Life cycle assessment method and system for power materials

Abstract

The present invention relates to the technical field of electric power material evaluation, and discloses a method and system for evaluating the life cycle of electric power materials, including: determining the target equipment of the electric power materials, obtaining the target data of the target equipment, performing a quality evaluation on the target equipment, obtaining a first evaluation value of the target equipment, and simultaneously performing an operation fault analysis on the target equipment to obtain a second evaluation value of the target equipment, constructing a life prediction model for the target equipment, predicting the remaining life value of the target equipment, and calculating the comprehensive value of the target equipment over its entire life cycle. The present invention makes the evaluation more comprehensive and accurate by performing quality evaluation and operation fault analysis separately, and constructs a life prediction model based on this, fully considering various factors that affect the life of the equipment, greatly improving the accuracy of the prediction, and improving the economic benefit evaluation system of electric power materials by calculating the comprehensive value of the equipment over its entire life cycle.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power material evaluation, and in particular to a method and system for evaluating the life cycle of electric power materials. Background Art

[0002] The life cycle of power materials refers to the entire use process of material equipment throughout its life cycle. The main goal is to ensure the quality and performance of materials throughout their life cycle, reduce the number of failures and repairs, and improve the operating efficiency of equipment. Through full life cycle management, resource allocation can be optimized, operating costs can be reduced, and the reliability and safety of the power system can be improved. In the field of power material management, the importance of equipment full life cycle management has been generally recognized, and some companies have also carried out related work. Some companies have established equipment ledgers to record basic equipment information, purchase prices, maintenance records, etc. At the same time, some simple equipment status monitoring will be carried out, such as through manual inspections to detect abnormal appearance of equipment, abnormal operating sounds, etc.

[0003] Existing data on power materials are usually scattered across different systems, lacking a unified data management and integration platform. This results in low data utilization, a single means of equipment evaluation, and mostly relies on empirical judgment and simple data analysis. There is a lack of systematic multi-dimensional evaluation methods, making it difficult to fully reflect the actual status of the equipment. Traditional life predictions are often based on fixed formulas or empirical formulas, which cannot fully consider the actual operating environment and usage of the equipment, resulting in inaccurate prediction results and incomplete benefit evaluation. Summary of the Invention

[0004] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides a method for life cycle assessment of electric power materials to solve the above problems.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for life cycle assessment of electric power materials, comprising:

[0008] Determine target equipment for electric power materials and obtain target data for the target equipment;

[0009] Based on the target data, a quality assessment is performed on the target device to obtain a first assessment value of the target device, and an operation failure analysis is performed on the target device to obtain a second assessment value of the target device;

[0010] Based on the first evaluation value and the second evaluation value, constructing a life prediction model for the target device to predict the remaining life value of the target device;

[0011] A second value assessment of the remaining life cycle is performed based on the remaining life value of the target device, and the comprehensive value of the entire life cycle of the target device is calculated in combination with the life cost of the target device and the first value.

[0012] As a preferred solution of the power material life cycle assessment method of the present invention, wherein: the target data of the target device obtained includes basic information of the device, device operation data and device maintenance fault data;

[0013] The basic equipment information includes equipment ID, equipment name, equipment type, equipment size, equipment location, equipment material data, equipment testing data, equipment activation date, and purchase and installation cost;

[0014] The equipment operation data includes the equipment operation time of each cycle, equipment operation status, equipment monitoring operation parameters, operation abnormality parameters, abnormality times, equipment energy consumption and equipment operation records;

[0015] The equipment maintenance failure data includes equipment maintenance cycle, maintenance content, maintenance time, maintenance cost, maintenance abnormal events, failure occurrence time, failure type, failure level, number of failures, failure duration, failure interval time, failure repair cost, and failure loss.

[0016] As a preferred solution of the electric power material life cycle assessment method of the present invention, obtaining the first assessment value of the target device includes:

[0017] Acquiring device material data of the target device, extracting key material features of the device, and obtaining attribute values ​​of the device material features;

[0018] Obtaining equipment test data of the target equipment, and calculating the test value of the target equipment through equipment material test data and factory operation test data;

[0019] Obtaining the equipment operation data of the target equipment, dividing the data according to the maintenance cycle of the target equipment, analyzing and calculating the actual operating parameters and abnormal operating parameters of the equipment in each cycle, and obtaining the performance index value of the target equipment;

[0020] Acquire multiple second devices of the same model as the target device over their entire life cycle, screen out a set of second devices whose similarity values ​​between the second devices and the target device are greater than a similarity threshold, and calculate a quality impact factor of the target device based on a quality assessment result of the set of second devices;

[0021] The first evaluation value of the target device is calculated by combining the attribute value, the detection value, the performance indicator value and the quality impact factor.

