Power Equipment Status Monitoring, Operation and Maintenance Control System Based on Data Monitoring

Abstract

The present invention discloses a power equipment status monitoring, operation and maintenance control system based on data monitoring, which relates to the technical field of power equipment operation and maintenance. It solves the technical problem in the prior art that it is impossible to evaluate the operation risk of the operation scenario in cooperation with power equipment with a low fault risk trend. Specifically, a historical fault analysis unit collects operation process data of power equipment, sets a top event and intermediate events according to the collected data, and constructs a fault tree; a fault feature recognition unit extracts and identifies the collected data features of power equipment with a high fault risk trend, and obtains fault events and non-fault events according to the feature extraction and identification; a parallel fault analysis unit conducts multi-device parallel fault risk analysis on power equipment with a low fault risk trend, and infers whether there is an operation fault risk when the power equipment with a low fault risk trend operates in cooperation according to the analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of operation and maintenance of power equipment, and specifically to a power equipment status monitoring and operation and maintenance control system based on data monitoring. Background Art

[0002] Power equipment mainly includes two categories: power generation equipment and power supply equipment; the operation and maintenance control of power equipment is a key link to ensure the safe, stable and efficient operation of the power system; the data monitoring of power equipment is an important part of operation and maintenance control. By collecting, analyzing and processing various data, the operation status of the equipment can be grasped in real time, and potential problems can be discovered in time.

[0003] The patent with the publication number of CN119205069A discloses an intelligent monitoring system for the operation and maintenance status of power equipment, which relates to the technical field of operation and maintenance of power equipment; the system realizes fault prediction and remote control by real-time monitoring the operation status of power equipment; through functions such as real-time monitoring, intelligent operation and maintenance, and personalized configuration, the operation and maintenance efficiency is effectively improved, the equipment failure rate is reduced, and significant improvement and promotion are brought to the operation and maintenance management of power equipment.

[0004] However, in the prior art, it is impossible to collect top events and intermediate events according to the impact of events, unable to construct a fault tree for the operation process of power equipment, and unable to perform fault risk trend analysis based on the collected data of each basic event in the fault tree; in addition, it is impossible to conduct risk assessment on power equipment with a low fault risk trend, unable to eliminate the operation impact of various types of events, reducing the operation and maintenance control efficiency of power equipment.

[0005] In view of the above technical defects, a solution is proposed now. Summary of the Invention

[0006] The purpose of the present invention is to solve the above-mentioned problems, and to propose a power equipment status monitoring and operation and maintenance control system based on data monitoring.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] A power equipment status monitoring and operation and maintenance control system based on data monitoring includes an operation and maintenance monitoring center, and the operation and maintenance monitoring center is communicatively connected to a historical fault analysis unit, a fault feature recognition unit, and a parallel fault analysis unit;

[0009] The historical fault analysis unit is used to collect operation process data of power equipment, set top events and intermediate events according to the collected data, construct a fault tree, set the fault risk trend of power equipment according to the fault tree analysis, and set it as a high fault risk trend and a low fault risk trend;

[0010] A fault feature recognition unit is used to extract and recognize the data characteristics of power equipment in a high-risk fault trend, obtain fault events and non-fault events according to the feature extraction and recognition, and perform operation and maintenance control on the collected data of the corresponding events.

[0011] A parallel fault analysis unit is used to perform multi-device parallel fault risk analysis on power equipment in a low-risk fault trend, and infer whether there is a risk of operation failure when the power equipment in the low-risk fault trend operates in cooperation.

[0012] As a preferred embodiment of the present invention, the process of the historical fault analysis unit is as follows:

[0013] Collect the operation time period of the power equipment, and continuously monitor the data during the operation time period to construct a historical operation time period with the current status monitoring moment and the start moment of the operation time period; according to the historical operation time period, obtain the fault moment of the power equipment, and set the fault type at the fault moment as the top event of the fault tree; according to the cause of the top event corresponding to the fault type, perform statistics on the cause type through the collected data in the historical operation time period and mark it as an intermediate event.

[0014] As a preferred embodiment of the present invention, according to the correlation analysis of the intermediate events, set the occurrence relationship between the top event and different intermediate events; set the currently constructed intermediate events on the same layer of the fault tree;

[0015] And construct the next layer of the fault tree, that is, collect the basic events of the current intermediate events;

[0016] Continuously update the fault tree according to the update of each event layer of the fault tree at each moment in the historical operation time period; and collect the sum of the increased quantity of the type of the basic event corresponding to the intermediate event on the same layer and the increased quantity of the type of the corresponding top event during the update process.

