Operation and maintenance system and method for whole life cycle of electrical equipment of thermal power plant

Abstract

The invention relates to the technical field of thermal power plant equipment operation and maintenance, and discloses a thermal power plant electrical equipment full life cycle operation and maintenance system and method, and the system comprises a setting module which is used for setting a plurality of equipment types, calculating the operation evaluation value of each equipment type, and constructing a to-be-operated and to-be-maintained node sequence according to the operation evaluation values; the construction module is used for constructing a node association table and a node-feature mapping table at each node to be operated and maintained, setting a plurality of monitoring time nodes and generating a plurality of operation and maintenance strategies; the operation and maintenance module is used for selecting a real-time node to be operated and maintained of the electrical equipment, collecting real-time operation data according to a plurality of monitoring time nodes of the real-time node to be operated and maintained, and setting a real-time operation and maintenance strategy based on the node-feature mapping table; and the correction module is used for acquiring a feedback data packet corresponding to the electrical equipment according to the preset feedback time node, and judging whether a correction instruction is generated or not, so that the operation and maintenance efficiency of the electrical equipment is improved, and the continuity and collaboration of the full-life-cycle operation and maintenance process are ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of operation and maintenance of thermal power plant equipment, in particular to a full life cycle operation and maintenance system and method for electrical equipment of a thermal power plant. BACKGROUND

[0002] The electrical equipment (such as generators, transformers, high-voltage switches, cables, etc.) of a thermal power plant is the core infrastructure for the production and operation of the thermal power plant, and its operation reliability directly affects the power generation efficiency and safety and stability of the thermal power plant.

[0003] In the prior art, the operation and maintenance system of the electrical equipment of the thermal power plant adopts a unified management mode, sets a fixed operation and maintenance period and a monitoring frequency, which causes the operation and maintenance nodes to be inconsistent with the actual operation and maintenance needs of the equipment, resulting in "excessive operation and maintenance" or "lack of operation and maintenance", and the monitoring frequency and the operation and maintenance strategy have poor adaptability, which reduces the operation and maintenance efficiency and service life of the electrical equipment. SUMMARY

[0004] To solve the above technical problems, the present application provides a full life cycle operation and maintenance system and method for electrical equipment of a thermal power plant, which sets the equipment categories and calculates the operation evaluation value, constructs the to-be-operated node sequence, constructs the node association table and the node-feature mapping table of each to-be-operated node, sets the monitoring time interval and a plurality of operation and maintenance strategies, selects the real-time to-be-operated node and collects the real-time operation data, obtains the real-time operation and maintenance strategy, and judges whether to correct the real-time operation and maintenance strategy in combination with the feedback data packet, thereby improving the operation and maintenance efficiency of the electrical equipment and ensuring the continuity and collaboration of the full life cycle operation and maintenance process.

[0005] In some embodiments of the present application, a full life cycle operation and maintenance system for electrical equipment of a thermal power plant is provided, which comprises:

[0006] A setting module is configured to set a plurality of equipment categories and calculate the operation evaluation value of each equipment category, construct the to-be-operated node sequence of the full life cycle of the corresponding equipment category according to the operation evaluation value, and the to-be-operated node sequence comprises a plurality of to-be-operated nodes;

[0007] A construction module is configured to construct the node association table and the node-feature mapping table at each to-be-operated node, set a plurality of monitoring time nodes according to the node association table, and generate a plurality of operation and maintenance strategies according to the node-feature mapping table;

[0008] An operation and maintenance module is configured to select the real-time to-be-operated node of the electrical equipment, collect real-time operation data according to the plurality of monitoring time nodes of the real-time to-be-operated node, and set the real-time operation and maintenance strategy of the real-time to-be-operated node based on the node-feature mapping table;

[0009] The correction module is configured to acquire a feedback data packet of the corresponding electrical equipment according to a preset feedback time node, and determine whether to generate a correction instruction for the real-time operation and maintenance strategy.

[0010] In some embodiments of the present application, a plurality of device categories are set and an operation evaluation value of each device category is calculated, including:

[0011] Based on the historical application parameters of each electrical equipment, a plurality of application feature indexes are generated;

[0012] The application feature indexes of all electrical equipment are subjected to cluster analysis, and a plurality of device categories are generated according to the analysis results;

[0013] Based on the historical operation and maintenance logs of a plurality of electrical equipment of the same device category, a historical operation and maintenance data packet is generated;

[0014] Based on the historical operation and maintenance data packet, an operation evaluation value of the corresponding device category is generated;

[0015] The calculation formula of the operation evaluation value is:

[0016]

[0017] Wherein, Y is the operation evaluation value, n is the operation evaluation index, yi is the reference evaluation value of the i-th operation evaluation index generated by the historical operation and maintenance data packet of the same device category, and ai is the weight coefficient of the i-th operation evaluation index.

[0018] In some embodiments of the present application, a to-be-operated node sequence of the full life cycle of the corresponding device category is constructed according to the operation evaluation value, including:

[0019] The number of to-be-operated nodes of the full life cycle of the corresponding device category is selected according to the relationship between the operation evaluation value of each device category and a preset operation evaluation value interval;

[0020] A first preset operation evaluation value interval, a second preset operation evaluation value interval, a third preset operation evaluation value interval and a fourth preset operation evaluation value interval are preset;

[0021] When the operation evaluation value is in the first preset operation evaluation value interval, the number of to-be-operated nodes of the full life cycle of the corresponding device category is set to be a fourth preset number of to-be-operated nodes;

[0022] When the operation evaluation value is in the second preset operation evaluation value interval, the number of to-be-operated nodes of the full life cycle of the corresponding device category is set to be a third preset number of to-be-operated nodes;

[0023] When the operation evaluation value is in the third preset operation evaluation value interval, the number of to-be-operated nodes of the full life cycle of the corresponding device category is set to be a second preset number of to-be-operated nodes;

[0024] When the running evaluation value is in the fourth preset running evaluation value interval, the number of to-be-maintained nodes in the full life cycle of the corresponding device category is set as a first preset number of to-be-maintained nodes;

[0025] According to the selected number of to-be-maintained nodes of each device category, the to-be-maintained nodes are evenly distributed to each stage of the full life cycle to form a to-be-maintained node sequence of the full life cycle of the corresponding device category.