[0022] As a preferred solution of the electric power material life cycle assessment method of the present invention, obtaining the second assessment value of the target device includes:

[0023] Obtain maintenance failure data of the target device, the maintenance failure data including the number of failures, failure interval time, failure level, failure loss, failure duration, and failure repair cost, and calculate a device failure coefficient, the device failure coefficient being obtained by calculating a failure frequency coefficient, a failure severity coefficient, and a failure repair difficulty coefficient;

[0024] The second evaluation value is calculated based on the equipment failure coefficient and combined with the failure impact factor of the second equipment.

[0025] As a preferred solution of the electric power material life cycle assessment method of the present invention, predicting the remaining life value of the target device includes:

[0026] performing data processing on the first evaluation value and the second evaluation value of the target device;

[0027] Obtain a third device of the same model as the target device and having a current life cycle greater than that of the target device, generate a third device set, filter device data of devices in the third device set at the used life cycle value based on the current used life cycle value of the target device, calculate the first evaluation value, the second evaluation value, and the remaining life value at the used life cycle, and generate a data set;

[0028] Constructing a life prediction model for the target device and using the data set for training, inputting the processed first evaluation value and second evaluation value of the target device into the life prediction model to predict the remaining life value of the target device;

[0029] The life prediction model is expressed as:

[0030] ;

[0031] in, Indicates the remaining health value of the target device. Indicates the first evaluation value of the device, Indicates the second evaluation value of the device, 、 、 represents the regression coefficient.

[0032] As a preferred embodiment of the method for evaluating the life cycle of electric power materials according to the present invention, the second value evaluation of the remaining life cycle includes:

[0033] Based on the remaining life of the target device, the device benefit index is determined, and combined with the historical benefits of the device, the second value of the device is calculated, which is expressed as:

[0034] ;

[0035] in, The expected annual benefit is represents the discount rate, Indicates the device's predicted remaining health value. Indicates the current health value of the device.

[0036] As a preferred embodiment of the electric power material life cycle assessment method of the present invention, the comprehensive value of the target equipment over its entire life cycle is calculated to include:

[0037] Based on the second value, the comprehensive value of the target device over its entire life cycle is calculated according to the life cost of the target device and the first value of the device actually generated during its actual use cycle, and is expressed as:

[0038] ;

[0039] in, Indicates the first value of the equipment, Indicates the second value of the device, Indicates the life cost of the equipment.

[0040] In a second aspect, the present invention provides a power material life cycle assessment system, comprising:

[0041] An acquisition module, configured to determine a target device of the electric power material and acquire target data of the target device;

[0042] an evaluation module, configured to perform a quality evaluation on the target device based on the target data to obtain a first evaluation value of the target device, and simultaneously perform an operation failure analysis on the target device to obtain a second evaluation value of the target device;

[0043] A prediction module, configured to construct a lifespan prediction model for the target device based on the first evaluation value and the second evaluation value, and predict the remaining lifespan of the target device;

[0044] The output module is used to perform a second value assessment of the remaining life cycle according to the remaining life value of the target device, and calculate the comprehensive value of the entire life cycle of the target device in combination with the life cost of the target device and the first value.

[0045] In a third aspect, the present invention provides an electronic device, comprising:

[0046] memory and processor;

[0047] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the power material life cycle assessment method are implemented.

[0048] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the electric power material life cycle assessment method.

[0049] Compared with the existing technology, the beneficial effects of the present invention are as follows: the present invention evaluates the equipment from two dimensions, namely, the inherent quality and actual operating performance, by performing quality assessment and operation fault analysis respectively, and then comprehensively obtains the evaluation results, so that the evaluation is more comprehensive and accurate, and avoids misjudgment caused by a single evaluation dimension; a life prediction model is constructed based on the results of quality assessment and operation fault analysis, which fully considers the various factors affecting the life of the equipment. Compared with the traditional single-factor life prediction method, the accuracy of the prediction is greatly improved, and a more reliable basis can be provided for the operation, maintenance and update of the equipment; by calculating the comprehensive value of the entire life cycle of the equipment, not only the various costs of the equipment are considered, but also the value during the period of use and the potential value of the remaining life cycle, thereby improving the economic benefit evaluation system of power materials, providing a scientific economic basis for the investment decision-making and equipment management of the enterprise, and helping the enterprise to optimize resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0051] Figure 1 This is a schematic diagram of the overall process of the power material life cycle assessment method according to one embodiment of the present invention;