[0017] As a preferred embodiment of the present invention, if the sum exceeds the set quantity threshold, or the continuous increase speed of the sum exceeds the set speed threshold, then mark the trend as a high-risk fault trend; if the sum does not exceed the set quantity threshold, and the continuous increase speed of the sum does not exceed the set speed threshold, then mark the trend as a low-risk fault trend.

[0018] As a preferred embodiment of the present invention, the process of the fault feature recognition unit is as follows:

[0019] Identify the characteristics of power equipment at high risk of failure trends and mark them as identified equipment. Analyze and statistically process the data collected for each layer of the fault tree of the identified equipment. During the time periods before and after the occurrence of the top event, collect the numerical floating waveforms of the data corresponding to the basic events within the fault tree of the identified equipment, and obtain the area of the floating waveform deviation region of the current data by comparing the floating waveforms.

[0020] At the same time, count the floating moments of the basic events, and obtain the floating moment waveform based on the floating at each moment point during the operation period. Specifically, set the floating threshold on the Y-axis of the coordinate system, that is, if floating occurs at the current moment point, the value of the corresponding moment point on the Y-axis of the coordinate system is the set floating threshold. If the floating continues, it is a horizontal line on the coordinate system. Otherwise, if there is no floating, the value is 0. Infer the floating frequency based on the floating moment waveform.

[0021] As a preferred embodiment of the present invention, after the occurrence of the top event, collect the floating waveform deviation region of the basic event and the floating frequency obtained based on the floating moment waveform. If the area of the floating waveform deviation region of the basic event exceeds the set area threshold during the time period when the top event appears, or the floating frequency obtained based on the floating moment waveform exceeds the set floating frequency threshold, then the current basic event is regarded as a fault event with a high risk of failure trend; and the parameters of the corresponding basic event that exceed the threshold for floating are used as the operation and maintenance control trend of the power equipment.

[0022] If the area of the floating waveform deviation region of the basic event does not exceed the set area threshold during the time period when the top event appears, and the floating frequency obtained based on the floating moment waveform does not exceed the set floating frequency threshold, then the current basic event is regarded as a non-fault event with a high risk of failure trend; and the collected data of the non-fault event is not preferentially used as the operation and maintenance control detection data.

[0023] As a preferred embodiment of the present invention, the process of the parallel fault analysis unit is as follows:

[0024] Mark the power equipment with a low risk of failure trend as cooperative monitoring equipment, and determine the cooperation type according to the power equipment required by the power process flow. During the cooperative operation of the cooperative monitoring equipment, collect the top event of the corresponding cooperative monitoring equipment, and conduct intermediate event analysis according to the type of the top event, that is, the same-type intermediate events and non-same-type intermediate events of the same-type top events are respectively marked as same-top same-middle events and same-top non-same-middle events; the same-type intermediate events and non-same-type intermediate events of non-same-type top events are respectively marked as non-same-top same-middle events and non-same-top non-same-middle events.

[0025] As a preferred embodiment of the present invention, collect the deviation ratio of the deviation between the set threshold of the acquisition data corresponding to the same-top and non-middle events of the corresponding monitoring device and the deviation between the real-time acquisition data values. If the deviation ratio exceeds the set threshold and continues to increase, it is inferred that the acquisition data corresponding to the same-top and non-middle events affects the operating state of the cooperative monitoring device, and the current acquisition data type is sent to the operation and maintenance monitoring center.

[0026] As a preferred embodiment of the present invention, collect the real-time peak value of the acquisition data of the non-same-top and same-middle events and the set data red line threshold of the corresponding top event, and calculate the red line distance value of the corresponding cooperative monitoring device according to the difference value; calculate the red line value ratio according to the ratio of the red line distance value to the data red line threshold;

[0027] Statistically analyze the red line value ratios of each cooperative monitoring device and the corresponding acquisition data at each moment, and obtain the floating speed of the red line value ratio and the numerical floating speed of the acquisition data;

[0028] When the floating speed of the red line value ratio of the current cooperative monitoring device exceeds the floating speed threshold, if the numerical floating speed of the acquisition data corresponding to the middle event of the cooperative monitoring device with which it cooperates increases, or the red line value ratio of the cooperative monitoring device with which it cooperates increases accordingly, then mark the corresponding cooperative monitoring device as a high-risk cooperative device;