[0026] In some embodiments of the present application, a node association table at each to-be-maintained node is constructed, including:

[0027] A to-be-maintained node of the electrical equipment is randomly set as a target node;

[0028] A first association value of the target node and the remaining to-be-maintained nodes of the same electrical equipment, a second association value of the target node and the remaining to-be-maintained nodes of different electrical equipment of the same device category, and a third association value of the target node and the remaining to-be-maintained nodes of several electrical equipment of different device categories are generated;

[0029] A preset first association value threshold, a preset second association value threshold, and a preset third association value threshold are set in advance;

[0030] A to-be-maintained node with a first association value greater than the preset first association value threshold is set as a first associated node of the target node, and a first node association table is constructed, and each first associated node in the first node association table is mapped with a corresponding first association value difference;

[0031] A to-be-maintained node with a second association value greater than the preset second association value threshold is set as a second associated node of the target node, and a second node association table is constructed, and each second associated node in the second node association table is mapped with a corresponding second association value difference;

[0032] A to-be-maintained node with a third association value greater than the preset third association value threshold is set as a third associated node of the target node, and a third node association table is constructed, and each third associated node in the third node association table is mapped with a corresponding third association value difference;

[0033] The node association table of the target node is constructed according to the first node association table, the second node association table, and the third node association table;

[0034] The node association table of each to-be-maintained node of each electrical equipment is sequentially generated.

[0035] In some embodiments of the present application, a node-feature mapping table is constructed, including:

[0036] determine historical fault data packets of each to-be-maintenance node based on a time corresponding relationship between a plurality of historical fault logs of each electrical equipment and each to-be-maintenance node in a to-be-maintenance node sequence of the corresponding electrical equipment;

[0037] perform feature extraction on the historical fault data packets to obtain a plurality of historical fault features of the to-be-maintenance node;

[0038] combine the historical fault features at the same to-be-maintenance node based on a preset fault probability to obtain a plurality of historical fault feature sets, wherein each historical fault feature set includes at least one historical fault feature;

[0039] map each to-be-maintenance node to the corresponding plurality of historical fault feature sets to obtain a node-feature mapping set.

[0040] In some embodiments of the present application, a plurality of monitoring time nodes are set according to the node association table, and a plurality of maintenance strategies are generated according to the node-feature mapping table, including:

[0041] generate a first influence coefficient according to a first number of associated nodes and a first difference value of the associated values in the first node association table in the node association table;

[0042] generate a second influence coefficient according to a second number of associated nodes, corresponding second difference values of the associated values, the number of electrical equipment, and the weight coefficient of the corresponding electrical equipment in the second node association table in the node association table;

[0043] generate a third influence coefficient according to a third number of associated nodes, corresponding third difference values of the associated values, the number of equipment categories, the weight coefficient of the corresponding equipment category, the number of electrical equipment in each equipment category, and the weight coefficient of the corresponding electrical equipment in the third node association table in the node association table;

[0044] generate a comprehensive influence coefficient of the to-be-maintenance node according to the first influence coefficient, the second influence coefficient, and the third influence coefficient;

[0045] select a monitoring time interval according to a corresponding relationship between the comprehensive influence coefficient and a preset comprehensive influence coefficient interval, and generate a plurality of monitoring time nodes of the to-be-maintenance node according to the monitoring time interval;

[0046] pre-construct a preset maintenance model of each to-be-maintenance node;

[0047] input each node-feature mapping set into the preset maintenance model of the corresponding to-be-maintenance node to obtain a plurality of maintenance strategies of the plurality of historical fault feature sets in the node-feature mapping set.

[0048] In some embodiments of the present application, the real-time to-be-maintenance node of the electrical equipment is selected, including:

[0049] Acquire runtime and real-time status data of electrical equipment;

[0050] Real-time health coefficients are generated based on the runtime and real-time status data of electrical equipment.

[0051] The difference between the real-time health coefficient and the preset health coefficient at each node in the sequence of nodes to be maintained in the current equipment category is calculated to obtain the health coefficient difference value.

[0052] Several nodes awaiting maintenance are sorted according to their health coefficient differences, and the node ranked first is set as the real-time node awaiting maintenance for the corresponding electrical equipment.

[0053] In some embodiments of this application, a real-time operation and maintenance strategy for nodes to be operated and maintained is set based on a node-feature mapping table, including:

[0054] Real-time operating data of the corresponding electrical equipment is collected according to the monitoring time node and compared with the corresponding standard operating data range. If it is not in the corresponding standard operating data range, the corresponding real-time operating data difference is calculated.

[0055] The difference in real-time running data is matched with the historical fault feature set in the node-feature mapping table of the real-time node to be maintained to obtain several matching degrees.

[0056] The operation and maintenance strategies corresponding to the historical fault feature set with a matching degree greater than the preset matching degree threshold are selected, and the selected operation and maintenance strategies are optimized and merged according to priority to generate the real-time operation and maintenance strategy for electrical equipment at the real-time maintenance node.

