An analysis method and system for automatic starting process exception of a hydropower station

Abstract

The application relates to the technical field of hydropower station detection, in particular to a hydropower station automatic starting process abnormality analysis method and system. The whole starting process is divided into multiple process steps, state monitoring points and timeout alarm points are set for each step, when process abnormality interruption is monitored, analysis and diagnosis are carried out, each step state point of scanning monitoring is located to find abnormal steps leading to process interruption, all associated data points and coupling logic between the data points of the abnormal steps are read, relevant real-time data are read, a source event leading to process abnormality interruption is found out by using a logic coupling analysis method, event records matched with data changes before and after the current fault are found, the current fault type and root cause are judged, a targeted maintenance and repair strategy module is formulated, and solution suggestions and preventive measures are generated. The application can quickly and accurately locate the source event of the fault, reduce fault troubleshooting time, and improve the operation efficiency and reliability of the hydropower station.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hydropower station detection, and particularly relates to a hydropower station automatic starting process anomaly analysis method and system. BACKGROUND

[0002] The starting process of a hydropower station is a highly complex process, involving the coordinated work of numerous devices and the accurate control of multiple links. The existing method for solving the problem of the starting process of a hydropower station usually focuses on decomposing the starting process into a series of starting steps determined based on device characteristics and operating experience. Each step is usually judged by a specific coupling relationship between a group of key data points to determine whether it can proceed smoothly. On this basis, a series of problems are derived:

[0003] (1) The abnormal interruption of the starting process of a hydropower station can be caused by more than one fault, such as abnormal bearing temperature, unsuccessful grid connection, and failure to raise voltage. The existing analysis method usually reports events in chronological order or relies on operation and maintenance personnel to check detailed log records and real-time monitoring data to manually screen events. It has no ability to automatically identify and judge the nonlinear coupling relationship between events and cannot quickly and accurately locate the source event that caused the failure of this starting;

[0004] (2) For the specific events found, the existing technology can only analyze real-time data and cannot accurately analyze from historical data which aspect of device failure, operation error, or external environmental factors caused the problem and give decision recommendations accordingly. In addition, it also lacks predictive analysis capability and cannot provide early warning of potential faults;

[0005] (3) The content and style of the analysis report generated by the existing technology are relatively single, the visualization degree is low, and there is a lack of key technical parameters of a hydropower station, in-depth professional insights, and other actual needs of users. It cannot achieve the ideal report style in the minds of users, affecting the user experience. SUMMARY

[0006] In view of the above-mentioned defects in the prior art, the present application provides a hydropower station automatic starting process anomaly analysis method and system.

[0007] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0008] In a first aspect, the present application provides a hydropower station automatic starting process anomaly analysis method, comprising the following steps:

[0009] S1, according to different devices involved, the entire starting process is divided into multiple process steps, state monitoring points and timeout alarm points are set for each step, the entire process is monitored in real time, and when a process anomaly interruption is monitored, the system is automatically triggered for analysis and diagnosis;

[0010] S2, scan each step state point of the monitoring, locate the abnormal step causing the process interruption, read all associated data points and coupling logic between data points under the abnormal step;

[0011] S3, read the relevant real-time data, find all the data points causing the process abnormality by using the logical coupling analysis method, record them, and find the source event causing the process abnormality interruption within the process time range without action record;

[0012] S4, trace the previous historical startup process data, analyze the past startup process log, find the event record matching the data change before and after the current fault, and judge the fault type and source;

[0013] S5, use the expert trace analysis method to develop a targeted maintenance and repair strategy module, input the source event and fault type, and generate targeted solutions and preventive measures;

[0014] S6, build a report generation model to summarize the fault analysis statistics in the report.

[0015] In some embodiments, in step S1, a timeout alarm point is set for each step. When the step does not meet the startup condition after the coupling logic operation of all conditions within the step is performed beyond the limited time, it is determined that the process is abnormal and interrupted, and the system is automatically triggered for analysis and diagnosis.

