Intelligent operation and maintenance decision-making method and system based on power facility asset state

Abstract

The invention discloses an intelligent operation and maintenance decision-making method and system based on an electric power facility asset state, and relates to the technical field of operation and maintenance, and the method comprises the following steps: obtaining all electric power equipment in a to-be-detected region, classifying the obtained electric power equipment, constructing an electric power equipment data set, and obtaining the operation and maintenance influence data of the electric power equipment; constructing an operation and maintenance influence model based on a machine learning algorithm according to the operation and maintenance influence data; obtaining an operation and maintenance influence coefficient of the power equipment based on the operation and maintenance influence model, carrying out descending sorting on the power equipment according to the operation and maintenance influence coefficient, carrying out classification according to the power equipment, and setting a threshold value for each classified power equipment; according to the method, by constructing the operation and maintenance influence model, the problems that a decision result is not comprehensive and accurate enough due to the fact that complex multi-dimensional influence of equipment is ignored, and meanwhile, equipment classification and threshold setting lack flexibility and are difficult to cope with operation and maintenance requirements of dynamic changes are solved.

Description

Technical Field

[0001] The present invention relates to the field of operation and maintenance technology, and more specifically, to an intelligent operation and maintenance decision-making method and system based on the asset status of power facilities. Background Art

[0002] With the rapid development of smart grid technology, the operation and maintenance mode of power facilities is undergoing a transformation from traditional manual management to intelligent management. Traditional power facility operation and maintenance methods mostly rely on manual inspections, regular inspections and empirical judgments, which have problems such as low efficiency, long response time and difficulty in fault prediction. Especially in the face of huge power networks and complex equipment conditions, traditional operation and maintenance methods seem to be inadequate and unable to detect potential risk points in a timely manner or conduct effective preventive maintenance.

[0003] In order to solve these problems, intelligent operation and maintenance decision-making methods based on the asset status of power facilities have emerged. This method relies on advanced technologies such as the Internet of Things, big data, artificial intelligence, and machine learning. By real-time monitoring of the operating status of power facilities, collecting equipment operating data, and combining historical data analysis and fault diagnosis models, it can achieve full life cycle management and accurate prediction of power equipment. Specifically, the intelligent operation and maintenance decision-making method obtains the operating parameters of various electrical equipment such as transformers, switchgear, and lines in real time through the equipment's online monitoring system, including current, voltage, temperature, and vibration indicators. Through real-time analysis of these data and combined with the prediction of the health status of the equipment by machine learning algorithms, the intelligent system identifies potential faults, evaluates the remaining service life of the equipment, and makes corresponding repair and replacement recommendations.

[0004] The above disclosed technical solutions have at least the following technical problems: The calculation of the operation and maintenance impact coefficient in the traditional intelligent operation and maintenance decision-making method based on the asset status of power facilities is too dependent on a single factor, ignoring the complex multi-dimensional impact of the equipment, resulting in incomplete and inaccurate decision results. At the same time, the equipment classification and threshold setting lack flexibility, making it difficult to cope with dynamically changing operation and maintenance needs. In view of the above problems, the present invention proposes a solution. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an intelligent operation and maintenance decision-making method based on the asset status of power facilities. By finding the three key influencing factors of equipment failure rate, operation and maintenance cost and operation and maintenance efficiency, and constructing an operation and maintenance impact model, the intelligent operation and maintenance decision-making method of the asset status of power facilities is optimized to solve the problem of ignoring the complex multi-dimensional impact of equipment, resulting in incomplete and inaccurate decision results, and at the same time, lacking flexibility in equipment classification and threshold setting, making it difficult to cope with dynamically changing operation and maintenance needs.

[0006] To achieve the above object, the present invention provides the following technical solutions: An intelligent operation and maintenance decision-making method based on the asset status of power facilities, comprising obtaining all power equipment in a test area, classifying the obtained power equipment to construct a power equipment data set, and obtaining operation and maintenance impact data of the power equipment; constructing an operation and maintenance impact model based on a machine learning algorithm according to the operation and maintenance impact data; obtaining the operation and maintenance impact coefficient of the power equipment based on the operation and maintenance impact model, sorting the power equipment in descending order according to the operation and maintenance impact coefficient, and classifying the power equipment, and setting a threshold for each classified power equipment.

