectric generating unit may be called an auxiliary device. In some embodiments, the auxiliary device may be, for example, a gas machine of a gas system of the hydroelectric station, a governor oil pump of the hydroelectric station, a water pump of a water supply and drainage system of the hydroelectric station, or any other possible auxiliary device, which is not limited in embodiments of the present disclosure. An auxiliary device for which an early warning currently needs to be performed may be called a target auxiliary device, which may be, for example, any one of the above auxiliary devices. The method for operating state analysis and early warning of an auxiliary device of a hydroelectric station will be illustrated below with reference to an example where the target auxiliary device is a governor oil pump. The start-stop time data is historical data of a starting time and a stopping time of the target auxiliary device (such as the governor oil pump) in the preset time period. The start-stop time data may include, for example, the starting time and a corresponding state (i.e., a starting state) as well as the stopping time and a corresponding state (i.e., a stopping state). For example, the start-stop time data may have the following format: started at 2022.01.01-12:00:00, stopped at 2022.01.01-13:00:00, started at 2022.01.01-18:00:00.... It can be understood that, the target auxiliary device may be started and stopped multiple times within the preset time period, and correspondingly, there may be multiple start-stop time data. The preset time period may be flexibly set according to an actual application scenario, for example, the preset time period may be 10 hours, which is not limited in the present disclosure. The working condition data is used to describe a working state of the generator unit of the hydroelectric station in the preset time period, and may be classified into, for example, a power generating state and a shutdown state. A method for determining the working condition of the generator unit is as that: when a guide vane of the generator unit is not in a fully closed position and a rotational speed is greater than or equal to 2, the generator unit is in the power generating state; otherwise, it is in the shutdown state. FIG. 2 is a schematic flowchart for early warning of the operation of a governor oil pump according to an embodiment of the present disclosure. As shown in FIG. 2, in practical application, the operating state of the target auxiliary device (such as the governor oil pump) may be monitored to obtain measured data, and in embodiments of the present disclosure, the start-stop time data may be determined according to the measured data. In practical application, operation early warning of the of the governor oil pump is performed using a model, which performs a batch of calculation on a hydroelectric station, and a time interval for each calculation of the D model is 10 hours (i.e., the preset time period). Considering that different types of oil pumps of the governor have different rules in the terms of the operating time duration and the start-stop time interval, in order to determine device data for a long operating or shutdown time, it is necessary to store historical operating data of the device. The specific method is as follows: (1) The state data time of the governor oil pump finally saved after the last operation is queried from the PostgreSQL database as a data starting time of the current calculation, and the operating time duration of the calculation model is a data ending time. (2) State data (i.e., the starting state or the stopping state) of the governor oil pump from the data starting time to the data ending time is queried from HBase. (3) The last two starting state data and the last two stopping state data reported by each device are queried D from the PostgreSQL database. (4) For the same device, the data obtained in step (2) and the data obtained in step (3) are combined and sorted, and recorded as data to be mined. (5) The mining data is subjected to calculations according to functional requirements, and calculation results and annotations are stored in respective testing points. (6) The last two starting state data and the last two stopping state data reported by each device in the data to be mined are stored in the PostgreSQL database, so that the start-stop time data may be obtained from the PostgreSQL database. In some embodiments, for the operation of the target auxiliary device, there are two control modes, i.e., an automatic control mode and a manual control mode, and there exists a deviation between the start-stop time data generated under the automatic control and that generated under the manual control, which affects the accuracy of state warning of the auxiliary device. For this, in embodiments of the present disclosure, the data generated under the manual control may be filtered off from the start-stop time data. In practical applications, the start-stop time data may include control mode labels, for example, the label for the manual control is 0, and the label for the automatic control is 1. In this way, the data generated under the manual control may be filtered off according to the labels. Specifically, FIG. 3 is a schematic diagram showing a time period of manual control according to an embodiment of the present disclosure. As shown in FIG. 3, in the process of determining a time range of manual control, for a time period from to to t2, there only exists the manual control state 0 at a moment tl, and the automatic control state 1 does not appear, then a starting moment of the time period of manual control is set to the moment tl, and an ending moment of the time period of manual control is set to the last moment t2, i.e., the current moment. For a time period from t2 to t4, there only exists the automatic control state 1 at a moment t3, then the starting moment t2 of the input data is determined as the starting moment of the time period of manual control, and the moment t3 of the automatic control state 1 is determined as the ending moment of the time period of manual control. In this way, the time period of manual control in the start-stop time interval is determined. Further, the time period of manual control in the start-stop time interval is filtered off. Further, a logical judgment may be added in the process for determining the final start-stop time data. Specifically, it is possible to determine a union of all the time periods of manual control of the governor oil pump in the preset time period, and then take an intersection of the union and the start-stop time data. If the intersection is empty, it means that there is no time period of manual control in the start-stop time data, and the start-stop time data may be output for subsequent calculation. In S102, a plurality of operating time durations and/or a plurality of start-stop time intervals of the target auxiliary device under different working conditions are determined according to the start-stop time data and the working condition data. In embodiments of the present disclosure, after the start-stop time data and the working condition data are obtained, the plurality of operating time durations and/or the plurality of start-stop time intervals of the target auxiliary device under different working conditions may be determined according to the start-stop time data and the working condition data. Specifically, as shown in FIG. 2, firstly, the plurality of operating time durations and/or the plurality of start-stop time intervals are determined according to the start-stop time data. The operating time duration is used to describe a time difference from a starting state of the target auxiliary device to a first stopping state after the starting state. For example, for the governor oil pump, when it is determined that the governor oil pump is in the stopping state, assuming that the oil pump is started for the first time at a moment Tstart, after started, the oil pump is stopped for the first time at a moment Tstop, the operating time duration Tmn of the oil pump may be expressed as: Trun = Tstop - Tstart. For example, the above start-stop time data has the following format: started at 2022.01.01-12:00:00, stopped at 2022.01.01-13:00:00, started at 2022.01.01-18:00:00..., then one of the operating time duration may be expressed as: 2022.01.01-13:00:00 - 2022.01.01-12:00:00= 1 hour. It can be understood that a plurality of operating time durations Tn may be determined according to the start-stop time data. It should be further illustrated that when there are a plurality of target auxiliary devices, for example, each ) generator unit of a hydroelectric station is equipped with 3 governor oil pumps, the determination of the operating time duration according to embodiments of the present disclosure only considers the operating time duration of a single target auxiliary device. The start-stop time interval is used to describe a time difference from a stopping state of the target auxiliary device to a first starting state after the stopping state. For example, the determination of the start-stop time interval considers cooperative work of multiple governor oil pumps. For example, each generator unit of a hydroelectric station is equipped with 3 governor oil pumps, a set {Pi, P2, ... } of the multiple governor oil pumps is regarded as a logical pump P*, and a working state S(P*,t) of the logical pump P* at a moment t is determined as follows: 0, if{PL Pz ... all are under the stopping state S(P*,t= 1, if any of {Pr, P .. }is under the starting state

D When the logic pump P* is in the operating state, i.e., S(P*,t) = 1, assuming that the logic pump P* is stopped for the first time at a moment ti, i.e., S(P*,t) = 0, the moment ti is recorded. After the logic pump P* is stopped, it is started for the first time at a moment t2, i.e., S(P*,t) = 1, then the start-stop time interval Tinty may be expressed as: TinN = t2 - t. For example, the above start-stop time data has the following format: started at 2022.01.01-12:00:00, stopped at 2022.01.01-13:00:00, started at 2022.01.01-18:00:00..., then one of the start-stop time intervals may be expressed as 2022.01.01-18:00:00 - 2022.01.01-13:00:00 = 5 hours. It can be understood that a plurality of start-stop time intervals Tint may be determined according to the start-stop time data. Further, as shown in FIG. 2, in embodiments of the present disclosure, based on the working condition data of the generator unit (i.e., the power generating state or the shutdown state), the plurality of operating time durations may be classified in accordance with the different working conditions, for example, the plurality of operating time durations may be classified into the operating time duration under the power generating state and the operating time duration under the shutdown state. For example, the generator unit is in the power generating state during partial operating time duration, so the partial operating time duration may be regarded as the operating time duration under the power generating state. Similarly, in embodiments of the present disclosure, the plurality of start-stop time intervals may be classified into start-stop time intervals under the power generating state and start-stop time intervals under the shutdown state. It can be understood that in embodiments of the present disclosure, the operating time duration under the power generating state and the operating time duration under the shutdown state may be determined, or the start-stop time intervals under the power generating state and the start-stop time intervals under the shutdown state may be determined, or the operating time duration under the power generating state, the operating time duration under the shutdown state, the start-stop time intervals under the power generating state and the start-stop time intervals under the shutdown state may be determined, which is not specifically limited herein. In an example, the operating time duration and/or the start-stop time intervals of the target auxiliary device (such as the governor oil pump) under different working conditions are shown in Table 1: Table 1 input time state operational state quantity of oil pump 1 of generator unit 1 operating time duration of oil pump 1 of generator unit 1 operating time operating time duration of oil pump 1 under the duration power generating state of generator unit 1 operating time duration of oil pump 1 under the output shutdown state of generator unit 1 start-stop time interval of oil pump of generator unit start-stop time start-stop time interval of oil pump under the power interval generating state of generator unit 1 start-stop time interval of oil pump under the shutdown state of generator unit 1 In practical applications, the operating time duration and/or the start-stop time interval may be determined using a data mining algorithm for the operating rule of the governor oil pump, such as the algorithm for determining the start-stop time interval and the operating time duration as described above, where the start-stop time data and the working condition data are used as inputs of the algorithm, and the operating time duration and/or the start-stop time interval are determined as outputs. It should be noted that for some auxiliary devices, the starting and stopping states are not distinguished, that is, the operating time duration and/or the start-stop time interval are determined without considering the working conditions (start-up or shutdown) of the generator unit. In S103, a health value range of the target auxiliary device is determined according to the plurality of operating time durations and/or the plurality of start-stop time intervals. D The health value range is used to describe an operating time range or a start-stop time interval range of the target auxiliary device during normal operation. For example, the health value range of the operating time duration of the governor oil pump under the power generating state may be [10 minutes, 30 minutes], and if the actual operating time duration of the governor oil pump exceeds the health value range, there may be a fault. It can be understood that different working conditions may correspond to different health value ranges, that is, the operating time duration under the power generating state, the operating time duration under the shutdown state, the start-stop time interval under the power generating state, and the start-stop time interval under the shutdown state may correspond to different health value ranges, respectively. In some embodiments of the present disclosure, the health value range corresponding to at least one of the operating time duration under the power generating state, the operating time duration under the shutdown state, the start-stop time interval under the D power generating state, and the start-stop time interval under the shutdown state may be determined. In some embodiments, in the process of determining the health value range, the plurality of operating time durations and/or the plurality of start-stop time intervals are sorted to obtain a sorting result; a target position in the sorting result is determined according to a data volume of the operating time duration and/or the start-stop time intervals, and respective confidences of the operating time duration and/or the start-stop time intervals; a time value of the target position is rounded, and an up health limit and a down health limit are determined according to the rounded time value; and determining the health value range according to the up health limit and the down health limit. The data volume is used to describe the number of the plurality of operating time durations or the plurality of start-stop time intervals, and may be represented by N. The confidence may also be called a confidence interval CI (%), the operating time duration and the start-stop time interval under different working conditions may have respective confidences, which may be flexibly set according to the actual data, and is not particularly limited herein. The target position is a specified sequential position in the above sorting result, which may be determined according to the data volume N and the confidence CI, and is not particularly limited herein. In the following, illustrations are made with reference to examples where the health value range corresponding to the operating time duration under the power generating state is determined. Specifically, in some embodiments, a data set of the plurality of operating time durations under the power generating state may be represented by DATA, and the data volume of the operating time duration may be represented by N. In some embodiments, the plurality of operating time durations in the data set DATA may be sorted in an order from large to small or from small to large to obtain a sorting result. Further, a target position in the sorting result is determined according to the data volume N of the operating time duration and/or the confidence CI corresponding to the operating time duration, and the target position may be expressed as (N*(1-CI)/2), for example. Further, a time value of the target position may be rounded, and an up health limit hvup and a down health limit hvdown may be determined according to the rounded time value. A formula for determining the up health limit may be expressed as: the up health limit hv up = ROUND(N*(1-CI)/2), where (N*(]-CI)/2) also represents the time value of the target position, and ROUND represents a rounding function. A formula for determining the down health limit may be expressed as: the down health limit hvdown = N- ROUND(N*(1-CI)/2). Alternatively, in some embodiments, the down health limit may be determined first, and then the up health limit is determined according to the down health limit, which is not limited herein. Regarding the rounding of time values of the up health limit and the down health limit, in some embodiments, the up health limit is rounded up, and the down health limit is rounded down. For example, if the time value of the target position is 4.3, the up health limit is rounded up to 5, and the down health limit is rounded down to 4. Further, a health value range is determined according to the up health limit and the down health limit, and the health value range may be expressed as [down health limit hv down, up health limit hvup]. In some embodiments, there may exist errors in the health value ranges corresponding to different target auxiliary devices (e.g., different models of governor oil pumps). In this case, a reasonable margin may be set for the health value range to eliminate some errors. In some embodiments, an up margin for the up health limit hvup may be determined, and the up margin may be expressed as mar up. A down margin for the down health limit hvdown may be determined, and the down margin may be expressed as mar down. Initial values of the up margin mar up and the down margin mardown are zero by default, which may be flexibly set according to actual application scenarios, and is not particularly limited herein. Further, the health value range is determined according to the up health limit, the up margin, the down health limit and the down margin. That is, an up health D limit hvup after added with the up margin mar up may be expressed as: up health limit hv up + up margin mar up; similarly, a down health limit hvdown after added with the down margin mardown may be expressed as: down health limit hvdown + down margin mardown, then the health value range may be expressed as

[down health limit hvdown + down margin mar down, up health limit hv_up + up margin mar up]. In this way, in some embodiments, corresponding margin values are set for the up health limit and the down health limit, so that the health threshold of the auxiliary device has a certain tolerance limit, i.e., adding the health margins to make the health performance have a certain tolerance limit, thereby reducing false alarm. It can be understood that even though the above examples are described with respect to the determination of the health value range corresponding to the operating time duration under the power generating state, the health value ranges corresponding to the operating time duration under the shutdown state, the start-stop time interval D under the power generating state, and the start-stop time interval under the shutdown state may be determined in the same way as the determination of the health value range corresponding to the operating time duration under the power generating state, which will not be elaborated here. In S104, an early warning condition is set according to the health value range, so as to perform an early warning for a current operating situation of the target auxiliary device. The current operating situation is used to describe a current real-time operating situation of the target auxiliary device, and may include, for example, real-time operating time duration and a real-time start-stop time interval when the generator unit is in the power generating state, or real-time operating time duration and the real-time start-stop time interval when the generator unit is in the shutdown state, etc., which are not particularly limited herein.

In some embodiments, early warning conditions may be set according to the health value ranges corresponding to the operating time duration under the power generating state, the operating time duration under the shutdown state, the start-stop time interval under the power generating state, and the tart-stop time interval under the shutdown state, respectively, and further, early warning may be performed for the current operating situation of the target auxiliary device according to the early warning conditions. For example, if the current operating situation exceeds a corresponding early warning condition, it indicates that there exists a fault in the current operation of the target auxiliary device, so that an alarm will be given. In some embodiments, multi-level early warning conditions may be set according to the health value range, including, for example, a first-level early warning condition (first-level early warning), a second-level early warning condition (second-level early warning), and so on. The first-level early warning is related to, for example, an accident point that requires immediate shutdown or power failure to process or an event that is highly concerned, and may be indicated by a red signal; and the second-level early warning is related to, for example, a fault point that requires an immediate emergency response measure or an event that requires close attention, and may be indicated by a yellow signal. In some embodiments of the present disclosure, the confidence includes a first confidence and a second confidence that are different from each other. That is, different confidence values may be set. Therefore, according to the first confidence and the second confidence, respective health value ranges may be determined, that is, different up health limits hvup and down health limits hvdown are determined. In some embodiments of the present disclosure, setting the early warning condition according to the health value range includes: setting the first-level early warning condition to be greater than or equal to an up health limit of the health value range corresponding to the first confidence, and/or less than or equal to a down health limit of the health value range corresponding to the first confidence. For example, the target auxiliary device is a governor oil pump, a health value range corresponding to the operating time duration under the starting state determined according to the first confidence may be expressed as a first health value range, and its up health limit and down health limit may be expressed as: hvup] and hvdown], respectively. In setting the early warning condition according to the health value range, two first-level early warning conditions may be configured, one of which is configured to be greater than or equal to the hv_up], and the other one of which is configured to be less than or equal to the hvdown]. That is, if the operating time duration of the target auxiliary device under the starting state of the generator unit is greater than or equal to the up health limit hv_up], or less than or equal to the down health limit hvdown], an alarm will be issued. Similarly, in some embodiments of the present disclosure, a health value range corresponding to the operating time duration under the starting state determined according to the second confidence may be expressed as a second health value range, and its up health limit and down health limit may be expressed as: hvup2 and hvdown2, respectively. In setting the early warning condition according to the health value range, two second-level early warning conditions may be configured, one of which is configured to be greater than or equal to the hvup2, and the other one of which is configured to be less than or equal to the hvdown2. That is, if the operating time duration of the target auxiliary device under the starting state of the generator unit is greater than or equal to the up health limit hvup2, or less than or equal to the down health limit hvdown2, an alarm will be issued. Therefore, in embodiments of the present disclosure, different early warning levels may be set to give D early warning to different accidents, thereby ensuring the operation efficiency of the hydroelectric station. In embodiments of the present disclosure, by obtaining the start-stop time data of the target auxiliary device of the hydroelectric station and the working condition data of the generator unit in the preset time period; determining the plurality of operating time durations and/or the plurality of start-stop time intervals of the target auxiliary device under different working conditions according to the start-stop time data and the working condition data; determining a health value range of the target auxiliary device according to the plurality of operating time durations and/or the plurality of start-stop time intervals; and setting the early warning condition according to the health value range so as to perform the early warning to the current operating situation of the target auxiliary device, analysis can be made in combination with different working conditions of the generator unit and the operating rule of the target auxiliary device to reasonably and accurately determine the health value D range of the target auxiliary device, thereby improving the early warning effect on the operation of the target auxiliary device. FIG. 4 is a schematic flowchart of a method for operating state analysis and early warning of an auxiliary device of a hydroelectric station according to an embodiment of the present disclosure. Referring to FIG. 4, the method includes the following actions. In S401, start-stop time data of a target auxiliary device of the hydroelectric station and working condition data of a generator unit in a preset time period are obtained. In S402, a plurality of operating ranges and/or a plurality of start-stop ranges of the target auxiliary device under different working conditions are determined according to start/stop state identifiers and the working condition data.