[0016] In some embodiments, in step S3, the process of finding the source event causing the process abnormality interruption includes:

[0017] S31, trace the historical data to find the last action time of each startup process step timeout alarm point, compare the time before and after to obtain the last timeout alarm point step, and record this time as the process end time endTime;

[0018] S32, record the process end time as the assumed start time, find the startup process abnormality interruption time in the historical data from the assumed start time to the process end time, and record it as the process start time startTime;

[0019] S33, use the coupling logic analysis method to analyze all coupling conditions associated with the abnormal step, and find the source event.

[0020] In some embodiments, in step S33, the coupling logic analysis method includes the following steps:

[0021] S331, start and traverse the specific steps under the abnormal step, and obtain the corresponding string of each specific step;

[0022] S332, convert the string into a logical relation formula, and split the logical relation formula according to the and logic and or logic therein into a specific multi-variable logical relation formula calculated by complex coupling logic of multiple variables;

[0023] S333, perform real-time data acquisition and logical judgment on each variable layer by layer from the outside to the inside, establish a variable numerical relation table, record all data points that do not meet the logic in the calculation process, if the innermost layer is not reached, temporarily record the state that does not meet the logic as a correct state, so that the operation process proceeds normally until the innermost layer is reached, find out all data points that do not meet the logic that cause the process to be abnormal, and record them down;

[0024] S334, query the historical data, from the start time of issuing the start-up process instruction to the end time of process interruption, record the action time of all recorded points that do not meet the logic, and the point without action time record is the source event point.

[0025] In some embodiments, in step S5, the fault type includes equipment failure, operation error, environmental factor or wear and tear.

[0026] In some embodiments, in step S5, the expert trace analysis method includes:

[0027] S51, create a parameter table associated with specific data points and state switching time limit values; the parameter table includes long-term concerned data points and their respective warning value t0 and limit value t1 of the action time, wherein t1>t0>0;

[0028] S52, query the time used by the source event from issuing the start-up process command to the action of the source event point in the previous start-up failure process, that is, calculate the abnormal process time errTimeLong: errTimeLong=process interruption end time-start time of issuing start-up process command, use artificial intelligence algorithm to eliminate outliers, and obtain a relative average value t;

[0029] S51, correspond the source event point to the parameter table, and determine the fault type according to the calculated abnormal process time:

[0030] If t≥t1, it is determined that the fault type of this time is equipment failure;

[0031] If t<t1 but t≥t0, and the time used for state switching in the previous n start-up processes gradually increases, it is determined that the fault type of this time may be environmental influence or material fatigue;

[0032] If t<t0, it is determined that the fault type of this time is an accidental event of operation error.

[0033] In some embodiments, if the source event point is not found in the parameter table, the historical data is traced to count the actual start-up times K;

[0034] If K>10, the start-up times K is regarded as 10, if 0

[0035] The historical data is traced to count the time from the issuance of the start-up process command to the source event action in the past K start-up processes, the time lengths of the K times are compared to find the maximum value and the minimum value, and the limit interval range range is calculated: [minimum value-(maximum value-minimum value)*5%, maximum value+(maximum value-minimum value)*5%];

[0036] If the abnormal time is in the limit interval range, it is judged whether the time from the order to the actual action at this point gradually lengthens in the K start-up processes, if it gradually lengthens, the fault type is environmental factors or wear, if it does not gradually lengthen, the fault type is environmental factors, if the abnormal time is not in the limit interval range, the fault type is equipment failure.