[0007] In a preferred embodiment, the operation and maintenance impact data includes an equipment failure rate coefficient, and the steps for obtaining the equipment failure rate coefficient are as follows: obtain the manual of the power equipment to obtain the standard temperature and rated load of the equipment operation; obtain the historical number of failures, the time of failure occurrence, the temperature when the failure occurred, and the load status of the power equipment to be tested, and find the number of failures and the time of failure occurrence under the standard temperature and optimal load status, and calculate based on the number of failures and the time of failure occurrence to obtain the basic failure rate of the power equipment; conduct a life cycle experiment on the power equipment, and obtain the failure rate under different service years from the life cycle experiment of the power equipment, analyze the failure rate under different service years based on the linear regression method, and obtain the equipment aging coefficient; obtain the rated load and actual operating load data of the power equipment to be tested, and record the equipment failure The load data at the time of test are obtained, and the relationship between load and failure is analyzed based on linear regression to obtain the load influence coefficient; the historical ambient temperature data of the equipment under test during actual operation is obtained, and the failure rate at different temperatures is analyzed according to the historical ambient temperature data, and the temperature influence coefficient is obtained based on the fitting method; the maintenance records of the equipment under test are collected, including maintenance frequency, maintenance content and maintenance quality, and the maintenance effect is evaluated according to the change in failure rate after equipment maintenance, including calculating the change in failure rate of equipment after regular maintenance and comparing the failure probability of equipment under different maintenance frequencies; the maintenance effect coefficient is obtained based on the regression analysis of the change in failure rate after equipment maintenance; the equipment failure rate coefficient is calculated based on the preset equipment failure rate coefficient formula according to the standard temperature, rated load, basic failure rate, equipment aging coefficient, temperature influence coefficient, maintenance effect coefficient, equipment load and real-time temperature.

[0008] In a preferred embodiment, the operation and maintenance impact data includes an operation and maintenance efficiency coefficient, and the steps for obtaining the operation and maintenance efficiency coefficient are as follows: obtain the historical actual daily operating time and daily planned available time data of the equipment to be tested, delete the time data of shutdown due to failure in the data, add and average the actual daily operating time and daily planned available time data respectively, and obtain the mean of the actual operating time of the equipment and the mean of the planned available time; compare the mean of the actual operating time of the equipment with the mean of the planned available time to obtain the equipment availability; obtain the total number of failures and the total operating time of the equipment to be tested, and compare the total number of failures and the total operating time of the equipment to obtain the equipment failure rate; obtain the equipment under test. The time point of each failure of the measured equipment and the time point of the start of maintenance are obtained, and each maintenance response time is obtained according to the time point of each failure of the equipment and the time point of the start of maintenance, and the average maintenance response time is obtained by adding up the average maintenance response time; the time point of each start of maintenance and the time point of the completion of maintenance of the measured equipment are obtained, and each maintenance completion time is obtained according to the time point of each start of maintenance and the time point of the completion of maintenance, and the average maintenance completion time is obtained by adding up the average maintenance completion time; the operation and maintenance efficiency coefficient is calculated based on the preset operation and maintenance efficiency coefficient formula according to the equipment availability, equipment failure rate, average maintenance response time and average maintenance completion time.

[0009] In a preferred embodiment, the operation and maintenance impact data includes an operation and maintenance impact range coefficient, and the steps for obtaining the operation and maintenance impact range coefficient are as follows: obtain the types of the operation and maintenance impact range coefficient of the equipment to be tested, the types of the operation and maintenance impact range coefficient include equipment importance, equipment status and operation and maintenance type, and score the types of the operation and maintenance impact range coefficient to obtain the actual impact value of the influencing factor; score the difficulty of the equipment fault repair and recovery process to obtain the recovery capacity coefficient; calculate based on the preset operation and maintenance impact range coefficient formula according to the actual impact value of the influencing factor, the number of types of influencing factors and the recovery capacity coefficient to obtain the operation and maintenance impact range coefficient.