[0029] When the floating speed of the red line value ratio of the current cooperative monitoring device exceeds the floating speed threshold, if the numerical floating speed of the acquisition data corresponding to the middle event of the cooperative monitoring device with which it cooperates does not increase, and the red line value ratio of the cooperative monitoring device with which it cooperates does not increase accordingly, then mark the corresponding cooperative monitoring device as a low-risk cooperative device;

[0030] When the floating speed of the red line value ratio of the current cooperative monitoring device does not exceed the floating speed threshold, continue to monitor the floating speed.

[0031] Compared with the prior art, the beneficial effects of the present invention are:

[0032] 1. In the present invention, parallel fault analysis of power equipment is carried out according to the acquisition data in combination with the fault tree technology. The events of each link of the power equipment are affected by acquisition through the fault tree technology. The occurrence probability of parallel faults and the events affecting the operating state are determined according to the event type, which improves the accuracy of power equipment data monitoring, effectively improves the availability of the acquisition data, and can improve the detection efficiency of power equipment, ensure the accuracy of fault prediction within the operating cycle, and improve the work efficiency of operation and maintenance control.

[0033] 2. In the present invention, by extracting and identifying features for accurate evaluation of the collected data, combining the influence of the collected data itself and the influence on power equipment, the pertinence of the operation and maintenance control of power equipment is improved. Moreover, when a fault occurs, it is possible to quickly trace the source according to the fault tree and also perform targeted control according to the tracing result, fundamentally reducing the influence brought by the fault and improving the operation efficiency of power equipment.

[0034] 3. In the present invention, a parallel multi-device fault risk analysis is performed on power equipment with a low-risk fault trend to infer whether there is a high-risk fault when the power equipment with the current low-risk fault trend operates in cooperation, avoiding the situation where the power equipment cannot be ensured to still be in a low-risk fault trend during operation and maintenance control in the cooperation operation scenario. By monitoring the cooperation operation, the accuracy of the condition monitoring of power equipment is effectively improved, the efficiency of monitoring operation and maintenance control is increased, the fault occurrence rate of power equipment is minimized, and the occurrence of faults is effectively avoided through timely operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] For the convenience of those skilled in the art to understand, the present invention will be further described below in conjunction with the accompanying drawings.

[0036] Figure 1 is the principle block diagram of the present invention;

[0037] Figure 2 is the flowchart of the implementation steps of the historical fault analysis unit in the present invention;

[0038] Figure 3 is the flowchart of the implementation steps of the fault feature recognition unit in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0040] The mention of "embodiment" in this article means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The phrase appears in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0041] Please refer to Figure 1As shown, the power equipment status monitoring and operation and maintenance control system based on data monitoring includes an operation and maintenance monitoring center, and the operation and maintenance monitoring center is communicatively connected to a historical fault analysis unit, a fault feature recognition unit, and a parallel fault analysis unit;

[0042] The historical fault analysis unit is used to collect operation process data of power equipment, perform parallel fault analysis of power equipment according to the collected data combined with fault tree technology, collect the influence of each link event of power equipment through fault tree technology, determine the occurrence probability of parallel faults and the operation state influence events according to the event type, improve the accuracy of power equipment data monitoring, effectively improve the availability of the collected data, and can improve the detection efficiency of power equipment, ensure the accuracy of fault prediction within the operation cycle, and improve the work efficiency of operation and maintenance control; It should be noted that the data monitoring of the fault tree is to collect the operation data related to power equipment, such as relevant data such as voltage, current, and temperature; and the number of power equipment is not unique during the data monitoring process, but the fault tree only matches a single power equipment;

[0043] It should be further explained that the flow chart of the implementation steps of the historical fault analysis unit is as Figure 2 shown;

[0044] Collect the operation period of the power equipment, and perform continuous data monitoring on the operation period to construct a historical operation period with the current status monitoring moment and the start moment of the operation period; According to the historical operation period, obtain the fault moment of the power equipment, and set the fault type at the fault moment as the top event of the fault tree; It should be noted that the top event is also an intermediate event, because after the power equipment fails, it will affect other cooperating equipment, but in this scenario, it is the status monitoring of the power equipment, so it is designated as the top event; Specifically, fault types such as the motor not being powered.

