CN117391663A - Equipment maintenance method and device, nonvolatile storage medium and computer equipment - Google Patents

Abstract

The invention discloses a device maintenance method, a device, a nonvolatile storage medium and computer equipment. Wherein the method comprises the following steps: acquiring connection relations among a plurality of power transmission and transformation devices and electrical parameters of the plurality of power transmission and transformation devices; constructing a fault tree model of a plurality of power transmission and transformation devices according to the connection relation among the plurality of power transmission and transformation devices, wherein the fault tree model is used for representing the reasons and results of the faults of the plurality of power transmission and transformation devices; determining a first probability of a plurality of power transmission and transformation devices to fail according to the fault tree model; determining a second probability of failure of the plurality of power transmission and transformation devices according to the electrical parameters of the plurality of power transmission and transformation devices; and overhauling the plurality of power transmission and transformation equipment according to the first probability and the second probability. The invention solves the technical problem that the operation state of the equipment cannot be comprehensively known in the related art, so that the equipment cannot be overhauled in time under the condition of higher fault risk.

Description

Equipment maintenance methods, devices, non-volatile storage media and computer equipment

Technical field

The present invention relates to the technical field of equipment maintenance, and specifically, to an equipment maintenance method, device, non-volatile storage medium and computer equipment.

Background technique

Traditional power transmission and transformation maintenance methods are usually based on experience and rely on manual judgment and operation. However, the complexity and large-scale nature of power systems make it difficult for traditional methods to comprehensively and accurately assess the health status of equipment, especially the lack of real-time monitoring and big data analysis. Therefore, timely intervention cannot be made before potential problems occur in the equipment, and countermeasures often need to be taken only after the failure occurs, resulting in long power outages and affecting the stability of power supply. Simple maintenance methods are often difficult to effectively deal with the diverse failure modes of equipment. Failures of power transmission and transformation equipment may involve many aspects such as electrical, mechanical, and thermal. However, traditional methods may only focus on a certain aspect and ignore the overall performance. Therefore, it is impossible to fully understand the operating status of the equipment and cannot solve the problem when the risk of failure is high. In this case, timely maintenance and troubleshooting are required, which makes it impossible to ensure the reliable operation of the equipment.

In response to the above problems, no effective solution has yet been proposed.

Contents of the invention

Embodiments of the present invention provide an equipment maintenance method, device, non-volatile storage medium and computer equipment to at least solve the problem of not being able to fully understand the operating status of the equipment in related technologies in a timely manner when the risk of failure is high. Technical issues for maintenance.

According to an aspect of an embodiment of the present invention, an equipment maintenance method is provided, including: obtaining the connection relationship between multiple power transmission and transformation equipment and the electrical parameters of the multiple power transmission and transformation equipment; according to the multiple power transmission and transformation equipment The connection relationship between multiple power transmission and transformation equipment is constructed to construct a fault tree model of multiple power transmission and transformation equipment. The fault tree model is used to represent the causes and consequences of the failure of multiple power transmission and transformation equipment. According to the fault tree model, multiple power transmission and transformation equipment are determined. The first probability of equipment failure; determining the second probability of failure of multiple power transmission and transformation equipment based on the electrical parameters of the multiple power transmission and transformation equipment; based on the first probability and the second probability, performing a test on the multiple power transmission and transformation equipment. Overhaul.

Optionally, constructing fault tree models of multiple power transmission and transformation equipment based on the connection relationships between multiple power transmission and transformation equipment includes: generating top nodes of the fault tree model based on predetermined fault events to be analyzed; respectively Generate multiple basic nodes of the fault tree model based on multiple basic events, where the multiple basic events are events that cause the fault event to be analyzed; determine multiple basic nodes based on the connection relationships between multiple power transmission and transformation equipment The connection relationship between multiple basic nodes and the connection relationship between multiple basic nodes and the top node; a fault tree model is constructed based on the connection relationship between multiple basic nodes and the connection relationship between multiple basic nodes and the top node.

Optionally, according to the connection relationship between multiple power transmission and transformation equipment, determine the connection relationship between multiple basic nodes, and the connection relationship between multiple basic nodes and the top node, and build a fault tree model, including: according to The connection relationship between multiple power transmission and transformation equipment determines the parent-child relationship between multiple basic nodes. The parent-child relationship between multiple basic nodes indicates that the event corresponding to the child node causes the event corresponding to the parent node to occur; according to The parent-child relationship between multiple basic nodes determines the connection relationship between multiple basic nodes; based on the connection relationship between multiple power transmission and transformation equipment, the parent-child relationship between multiple basic nodes and the top node is determined, where, The top node is connected to the basic node corresponding to the basic event that directly causes the fault to be analyzed; according to the parent-child relationship between multiple basic nodes and the top node, the connection relationship between multiple basic nodes and the top node is determined.

Optionally, building a fault tree model based on the connection relationship between multiple basic nodes and the connection relationship between multiple basic nodes and the top node includes: determining based on the connection relationship between multiple power transmission and transformation equipment. The attributes of the connecting edges between multiple basic nodes and the top node, where the attributes of the connecting edges represent that the occurrence of the event corresponding to the child node alone causes the occurrence of the event corresponding to the corresponding parent node, or the occurrence of events corresponding to multiple child nodes Together, the events corresponding to the corresponding parent nodes occur; according to the attributes of the connecting edges between multiple basic nodes and the top node, the connection relationship between multiple basic nodes, and the connection relationship between multiple basic nodes and the top node , connect multiple basic nodes and top nodes to build a fault tree model.

Optionally, according to the fault tree model, determining the first probability of failure of multiple power transmission and transformation equipment includes: determining the probability of occurrence of events corresponding to multiple basic nodes; based on the connection edges between the multiple basic nodes and the top node attributes, determine the probability of occurrence of the fault event to be analyzed corresponding to the top node; determine the first probability of failure of multiple power transmission and transformation equipment based on the probability of the occurrence of the fault event to be analyzed.

Optionally, determining the second probability of failure of the multiple power transmission and transformation equipment based on the electrical parameters of the multiple power transmission and transformation equipment includes: inputting the electrical parameters of the multiple power transmission and transformation equipment into a predetermined time series prediction model. , the time series prediction model outputs the second probability, where the time series prediction model is obtained by training multiple groups of training samples. Each group of samples in the multiple groups of training samples includes historical electrical parameters of multiple power transmission and transformation equipment in the historical time period, and the probability of failure over a historical time period.