[0057] In some embodiments of this application, the feedback data packet of the corresponding electrical equipment is obtained according to a preset feedback time node, and it is determined whether to generate a correction instruction for the real-time operation and maintenance strategy, including:

[0058] Feature extraction is performed on the feedback data packets to obtain key operational features;

[0059] Key operational characteristics are compared with corresponding standard operational characteristics, and operational evaluation values ​​are generated based on the comparison results.

[0060] Pre-set operation and maintenance evaluation thresholds;

[0061] When the operation and maintenance evaluation value is greater than the operation and maintenance evaluation value threshold, no correction instruction will be generated for the real-time operation and maintenance strategy.

[0062] When the operation and maintenance evaluation value is not greater than the operation and maintenance evaluation value threshold, a correction instruction is generated for the implementation of the operation and maintenance strategy.

[0063] In some embodiments of this application, a method for the operation and maintenance of electrical equipment in a thermal power plant throughout its entire lifecycle is also included:

[0064] Multiple equipment categories are defined and an operational evaluation value is calculated for each equipment category. A sequence of nodes to be maintained throughout the entire lifecycle of the corresponding equipment category is constructed according to the operational evaluation value. The sequence of nodes to be maintained includes several nodes to be maintained.

[0065] Construct a node association table and a node-feature mapping table for each node to be maintained. Set several monitoring time nodes based on the node association table and generate several maintenance strategies based on the node-feature mapping table.

[0066] Select the real-time maintenance nodes of electrical equipment, collect real-time operation data according to several monitoring time nodes of the real-time maintenance nodes, and set the real-time maintenance strategy of the real-time maintenance nodes based on the node-feature mapping table.

[0067] The system obtains feedback data packets from the corresponding electrical equipment based on preset feedback time nodes and determines whether to generate correction instructions for the real-time operation and maintenance strategy.

[0068] The operation and maintenance system and method for the entire life cycle of electrical equipment in thermal power plants, as described in this application, have the following advantages compared to the prior art:

[0069] By setting equipment categories and calculating operational evaluation values, a sequence of nodes to be maintained is constructed, along with a node association table and a node-feature mapping table for each node. Monitoring time intervals and several maintenance strategies are set, real-time nodes to be maintained are selected, and real-time operational data is collected to obtain real-time maintenance strategies. Based on feedback data packets, it is determined whether to modify the real-time maintenance strategies, thereby improving the maintenance efficiency of electrical equipment and ensuring the continuity and collaboration of the entire lifecycle maintenance process. Attached Figure Description

[0070] Figure 1 This is a schematic diagram of an operation and maintenance system for the entire life cycle of electrical equipment in a thermal power plant, as described in an embodiment of this application.

[0071] Figure 2 This is a flowchart illustrating a method for the operation and maintenance of electrical equipment in a thermal power plant throughout its entire lifecycle, as described in an embodiment of this application. Detailed Implementation

[0072] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this application, but are not intended to limit the scope of this application.

[0073] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0074] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0075] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0076] like Figure 1 As shown in the figure, an embodiment of this application provides an operation and maintenance system for the entire life cycle of electrical equipment in a thermal power plant, comprising:

[0077] The setting module is used to set multiple device categories and calculate the operation evaluation value of each device category, and construct a sequence of nodes to be maintained for the entire life cycle of the corresponding device category according to the operation evaluation value. The sequence of nodes to be maintained includes several nodes to be maintained.

[0078] The module is used to build a node association table and a node-feature mapping table for each node to be maintained. Based on the node association table, several monitoring time nodes are set, and based on the node-feature mapping table, several maintenance strategies are generated.

[0079] The operation and maintenance module is used to select the real-time nodes to be operated and maintained of electrical equipment, collect real-time operation data according to several monitoring time nodes of the real-time nodes to be operated and maintained, and set real-time operation and maintenance strategies for the real-time nodes to be operated and maintained based on the node-feature mapping table.

[0080] The correction module is used to obtain the feedback data packets of the corresponding electrical equipment according to the preset feedback time nodes, and to determine whether to generate correction instructions for the real-time operation and maintenance strategy.

[0081] In this embodiment, each node to be maintained is mapped to a corresponding maintenance period, which refers to the interval between the current node to be maintained and the next node to be maintained.

[0082] In some embodiments of this application, multiple device categories are defined and an operational evaluation value is calculated for each device category, including:

[0083] Several application characteristic indicators are generated based on the historical application parameters of each electrical device.

[0084] Cluster analysis is performed on the application characteristic indicators of all electrical equipment, and multiple equipment categories are generated based on the analysis results.

[0085] Historical maintenance data packages are generated based on the historical maintenance logs of several electrical devices of the same equipment category;

[0086] Generate operational evaluation values ​​for the corresponding device categories based on historical maintenance data packages;

[0087] The formula for calculating the operational evaluation value is as follows:

[0088]

[0089] Where Y is the operation evaluation value, n is the operation evaluation index, yi is the reference evaluation value of the i-th operation evaluation index generated from the historical operation and maintenance data packets of the same equipment category, and ai is the weight coefficient of the i-th operation evaluation index.

[0090] In this embodiment, the application characteristic indicators are determined comprehensively based on the key performance parameters of the electrical equipment, the application's operating parameters, operational stability indicators, and the frequency of fault occurrence. These application characteristic indicators cover all important operational information of the equipment from its commissioning to the present moment, including but not limited to the equipment's load rate, temperature variation range, voltage fluctuation, current stability, and the type, occurrence time, and repair time of each fault.

[0091] In this embodiment, cluster analysis refers to the use of advanced algorithm models that can truly reflect the similarities and differences between equipment parameters, ensuring that the classification of equipment categories is both scientific and reasonable.