[0010] In a preferred embodiment, the operation and maintenance impact coefficient of the power equipment is obtained based on the operation and maintenance impact model, the power equipment is sorted in descending order according to the operation and maintenance impact coefficient, and the power equipment is classified, and a threshold value is set for the power equipment in each category, as follows: The operation and maintenance impact coefficient of the power equipment to be tested is obtained according to the operation and maintenance impact model; the power equipment is classified according to its function, and the power equipment in each category is sorted in descending order based on the operation and maintenance impact coefficient to obtain multiple groups of descending sorting forms; different thresholds are set for each category according to the classification of power equipment; when the operation and maintenance impact coefficient exceeds the set threshold, an alarm will be sent to remind the administrator to perform maintenance.

[0011] A system for an intelligent operation and maintenance decision-making method based on the asset status of power facilities is characterized in that it includes a data acquisition module, an operation and maintenance impact model construction module and an operation and maintenance impact coefficient calculation module, and there is a connection between the modules; the data acquisition module is used to obtain all power equipment in the test area, and classify the acquired power equipment to construct a power equipment data set, and obtain the operation and maintenance impact data of the power equipment; the operation and maintenance impact model construction module is used to construct an operation and maintenance impact model based on the operation and maintenance impact data based on a machine learning algorithm; the operation and maintenance impact coefficient calculation module is used to obtain the operation and maintenance impact coefficient of the power equipment based on the operation and maintenance impact model, sort the power equipment in descending order according to the operation and maintenance impact coefficient, and classify the power equipment, and set a threshold for each classified power equipment.

[0012] Technical effects and advantages of an intelligent operation and maintenance decision-making method based on the asset status of power facilities of the present invention: 1. The present invention collects multi-dimensional operation and maintenance impact data of power equipment and uses machine learning algorithms to build an operation and maintenance impact model. Based on the historical operation data of the equipment, the model can analyze the failure risk, operation and maintenance cost and impact range of various types of equipment, provide a scientific basis for equipment management, and accurately predict the health status of the equipment. Based on the operation and maintenance impact coefficient generated by this model, the equipment can be sorted in descending order, high-risk equipment can be given priority, the maintenance order can be optimized, and the timeliness and effectiveness of the operation and maintenance response can be improved.

[0013] 2. The present invention calculates the influence coefficients of different factors on equipment failures and establishes a detailed fault prediction model in combination with the actual operation of the equipment. This model can dynamically adjust the failure rate estimate of the equipment and provide an important reference for equipment life cycle management, especially in equipment failure prediction and prevention, which can effectively reduce the occurrence of sudden failures and improve the reliability of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The present invention is a schematic structural diagram of an intelligent operation and maintenance decision-making method based on the asset status of power facilities.

[0015] Figure 2 The present invention is a schematic diagram of the system structure of an intelligent operation and maintenance decision-making method based on the asset status of power facilities. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Embodiment 1, Figure 1 The present invention provides an intelligent operation and maintenance decision-making method based on the asset status of power facilities, comprising: S1, obtain all the power equipment in the area to be tested, classify the obtained power equipment to construct a power equipment data set, and obtain the operation and maintenance impact data of the power equipment.

[0018] In this embodiment, the steps of acquiring all power equipment in the area to be tested, classifying the acquired power equipment to construct a power equipment data set, and acquiring operation and maintenance impact data of the power equipment include: Equipment failure rate coefficient, equipment failure rate coefficient, is a comprehensive indicator used to quantitatively evaluate the possibility of equipment failure under specific operating conditions. It takes into account multiple factors such as the equipment's service life, operating environment, and maintenance status, and is calculated through a mathematical formula to guide equipment maintenance strategies, predict failure trends, and optimize equipment management to ensure stable equipment operation.