[0052] Figure 2 This is a schematic diagram of the structure of an electric power material life cycle assessment system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0053] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0054] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0055] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0056] The present invention is described in detail with reference to schematic diagrams. For ease of illustration, cross-sectional views of device structures may be partially enlarged and not to scale when describing embodiments of the present invention. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0057] In the description of the present invention, it should be noted that the terms "upper, lower, inner, and outer" and other references to orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0058] In this disclosure, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be interpreted broadly. For example, they may refer to fixed, removable, or integral connections. They may also refer to mechanical, electrical, or direct connections, indirect connections through an intermediary, or internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in this disclosure.

[0059] Reference Figure 1-Figure 2, is an embodiment of the present invention, which provides a method for evaluating the life cycle of electric power materials, such as Figure 1 As shown, including:

[0060] S100, determining target equipment for electric power materials and obtaining target data of the target equipment;

[0061] S200, performing a quality assessment on the target device based on the target data to obtain a first assessment value of the target device, and simultaneously performing an operation failure analysis on the target device to obtain a second assessment value of the target device;

[0062] S300, constructing a life prediction model for the target device based on the first evaluation value and the second evaluation value to predict the remaining life value of the target device;

[0063] S400: Perform a second value assessment of the remaining life cycle based on the remaining life value of the target device, and calculate the comprehensive value of the entire life cycle of the target device by combining the life cost of the target device and the first value.

[0064] In a preferred embodiment, the target data of the target device is obtained, including but not limited to basic device information, device operation data, and device maintenance fault data;

[0065] Basic equipment information includes equipment ID, equipment name, equipment type, equipment size, equipment location, equipment material data, equipment inspection data, equipment activation date, and purchase and installation cost;

[0066] Equipment operation data includes equipment operation time per cycle, equipment operation status, equipment monitoring operation parameters, abnormal operation parameters, abnormality times, equipment energy consumption and equipment operation records;

[0067] Equipment maintenance failure data includes equipment maintenance cycle, maintenance content, maintenance time, maintenance cost, maintenance abnormal events, failure occurrence time, failure type, failure level, number of failures, failure duration, failure interval, failure repair cost, and failure loss.

[0068] Specifically, detailed data on various basic information of the equipment can be obtained from the supplier. After the equipment is installed and put into use, monitoring equipment is set up for each device. Equipment data is regularly obtained during use and recorded in the database. Specific equipment operation data and maintenance fault data are extracted from the database for subsequent analysis and evaluation of the equipment.

[0069] It should be noted that by identifying the target equipment and collecting its detailed data (basic equipment attributes, technical specifications, historical records, etc.), we ensure that subsequent analysis is based on complete and accurate information. High-quality data helps improve the reliability and accuracy of the assessment results.

[0070] In a preferred embodiment, obtaining the first evaluation value of the target device includes:

[0071] Step 1: Obtain the equipment material data of the target equipment, extract the key material characteristics of the equipment, and obtain the attribute values ​​of the equipment material characteristics;

[0072] Step 2: Obtain the equipment test data of the target equipment, calculate the test value of the target equipment through the equipment material test data and factory operation test data;

[0073] Step 3: Obtain the target device's operating data, divide the data according to the target device's maintenance cycle, analyze and calculate the actual operating parameters and abnormal operating parameters of the device in each cycle, and obtain the performance index value of the target device;

[0074] Step 4: Obtain multiple second devices of the same model as the target device throughout their entire life cycle, select a set of second devices whose similarity values ​​with the target device are greater than a similarity threshold, and calculate the quality impact factor of the target device based on the quality assessment results of the set of second devices;

[0075] Step 5: Calculate the first evaluation value of the target device by combining the attribute value, the detection value, the performance index value and the quality impact factor.