Optionally, performing maintenance on multiple power transmission and transformation equipment based on the first probability and the second probability includes: determining the comprehensive probability of failure of the multiple power transmission and transformation equipment based on the first probability and the second probability; based on the comprehensive probability , determine the maintenance plan for multiple power transmission and transformation equipment; perform maintenance on multiple power transmission and transformation equipment according to the maintenance plan for multiple power transmission and transformation equipment.

According to another aspect of the embodiment of the present invention, an equipment maintenance device is also provided, including: an acquisition module for acquiring the connection relationship between multiple power transmission and transformation equipment and the electrical parameters of the multiple power transmission and transformation equipment; The modeling module is used to construct fault tree models of multiple power transmission and transformation equipment based on the connection relationships between multiple power transmission and transformation equipment. The fault tree model is used to characterize the causes and reasons for the failure of multiple power transmission and transformation equipment. Result; the first determination module is used to determine the first probability of failure of multiple power transmission and transformation equipment according to the fault tree model; the second determination module is used to determine the first probability of failure of multiple power transmission and transformation equipment based on the electrical parameters of the multiple power transmission and transformation equipment. The second probability of failure of the power transmission equipment; the maintenance module is used to perform maintenance on multiple power transmission and transformation equipment according to the first probability and the second probability.

According to another aspect of the embodiment of the present invention, a non-volatile storage medium is also provided. The non-volatile storage medium includes a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute the above-mentioned Any equipment maintenance method.

According to yet another aspect of the embodiment of the present invention, a computer device is also provided. The computer device includes a processor, and the processor is configured to run a program, wherein any one of the above equipment maintenance methods is executed when the program is running.

In the embodiment of the present invention, by obtaining the connection relationship between multiple power transmission and transformation equipment and the electrical parameters of the multiple power transmission and transformation equipment; constructing multiple power transmission and transformation equipment based on the connection relationship between multiple power transmission and transformation equipment. A fault tree model of electrical equipment, where the fault tree model is used to characterize the causes and consequences of failures of multiple power transmission and transformation equipment; according to the fault tree model, the first probability of failure of multiple power transmission and transformation equipment is determined; according to multiple power transmission and transformation equipment The electrical parameters of power transmission and transformation equipment determine the second probability of failure of multiple power transmission and transformation equipment; based on the first probability and the second probability, multiple power transmission and transformation equipment are inspected and repaired, achieving the goal of considering multiple power transmission and transformation equipment at the same time. The current electrical parameters of the equipment and the connection status of multiple power transmission and transformation equipment are used to determine the current risk of failure of multiple power transmission and transformation equipment, so as to comprehensively understand the operating status of the equipment and achieve accurate and timely prediction of multiple power transmission and transformation equipment. The probability of electrical equipment failure, and the technical effect of repairing multiple power transmission and transformation equipment accordingly, thus solving the problem of not being able to fully understand the operating status of the equipment in related technologies and not being able to carry out timely maintenance when the risk of failure is high. Maintenance technical issues.

Description of the drawings

The drawings described here are used to provide a further understanding of the present invention and constitute a part of this application. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the attached picture:

Figure 1 shows a hardware structure block diagram of a computer terminal used to implement an equipment maintenance method;

Figure 2 is a schematic flow chart of an equipment maintenance method according to an embodiment of the present invention;

Figure 3 is a structural block diagram of an equipment maintenance device provided according to an embodiment of the present invention.

Detailed ways

In order to enable those skilled in the art to better understand the solutions 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 These are some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts should fall within the scope of protection of the present invention.

It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances so that the embodiments of the invention described herein are capable of being practiced in sequences other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, e.g., a process, method, system, product, or apparatus that encompasses a series of steps or units and need not be limited to those explicitly listed. Those steps or elements may instead include other steps or elements not expressly listed or inherent to the process, method, product or apparatus.

According to an embodiment of the present invention, a method embodiment for equipment maintenance is provided. It should be noted that the steps shown in the flow chart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although A logical order is shown in the flowcharts, but in some cases, the steps shown or described may be performed in a different order than herein.

The method embodiment provided in Embodiment 1 of the present application can be executed in a mobile terminal, a computer terminal, or a similar computing device. Figure 1 shows a hardware structural block diagram of a computer terminal used to implement an equipment maintenance method. As shown in Figure 1, the computer terminal 10 may include one or more (shown as 102a, 102b, ..., 102n in the figure) processors (the processors may include but are not limited to microprocessors MCU or programmable logic devices) A processing device such as an FPGA) and a memory 104 for storing data. In addition, it may also include: a display, an input/output interface (I/O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and/or Or camera. Persons of ordinary skill in the art can understand that the structure shown in FIG. 1 is only illustrative, and it does not limit the structure of the above-mentioned electronic device. For example, the computer terminal 10 may also include more or fewer components than shown in FIG. 1 , or have a different configuration than shown in FIG. 1 .

It should be noted that the one or more processors and/or other data processing circuitry described above may generally be referred to herein as "data processing circuitry." The data processing circuit may be embodied in whole or in part as software, hardware, firmware or any other combination. In addition, the data processing circuit may be a single independent processing module, or may be fully or partially integrated into any of the other components in the computer terminal 10 . As referred to in the embodiments of the present application, the data processing circuit serves as a processor control (eg, selection of a variable resistor terminal path connected to the interface).

The memory 104 can be used to store software programs and modules of application software, such as the program instructions/data storage device corresponding to the equipment maintenance method in the embodiment of the present invention. The processor executes various operations by running the software programs and modules stored in the memory 104. A kind of functional application and data processing, that is, the equipment maintenance method that implements the above-mentioned application program. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely relative to the processor, and these remote memories may be connected to the computer terminal 10 through a network. Examples of the above-mentioned networks include but are not limited to the Internet, intranets, local area networks, mobile communication networks and combinations thereof.

The display may be, for example, a touch-screen liquid crystal display (LCD), which may enable a user to interact with the user interface of the computer terminal 10 .

Figure 2 is a schematic flow chart of an equipment maintenance method according to an embodiment of the present invention. As shown in Figure 2, the method includes the following steps:

Step S202: Obtain the connection relationship between multiple power transmission and transformation equipment and the electrical parameters of the multiple power transmission and transformation equipment.

In this step, the multiple power transmission and transformation equipment may be power equipment located in the same area, and there are various connection relationships between the multiple power transmission and transformation equipment. The electrical parameters of multiple power transmission and transformation equipment may include electrical data such as voltage, current, and power of the equipment at the current moment. It should be noted that in this patent, multiple power transmission and transformation equipment may not only refer to large-scale, highly integrated power equipment, but may also refer to a certain module or component in the equipment.