[0037] In a second aspect, an automatic start-up process abnormality analysis system of a hydropower station comprises:

[0038] A state monitoring module is used to monitor each step and key data point in the entire start-up process in real time;

[0039] A timeout alarm module is used to determine the process interruption and automatically trigger the system to analyze and diagnose;

[0040] A fault analysis and diagnosis module is used to scan each step state point, locate the specific failure step causing the process interruption, and read all associated data points and coupling logic between the data points under the step;

[0041] A historical data tracing and analysis module is used to trace the source event, find the event record matching the data change before and after the current fault, and determine the current fault type and root cause;

[0042] A strategy development module is used to develop a targeted maintenance and repair strategy module, input the source event and the fault type, and generate a targeted solution suggestion and preventive measure;

[0043] A report generation module is used to provide intuitive visual elements to intuitively display the key technical parameters of the hydropower station and their change trends.

[0044] Based on the above technical solution, the embodiments of the present application can at least produce the following technical effects:

[0045] The application provides an automatic starting process abnormality analysis method and system for a hydropower station, which further accurately divides key event coupling points and realizes fine management of the starting process of the hydropower station. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from the structures shown in the drawings without creative labor.

[0047] Figure 1 is a flowchart of the automatic starting process abnormality analysis method for a hydropower station of the present application;

[0048] Figure 2 is a flowchart of the coupling logic analysis method of the present application;

[0049] Figure 3 is a program interface diagram of the embodiment of the present application for high-pressure oil injection;

[0050] Figure 4 is a principle diagram of the coupling logic analysis algorithm model of the embodiment of the present application;

[0051] Figure 5 is a logic relationship calculation principle diagram of the embodiment of the present application;

[0052] Figure 6 is a principle diagram of the calculation method for each minimum variable of the embodiment of the present application;

[0053] Figure 7 is a principle diagram of the calculation method for each layer of logic of the embodiment of the present application;

[0054] Figure 8 is a structure diagram of the automatic starting process abnormality analysis system for a hydropower station of the present application. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present application will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative effort should fall within the protection scope of the present application. In addition, the technical solutions in each embodiment can be combined with each other, but the combination of the technical solutions should be based on the fact that a person of ordinary skill in the art can realize the combination, and when the combination of the technical solutions appears contradictory or unachievable, it should be considered that the combination of the technical solutions does not exist and is not within the protection scope of the present application.

[0056] The object of the present application is achieved by the following technical solutions.

[0057] Please refer to Figure 1 The present application provides an analysis method for automatic starting process exception of a hydropower station, comprising:

[0058] S1, according to different devices involved, the whole starting process is divided into multiple process steps, a state monitoring point and an overtime alarm point are set for each step, the whole process is monitored in real time, when a process exception interruption is monitored, the system is automatically triggered to analyze and diagnose.

[0059] In the specific implementation process, the process steps include starting the cooling equipment, auxiliary equipment operation, pneumatic hydraulic system, high-pressure oil injection, opening the cylindrical valve, pulling the lock spindle, starting the speed regulator, injecting the excitation, grid connection operation, and each process step further includes multiple specific operation steps.

[0060] S2, scanning the state points of each step monitored, locating the abnormal step causing the process interruption, reading all associated data points and coupling logic between the data points under the abnormal step.

[0061] In the specific embodiment, the high-pressure oil injection includes starting the high-pressure oil pump, high-pressure oil pump operation, high-pressure oil lifting pressure > 6.8Mpa, water guide external circulation oil pump operation, push guide external circulation oil pump operation, and brake valve falling.

[0062] Among them, the high-pressure oil pump operation includes high-pressure oil 1 pump operation and high-pressure oil 2 pump operation, the water guide external circulation oil pump operation includes water guide external circulation 1 oil pump operation and water guide external circulation 2 oil pump operation, and the push guide external circulation oil pump operation includes push guide external circulation 1 oil pump operation and push guide external circulation 2 oil pump operation.

[0063] S3, reading the related real-time data, using a logic coupling analysis method to find out the source event causing the process exception interruption.