[0076] Specifically, in the above step 1, the basic data of the target equipment is retrieved and the main material components of the equipment are obtained. Each type of power material equipment is entered and archived in the database, and the various materials of the equipment are statistically classified, and the key characteristics (such as strength, conductivity, insulation, etc.) and quality grade of each material are noted. For example, for power cables, the purity of the conductor material (copper or aluminum), the voltage resistance grade of the insulating material, etc. are listed, and then weights are assigned according to the importance of each material to the overall performance of the equipment. For example, for generators, the weight of the winding material is higher because it is directly related to the generation of electric energy, while the weight of the casing material is relatively low. Then, the quality grade of each material is scored according to the application scenario, and the total score of the equipment material evaluation, that is, the attribute value of the equipment material characteristics, is obtained through weighted calculation.

[0077] Step 2: Obtain the test values. Before the equipment is installed and used, the manufacturer will conduct a quality inspection on the equipment. Based on the material test results and operational test results of the target equipment, the test values ​​of the equipment are evaluated and the data of each test result is standardized and converted into a standard score (Z-score) to make the data of different equipment comparable. Weights are assigned to each test data based on its importance to the equipment quality, and then the inspection evaluation score is calculated by weighted average. For example, for power transformers, the weight of the withstand voltage test data may be higher because insulation performance is a key indicator of the transformer.

[0078] Step three is to obtain the performance index value. Various sensors are installed on the equipment to monitor the operating parameters in real time. For example, for electric motors, current sensors, temperature sensors and vibration sensors are installed to monitor the operating current, temperature and vibration of the motor respectively. A maintenance cycle is set for each device, which can be two weeks or one month. It is set according to the actual scenario, and its key operating parameters are extracted according to the target device. The key operating parameter values ​​are recorded regularly according to the equipment maintenance cycle, including the normal operating data and abnormal operating data of the equipment. Specifically, the operating status, parameters and appearance of the equipment can be recorded. The recorded parameter values ​​are divided according to the maintenance cycle, and the performance index value of each cycle is calculated. First, the parameter deviation is calculated based on the actual operating data of the equipment. The parameters with a parameter deviation greater than the deviation threshold are recorded as abnormal parameters, and the abnormality of the abnormal parameters is calculated. The deviation threshold is set as needed. The parameter deviation and the abnormality are added to obtain the performance index value of each cycle. Then, the performance index value of the target device during the operating time is obtained by weighted average based on the performance index value of each cycle.

[0079] For example, the parameter deviation of the target device Expressed as:

[0080] ;

[0081] in, Indicates the actual parameter value, Indicates the middle value of the normal range of the parameter, Indicates the normal range width of the parameter. For example, if the actual operating temperature of the motor is 90°C, the normal range of the parameter is 60-80°C, the middle value of the normal range is 70°C, and the normal range width is 10°C, then the temperature deviation is 0.02.

[0082] Furthermore, according to the set deviation threshold, in this embodiment, the deviation threshold is 0.1. When the deviation of the operating parameter exceeds the set threshold, the parameter is determined to be an abnormal parameter. The severity of the abnormal situation is graded based on factors such as the type of abnormal parameter, the size of the deviation, and the duration of the abnormality. The number of abnormalities is multiplied by the abnormality severity score to obtain the abnormality degree of the parameter.

[0083] Based on the parameter deviation and anomaly, the performance index value of the target device is calculated , expressed as:

[0084] ;

[0085] in, is the number of running parameters, is the number of abnormal situations, Indicates the equipment maintenance cycle, Indicates device parameters The parameter deviation of Indicates device parameters The abnormality of Indicates the number of equipment maintenance cycle divisions.

[0086] In the above step 4, devices of the same model as the target device and having completed their entire life cycle, i.e., devices that have completed the entire life cycle from factory production to scrapping, are screened out and defined as second devices. First, device similarity indicators are defined, which may include basic parameter similarity, such as rated power, voltage level, and operating environment; material similarity, such as the type and quality of materials used in the devices; and operating condition similarity, such as the actual operating conditions of the devices, such as load rate and operating time. A similarity value is calculated for each pair of the target device and the second device using the Euclidean distance method or cosine similarity. When the Euclidean distance is used to measure the difference between the two devices, the smaller the distance, the higher the similarity. Alternatively, the device parameters are normalized into vectorized data, and the cosine value of the angle between the two vectors is measured. The closer the value is to 1, the higher the similarity. Then, second devices with similarity values ​​above the threshold are screened out to generate a second device set. Based on the quality assessment results of the entire life cycle, a weighted average is performed, and the weighted average is multiplied and divided by the similarity value between the two devices to obtain the quality impact factor of the target device. The quality assessment result of the second device may be the first assessment value obtained for the device based on its entire life cycle.