[0045] According to the cause of the top event corresponding to the fault type, perform a statistical analysis of the cause type through the collected data of the historical operation period, and mark it as an intermediate event; such as the motor is running but not powered or the motor is not running.

[0046] According to the correlation analysis of the intermediate events, set the occurrence relationship between the top event and different intermediate events, specifically as an OR relationship; Multiple intermediate events correspond to a single intermediate event affecting the generation of the top event, and they cannot exist simultaneously, so the corresponding relationship is OR; such as the motor is running but not powered and the motor is not running, which is an OR relationship.

[0047] Set the currently constructed intermediate events on the same layer of the fault tree.

[0048] Construct the next-level fault tree, that is, collect basic events for the current intermediate event. It should be noted that at this time, the intermediate event and the basic event are in an AND relationship, that is, the generation of basic events will all affect the generation of the intermediate event. It should be noted that the number of intermediate events between the top event and the basic event is not unique according to the actual scenario, and they jointly construct the fault tree.

[0049] Continuously update the fault tree according to the update of each layer of events in the fault tree at each moment during the historical operation period; and during the update process, collect the sum of the increased quantity of the basic event types corresponding to the intermediate events of the same layer and the increased quantity of the top event types. If the sum exceeds the set quantity threshold, or the continuous increase speed of the sum exceeds the set speed threshold, then mark the operation state trend of the power equipment in the current historical operation period as a high-risk fault trend; if the sum does not exceed the set quantity threshold, and the continuous increase speed of the sum does not exceed the set speed threshold, then mark the operation state trend of the power equipment in the current historical operation period as a low-risk fault trend.

[0050] When the power equipment is in a high-risk fault trend, the operation and maintenance monitoring center conducts operation and maintenance processing on the power equipment; on the contrary, when the power equipment is in a low-risk fault trend, continue to monitor the data during the operation process.

[0051] The fault feature recognition unit is used to extract and recognize the characteristics of the collected data of the power equipment, accurately evaluate the impact of the collected data through the extracted and recognized characteristics, combine the impact of the collected data itself and the impact on the power equipment, improve the pertinence of the operation and maintenance control of the power equipment, and can quickly trace the source according to the fault tree when a fault occurs, and can also perform targeted control according to the tracing result, fundamentally reducing the impact of the fault and improving the operation efficiency of the power equipment.

[0052] It should be further explained that the flow chart of the implementation steps of the fault feature recognition unit is as Figure 3 shown;

[0053] Identify the characteristics of the power equipment in a high-risk fault trend and mark it as an identified device. Analyze and statistically process the collected data of each layer of the fault tree of the identified device. Before and after the generation of the top event, collect the numerical floating waveform of the data corresponding to the basic events in the fault tree of the identified device, and obtain the area of the floating waveform deviation region of the current data according to the comparison of the floating waveforms. It should be noted that the area of the waveform deviation region corresponding to the numerical floating waveform can reflect the magnitude of the floating impact of the numerical value; specifically, when counting the current waveform, the basic event is the current floating, and different current floating spans before and after the generation of the top event will result in waveform deviations.

[0054] Meanwhile, the floating moments of basic events are statistically analyzed, and the floating moment waveform is obtained based on the floating at each time point within the operation period. Specifically, the floating threshold of the Y-axis of the coordinate system is set. That is, if floating occurs at the current time point, the value of the corresponding time point on the Y-axis of the coordinate system is the set floating threshold. If the floating continues, it is a horizontal line on the coordinate system. Otherwise, if there is no floating, the value is 0. The floating frequency is inferred based on the floating moment waveform.

[0055] After the top event occurs, the floating waveform deviation area of the basic event is collected, and the floating frequency is obtained based on the floating moment waveform. If the area of the floating waveform deviation area of the basic event within the time period when the top event appears exceeds the set area threshold, or the floating frequency obtained based on the floating moment waveform exceeds the set floating frequency threshold, then the current basic event is regarded as a fault event with a high risk trend of failure; and the parameters of the corresponding basic event that exceed the threshold for floating are used as the operation and maintenance control trend of the power equipment.

[0056] If the area of the floating waveform deviation area of the basic event within the time period when the top event appears does not exceed the set area threshold, and the floating frequency obtained based on the floating moment waveform does not exceed the set floating frequency threshold, then the current basic event is regarded as a non-fault event with a high risk trend of failure; and the collected data of the non-fault event is not preferentially used as the operation and maintenance control detection data.