[0019] The steps for obtaining the equipment failure rate coefficient are specifically as follows: Obtain the manual of the power equipment to obtain the standard operating temperature and rated load of the equipment; Obtain the historical number of failures, the time of failure, the temperature at which the failure occurred, and the load state of the power equipment to be tested, and find the number of failures and the time of failure under the standard temperature and optimal load state, and calculate based on the number of failures and the time of failure to obtain the basic failure rate of the power equipment; Conduct life cycle experiments on power equipment, and obtain failure rates at different service years from the life cycle experiments of power equipment. Analyze the failure rates at different service years based on the linear regression method to obtain the equipment aging coefficient. Obtain the rated load and actual operating load data of the power equipment to be tested, and record the load data when the equipment fails. Analyze the relationship between load and failure based on linear regression to obtain the load influence coefficient; Obtain historical ambient temperature data during the actual operation of the equipment under test, analyze the failure rate at different temperatures based on the historical ambient temperature data, and obtain the temperature influence coefficient based on the fitting method; Collect maintenance records of the equipment under test, including maintenance frequency, maintenance content and maintenance quality, and evaluate the maintenance effect based on the change in failure rate of the equipment after maintenance, including calculating the change in failure rate of the equipment after regular maintenance and comparing the failure probability of the equipment under different maintenance frequencies; The maintenance effect coefficient is obtained based on the regression analysis of the change in failure rate after equipment maintenance; The equipment failure rate coefficient is calculated based on the preset equipment failure rate coefficient formula according to the standard temperature, rated load, basic failure rate, equipment aging coefficient, temperature influence coefficient, maintenance effect coefficient, equipment load and real-time temperature to obtain the equipment failure rate coefficient.

[0020] The calculation formula of the equipment failure rate coefficient is as follows:

[0021] In the formula, is the equipment failure rate coefficient, is the basic failure rate, is the equipment aging factor, Is the useful life, is the load influence coefficient, is the equipment load, is the rated load of the equipment, is the temperature influence coefficient, is the real-time temperature, is the standard temperature, is the maintenance effectiveness coefficient, For maintenance frequency.

[0022] The operation and maintenance efficiency coefficient refers to a comprehensive indicator that measures the work effectiveness and efficiency of the operation and maintenance team. By comparing the key indicators of the completion of operation and maintenance tasks, fault recovery speed, system stability improvement, and resource utilization with the preset standards, it quantitatively evaluates the overall performance and optimization space of the operation and maintenance work, aiming to promote the optimization of the operation and maintenance process and improve efficiency.

[0023] The steps for obtaining the operation and maintenance efficiency coefficient are as follows: Obtain the historical actual daily operating time and daily planned available time data of the equipment under test, delete the time data of downtime due to failure in the data, add and average the actual daily operating time and daily planned available time data respectively, and obtain the mean value of the actual operating time and the mean value of the planned available time of the equipment; Compare the average of the actual operating time of the equipment with the average of the planned available time to obtain equipment availability; Obtain the total number of failures and the total operating time of the device under test, and compare the total number of failures and the total operating time of the device to obtain the device failure rate; Obtain the time point when each failure of the device under test occurs and the time point when maintenance begins, obtain each maintenance response time according to the time point when each failure of the device occurs and the time point when maintenance begins, and add up and average each maintenance response time to obtain an average maintenance response time; Obtain the time point at which each maintenance of the device under test is started and the time point at which the maintenance is completed, obtain the completion time of each maintenance according to the time point at which each maintenance of the device is started and the time point at which the maintenance is completed, and add up and average the completion time of each maintenance to obtain the average completion time of the maintenance; The operation and maintenance efficiency coefficient is calculated based on the equipment availability, equipment failure rate, average maintenance response time and average maintenance completion time based on a preset operation and maintenance efficiency coefficient formula to obtain the operation and maintenance efficiency coefficient.

[0024] The calculation formula of the operation and maintenance efficiency coefficient is as follows:

[0025] In the formula, is the operation and maintenance efficiency coefficient, is the equipment availability, is the equipment failure rate, is the average response time for maintenance, is the average time to complete repairs.

[0026] The operation and maintenance impact coefficient is a comprehensive evaluation index of the impact of operation and maintenance activities on system stability, business continuity and user experience. This coefficient measures the actual impact of operation and maintenance events by quantitatively analyzing the service interruption time, number of affected users and degree of business loss that may be caused by operation and maintenance operations, and provides a basis for operation and maintenance decisions.