[0092] In this embodiment, the historical maintenance data package includes maintenance frequency, maintenance difficulty, maintenance cost, maintenance time, and the degree of impact on other equipment or operational needs during maintenance. The operation evaluation indicators include, but are not limited to, key indicators such as mean time between failures (MTBF), mean time to repair (MTBT), and maintenance cost-effectiveness ratio. These indicators together constitute an important basis for evaluating the operating status and maintenance needs of equipment categories. When the reference evaluation value is larger, it indicates that the corresponding equipment category has a longer MTBF, a shorter repair time, or a lower maintenance cost. This provides a basis for setting the number of maintenance nodes to be performed on different electrical equipment throughout their entire life cycle, thereby improving the timeliness and efficiency of electrical equipment maintenance.

[0093] In some embodiments of this application, a sequence of nodes to be maintained throughout the entire lifecycle of the corresponding equipment category is constructed according to the operational evaluation value, including:

[0094] The number of nodes to be maintained throughout the entire lifecycle of each equipment category is selected based on the relationship between the operational evaluation value of each equipment category and the preset operational evaluation value range.

[0095] The first preset operating evaluation value range, the second preset operating evaluation value range, the third preset operating evaluation value range, and the fourth preset operating evaluation value range are preset.

[0096] When the operation evaluation value is within the first preset operation evaluation value range, the number of nodes to be maintained for the entire life cycle of the corresponding equipment category is set to the fourth preset number of nodes to be maintained.

[0097] When the operation evaluation value is within the second preset operation evaluation value range, the number of nodes to be maintained for the entire life cycle of the corresponding equipment category is set to the third preset number of nodes to be maintained.

[0098] When the operation evaluation value is within the third preset operation evaluation value range, the number of nodes to be maintained for the entire life cycle of the corresponding equipment category is set to the second preset number of nodes to be maintained.

[0099] When the operation evaluation value is in the fourth preset operation evaluation value range, the number of nodes to be maintained for the entire life cycle of the corresponding equipment category is set to the first preset number of nodes to be maintained.

[0100] The number of nodes to be maintained for each selected equipment category is evenly distributed across all stages of the entire lifecycle, forming a sequence of nodes to be maintained for the entire lifecycle of the corresponding equipment category.

[0101] In this embodiment, the first preset operation evaluation value range < the second preset operation evaluation value range < the third preset operation evaluation value range < the fourth preset operation evaluation value range, and the first preset number of nodes to be maintained < the second preset number of nodes to be maintained < the third preset number of nodes to be maintained < the fourth preset number of nodes to be maintained.

[0102] In this embodiment, the preset operation evaluation value and the preset number of nodes to be maintained are set comprehensively based on factors such as the historical operation data of electrical equipment, the fault occurrence pattern, and maintenance needs.

[0103] In this embodiment, the larger the preset operating evaluation value range of the operating evaluation value, the lower the operation and maintenance frequency, that is, the lower the failure frequency of the electrical equipment of the corresponding equipment category. Therefore, the number of nodes to be maintained throughout the entire life cycle of the corresponding equipment category is less, and vice versa. Through this differentiated setting method, operation and maintenance resources can be allocated more reasonably. For equipment categories with low failure frequency, the number of nodes to be maintained is appropriately reduced, while for equipment categories with higher failure frequency, the number of nodes to be maintained is appropriately increased, so as to more comprehensively monitor the equipment status and discover potential problems in advance.

[0104] In some embodiments of this application, a node association table is constructed for each node to be maintained, including:

[0105] Randomly select one of the electrical equipment nodes to be maintained as the target node;

[0106] Generate a first association value between the target node and the remaining nodes to be maintained of the same electrical equipment, a second association value between the target node and the remaining nodes to be maintained of different electrical equipment of the same equipment category, and a third association value between the target node and the remaining nodes to be maintained of several electrical equipment of different equipment categories;

[0107] Pre-set a first correlation value threshold, a second correlation value threshold, and a third correlation value threshold;

[0108] The nodes to be maintained that have a first association value greater than a preset first association value threshold are set as the first association nodes of the target node, and a first node association table is constructed, wherein each first association node in the first node association table is mapped to a corresponding first association value difference.

[0109] The nodes to be maintained that have a second association value greater than a preset second association value threshold are set as the second association nodes of the target node, and a second node association table is constructed, wherein each second association node in the second node association table is mapped to a corresponding second association value difference.

[0110] Nodes to be maintained whose third association value is greater than the preset third association value threshold are set as the third association nodes of the target node, and a third node association table is constructed, wherein each third association node in the third node association table is mapped to a corresponding third association value difference.

[0111] Construct the node association table of the target node based on the first node association table, the second node association table, and the third node association table;

[0112] Generate a node association table for each node to be maintained for each electrical device in sequence.

[0113] In this embodiment, the remaining nodes to be maintained are all nodes whose maintenance time is later than that of the target node.

[0114] In this embodiment, the correlation value is based on the different historical fault levels at the target node as the main influencing factor. It evaluates the historical operating status of the remaining nodes to be maintained for the same electrical equipment, the remaining nodes to be maintained for different electrical equipment of the same equipment category, and the remaining nodes to be maintained for several electrical equipment of different equipment categories. If the historical operating status of the remaining nodes to be maintained changes more significantly as the historical fault level of the target node increases, the corresponding correlation value increases accordingly, indicating that the influence of the target node on the node gradually increases. In this way, the correlation between each node to be maintained can be accurately quantified, providing a scientific basis for setting monitoring parameters in the future.

[0115] In some embodiments of this application, constructing a node-feature mapping table includes:

[0116] Based on the time correspondence between several historical fault logs of each electrical device and each node to be maintained in the sequence of nodes to be maintained of the corresponding electrical device, the historical fault data packets of each node to be maintained are determined.