[0087] Calculate the first evaluation value of the target device Expressed as:

[0088] ;

[0089] in, Attribute values ​​representing the material characteristics of the target device, Indicates the detection value of the target device. Indicates the performance indicator value of the target device. Indicates the quality impact factor of the target device.

[0090] It should be noted that this embodiment combines multiple dimensions such as material evaluation, inspection evaluation, and operational performance evaluation to comprehensively reflect the initial quality status of the equipment. Through operational failure analysis, it can reflect the actual usage of the equipment and effectively provide data support for the evaluation of the equipment life cycle.

[0091] In a preferred embodiment, obtaining the second evaluation value of the target device includes:

[0092] Obtain maintenance failure data of the target equipment, including the number of failures, failure interval time, failure level, failure loss, failure duration, and failure repair cost, and calculate the equipment failure coefficient. The equipment failure coefficient is obtained by calculating the failure frequency coefficient, failure severity coefficient, and failure repair difficulty coefficient.

[0093] A second evaluation value is calculated based on the equipment failure coefficient and in combination with the failure impact factor of the second equipment.

[0094] Specifically, first divide the number of failures during the actual operation of the target device by the total operating time to calculate the failure frequency. Assuming that the expected service life of the device is , combined with the average time between failures of the equipment, the failure frequency coefficient Expressed as:

[0095] ;

[0096] in, Indicates the frequency of equipment failure, Indicates the mean time between failures of the equipment;

[0097] Calculate the fault severity coefficient based on the fault level and fault loss , expressed as:

[0098] ;

[0099] in, Indicates the equipment failure level, Indicates the actual failure loss, Indicates the set maximum failure loss;

[0100] Calculate the equipment failure repair difficulty coefficient based on the failure duration and failure repair cost , expressed as:

[0101] ;

[0102] in, Indicates the duration of equipment failure, Indicates the total operating time of the device. represents the failure repair cost, Indicates the total value of the equipment.

[0103] Furthermore, the second device set obtained based on the similarity is weighted averaged according to the fault assessment results of its entire life cycle, and then multiplied by the similarity value between the two devices and divided by the similarity value to obtain the fault impact factor of the target device, wherein the quality assessment result of the second device can be the second assessment value obtained by the device based on its entire life cycle.

[0104] Second evaluation value of the target device Expressed as:

[0105] ;

[0106] in, Indicates the failure impact factor of the target device.

[0107] It should be noted that the fault evaluation value in this embodiment can be used to obtain the evaluation value of the target device based on the fault condition of the device itself and the fault occurrence condition of similar devices throughout their life cycle as a correction factor. The higher the evaluation value, the higher the failure risk of the device. During implementation, parameters such as parameter weights can be adjusted according to actual conditions to better adapt to specific equipment and evaluation requirements. Through the above steps, the target device can be systematically analyzed for operational faults and the fault evaluation value can be calculated to predict the remaining life of the device. Not only the historical fault data of the device is taken into account, but also the actual parameters of similar devices are used to provide more accurate and reliable prediction results.

[0108] The equipment quality assessment of this embodiment can understand the inherent quality status of the equipment from aspects such as the equipment's design, manufacturing, and raw materials. The first assessment value provides a quantitative basis for judging the overall quality level of the equipment. The operational failure analysis starts from the actual operating performance of the equipment. The second assessment value reflects the stability and reliability of the equipment during operation. The combination of the two can comprehensively evaluate the performance and status of the equipment.

[0109] In a preferred embodiment, predicting the remaining life value of the target device includes:

[0110] performing data processing on the first evaluation value and the second evaluation value of the target device;

[0111] Obtain a third device of the same model as the target device and with a current life cycle longer than the target device, that is, screen out devices whose actual use cycle is longer than the current actual use cycle of the target device, which can also be devices that have completed their entire life cycle, generate a third device set, and based on the current used life cycle value of the target device, screen out device data of the devices in the third device set at the used life cycle value (device operation data and maintenance failure data before the used life cycle), calculate the first evaluation value, second evaluation value and remaining life value of the third device at the used life cycle, generate a data set, and divide the data set into a training set, a validation set and a test set according to a certain proportion;

[0112] Constructing a life prediction model for the target device and training it using the data set, inputting the processed first evaluation value and the second evaluation value of the target device into the life prediction model to predict the remaining life value of the target device;

[0113] The lifespan prediction model is expressed as:

[0114] ;

[0115] in, Indicates the remaining health value of the target device. Indicates the first evaluation value of the device, Indicates the second evaluation value of the device, 、 、 represents the regression coefficient.