[0027] The steps for obtaining the operation and maintenance impact range coefficient are as follows: Obtain the types of the operation and maintenance impact coefficients of the equipment under test, where the types of the operation and maintenance impact coefficients include equipment importance, equipment status, and operation and maintenance type, and score the types of the operation and maintenance impact coefficients to obtain actual impact values ​​of the influencing factors; Score the difficulty of equipment failure repair and recovery process to obtain the recovery capacity coefficient; The operation and maintenance influence range coefficient is calculated based on the actual influence value of the influence factor, the number of types of the influence factor and the recovery capacity coefficient based on the preset operation and maintenance influence range coefficient formula to obtain the operation and maintenance influence range coefficient.

[0028] The calculation formula of the operation and maintenance impact range coefficient is as follows:

[0029] In the formula, is the operation and maintenance impact range coefficient, is the weight coefficient of the i-th influencing factor, is the actual impact value of the i-th influencing factor, is the number of influencing factors, is the recovery capacity coefficient.

[0030] The types of coefficients affecting the scope of operation and maintenance are scored as follows: The grading criteria is a 1-5 point rating system; The health status of the equipment can be evaluated through the equipment's operating data and historical fault records. If the equipment is running under high load, the risk of failure will increase, the impact value will be large, and the score will be 4. If the equipment is in good condition, the impact value will be small, and the score will be 1. The types of O&M activities are evaluated, including regular inspections with a low impact value of 1, and troubleshooting and emergency repairs with a high impact value of 5; The impact value of environmental factors can be quantified based on weather, geographical location, environmental pollution, etc., including that the impact value of operation and maintenance in bad weather is higher, with a score of 5.

[0031] S2: constructing an operation and maintenance impact model based on the operation and maintenance impact data and a machine learning algorithm.

[0032] In this embodiment, the operation and maintenance impact model is constructed based on the operation and maintenance impact data based on a machine learning algorithm, as follows: The calculation formula of the operation and maintenance impact model is as follows:

[0033] In the formula, is the operation and maintenance impact coefficient, , and is the weight coefficient, is the equipment failure rate coefficient, is the operation and maintenance efficiency coefficient, is the operation and maintenance impact range coefficient.

[0034] S3, obtaining the operation and maintenance impact coefficient of the power equipment based on the operation and maintenance impact model, sorting the power equipment in descending order according to the operation and maintenance impact coefficient, classifying the power equipment, and setting a threshold for each classified power equipment.

[0035] In this embodiment, the operation and maintenance impact coefficient of the power equipment is obtained based on the operation and maintenance impact model, the power equipment is sorted in descending order according to the operation and maintenance impact coefficient, and the power equipment is classified according to the power equipment, and a threshold is set for each classified power equipment, as follows: Obtaining the operation and maintenance impact coefficient of the power equipment to be tested according to the operation and maintenance impact model; Classify the power equipment according to its function, and sort the power equipment in each category in descending order based on the operation and maintenance impact coefficient, to obtain multiple groups of descending sorting tables; According to the classification of power equipment, different thresholds are set for each category; When the operation and maintenance impact coefficient exceeds the set threshold, an alarm will be sent to remind the administrator to perform maintenance.

[0036] Embodiment 2, Figure 2 The present invention provides a system for an intelligent operation and maintenance decision-making method based on the asset status of power facilities, which is characterized by comprising a data acquisition module, an operation and maintenance impact model construction module and an operation and maintenance impact coefficient calculation module, and there is a connection between the modules; The data acquisition module is used to obtain all the power equipment in the area to be tested, classify the obtained power equipment to construct a power equipment data set, and obtain the operation and maintenance impact data of the power equipment; An operation and maintenance impact model building module, used to build an operation and maintenance impact model based on the operation and maintenance impact data based on a machine learning algorithm; The operation and maintenance impact coefficient calculation module is used to obtain the operation and maintenance impact coefficient of the power equipment based on the operation and maintenance impact model, sort the power equipment in descending order according to the operation and maintenance impact coefficient, classify the power equipment, and set a threshold for each classified power equipment.