Step S204: Construct fault tree models of multiple power transmission and transformation equipments based on the connection relationships between the multiple power transmission and transformation equipments, where the fault tree models are used to represent the causes and consequences of failures of multiple power transmission and transformation equipments.

Step S206: Determine the first probability of failure of multiple power transmission and transformation equipment based on the fault tree model.

Fault Tree Analysis (FTA) is a reliability tool used to analyze and predict the occurrence of system failures. It is a qualitative and quantitative reliability analysis method that can be used to evaluate the safety and reliability of the system. The fault tree model can help identify various events in the system that may cause faults and construct a tree structure through logical relationships to represent the possibility of fault occurrence. In the above two steps, through the connection relationships between multiple power transmission and transformation equipment, various events that may cause failures of multiple power transmission and transformation equipment can be determined, and fault tree models of multiple power transmission and transformation equipment can be constructed, and then multiple fault tree models can be constructed. The first probability of failure of a power transmission and transformation equipment.

Step S208: Determine the second probability of failure of the multiple power transmission and transformation equipments based on the electrical parameters of the multiple power transmission and transformation equipments.

In this step, the electrical parameters of multiple power transmission and transformation equipment at the current moment can also be analyzed, and the second probability of failure of the multiple power transmission and transformation equipment can be determined based on whether the electrical parameters are abnormal. It should be noted that the first probability and the second probability are the probabilities of failure of multiple power transmission and transformation equipment determined from different angles. Specifically, the first probability is based on determining various events that may occur in the equipment leading to failure. on, analyze the actual connection relationships of multiple power transmission and transformation equipment, and determine the probability that multiple power transmission and transformation equipment may fail based on logical relationships. That is to say, the determination of the first probability does not involve each of the multiple power transmission and transformation equipment. In the personalized situation, only the probability of failure of this type of equipment under the current connection relationship is considered. For example, the first probability is to consider the probability of failure of a certain type of transformer, without considering the specific current transmission and transformation conditions. The working condition of the transformer itself; and the second probability is determined based on the electrical parameters of multiple power transmission and transformation equipment, that is to say, the second probability involves the individualized conditions of multiple power transmission and transformation equipment, based on the current transmission and transformation equipment. The working condition of the specific transformer itself determines the probability of failure of multiple power transmission and transformation equipment.

Step S210, perform maintenance on multiple power transmission and transformation equipment according to the first probability and the second probability.

In this step, the first probability and the second probability obtained by the above two methods can be comprehensively considered to determine the current risk of failure of multiple power transmission and transformation equipment, and perform maintenance on the multiple power transmission and transformation equipment accordingly.

Through the above steps, the purpose of determining the current risk of failure of multiple power transmission and transformation equipment by simultaneously considering the current electrical parameters of multiple power transmission and transformation equipment and the connection conditions of multiple power transmission and transformation equipment can be achieved, thereby comprehensively understanding the operation of the equipment. status, achieving the technical effect of accurately and timely predicting the probability of failure of multiple power transmission and transformation equipment, and performing maintenance on multiple power transmission and transformation equipment accordingly, thereby solving the problem of the inability to fully understand the operating status of the equipment in related technologies. The technical problems caused by the inability to carry out timely maintenance when the risk of failure is high.

As an optional embodiment, constructing fault tree models of multiple power transmission and transformation devices based on the connection relationships between the multiple power transmission and transformation devices includes: generating a fault tree model based on predetermined fault events to be analyzed. The top node; generate multiple basic nodes of the fault tree model based on multiple basic events, where the multiple basic events are events that cause the fault event to be analyzed; according to the connection relationship between multiple power transmission and transformation equipment, Determine the connection relationship between multiple basic nodes, and the connection relationship between multiple basic nodes and the top node; based on the connection relationship between multiple basic nodes, and the connection relationship between multiple basic nodes and the top node, Build a fault tree model.

Optionally, possible fault events can be identified based on the connection relationships between devices and known fault modes, and the main equipment components and possible fault events can be listed as nodes of the fault tree. Among the possible fault events, one or more final fault events can be selected as the fault event to be analyzed, and the fault event to be analyzed can be used as the top node of the fault tree, causing the fault event to be analyzed to be the basic node of the fault tree. . Then the causal relationship between fault events can be determined based on the connection relationships between multiple power transmission and transformation equipment, and then the connection relationships between multiple basic nodes and top nodes can be determined.

As an optional embodiment, according to the connection relationship between multiple power transmission and transformation equipment, the connection relationship between multiple basic nodes is determined, and the connection relationship between multiple basic nodes and the top node is determined, and a fault tree is constructed. The model includes: determining the parent-child relationship between multiple basic nodes based on the connection relationship between multiple power transmission and transformation equipment. The parent-child relationship between multiple basic nodes represents that the event corresponding to the child node causes the corresponding parent node to occur. event occurs; according to the parent-child relationship between multiple basic nodes, the connection relationship between multiple basic nodes is determined; according to the connection relationship between multiple power transmission and transformation equipment, the connection relationship between multiple basic nodes and the top node is determined Parent-child relationship, in which the top node is connected to the basic node corresponding to the basic event that directly causes the fault to be analyzed; according to the parent-child relationship between multiple basic nodes and the top node, the connection relationship between multiple basic nodes and the top node is determined .

Optionally, for each fault event, determine its fault cause and effect, and express the logical relationship between the cause and effect as a branch of the fault tree, that is, the fault event, cause and effect can be associated in a tree structure stand up. Each failure event acts as a node in the tree, and the cause and effect serve as branches connected to the corresponding event node.

As an optional embodiment, constructing a fault tree model based on the connection relationship between multiple basic nodes and the connection relationship between multiple basic nodes and the top node includes: based on the connection relationship between multiple power transmission and transformation equipment The connection relationship determines the attributes of the connecting edges between multiple basic nodes and the top node, where the attributes of the connecting edges represent that the occurrence of the event corresponding to the child node alone causes the occurrence of the event corresponding to the corresponding parent node, or, multiple child nodes The occurrence of the corresponding event jointly causes the occurrence of the corresponding event of the corresponding parent node; according to the attributes of the connecting edges between multiple basic nodes and the top node, the connection relationship between multiple basic nodes, and the multiple basic nodes and the top node The connection relationship between multiple basic nodes and top nodes is connected to build a fault tree model.