In embodiments of the present disclosure, the start-stop time data may include start/stop state identifiers. For example, "0" may be used to indicate that the target auxiliary device is stopped, and "1" may be used to indicate that the target auxiliary device is started. Table 2 shows the start-stop time data with the start/stop state identifiers as follows: Table 2 start-stop time data start/stop state identifier 2021-10-31 16:23:18 0 2021-10-31 16:21:43 1 2021-10-31 10:30:00 0 2021-10-3108:19:58 0 2021-10-31 08:18:42 1 In some embodiments of the present disclosure, in the action of determining the plurality of operating time durations and/or the plurality of start-stop time intervals of the target auxiliary device under different working conditions according to the start-stop time data and the working condition data, a plurality of operating ranges and/or a plurality of start-stop ranges of the target auxiliary device under different working conditions may be determined first according to start/stop state identifiers and the working condition data. The operating range is used to describe a time range from a starting state to a stopping state of the target auxiliary device, and correspondingly, the start-stop range is used to describe a time range from a stopping state to a starting state of the target auxiliary device. Specifically, in embodiments of the present disclosure, a plurality of operating ranges and/or a plurality of start-stop ranges may be determined first according to the start/stop state identifiers. For example, "01" indicates that the target auxiliary device is from a stopping state to a starting state, and may be used as a start-stop range; and "10" indicates that the target auxiliary device is from a starting state to a stopping state, and may be used as an operating range. Further, in embodiments of the present disclosure, the plurality of operating ranges and/or the plurality of start-stop ranges may be divided into operating ranges and start-stop ranges under different working conditions according to the working condition data of the generator unit. It should be noted that this step does not involve the determination of lengths of time ranges. It should be noted that in embodiments, since a part of the start-stop data may be lost during data transmission, abnormal data such as "001" or "110" may exist in the identifiers of the operating ranges and start-stop ranges. In some embodiments, in order to ensure both the traceability of the determined result and the accuracy of the subsequent statistical analysis, a range that has an abnormal start/stop state identifier may be marked in the plurality of operating ranges and/or the plurality of start-stop ranges, and the abnormal data marked may be filtered off in the subsequent action. Specifically, for the operating time duration determined according to "110" and the start-stop time interval determined according to "001", the determined results will be marked with q = 11. Since the determination of the start-stop time interval needs to consider the states of multiple target auxiliary devices (for example, multiple governor oil pumps), there may exist a case where the states of the multiple target auxiliary devices are indicated as 0 at the same time. Starting at this time, if the state data of any of the governor oil pumps is "001", the determined result of the start-stop time interval will be marked with q = 11, and other data will be marked with q = 0. In S403, the plurality of operating time durations and/or the plurality of start-stop time intervals are determined according to the start-stop time data and the plurality of operating ranges and/or the plurality of start-stop ranges. In other words, in some embodiments of the present disclosure, after the plurality of operating ranges and/or the plurality of start-stop ranges are determined, the plurality of operating time durations corresponding to the D plurality of operating ranges and/or the plurality of start-stop time intervals corresponding to the plurality of start-stop ranges may be determined according to the start-stop time data. For example, start-stop time data corresponding to an operating range ("10") includes 2021-10-31 16:21:43 (1) and 2021-10-31 16:23:18 (0), and the operating time duration corresponding to this operating range is determined according to start-stop time data as: 2021-10-31 16:23:18 - 2021-10-31 16:21:43 = 95 seconds. Therefore, with embodiments of the present disclosure, the plurality of operating time durations and the plurality of start-stop time intervals in the start-stop time data may be quickly determined according to the start/stop state identifiers, and abnormal time data may be filtered off according to the identifiers, thereby improving the accuracy of subsequent warnings. In S404, a health value range of the target auxiliary device is determined according to the plurality of D operating time durations and/or the plurality of start-stop time intervals. In S405, an early warning condition is set according to the health value range, so as to perform an early warning for a current operating situation of the target auxiliary device. For the descriptions of S404 to S405, reference may be made to the foregoing embodiments for details, which will not be elaborated here. In embodiments of the present disclosure, by obtaining the start-stop time data of the target auxiliary device of the hydroelectric station and the working condition data of the generator unit in the preset time period; determining the plurality of operating time durations and/or the plurality of start-stop time intervals of the target auxiliary device under different working conditions according to the start-stop time data and the working condition data; determining a health value range of the target auxiliary device according to the plurality of operating time durations and/or the plurality of start-stop time intervals; and setting the early warning condition according to the health value range so as to perform the early warning to the current operating situation of the target auxiliary device, analysis can be made in combination with different working conditions of the generator unit and the operating rule of the target auxiliary device to reasonably and accurately determine the health value range of the target auxiliary device, thereby improving the early warning effect on the operation of the target auxiliary device. In addition, with embodiments of the present disclosure, the plurality of operating time durations and the plurality of start-stop time intervals in the start-stop time data may be quickly determined according to the start/stop state identifiers, and abnormal time data may be filtered off according to the identifiers, thereby improving the accuracy of subsequent warnings. It should be noted that embodiments of the present disclosure also provide an auxiliary device state intelligent analysis system based on data mining, which is able to intelligently analyze operating rules and performances of pumps and gas machines involved in an oil system, a gas system, and a water system of a hydroelectric plant, and perform early warning, analysis and diagnosis on abnormal cases of the auxiliary device, such as too long or too short operating time duration and/or start-stop time interval, start/stop setting value drift, water-inflow-oil, oil leakage and the like of the auxiliary device. FIG. 5A is a schematic diagram showing a functional structure of an auxiliary device state intelligent analysis system according to an embodiment of the present disclosure. As shown in FIG. 5A, the intelligent analysis system including the following functions: 1) oil system-oil pump; 2) gas system-gas machine; 3) water system-water pump; 4) auxiliary device state analysis report; 5) Auxiliary device state analysis statement. In order to realize the comprehensive analysis of states of the auxiliary device of the hydroelectric station, the auxiliary device state intelligent analysis system based on data mining comprehensively considers the analysis requirements of the oil pump, the water pump and the gas machine involved in auxiliary devices of the oil system, the water supply and drainage system and the gas system of the hydroelectric station. FIG. 5B is a schematic structure diagram showing operating rule analysis functions of an auxiliary device state intelligent analysis system according to an embodiment of the present disclosure. As shown in FIG. 5B, the analysis functions include: time trend analysis, a statistical value and health value determining function, a normal analysis function, a value listing function, a detail capturing and enlarging function, an automatic report function and any other possible functions, which will not be particularly limited herein. 1. The function of time trend analysis is as follows: Taking the governor oil pump as an example, if the operating time duration is too long, the problem may be that the efficiency of the oil pump declines, and if the operating time duration is too short, there may exist the problem of setting value drift. When mining the operating rule of the auxiliary device from the operation and start-stop time intervals of the auxiliary device, based on whether the operation of the auxiliary device is D associated with the working conditions of the generator unit, a plurality of operating time durations and a plurality of start-stop time intervals may be classified into three types of data, i.e., a type of data without distinguishing the starting state and the shutdown state, a type of data under the starting state, and a type of data under the shutdown state, and automatic mining is performed for these three types of data. (1) Analysis list For the oil system, the gas system and the water system, the original data, the operating time duration, the start-stop time interval, the analog quantity, the control mode, and the working condition parameter of each device may be analyzed individually or associatively through selection of testing points to obtain the time trend performance and value performance. In the auxiliary device state intelligent analysis system, the testing points are sorted as follows. The original data is sorted according to serial numbers of pumps. The plurality of operating D time durations are sorted in an order of the operating time duration (without distinguishing the power generating state and the shutdown state), the operating time duration under the power generating state, and the operating time duration under the shutdown state. The plurality of start-stop time intervals are sorted in an order of the start-stop time intervals (without distinguishing the power generating state and the shutdown state), the start-stop time intervals under the power generating state, and the start-stop time intervals under the shutdown state. For the operating rule that is associated with the working conditions, there are working condition parameters in the analysis configuration for associated analysis. FIG. 5C is a schematic diagram showing an analysis list structure of an auxiliary device state intelligent analysis system according to an embodiment of the present disclosure. Referring to FIG. 5C where a water system of a hydroelectric plant is shown as an example, data of various drainage pumps in the water system may be analyzed. In addition, an analysis range may be determined by time selection, and the original data analysis can display an operating rule of a pump in a form of a ladder diagram on the system operation interface. (2) Trend analysis The operating time duration of any auxiliary device (such as the governor oil pump) can be analyzed, and operating time duration trends of the auxiliary devices can be analyzed overall. On the operation interface, move the mouse to the operating time duration trend graph to display the operating time duration of different pumps at the same time (3) Association Analysis The association analysis function provides the association analysis of the operating rule of the auxiliary device with the operating conditions of the generator unit, the control mode of the auxiliary device, and the analog quantity of the auxiliary device control, and supports the analysis of the operating rule of the auxiliary device and the abnormal analysis of the star/stop setting values, and the like business. By using testing points of the control modes, it is possible to analyze the causes of special values of the operating time duration and the start-stop time interval. For example, when the control mode is switched to a manual control mode on site, the operating time duration and the start-stop time interval of the water pump both show abnormally low values. Association analysis with the working condition parameters can compare the numerical performances of the operating time duration and the start-stop time interval of the relevant oil pump in the starting and stopping states or in the process of changing the opening of the guide vane. Association analysis with the analog quantity can analyze the numerical performances of related analog quantities when the auxiliary device is started and stopped, and analyze and diagnose abnormalities of the auxiliary device, such as start/stop setting value drift. (4) Check all The check all function can realize the selection of operational state quantities at all testing points of the oil pump, the gas machine and the water pump in the oil system, the gas system and water system with one click. When the query is clicked, a ladder diagram of the original data within the selected time range is displayed in the display area, which supports the judgment and analysis of the switch, the operating time duration, and the start-stop time intervals of different pumps. 2. Statistical value and health value determining function: The statistical data of the operating time duration and the start-stop time interval of the selected auxiliary device in the selected time period as well as the up and down health limits of the first health value range and the up and down health limits of the second health value range mined based on historical data may be displayed on the operation interface, and the time is displayed in the form of hours, minutes and seconds. The statistical value includes a maximum value, a minimum value and an average value. 3. Normal analysis function When a normal distribution analysis button on the operation interface is clicked, statistical analysis is performed on the value range and the frequency of occurrence. For example, for the operating time duration of the governor oil pump, when the normal distribution analysis button is clicked, a normal analysis graph is displayed. 4. Value listing function D The value listing function supports the trend analysis on the data of the selected pump or gas machine, the browse of the specific values of the operational state quantity, the operating time duration and the start-stop time interval of the auxiliary device, and the export of the excel. The image exporting button on the right side of the value list can realize the export of the time trend graph in the display area. The confidence, the up margin, the down margin, the determination of the health value, and the write-in of the first-level early warning and the second-level early warning are configuration functions that can be viewed by maintenance authority. The data listing diagram may include data lists of the original data obtained under the operation state 0/1, the operating time duration and the start-stop time interval arranged in order, and display the maximum, minimum and average statistical values under the operating time duration and the start-stop time interval. 5. Shared Y-axis function D In order to facilitate the analysis of the alternate operation of several pumps, the operating time duration of different pumps of the same device, the operating time duration and the start-stop time interval under the power generating state/ shutdown state or other situations, a shared Y-axis function has been developed. When the related button is clicked, the original data, the operating time duration, the start-stop time interval and the analog quantity will shares the Y-axis. 6. Detail capturing and enlarging function The detail capturing and enlarging function can be used to analyze abnormal problems in terms of such as the timeliness of the change in oil level after the auxiliary device is started or the abnormal value of the analog quantity when the auxiliary device is started or stopped. If the selected analysis time is too long, the data volume of the working condition parameters or the analog quantity is too large, which will cause the browser to take up too much memory and get stuck. In this case, the detail capturing and enlarging function will be used to solve the incompatibility problem caused by the sparse analog quantity. On the operation interface, click the detail capturing and enlarging function button, move the mouse to the position to be enlarged in the figure, press and hold the left button mouse to select the content to be enlarged, and then release the mouse to realize the enlarged analysis of the selected range. When the restore button is clicked, the original state is restored. Based on this function, the check of the start-up values of the pump and the gas machine can be achieved, and the detailed analysis of the change in the analog quantity after an action of the auxiliary device can be carried out. 7. Auxiliary device state analysis report (1) Overall introduction With this function, reports on the trends and statistical values of the operating time duration and the start-stop time intervals of all pumps and gas machines involved in the oil system, the gas system, and the water system of a power plant can be generated automatically, and the reports in word format can be exported with one click, thereby reducing the time of the staff of the power plant to perform statistical analysis and report writing. In the analysis list, systems and the devices are grouped, testing points of the operating time duration and the start-stop time interval are selectable to meet the needs of various professional users. It should be noted that all the testing points are selected by default. (2) Report type Report types include daily, weekly, monthly, quarterly and annual reports, and the time range analyzed in a report is selectable as required. (3) Report example Taking auxiliary device state analysis report of Wuqiangxi power plant in December as an example, the automatically exported report is shown as follows: Report Name: auxiliary device state analysis report Power Plant: Wuqiangxi Power Plant Time range analyzed: 2021-12-0100:00:00 to 2021-12-3123:59:59 Report generation time: 2022-01-12 19:55:15 I. Oil system (i) Oil pressure device of a governor of generator unit 1 Operating time duration: Time trend graph, operating time duration statistical table Start-stop time interval: Start-stop time interval trend, start-stop time statistical table (ii) Oil pressure device of a governor of generator unit 2

I. Gas system

11. Water system Pump of #22 Dam Section Operating time duration: D Time trend graph, operating time duration statistical table Start-stop time interval: Start-stop time interval trend, start-stop time statistical table Operating time duration of a fire pump of a 110 m platform: Time trend graph, operating time duration statistical table Start-stop time interval: Start-stop time interval trend, start-stop time statistical table

8. Auxiliary device state analysis statement The functions of the auxiliary device status analysis report are as follows: D (1) Overall introduction This function mainly includes statistical analysis of the monthly average values of the operation and start-stop time intervals of the auxiliary device, as well as the change trend analysis of the monthly average values, and supports the monthly statistical analysis of the operating rule of the auxiliary device. Its operation interface may include for example analyzed time range, analysis list, monthly statistical value/trend chart switch, excel export and other functions. (2) Time selection Time selection shortcut options include: up to now, the last 6 months, and the last 12 months; the time range to be analyzed can be freely selected on the right side. (3) Analysis list