[0037] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0038] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0039] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0040] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0041] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0042] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent operation and maintenance decision-making method based on the asset status of power facilities, characterized in that: include: Obtain all power equipment in the area to be tested, classify the acquired power equipment to construct a power equipment data set, and obtain operation and maintenance impact data of the power equipment; Building an operation and maintenance impact model based on the operation and maintenance impact data and a machine learning algorithm; The operation and maintenance impact coefficient of the power equipment is obtained based on the operation and maintenance impact model, the power equipment is sorted in descending order according to the operation and maintenance impact coefficient, and the power equipment is classified, and a threshold is set for each classified power equipment.

2. The intelligent operation and maintenance decision-making method based on the asset status of power facilities according to claim 1 is characterized in that: The operation and maintenance impact data includes an equipment failure rate coefficient, and the steps for obtaining the equipment failure rate coefficient are as follows: Obtain the manual of the power equipment to obtain the standard operating temperature and rated load of the equipment; Obtain the historical number of failures, the time of failure, the temperature at which the failure occurred, and the load state of the power equipment to be tested, and find the number of failures and the time of failure under the standard temperature and optimal load state, and calculate based on the number of failures and the time of failure to obtain the basic failure rate of the power equipment; Conduct life cycle experiments on power equipment, and obtain failure rates at different service years from the life cycle experiments of power equipment. Analyze the failure rates at different service years based on the linear regression method to obtain the equipment aging coefficient. Obtain the rated load and actual operating load data of the power equipment to be tested, and record the load data when the equipment fails. Analyze the relationship between load and failure based on linear regression to obtain the load influence coefficient; Obtain historical ambient temperature data during the actual operation of the equipment under test, analyze the failure rate at different temperatures based on the historical ambient temperature data, and obtain the temperature influence coefficient based on the fitting method; Collect maintenance records of the equipment under test, including maintenance frequency, maintenance content and maintenance quality, and evaluate the maintenance effect based on the change in failure rate of the equipment after maintenance, including calculating the change in failure rate of the equipment after regular maintenance and comparing the failure probability of the equipment under different maintenance frequencies; The maintenance effect coefficient is obtained based on the regression analysis of the change in failure rate after equipment maintenance; The equipment failure rate coefficient is calculated based on the preset equipment failure rate coefficient formula according to the standard temperature, rated load, basic failure rate, equipment aging coefficient, temperature influence coefficient, maintenance effect coefficient, equipment load and real-time temperature to obtain the equipment failure rate coefficient.

3. The intelligent operation and maintenance decision-making method based on the asset status of power facilities according to claim 2 is characterized in that: The operation and maintenance impact data includes an operation and maintenance efficiency coefficient. The steps for obtaining the operation and maintenance efficiency coefficient are as follows: Obtain the historical actual daily operating time and daily planned available time data of the equipment to be tested, delete the time data of downtime due to failure in the data, add and average the actual daily operating time and daily planned available time data respectively, and obtain the average value of the actual operating time and the average value of the planned available time of the equipment; Compare the average of the actual operating time of the equipment with the average of the planned available time to obtain equipment availability; Obtain the total number of failures and the total operating time of the device under test, and compare the total number of failures and the total operating time of the device to obtain the device failure rate; Obtain the time point when each failure of the device under test occurs and the time point when maintenance begins, obtain each maintenance response time according to the time point when each failure of the device occurs and the time point when maintenance begins, and add up and average each maintenance response time to obtain an average maintenance response time; Obtain the time point at which each maintenance of the device under test is started and the time point at which the maintenance is completed, obtain the completion time of each maintenance according to the time point at which each maintenance of the device is started and the time point at which the maintenance is completed, and add up and average the completion time of each maintenance to obtain the average completion time of the maintenance; The operation and maintenance efficiency coefficient is calculated based on the equipment availability, equipment failure rate, average maintenance response time and average maintenance completion time based on a preset operation and maintenance efficiency coefficient formula to obtain the operation and maintenance efficiency coefficient.