[0116] Specifically, data integrity is checked through data processing, and missing values ​​are filled. For some missing values ​​that cannot be filled, whether to delete the corresponding data records is decided based on the importance of the data and the missing ratio. Duplicate data is removed to ensure the uniqueness of the data and avoid duplicate data from causing deviation to the model. Outliers are detected and processed. Box plots, 3σ principle and other methods can be used to identify outliers, and they can be corrected or deleted according to the specific situation. The collected data of different dimensions and magnitudes are standardized to make the data comparable. The Z-score standardization method can be used to convert the data into standard normal distribution data with a mean of 0 and a standard deviation of 1. Furthermore, the training set data is used to train the life prediction model, and the regression coefficient is solved by methods such as least squares method to minimize the sum of squared errors between the predicted value and the true value, and finally the remaining life value of the target equipment is predicted.

[0117] It should be noted that this embodiment constructs a life prediction model by comprehensively considering equipment quality and operational failure conditions, and combines historical data and real-time monitoring information. Compared with the prediction of a single factor, it provides a more accurate remaining life prediction, which helps to reasonably arrange equipment maintenance and update plans, avoid production interruptions caused by unexpected equipment failures, improve the stability and reliability of power system operation, and at the same time optimize resource allocation and reduce equipment operation and maintenance costs.

[0118] In a preferred embodiment, performing the second value assessment of the remaining life cycle includes:

[0119] Based on the remaining life of the target equipment, the equipment benefit index is determined, and combined with the historical benefits of the equipment, the second value of the equipment is calculated, which is expressed as:

[0120] ;

[0121] in, Indicates the expected annual benefit of the equipment, represents the discount rate, Indicates the device's predicted remaining health value. Indicates the current health value of the device.

[0122] Specifically, the benefit indicators mainly include analyzing the actual economic benefits of the equipment in the past few years, finding out its average annual income, considering the power market demand factors, and combining the aging of the equipment to estimate the expected annual benefits of the equipment in the future.

[0123] In a preferred embodiment, calculating the comprehensive value of the target device over its entire life cycle includes:

[0124] Based on the second value, the comprehensive value of the target equipment over its entire life cycle is calculated according to the life cost of the target equipment and the first value of the equipment actually generated during its actual use cycle, which is expressed as:

[0125] ;

[0126] in, Indicates the first value of the equipment, Indicates the second value of the device, Indicates the life cost of the equipment.

[0127] Specifically, the life cost of equipment mainly includes purchase cost, installation cost, maintenance cost, repair cost, equipment energy consumption, etc., that is, the initial cost of purchasing the equipment, the cost incurred during the equipment installation process, the cost required for regular equipment maintenance, the cost required for repairing the equipment when it fails, and the energy consumption during the use of the trademark. These costs are added together to obtain the life cost of the equipment; then the actual economic benefits generated by the target equipment during its use are calculated, and the first value of the equipment is obtained by adding up the total number of years the equipment has been in use and the actual annual income.

[0128] It should be noted that by evaluating the expected benefits within the remaining life cycle and combining it with the total life cost of the equipment, the benefits of the equipment throughout its entire life cycle can be comprehensively measured, and the historical performance and future expected benefits of the equipment can be comprehensively considered to optimize resource allocation and improve operational efficiency.

[0129] The present invention evaluates the equipment from two dimensions, namely, the inherent quality and actual operating performance, by conducting quality assessment and operational fault analysis respectively, and then comprehensively obtains the evaluation results, making the evaluation more comprehensive and accurate, and avoiding misjudgment caused by a single evaluation dimension; constructing a life prediction model based on the results of quality assessment and operational fault analysis, fully considering the various factors affecting the equipment life, compared with the traditional single-factor life prediction method, greatly improving the accuracy of the prediction, and providing a more reliable basis for the operation, maintenance and update of the equipment; by calculating the comprehensive value of the entire life cycle of the equipment, not only the various costs of the equipment are taken into account, but also the value during the period of use and the potential value of the remaining life cycle, thereby improving the economic benefit evaluation system of power materials, providing a scientific economic basis for the investment decision-making and equipment management of enterprises, and helping enterprises to optimize resource allocation.