[0064] Step S3 specifically includes:

[0065] S31, trace historical data, find the last action time of each start-up process step timeout alarm point, compare the time before and after to get the last timeout alarm point step, and record this time as the process end time endTime;

[0066] S32, record the process end time as the assumed start time 10 minutes ago, find the start-up process abnormal interruption time in the historical data from the assumed start time to the process end time, and record it as the process start time startTime;

[0067] S33, using the coupling logic analysis method, analyze all coupling conditions associated with the abnormal step to find the source event.

[0068] In step S33, the coupling logic analysis method includes the following steps:

[0069] S331, start and traverse the specific steps under the abnormal step, including at least traversing the start of the high-pressure oil pump, the operation of the high-pressure oil No. 1 pump, the operation of the high-pressure oil No. 2 pump, the high-pressure oil lifting pressure > 6.8Mpa, the operation of the water guide external circulation No. 1 oil pump, the operation of the water guide external circulation No. 2 oil pump, the operation of the water guide external circulation No. 1 oil pump, the operation of the water guide external circulation No. 2 oil pump, and the brake valve falling down, to obtain the corresponding string of each specific step;

[0070] S332, convert the string into a logical relationship formula, and split the logical relationship formula according to the AND logic and OR logic in it into a specific multi-variable logical relationship formula calculated by multiple variables through complex coupling logic;

[0071] S333, from outside to inside, layer by layer, real-time data acquisition and logical judgment are performed on each variable, a variable value relationship table is established, all data points that do not meet the logic in the calculation process are recorded, if the innermost layer is not reached, the state that does not meet the logic is temporarily recorded as a correct state, so that the operation process is normally down until the innermost layer is reached, all data points that do not meet the logic leading to the process abnormality are found out and recorded;

[0072] S334, query the historical data, from the start of the start-up process instruction startTime to the process abnormal interruption endTime, all recorded action time records of the points that do not meet the logic, and the points without action time records are the source event points.

[0073] Specifically, please refer to Figure 2 , the coupling logic analysis method includes: reading the string containing coupling logic and data points associated with the Nth step in the configuration file C1 corresponding to the report; layer by layer deciphering the string in the program against the symbol conversion table defined in the method, building a structure in the program, and the built structure is as follows Figure 3As shown; filter out all data points and query their real-time data, convert the analog data into 0 or 1 through real-time data and reference value calculation; start calculation from the outermost layer to the inner layer: when encountering "AND logic", all its input results must be 1; when encountering "OR logic", only one of its input results must be 1; build a logical coupling analysis algorithm model for cyclic calculation, and the schematic diagram of the model is shown below. Figure 4 As shown, all illogical data points in the calculation process are recorded. If they have not reached the innermost layer, the state that does not meet the logic is temporarily recorded as the correct state, so that the calculation process can proceed normally until it reaches the innermost layer. In the query historical data, the action time records of all recorded illogical points during the period from the startTime when the power-on process instruction is issued to the endTime when the process is abnormally interrupted are recorded. The point without an action time record is the source event point.

[0074] In specific implementation, for a string containing complex coupled logic and data points, the symbol conversion surface defined by the control method is deciphered layer by layer. For example, for the step of injecting high-pressure oil, the outermost layer is to start the high-pressure oil pump, which is defined as base_points1. The operation of high-pressure oil pump No. 1 is defined as points1. The operation of high-pressure oil pump No. 2 is defined as points2. The high-pressure oil top-up pressure > 6.8Mpa is defined as points3>6.8. The operation of water-guided external circulation oil pump No. 1 is defined as points4. The operation of water-guided external circulation oil pump No. 2 is defined as points5. The derivation of external circulation oil pump No. 1 is defined as points6. The derivation of external circulation oil pump No. 2 is defined as points7. The brake valve drops as points8. According to the logical relationship between each step, "AND logic" and "OR logic" are used to express it in the configuration file as follows:

[0075] base_points1&((points1||points2)&(points3>6.8)&(points4||points5)&(points6||points7)&(points8)). Filter out all data points and query real-time data. Convert the analog data (such as points3>6.8) to 0 (points3 real-time value <= 6.8) or 1 (points3 real-time value > 6.8) through real-time data and reference value calculation. Calculate from the outermost layer to the inner layer and establish a variable value relationship table. The calculation method for each minimum variable is as follows: Figure 6 As shown, the calculation method for each layer of logic is as follows Figure 7As shown: encounter "and logic" then all its input results are 1; encounter "or logic" then only one of its all input results is 1. According to this basic logic to create algorithm model, in the calculation process, record the data points that do not meet the logic, if not to the innermost layer, temporarily record the state that does not meet the logic as the correct state, make the operation process normally down until reaching the innermost layer, the numerical relationship table established is shown in Table 1, and the corresponding logic relationship calculation principle is shown in Figure 5 As shown. Record all the recorded points that do not meet the logic as fault points, query the historical database event record table, and find the action time of all fault points from the start time of the issued start-up process command to the time when the process is abnormally interrupted. The fault points that have no action are the source event points.

[0076] Table 1 Variable numerical relationship table

[0077]

[0078] Step S4, trace the previous historical start-up process data, analyze the past start-up process log, find the event record matched with the data change before and after the current fault, and judge the current fault type and root cause.

[0079] Step S5, use the artificial intelligence method fused with the long-term operation and maintenance experience of hydropower station experts to verify and deeply analyze the fault cause, provide targeted solution suggestions and preventive measures, and if necessary, use charts and other methods to warn the trend of changes before and after the focus point.

[0080] The expert trace analysis method is to trace the specified fault event, summarize the operation and maintenance experience of experts in the field of hydropower, divide the fault into equipment fault, operation error, environmental influence and other reasons, and create a parameter table associated with specific data points and state switching time limit values.

[0081] Step S4 specifically includes:

[0082] Step S41: create a parameter table associated with specific data points and state switching time limit values; the parameter table includes long-term attention data points and their respective corresponding action time warning value t0 and limit value t1, wherein t1>t0>0;

[0083] Step S42: query the time used by the source event from the issuance of the start-up process command to the action of the source event point in the previous n start-up failure processes, that is, calculate the abnormal process time errTimeLong: errTimeLong=process abnormal interruption endTime-start time of the start-up process command start, use artificial intelligence algorithm, eliminate outliers, and obtain the relative average value t;

[0084] Step S43: Corresponding the source event point with the parameter table, judging the fault type according to the calculated abnormal flow time;

[0085] If t≥t1, it is judged that the fault type is equipment failure, which needs timely maintenance;

[0086] If t<t1 but t≥t0, and the time for state switching gradually increases in the previous n start-up processes, it is judged that the fault type is likely to be environmental influence or material fatigue, which needs close attention to the data point data;

[0087] If t<t0, it is judged that the fault type is an accidental event of operation error, which can be judged according to the actual situation on site, and the method is summarized in the algorithm module.

[0088] If the source event point is not found in the parameter table, the historical data is traced back to count the actual start-up times K;

[0089] If K>10, the start-up times K is regarded as 10, and if 0<K<10, K=the actual start-up times;

[0090] The historical data is traced back to count the time from the start-up flow command to the source event action in the past K start-up processes, the length of the K times is compared to find the maximum value and the minimum value, and the limit interval range range is calculated: [minimum value-(maximum value-minimum value)*5%, maximum value+(maximum value-minimum value)*5%];

[0091] If the abnormal time is in the limit interval range, it is judged whether the time from the order to the actual action at this point gradually increases in the K start-up processes, if it gradually increases, the fault type is environmental factors or wear, if it does not gradually increase, the fault type is environmental factors; if the abnormal time is not in the limit interval range, the fault type is equipment failure.