Options in the analysis list include the operating time duration and the start-stop time interval of the oil pump, the gas machine and the water pump in the oil system, the gas system and the water system, which can be selected by click it. (4) Analysis function In the display area of the auxiliary device state analysis statement, there are two options, i.e., statistical table and trend graph. 1) Statistical table: for each testing point, the average value of each month is determined through statistic analysis, which can be export to an excel table by clicking a table export button. 2) Trend graph: click on the trend graph to display the monthly average trend of the testing point. (5) Month-to-month function and year-on-year function Click on a row of the statistical table to display a histogram of the monthly average values. For example, click the row of the operating time duration of #1 pump of an inner corridor drainage system in an auxiliary dam, the monthly average values will be displayed in the form of a histogram. Month-to-month and year-on-year analysis can be performed. For example, click on the monthly year-on-year analysis, the year-on-year analysis of the currently analyzed month with respect to the same month of the previous year is performed. 9. Function configuration Function configuration of the auxiliary device state analysis system supports the underlying logic configuration and display configuration of a function, and mainly includes the following options: (1) standard name (group name, without parentheses); (2) sparseness of data of a testing point - needs to be sparse; (3) value list - needs to be displayed; (4) statistical value and health index lists - need to be displayed; (5) warning writing - can be written (display a warning line - icon maximum or minimum value); (6) original unit conversion on the chart title - do not fill if no conversion; (7) value conversion - do not fill if no conversion; chart Y-axis/1000/60, in other places, *1000*60, then being converted to hhmmss tooltip through upperCaseTime, the unit being directly emptied due to the value conversion; (8) which of up margins and down margins is divided by 60; (9) early warning state overview of the auxiliary device - needs to be hidden; (10) auxiliary device state analysis report - needs to be displayed; (11) auxiliary device state analysis statement needs to be hidden. In addition, embodiments of the present disclosure also provide an auxiliary device state index early warning system based on a health threshold. The early warning system is configured to obtain operating data and the start-stop time data of the auxiliary device based on original start-stop data of the auxiliary device, and obtain a health index threshold of an auxiliary device state using a health threshold automatic mining algorithm so as to perform early warning of the health index. There are two ways to display the function front end of the early warning system, including an early warning state overview of the auxiliary device and a health index early warning form of the auxiliary device, which can be switched. The status early warning overview of the auxiliary device is first displayed by default. (1) Early warning state overview of the auxiliary device The front end of the auxiliary device status overview displays the overall status of the auxiliary device in the form of an architecture diagram. The architecture data comes from the hierarchical relationship of the menu D bar of the auxiliary device state system. Consistent with the legend of the auxiliary device state analysis system, the first-level early warning box is displayed as red, and the second-level early warning box is displayed as yellow. In the early warning state overview interface, the state overview diagram can be zoomed by the scroll wheel can be movably displayed by pressing and holding the left mouse button. (2) Details on health index early warning of the auxiliary device Health index early warning functions of the auxiliary device are displayed in a list form, the existing early warning information is queried from an early warning output table, and the early warning information in the selected time range is displayed in a list form. The time range is selectable, and the early warning information of the last day is displayed by default. The field information displayed on the function interface includes serial number, power plant name, early warning type, device name, early warning parameter, early warning testing D point, value, early warning state, and early warning time. The first-level early warning state is displayed in red, and the second-level early warning state is displayed in yellow. FIG. 6 is a schematic block diagram of an apparatus for operating state analysis and early warning of an auxiliary device of a hydroelectric station according to an embodiment of the present disclosure. As shown in FIG. 6, the apparatus 60 includes an obtaining module 601, a first determining module 602, a second determining module 603, and an early warning module 604. The obtaining module 601 is configured to obtain start-stop time data of a target auxiliary device of the hydroelectric station and working condition data of a generator unit in a preset time period. The first determining module 602 is configured to determine a plurality of operating time durations and/or a plurality of start-stop time intervals of the target auxiliary device under different working conditions according to the start-stop time data and the working condition data. The second determining module 603 is configured to determine a health value range of the target auxiliary device according to the plurality of operating time durations and/or the plurality of start-stop time intervals. The early warning module 604 is configured to set an early warning condition according to the health value range, so as to perform an early warning for a current operating situation of the target auxiliary device. In some embodiments, the second determining module 603 is further configured to: sort the plurality of operating time durations and/or the plurality of start-stop time intervals to obtain a sorting result; determine a target position in the sorting result according to a data volume of the operating time duration and/or the start-stop time intervals, and respective confidences of the operating time duration and/or the start-stop time intervals; round a time value of the target position, and determine an up health limit and a down health limit according to the rounded time value; and determine the health value range according to the up health limit and the down health limit. In some embodiments, the second determining module 603 is further configured to: determine an up margin for the up health limit and determine a down margin for the down health limit; and determine the health value range according to the up health limit, the up margin, the down health limit, and the down margin. In some embodiments, the confidence includes a first confidence and a second confidence, and the early warning module 604 is further configured to: set a first-level early warning condition to be greater than or equal to an up health limit of a health value range corresponding to the first confidence, and/or less than or equal to a down health limit of the health value range corresponding to the first confidence; and set a second-level early warning condition to be greater than or equal to an up health limit of a health value range corresponding to the second confidence, and/or less than or equal to a down health limit of the health value range corresponding to the second confidence. In some embodiments, the start-stop time data includes start/stop state identifiers, and the first determining module 602 is further configured to: determine a plurality of operating ranges and/or a plurality of start-stop ranges of the target auxiliary device under different working conditions according to the start/stop state identifiers and the working condition data; and determine the plurality of operating time durations and/or the plurality of start-stop time intervals according to the start-stop time data and the plurality of operating ranges and/or the plurality of start-stop ranges. In some embodiments, the apparatus further includes a marking module, and the marking module is configured to mark a range with an abnormal start/stop state identifier in the plurality of operating ranges and/or the plurality of start-stop ranges. In some embodiments, the apparatus further includes a screening module, the screening module is configured to filter off data generated under manual control in the start-stop time data. In some embodiments, the target auxiliary device is any one of: a gas machine of a gas system of the hydroelectric station, a governor oil pump of the hydroelectric station, or a water pump of a water supply and drainage system of the hydroelectric station. In embodiments of the present disclosure, by obtaining the start-stop time data of the target auxiliary device of the hydroelectric station and the working condition data of the generator unit in the preset time period; determining the plurality of operating time durations and/or the plurality of start-stop time intervals of the target D auxiliary device under different working conditions according to the start-stop time data and the working condition data; determining a health value range of the target auxiliary device according to the plurality of operating time durations and/or the plurality of start-stop time intervals; and setting the early warning condition according to the health value range so as to perform the early warning to the current operating situation of the target auxiliary device, analysis can be made in combination with different working conditions of the generator unit and the start-stop time data of the target auxiliary device to reasonably and accurately determine the health value range of the target auxiliary device, thereby improving the early warning effect on the operation of the target auxiliary device. In some embodiments of the present disclosure, there is provided an electronic device, a readable storage medium and a computer program product. D In some embodiments, there is provided a computer program product having an instruction that, when executed by a processor, causes the processor to perform the method for operating state analysis and early warning of an auxiliary device of a hydroelectric station as described in any embodiment hereinbefore. FIG. 7 is a schematic block diagram of an illustrative computer device suitable for implementing embodiments of the present disclosure. The computer device 12 shown in FIG. 7 is only illustrated as an example, and should not be considered as any restriction on the functions and the usage ranges of embodiments of the present disclosure. As shown in FIG. 7, the computer device 12 may be represented as a general computing device form. Components of the computer device 12 may include, but are not limited to, one or more processors or processing units 16, a system memory 28, and a bus 18 connecting different system components (including the system memory 28 and the processing units 16). The bus 18 represents one or more of several types of bus architectures, including a memory bus or a memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any bus architecture of a variety of bus architectures. For example, these architectures include, but are not limited to, an industry standard architecture (hereinafter referred to as ISA) bus, a micro channel architecture (hereinafter referred to as MAC) bus, an enhanced ISA bus, a video electronics standards association (hereinafter referred to as VESA) local bus and a peripheral component interconnection (hereinafter referred to as PCI) bus. The computer device 12 typically includes a variety of computer system readable media. These media may be any available media accessible by the computer device 12 and include both volatile and non-volatile media, removable and non-removable media. The memory 28 may include a computer system readable medium in the form of volatile memory, such as a random access memory (hereinafter referred to as RAM) 30 and/or a high speed cache memory 32. The computer device 12 may further include other removable or non-removable, volatile or non-volatile computer system storage media. For example, the storage system 34 may be configured to read and write a non-removable and non-volatile magnetic medium (not shown in FIG. 7, commonly referred to as a "hard drive"). Although not shown in FIG. 7, a magnetic disk drive for reading and writing a removable and non-volatile magnetic disk (such as a "floppy disk") and an optical disk driver for reading and writing a removable and non-volatile optical disk (such as a compact disc read only memory (hereinafter referred to as CD-ROM), a digital video disc read only memory (hereinafter referred to as DVD-ROM) or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product. The program product has a set (such as, at least one) of program modules configured to perform the functions of various embodiments of the present disclosure. A program/utility 40 having a set (at least one) of program modules 42 may be stored in for example the memory 28. Such a program module 42 includes, but is not limited to, an operating system, one or more applications, other program modules, and program data. Each or a certain combination of these examples may include an implementation of a network environment. The program modules 42 generally perform the functions and/or methods as described in embodiments of the present disclosure. The computer device 12 may also communicate with one or more external devices 14 (such as, a keyboard, a pointing device, a display 24, etc.), one or more devices enabling a user to interact with the computer device 12, and/or any other devices (such as a network card, a modem, etc.) enabling the computer device 12 to communicate with one or more other computing devices. This communication can be performed via an input/output (I/O) interface 22. Also, the computer device 12 may communicate with one or more networks (such as a local area network (hereafter referred to as LAN), a wide area network (hereafter referred to as WAN) and/or a public network such as an Internet) through a network adapter 20. As shown in FIG. 7, the network adapter 20 communicates with other modules of the computer device 12 over the bus 18. It should be understood that, although not shown in FIG. 7, other hardware and/or software modules may be used in connection with the computer device 12. The hardware and/or software includes, but is not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tap drive and data backup storage system. The processing unit 16 is configured to execute various functional applications and data processing by D running programs stored in the system memory 28, for example, to implementing the method described above. In view of the technical problems mentioned in the background part that the inspection accuracy of the hydroelectric plant is not high, the service life of the device is low, and the economic benefits need to be improved, embodiments of the present disclosure provide a decision support system for hydroelectric production. The decision support system for hydroelectric production is applicable to any hydroelectric plant to control hydroelectric device. The method will be described below with reference to specific embodiments. It should be noted that the decision support system for hydroelectric production according to embodiments of the present disclosure can be implemented by software and/or hardware. The decision support system for hydroelectric production can run in electronic device, which may include, but is not limited to, a terminal, a server side and so on. D FIG. 8 is a schematic block diagram of a decision support system for hydroelectric production according to embodiments of the present disclosure. As shown in FIG. 8, generally, the decision support system for hydroelectric production may include: a data acquiring subsystem, an inspection subsystem, an operation optimizing subsystem, a condition based maintenance (CBM) support subsystem, a diagnosis subsystem, and a knowledge center subsystem. The data acquiring subsystem may include, for example, various sensors and image acquisition device, as well as any other possible data acquisition devices. The various sensors may include such as a temperature sensor, a humidity sensor, a vibration sensor, an odor sensor, and any other possible sensors, which is not specifically limited in the present disclosure. The data acquiring subsystem is connected with each of a plurality of hydroelectric devices (such as a hydroelectric generator, a governor, etc.) in a hydroelectric plant to obtain operating data of each of the plurality of hydroelectric devices. That is, various operating data of the hydroelectric plant is obtained through the data acquiring subsystem. The inspection subsystem is connected with the data acquiring subsystem, and is configured to acquire the operating data of each of the plurality of hydroelectric devices from the data acquiring subsystem, and determine whether an abnormal event occurs in an observation item of a hydroelectric plant inspection scenario according to the operating data. The hydroelectric plant inspection scenario may be any inspection scenario. For example, a generator, a hydroturbine or the like may be included in the inspection scenario. That is, the inspection can be performed on the generator or the hydroturbine. The observation item may refer to a state quantity, an analog quantity, or a video signal of a device in the hydroelectric plant, or various testing points of an online monitoring device, etc., which will not be limited in the present disclosure. That is, the inspection subsystem can determine whether a malfunction occurs in the state quantity, the analog quantity, or the video signal of the device in the hydroelectric plant, or in various testing points of the online monitoring device according to the operating data, and then determine whether a malfunction occurs in the hydroelectric device. The operation optimizing subsystem is connected with the data acquiring subsystem, and configured to acquire the operating data of each of the plurality of hydroelectric devices from the data acquiring subsystem, and determine a startup sequence of hydroelectric generating units under different working conditions according to the operating data, for example determine a startup sequence of the hydroelectric generating units under a steady-state condition or determine a startup sequence of the hydroelectric generating units under an unsteady-state condition, which will not be limited in the present disclosure. The CBM support subsystem is connected with the data acquiring subsystem, and configured to acquire the operating data of each of the plurality of hydroelectric devices from the data acquiring subsystem, determine a current operating state of an electrical device according to the operating data, and support condition based maintenance of the electrical device. For example, the CBM support subsystem may determine a temperature, a service life or the like of a transformer, so as to support the maintenance of the electrical device or other device. The diagnosis subsystem is connected with the data acquiring subsystem, and configured to acquire the operating data of each of the plurality of hydroelectric devices from the data acquiring subsystem, and construct a data sample corresponding to each of the plurality of hydroelectric devices according to the operating data. In some embodiments, the diagnostic subsystem may be configured to construct a data sample of an electromagnetic vibration state of a hydroelectric generating unit. FIG. 9 is a schematic flowchart of a method for extracting and constructing electromagnetic vibration state samples of a hydroelectric generating unit according to embodiments of the present disclosure. As shown in FIG. 9, the method includes the following actions. In S901, stability state samples of a hydroelectric generating unit in a transition process of excitation and voltage buildup is acquired. In embodiments of the present disclosure, since the guide vane opening and rotation speed are relatively stable in the transition process of excitation and voltage buildup, the transition process is a process where the response of vibration of the hydroelectric generating unit to an electromagnetic factor is clearest. In D embodiments of the present disclosure, samples of the transition process of excitation and voltage buildup of the hydroelectric generating unit are acquired and accumulated based on a state monitoring system of the hydroelectric generating unit and data of the state monitoring system. Specifically, the stability state samples of the hydroelectric generating unit in the transition process of excitation and voltage buildup may be acquired as follows. First, a starting condition for the transition process of excitation and voltage buildup is that an excitation current of the hydroelectric generating unit is greater than or equal to a preset value; and an ending condition for the transition process of excitation and voltage buildup is that an outlet switch of the generator is on. In an embodiment of the present disclosure, for example, the preset value may be 2. In the case where the preset value is 2, a starting time (TEUO) of the transition process of excitation and voltage buildup is a time D when the excitation current (Ec) is greater than or equal to 2; and an ending time (TEUl) of the transition process of excitation and voltage buildup is a time when the outlet switch of the generator changes from 0 to 1, where 0 indicates turning off, and 1 indicates turning on. Afterwards, all the vibration waveform signals in a storage period are acquired as the stability state samples. The storage period is a period from the starting time to the ending time. Specifically, all the vibration waveform signals and point value data in the storage period may be acquired as the stability state samples. It should be noted that based on this strategy, the samples of the transition process of excitation and voltage buildup are obtained each time the hydroelectric generating unit is started up, and the stability state samples of each hydroelectric generating unit in the transition process of excitation and voltage buildup are obtained each time the hydroelectric generating unit is started up, so massive samples are obtained in long-term operation of the hydroelectric generating units, which provide supports for extraction of a tendency of the stability state of the hydroelectric generating unit with the change of the excitation current, for establishment of a model for imbalance electromagnetic tension of the hydroelectric generating unit, and for statistical analysis on time-consuming of the transition process of excitation and voltage buildup, concurrent time-consuming, no-load guide vane opening, and the like, including but not limited to the following information in the transition process of excitation and voltage buildup when the hydroelectric generating unit is started up: the starting time and the ending time of the transition process of excitation and voltage buildup; the time-consuming of the transition process of excitation and voltage buildup, and the concurrent time-consuming; and the excitation current and the guide vane opening of the hydroelectric generating unit under a no-load condition. In S902, an electromagnetic vibration characteristic is extracted based on the stability state samples. In embodiments of the present disclosure, the stability state samples include the vibration waveform signals of the hydroelectric generating unit in the transition process of excitation and voltage buildup. Correspondingly, a process for extracting and constructing electromagnetic vibration state sample of the hydroelectric generating unit may for example include: performing Fourier transform analysis on the vibration waveform signals to extract the electromagnetic vibration characteristic in the vibration waveform signals. The electromagnetic vibration characteristic includes an electromagnetic vibration frequency characteristic, and/or a correlation characteristic between an electromagnetic vibration amplitude and an excitation current. FIG. 10 is a schematic diagram of the electromagnetic vibration characteristic of a hydroelectric generating unit according to embodiments of the present disclosure. In FIG. 10, the electromagnetic vibration frequency characteristics may include: a rotation frequency vibration characteristic, and/or a pole frequency vibration characteristic. What needs to be considered is that, during the operation of the hydroelectric generating unit, vibration that causes the unstable operation of the hydroelectric generating unit due to an interference force generated by an electromagnetic vibration source is called electromagnetic vibration. Common causes of the electromagnetic vibration of the hydroelectric generating unit include out-of-round rotor, short-circuit of magnetic poles, loose rotor, loose stator core, and poor stator winding fixation. Therefore, the electromagnetic vibration state of the hydroelectric generating unit can be described by the vibration frequency characteristic and the correlation characteristic between the electromagnetic vibration amplitude and the excitation current. In S903, electromagnetic vibration characteristic samples are constructed according to the extracted electromagnetic vibration characteristic. In summary, the stability state samples of the hydroelectric generating unit in the transition process of excitation and voltage buildup is acquired; the electromagnetic vibration characteristic is extracted based on the stability state samples; and the electromagnetic vibration characteristic samples are constructed according to the extracted electromagnetic vibration characteristic. In this way, the stability state evaluation and fault diagnosis research are carried out to realize the tracking evaluation of the stability state and the early identification of an abnormal state of the hydroelectric generating unit. In embodiments of the present disclosure, the stability state samples obtained in S901, i.e., the vibration waveform signals in the transition process of excitation and voltage buildup are analyzed through Fourier transform to extract the electromagnetic vibration characteristic (i.e., electromagnetic vibration frequency D characteristic) in the vibration waveform signals. Commonly used Fourier transform methods include discrete Fourier transform (DFT) and fast Fourier transform (FFT). In the Fourier transform, assuming that t represents time, x(t) represents a continuous time signal, and T represents a signal duration, a periodic signal is expressed in the form of superimposition of several simple harmonic signals, then: co(acos2znt+bsn2nft) f=1 (2.1);

=A4+ZAsin(2znf0 t + )

(2.2), where Ao = ao, representing is a direct current (DC) component (a static component) of a signal; fo represents a frequency of a fundamental wave; nfo represents a frequency of an nth (n=1, 2, 3,...) harmonic wave;

A " =a 2"2 b "2 , representing an amplitude of the nth harmonic wave; and

# = cta (a b, , representing an initial phase of the nth harmonic wave.

1jBr ao = x(t)dt T 0 (2.3);

a. =2 x(t) cos 2rmfotdt T0 (2.4); 2 T b= T x(t)sin2rnftdt where T=i/fo, representing a basic period of the signal. From formulas (2.2) to (2.5), it can be seen that after the Fourier transform, the signal is formed by the superposition of n harmonic waves with different frequencies. According to Euler's formula, the Fourier transform of a complex exponential form is shown in formula (2.6):

x(t) =( X(f -- o jAndf (2.6). For discrete signals collected in reality, the discrete Fourier transform (DFT) needs to be used, with the formula as shown in formula (2.7), and the corresponding inverse discrete Fourier transform is shown in formula (2.8), to associate a time domain sampling sequence and a spectrum sampling sequence each having a length of N together: N-1 X(_X ( 'a k-0O )= X(kte ,~(n=1, 2,..N- 1) (2.7);

1 N-i x(kt) = 1IX( n j2xnkN jx

Tn, (k=1,2,...N-1)(2.8), where x(kt) represents a discrete signal value sampled, N represents the number of signal sampling points, T represents a sampling interval, n represents a sequence number of a spectrum discrete value, and k represents a sequence number of a time-domain discrete value. In the case of DFT, when the signal sequence length N increases, the amount of calculation is increased by N 2, so it will take a lot of time. To solve this problem, the fast Fourier transform (FFT) was proposed, which is consistent with the DFT in principle, but in the calculation process, the data sequence is divided into two shorter subsequences for transformation, respectively, and then the transformed results are combined to obtain the discrete sequence of the entire sequence. In this way, the calculation time is shortened, so that the Fourier transform is widely used in signal spectrum analysis. For the analysis process, differing from obtaining signals by sampling with equal interval in the time domain, the FFT analysis of signals obtained by integer-period sampling with equal interval in the time domain is based on a frequency resolution corresponding to the equal time interval to obtain an amplitude spectrum. For the FFT analysis of signals obtained by equal-angle sampling is based on a rotation frequency of the D hydroelectric generating unit corresponding to the equal circumferential angle to obtain an order amplitude spectrum, where the order corresponds to a ratio of the number of vibration fluctuations to the number of rotations of the hydroelectric generating unit, so as to realize the order feature extraction of the rotation frequency and its frequency multiplication in a dynamic process of increasing rotational speed. The rotation frequency is first ordered, and a characteristic order frequency at the corresponding speed is: n 60 (2.9), wherefo(i) is an i' order frequency, and n is the rotational speed of the hydroelectric generating unit. For angle-domain stationary signals, the Fourier analysis is performed according to the following formula to obtain an order amplitude characteristic:

N-1

X( )= x(ktje k-O ,(n = 1, 2, ... N-1) (2.10), where x(kt) represents a discrete signal value obtained by equal-angle sampling, X(n/N) represents a corresponding spectrum line amplitude in the order spectrum, N represents the number of signal sampling points, 0 represents an angle-domain interval for sampling, n represents the sequence number of the spectrum discrete value, and k represents the sequence number of the time-domain discrete value. During the operation of the hydroelectric generating unit, due to some reasons, for example, the outer periphery of the rotor is out-of-round, the rotor and stator are not concentric, or stator cavity is out-of-round, the gap between the rotor and the stator is uneven, which causes unbalanced magnetic pull. The eccentric rotor mainly causes the amplitude of a component with one time the rotation frequency in the vibration to increase. The out-of-round rotor may cause the amplitudes of the harmonic components with two, three or more times the rotation frequency in the vibration to increase. The electromagnetic vibration frequency of such faults is:

k= n 60(Hz),k1= 1,2,3,... (2.11), where n represents the rotational speed of the hydroelectric generating unit, in r/min. Due to the looseness of the stator core, the looseness of the stator joint, the buckling of the stator core, the stator fractional slot subharmonic, or the like, electromagnetic pole-frequency vibration of the stator core or the stator frame will be generated in the hydroelectric generating unit, with a vibration frequency being: 3000 2 60 (Hz), k2 = 1, 2, 3,... (2.12), where k2 represents the order, and generally is 1 or 2. In addition, the looseness of the rotor magnetic poles, the looseness of the stator joint and the stator silicon steel sheet caused by the thermal expansion of the stator will cause electromagnetic vibration related to the number of magnetic pole pairs, with the vibration frequency being: n 60 (2.13), where p represents the number of the magnetic pole pairs of the rotor, and n represents the rotational speed of the hydroelectric generating unit, in r/min. The electromagnetic vibration amplitude of the hydroelectric generating unit caused by electromagnetic factors is generally positively correlated with the size of the excitation current. Therefore, the correlation between the vibration and the excitation current is also a main consideration in electromagnetic vibration analysis. The correlation characteristic between the electromagnetic vibration amplitude and the excitation current is determined by the following formula:

-_ Cov(VEC) JVar(V)Var(EC) (2.14), where R(W EQ represents the correlation characteristic between the electromagnetic vibration amplitude and the excitation current; Cov(K EQ represents a covariance of the electromagnetic vibration amplitude and the excitation current; Var(V) represents the electromagnetic vibration amplitude; and Var(EC) represents a variance of the excitation current. Based on the above electromagnetic vibration characteristic of the hydroelectric generating unit, the electromagnetic vibration characteristic samples are constructed, the data of the transition process of excitation and voltage buildup is analyzed, and relevant characteristics are extracted, an electromagnetic vibration state

evaluation matrix is constructed as shown in formula (2.15), i.e.,E

Y 11 Y 12 Yi m- _ Y21 Y 22 2. T 1 y 2 N

YN1 YN2 . Nm (2.15), where m represents a dimension of a characteristic variable, N represents the number of electromagnetic vibration samples. The characteristic variable may include a vibration peak-to-peak value (PtP), amplitudes of 1fn (fn is the rotation frequency of the hydroelectric generating unit), 2fn, 3fn, p*fn (p is the number of magnetic pole pairs), 50 Hz and 100 Hz frequency components, an association coefficient (RPtPEC) between the vibration peak-to-peak value and the excitation current, an association coefficient (RVEC) between a characteristic frequency amplitude and the excitation current and the like. An electromagnetic vibration characteristic normal state sample is represented by Ynormai, and an electromagnetic vibration characteristic monitoring sample is represented by Ytest. The electromagnetic vibration of the hydroelectric generating unit has two main characteristics: the electromagnetic vibration frequency and the correlation between the electromagnetic vibration amplitude and the excitation current. If noticeable electromagnetic vibration occurs, a corresponding frequency is a power frequency and its frequency multiplication, or vibration components of the polar frequency and its frequency multiplication appear, and the vibration amplitude tends to increase with the increase of the excitation current. For example, taking a hydroelectric generating unit of a power station as the object, the electromagnetic vibration frequency amplitude and correlation characteristics of the hydroelectric generating unit are extracted based on sample data obtained by monitoring the stability state of the transition process of excitation and voltage buildup of the hydroelectric generating unit to construct electromagnetic vibration state samples. It is understandable that the diagnostic subsystem can also construct corresponding data samples for other hydroelectric device or different working conditions, which is not limited herein. The knowledge center subsystem is connected with the data acquiring subsystem, and may acquire the operating data of each of the plurality of hydroelectric devices from the data acquiring subsystem, and generate a knowledge map corresponding to the system according to the operating data. FIG. 11 is a schematic flowchart of an operating method of a knowledge center subsystem according to embodiments of the present disclosure. As shown in FIG. 11, the method includes the following actions. In Sio1, attribute information of each of a plurality of devices in the system and operating data sets acquired by sensors in each of the plurality of devices are acquired. The device in the system may be any hydroelectric device in the hydroelectric plant, including for example a generator, a transformer and other electrical device, which will not be limited in the present disclosure. The attribute information of the device may include a manufacturer, an installation company, working parameters, a device code, a responsible person of the device, etc., which are not limited in the present disclosure. The operating data sets acquired by sensors are operating data collected by the data acquiring system. For example, for the generator of the hydroelectric plant, its operating data set may include centralized control monitoring data, monitoring data of an excitation system, monitoring data of a governor system, monitoring data of an oscillating system, and so on. In S1102, structured data corresponding to each of the plurality of devices and a relationship among the plurality of devices are determined according to the attribute information of each of the plurality of devices. By forming the structured data of each device based on the attribute information of the device, an D association among the attribute information of the device can be established. For example, for transformers of the hydroelectric plant, the manufacturer, the installation company, the working parameters, the device code, the responsible person, and other attribute information of each transformer may be constructed into the structured data for storage. In this way, by querying the structured data, various attribute information of the device can be obtained quickly. In addition, the association relationship among the plurality of devices can be determined according to the attribute information of each of the plurality of devices. For example, if Transformer 1and Transformer 2 have the same manufacturer, the association relationship between Transformer 1 and Transformer 2 may be determined based on the manufacturer. In S1103, a data code is determined for each operating data set according to a position and a type of the D sensor corresponding to the operating data set. It is understandable that the operating data sets come from various sensors of the device and reflect the operating state of the device in real time. Determining the data code for each operating data set according to the position and the type of the corresponding sensor can establish an association relationship between the operating data set and the device. For example, if an operating data set records a C-phase temperature of an excitation transformer of a hydroelectric generating unit 1 of the hydroelectric plant, the corresponding sensor is located at a C-phase winding of the excitation transformer of the hydroelectric generating unit 1, and the type of the corresponding sensor is a temperature sensor. According to the position and the type of this sensor, a corresponding data code can be uniquely determined. The form of the data code may be selected as required, which will not be limited in the present disclosure. It should be noted that the above example is only for illustration, and cannot be used as limitations on the position and type of the sensor as well as the data code in embodiments of the present disclosure. In S1104, a knowledge map corresponding to the system is generated based on the structured data corresponding to each of the plurality of devices, the relationship among the plurality of devices, and the data codes. In embodiments of the present disclosure, a classification map may be constructed for each device according to the corresponding structured data and data code. Then, the knowledge map corresponding to the system is generated based on the relationship among the plurality of devices. For example, a basic information map of a device may be constructed according to the corresponding structured data. Nodes of the basic information map of the device may include a system device, a sub-device, components, a manufacturer, an installation company, a device code, working parameters, a responsible person of the device, and the like. The corresponding structured data is stored in the respective nodes, and then the knowledge map is formed based on the relationship among these nodes. It is understandable that the above example is only an example for illustrating the knowledge map construction by the knowledge center subsystem. In practical, the knowledge map may also be constructed in other ways or using other data, which will not be limited in the present disclosure. Embodiments of the present disclosure not only improve the inspection accuracy and reduce labor costs for the operation and maintenance of the hydroelectric plant, but also improve the economic benefits of hydroelectric plant and prolong the service life of electrical device, thereby realizing the intelligent operation and maintenance of the hydroelectric plant. FIG. 12 is a schematic flowchart of an operating method of an inspection subsystem according to embodiments of the present disclosure. As shown in FIG. 12, determining whether the abnormal event occurs in the observation item of the hydroelectric plant inspection scenario according to the operating data includes the following actions. In S1201, multimedia data and sensory modal data of the hydroelectric plant inspection scenario are acquired from the operating data. Data acquired by a sensory modal device of the data acquiring subsystem may be called the sensory modal data, and data acquired by an image acquisition device of the data acquiring subsystem may be called the multimedia data. The sensory modal data may be, for example, odor information data captured by an electronic nose in the D hydroelectric plant inspection scenario, or data generated by other electronic devices with various sensory modal functions such as odor recognition in the hydroelectric plant inspection scenario. The sensory modal data may also be visual data or auditory data collected in the hydroelectric plant inspection scenario, which will not be limited in the present disclosure. The multimedia data may be, for example, a real-time video captured by a camera device in the hydroelectric plant inspection scenario, a real-time audio recorded by a recording device in the hydroelectric plant inspection scenario, or other texts, videos or audios generated by other electronic devices with a shooting or recording function in the hydroelectric plant inspection scenario, such as a video or an audio generated for screen recording. In S1202: an inspection result corresponding to the hydroelectric plant is determined based on the D multimedia data and the sensory modality data. The inspection result may be various state data and environmental data on the inspection scenario acquired during the inspection process, or it may be a judgment result of whether a fault has occurred and the situation of the fault obtained based on the analysis of the state data and the environmental data. The inspection result corresponding to the hydroelectric plant can be used as reference data for the state of the hydroelectric plant to determine whether a fault occurs in a current hydroelectric plant and the situation of the fault. The inspection result may be data about various states of the hydroelectric plant. For example, the inspection result may be monitoring data on the speed and sound of a rotor of a generator of the hydroelectric plant, video monitoring data of a stator and windings of the generator of the hydroelectric plant, or a judgment result of a fault of the hydroelectric plant obtained by processing state data acquired from other device of the hydroelectric plant. The above inspection result may be used as basic data for assisting in the construction of a state model of the hydroelectric plant. The state model may be a mathematical model, an algorithm for determining a state, a graph representing a state change, a broken line graph, or the like, which will not be limited in the present disclosure. In S1203, an actual measurement value corresponding to the observation item of the hydroelectric plant inspection scenario is determined according to the inspection result. The observation item may refer to a state quantity, an analog quantity, or a video signal of an electromechanical device in the hydroelectric plant, or various testing points of an online monitoring device, etc., which will not be limited in the present disclosure. In embodiments of the present disclosure, each observation item may be detected in real time or periodically. For example, different observation items each can be configured with a logical association in advance based on a detection processing logic, and the state of each observation item is automatically analyzed to determine whether the electromechanical device is normal. The measurement value obtained by real-time or periodic detection of each observation item may be called the actual measurement value. One observation item may correspond to one or more actual measurement values, which will not be limited in the present disclosure. The actual measurement value may be a specific numerical value, or it may be a reference symbol or range interval indicating a degree. For example, an operating temperature of a motor (the motor is a kind of electromechanical device) in the hydroelectric plant inspection scenario may be detected by a temperature sensor; a relationship curve between voltage and current of the motor in operation may be obtained through the detection by a voltmeter and an ammeter; a vibration frequency of the motor in operation may be obtained through a sound sensor. The operating temperature, the relationship between voltage and current, and the vibration frequency may all be called observation items, and values actually measured for the operating temperature, the relationship between voltage and current, and the vibration frequency, such as in a form of a numerical value or a curve, may all be called actual measurement values. In S1204, multiple reference items corresponding to the observation item are determined, and multiple reference values corresponding to the multiple reference items respectively are determined. The reference item may also refer to a state quantity, an analog quantity, or a video signal of an electromechanical device in the hydroelectric plant, or various testing points of an online monitoring device, etc., which will not be limited in the present disclosure. In embodiments of the present disclosure, the multiple reference items corresponding to the observation item refer to reference items that have an association relationship with the observation item. The association relationship indicates an association relationship between a corresponding measurement item and other measurement items when a plurality of electromechanical device operates, respectively. For example, an actual measurement value of an observation item A has a certain association relationship with a reference value of a reference item A; and an actual measurement value of an observation item B has a certain association relationship with a reference value of a reference item B, which will not be limited in the present disclosure. SA reference value may be a value actually observed for a reference item, or it may be a value presented by the reference item when the electromechanical device is in the normal operating state, or it may be a value calibrated for the reference item based on actual work experience when the electromechanical device is in the normal operating state, which will not be limited in the present disclosure. The reference value may be a numerical value, or it may refer to a reference identification or range interval indicating a degree. The determination of the reference value may be a record of past experience or a real-time record in normal operation, which will not be limited in the present disclosure. For example, the operating temperature of the motor in normal operation is used as a reference item, and the reference value corresponding to the reference item may be a temperature value or a temperature range. For another example, the reference items may include a relationship between current and voltage, a vibration D frequency of the motor and so on when the motor operates normally, and the reference values may include, for example, a relationship curve between current and voltage, the vibration frequency of the motor and so on, which will not be limited in the present disclosure. In S1205, a measurement threshold corresponding to the observation item is determined according to the multiple reference values. The measurement threshold refers to a critical value corresponding to the observation item that may cause malfunction of the electromechanical device. The measurement threshold may be a minimum value that affects normal operation, a maximum value that affects normal operation, or an interval range including the minimum value and the maximum value. The measurement threshold shown above may have a certain error range. The specific measurement threshold may be determined according to an actual working state of the electromechanical device, which will not be limited in the present disclosure. For example, a motor works normally at 4200 r/min, when the motor works at a speed of more than 4250 r/min or less than 4150 r/min, it is determined that the motor works abnormally, then the measurement thresholds are 4250 r/min and 4150 r/min. For example, the observation item is the state quantity, the analog quantity, or the video signal of an electromechanical device in the hydroelectric plant, or various testing points of an online monitoring device, etc., then the measurement threshold may be a critical value corresponding to the state quantity, the analog quantity or the video signal of the electromechanical device in the hydroelectric plant, or various testing points of the online monitoring device, which will not be limited in the present disclosure. In S1206, it is determined whether the abnormal event occurs in the observation item according to the actual measurement value and the measurement threshold. After determining the measurement threshold corresponding to the observation item according to the multiple reference values, the actual measurement value may be compared with the measurement threshold value. For example, when the actual measurement value exceeds this critical value, it indicates that the observation item has a high probability of failure; while when the actual measurement value does not exceed the critical value, it indicates that there is no abnormal event in the observation item, which will not be limited in the present disclosure. For example, for a winding temperature of a transformer (a main transformer), it is learned from operation experiences of a main transformer of a hydroelectric plant that a top oil temperature of the main transformer is normally lower than a winding temperature of the main transformer by about 5 °C. Therefore, a sum of the top oil temperature of the main transformer + 5 °C + an error value may be used as a threshold. When the winding temperature of the main transformer is greater than this sum, it indicates that the main transformer winding is faulty. In embodiments of the present disclosure, the multimedia data and the sensory modality data of the hydroelectric plant inspection scenario are acquired; the inspection result corresponding to the hydroelectric plant is determined based on the multimedia data and the sensory modality data; the actual measurement value corresponding to the observation item is determined according to the inspection result; the multiple reference items corresponding to the observation item are determined, the multiple reference values respectively corresponding to the multiple reference items are determined; the measurement threshold corresponding to the observation item is determined according to the multiple reference values; and it is determined whether the abnormal event occurs in the observation item according to the actual measurement value and the measurement threshold. Since the multimedia data and the sensory modal data are used to determine the inspection result, and the actual measurement value and the measurement threshold are used to determine the occurrence of the fault event, the system according to embodiments of the present disclosure can improve the inspection efficiency of the hydroelectric plant, reduce the labor intensity of related personnel, and ensure the safe and stable operation of the device in the hydroelectric plant. In some embodiments, the inspection subsystem may also perform the following actions. In step 11, first state information of a component to which the observation item belongs, environmental state information of the hydroelectric plant inspection scenario, and second state information of a component to D which an associated observation item associated with the observation item belongs are determined. The first state information, the environment state information, and the second state information may also be understood as associated reference information of the observation item. The associated reference information may be information corresponding to the associated observation item that has an influence on the threshold to be configured corresponding to the observation item of the hydroelectric plant, and the associated observation item has an association relationship with the observation item. The observation item may be, for example, a certain point of a hardware part of the device in the hydroelectric plant when it works. This point is subordinate to the hardware part, and its state parameter will change in the working state. For example, a high-current heating component of the hydroelectric plant, a generator rotor of the hydroelectric plant, a motor of the hydroelectric plant and the like each may be referred to D as an observation item, which will not be limited in the present disclosure. For example, when the device of the hydroelectric plant is in operation, a state parameter of an observation item A will be affected by a state parameter of an observation item B, so that the observation item A may be regarded as having an association relationship with the observation item B. When the threshold to be configured for the observation item A is adjusted, the observation item B may be used as the associated observation item, and reference information of the observation item B may be obtained and used as the associated reference information. The reference information of the observation item B may be, for example, a state parameter value corresponding to the observation item B, a threshold corresponding to the observation item B, or any other information related to the observation item B that may affect the operating state of the device, which will not be limited in the present disclosure. The observation item of the hydroelectric plant may correspond to one or more associated observation items, and correspondingly, there may be one or more kinds of associated reference information, which will not be limited in the present disclosure. The associated reference information determined in embodiments of the present disclosure may be used to adjust the threshold to be configured for the above observation item. Because the associated reference information is the reference information corresponding to the associated observation item that has an association relationship with the observation item, and it has a high reference value for the adjustment of the threshold corresponding to the observation item, so that the rationality of the adjustment of the threshold corresponding to the observation item can be effectively guaranteed. For example, the ambient temperature has a certain impact on the temperature generated in the operation of the generator in the hydroelectric plant, so the temperature of the generator in operation may be regarded as an observation item, the ambient temperature may be regarded as an associated observation item, and reference information corresponding to the ambient temperature (such as an actual ambient temperature value, change of the ambient temperature, etc.) may be used as the associated reference information. The reference information corresponding to the ambient temperature may be used to adjust a threshold to be configured corresponding to the temperature of the generator in operation. For another example, for the detection of the efficiency of a hydroturbine in operation, work condition information of a hydroturbine unit when it operates may also be regarded as the associated reference information, and may be used as a reference for the adjustment of an operating efficiency threshold of the hydroturbine. In some embodiments, the associated reference information may include the first state information of the component to which the observation item belongs, the environmental state information of the hydroelectric plant inspection scenario, and the second state information of the component to which the associated observation item associated with the observation item belongs. That is, the first state information, the environmental state information and the second state information are collectively used as the associated reference information. The component to which the observation item belongs is a component that has a direct influence on the observation item, and it may be expressed as a hardware module to which the observation item belongs, or an electronic or mechanical device to which the observation item belongs. For example, when a rotor of a generator of the hydroelectric plant rotates, the observation item may be for example an electromotive force generated by the rotor, and a power generation system to which the rotor belongs may be called the component to which the observation item (i.e., the electromotive force generated by the rotor) belongs. For example, when a rotor of an engine operates, heat may be generated due to frictional force, the observation item may be, for example, the heat generated by the rotor, and a heat generation and dissipation system to which the rotor belongs may be called the component to which the observation item (heat generated by the rotor) belongs. Operating state information corresponding to the component to which the observation item belongs may be referred to as the first state information. The first state information is state information exhibited by the component to which the observation item belongs during the operation process. For example, the state information exhibited by the component to which D the observation item belongs during the operation process may be measured or detected in real time and used as the first state information. Alternatively, the first state information may be operating state information corresponding to the component to which the observation item belongs in a historical state. The component to which the observation item belongs may include one or more components, and correspondingly, the first state information may include one or more types of state information, which will not be limited in the present disclosure. For example, the first state information may include, for example, the heat generated when the rotor of the engine of the hydroelectric plant rotates, the temperature of the rotor of the engine, displacement information when the hydroturbine rotates, and rotational speed information of the hydroturbine. That is, these information all can be used as the first state information. D The environmental state information may be, for example, information related to the environmental state in the hydroelectric plant inspection scenario, such as temperature, humidity, wind power, water flow speed and other environmental state information. In embodiments of the present disclosure, the environmental state information of the hydroelectric plant inspection scenario may be determined in real time, or may be acquired from historical state information as required, and the environmental state information may be dynamically changed information, which will not be limited in the present disclosure. For example, the water flow speed when a hydroelectric generator operates and information on ambient temperature and humidity when the rotor of the engine operates can all be called the environmental state information, which will not be limited in the present disclosure.

The associated observation item associated with the observation item refer to anther observation item that has a direct or indirect impact on the observation item, and the associated observation item may be present in the same component as the observation item, or may be present in a different component from the observation item, which will not be limited in the present disclosure. Operating state information corresponding to the component to which the associated observation item belongs may be referred to as the second state information. The second state information is state information exhibited by the component to which the associated observation item belongs during the operation process. For example, the state information exhibited by the component to which the associated observation item belongs during the operation process may be measured or detected in real time and used as the second state information. Alternatively, the second state information may be operating state information corresponding to the component to which the associated observation item belongs in a historical state. The component to which the associated observation item belongs may include one or more components, and correspondingly, the second state information may include one or more types of state information, which will not be limited in the present disclosure. For example, when the temperature of a high-current heating component of the hydroelectric plant is used as the observation item, and the temperature when the rotor operates is used as the associated observation item, the second state information may be the temperature when the rotor operates or current change information, which will not be limited in the present disclosure. In step 12, threshold change information corresponding to the observation item is determined according to the first state information, and/or the environmental state information, and/or the second state information. The threshold change information may be obtained based on the associated reference information, and is used to describe the threshold change situation corresponding to the threshold to be configured, such as a threshold change range corresponding to the threshold to be configured, and the threshold change range may be determined based on the associated reference information, which will not be limited in the present disclosure. It is understandable that different associated reference information may have different degrees of impact on the threshold to be configured. Therefore, in embodiments of the present disclosure, different associated reference information may be tested to calculate the respective threshold change information. Then, the threshold may be configured according to one or more different threshold change information. In embodiments of the present disclosure, the threshold change information may be determined based on any one or any combination of the first state information, the environment state information, and the second state information, which will not be limited in the present disclosure. For example, any one or any combination of the first state information, the environment state information, and second state information may be input into a pre-configured model to subject to model calculation to determine the threshold change information. Alternatively, the threshold change information may be determined based on any one or any combination of the first state information, the environment state information, and the second state information in any other possible way, which will not be limited in the present disclosure. In some embodiments, during the determination of the threshold change information corresponding to the observation item is determined according to the first state information, and/or the environmental state D information, and/or the second state information, a first association coefficient corresponding to the first state information may also be determined. The first association coefficient corresponds to the first state information, and may be used as a reference for determining the threshold change information. The first association coefficient may be used to indicate the influence of the first state information on the threshold change information. The first association coefficient may be a numerical value or a symbol representing a degree, which will not be limited in the present disclosure. For example, when a hydroturbine of the hydroelectric plant operates, the rotational speed of the hydroturbine is used as the first state information, a specific value of the rotational speed of the hydroturbine, a numerical range representing the working speed of the hydroturbine, or an identifier like "fast" and "slow" representing the degree of the speed may be called the first association coefficient D Further, a second association coefficient corresponding to the environmental state information is determined. The association coefficient corresponding to the environmental state information may be referred to as the second association coefficient, which may be used as a reference for determining the threshold change information. The second association coefficient may be used to indicate the influence of the environmental state information on the threshold change information. The second association coefficient may be a numerical value or a symbol representing a degree, which will not be limited in the present disclosure. For example, if the environmental state information is the air temperature around the high-current heating component of the hydroelectric plant during operation, the second association coefficient may be a coefficient for indicating the influence of the air temperature around the high-current heating component on the threshold change information