[0057] After the fault feature recognition is completed, the data type of the corresponding fault event and the numerical floating trajectory of the corresponding type of data in the corresponding operation and maintenance fault are synchronized to the operation and maintenance monitoring center. The operation and maintenance monitoring center stores them and issues an early warning based on the data floating trajectory of the fault event. And when necessary, the set threshold is adjusted and updated according to the floating value.

[0058] It should be noted that the set thresholds are all threshold values artificially set by personnel in the field of power equipment operation and maintenance according to the historical operation fault generation scenarios; if the set thresholds do not match the current scenario, the thresholds are updated to ensure accurate data detection in the current scenario.

[0059] The parallel fault analysis unit is used to perform multi-device parallel fault risk analysis on power equipment with a low risk trend of failure, and infer whether there is a high risk of failure when the current power equipment with a low risk trend of failure operates in cooperation. This avoids the situation where the power equipment cannot be guaranteed to still be in a low risk trend of failure during operation and maintenance control. By monitoring the cooperation operation, the accuracy of the state monitoring of power equipment is effectively improved, the monitoring operation and maintenance control efficiency is improved, the failure rate of power equipment is minimized to the greatest extent, and the occurrence of failures is effectively avoided through timely operation and maintenance.

[0060] Mark the power equipment with a low-risk trend of faults as cooperative monitoring equipment, determine the cooperation type according to the power equipment required by the power process flow. During the cooperative operation of the cooperative monitoring equipment, collect the top events of the corresponding cooperative monitoring equipment, and conduct intermediate event analysis according to the top event type, that is, the same-type intermediate events and non-same-type intermediate events of the same-type top events are respectively marked as same-top same-middle events and same-top non-same-middle events; mark the same-type intermediate events and non-same-type intermediate events of non-same-type top events as non-same-top same-middle events and non-same-top non-same-middle events respectively; it should be noted that the same-top same-middle event means that the top events of the collected cooperative monitoring equipment are the same and the intermediate events are the same; the same-top non-same-middle event means that the top events of the collected cooperative monitoring equipment are the same but the intermediate events are different; the non-same-top same-middle event means that the top events of the collected cooperative monitoring equipment are different but the intermediate events are the same; the non-same-top non-same-middle event means that the top events of the collected cooperative monitoring equipment are different and the intermediate events are different;

[0061] Collect the deviation ratio of the deviation between the set threshold of the collected data corresponding to the same-top non-same-middle event of the corresponding cooperative monitoring equipment and the value of the real-time collected data. If the deviation ratio exceeds the set threshold and continues to increase, it is inferred that the collected data corresponding to the same-top non-same-middle event affects the operation state of the cooperative monitoring equipment, and send the current type of collected data to the operation and maintenance monitoring center. After receiving it, the operation and maintenance monitoring center monitors the collected data and the corresponding numerical fluctuations of the current same-top non-same-middle event, and adjusts in time. At the same time, monitor and give early warning to the corresponding top event with the same top event as the intermediate event;

[0062] Collect the real-time peak value of the collected data of the non-same-top same-middle event and the set data red line threshold of the corresponding top event, and calculate the red line distance value of the corresponding cooperative monitoring equipment according to the difference value; calculate the red line value ratio according to the ratio of the red line distance value to the data red line threshold;

[0063] Statistically analyze the red line value ratios of each cooperative monitoring equipment and the corresponding collected data at each moment, and obtain the floating speed of the red line value ratio and the numerical floating speed of the collected data. When the floating speed of the red line value ratio of the current cooperative monitoring equipment exceeds the floating speed threshold, if the numerical floating speed of the collected data of the corresponding intermediate event of the cooperative monitoring equipment that cooperates with it increases, or the red line value ratio of the cooperative monitoring equipment that cooperates with it increases, it is inferred that the current cooperative monitoring equipment has a risk of cooperative operation, mark the corresponding cooperative monitoring equipment as a high-risk cooperative equipment, and monitor the collected data of the intermediate event during the operation of the high-risk cooperative equipment to avoid the generation of collected data of the same intermediate event corresponding to different cooperative monitoring equipment;