[0117] Feature extraction is performed on historical fault data packets to obtain several historical fault features of the corresponding nodes to be maintained;

[0118] Based on the preset failure probability, the historical failure features at the same node to be maintained are combined to obtain several historical failure feature sets, wherein each historical failure feature set includes at least one historical failure feature.

[0119] Each node to be maintained is mapped to a set of corresponding historical fault features to obtain a node-feature mapping set.

[0120] In this embodiment, historical fault features refer to key features in historical fault data packets that have a high probability of failure. These features include, but are not limited to, fault type, fault occurrence time, fault duration, and equipment operating parameters at the time of the fault. By extracting and combining these features, the possible fault situations of the nodes to be maintained can be reflected more accurately.

[0121] In this embodiment, the preset failure probability is set based on historical data statistics and expert experience, and is used to filter out representative historical failure features to ensure that each set of historical failure features can effectively reflect the failure risk of the node to be maintained.

[0122] In this embodiment, the node-feature mapping set not only includes the mapping relationship between the node to be maintained and the historical fault feature set, but also records the frequency of occurrence and degree of influence of each historical fault feature set at the node to be maintained, providing an important basis for the subsequent generation of maintenance strategies.

[0123] In some embodiments of this application, several monitoring time nodes are set according to a node association table, and several operation and maintenance strategies are generated according to a node-feature mapping table, including:

[0124] The first influence coefficient is generated based on the number of first associated nodes in the first node association table and the difference in the first association value.

[0125] The second influence coefficient is generated based on the number of second associated nodes in the second node association table, the difference in the corresponding second association values, the number of electrical equipment, and the weight coefficient of the corresponding electrical equipment.

[0126] The third influence coefficient is generated based on the number of third-related nodes in the third-related node association table, the difference of the corresponding third-related values, the number of equipment categories, the weight coefficient of the corresponding equipment category, the number of electrical devices in each equipment category, and the weight coefficient of the corresponding electrical devices.

[0127] The comprehensive impact coefficient of the corresponding node to be maintained is generated based on the first impact coefficient, the second impact coefficient, and the third impact coefficient.

[0128] The monitoring time interval is selected based on the correspondence between the comprehensive impact coefficient and the preset comprehensive impact coefficient range, and several monitoring time nodes corresponding to the nodes to be maintained are generated according to the monitoring time interval.

[0129] Pre-build a preset operation and maintenance model for each node to be operated and maintained;

[0130] Each node-feature mapping set is input into the preset operation and maintenance model of the corresponding node to be operated and maintained, thereby obtaining several operation and maintenance strategies for several historical fault feature sets in the node-feature mapping set.

[0131] In this embodiment, when the number of first associated nodes m1 is greater and the difference in the first associated value g1 is greater, the corresponding first influence coefficient x1 is greater. When the number of electrical devices L1 is greater, and the number of second associated nodes m2 in the same electrical device is greater and the difference in the corresponding second associated value g2 is greater, the second influence coefficient x2 is greater when the weight coefficient w of the corresponding electrical device is greater. Similarly, when the number of device categories u is greater, and the number of electrical devices L2 in the same device category is greater, the number of third associated nodes m3 in the electrical devices is greater, the difference in the corresponding third associated value g3 is greater, and the weight coefficient w of the electrical devices is greater, the third influence coefficient is greater when the weight coefficient of the corresponding device category is greater.

[0132] In this embodiment, Where z1, z2, and z3 refer to the values ​​of the first correlation value difference, the second correlation value difference, and the third correlation value difference, respectively, converted into values ​​with the same dimension as the first influence coefficient, the second influence coefficient, and the third influence coefficient.

[0133] In this embodiment, a first preset comprehensive influence coefficient range, a second preset comprehensive influence coefficient range, a third preset comprehensive influence coefficient range, and a fourth preset comprehensive influence coefficient range are preset. When the comprehensive influence coefficient is within the first preset comprehensive influence coefficient range, the monitoring time interval is selected as the fourth preset time interval. When the comprehensive influence coefficient is within the second preset comprehensive influence coefficient range, the monitoring time interval is selected as the third preset time interval. When the comprehensive influence coefficient is within the third preset comprehensive influence coefficient range, the monitoring time interval is selected as the second preset time interval. When the comprehensive influence coefficient is within the fourth preset comprehensive influence coefficient range, the monitoring time interval is selected as the first preset time interval. The first preset comprehensive influence coefficient range < the second preset comprehensive influence coefficient range < the third preset comprehensive influence coefficient range < the fourth preset comprehensive influence coefficient range, and the first preset time interval < the second preset time interval < the third preset time interval < the fourth preset time interval.

[0134] In this embodiment, the preset comprehensive influence coefficient range and the corresponding preset time interval are set based on the historical comprehensive influence coefficient and a reasonable historical monitoring time interval. When the preset comprehensive influence coefficient range in which the comprehensive influence coefficient is located is larger, the monitoring time interval should be shorter, so as to avoid the situation of untimely monitoring.

[0135] In this embodiment, the preset maintenance model for each node to be maintained is obtained through comprehensive training based on the node's location throughout its lifecycle, historical failure modes, historical failure characteristics, and corresponding historical maintenance strategies. The preset maintenance model employs machine learning algorithms, such as neural networks or decision trees, and through learning from a large number of historical maintenance strategies, it can automatically identify trends in equipment status changes, predict potential failures, and generate targeted maintenance recommendations. These recommendations include, but are not limited to, adjusting operating parameters, scheduling preventative maintenance, replacing vulnerable parts, or upgrading software, aiming to minimize equipment downtime, extend equipment lifespan, and reduce maintenance costs. Furthermore, the preset maintenance model also possesses self-optimization capabilities, adjusting model parameters based on actual maintenance feedback to continuously improve the accuracy and effectiveness of maintenance strategies.