Optionally, the edges connecting multiple nodes can have their own attributes. The attributes of the edges can represent the causal relationship between the events corresponding to the nodes. That is, the attributes of the edges can represent the logical relationships between the events corresponding to the nodes. Logical relationships can be "and", "or", etc. For logical operations in branch relationships, Boolean algebra can be used to express them. For example, the "AND" relationship is expressed as a logical AND operation, using the AND operator; the "OR" relationship is expressed as a logical OR operation, using the OR operator. For example: Suppose you want to conduct a transformer fault tree analysis to identify possible fault events, such as transformer overload, insulation damage, etc. For transformer overload, determine the causes that may include overcurrent and voltage fluctuations, and use the logical AND operator (AND) to represent overcurrent. Overload occurs only when the two causes of voltage fluctuation and voltage fluctuation occur at the same time; for insulation damage, possible causes include environmental damage and aging. The logical OR operator (OR) can be used to indicate insulation damage caused by environmental damage or aging.

As an optional embodiment, determining the first probability of failure of multiple power transmission and transformation equipment according to the fault tree model includes: determining the probability of occurrence of events corresponding to multiple basic nodes; The attributes of the connecting edges between them determine the probability of occurrence of the fault event to be analyzed corresponding to the top node; based on the probability of the fault event to be analyzed, the first probability of failure of multiple power transmission and transformation equipment is determined.

Optionally, you can first make a preliminary estimate of the probability of events corresponding to multiple basic nodes. Specifically, you can make an estimate based on historical data, expert judgment, and relevant literature. The estimated probability represents the possibility of the failure event, for example, The probability of transformer overload is 0.05 and the probability of insulation damage is 0.1. Then the probability of occurrence of each fault event (first probability) can be calculated through the fault tree model based on the logical relationship between nodes and the preliminary probability estimate. You can start from the basic events at the bottom of the fault tree model, and gradually calculate the probability of the event corresponding to the top node along the branches of the tree. According to the logical relationship (AND, OR), pass the probability of the child node to the parent node, and use Boolean algebra to perform logical operations. Specifically, if the parent node represents AND logic, that is, multiple child nodes occur at the same time, then the probability of the parent node is equal to the product of the probabilities of all child nodes; if the parent node represents OR logic, that is, at least one of the multiple child nodes occurs, then the parent node's The probability is equal to 1 minus the product of the probabilities of all child nodes that have not occurred; through step-by-step calculation, the probability is passed from the bottom node to the upper node until it is passed to the top node. The probability of the event corresponding to the top node represents the possibility of equipment failure. sex. Finally, the risk of equipment failure can be judged based on the calculated top-level event probability.

Among them, preliminary probability estimates can be derived based on historical data and professional knowledge. Historical data on various types of power transmission and transformation equipment can be collected to calculate the frequency and number of occurrences of fault events. For example, for a specific failure event, you can count the number of times it occurred within the past year. The probability can be simply defined as the number of failure events divided by the total number of operations. For example, if a fault event occurs 20 times and a total of 100 operations are performed, the probability of the fault event is 20/100=0.2.

As an optional embodiment, determining the second probability of failure of the multiple power transmission and transformation equipment based on the electrical parameters of the multiple power transmission and transformation equipment includes: inputting the electrical parameters of the multiple power transmission and transformation equipment into a predetermined Among the time series prediction models, there is a time series prediction model that outputs the second probability. Among them, the time series prediction model is obtained by training multiple groups of training samples. Each group of samples in the multiple groups of training samples includes multiple power transmission and transformation equipment within a historical time period. historical electrical parameters, and the probability of failure within the historical time period.

Optionally, sensors can be installed for each power transmission and transformation equipment or equipment element to collect real-time data, and based on the real-time sensor data, a time series prediction algorithm can be applied to predict the equipment operating status, including potential failures and performance degradation. Among them, data acquisition and preprocessing are to collect real-time data of equipment components and prepare them for subsequent predictive maintenance algorithms and fault tree analysis algorithms.

The following details how to collect real-time data from multiple power transmission and transformation equipment: For each equipment component, select the appropriate sensor according to the parameters that need to be monitored (such as temperature, humidity, current, voltage, etc.). The selection of the sensor should be based on its accuracy, Reliability and adaptability, the location and installation method of the sensor will not affect the normal operation of the equipment; you can configure the data acquisition system, which can connect and read the data of the sensor; set the data collection frequency, that is, how often the data is obtained, which can Adjusting based on the importance of the equipment, failure modes and data storage capabilities can ensure that the collected data has consistent timestamps for subsequent time series analysis; monitor the quality of sensor data to detect outliers or data drift. If data is found If there are abnormalities, you may need to check whether there are problems with sensors, connecting lines, etc.; preprocess the collected data to ensure the accuracy and consistency of the data, which may include removing noise, filling in missing values, data interpolation and outlier processing; Store preprocessed data in a database, data warehouse, or similar storage system for subsequent analysis. For example: Suppose you want to monitor the temperature and current of a transformer, select a temperature sensor and a current sensor that are suitable for the working environment and parameter range of the transformer, install the temperature sensor on the surface of the transformer, and the current sensor measures the current of the transformer through electrical connections. Configure the data acquisition system to obtain temperature and current data every 15 minutes. Set up an alarm mechanism in the data acquisition system to promptly notify maintenance personnel when the temperature is abnormally high or the current is overloaded. Preprocess the collected data, such as removing The instantaneous noise is then stored in the database. Through the above steps, you can ensure that accurate and reliable real-time data are obtained from equipment components, providing a basis for subsequent predictive analysis.

The following details how to apply the time series prediction algorithm to predict equipment operating status: collect historical data, including equipment operating status, temperature, current, voltage and other sensor data, and use these data as a training set to build a time series prediction model ; Preprocess the collected historical data, including removing outliers, filling in missing values, and smoothing data, to ensure the quality and stability of the data; perform time series analysis on historical data to check whether the data has trends, seasonality, and cycles property, select appropriate analysis model parameters based on the analysis results; determine the parameters of the analysis model based on the results of the time series analysis, including the autoregressive order (p), difference order (d) and moving average order (q), and based on Select the parameters to establish an analysis model; use the training data to train the established analysis model and estimate the parameter values of the model; use part of the training data as a validation set to evaluate the prediction performance of the analysis model. Specifically, you can use the root mean square Error (RMSE) and other indicators to measure the prediction error; you can use the trained analysis model to predict real-time sensor data, and compare the predicted results with the actual observed values to check the accuracy of the prediction; according to the prediction results of the analytical model, Determine whether the equipment operating status is likely to malfunction or degrade in performance. If the predicted values are consistent with anomalies in historical data, maintenance may need to be considered. For example: Suppose you want to predict the temperature of a transformer. You can collect historical temperature data for the past year and record it once a day. Perform smoothing and trend analysis on the historical data and find that the data has seasonality and trend. Based on time series analysis, select an analysis model. The parameters of are (p=1, d=1, q=1), which represent first-order autoregression, first-order difference and first-order moving average. The analysis model is trained using the data of the past 11 months and the data of the last month is used. Model evaluation, and then using the trained analytical model to perform temperature predictions on the latest sensor data. Through the above steps, analytical models can be used to predict the operating status of equipment based on historical data and real-time sensor data, identify potential failures and performance degradation, and develop more effective maintenance plans.