4. The intelligent operation and maintenance decision-making method based on the asset status of power facilities according to claim 3 is characterized in that: The operation and maintenance impact data includes an operation and maintenance impact range coefficient, and the steps for obtaining the operation and maintenance impact range coefficient are as follows: Obtain the types of the operation and maintenance impact coefficients of the equipment under test, where the types of the operation and maintenance impact coefficients include equipment importance, equipment status, and operation and maintenance type, and score the types of the operation and maintenance impact coefficients to obtain actual impact values ​​of the influencing factors; Score the difficulty of equipment failure repair and recovery process to obtain the recovery capacity coefficient; The operation and maintenance influence range coefficient is calculated based on the actual influence value of the influence factor, the number of types of the influence factor and the recovery capacity coefficient based on the preset operation and maintenance influence range coefficient formula to obtain the operation and maintenance influence range coefficient.

5. The intelligent operation and maintenance decision-making method based on the asset status of power facilities according to claim 4 is characterized in that: The operation and maintenance impact coefficient of the power equipment is obtained based on the operation and maintenance impact model, the power equipment is sorted in descending order according to the operation and maintenance impact coefficient, and the power equipment is classified according to the power equipment, and a threshold is set for each classified power equipment, as follows: Obtaining the operation and maintenance impact coefficient of the power equipment to be tested according to the operation and maintenance impact model; Classify the power equipment according to its function, and sort the power equipment in each category in descending order based on the operation and maintenance impact coefficient, to obtain multiple groups of descending sorting tables; According to the classification of power equipment, different thresholds are set for each category; When the operation and maintenance impact coefficient exceeds the set threshold, an alarm will be sent to remind the administrator to perform maintenance.

6. The intelligent operation and maintenance decision-making method based on the asset status of power facilities according to claim 5 is characterized in that: The calculation formula of the equipment failure rate coefficient is as follows: In the formula, is the equipment failure rate coefficient, is the basic failure rate, is the equipment aging factor, Is the useful life, is the load influence coefficient, is the equipment load, is the rated load of the equipment, is the temperature influence coefficient, is the real-time temperature, is the standard temperature, is the maintenance effectiveness coefficient, For maintenance frequency.

7. The intelligent operation and maintenance decision-making method based on the asset status of power facilities according to claim 6 is characterized in that: The calculation formula of the operation and maintenance efficiency coefficient is as follows: In the formula, is the operation and maintenance efficiency coefficient, is the equipment availability, is the equipment failure rate, is the average response time for maintenance, is the average time to complete repairs.

8. The intelligent operation and maintenance decision-making method based on the asset status of power facilities according to claim 7 is characterized in that: The calculation formula of the operation and maintenance impact range coefficient is as follows: In the formula, is the operation and maintenance impact range coefficient, is the weight coefficient of the i-th influencing factor, is the actual impact value of the i-th influencing factor, is the number of influencing factors, is the recovery capacity coefficient.

9. The intelligent operation and maintenance decision-making method based on the asset status of power facilities according to claim 8 is characterized in that: The calculation formula of the operation and maintenance impact model is as follows: In the formula, is the operation and maintenance impact coefficient, , and is the weight coefficient, is the equipment failure rate coefficient, is the operation and maintenance efficiency coefficient, is the operation and maintenance impact range coefficient.

10. A system using the intelligent operation and maintenance decision-making method based on the asset status of power facilities as claimed in any one of claims 1 to 9, characterized in that: It includes a data collection module, an operation and maintenance impact model building module and an operation and maintenance impact coefficient calculation module, and there are connections between the modules; The data acquisition module is used to obtain all the power equipment in the area to be tested, classify the obtained power equipment to construct a power equipment data set, and obtain the operation and maintenance impact data of the power equipment; An operation and maintenance impact model building module, used to build an operation and maintenance impact model based on the operation and maintenance impact data based on a machine learning algorithm; The operation and maintenance impact coefficient calculation module is used to obtain the operation and maintenance impact coefficient of the power equipment based on the operation and maintenance impact model, sort the power equipment in descending order according to the operation and maintenance impact coefficient, classify the power equipment, and set a threshold for each classified power equipment.

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