[0136] In some embodiments of this application, the selected real-time maintenance nodes of electrical equipment include:

[0137] Acquire runtime and real-time status data of electrical equipment;

[0138] Real-time health coefficients are generated based on the runtime and real-time status data of electrical equipment.

[0139] The difference between the real-time health coefficient and the preset health coefficient at each node in the sequence of nodes to be maintained in the current equipment category is calculated to obtain the health coefficient difference value.

[0140] Several nodes awaiting maintenance are sorted according to their health coefficient differences, and the node ranked first is set as the real-time node awaiting maintenance for the corresponding electrical equipment.

[0141] In this embodiment, the preset health coefficient is a health level that electrical equipment should achieve in a fault-free state at the corresponding maintenance node. It is calculated based on the health levels of several components contained in the electrical equipment.

[0142] In this embodiment, runtime reflects the frequency of device use and the degree of aging; real-time status data includes the health level of device components.

[0143] In this embodiment, sorting by health coefficient difference means that the smaller the health coefficient difference, the higher the ranking. This selects reasonable nodes to be maintained, ensuring that maintenance work can accurately meet the actual needs of the equipment, effectively prevent potential faults, and improve the stability and reliability of equipment operation.

[0144] In some embodiments of this application, a real-time operation and maintenance strategy for nodes to be operated and maintained is set based on a node-feature mapping table, including:

[0145] Real-time operating data of the corresponding electrical equipment is collected according to the monitoring time node and compared with the corresponding standard operating data range. If it is not in the corresponding standard operating data range, the corresponding real-time operating data difference is calculated.

[0146] The difference in real-time running data is matched with the historical fault feature set in the node-feature mapping table of the real-time node to be maintained to obtain several matching degrees.

[0147] The operation and maintenance strategies corresponding to the historical fault feature set with a matching degree greater than the preset matching degree threshold are selected, and the selected operation and maintenance strategies are optimized and merged according to priority to generate the real-time operation and maintenance strategy for electrical equipment at the real-time maintenance node.

[0148] In this embodiment, priority is set according to matching degree. The higher the matching degree, the higher the priority. Optimizing and merging operation and maintenance strategies means integrating multiple similar or complementary operation and maintenance strategies into a comprehensive strategy to avoid conflicts and duplication between strategies and improve operation and maintenance efficiency. Specifically, for historical fault feature sets with high matching degree, the corresponding operation and maintenance strategy types are first analyzed, such as adjusting operating parameters and arranging preventive maintenance. Then, the operation and maintenance strategies are grouped according to strategy type, with strategies within the same group having similar operational goals and implementation methods. Next, within each group, the strategies are sorted in descending order of matching degree, with strategies with higher matching degree having higher priority. During the optimization and merging process, high-priority strategies are used first. For low-priority strategies, if they do not conflict with high-priority strategies and can provide additional operation and maintenance effects, they are integrated into high-priority strategies; if conflicts exist, the current state of the equipment and the operation and maintenance goals are weighed to select the most suitable strategy or to make appropriate adjustments to the strategy. Ultimately, all optimized and merged strategies are integrated into a complete real-time operation and maintenance strategy. This strategy comprehensively considers factors such as the current status of the equipment, historical failure modes, and operation and maintenance efficiency, which can more accurately guide operation and maintenance work and improve the operational stability and reliability of electrical equipment.

[0149] In some embodiments of this application, the feedback data packet of the corresponding electrical equipment is obtained according to a preset feedback time node, and it is determined whether to generate a correction instruction for the real-time operation and maintenance strategy, including:

[0150] Feature extraction is performed on the feedback data packets to obtain key operational features;

[0151] Key operational characteristics are compared with corresponding standard operational characteristics, and operational evaluation values ​​are generated based on the comparison results.

[0152] Pre-set operation and maintenance evaluation thresholds;

[0153] When the operation and maintenance evaluation value is greater than the operation and maintenance evaluation value threshold, no correction instruction will be generated for the real-time operation and maintenance strategy.

[0154] When the operation and maintenance evaluation value is not greater than the operation and maintenance evaluation value threshold, a correction instruction is generated for the implementation of the operation and maintenance strategy.

[0155] In this embodiment, key operational characteristics refer to real-time operational data that shows anomalies before maintenance, as well as the trend, magnitude, and rate of change of key performance data. Standard operational characteristics include the trend, range, and rate of change of key performance data that the device should exhibit during normal operation.

[0156] In this embodiment, by comparing the extracted key operational features with preset standard operational features one by one, the system can quantitatively evaluate the performance recovery degree and operational stability changes of the equipment after implementing real-time operation and maintenance strategies. Specifically, during the comparison process, if the deviation between the key operational features and the standard operational features is within the allowable error range, the larger the operation and maintenance evaluation value, the less need to generate a correction instruction; if the deviation exceeds the preset threshold, the smaller the operation and maintenance evaluation value, the more necessary the correction.

[0157] In this embodiment, when a correction instruction is generated, the direction and extent of the deviation are further analyzed. Combining historical equipment fault data with the current operating environment, it is determined whether the strategy has failed due to inadequate execution of the operation and maintenance strategy or changes in the external environment. A correction instruction containing specific correction direction and adjustment range will be automatically generated. This instruction will clearly indicate the operation and maintenance parameters to be optimized, the target value range after adjustment, and the expected operating effect, ensuring that the operation and maintenance strategy can dynamically adapt to the state changes of the equipment throughout its entire life cycle and continuously improve the accuracy of operation and maintenance and the reliability of the equipment.