As an optional embodiment, performing maintenance on multiple power transmission and transformation equipment according to the first probability and the second probability includes: determining the comprehensive cause of failure of the multiple power transmission and transformation equipment according to the first probability and the second probability. Probability; determine the maintenance plan for multiple power transmission and transformation equipment based on the comprehensive probability; perform maintenance on multiple power transmission and transformation equipment based on the maintenance plan for multiple power transmission and transformation equipment.

Optionally, the first probability and the second probability may be obtained and used as a basis for formulating the maintenance plan. According to the first probability of the fault tree analysis, the occurrence probability and impact degree of each fault event are determined, and then combined with the second probability determined by the time series prediction algorithm, a priority is assigned to each fault event, such as high, medium, and low priority. level, or urgent, important, general, etc., which will help distinguish which faults require urgent handling and which can be handled later. It is also possible to evaluate the impact of each failure event, that is, the possible losses to system operation after the failure occurs, taking into account the economic cost of the loss, safety risks and reliability impacts. For high-priority fault events, corresponding maintenance strategies can be formulated, which can include immediate shutdown for maintenance, partial shutdown for maintenance, or online maintenance. For example: Suppose in the fault tree analysis, it is found that the probability of overloading the transformer is high and its impact is serious; the predictive maintenance algorithm also shows that the temperature of the transformer may exceed the safe range in the next week; given the high probability and serious impact, the transformer is Set the overload event as high priority; formulate a maintenance plan and decide to shut down the transformer for maintenance within the next two days to clean up the transformer and ensure its normal operation; through the above steps, a reasonable maintenance plan can be formulated based on the probability of failure and the degree of impact, and priority processing can be made Failure events that may affect the stable operation and safety of the equipment.

Specifically, the optimal maintenance time is determined based on the equipment's operation schedule, production plan and maintenance window. Taking into account the load condition of the equipment and the impact on production, choose to perform maintenance at an appropriate time period. You can also choose an appropriate maintenance method based on maintenance objectives and equipment conditions, which can be preventive maintenance, corrective maintenance or online maintenance. Consider the condition of the equipment and probability of failure to select the most appropriate maintenance method. The required maintenance personnel, tools, materials and equipment can also be determined, and the appropriate personnel and resources can be allocated based on the complexity of the maintenance task and the skills required. Detailed maintenance steps and processes can also be developed, starting from preparation work and guiding maintenance personnel step by step to inspect, clean, repair and test the equipment. Possible risks and safety hazards can also be identified, and corresponding risk management measures can be formulated to ensure that the maintenance process is safe and reliable and to avoid accidents. The required spare parts and components can be prepared according to the maintenance plan to ensure the adequacy of spare parts and avoid maintenance work being blocked due to lack of spare parts. Before maintenance begins, necessary preparatory work can be performed, such as equipment shutdown, disassembly, equipment cleaning, etc., to ensure the smooth progress of the maintenance process. Carry out maintenance work according to the established maintenance procedures, and record key information during the maintenance process, such as operation records, maintenance status, spare parts used, etc. Record all information during the maintenance process in detail, including problems, solutions, time and resources consumed, etc. This will provide a useful reference for future maintenance decisions. After the maintenance is completed, feedback from the maintenance personnel is collected, the effect of the maintenance execution is evaluated, and the maintenance plan and execution process are improved and optimized based on the feedback results.

For example: Suppose the maintenance plan decides to shut down the transformer for maintenance. Maintenance optimization decisions include choosing to conduct maintenance during low production periods and preparing the required maintenance personnel, tools, spare parts and equipment. Detailed maintenance steps include cleaning insulation materials, checking cable connections, testing current and voltage, etc. During the maintenance process, every step of the overhaul was recorded, and an aging cable connection was discovered that needed to be replaced in time. The maintenance personnel noticed the temperature rise of the equipment during the maintenance process, and based on the results of the predictive maintenance algorithm, decided to replace the radiator during the maintenance process. Through the above steps, detailed maintenance decisions can be made based on the formulated maintenance plan to ensure the smooth execution of the maintenance process and optimize the maintenance effect. This helps improve device reliability and performance.

The equipment maintenance method proposed by the present invention combines the predictive maintenance algorithm, the fault tree analysis algorithm and the topology (connection relationship) of the power transmission and transformation equipment, which can bring many outstanding advantages in the process of power transmission and transformation equipment maintenance:

(1) Comprehensive performance analysis: Combining topology, predictive maintenance algorithms and fault tree analysis, the overall performance of the equipment can be comprehensively analyzed. The predictive maintenance algorithm provides the possible future status of the equipment through time series analysis, the fault tree analysis evaluates the failure probability from the perspective of logical relationships, and the topology simulation integrates this information to help determine the appropriate maintenance timing and strategy.

(2) Accurate maintenance prediction: Predictive maintenance algorithms can use historical data and real-time sensor data to accurately predict the future status of equipment. By combining fault tree analysis, the probability of occurrence of different fault events can be more accurately estimated, helping to identify possible faults in advance and take targeted maintenance measures.

(3) Optimize maintenance decisions: Fault tree analysis and topology simulation help identify possible failure modes and impact paths. Predictive maintenance algorithms provide status information about equipment. Combining this information can optimize maintenance decisions, develop more targeted maintenance plans, and minimize the impact of maintenance on production.

(4) System level consideration: Fault tree analysis considers the logical relationship between different fault events, while topology simulation takes the physical connections of the equipment into consideration. This system-level consideration enables a more complete assessment of failure risk and maintenance needs, providing a more complete maintenance decision.

(5) Optimal utilization of resources: Combined with topology simulation, the interaction between equipment can be considered when formulating maintenance plans. This helps to rationally allocate human, material and time resources and avoid resource waste and duplication of work.

(6) Risk reduction: Fault tree analysis can clarify the possible paths and potential causes of failure. Combined with predictive maintenance algorithms, potential failures can be predicted early. Through comprehensive analysis, targeted maintenance strategies can be adopted to reduce risks caused by equipment failure.

By comprehensively utilizing the advantages of these three methods, we can achieve a more accurate, efficient, and comprehensive maintenance plan for power transmission and transformation equipment, ensuring the stable operation and safety of the equipment to the greatest extent.