[0064] When the floating speed of the red line ratio of the current cooperative monitoring device exceeds the floating speed threshold, if the floating speed of the collected data value of the corresponding intermediate event of the cooperative monitoring device it cooperates with does not increase, and the red line ratio of the cooperative monitoring device it cooperates with does not increase accordingly, it is inferred that there is no risk of cooperative operation for the current cooperative monitoring device, and the corresponding cooperative monitoring device is marked as a low-risk cooperative device;

[0065] When the floating speed of the red line ratio of the current cooperative monitoring device does not exceed the floating speed threshold, the floating speed monitoring is continuously carried out;

[0066] For the cooperative monitoring devices of the same-top and same-middle events, the collected data of the intermediate events are synchronously identified and monitored; for the cooperative monitoring devices of different-top and different-middle events, the influence analysis of various types of intermediate events is carried out to infer whether there is a floating influence on the collected data corresponding to the intermediate events. If the floating among the collected data does not affect each other, the collected data of different intermediate events are correspondingly monitored. If there is an influence, the monitoring is carried out according to the monitoring process of the cooperative monitoring of different-top and same-middle events;

[0067] When the present invention is in use, the historical fault analysis unit collects the operation process data of the power equipment, sets the top event and intermediate events according to the collected data, constructs a fault tree, sets the power equipment fault risk trend according to the fault tree analysis, and sets it as a high fault risk trend and a low fault risk trend; the fault feature recognition unit extracts and recognizes the collected data features of the power equipment in the high fault risk trend, obtains the fault events and non-fault events according to the feature extraction and recognition, and performs operation and maintenance control on the collected data of the corresponding events; the parallel fault analysis unit performs multi-device parallel fault risk analysis on the power equipment in the low fault risk trend, and infers whether there is a risk of operation failure when the power equipment in the low fault risk trend operates in cooperation.

[0068] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to only the specific embodiments. Obviously, many modifications and changes can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principle and practical application of the present invention, so that those skilled in the art in the technical field can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. The power equipment status monitoring and operation and maintenance control system based on data monitoring is characterized by: It includes an operation and maintenance monitoring center, and the operation and maintenance monitoring center is communicatively connected with a historical fault analysis unit, a fault feature identification unit, and a parallel fault analysis unit; The historical fault analysis unit is used to collect the operation process data of the power equipment, set the top event and the intermediate event according to the collected data, and build a fault tree. According to the fault tree analysis, the power equipment fault risk trend is set, and it is set as a high-risk fault trend and a low-risk fault trend; The fault feature recognition unit is used to extract and identify the features of the collected data of the power equipment with a high risk of failure, obtain the fault events and non-fault events based on the feature extraction and recognition, and perform operation and maintenance control on the collected data of the corresponding events; The process of the fault feature recognition unit is as follows: The power equipment with high risk of failure is characterized and marked as identification equipment. The data collected at each level of the fault tree of the identification equipment is analyzed and counted. Before and after the top event occurs, the numerical floating waveform of the data corresponding to the basic event in the fault tree of the identification equipment is collected, and the floating waveform deviation area of ​​the current data is obtained based on the floating waveform comparison; At the same time, the floating time of the basic event is counted, and the floating time waveform is obtained according to the floating of each time point in the running period. Specifically, the Y-axis floating threshold of the coordinate system is set, that is, if the current time point floats, the value of the corresponding time point on the Y-axis of the coordinate system is the set floating threshold. If it continues to float, it will be a horizontal line on the coordinate system, otherwise it will be 0 if there is no floating; the floating frequency is inferred according to the floating time waveform; After the top event occurs, the floating waveform deviation area of ​​the basic event is collected, and the floating frequency is obtained according to the floating moment waveform. If the floating waveform deviation area of ​​the basic event during the period of the top event exceeds the set area threshold, or the floating frequency obtained according to the floating moment waveform exceeds the set floating frequency threshold, the current basic event is regarded as a fault event with a high risk trend of fault; and the parameter corresponding to the basic event exceeding the threshold floating is used as the operation and maintenance control trend of the power equipment; If the area of ​​the floating waveform deviation area corresponding to the basic event during the period of the top event does not exceed the set area threshold, and the floating frequency obtained according to the floating time waveform does not exceed the set floating frequency threshold, then the current basic event is regarded as a non-fault event with a high risk trend of fault; and the collected data of the non-fault event is not given priority as the operation and maintenance control detection data; The parallel fault analysis unit is used to perform multi-device parallel fault risk analysis on power equipment with a low fault risk trend, and infer whether there is an operational failure risk when the power equipment with a low fault risk trend is operated in coordination based on the analysis.