[0158] In some embodiments of this application, such as Figure 2 As shown, it also includes a method for the operation and maintenance of electrical equipment in thermal power plants throughout their entire life cycle:

[0159] S201: Set multiple equipment categories and calculate the operation evaluation value for each equipment category. Construct a sequence of nodes to be maintained for the entire life cycle of the corresponding equipment category according to the operation evaluation value. The sequence of nodes to be maintained includes several nodes to be maintained.

[0160] S202: Construct a node association table and a node-feature mapping table for each node to be maintained. Set several monitoring time nodes according to the node association table and generate several maintenance strategies according to the node-feature mapping table.

[0161] S203: Select the real-time maintenance nodes of electrical equipment, collect real-time operation data according to several monitoring time nodes of the real-time maintenance nodes, and set the real-time maintenance strategy of the real-time maintenance nodes based on the node-feature mapping table.

[0162] S204: Obtain the feedback data packet of the corresponding electrical equipment according to the preset feedback time node, and determine whether to generate a correction instruction for the real-time operation and maintenance strategy.

[0163] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of this application, and these improvements and substitutions should also be considered within the scope of protection of this application.

Claims

1. A full life-cycle operation and maintenance system for electrical equipment in thermal power plants, characterized in that, include: The setting module is used to set multiple device categories and calculate the operation evaluation value of each device category, and construct a sequence of nodes to be maintained for the entire life cycle of the corresponding device category according to the operation evaluation value. The sequence of nodes to be maintained includes several nodes to be maintained. The module is used to build a node association table and a node-feature mapping table for each node to be maintained. Based on the node association table, several monitoring time nodes are set, and based on the node-feature mapping table, several maintenance strategies are generated. The operation and maintenance module is used to select the real-time nodes to be operated and maintained of electrical equipment, collect real-time operation data according to several monitoring time nodes of the real-time nodes to be operated and maintained, and set real-time operation and maintenance strategies for the real-time nodes to be operated and maintained based on the node-feature mapping table. The correction module is used to obtain the feedback data packets of the corresponding electrical equipment according to the preset feedback time nodes, and to determine whether to generate correction instructions for the real-time operation and maintenance strategy.

2. The operation and maintenance system for the entire life cycle of electrical equipment in thermal power plants as described in claim 1, characterized in that, Define multiple equipment categories and calculate the operational evaluation value for each equipment category, including: Several application characteristic indicators are generated based on the historical application parameters of each electrical device. Cluster analysis is performed on the application characteristic indicators of all electrical equipment, and multiple equipment categories are generated based on the analysis results. Historical maintenance data packages are generated based on the historical maintenance logs of several electrical devices of the same equipment category; Generate operational evaluation values ​​for the corresponding device categories based on historical maintenance data packages; The formula for calculating the operational evaluation value is as follows: Where Y is the operation evaluation value, n is the operation evaluation index, yi is the reference evaluation value of the i-th operation evaluation index generated from the historical operation and maintenance data packets of the same equipment category, and ai is the weight coefficient of the i-th operation evaluation index.

3. The operation and maintenance system for the entire life cycle of electrical equipment in thermal power plants as described in claim 2, characterized in that, Construct a sequence of maintenance nodes for the entire lifecycle of the corresponding equipment category based on operational evaluation values, including: The number of nodes to be maintained throughout the entire lifecycle of each equipment category is selected based on the relationship between the operational evaluation value of each equipment category and the preset operational evaluation value range. The first preset operating evaluation value range, the second preset operating evaluation value range, the third preset operating evaluation value range, and the fourth preset operating evaluation value range are preset. When the operation evaluation value is within the first preset operation evaluation value range, the number of nodes to be maintained for the entire life cycle of the corresponding equipment category is set to the fourth preset number of nodes to be maintained. When the operation evaluation value is within the second preset operation evaluation value range, the number of nodes to be maintained for the entire life cycle of the corresponding equipment category is set to the third preset number of nodes to be maintained. When the operation evaluation value is within the third preset operation evaluation value range, the number of nodes to be maintained for the entire life cycle of the corresponding equipment category is set to the second preset number of nodes to be maintained. When the operation evaluation value is in the fourth preset operation evaluation value range, the number of nodes to be maintained for the entire life cycle of the corresponding equipment category is set to the first preset number of nodes to be maintained. The number of nodes to be maintained for each selected equipment category is evenly distributed across all stages of the entire lifecycle, forming a sequence of nodes to be maintained for the entire lifecycle of the corresponding equipment category.

4. The operation and maintenance system for the entire life cycle of electrical equipment in thermal power plants as described in claim 3, characterized in that, Construct a node association table for each node to be maintained, including: Randomly select one of the electrical equipment nodes to be maintained as the target node; Generate a first association value between the target node and the remaining nodes to be maintained of the same electrical equipment, a second association value between the target node and the remaining nodes to be maintained of different electrical equipment of the same equipment category, and a third association value between the target node and the remaining nodes to be maintained of several electrical equipment of different equipment categories; Pre-set a first correlation value threshold, a second correlation value threshold, and a third correlation value threshold; The nodes to be maintained that have a first association value greater than a preset first association value threshold are set as the first association nodes of the target node, and a first node association table is constructed, wherein each first association node in the first node association table is mapped to a corresponding first association value difference. The nodes to be maintained that have a second association value greater than a preset second association value threshold are set as the second association nodes of the target node, and a second node association table is constructed. Each second association node in the second node association table is mapped to a corresponding second association value difference. Nodes to be maintained whose third association value is greater than the preset third association value threshold are set as the third association nodes of the target node, and a third node association table is constructed, wherein each third association node in the third node association table is mapped to a corresponding third association value difference. Construct the node association table of the target node based on the first node association table, the second node association table, and the third node association table; Generate a node association table for each node to be maintained for each electrical device in sequence.