It should be noted that for the sake of simple description, the foregoing method embodiments are expressed as a series of action combinations. However, those skilled in the art should know that the present invention is not limited by the described action sequence. Because in accordance with the present invention, certain steps may be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily necessary for the present invention.

Through the description of the above embodiments, those skilled in the art can clearly understand that the equipment maintenance method according to the above embodiments can be implemented by means of software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases The former is a better implementation. Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product in essence or the part that contributes to the existing technology. The computer software product is stored in a storage medium (such as ROM/RAM, disk, CD), including several instructions to cause a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in various embodiments of the present invention.

According to the embodiment of the present invention, an equipment maintenance device for implementing the above equipment maintenance method is also provided. Figure 3 is a structural block diagram of the equipment maintenance device provided according to the embodiment of the present invention. As shown in Figure 3, the equipment maintenance device It includes: an acquisition module 31, a modeling module 32, a first determination module 33, a second determination module 34 and a maintenance module 35. The equipment maintenance device will be described below.

The acquisition module 31 is used to acquire the connection relationship between multiple power transmission and transformation equipment and the electrical parameters of the multiple power transmission and transformation equipment.

The modeling module 32 is connected to the acquisition module 31 and is used to construct fault tree models of multiple power transmission and transformation equipment according to the connection relationships between the multiple power transmission and transformation equipment, where the fault tree model is used to represent the multiple power transmission and transformation equipment. Causes and consequences of electrical equipment failures.

The first determination module 33 is connected to the modeling module 32 and is used to determine the first probability of failure of multiple power transmission and transformation equipment according to the fault tree model.

The second determination module 34 is connected to the first determination module 33 and is used to determine the second probability of failure of the plurality of power transmission and transformation equipments based on the electrical parameters of the plurality of power transmission and transformation equipments.

The maintenance module 35 is connected to the second determination module 34 and is used to perform maintenance on multiple power transmission and transformation equipment according to the first probability and the second probability.

It should be noted here that the above-mentioned acquisition module 31, modeling module 32, first determination module 33, second determination module 34 and maintenance module 35 correspond to steps S202 to S210 in the embodiment, and the multiple modules are related to the corresponding steps. The examples and application scenarios implemented by the steps are the same, but are not limited to the contents disclosed in the above embodiments. It should be noted that the above modules as part of the device can run in the computer terminal 10 provided in the embodiment.

An embodiment of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one network device among multiple network devices in a computer network. The computer device includes a memory and a processor.

The memory can be used to store software programs and modules, such as the program instructions/modules corresponding to the equipment maintenance method and device in the embodiment of the present invention. The processor executes various functional applications by running the software programs and modules stored in the memory. And data processing, that is, to implement the above-mentioned equipment maintenance method. Memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include memory located remotely relative to the processor, and these remote memories may be connected to the computer terminal through a network. Examples of the above-mentioned networks include but are not limited to the Internet, intranets, local area networks, mobile communication networks and combinations thereof.

The processor can call the information stored in the memory and the application program through the transmission device to perform the following steps: obtain the connection relationship between multiple power transmission and transformation equipment, and the electrical parameters of the multiple power transmission and transformation equipment; The connection relationship between power equipment is used to construct a fault tree model of multiple power transmission and transformation equipment. The fault tree model is used to represent the causes and consequences of the failure of multiple power transmission and transformation equipment. According to the fault tree model, multiple power transmission and transformation equipment are determined. The first probability of failure of the power transmission equipment; determining the second probability of failure of the multiple power transmission and transformation equipment based on the electrical parameters of the multiple power transmission and transformation equipment; based on the first probability and the second probability, determining the multiple power transmission and transformation equipment Equipment for maintenance.

Optionally, constructing fault tree models of multiple power transmission and transformation equipment based on the connection relationships between multiple power transmission and transformation equipment includes: generating top nodes of the fault tree model based on predetermined fault events to be analyzed; respectively Generate multiple basic nodes of the fault tree model based on multiple basic events, where the multiple basic events are events that cause the fault event to be analyzed; determine multiple basic nodes based on the connection relationships between multiple power transmission and transformation equipment The connection relationship between multiple basic nodes and the connection relationship between multiple basic nodes and the top node; a fault tree model is constructed based on the connection relationship between multiple basic nodes and the connection relationship between multiple basic nodes and the top node.

Optionally, according to the connection relationship between multiple power transmission and transformation equipment, determine the connection relationship between multiple basic nodes, and the connection relationship between multiple basic nodes and the top node, and build a fault tree model, including: according to The connection relationship between multiple power transmission and transformation equipment determines the parent-child relationship between multiple basic nodes. The parent-child relationship between multiple basic nodes indicates that the event corresponding to the child node causes the event corresponding to the parent node to occur; according to The parent-child relationship between multiple basic nodes determines the connection relationship between multiple basic nodes; based on the connection relationship between multiple power transmission and transformation equipment, the parent-child relationship between multiple basic nodes and the top node is determined, where, The top node is connected to the basic node corresponding to the basic event that directly causes the fault to be analyzed; according to the parent-child relationship between multiple basic nodes and the top node, the connection relationship between multiple basic nodes and the top node is determined.

Optionally, building a fault tree model based on the connection relationship between multiple basic nodes and the connection relationship between multiple basic nodes and the top node includes: determining based on the connection relationship between multiple power transmission and transformation equipment. The attributes of the connecting edges between multiple basic nodes and the top node, where the attributes of the connecting edges represent that the occurrence of the event corresponding to the child node alone causes the occurrence of the event corresponding to the corresponding parent node, or the occurrence of events corresponding to multiple child nodes Together, the events corresponding to the corresponding parent nodes occur; according to the attributes of the connecting edges between multiple basic nodes and the top node, the connection relationship between multiple basic nodes, and the connection relationship between multiple basic nodes and the top node , connect multiple basic nodes and top nodes to build a fault tree model.

Optionally, according to the fault tree model, determining the first probability of failure of multiple power transmission and transformation equipment includes: determining the probability of occurrence of events corresponding to multiple basic nodes; based on the connection edges between the multiple basic nodes and the top node attributes, determine the probability of occurrence of the fault event to be analyzed corresponding to the top node; determine the first probability of failure of multiple power transmission and transformation equipment based on the probability of the occurrence of the fault event to be analyzed.

Optionally, determining the second probability of failure of the multiple power transmission and transformation equipment based on the electrical parameters of the multiple power transmission and transformation equipment includes: inputting the electrical parameters of the multiple power transmission and transformation equipment into a predetermined time series prediction model. , the time series prediction model outputs the second probability, where the time series prediction model is obtained by training multiple groups of training samples. Each group of samples in the multiple groups of training samples includes historical electrical parameters of multiple power transmission and transformation equipment in the historical time period, and the probability of failure over a historical time period.