[0092] Specifically, after identifying the fault type this time, combined with the solutions of previous cases and the best practices of operation and maintenance, etc. professional knowledge and experience, the specific maintenance and repair strategy module (in particular, the historical data trend chart of the close attention point will be automatically generated to facilitate the operation and maintenance personnel to view) is written and formulated, and the system automatically generates the preliminary maintenance decision suggestion for this fault.

[0093] Step S6, a report generation model is constructed, and the above fault analysis statistical results are summarized into a report to facilitate later review. Users are allowed to customize analysis reports according to their own needs, including data points, parameters, logic, charts, hydropower station professional terms, and other specific report display information, to meet the practical needs of users.

[0094] Specifically, the report generation model is built by using an SSRS (SQL Server Reporting Services) framework, and the report generation model comprises a report design unit, a report management unit and a report generation unit. The report design unit is configured to allow a user to create and manage report content, format and data, and the report design unit provides a visual design interface, and the user can quickly create various reports by dragging report elements, setting properties, defining data sources and the like. The report management unit is configured to view, search and subscribe to reports, and the report management unit can create, edit, delete, export and the like of the reports, and the report management unit is also used for role definition and role allocation to control access rights of the user. The report generation unit is configured to process a report request, and the report generation unit can retrieve attribute, format setting information and data of the report through a programming interface and an extension plug-in, combine the format setting information with the data, and finally present a generated report.

[0095] In summary, the application provides a complete set of abnormal interruption analysis and tracing scheme for the starting process of a hydropower station: the process is divided into multiple steps according to the equipment, each step is coupled with multiple key data points and logic points, when the process is interrupted abnormally, the specific step failure can be directly monitored and located, the coupled logic under the step is further analyzed to obtain the source event, the experience of experts in operation and maintenance for many years and artificial intelligence algorithms are combined to analyze the fault type and give maintenance strategy suggestions, and trend early warning is performed on related data points as needed.

[0096] Referring to Figure 8 The application provides a kind of analysis system of abnormal automatic starting process of hydropower station, comprising:

[0097] State monitoring module: for real-time monitoring each step and key data point in the whole starting process;

[0098] Timeout alarm module: for determining process abnormal interruption, and automatically triggering system to analyze and diagnose;

[0099] Fault analysis and diagnosis module: scanning each step state point monitored, locating the specific failure step that leads to process interruption, and reading all associated data points and coupling logic between data points under the step;

[0100] Historical data tracing and analysis module: for historical tracing source event, find event record matched with data change before and after current fault, to determine current fault type and root;

[0101] Strategy formulation module: for formulating targeted maintenance and repair strategy module, input source event and fault type, generate targeted solution suggestion and preventive measures;

[0102] The report generation module is used to provide intuitive visual elements to intuitively display key technical parameters of the hydropower station and their change trends.

[0103] The above shows and describes the basic principles and main features of the present application and the advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for analyzing abnormalities in the automatic startup process of a hydropower station, characterized in that: The following steps are involved: S1. Divide the entire startup process into multiple steps based on the different devices involved. Set status monitoring points and timeout alarm points for each step to monitor the entire process in real time. When an abnormal interruption in the process is detected, the system will automatically trigger analysis and diagnosis. S2. Scan the status monitoring points of each step to locate the abnormal step that caused the process interruption, read all the associated data points under the abnormal step and the coupling logic between the data points; S3. Read relevant real-time data and use coupled logic analysis methods to identify the source event that caused the abnormal process interruption; The coupling logic analysis method comprises the following steps: Start and traverse the specific steps under the abnormal step, and obtain the string corresponding to each specific step; Convert the character string into a logical relational expression, and split the logical relational expression into a specific multi-variable logical relational expression consisting of multiple variables and complex coupled logic calculations according to the AND logic and OR logic; Perform real-time data acquisition and logical judgment on each variable layer by layer from the outside to the inside, establish a variable value relationship table, and record all illogical data points in the calculation process. If it has not reached the innermost layer, temporarily record the illogical state as the correct state, so that the calculation process can proceed normally until it reaches the innermost layer. Find all illogical data points that cause process abnormalities and record them; In the query history data, the action time records of all illogical points recorded from the startTime when the startup process instruction is issued to the endTime when the process is abnormally interrupted are recorded. The points without action time records are the source event points. S4. Trace back the historical startup process data, analyze the past startup process logs, find the event records that match the data changes before and after the current fault, and determine the current fault type and root cause; S5. Use expert tracing and analysis methods to develop targeted maintenance and repair strategy modules, input source events and fault types, and provide targeted solution suggestions and preventive measures; The expert retrospective analysis method is to conduct historical tracing of specified fault events, summarize the operation and maintenance experience of experts in the hydropower field, classify the faults into multiple causes, and create a parameter table that associates specific data points with state switching time limits; S6. Build a report generation model and summarize the fault analysis statistical results into a report.