5. The operation and maintenance system for the entire life cycle of electrical equipment in thermal power plants as described in claim 4, characterized in that, Construct a node-feature mapping table, including: Based on the time correspondence between several historical fault logs of each electrical device and each node to be maintained in the sequence of nodes to be maintained of the corresponding electrical device, the historical fault data packets of each node to be maintained are determined. Feature extraction is performed on historical fault data packets to obtain several historical fault features of the corresponding nodes to be maintained; Based on the preset failure probability, the historical failure features at the same node to be maintained are combined to obtain several historical failure feature sets, wherein each historical failure feature set includes at least one historical failure feature. Each node to be maintained is mapped to a set of corresponding historical fault features to obtain a node-feature mapping set.

6. The operation and maintenance system for the entire life cycle of electrical equipment in thermal power plants as described in claim 4, characterized in that, Several monitoring time nodes are set based on the node association table, and several operation and maintenance strategies are generated based on the node-feature mapping table, including: The first influence coefficient is generated based on the number of first associated nodes in the first node association table and the difference in the first association value. The second influence coefficient is generated based on the number of second associated nodes in the second node association table, the difference in the corresponding second association values, the number of electrical equipment, and the weight coefficient of the corresponding electrical equipment. The third influence coefficient is generated based on the number of third-related nodes in the third-related node association table, the difference of the corresponding third-related values, the number of equipment categories, the weight coefficient of the corresponding equipment category, the number of electrical devices in each equipment category, and the weight coefficient of the corresponding electrical devices. The comprehensive impact coefficient of the corresponding node to be maintained is generated based on the first impact coefficient, the second impact coefficient, and the third impact coefficient. The monitoring time interval is selected based on the correspondence between the comprehensive impact coefficient and the preset comprehensive impact coefficient range, and several monitoring time nodes corresponding to the nodes to be maintained are generated according to the monitoring time interval. Pre-build a preset operation and maintenance model for each node to be operated and maintained; Each node-feature mapping set is input into the preset operation and maintenance model of the corresponding node to be operated and maintained, thereby obtaining several operation and maintenance strategies for several historical fault feature sets in the node-feature mapping set.

7. The operation and maintenance system for the entire life cycle of electrical equipment in thermal power plants as described in claim 6, characterized in that, Select the real-time maintenance nodes of electrical equipment, including: Acquire runtime and real-time status data of electrical equipment; Real-time health coefficients are generated based on the runtime and real-time status data of electrical equipment. The difference between the real-time health coefficient and the preset health coefficient at each node in the sequence of nodes to be maintained in the current equipment category is calculated to obtain the health coefficient difference value. Several nodes awaiting maintenance are sorted according to their health coefficient differences, and the node ranked first is set as the real-time node awaiting maintenance for the corresponding electrical equipment.

8. The operation and maintenance system for the entire life cycle of electrical equipment in thermal power plants as described in claim 7, characterized in that, Based on the node-feature mapping table, real-time operation and maintenance strategies are set for nodes to be maintained, including: Real-time operating data of the corresponding electrical equipment is collected according to the monitoring time node and compared with the corresponding standard operating data range. If it is not in the corresponding standard operating data range, the difference in real-time operating data is calculated. The difference in real-time running data is matched with the historical fault feature set in the node-feature mapping table of the real-time node to be maintained to obtain several matching degrees. The operation and maintenance strategies corresponding to the historical fault feature set with a matching degree greater than the preset matching degree threshold are selected, and the selected operation and maintenance strategies are optimized and merged according to priority to generate the real-time operation and maintenance strategy for electrical equipment at the real-time maintenance node.

9. The operation and maintenance system for the entire life cycle of electrical equipment in thermal power plants as described in claim 8, characterized in that, Based on the preset feedback time nodes, obtain the feedback data packets of the corresponding electrical equipment, and determine whether to generate correction instructions for the real-time operation and maintenance strategy, including: Feature extraction is performed on the feedback data packets to obtain key operational features; Key operational characteristics are compared with corresponding standard operational characteristics, and operational evaluation values ​​are generated based on the comparison results. Pre-set operation and maintenance evaluation thresholds; When the operation and maintenance evaluation value is greater than the operation and maintenance evaluation value threshold, no correction instruction will be generated for the real-time operation and maintenance strategy. When the operation and maintenance evaluation value is not greater than the operation and maintenance evaluation value threshold, a correction instruction is generated for the implementation of the operation and maintenance strategy.

10. A method for the operation and maintenance of electrical equipment in a thermal power plant throughout its entire life cycle, characterized in that, include: Multiple equipment categories are defined and an operational evaluation value is calculated for each equipment category. A sequence of nodes to be maintained throughout the entire lifecycle of the corresponding equipment category is constructed according to the operational evaluation value. The sequence of nodes to be maintained includes several nodes to be maintained. Construct a node association table and a node-feature mapping table for each node to be maintained. Set several monitoring time nodes based on the node association table and generate several maintenance strategies based on the node-feature mapping table. Select the real-time maintenance nodes of electrical equipment, collect real-time operation data according to several monitoring time nodes of the real-time maintenance nodes, and set the real-time maintenance strategy of the real-time maintenance nodes based on the node-feature mapping table. The system obtains feedback data packets from the corresponding electrical equipment based on preset feedback time nodes and determines whether to generate correction instructions for the real-time operation and maintenance strategy.

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