(threshold change information corresponding to the environmental state information). Further, a third association coefficient corresponding to the second state information is determined. The third association coefficient corresponds to the second state information, and may be used as a reference for determining the threshold change information. The third association coefficient may be used to indicate a coefficient of influence of the second state information on the threshold change information. The third association coefficient may be a numerical value or a symbol representing a degree, which will not be limited in the present disclosure. For example, when the heat generated by the high-current heating component of the hydroelectric plant is configured as a testing point, and a heat threshold corresponding to the heat generated by the high-current heating component is used as the threshold to be configured, heat generation and dissipation information of the associated component may be used as the second state information, then an influence coefficient of the heat generation and dissipation information of the associated component on the threshold change information is determined as the third association coefficient. After the first association coefficient, and/or the second association coefficient, and/or the third association coefficient are determined, threshold change information corresponding to a testing point may be determined according to any one or any combination of the first association coefficient, the second association coefficient, and the third association coefficient. For example, the first association coefficient, the second association coefficient, and the third association coefficient may be fused to obtain a fused coefficient, and the fused coefficient may be used as an input of a preset function, and an output of the preset function is used as the threshold change information. However, the present disclosure is not limited thereto. For example, for the high-current heating component of the hydroelectric plant, its testing point may be the temperature of the heating component, and the threshold to be configured may be a temperature threshold. When the environment is hot, the real-time ambient temperature c may be introduced as the associated reference information. Then, the threshold change information may be determined based on the associated reference information, i.e., the real-time ambient temperature c, and an actual threshold corresponding to the high-current heating component may be set to (w]+F1(c), w2+F2(c)), where w1 and w2 are thresholds to be configured, F1 and F2 are ambient temperature associated functions, which are used to determine the threshold change information. That is, the associated reference information, i.e., the real-time ambient temperature c, is used as an input parameter of F1 and F2, respectively, F1 and F2 output the threshold change information F(c) and F2(c), respectively. The threshold change information F1(c) and F2(c) are added to w], w2, respectively to obtain target thresholds (wl+Fl(c), w2+F2(c)), and then the thresholds to be configured may be set to the target thresholds (wl+F1(c), w2+F2(c)). In embodiments of the present disclosure, since any one or any combination of the first association coefficient, the second association coefficient, and the third association coefficient is considered, the reference value of the threshold change information is greatly improved, thereby increasing the accuracy and objectivity of threshold configuration, improving the threshold configuration effect, and improving the accuracy of the inspection result. In step 13, the measurement threshold is configured to be a target threshold according to the threshold change information. D In some embodiments, when the observation item is in a working state, the target threshold may be determined in real time according to the threshold change information, and the threshold to be configured may be configured to be the target threshold. In some embodiments, it may be possible to determine the target threshold according to the threshold change information when a set period is reached, and the threshold to be configured may be configured to be the target threshold. In some embodiments, the target threshold may be determined in real time according to the threshold change information, and the threshold to be configured may be configured to be the target threshold when a set period is reached. Therefore, in embodiments of the present disclosure, by determining the target threshold in real time D according to the threshold change information when the observation item is in the working state, and configuring the threshold to be the target threshold, the timeliness of the threshold configuration is effectively improved, so that the configured target threshold can be included in the device inspection scenario in time to ensure the inspection effect. In step 14, it is determined whether the abnormal event occurs in the observation item according to the actual measurement value and the target threshold. After the target threshold corresponding to the associated reference information is determined according to the associated reference information, the actual measurement value may be compared with the target threshold. When the actual measurement value exceeds this critical value, it indicates that the observation item has a high probability of failure; while when the actual measurement value does not exceed the critical value, it indicates that there is no abnormal event in the observation item, which will not be limited in the present disclosure. For example, for a winding temperature of a transformer (a main transformer), it is learned from operation experiences of a main transformer of a hydroelectric plant that the actual ambient temperature value and ambient temperature change have a certain influence on a temperature threshold of the main transformer winding. If the ambient temperature affects the temperature threshold of the main transformer winding by about 5 °C, it is necessary to add 5 °C to the measured target threshold when determining whether a temperature fault event occurs in the main transformer winding. When the temperature of the main transformer winding is greater than a sum of the target threshold + 5 °C + an error value, it indicates that the main transformer winding has failed. In this way, the threshold configuration is more reasonable, which effectively reduce the influence of external factors on the threshold, makes the threshold configuration more accurate and objective, and realizes accurate adjustment and modification of the threshold to be configured, so as to ensure the objectivity and accuracy of the inspection result, and ensure the stable operation of device. In some embodiments, the hydroelectric plant inspection scenario may include the generator, the hydroturbine, and any other possible hydroelectric device. In the operation of acquiring the multimedia data of the hydroelectric plant inspection scenario, image data corresponding to each of a governor system, an excitation system and a protective system associated with the generator may be acquired first. Data related to images collected in the inspection process of the hydroelectric plant may be called image data. The image data may be related videos or pictures recorded in real time in the working environment of the hydroelectric plant, related videos or screenshots obtained through screen recording, or image signals of recording the system monitoring panel, which will not be limited in the present disclosure. The image data in embodiments of the present disclosure may be the image data corresponding to each of the governor system, the excitation system, and the protective system associated with the generator of the hydroelectric plant, or it may be the image data of any other system associated with the generator, which will not be limited in the present disclosure. In some embodiments, in the action of determining the inspection result corresponding to the generator based on the multimedia data, image feature extraction may be performed on the image data corresponding to each of the governor system, the excitation system, and the protective system associated with the generator to obtain image features to be matched. The image features to be matched include: an image feature corresponding to an indicator signal, an image feature corresponding to a pressure plate position, and an image feature corresponding to an operating state of a switch. The image features to be matched are compared with reference image features to determine indicator signal information, pressing plate position information, and switch operating state information corresponding to each of the governor system, the excitation system and the protective system. The inspection result is acquired by analyzing the indicator signal information and the pressing plate position information. In this way, efficient inspection can be performed on the governor system, the excitation system, and the protective system associated with the generator, and empirical and subjective judgments of on-site personnel on the governor system, the excitation system, and the protective system associated with the generator are reduced, so that the work intensity of the on-site personnel is reduced, and at the same time, the objectivity, accuracy and reliability of the inspection result are greatly improved. The image features to be matched may be image-dimensional features obtained by image processing the D collected image data of the governor system, the excitation system and the protective system. For example, the image features to be matched may be image features of image frames in a video of a fixed time period, or the image features to be matched may be used to indicate the indicator signal information, the pressing plate position information and the like in a control panel for controlling the governor system, the excitation system, and the protective system, which will not be limited in the present disclosure. A variety of reference images may be pre-labeled, which may be images of the governor system, the excitation system, and the protective system recorded when are in normal operation (i.e., images corresponding to these systems when they are not faulty), or may be the indicator signal information and the pressing plate position information corresponding to each of these systems when they are not faulty. The image features to be matched may have a one-to-one correspondence with the pre-labeled reference images in the time and periodic frequency band. The governor system, the D excitation system, and the protective system associated with the generator may be monitored through directly monitoring and recording working images of these systems, or through monitoring the control panel that reflects the operating state of these systems, for example, through monitoring a turn-on state, a flashing state, a turn-off state or other states of one or more signal lights on the panel, to determine the operating states of these systems. For example, in the hydroelectric plant inspection scenario, the inspection of a small room next to the generator can use an algorithm based on image recognition and analysis technology to identify indicator signals of a display cabinet or the pressing plate position of the governor system, the excitation system, and the protective system, to determine the operating states of the respective systems. Further, thermal imaging data and temperature data of a power generating layer of the generator are acquired.

The power generating layer of the generator may represent the stator and rotor that cut magnetic induction lines. A part of the generator that converts energy of other states into electrical energy may also be called the power generating layer. The thermal imaging data and the temperature data may be recorded numbers representing thermal imaging and temperature, or may be tables, graphs or symbols generated by software that represent thermal imaging and temperature. The thermal imaging data and the temperature data of the power generating layer may be acquired by measuring instruments such as a thermal imager and a thermometer, electronic device integrated with a thermal imaging function, or electronic device integrated with temperature detection, which will not be limited in the present disclosure. For example, during the hydroelectric plant inspection, image data of the stator and rotor of the generator may be collected to determine the rotational speed of the rotor of the generator and whether the generator is damaged. Further, thermal imaging data and temperature data of each of an excitation slip ring and a wind tunnel outlet of the generator are acquired. For explanatory illustrations of the thermal imaging data and temperature data of the excitation slip ring and the wind tunnel outlet, reference may be made to the above examples and illustrations with respect to the thermal imaging data and temperature data of the power generating layer of the generator, which will not be elaborated here. For example, during the hydroelectric plant inspection, infrared thermal imaging data of a slip-ring carbon brush and a connecting line of a generator layer (such as the excitation slip ring) may be collected, and infrared thermal imaging data of a busbar at the wind tunnel outlet may be collected, so as to collect a current temperature, a maximum temperature and other signal data. Further, audio data of the stator of the generator is acquired. The image data, the thermal imaging data, the temperature data, and the audio data are used as the multimedia data. The audio data of the stator of the generator may be a real-time audio recorded by a recording device for the stator of the generator in the hydroelectric plant inspection scenario, or an audio file on the operating state of the stator generated by any other electronic devices with recording functions in the hydroelectric plant inspection scenario, which will not be limited in the present disclosure. In some embodiments, in the action of determining the inspection result corresponding to the hydroelectric plant based on the multimedia data, audio feature extraction may be performed on the audio data of the stator of the generator to obtain a voiceprint feature to be matched, the voiceprint feature is compared with pre-labeled reference voiceprint features to determine a reference voiceprint feature matching the voiceprint feature, and a fault type of the stator of the generator to which the reference voiceprint feature matching the voiceprint feature belongs is determined as the inspection result, so that the audio data of the stator of the generator is processed, personal empirical judgments of the on-site personnel on the sound in the inspection scenario is reduced, and the accuracy of the processing result is ensured by using the more objective data. The voiceprint feature to be matched may be obtained by processing the collected audio data. For example, the voiceprint feature may be acquired from a fixed time period, or may be periodically acquired from a certain D frequency band, which will not be limited in the present disclosure. The pre-labeled reference voiceprints may be voiceprints recorded for the stator of the same one generator in normal operation, or voiceprints generated by the stator of other generators of the same model or with the same voiceprint feature. The voiceprint features to be matched may have a one-to-one correspondence with the pre-labeled voiceprint features in the time and periodic frequency band. For example, the audio signal of the stator of the generator is recorded in real time using recording device. When a stator bar or a bolt of the stator of the generator is loosened, an abnormal voiceprint signal of a specific frequency band will be generated, based on which it is determined that an abnormality and fault occurs. Further, operating audio data of each of a runner and a draft tube of the hydroturbine in a hydroturbine inspection scenario is acquired. D Audio data generated by the runner and draft tube of the hydroturbine in operation may be called operating audio data. In embodiments of the present disclosure, a library of pre-labeled reference voiceprint features may be established, which includes voiceprint features of the runner and draft tube of the hydroturbine in normal operation. Acquiring the operating audio data of each of the runner and draft tube of the hydroturbine in the hydroturbine inspection scenario may include acquiring the operating audio data of each of the runner and the draft tube of the hydroturbine generated when they are in operation. The operating audio data is subjected to feature extraction to extract voiceprint signal features to be matched. The voiceprint signal features to be matched are compared with the library of the pre-labeled reference voiceprint features. For a possible abnormal voiceprint signal feature, the fault type is marked. The fault type detected, the corresponding voiceprint signal feature, and a component to which the voiceprint signal feature belongs (such as the runner or the draft tube of the hydroturbine) are used as the inspection result. Further, operating video data of each of a bearing, an oil tank, and a pipeline of the hydroturbine in the hydroturbine inspection scenario is acquired. The operating audio data and the operating video data are used as the multimedia data. In embodiments of the present disclosure, a library of pre-labeled reference image features may be established, which includes reference image features or reference video features corresponding to the operating video data of the bearing, the oil tank, and the pipeline of the hydroturbine. The operating video data of the bearing, the oil tank, and the pipeline of the hydroturbine in the hydroelectric plant inspection scenario is acquired. Video signal features to be matched are extracted from the operating video data. The video signal features to be matched are compared with the reference video features in the library of the pre-labeled reference image features. For a possible abnormal video signal feature (such as a hole, a crack, or a video image with a large color difference), the fault type is marked. The fault type detected, the video signal feature to be matched, and a component to which the video signal feature to be matched belongs (such as the bearing, the oil tank, and the pipeline of the hydroturbine) are used as the inspection result. In some embodiments, in the action of determining the inspection result corresponding to the hydroturbine based on the multimedia data, the video feature extraction may be performed on the operating video data of the bearing, the oil tank, and the pipeline of the hydroturbine to extract image features to be matched. The image features to be matched are matched with reference image features to determine crack position information of the bearing, the oil tank, and the pipeline of the hydroturbine and start/stop position information corresponding to each of a water pump and an oil pump of the hydroturbine. The crack position information and the start/stop position information are determined as the inspection result. As a result, the crack position information of the bearing, the oil tank and the pipeline of the hydroturbine, and the start/stop position information corresponding to each of the water pump and the oil pump of the hydroturbine can be automatically identified based on the multimedia data, which effectively improves the completeness and comprehensiveness of the hydroturbine inspection. For example, in the hydroelectric plant inspection scenario, a crack identification algorithm for a mechanical part of a hydroturbine unit is pre-set based on an image comparison technology. In an inspection area, a static part of the hydroturbine when it operates and a rotating part of the hydroturbine when it is shut down are monitored, and an early warning is issued when there is a crack. The start/stop position information corresponding to each of the water pump and the oil pump of the hydroturbine is provided, and an early warning algorithm for monitoring water leakage or oil leakage is pre-set based on the image comparison technology. In terms of voiceprint monitoring, the bearing, the oil tank, the pipeline and other parts of the hydroturbine unit are monitored in real time, a runner abnormal discrimination algorithm is preset based on a spectrum analysis method of big data mining, with which some fault sounds (such as scratch, collision, jam, etc.) of the runner of the hydroturbine are early warned. In some embodiments, in the action of acquiring the sensory modal data of the hydroelectric plant inspection scenario, the odor data in the hydroelectric plant inspection scenario may also be acquired, and the odor data may be used as the sensory modal data. The odor data in the hydroelectric plant inspection scenario may be odor information data captured by an D electronic nose in the hydroelectric plant inspection scenario, or it may be data files generated by various odor mode electronic devices that have odor recognition or gas collection functions in the hydroelectric plant inspection scenario, which will not be limited in the present disclosure. In some embodiments, odor data of the stator of the generator may be acquired, and odor data of an outlet switch of the generator and ozone concentration data in the wind tunnel of the generator may be detected, so that the odor data of the stator, the outlet switch and the wind tunnel of the generator may be accurately detected. For example, a gas collection probe may be installed on the generator in the hydroelectric plant inspection scenario to detect changes in the content and concentration of the ozone in the wind tunnel and generate the change period and characteristic graph, based on which to determine the occurrence of local discharge, electrical corrosion of the stator bar, and the like. D In some embodiments, odor feature analysis is performed on the odor data to obtain an odor type to be matched; it is determined that an electrical corrosion event and/or a heating event and/or a damage event and/or a discharge event occurs in the hydroelectric plant inspection scenario in response to determining that the odor type to be matched is a target odor type; and respective position information corresponding to the electrical corrosion event and/or the heating event and/or the damage event and/or the discharge event is determined according to the image data, the thermal imaging data and the temperature data, and the respective position information is determined as the inspection result, so that the electrical corrosion event and/or the heating event in the generator inspection scenario can be accurately detected, empirical subjective judgment of the on-site personnel on whether the electrical corrosion event and/or the heating event occurs in the generator inspection scenario is reduced, the work intensity of the on-site personnel is reduced, and the objectivity, accuracy and reliability of the inspection result is improved. The odor type to be matched may be an odor type (such as an ozone type, etc.) obtained by analyzing the odor information data captured by the electronic nose, or it may be an odor type obtained by analyzing the data files generated by various odor mode electronic devices that have odor recognition or gas collection functions in the hydroelectric plant inspection scenario, which will not be limited in the present disclosure. The target odor type may be an odor type acquired in advance about the environment after the occurrence of the electrical corrosion event and/or the heating event and/or the damage event and/or the discharge event, or it may be an odor type obtained by detecting and classifying one or more special odors in the air. In some embodiments, feature analysis is performed on visual data and/or auditory data to obtain a visual type and/or an auditory type to be matched, and the visual type and/or the auditory type to be matched is compared with a pre-labeled target visual type and/or target auditory type to determine the situation in the hydroturbine inspection scenario, so as to further determine whether an oil leakage event occurs. If the visual type and/or the auditory type to be matched is/are the target visual type and/or the target auditory type, it is determined that an oil leakage event occurs in the hydroturbine inspection scenario. Oil leakage position information corresponding to the oil leakage event is determined according to the multimedia data, and the oil leakage position information is determined as the inspection result. In this way, the oil leakage event in the hydroturbine inspection scenario can be accurately detected, empirical subjective judgment of the on-site personnel on whether the oil leakage event occurs in the hydroturbine inspection scenario and on the oil leakage position is reduced, the work intensity of the on-site personnel is reduced, and the objectivity, accuracy and reliability of the inspection result is improved. FIG. 13 is a schematic flowchart of an operating method of an operation optimizing subsystem according to embodiments of the present disclosure. As shown in FIG. 13, determining the startup sequence of the hydroelectric generating units under different working conditions according to the operating data includes the following actions. In S1301, an operating efficiency, a first operating state, a first cumulative operating time corresponding to each of the hydroelectric generating units under a steady-state condition are determined according to the operating data, and a first target startup sequence of the hydroelectric generating units under the steady-state condition is determined according to the operating efficiency, the first operating state, the first cumulative operating time corresponding to each of the hydroelectric generating units. The hydroelectric generating units are provided in the hydroelectric plant for hydroelectric generation, and the number of the hydroelectric generating units may be determined according to the scale of the hydroelectric plant, which will not be limited in the present disclosure. Operation under the steady-state condition may be understood as that the hydroelectric generating units operate under a good working condition, and when operating under the steady-state condition, the hydroelectric generating units each may correspondingly have an operating efficiency, an operating state (i.e., the first operating state), and a cumulative operating time (i.e., the first cumulative operating time). Further, the operating efficiency, the first operating state, and the first cumulative operating time may be determined from the operating data acquired by the data acquiring subsystem. An order obtained by ranking the startup sequence of the hydroelectric generating units according to the D operating efficiency, the first operating state, the first cumulative operating time and other factors may be called the first target starting sequence. In some embodiments, determining the first target startup sequence of the hydroelectric generating units under the steady-state condition includes the following actions. In step 20, guide vane opening data of each of the hydroelectric generating units within a predetermined time is acquired. In embodiments of the present disclosure, the guide vane opening data of each of the hydroelectric generating units within the predetermined time under the steady-state condition may be acquired. For example, the guide vane opening data of each hydroelectric generating unit within one year may be obtained from the operating data acquired by the data acquiring subsystem, and the guide vane opening data may also correspond D to load data of the hydroelectric generating unit. In addition, in the process of obtaining the guide vane opening data, in order to improve data accuracy, the guide vane opening data may be filtered, for example, data obtained in the maintenance of the hydroelectric generating unit and abnormal guide vane opening sensor data may be excluded. In step 21, a guide vane opening mean of the hydroelectric generating unit in a first load interval preset is determined according to the guide vane opening data of the hydroelectric generating unit, and the guide vane opening mean is determined as the operating efficiency of the hydroelectric generating unit. The first load interval may be a load interval of the hydroelectric generator set under a normal operation state. For example, the first load interval may be an interval from 180 to 250 MW. In embodiments of the present disclosure, the guide vane opening mean of the hydroelectric generating unit in the first load interval may be calculated as the operating efficiency of the hydroelectric generating unit. Generally, in the same load interval, the lower the guide vane opening mean is, the higher the operating efficiency is. For example, the guide vane opening mean of a hydroelectric generating unit 1 is 63%, and the guide vane opening mean of a hydroelectric generating unit 2 is 67%, then the operating efficiency of the hydroelectric generating unit 1 is greater than the operating efficiency of the hydroelectric generating unit 2. That is, in embodiments of the present disclosure, the guide vane opening data of the hydroelectric generating unit may be used as the operating efficiency, so the operating efficiency of the hydroelectric generating unit can be intuitively and accurately reflected through the guide vane opening. In some embodiments, in the action of determining the guide vane opening mean of the hydroelectric generating unit in the first load interval preset according to the guide vane opening data of the hydroelectric generating unit, a two-dimensional distribution diagram between the guide vane opening data and the corresponding load data. FIG. 14a is a two-dimensional distribution diagram of guide vane opening data according to embodiments of the present disclosure, where the ordinate represents the load data of a hydroelectric generating unit, and the abscissa represents the guide vane opening data of the hydroelectric generating unit. Further, a first regression model for indicating a relationship between guide vane opening and loads may be constructed according to the guide vane opening data and the corresponding load data. For example, a curve (i.e., the first regression model) is fitted according to data distribution in the two-dimensional distribution diagram. In some embodiments, the single-machine guide vane opening data is used as a dependent variable, the load is used as an independent variable, and a regression equation between the dependent variable Y and the independent variable x may be: Y = po + Pixi + 8, wherefpo and i represents regression coefficients, c represents a random error and independently obeys the normal distribution. The influencing factor x is brought into the above formula to obtain: yi= po + iXi+ 8i.