Optionally, performing maintenance on multiple power transmission and transformation equipment based on the first probability and the second probability includes: determining the comprehensive probability of failure of the multiple power transmission and transformation equipment based on the first probability and the second probability; based on the comprehensive probability , determine the maintenance plan for multiple power transmission and transformation equipment; perform maintenance on multiple power transmission and transformation equipment according to the maintenance plan for multiple power transmission and transformation equipment.

Using the embodiment of the present invention, a solution for equipment maintenance is provided. By obtaining the connection relationships between multiple power transmission and transformation equipment and the electrical parameters of multiple power transmission and transformation equipment; building fault tree models of multiple power transmission and transformation equipment based on the connection relationships between multiple power transmission and transformation equipment, Among them, the fault tree model is used to characterize the causes and consequences of the failure of multiple power transmission and transformation equipment; according to the fault tree model, the first probability of failure of multiple power transmission and transformation equipment is determined; according to the electrical parameters of multiple power transmission and transformation equipment , determine the second probability of failure of multiple power transmission and transformation equipment; perform maintenance on multiple power transmission and transformation equipment based on the first probability and the second probability, achieving the simultaneous consideration of the current electrical parameters and multiple power transmission and transformation equipment The purpose of determining the current risk of failure of multiple power transmission and transformation equipment is based on the connection status of each power transmission and transformation equipment, so as to comprehensively understand the operating status of the equipment and achieve accurate and timely prediction of the probability of failure of multiple power transmission and transformation equipment. Based on this, the technical effect of overhauling multiple power transmission and transformation equipment is solved, which solves the technical problem of being unable to conduct timely maintenance when the risk of failure is high due to the inability to fully understand the operating status of the equipment in related technologies.

Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program. The program can be stored in a non-volatile storage medium. The storage medium It can include: flash disk, read-only memory (Read-Only Memory, ROM), random access memory (RandomAccess Memory, RAM), magnetic disk or optical disk, etc.

Embodiments of the present invention also provide a non-volatile storage medium. Optionally, in this embodiment, the above-mentioned non-volatile storage medium can be used to save the program code executed by the equipment maintenance method provided in the above-mentioned embodiment.

Optionally, in this embodiment, the above-mentioned non-volatile storage medium can be located in any computer terminal in the computer terminal group in the computer network, or in any mobile terminal in the mobile terminal group.

Optionally, in this embodiment, the non-volatile storage medium is configured to store program codes for performing the following steps: obtaining connection relationships between multiple power transmission and transformation equipment, and Electrical parameters; based on the connection relationships between multiple power transmission and transformation equipment, fault tree models of multiple power transmission and transformation equipment are constructed, where the fault tree model is used to characterize the causes and consequences of failures of multiple power transmission and transformation equipment; according to The fault tree model determines the first probability of failure of multiple power transmission and transformation equipment; determines the second probability of failure of multiple power transmission and transformation equipment based on the electrical parameters of multiple power transmission and transformation equipment; based on the first probability and the second probability of failure of multiple power transmission and transformation equipment Probability, perform maintenance on multiple power transmission and transformation equipment.

Optionally, constructing fault tree models of multiple power transmission and transformation equipment based on the connection relationships between multiple power transmission and transformation equipment includes: generating top nodes of the fault tree model based on predetermined fault events to be analyzed; respectively Generate multiple basic nodes of the fault tree model based on multiple basic events, where the multiple basic events are events that cause the fault event to be analyzed; determine multiple basic nodes based on the connection relationships between multiple power transmission and transformation equipment The connection relationship between multiple basic nodes and the connection relationship between multiple basic nodes and the top node; a fault tree model is constructed based on the connection relationship between multiple basic nodes and the connection relationship between multiple basic nodes and the top node.

Optionally, according to the connection relationship between multiple power transmission and transformation equipment, determine the connection relationship between multiple basic nodes, and the connection relationship between multiple basic nodes and the top node, and build a fault tree model, including: according to The connection relationship between multiple power transmission and transformation equipment determines the parent-child relationship between multiple basic nodes. The parent-child relationship between multiple basic nodes indicates that the event corresponding to the child node causes the event corresponding to the parent node to occur; according to The parent-child relationship between multiple basic nodes determines the connection relationship between multiple basic nodes; based on the connection relationship between multiple power transmission and transformation equipment, the parent-child relationship between multiple basic nodes and the top node is determined, where, The top node is connected to the basic node corresponding to the basic event that directly causes the fault to be analyzed; according to the parent-child relationship between multiple basic nodes and the top node, the connection relationship between multiple basic nodes and the top node is determined.

Optionally, building a fault tree model based on the connection relationship between multiple basic nodes and the connection relationship between multiple basic nodes and the top node includes: determining based on the connection relationship between multiple power transmission and transformation equipment. The attributes of the connecting edges between multiple basic nodes and the top node, where the attributes of the connecting edges represent that the occurrence of the event corresponding to the child node alone causes the occurrence of the event corresponding to the corresponding parent node, or the occurrence of events corresponding to multiple child nodes Together, the events corresponding to the corresponding parent nodes occur; according to the attributes of the connecting edges between multiple basic nodes and the top node, the connection relationship between multiple basic nodes, and the connection relationship between multiple basic nodes and the top node , connect multiple basic nodes and top nodes to build a fault tree model.

Optionally, according to the fault tree model, determining the first probability of failure of multiple power transmission and transformation equipment includes: determining the probability of occurrence of events corresponding to multiple basic nodes; based on the connection edges between the multiple basic nodes and the top node attributes, determine the probability of occurrence of the fault event to be analyzed corresponding to the top node; determine the first probability of failure of multiple power transmission and transformation equipment based on the probability of the occurrence of the fault event to be analyzed.

Optionally, determining the second probability of failure of the multiple power transmission and transformation equipment based on the electrical parameters of the multiple power transmission and transformation equipment includes: inputting the electrical parameters of the multiple power transmission and transformation equipment into a predetermined time series prediction model. , the time series prediction model outputs the second probability, where the time series prediction model is obtained by training multiple groups of training samples. Each group of samples in the multiple groups of training samples includes historical electrical parameters of multiple power transmission and transformation equipment in the historical time period, and the probability of failure over a historical time period.