2. The method for analyzing abnormalities in the automatic startup process of a hydropower station according to claim 1, characterized in that: In step S1, a timeout alarm point is set for each step. When the time limit is exceeded and all conditions in the step do not meet the startup conditions after coupled logic operations, it is determined that the process is abnormally interrupted and the system is automatically triggered for analysis and diagnosis.

3. The method for analyzing abnormalities in the automatic startup process of a hydropower station according to claim 1, characterized in that: In step S3, the process of finding the source event that caused the abnormal process interruption includes: S31. Trace back the historical data to find the last action time of the timeout alarm point of each startup process step, compare the time before and after to obtain the step of the last timeout alarm point, and record this time as the process end time endTime; S32. Record 10 minutes before the process end time as the assumed start time, and find the time when the startup process is abnormally interrupted from the assumed start time to the process end time in the historical data, and record it as the process start time startTime; S33. Use the coupled logic analysis method to analyze all the coupled conditions associated with the abnormal steps and find the source event.

4. The method for analyzing abnormalities in the automatic startup process of a hydropower station according to claim 1, characterized in that: In step S5, the fault types include equipment failure, operation error, environmental factors or wear.

5. The method for analyzing abnormalities in the automatic startup process of a hydropower station according to claim 1, characterized in that: If the source event point is not found in the parameter table, trace the historical data and count the actual number of startups K. If K > 10, then regard the number of startups K as 10. If 0 < K < 10, then K = the actual number of startups. Trace the historical data and count the time from issuing the startup process command to the action of the source event during the past K startups. Compare the lengths of the times for K startups, find the maximum value and the minimum value, and calculate the limit interval range range: [minimum value - (maximum value - minimum value) * 5%, maximum value + (maximum value - minimum value) * 5%]. If the abnormal time is within the limit interval range, judge whether the time from the order to the actual action at this point gradually increases during the K startups. If it gradually increases, the fault type is environmental factors or wear. If it does not gradually increase, the fault type is environmental factors. If the abnormal time is not within the limit interval range, the fault type is equipment failure.

6. A hydropower station automatic startup process abnormality analysis system, used to implement the hydropower station automatic startup process abnormality analysis method according to any one of claims 1 to 5, characterized in that: Include: Status monitoring module: used to monitor all steps and key data points in the entire startup process in real time. Timeout alarm module: used to determine that the process is abnormally interrupted and automatically trigger the system for analysis and diagnosis. Fault analysis and diagnosis module: scan the status points of each monitored step, locate the specific failure step that causes the process interruption, and read all the associated data points subordinate to this step and the coupled logic between the data points. Historical data traceability and analysis module: used to trace the history of the source event, search for event records that match the data changes before and after the current fault, so as to judge the current fault type and root cause. Strategy formulation module: used to formulate targeted maintenance and repair strategy modules, input the source event and fault type, and generate targeted solution suggestions and preventive measures. Report generation module: used to provide intuitive visual elements to intuitively display the key technical parameters of the hydropower station and their change trends.

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