A linear sample regression equation is obtained as: Y ±/AA" The regression coefficient in the linear regression equation is estimated by the least square method, and

represented by a sum of square error (SSE): SSE Taking the partial derivative of SSE with respect to each of Po and i, and making each partial derivative equal to zero, to obtain standard equations: 5SSE ME =-2- (y--y) = 0

OE= -2y (y -y-)xi = 0 Safli , where i=1, 2, 3...m. By solving the above equations, estimated values of the regression coefficients o and §j may be obtained, so as to obtain the first regression model. In some embodiments, the first regression model may also be expressed as: y = CXa + bx, ory = c 'ln(X+ a') + b'x, where a, a', b, b', c, and c' are coefficient values of respective parts (a >1), and the solving method is similar to the above solving process, which will not be elaborated here. Further, the first load interval and a step length of the first load interval are determined. FIG. 14b is a schematic diagram showing a first load interval according to embodiments of the present disclosure, as shown in FIG. 14b, the first load interval is from 180 to 250 MW, for example. The step length of the first load interval may be determined according to actual application scenarios. FIG. 14c is a schematic diagram showing a step D length of the first load interval according to embodiments of the present disclosure, as shown in FIG. 14c, the step length in some embodiments may be 1 MW. Further, a plurality of guide vane opening sample data is determined by sampling in the first regression model based on the first load interval and the step length of the first load interval. For example, as shown in FIG. 14c, a plurality of guide vane opening data corresponding to 180, 181, 182, ... , and 250 MW respectively are taken from the curve of the first regression model, and used as the plurality of guide vane opening sample data. Further, a first arithmetic mean of the plurality of guide vane opening sample data is determined as the guide vane opening mean. In step 22, a first startup sequence of the hydroelectric generating units is determined according to the operating efficiency corresponding to each of the hydroelectric generating units under the steady-state condition.

Further, an order obtained by ranking the hydroelectric generating units according to the guide vane opening mean (i.e., the operating efficiency) may be called the first startup sequence. For example, the hydroelectric generating units are ranked in operating efficiency from high to low, obtaining the first startup sequence. That is, in embodiments of the present disclosure, the startup sequence of the hydroelectric generating units may be determined according to the operating efficiency of each of the hydroelectric generating units. For example, the hydroelectric generating units include such as Unit 1, Unit 2, Unit 3, Unit 4, and Unit 5, and the first startup sequence for example is: Unit 1, Unit 2, Unit 3, Unit 4, Unit 5. In step 23, heating data and oscillation data of multiple components in each of the hydroelectric generating units within a predetermined time are acquired. Further, the heating data and the oscillation data of the multiple components in each of the hydroelectric generating units within the predetermined time may be acquired. The heating data and the oscillation data in embodiments of the present disclosure are those of the hydroelectric generating unit acquired under the steady-state condition. In some embodiments, the multiple components include, for example, one or more of an upper guide, a lower guide, a water guide, a thrust-bearing shell, a stator, a rotor, and any other possible components of the hydroelectric generator, and the heating data may be heating data of each of the above components in operation, which will not be limited in the present disclosure. The oscillation data includes, for example, one or more of an upper guide X-direction runout, an upper guide Y-direction runout, a water guide X-direction runout, a water guide Y-direction runout, a thrust X-direction runout, a thrust Y-direction runout, an upper bracket X-direction vibration, an upper frame Y-direction vibration, an upper frame vertical vibration, a lower frame X-direction vibration, a lower frame Y-direction vibration, a lower frame vertical vibration, a top cover X-direction horizontal vibration, a top cover Y-direction horizontal vibration, a top cover Z-direction horizontal vibration, a stator core X-direction horizontal vibration, a stator base Z-direction horizontal vibration of the hydroelectric generator, which will not be limited in the present disclosure. In step 24, a temperature mean of the hydroelectric generating unit in a second load interval preset is determined according to the heating data. The second load interval may be a load interval of the hydroelectric generating unit under a normal operating state. For example, the second load interval may be an interval from 180 to 250 MW. That is, when the hydroelectric generating unit operates in the load interval from 180 to 250 MW, a mean of the heating data of various components is determined to obtain the temperature mean. In some embodiments, in the action of determining the temperature mean of the hydroelectric generating unit in the second load interval preset, a two-dimensional distribution diagram may be established between the heating data of each component and the corresponding load data. For example, the component is the stator of the hydroelectric generator. The heating data of the stator and the corresponding load data within one year are obtained from the operating data acquired by the data acquiring subsystem through big data technology, and a coordinate system is established, where the abscissa represents the load data of the hydroelectric generating unit, and the ordinate represents the heating data of the stator (i.e., the stator temperature). The two-dimensional distribution diagram of other components may be established in a D similar way to that of the stator of the hydroelectric generator, which will not be elaborated here. Therefore, for each component, there may be a corresponding two-dimensional distribution diagram. Further, according to the heating data of each component and the corresponding load data, a second regression model is constructed to represent the relationship between the heating situation of the component and the load of the hydroelectric generating unit, and a curve (i.e., the second regression model) is fitted according to the data distribution in the two-dimensional distribution diagram. In this way, for each component, a corresponding second regression model may be obtained. The form of the second regression model may be the same as that of the first regression model, which will not be elaborated here. Further, the second load interval and a step length of the second load interval are determined. For example, the second load interval may be an interval from 80 to 250 MW. The step length of the second load interval may D be determined according to an actual application scenario. For example, the step length of the second load interval may be 1 MW. Further, a plurality of temperature sample data of a corresponding component is determined by sampling in the second regression model based on the second load interval and the step length of the second load interval, and the plurality of temperature sample data is determined as the stator temperature sample data. Further, a second arithmetic mean of the plurality of temperature sample data of each component is determined. For example, a mean of a plurality of temperature sample data of the stator is determined as the second arithmetic mean of the stator. It may be understood that the determining process of the second arithmetic mean of other component may be the same as that of the stator, which will not be elaborated here. In this way, for each component, a corresponding second arithmetic mean may be determined. Further, a weighted average calculation is performed on a plurality of second arithmetic means corresponding to a plurality of components to determine the temperature mean. In other words, different components may correspond to different weights, and the weighted average calculation may be performed according to the second arithmetic mean of each component and the corresponding weight to obtain the temperature mean of each hydroelectric generating unit. For example, the temperature mean = (the second arithmetic mean of stator temperature * its weight + the second arithmetic mean of rotor temperature * its weight...)/n. In step 25, an oscillation mean of the hydroelectric generating unit in a third load interval preset is determined according to the oscillation data. The determining process of the oscillation mean may be the same as that of the temperature mean, which will not be elaborated here. In step 26, the first operating state of the hydroelectric generating unit is determined according to the temperature mean, the oscillation mean, a weight of the temperature mean, and a weight of the oscillation mean. For example, the temperature mean may be represented by JR, the oscillation mean may be represented by JZ, the weight corresponding to the temperature mean is for example 0.3, and the weight corresponding to the oscillation mean is for example 0.7. Then, the first operating state of each hydroelectric generating unit may be determined as 0.3JR + 0.7JZ. In step 27, a second startup sequence of the hydroelectric generating units is determined according to the first operating state corresponding to each of the hydroelectric generating units under the steady-state condition. In embodiments of the present disclosure, an order obtained by ranking the hydroelectric generating units according to the first operating state may be called as the second startup sequence. For example, the hydroelectric generating units are ranked in operating state from low to high, obtaining the second startup sequence. That is, in embodiment of the present disclosure, the startup sequence of the hydroelectric generating units may be determined according to the first operating state of each of the hydroelectric generating units. The second startup sequence may be, for example, Unit 4, Unit 1, Unit 2, Unit 3, Unit 5. In step 28, the first cumulative operating time corresponding to each of the hydroelectric generating units under the steady-state condition is determined. The first cumulative operating time may be an annual cumulative operating time under the steady-state condition, or it may be a cumulative operating time starting from the installation time, which will not be limited in the present disclosure. In step 29, the first target startup sequence is determined according to the first startup sequence, the second startup sequence, and the first cumulative operating time. In some embodiments, a first time difference between a maximum first cumulative operating time and a minimum first cumulative operating time may be determined. For example, the respective first cumulative operating time of Unit 1, Unit 2, Unit 3, Unit 4, and Unit 5 may be represented by TL1, TL2, TL3, TL4, and TL5, respectively, then the maximum first cumulative operating time may be expressed as TLmax = Max{TL1, TL2, TL3, TL4, TL5}, the minimum first cumulative operating time may be expressed as TLmin = Min{TL1, TL2, D TL3, TL4, TL5}, and the first time difference = TLmax - Tlmin. Further, the first time difference is compared with a first threshold to determine whether the first time difference is greater than or equal to the first threshold. The first threshold may be flexibly determined according to an application scenario. For example, if the first threshold is 1000 h, then determining whether the first time difference is greater than or equal to the first threshold is to determine whether the first time difference 1000 h. If the first time difference is greater than or equal to the first threshold (i.e., 1000 h), the unit having the maximum first cumulative operating time in the first startup sequence is moved to a last startup position to obtain a fifth startup sequence. For example, the unit having the maximum first cumulative operating time is Unit 2, whose first cumulative operating time is 1500 h, the unit having the minimum first cumulative operating time is Unit 3, whose first cumulative operating time is 300 h, then the first time difference is > 1000 h. In this case, Unit 2 having the maximum first cumulative D operating time in the first startup sequence is moved to the last startup position, obtaining the fifth startup sequence: Unit 1, Unit 3, Unit 4, Unit 5, Unit 2. In a case where the first time difference is less than the first threshold, the first startup sequence is determined as the fifth startup sequence. Further, a predetermined number of units with a low first operating state are determined according to the second startup sequence, and the units with the low first operating state in the fifth startup sequence are moved backwards to obtain the first target startup sequence. For example, the predetermined number is 2, then two units with the low first operating state are selected according to the second startup sequence, which are unit 4 and Unit 1, and Unit 4 and Unit 1 in the fifth startup sequence are moved backwards for example by one position, obtaining the first target startup sequence: Unit 3, Unit 1, Unit 5, Unit 4, Unit 2. Therefore, using the first target startup sequence determined by the ranking manner of embodiments of the present disclosure, the most efficient and healthy units can be selected to start up firstly, which ensures both the safety and the economic benefits of the device in the hydroelectric plant. In some embodiments, a shutdown sequence of the hydroelectric generating units under the steady-state condition may also be determined. Specifically, the first target startup sequence is reversed to obtain a candidate shutdown sequence. According to the above-mentioned first target startup sequence: Unit 3, Unit 1, Unit 5, Unit 4, Unit 2, the candidate shutdown sequence is determined as: Unit 2, Unit 4, Unit 5, Unit 1, Unit 3. Further, for each of the hydroelectric generating units, a corresponding continuous operating time under the steady-state condition is determined. The continuous operating time for example is an operating time of a hydroelectric generating unit from the last startup to the statistical time node. For example, the respective continuous operating time of Unit 1, Unit 2, Unit 3, Unit 4, and Unit 5 may be represented by TC1, TC2, TC3, TC4, and TC5, respectively. Further, a second time difference between a maximum continuous operating time and a minimum continuous operating time is determined, and it is determined whether the second time difference is greater than or equal to a second threshold. The maximum continuous operating time may be expressed as TCmax = Max{TC, TC2, TC3, TC4, TC5}, the minimum continuous operating time may be expressed as TCmin = Min{TC, TC2, TC3, TC4, TC5}, and the second time difference = TCmax - Tcmin. Further, the second time difference is compared with the second threshold to determine whether the second time difference is greater than or equal to the second threshold. The second threshold may be flexibly determined according to an application scenario. For example, if the second threshold is 100 h, then determining whether the second time difference is greater than or equal to the second threshold is to determine whether the second time difference > 100 h. If the second time difference is greater than or equal to the second threshold, the unit having the maximum continuous operating time in the candidate shutdown sequence is moved to a first shutdown position, and the unit having the minimum continuous operating time is moved to a last shutdown position, obtaining a target shutdown sequence. For example, if the unit having the maximum continuous operating time is Unit 4, and the unit having the minimum continuous operating time is Unit 5, then Unit 4 in the candidate shutdown sequence is moved to the first shutdown position, and Unit 5 in the candidate shutdown sequence is moved to the last shutdown position, obtaining the target shutdown sequence: Unitn4, Unitn2, Unitn1, Unit3,Unit 5. If the second time difference is less than the second threshold, the candidate shutdown sequence is determined as the target shutdown sequence. Therefore, the respective continuous operating time of the units may be considered when determining the shutdown sequence, so that the service life of the units may be increased while ensuring the economic benefits. In S1302, a defect level, a second operating state, and a second cumulative operating time corresponding to each of the hydroelectric generating units under an unsteady-state condition are determined according to the operating data, and a second target startup sequence of the hydroelectric generating units under the unsteady-state condition is determined according to the defect level, the second operating state, and the second cumulative operating time corresponding to each of the hydroelectric generating units. Operation under unsteady-state condition may be understood as the operation of hydroelectric generating units deviating from the good working condition, such as voltage-regulation operation, no-load operation, D operation in non-recommended interval, etc, which will not be limited in the present disclosure. In addition, when operating under the unsteady-state condition, the hydroelectric generating units each may correspondingly have a defect level, an operating state (i.e., the second operating state), and a cumulative operating time (i.e., the second cumulative operating time). An order obtained by ranking the startup sequence of the hydroelectric generating units according to the defect level, the second operating state, the second cumulative operating time and other factors may be called the second target startup sequence. In some embodiments, determining the second target startup sequence of the hydroelectric generating units under the unsteady-state condition includes the following actions. In step 31, the number of runner cavitations, a length of a runner crack, and the number of untreated defects D of each of the hydroelectric generating units within a predetermined time are acquired. In embodiments of the present disclosure, the number of the runner cavitations, the length of the runner crack, and the number of the untreated defects of each of the hydroelectric generating units within the predetermined time are acquired firstly. The number of the runner cavitations, the length of the runner crack, and the number of the untreated defects are data of the hydroelectric generating unit acquired under the unsteady-state condition. The predetermined time may be a maintenance period. That is, the number of the runner cavitations, the length of the runner crack, and the number of the untreated defects found during the maintenance may be obtained. In step 32, a cavitation grade corresponding to the number of the runner cavitations, a crack grade corresponding to the length of the runner crack, and a defect grade corresponding to the number of the untreated defects are determined according to preset grading rules. For example, cavitations may be divided into five grades according to the number of the cavitations from low to high: 0-19, 20-50, 50-100, 100-200, more than 200. According to this grading rule, the cavitation grade corresponding to the number of the runner cavitations of each hydroelectric generating unit may be determined. Cracks may be divided into five grades according to the length of the cracks from long to short: 0-29 mm, 30-79 mm, 80-149 mm, 150-300 mm, more than 300 mm. According to this grading rule, the crack grade corresponding to the length of the crack of each hydroelectric generating unit may be determined. Untreated defects may be divided into three types: type A, type B, and type C. If there exists a unit with a defect of type A, the unit will be deleted from the startup sequence (that is, it does not participate in the ranking); if there exists a unit with a defect of type B, the unit is arranged to the last startup position of the startup sequence; if there exists a unit with defects of type C, the number of defects of type C is regarded as the defect level. In step 33, the defect level of each of the hydroelectric generating units is determined according to the cavitation grade and a weight thereof, the crack grade and a weight thereof, and the defect grade and a weight thereof The cavitation grade, the crack grade, and the defect grade may have respective second weights. In the process of determining the defect level, weighted summation may be performed based on the cavitation grade, the crack grade, the defect grade and their respective second weights of each hydroelectric generating unit to obtain a weighted sum, which may be determined as the defect level of the hydroelectric generating unit. For example, the cavitation grade may be represented by Zq, and the second weight corresponding to the cavitation grade may be 0.2; the crack grade may be represented by Z, and the second weight corresponding to the crack grade may be 0.4; the defect grade may be represented by QX, and the second weight corresponding to the defect grade is 0.4, then the defect level of each hydroelectric generating unit = Zq*0.2 + Z*0.4 + QX*0.4. In step 34, a third startup sequence of the hydroelectric generating units is determined according to the defect level corresponding to each of the hydroelectric generating units under the unsteady-state condition. In other words, an order obtained by ranking the hydroelectric generating units under the unsteady-state condition according to the defect level may be called the third startup sequence. In step 35, a fourth startup sequence of the hydroelectric generating units is determined according to the second operating state corresponding to each of the hydroelectric generating units under the unsteady-state condition. The second operating state may be determined based on for example the heating data and the oscillation data of multiple components of each hydroelectric generating unit within a predetermined time under the unsteady-state condition. Further, the hydroelectric generating units are ranked according to their respective second operating states to obtain the fourth startup sequence under the unsteady-state condition. The determination of the second operating state is similar to that of the first operating state, which will not be elaborated here. In step 36, the second cumulative operating time corresponding to each of the hydroelectric generating units under the unsteady-state condition is determined. D The second cumulative operating time may be an annual cumulative operating time under the unsteady-state condition. For example, it may be the time of operation deviating from the good working condition, such as voltage-regulation operation, no-load operation, operation in non-recommended interval, etc, which will not be limited in the present disclosure. That is, an operating time of each hydroelectric generating unit deviating from the optimal working condition each year is determined. In step 37, the second target startup sequence is determined according to the third startup sequence, the fourth startup sequence, and the second cumulative operating time. In some embodiments, a time difference between a maximum second cumulative operating time and a minimum second cumulative operating time may be determined. For example, the respective second cumulative operating time of Unit 1, Unit 2, Unit 3, Unit 4, and Unit 5 may be represented by TL1, TL2, TL3, TL4, and TL5, ) respectively, then the maximum second cumulative operating time may be expressed as TLmax = Max{TL1, TL2, TL3, TL4, TL5}, the minimum second cumulative operating time may be expressed as TLmin = Min{TL1, TL2, TL3, TL4, TL5}, and the time difference = TLmax - Tlmin. Further, the time difference is compared with the second threshold to determine whether the time difference is greater than or equal to the second threshold. The second threshold may be flexibly determined according to an application scenario. For example, if the second threshold is 100 h, then determining whether the time difference is greater than or equal to the second threshold is to determine whether the time difference > 100 h. If the time difference is greater than or equal to the second threshold (i.e., 100 h), the unit having the maximum second cumulative operating time in the third startup sequence is moved to a last startup position to obtain a sixth startup sequence. For example, the unit having the maximum second cumulative operating time is Unit 2, whose second cumulative operating time is 150 h, the unit having the minimum second cumulative operating time is Unit 3, whose second cumulative operating time is 30 h, then the time difference is > 100 h. In this case, Unit 2 having the maximum second cumulative operating time in the third startup sequence is moved to the last startup position, obtaining the sixth startup sequence: Unit 1, Unit 3, Unit 4, Unit 5, Unit 2. In a case where the time difference is less than the second threshold, the third startup sequence is determined as the sixth startup sequence. Further, the second target startup sequence of the hydroelectric generating units under the unsteady-state condition is determined according to the fourth startup sequence and a third weight thereof, and the sixth startup sequence and a third weight thereof. In some embodiments, the operation optimizing subsystem is further configured to: determine information of target hydroelectric generating units for performing a power generation task according to a load distribution table that records multiple load distribution schemes of the hydroelectric generating units. The load distribution schemes are determined by an optimization algorithm based on a minimum water consumption model and a hydroelectric plant constraint condition. The power generation task is configured to instruct the hydroelectric plant to generate power, and the power generation task may correspond to a total load, i.e., the total load of the hydroelectric plant to perform the power generation task. In embodiments of the present disclosure, the total load of the power generation task may be received first. Further, the load distribution table that records the multiple load distribution schemes of the hydroelectric generating units may be read. For example, the hydroelectric generating units include Unit 1, Unit 2, Unit 3, Unit 4, and Unit 5. Under different total loads, the hydroelectric generating units may have different load distribution schemes. FIG. 15 shows a load distribution table according to embodiments of the present disclosure, as shown in FIG. 15, each row of the load distribution table represents a load distribution scheme, for example, the first row of the load distribution table represents a power generation task with a total load of 700,000 kw, where Unit 2, Unit 4, and Unit 5 will be started up, and Unit 2 is assigned with a load of 240,000 kw, Unit 4 is assigned with a load of 230,000 kw, and Unit 5 is assigned with a load of 230,000 kw, so as to complete the power generation task of 700,000 kw. The multiple load distribution schemes in this load distribution table are determined using the optimization algorithm based on the minimum water consumption model and the hydroelectric plant constraint condition. That is to say, each load distribution scheme is determined using the optimization algorithm under the premise of meeting the hydroelectric plant constraint condition and the minimum water consumption. Further, the load distribution table may be continuously determined, so that the load distribution table may be determined in real time with the change in the constraint condition and the water consumption. Therefore, the load distribution schemes in the load distribution table are optimal distribution schemes. In some embodiments, the minimum water consumption model is expressed as TN min W = 2[(H',P)-AT -u,+u(1-u-)Q + uj-(1-ui)Q,] t=1 i=1