2. The power equipment status monitoring and operation and maintenance control system based on data monitoring according to claim 1 is characterized in that: The process of the historical fault analysis unit is as follows: Collect the operating time of power equipment and continuously monitor the operating time, and build the historical operating time based on the current status monitoring time and the starting time of the operating time; According to the historical operation period, the fault time of the power equipment is obtained, and the fault type at the fault time is set as the top event of the fault tree; according to the cause of the top event corresponding to the fault type, the cause type is counted through the collected data of the historical operation period and marked as an intermediate event.

3. The power equipment status monitoring and operation and maintenance control system based on data monitoring according to claim 2 is characterized in that: According to the correlation analysis of intermediate events, the occurrence relationship between the top event and different intermediate events is set; the currently constructed intermediate events are set at the same level of the fault tree; And build the next level of fault tree, that is, collect basic events for the current intermediate events; The fault tree is continuously updated according to the event updates of each layer of the fault tree at each moment in the historical operation period; During the updating process, the sum of the number of added basic event types corresponding to the intermediate events at the same layer and the number of added top event types corresponding to the intermediate events at the same layer is collected.

4. The data-based monitoring and control system for power equipment status monitoring and operation according to claim 3 is characterized in that: If the sum of the quantities exceeds the set quantity threshold, or the sum of the quantities continues to increase at a rate exceeding the set speed threshold, the trend is marked as a high-risk trend for failure; If the sum of the quantities does not exceed the set quantity threshold, and the continuous increase rate of the sum of the quantities does not exceed the set speed threshold, the trend is marked as a low risk trend for failure.

5. The power equipment status monitoring and operation and maintenance control system based on data monitoring according to claim 1 is characterized in that: The process of the parallel fault analysis unit is as follows: The power equipment with a low risk trend of failure is marked as cooperative monitoring equipment, and the cooperation type is determined according to the power equipment required by the power process flow. During the cooperative operation of the cooperative monitoring equipment, the top events of the corresponding cooperative monitoring equipment are collected, and intermediate event analysis is performed according to the top event type, that is, the same type of intermediate events of the same type of top events and non-same type of intermediate events are marked as same-top-same-middle events and same-top-non-same-middle events, respectively; the same type of intermediate events of non-same type of top events and non-same type of intermediate events are marked as non-same-top-same-middle events and non-same-top-non-same-middle events, respectively.

6. The power equipment status monitoring and operation and maintenance control system based on data monitoring according to claim 5 is characterized in that: The deviation of the collected data corresponding to the same-top but different-center events of the corresponding monitoring equipment is collected, and the deviation of the real-time collected data value corresponds to the deviation of the set threshold. If the deviation value ratio exceeds the set threshold and continues to increase, it is inferred that the collected data corresponding to the same-top but different-center events affects the operating status of the corresponding monitoring equipment, and the current collected data type is sent to the operation and maintenance monitoring center.

7. The power equipment status monitoring and operation and maintenance control system based on data monitoring according to claim 6 is characterized in that: Collect the real-time peak value of the collected data of the non-same top and same center events and the data red line threshold set for the corresponding top event, and calculate the red line distance value of the corresponding monitoring equipment according to the difference; calculate the red line value ratio according to the ratio of the red line distance value and the data red line threshold; The red line value ratios of each coordinated monitoring device and the corresponding collected data are statistically analyzed at each moment, and the floating speed of the red line value ratio and the numerical floating speed of the collected data are obtained.

8. The power equipment status monitoring and operation and maintenance control system based on data monitoring according to claim 7 is characterized in that: When the floating speed of the red line value ratio of the current cooperative monitoring device exceeds the floating speed threshold, if the floating speed of the collected data value of the corresponding intermediate event of the cooperative monitoring device increases, or the red line value ratio of the cooperative monitoring device increases accordingly, the corresponding cooperative monitoring device will be marked as a high-risk cooperative device; When the floating speed of the red line value ratio of the current cooperative monitoring device exceeds the floating speed threshold, if the floating speed of the collected data value of the corresponding intermediate event of the cooperative monitoring device does not increase, and the red line value ratio of the cooperative monitoring device does not increase accordingly, the corresponding cooperative monitoring device will be marked as a low-risk cooperative device; When the red line value of the current cooperative monitoring device does not exceed the floating speed threshold compared to the floating speed, the floating speed monitoring will continue.

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