[0130] The above is a schematic diagram of a method for life cycle assessment of electric power materials according to this embodiment. It should be noted that the technical solution of this electric power material life cycle assessment system and the technical solution of the above-mentioned method for life cycle assessment of electric power materials are based on the same concept. For details not described in detail in the technical solution of the electric power material life cycle assessment system according to this embodiment, please refer to the description of the technical solution of the above-mentioned method for life cycle assessment of electric power materials.

[0131] In this embodiment, the power material life cycle assessment system, such as Figure 2 As shown, including:

[0132] An acquisition module is used to determine the target device of the electric power material and obtain the target data of the target device;

[0133] An evaluation module is used to perform a quality evaluation on the target device based on the target data to obtain a first evaluation value of the target device, and simultaneously perform an operation failure analysis on the target device to obtain a second evaluation value of the target device;

[0134] A prediction module, configured to construct a life prediction model for the target device based on the first evaluation value and the second evaluation value, and predict the remaining life value of the target device;

[0135] The output module is used to perform a second value assessment of the remaining life cycle based on the remaining life value of the target device, and calculate the comprehensive value of the target device's entire life cycle by combining the life cost of the target device and the first value.

[0136] This embodiment further provides an electronic device suitable for life cycle assessment of electric power materials, including:

[0137] Memory and processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the method for life cycle assessment of electric power materials proposed in the above embodiment.

[0138] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for implementing the life cycle assessment of electric power materials as proposed in the above embodiment.

[0139] The storage medium proposed in this embodiment and the method for implementing the life cycle assessment of electric power materials proposed in the above embodiment belong to the same inventive concept. For technical details not fully described in this embodiment, please refer to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0140] Through the above description of the embodiments, those skilled in the art will clearly understand that the present invention can be implemented using software and necessary general-purpose hardware. Of course, it can also be implemented using hardware, but in many cases the former is a more preferred embodiment. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk, or optical disk, and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0141] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for life cycle assessment of electric power materials, characterized in that: include: Determine target equipment for electric power materials and obtain target data for the target equipment; Based on the target data, a quality assessment is performed on the target device to obtain a first assessment value of the target device, and an operation failure analysis is performed on the target device to obtain a second assessment value of the target device; Based on the first evaluation value and the second evaluation value, constructing a life prediction model for the target device to predict the remaining life value of the target device; Performing a second value assessment of the remaining life cycle based on the remaining life value of the target device, and calculating a comprehensive value of the entire life cycle of the target device in combination with the life cost of the target device and the first value; Obtaining a first evaluation value of the target device includes: Acquiring device material data of the target device and extracting key material characteristics of the device to obtain attribute values ​​of the device material characteristics, wherein the attribute values ​​are obtained by weighted calculation of key characteristics and quality grades of each material; Obtaining equipment test data of the target equipment, and calculating the test value of the target equipment through equipment material test data and factory operation test data; Obtaining device operation data of the target device, dividing the data according to the maintenance cycle of the target device, analyzing and calculating the actual operation parameters and abnormal operation parameters of the device in each cycle to obtain a performance index value of the target device, wherein parameter deviation is calculated based on the actual operation data of the device, parameters with parameter deviations greater than a deviation threshold are recorded as abnormal parameters, the abnormality of the abnormal parameters is calculated, the parameter deviation and the abnormality are added to obtain the performance index value of each cycle, and then a weighted average is performed based on the performance index value of each cycle to obtain the performance index value of the target device during the operating time; Acquire multiple second devices of the same model as the target device over their entire life cycle, screen out a set of second devices whose similarity values ​​between the second devices and the target device are greater than a similarity threshold, and calculate a quality impact factor of the target device based on a quality assessment result of the set of second devices, wherein a weighted average is taken based on the quality assessment results of the second devices over their entire life cycle, and the weighted average is multiplied by the similarity value between the two devices and divided by the similarity value to obtain the quality impact factor of the target device; Calculate a first evaluation value of the target device by combining the attribute value, the detection value, the performance indicator value, and the quality impact factor; Obtaining a second evaluation value of the target device includes: Obtain maintenance failure data of the target device, the maintenance failure data including the number of failures, failure interval time, failure level, failure loss, failure duration, and failure repair cost, and calculate a device failure coefficient, the device failure coefficient being obtained by calculating a failure frequency coefficient, a failure severity coefficient, and a failure repair difficulty coefficient; Calculating the second evaluation value based on the equipment failure coefficient and in combination with the failure impact factor of the second equipment; Among them, the fault frequency coefficient Expressed as: ； in, Indicates the frequency of equipment failure, Indicates the mean time between failures of the equipment; Calculate the fault severity coefficient based on the fault level and fault loss , expressed as: ； in, Indicates the equipment failure level, Indicates the actual failure loss, Indicates the set maximum failure loss; Calculate the equipment failure repair difficulty coefficient based on the failure duration and failure repair cost , expressed as: ； in, Indicates the duration of equipment failure, Indicates the total operating time of the device. represents the failure repair cost, Indicates the total value of the equipment; Predicting the remaining life value of the target device includes: performing data processing on the first evaluation value and the second evaluation value of the target device; Obtain a third device of the same model as the target device and having a current life cycle greater than that of the target device, generate a third device set, filter device data of devices in the third device set at the used life cycle value based on the current used life cycle value of the target device, calculate the first evaluation value, the second evaluation value, and the remaining life value at the used life cycle, and generate a data set; A life prediction model for the target device is constructed and trained using the data set. The processed first evaluation value and second evaluation value of the target device are input into the life prediction model to predict the remaining life value of the target device.