Optionally, performing maintenance on multiple power transmission and transformation equipment based on the first probability and the second probability includes: determining the comprehensive probability of failure of the multiple power transmission and transformation equipment based on the first probability and the second probability; based on the comprehensive probability , determine the maintenance plan for multiple power transmission and transformation equipment; perform maintenance on multiple power transmission and transformation equipment according to the maintenance plan for multiple power transmission and transformation equipment.

The above serial numbers of the embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

In the above-mentioned embodiments of the present invention, each embodiment is described with its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the units may be a logical functional division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or may be Integrated into another system, or some features can be ignored, or not implemented. On the other hand, the coupling or direct coupling or communication connection between each other shown or discussed may be through some interfaces, and the indirect coupling or communication connection of the units or modules may be in electrical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or software functional units.

If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention is essentially or contributes to the existing technology or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , including several instructions to cause a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in various embodiments of the present invention. The aforementioned storage media include: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program code. .

The above are only preferred embodiments of the present invention. It should be noted that those skilled in the art can make several improvements and modifications without departing from the principles of the present invention. These improvements and modifications can also be made. should be regarded as the protection scope of the present invention.

Claims (10)

1. A method of servicing an apparatus, comprising:

acquiring connection relations among a plurality of power transmission and transformation devices and electrical parameters of the plurality of power transmission and transformation devices;

constructing a fault tree model of the power transmission and transformation equipment according to the connection relation among the power transmission and transformation equipment, wherein the fault tree model is used for representing the reasons and results of the faults of the power transmission and transformation equipment;

determining a first probability of the plurality of power transmission and transformation devices to fail according to the fault tree model;

determining a second probability of failure of the plurality of power transmission and transformation equipment according to the electrical parameters of the plurality of power transmission and transformation equipment;

and overhauling the plurality of power transmission and transformation equipment according to the first probability and the second probability.

2. The method according to claim 1, wherein the constructing a fault tree model of the plurality of power transmission and transformation devices according to the connection relationship between the plurality of power transmission and transformation devices includes:

generating a top node of the fault tree model according to a predetermined fault event to be analyzed;

generating a plurality of basic nodes of the fault tree model according to a plurality of basic events respectively, wherein the plurality of basic events are events which lead to the occurrence of the fault event to be analyzed;

Determining the connection relation between the plurality of basic nodes and the top node according to the connection relation between the plurality of power transmission and transformation devices;

and constructing the fault tree model according to the connection relation among the plurality of basic nodes and the connection relation between the plurality of basic nodes and the top node.

3. The method according to claim 2, wherein the determining the connection relationship between the plurality of basic nodes and the top node according to the connection relationship between the plurality of power transmission and transformation devices, and constructing the fault tree model, includes:

determining father-son relations among the plurality of basic nodes according to the connection relations among the plurality of power transmission and transformation devices, wherein the father-son relations among the plurality of basic nodes represent event occurrence corresponding to the child nodes and cause event occurrence corresponding to the father nodes;

determining the connection relation among the plurality of basic nodes according to the parent-child relation among the plurality of basic nodes;

determining father-son relationships between the plurality of basic nodes and the top node according to the connection relationships among the plurality of power transmission and transformation devices, wherein the top node is connected with the basic node corresponding to the basic event which directly causes the fault to be analyzed;

And determining the connection relation between the plurality of basic nodes and the top node according to the parent-child relation between the plurality of basic nodes and the top node.

4. The method of claim 3, wherein constructing the fault tree model based on the connection relationships between the plurality of base nodes and the top node comprises:

determining the attribute of a connection edge between the plurality of basic nodes and the top node according to the connection relation among the plurality of power transmission and transformation devices, wherein the attribute of the connection edge represents that the occurrence of an event corresponding to a child node singly causes the occurrence of an event corresponding to a corresponding parent node, or the occurrence of the event corresponding to the plurality of child nodes commonly causes the occurrence of the event corresponding to the corresponding parent node;

and connecting the plurality of basic nodes with the top node according to the attribute of the connecting edges between the plurality of basic nodes and the top node, the connection relation between the plurality of basic nodes and the top node, and constructing the fault tree model.

5. The method of claim 4, wherein determining a first probability of the plurality of power transmission and transformation devices failing according to the fault tree model comprises:

Determining the occurrence probability of the events corresponding to the plurality of basic nodes;

determining the probability of occurrence of the fault event to be analyzed corresponding to the top node according to the attribute of the connecting edges between the plurality of basic nodes and the top node;

and determining a first probability of the faults of the power transmission and transformation equipment according to the probability of the faults to be analyzed.

6. The method of claim 1, wherein determining a second probability of the plurality of power transmission and transformation devices failing based on the electrical parameters of the plurality of power transmission and transformation devices comprises:

and inputting the electrical parameters of the power transmission and transformation devices into a predetermined time sequence prediction model, wherein the time sequence prediction model outputs the second probability, the time sequence prediction model is obtained through training of a plurality of groups of training samples, and each group of samples in the plurality of groups of training samples comprises historical electrical parameters of the power transmission and transformation devices in a historical time period and the probability of failure in the historical time period.

7. The method of any one of claims 1 to 6, wherein the overhauling the plurality of power transmission and transformation devices according to the first probability and the second probability comprises:

Determining the comprehensive probability of the faults of the power transmission and transformation equipment according to the first probability and the second probability;

determining a scheme for overhauling the plurality of power transmission and transformation equipment according to the comprehensive probability;

and overhauling the plurality of power transmission and transformation equipment according to the overhauling scheme of the plurality of power transmission and transformation equipment.

8. An equipment servicing device, comprising:

the acquisition module is used for acquiring the connection relation among the power transmission and transformation equipment and the electrical parameters of the power transmission and transformation equipment;

the modeling module is used for constructing fault tree models of the power transmission and transformation devices according to the connection relation among the power transmission and transformation devices, wherein the fault tree models are used for representing the reasons and results of the faults of the power transmission and transformation devices;

the first determining module is used for determining a first probability of the faults of the power transmission and transformation equipment according to the fault tree model;

a second determining module, configured to determine a second probability of failure of the plurality of power transmission and transformation devices according to the electrical parameters of the plurality of power transmission and transformation devices;

and the overhaul module is used for overhauling the plurality of power transmission and transformation equipment according to the first probability and the second probability.

9. A non-volatile storage medium, characterized in that the non-volatile storage medium comprises a stored program, wherein the program, when run, controls a device in which the non-volatile storage medium is located to perform the method of equipment servicing according to any of claims 1 to 7.

10. A computer device, comprising: a memory and a processor, wherein the memory is configured to store,

the memory stores a computer program;

the processor being configured to execute a computer program stored in the memory, the computer program when run causing the processor to perform the method of equipment servicing as claimed in any of claims 1 to 7.