O' H' P' where W represents a total water consumption of a hydroelectric plant; ria . I( represents Pt a power discharge of an i'f hydroelectric generating unit at a working head of Ht and a load of I in a period t t; AT represents a length of a period; 1 represents a state of the ith hydroelectric generating unit in the

ut 0 uI =1 period t, i when the ith hydroelectric generating unit is shut down, and i when the S ith hydroelectric generating unit operates; Qupirepresents water consumption during a startup process, including the amount of water equivalent to mechanical wear of the ith hydroelectric generating unit during the start-up process; Qdn,i represents water consumption during a shutdown process, including the amount of water equivalent to mechanical wear of the ith hydroelectric generating unit during the shutdown process; N represents the number of hydroelectric generating units; and T represents the number of periods during the scheduling. In some embodiments, the hydroelectric plant constraint condition includes at least one of: a hydroelectric plant load balance constraint, a water level variation constraint, a power output constraint of the hydroelectric generating unit, a power discharge constraint of the hydroelectric generating unit, an operating head constraint of the hydroelectric generating unit, and a spinning reserve capacity constraint, etc. In some embodiments, the optimization algorithm may be, for example, a dynamic programming algorithm, D and the load distribution table may be determined using the dynamic programming algorithm as follows.

Taking k as a stage number, where k = 1, 2...n, a corresponding optimal power discharge of the hydroelectric plant may be determined stepwise according to the number of the hydroelectric generating units and the load of the hydroelectric plant in an order from high to low using the following recursion formulas:

Q*(Nk, H)= min [Qk(Nk, H)+Qk*_,(N _k, H )]

Nk-_=Nk-Nk(k=l, 2 ,---,n)

Q,(N0 ,H)rO VN0

where Nk represents a total load of units 1 to k in a k' stage, QO7(Nki, H represents a total

working flow when load distribution among the units 1 to k is optimized under the total load of Nk and a

water head of H, represents a boundary condition with an initial value of 0. It is understandable that the above example is just an example of using the dynamic programming algorithm to solve the load distribution table. In practical applications, any other possible optimization algorithm can also be used to solve the load distribution table. For example, an annealing particle swarm algorithm may also be used, which will not be limited in the present disclosure. Further, the information of the target hydroelectric generating units for performing the power generation task may be determined from the hydroelectric generating units according to the total load and the load distribution table. The information of the target hydroelectric generating units includes, for example, the number of the target hydroelectric generating units, a serial number and a load of each of the target hydroelectric generating units, and any other possible information, which will not be limited in the present disclosure. For example, if the total load of the power generation task is 700,000 kw, the load distribution scheme determined according to the load distribution table is that Unit 2 is assigned with a load of 240,000 kw, Unit 4 is assigned with a load of 230,000 kw, and Unit 5 is assigned with a load of 230,000 kw. That is, the number of the target hydroelectric generating units is 3, their serial numbers are 2, 4, and 5, respectively, and the loads of the target hydroelectric generating units are 240,000 kw, 230,000 kw, and 230,000 kw, respectively. In some embodiments, each data row in the load distribution table represents a load distribution scheme. The action of determining the number of the target hydroelectric generating units for performing the power generation task from the hydroelectric generating units, and the serial number and load of each of the target hydroelectric generating units according to the total load and the load distribution table may include the following steps. In step 1, a row J is selected from the load distribution table, where the row J is any row of the load distribution table. D In step 2, a total load of the row Jis determined according to the following formula: n

J, =N(J,k) k-1 where N(J,k) represents a load of a kt unit in the row J. In step 3, it is determined whether Ji is equal to the total load of the power generation task, if Ji is equal to the total load of the power generation task, the number of units in the row Jis determined as the number of the target hydroelectric generating units, the serial numbers of the units in the row J are determined as the serial numbers of the target hydroelectric generating units, and the loads of the units in the row J are determined as the loads of the target hydroelectric generating units; if Ji is less than the total load of the power generation task, make J= J+ 1 and return to the step2; if J is greater than the total load of the power generation task, the loads of the units in the row J are adjusted using a two-point linear interpolation method to obtain the loads of the D target hydroelectric generating units. In practical applications, each row of the load distribution table may be traversed to determine whether there exists a row (i.e., a load distribution scheme) whose total load is equal to the total load of the power generation task; if yes, the operating units in this row are determined as the target hydroelectric generating units; if no, a row whose total load is greater than and closest to the total load of the power generation task is determined, and the operating units in this row are determined as the target hydroelectric generating units. For example, if J= 1, the first row of the load distribution table is selected, a total load Ji of the first row is R

J, =I N(J,k) determined as: k-1 = 240,000 kw + 230,000 kw + 230,000 kw = 700,000 kw, and J = the total load (such as 700,000 kw) of the power generation task, then the number of operating units in the first row is determined as the number of the target hydroelectric generating units, correspondingly, the serial numbers of the target hydroelectric generating units are Unit 2, Unit 4, and Unit 5, and the loads of the target hydroelectric generating units are 240,000 kw, 230,000 kw, and 230,000 kw, respectively. If J = 4, the fourth row of the load distribution table is selected, a total load Ji of the fourth row is n

J,= N(J,k) determined as: k-1 = 500,000 kw, which is less than the total load (such as 700,000 kw) of the power generation task, then make J= J+ 1. It should be illustrated that rows of the load distribution table may be traversed circularly. For example, if traverse to the first row, then continue to execute step 2; if the first row does not exist in the load distribution table, and it is determined after traversing that the total power of the second row (J=2) is greater than and closest to the total load of the power generation task, then operating units in the second row are determined as the target hydroelectric generating units, and loads of the units in the second row are adjusted using for example the two-point linear interpolation method to obtain the loads of the target hydroelectric generating units. FIG. 16 is a schematic flowchart of an operating method of a CBM support subsystem according to embodiments of the present disclosure. As shown in FIG. 16, determining the current operating state of the electrical device according to the operating data includes the following actions. In S1601, a target detection mode is determined according to a type of a target electrical device to be detected. The target electrical device may be an electrical device to be detected, which may have different types. For example, the target electrical device may be such as a generator, a transformer, a hydroelectric device, a breaker, a power line, etc., which will not be limited here. It is understandable that, in the present disclosure, for various types of target electrical device, a corresponding target detection mode may be determined according to the specific type of the target electrical device. For example, for the transformer, as one of the most important electrical device in a power system, the safety and reliability of its operation directly influence the safety and stability of the power system. The operating temperature of the transformer is a factor that has a crucial influence on the transformer itself. When the operating temperature of the transformer rises, the transformer will suffer a certain degree of danger and may accelerate the life reduction. Generally, within the temperature range of [80, 140], for every six degrees increase in temperature, the life of the transformer is reduced by half. Therefore, optionally, when the type of the target electrical device to be detected is a transformer, it may be determined that the target detection mode is working temperature detection. Alternatively, when the type of the target electrical device to be detected is a generator, it may be determined that the target detection mode is insulation performance detection. It is understandable that an electrical test parameter of the generator mainly characterizes the deterioration of the insulation performance of D the generator, and when determining the progress of the insulation aging of the generator, a power generation enterprise can reasonably arrange technical maintenance and modification works of the units according to the insulation aging situation of the generator. It should be noted that for the same type of target electrical device, there may be different target detection modes, which will not be limited in the present disclosure. In S1602, target detection data to be acquired and reference data are determined according to the target detection mode. The target detection data may be related data of the target electrical device, such as operating data, test data, environmental temperature data and so on of the target electrical device, which are not limited here. The reference data may be electrical data or other data of device of the same type as the target electrical D device. It is understandable that through the reference data, various references of the target electrical device may be integrated to evaluate the operating state of the target electrical device, which provides support for revealing the operating rule of the target electrical device and characterizing the state of the target electrical device. A reference electrical device may be an electrical device of the same type as the target electrical device, and a reference data set may be a collection of data sets of various periods established for the same type of electrical device. According to the target detection mode of the target electrical device, the inspection subsystem is able to acquire corresponding reference data from the reference data set. In some embodiments, in a case where the target detection mode is the insulation performance detection, it may be determined that the target detection data is a current capacitance value of a stator bar of the generator, and the reference data is historical operating data of multiple reference generators; or in the case where the target detection mode is the insulation performance detection, it is determined that the target detection data is a historical operating state parameter of the target electrical device, and the reference data is data of an electrical device of the same type as the target electrical device in various period. For example, if the target detection mode is the insulation performance detection, the inspection subsystem can determine that the target detection data to be acquired is the current capacitance value of the stator bar of the generator, and the reference data is the test result of the capacitance of other units of the same type. For different generators, the target detection data may be different. The generators may be large, medium or small hydroelectric generators, turbo-generators, or alternating-current generator, so the stator bar may be respective stator bar of various generators, which will not be limited here. In some embodiments, in a case where the target detection mode is the working temperature detection, in the action of determining the target detection data to be acquired and the reference data, it may be firstly determined that the target detection data to be acquired is current operating data of the transformer. The current operating data includes a testing point temperature, a current ambient temperature, and a current load of the transformer. It is understandable that the current operating data of the transformer may include various kinds of data, such as the current ambient temperature, the current load, a current operating state of a cooler, the number of coolers currently activated, a current inlet/outlet water temperature of a cooler, and a current inlet/outlet flow rate of a cooler, a coil temperature, and a top oil temperature of the transformer and so on, which will not be limited here. It should be noted that the operating temperature of the transformer has a very important impact on the transformer itself. When the temperature of the transformer increases, the transformer may suffer a certain degree of danger. Generally, a faulty cooling system, poor internal contact, overload, oil circuit blockage, or short circuit may cause the operating temperature of the transformer to rise, which is not limited here. Therefore, in order to discover the fault of the transformer in time, in embodiments of the present disclosure, the testing point temperature of the transformer may be acquired, and it may be determined whether the transformer operates under a fault condition according to the testing point temperature of the transformer. The testing point temperature may be the top oil temperature and/or coil temperature of the transformer. The ambient temperature may be measured in real time by a thermometer or other devices, a temperature sensor may be used to contact the temperature testing points to obtain the coil temperature and the top oil temperature, and the current load of the transformer may be determined by an ammeter, a voltmeter, a power meter or other instrument. Further, it is determined that the reference data is an ambient temperature and a load corresponding to a historical temperature sample interval. For example, for the top oil temperature of the transformer, since the D transformer relies on oil circulation, when a potential fault occurs in the transformer, the top oil temperature has not reached a warning value, so there exists a time delay. For this, in embodiments of the present disclosure, in order to discover the potential hazards of the transformer and discover the fault of the transformer in time, historical temperature sample intervals of the transformer during operation under various operating conditions may be determined according to the data of the transformer in various previous periods. The historical temperature sample interval may be a temperature interval of the testing point temperature when the transformer operates. It is understandable that the ambient temperature and the load are two factors that have a relatively large impact on the temperature of the transformer. In embodiments of the present disclosure, the ambient temperature and the load may be regarded as working conditions of the transformer, and the inspection subsystem is able to D determine various working conditions of the transformer according to the ambient temperature and load data of the transformer in various previous periods. For the same working condition, i.e., the same ambient temperature and load, the testing point temperature of the transformer when it operates may be in different temperature interval, so the historical temperature sample interval corresponding to each working condition may also be different. Therefore, in the present disclosure, the reference data corresponding to the working temperature detection may be the environmental temperature and load corresponding to the historical temperature sample interval. Further, historical ambient temperatures, historical loads, and historical testing point temperatures of the transformer are acquired. The historical ambient temperatures may be ambient temperatures of the transformer in various previous periods, the historical loads may be the loads of the transformer in various previous periods, and the historical testing point temperatures may be top oil temperatures and/or coil temperatures corresponding to the ambient temperatures and loads at the same time of various previous periods, which will not be limited here. Further, historical temperature sample intervals are determined according to the historical ambient temperatures, the historical loads, and the historical testing point temperatures of the transformer. In some embodiments, in the action of determining the historical temperature sample intervals, the historical ambient temperatures, the historical loads, and the historical testing point temperatures of the transformer may be acquired; the historical ambient temperatures are evenly divided into multiple temperature intervals; the historical loads are evenly divided into multiple load intervals; working condition sample groups of the transformer are determined according to the multiple temperature intervals and the multiple load intervals; and the historical temperature sample intervals are determined according to the respective historical testing point temperatures corresponding to the working condition sample groups of the transformer. The historical ambient temperatures may be the ambient temperatures of the transformer in various previous periods, the historical loads may be the loads of the transformer in various previous periods, and the historical testing point temperatures may be the top oil temperatures and/or coil temperatures corresponding to the ambient temperatures and loads at the same time of various previous periods, which will not be limited here. Specifically, the historical ambient temperatures may be evenly divided into multiple temperature intervals, and the historical loads may be evenly divided into multiple load intervals. For example, if the historical ambient temperatures of the transformer in the previous year are in the range of 18 °C to 35 °C, the system according to embodiments of the present disclosure can divide the historical ambient temperatures into multiple temperature intervals by a step of 0.2 °C. That is, the historical ambient temperatures in the range of 18 °C to 35 °C may be divided into A[18 °C, 18.2 °C], A2[18.2 °C, 18.4 °C], A3[18.4 °C, 18.6 °C]...A11[39.8 °C, 40 °C]. For example, the historical load may be divided by a step of 0.5 MW into multiple load intervals, such as B1, B2, B3, B4... Bn. The above examples are used to illustrate the present disclosure, and for specific division fineness of the historical ambient temperatures and the historical loads, it can be determined by those skilled in the art as required, which will not be limited here. Further, the temperature intervals and the load intervals are combined to determine the working condition sample groups, such as GI (Al, BI), G2 (Al, B2), G3 (A2, B2)... which is not limited here. By determining the historical testing point temperatures of each working condition sample group, the system according to embodiments of the present disclosure can obtain the historical temperature sample interval corresponding to each working condition sample group. For example, if the top oil temperature is used as the testing point temperature, the system according to embodiments of the present disclosure can obtain an interval of the top oil temperatures (i.e., the historical temperature sample interval) corresponding to the transformer under the working condition of G I(Al, B1). In S1603, the target detection data is acquired from the operating data of the target electrical device. It should be noted that in embodiments of the present disclosure, the target detection data required for the detection of the target electrical device may be extracted from the operating data of the target electrical device. For example, if the target detection mode is the insulation performance detection, the system according to ) embodiments of the present disclosure can obtain target detection data corresponding to the insulation performance detection. Taking the insulation resistance of the generator as an example, the system according to embodiments of the present disclosure can extract historical operating state parameters from the operating data of the generator as the target detection data. The historical operating state parameters may be operating state parameters of various previous periods corresponding to the target electrical device. For example, if the target electrical device is the generator, the operating state parameters may include insulation resistance data, leakage current data, partial discharge data, direct-current resistance data, dielectric loss data, capacitance data of the generator, and so on, which will not be limited here. It should be noted that an operating data library for the target electrical device may be established in ) advance. The operating data library may be a data set containing various types of electrical test data of various electrical device and other data obtained by analysis and calculation. In S1604, a current operating state of the target electrical device is determined according to the reference data and the target detection data. As a possible implementation, when the reference data is data of an electrical device of the same type as the target electrical device, to a certain extent, the reference data may be used as a reference for the related electrical data of the current target electrical device, that is, the reference data can provide support for the prediction of the current operating state of the target electrical device. Further, based on the operating state, a recommended condition based maintenance decision-making scheme is given. In some embodiments, if the operating state parameters of the current target electrical device are the same as or close to operating state parameters of a reference electrical device in various periods, for example, a difference therebetween is less than a preset threshold, then electrical test data of this reference electrical device operating for the same time may be used as reference, or the operating state parameter of the reference electrical device is used as the operating state parameter of the target electrical device in the current period. For example, if the operating state parameters of a current target electrical device A in the past four years are 12%, 22%, 32%, and 40%, respectively, and the operating state parameters of a reference electrical device B in the past six years are 11% , 22%, 33%, 39%, 15% and 26%, respectively, since the operating state parameters of the target electrical device in the past four years have a small difference (less than 1%) from that of the reference electrical device in the first four years of the past six years, it is considered that the current target electrical device A may have a same aging process as the reference electrical device B, so the operating state parameter of the reference electrical device in the fifth year, i.e., 15%, may be used as a predicted value of the target electrical device in the fifth year. It should be noted that this example is used for illustrating the present disclosure, and embodiments of the present disclosure are not limited thereto. It should be noted that, according to the current operating state parameter of the target electrical device, the system according to embodiments of the present disclosure is able to determine whether the current target electrical device is in a normal operating state. For example, a threshold may be set for the operating state parameter, if the current operating state parameter exceeds the threshold, it indicates that the current operating state of the target electrical device is not good, and a fault or damage may happen. In this case, an early warning may be given to a staff in time, so as to enable the staff to reasonably arrange technical maintenance and modification works of the units. In some embodiments, a target temperature interval corresponding to the current ambient temperature and the current load may be determined according to ambient temperatures and loads corresponding to historical temperature sample intervals. The target temperature interval may be a temperature interval when the transformer operates normally. It is understandable that for different ambient temperatures and different loads, the target temperature interval of the transformer may be different, which will not be limited in the present disclosure. It should be noted that the target temperature intervals of the transformer under various working conditions may be determined according to operating data of various dimensions of the transformer in various previous periods in the data library. Alternatively, a temperature boundary interval of the transformer may be obtained. The temperature boundary interval may be a temperature boundary range of the transformer in normal operation. If the temperature of the transformer exceeds this interval, it indicates that the transformer operates beyond historical working conditions and may fall into a fault operating interval. Alternatively, a temperature boundary interval of the transformer may be obtained. The temperature boundary interval may be a temperature boundary range of the transformer in normal operation. If the temperature of the transformer exceeds this interval, it indicates that the transformer operates beyond historical working conditions and may fall into a fault operating interval. It should be noted that after acquiring the current ambient temperature and the current load corresponding to the current transformer, the working condition sample group corresponding to the current ambient temperature and the current load may be determined, and then the target temperature interval corresponding to this working D condition sample group may be determined. Further, the current operating state of the transformer is determined according to the testing point temperature and the target temperature interval. For example, if the testing point temperature is not within the target temperature interval, it indicates that the current transformer may have deviated from the normal operation, and a fault may occur. In this case, it may be determined that the current operating state of the transformer is abnormal. If the testing point temperature is within the target temperature interval, it indicates that the current transformer is in normal operation, that is, the operating state of the transformer is normal. In embodiments of the present disclosure, a corresponding maintenance mode can be determined for an electrical device according to the specific type of the electrical device, so that the operating state of the electrical D device may be accurately and effectively detected, which provides guarantee for maintaining device safety and safe production. In some embodiments, when the target electrical device to be detected is the generator, determining the current operating state of the target electrical device may include the following actions. First, a material of an insulating medium in the stator bar may be determined. The insulating medium may be composed of a solid material, such as rubber, plastic, glass, ceramic, etc., and may also be composed of a gaseous material, such as air, carbon dioxide, etc., which will not be elaborated here. Further, a mapping relationship model is acquired according to the material of the insulating medium. It should be noted that the insulating medium may be broken down under certain external conditions, such as under high temperature or high voltage. For insulating media of different materials, the corresponding residual breakdown voltages are often different. Therefore, in embodiments of the present disclosure, for insulating media of different materials, calculation can be performed based on the different mapping relationship models. The mapping relationship model may be a mathematical model (such as a unary linear function model) or a neural network model, which may be used to represent a relationship between preset capacitance values and residual breakdown voltages. The preset capacitance values may have a one-to-one correspondence with the residual breakdown voltages. For example, the mapping relationship model may be a unary linear regression function. For example, the following formula may be used: y = #o + #ix + c, where x is a variable representing the capacitance value, y is a variable representing the residual breakdown voltage, #o is a regression constant, #3 is a regression coefficient, and c is an influence factor. It is understandable that the relationship indicated by the above formula may be divided into two parts, one part is #o + #x, which indicates the change of y caused by the change of x, and the other part is c, which indicates the change caused by any random factor. Further, a current residual breakdown voltage corresponding to the current capacitance value is determined based on the mapping relationship model between preset capacitance values and residual breakdown voltages. It should be noted that, since the preset capacitance values are in the one-to-one correspondence with the residual breakdown voltages, the system according to embodiments of the present disclosure can determine a residual breakdown voltage corresponding to a preset capacitance value according to the mapping relationship model. Further, a remaining operating time of the target electrical device is determined according to the current residual breakdown voltage and a safe voltage threshold corresponding to the target electrical device. In some embodiments, historical operating data of the generator is acquired. The historical operating data includes breakdown voltages and respective historical operating time of the generator. The historical operating data may be electrical data of the generator over the years, including such as capacitance values, breakdown voltage values, historical operating time lengths and the like in various periods, which will not be limited here. It should be noted that a data library of various generators may be established in advance, and the data library includes electrical data of various dimensions of various types of generators in various operating periods. Therefore, in embodiments of the present disclosure, various kinds of historical operating data may be extracted from the aforementioned data library, which provides data support for characterizing the operating and aging rules of the generator, so as to construct models and rules more accurately. Further, a mapping relationship between breakdown voltages and operating time corresponding to the generator is determined according to the historical operating data. It should be noted that after collecting the historical operating data of various periods, a mapping relationship between respective breakdown voltages and operating time may be determined according to test results of capacitances of generators of various units of the same type. Further, the remaining operating time of the generator is determined based on the mapping relationship, the current residual breakdown voltage, and the safe voltage threshold corresponding to the generator. It should be noted that in embodiments of the present disclosure, a preset rule may be established, such as ) the mapping relationship between residual breakdown voltages and operating time of the generator, such as a functional relationship. Alternatively, a neural network model may be trained in advance. For example, the safety voltage threshold is 22 k, and the linear model between breakdown voltages and remaining operating time is y = -0.1415x + 79.966 + 0.991. If the current breakdown voltage is 70 k, then the corresponding operating time is 77 months. The operating time corresponding to the safe voltage threshold is 410 months, so the remaining operating time is 410-77=333 months. To sum up, with the embodiments of the present disclosure, in the operation and maintenance of the hydroelectric plant, the inspection accuracy is improved, the labor costs are reduced, the economic benefits of hydroelectric plant are improved, and the service life of electrical device is prolonged, and intelligent operation and maintenance of the hydroelectric plant is achieved. D Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed here. This application is intended to cover any variations, uses, or adaptations of the disclosure following the general principles thereof and including such departures from the present disclosure as come within known or customary practice in the art. It is intended that the specification and examples be considered as illustrative only, with a true scope and spirit of the disclosure being indicated by the following claims. It will be appreciated that the present disclosure is not limited to the exact construction that has been described above and illustrated in the accompanying drawings, and that various modifications and changes can be made without departing from the scope thereof. It is intended that the scope of the disclosure only be limited by the appended claims.

Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the present disclosure disclosed here. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure following the general principles thereof and including such departures from the present disclosure as come within known or customary practice in the art. It is intended that the specification and examples be considered as illustrative only, with a true scope and spirit of the present disclosure being indicated by the following claims. It will be appreciated that the present disclosure is not limited to the exact construction that has been described above and illustrated in the accompanying drawings, and that various modifications and changes can be made without departing from the scope thereof. It is intended that the scope of the present disclosure only be limited by the appended claims. It should be illustrated that, in descriptions of the present disclosure, terms such as "first" and "second" are used herein for purposes of description and are not construed as indicating or implying relative importance or significance. Furthermore, in the description of the present disclosure, the phrase "a plurality of' means two or more than two, unless specified otherwise. Any process or method described in a flow chart or described herein in other ways may be understood to represent a module, segment, or portion that includes codes of one or more executable instructions for implementing specific logical functions or steps in the process. Moreover, advantageous embodiments of the present disclosure include other implementations in which functions may be executed in an order different from what is shown or discussed herein, for example, the functions may be executed substantially the same time or in an opposite order based on the involved functions, which could be understood by those skilled in the art. It should be understood that each part of the present disclosure may be realized by hardware, software, firmware or their combination. In the above embodiments, a plurality of steps or methods may be realized by the software or firmware stored in the memory and executed by the appropriate instruction execution system. For example, if to be realized by the hardware, likewise in another embodiment, the steps or methods may be realized by any one or a combination of the following techniques well known in the art: a discrete logic circuit having a logic gate circuit for realizing a logic function of a data signal, an application-specific integrated circuit having an appropriate combination logic gate circuit, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. Those skilled in the art shall understand that all or a part of steps in the above method according to embodiments of the present disclosure may be achieved by commanding the related hardware with programs. The programs may be stored in a computer readable storage medium, and the programs, when executed, include one or a combination of the steps in the method embodiments of the present disclosure. In addition, various function units in various embodiments of the present disclosure may be integrated in a processing module, or these units may physically exist separately, or two or more units are integrated in a module. The integrated module may be realized in a form of hardware or in a form of a software function module. The integrated module, when realized in the form of software function module and sold or used as a standalone product, may be stored in a computer readable storage medium. The storage medium mentioned above may be a read-only memory, a magnetic disk or a CD, etc. In the description of the present disclosure, reference throughout this specification to "an embodiment," "some embodiment," "example," "a specific example," or "some examples," means that a particular feature, D structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In the specification, the illustrative representations of the above terms are not necessarily referring to the same embodiment or example of the present disclosure. Furthermore, the described particular features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples. Although explanatory embodiments have been illustrated and described above, it would be appreciated by those skilled in the art that these embodiments are illustrative and explanatory and cannot be construed to limit the present disclosure, and changes, modifications, alternatives and varieties can be made in the embodiments by those skilled in the art without departing from scope of the present disclosure.

Claims (20)

What is claimed is:

1. A method for operating state analysis and early warning of an auxiliary device of a hydroelectric station, comprising: obtaining start-stop time data of a target auxiliary device of the hydroelectric station and working condition data of a generator unit in a preset time period; determining a plurality of operating time durations and/or a plurality of start-stop time intervals of the target auxiliary device under different working conditions according to the start-stop time data and the working condition data; determining a health value range of the target auxiliary device according to the plurality of operating time durations and/or the plurality of start-stop time intervals; and setting an early warning condition according to the health value range, so as to perform an early warning for a current operating situation of the target auxiliary device; wherein determining the health value range of the target auxiliary device according to the plurality of operating time durations and/or the plurality of start-stop time intervals comprises: sorting the plurality of operating time durations and/or the plurality of start-stop time intervals to obtain a sorting result; determining a target position in the sorting result according to a data volume of the operating time duration and/or the start-stop time intervals, and respective confidences of the operating time duration and/or the start-stop time intervals; rounding a time value of the target position, and determining an up health limit and a down health limit according to the rounded time value; and determining the health value range according to the up health limit and the down health limit.

2. The method according to claim 1, wherein determining the health value range according to the up health limit and the down health limit comprises: determining an up margin for the up health limit, and determining a down margin for the down health limit; and determining the health value range according to the up health limit, the up margin, the down health limit, and the down margin.

3. The method according to claim 1, wherein the confidence comprises a first confidence and a second confidence, and wherein setting the early warning condition according to the health value range comprises: setting a first-level early warning condition to be greater than or equal to an up health limit of a health value range corresponding to the first confidence, and/or less than or equal to a down health limit of the health value range corresponding to the first confidence; and setting a second-level early warning condition to be greater than or equal to an up health limit of a health value range corresponding to the second confidence, and/or less than or equal to a down health limit of the health value range corresponding to the second confidence.

4. The method according to claim 1, wherein the start-stop time data comprises start/stop state identifiers, and wherein determining the plurality of operating time durations and/or the plurality of start-stop time intervals D of the target auxiliary device under different working conditions according to the start-stop time data and the working condition data comprises: determining a plurality of operating ranges and/or a plurality of start-stop ranges of the target auxiliary device under different working conditions according to the start/stop state identifiers and the working condition data; and determining the plurality of operating time durations and/or the plurality of start-stop time intervals according to the start-stop time data and the plurality of operating ranges and/or the plurality of start-stop ranges.

5. The method according to claim 4, before determining the plurality of operating time durations and/or the plurality of start-stop time intervals, further comprising: marking a range with an abnormal start/stop state identifier in the plurality of operating ranges and/or the D plurality of start-stop ranges.

6. The method according to claim 1, before determining the plurality of operating time durations and/or the plurality of start-stop time intervals, further comprising: filtering off data generated under manual control in the start-stop time data.

7. The method according to claim 1, wherein the target auxiliary device is any one of: a gas machine of a gas system of the hydroelectric station, a governor oil pump of the hydroelectric station, or a water pump of a water supply and drainage system of the hydroelectric station.

8. An apparatus for operating state analysis and early warning of an auxiliary device of a hydroelectric station, comprising: an obtaining module configured to obtain start-stop time data of a target auxiliary device of the hydroelectric station and working condition data of a generator unit in a preset time period; a first determining module configured to determine a plurality of operating time durations and/or a plurality of start-stop time intervals of the target auxiliary device under different working conditions according to the start-stop time data and the working condition data; a second determining module configured to determine a health value range of the target auxiliary device according to the plurality of operating time durations and/or the plurality of start-stop time intervals; and an early warning module configured to set an early warning condition according to the health value range, so as to perform an early warning for a current operating situation of the target auxiliary device; wherein the second determining module is further configured to: sort the plurality of operating time durations and/or the plurality of start-stop time intervals to obtain a sorting result; determine a target position in the sorting result according to a data volume of the operating time duration and/or the start-stop time intervals, and respective confidences of the operating time duration and/or the start-stop time intervals; round a time value of the target position, and determine an up health limit and a down health limit according to the rounded time value; and determine the health value range according to the up health limit and the down health limit.

9. A non-transitory computer-readable storage medium having stored therein computer instructions, wherein the computer instructions are configured to cause a computer to perform the method according to any one of claims 1 to 7.

10. A decision support system for hydroelectric production, comprising: a data acquiring subsystem, connected with each of a plurality of hydroelectric devices, and configured to acquire operating data of each of the plurality of hydroelectric devices; an inspection subsystem, connected with the data acquiring subsystem, and configured to acquire the operating data of each of the plurality of hydroelectric devices from the data acquiring subsystem, and determine whether an abnormal event occurs in an observation item of a hydroelectric plant inspection scenario according to the operating data; an operation optimizing subsystem, connected with the data acquiring subsystem, and configured to acquire the operating data of each of the plurality of hydroelectric devices from the data acquiring subsystem, and determine a startup sequence of hydroelectric generating units under different working conditions according to the operating data; and a condition based maintenance (CBM) support subsystem, connected with the data acquiring subsystem, and configured to acquire the operating data of each of the plurality of hydroelectric devices from the data acquiring subsystem, determine a current operating state of an electrical device according to the operating data, and support condition based maintenance of the electrical device; wherein determining the startup sequence of the hydroelectric generating units under different working conditions according to the operating data comprises: determining an operating efficiency, a first operating state, a first cumulative operating time corresponding to each of the hydroelectric generating units under a steady-state condition according to the operating data, and D determining a first target startup sequence of the hydroelectric generating units under the steady-state condition according to the operating efficiency, the first operating state, the first cumulative operating time corresponding to each of the hydroelectric generating units; determining a defect level, a second operating state, and a second cumulative operating time corresponding to each of the hydroelectric generating units under an unsteady-state condition according to the operating data, and determining a second target startup sequence of the hydroelectric generating units under the unsteady-state condition according to the defect level, the second operating state, and the second cumulative operating time corresponding to each of the hydroelectric generating units.

11. The system according to claim 10, wherein determining whether the abnormal event occurs in the observation item of the hydroelectric plant inspection scenario according to the operating data comprises: D acquiring multimedia data and sensory modal data of the hydroelectric plant inspection scenario from the operating data; determining an inspection result corresponding to a hydroelectric plant based on the multimedia data and the sensory modal data; determining an actual measurement value corresponding to the observation item of the hydroelectric plant inspection scenario according to the inspection result; determining multiple reference items corresponding to the observation item, and determining multiple reference values corresponding to the multiple reference items, respectively; determining a measurement threshold corresponding to the observation item according to the multiple reference values; and determining whether the abnormal event occurs in the observation item according to the actual measurement value and the measurement threshold.

12. The system according to claim 10, wherein determining the current operating state of the electrical device according to the operating data comprises: determining a target detection mode according to a type of a target electrical device to be detected; determining target detection data to be acquired and reference data according to the target detection mode; acquiring the target detection data from operating data of the target electrical device; and determining a current operating state of the target electrical device according to the reference data and the target detection data.

13. The system according to claim 11, wherein the inspection subsystem is further configured to: determine first state information of a component to which the observation item belongs, environmental state information of the hydroelectric plant inspection scenario, and second state information of a component to which an associated observation item associated with the observation item belongs; determine threshold change information corresponding to the observation item according to the first state information, and/or the environmental state information, and/or the second state information; and configure the measurement threshold to be a target threshold according to the threshold change information; wherein determining whether the abnormal event occurs in the observation item according to the actual measurement value and the measurement threshold comprises: determining whether the abnormal event occurs in the observation item according to the actual measurement value and the target threshold.

14. The system according to claim 11, wherein a generator and a hydroturbine are comprised in the hydroelectric plant inspection scenario; wherein acquiring the multimedia data of the hydroelectric plant inspection scenario comprises: acquiring image data corresponding to each of a governor system, an excitation system, and a protective system associated with the generator; acquiring thermal imaging data and temperature data of a power generating layer of the generator; acquiring thermal imaging data and temperature data of each of an excitation slip ring and a wind tunnel outlet of the generator; acquiring audio data of a stator of the generator, wherein the image data, the thermal imaging data, the temperature data, and the audio data are used as the multimedia data; acquiring operating audio data of each of a runner and a draft tube of the hydroturbine in a hydroturbine inspection scenario; and acquiring operating video data of each of a bearing, an oil tank, and a pipeline of the hydroturbine in the hydroturbine inspection scenario, wherein the operating audio data and operating video data are used as the multimedia data.

15. The system according to claim 10, wherein determining the first target startup sequence of the hydroelectric generating units under the steady-state condition according to the operating efficiency, the first operating state, the first cumulative operating time corresponding to each of the hydroelectric generating units comprises: D determining a first startup sequence of the hydroelectric generating units according to the operating efficiency corresponding to each of the hydroelectric generating units under the steady-state condition; determining a second startup sequence of the hydroelectric generating units according to the first operating state corresponding to each of the hydroelectric generating units under the steady-state condition; determining the first cumulative operating time corresponding to each of the hydroelectric generating units under the steady-state condition; and determining the first target startup sequence according to the first startup sequence, the second startup sequence, and the first cumulative operating time.

16. The system according to claim 10, wherein determining the second target startup sequence of the hydroelectric generating units under the unsteady-state condition according to the defect level, the second D operating state, and the second cumulative operating time corresponding to each of the hydroelectric generating units comprises: determining a third startup sequence of the hydroelectric generating units according to the defect level corresponding to each of the hydroelectric generating units under the unsteady-state condition; determining a fourth startup sequence of the hydroelectric generating units according to the second operating state corresponding to each of the hydroelectric generating units under the unsteady-state condition; determining the second cumulative operating time corresponding to each of the hydroelectric generating units under the unsteady-state condition; and determining the second target startup sequence according to the third startup sequence, the fourth startup sequence, and the second cumulative operating time.

17. The system according to claim 15, wherein the operation optimizing subsystem is further configured to: acquire guide vane opening data of each of the hydroelectric generating units within a predetermined time; and determine a guide vane opening mean of the hydroelectric generating unit in a first load interval preset according to the guide vane opening data of the hydroelectric generating unit, and determining the guide vane opening mean as the operating efficiency of the hydroelectric generating unit.

18. The system according to claim 17, wherein determining the guide vane opening mean of the hydroelectric generating unit in the first load interval preset according to the guide vane opening data of the hydroelectric generating unit comprises: constructing a first regression model for indicating a relationship between guide vane openings and loads according to the guide vane opening data and respective load data; determining the first load interval and a step length of the first load interval; determining a plurality of guide vane opening sample data by sampling in the first regression model based on the first load interval and the step length of the first load interval; and determining a first arithmetic mean of the plurality of guide vane opening sample data as the guide vane opening mean.

19. The system according to claim 15, wherein the operation optimizing subsystem is further configured to: acquire heating data and oscillation data of multiple components in each of the hydroelectric generating units within a predetermined time; determine a temperature mean of the hydroelectric generating unit in a second load interval preset according to the heating data; determine an oscillation mean of the hydroelectric generating unit in a third load interval preset according to the oscillation data; and determine the first operating state of the hydroelectric generating unit according to the temperature mean, the oscillation mean, a weight of the temperature mean, and a weight of the oscillation mean.

20. The system according to claim 15, wherein determining the first target startup sequence according to the first startup sequence, the second startup sequence, and the first cumulative operating time comprises: determining a first time difference between a maximum first cumulative operating time and a minimum first cumulative operating time, and determining whether the first time difference is greater than or equal to a first threshold; moving a hydroelectric generating unit having the maximum first cumulative operating time in the first startup sequence to a last startup position to obtain a fifth startup sequence, in response to determining that the first time difference is greater than or equal to the first threshold; otherwise, determining the first startup sequence as the fifth startup sequence; and determining a predetermined number of hydroelectric generating units with a low first operating state according to the second startup sequence, and moving backwards the hydroelectric generating units with the low first operating state in the fifth startup sequence to obtain the first target startup sequence.