2. The electric power material life cycle assessment method according to claim 1, characterized in that: Acquiring target data of the target device including basic device information, device operation data, and device maintenance fault data; The basic equipment information includes equipment ID, equipment name, equipment type, equipment size, equipment location, equipment material data, equipment testing data, equipment activation date, and purchase and installation cost; The equipment operation data includes the equipment operation time of each cycle, equipment operation status, equipment monitoring operation parameters, operation abnormality parameters, abnormality times, equipment energy consumption and equipment operation records; The equipment maintenance failure data includes equipment maintenance cycle, maintenance content, maintenance time, maintenance cost, maintenance abnormal events, failure occurrence time, failure type, failure level, number of failures, failure duration, failure interval time, failure repair cost, and failure loss.

3. The electric power material life cycle assessment method according to claim 1, characterized in that: The life prediction model is expressed as: ； in, Indicates the remaining health value of the target device. Indicates the first evaluation value of the device, Indicates the second evaluation value of the device, 、 、 represents the regression coefficient.

4. The electric power material life cycle assessment method according to claim 1 or 3, characterized in that: Conducting a second value assessment of the remaining life cycle includes: Based on the remaining life of the target device, the device benefit index is determined, and combined with the historical benefits of the device, the second value of the device is calculated, which is expressed as: ； in, The expected annual benefit is represents the discount rate, Indicates the device's predicted remaining health value. Indicates the current health value of the device.

5. The electric power material life cycle assessment method according to claim 4, characterized in that: The comprehensive value of the target equipment over its entire life cycle is calculated to include: Based on the second value, the comprehensive value of the target device over its entire life cycle is calculated according to the life cost of the target device and the first value of the device actually generated during its actual use cycle, and is expressed as: ； in, Indicates the first value of the equipment, Indicates the second value of the device, Indicates the life cost of the equipment.

6. A power material life cycle assessment system, used in the power material life cycle assessment method according to any one of claims 1 to 5, characterized in that: include, An acquisition module, configured to determine a target device of the electric power material and acquire target data of the target device; an evaluation module, configured to perform a quality evaluation on the target device based on the target data to obtain a first evaluation value of the target device, and simultaneously perform an operation failure analysis on the target device to obtain a second evaluation value of the target device; A prediction module, configured to construct a lifespan prediction model for the target device based on the first evaluation value and the second evaluation value, and predict the remaining lifespan of the target device; an output module, configured to perform a second value assessment of the remaining life cycle based on the remaining life value of the target device, and calculate a comprehensive value of the entire life cycle of the target device by combining the life cost of the target device and the first value; Obtaining a first evaluation value of the target device includes: Acquiring device material data of the target device and extracting key material characteristics of the device to obtain attribute values ​​of the device material characteristics, wherein the attribute values ​​are obtained by weighted calculation of key characteristics and quality grades of each material; Obtaining equipment test data of the target equipment, and calculating the test value of the target equipment through equipment material test data and factory operation test data; Obtaining device operation data of the target device, dividing the data according to the maintenance cycle of the target device, analyzing and calculating the actual operation parameters and abnormal operation parameters of the device in each cycle to obtain a performance index value of the target device, wherein parameter deviation is calculated based on the actual operation data of the device, parameters with parameter deviations greater than a deviation threshold are recorded as abnormal parameters, the abnormality of the abnormal parameters is calculated, the parameter deviation and the abnormality are added to obtain the performance index value of each cycle, and then a weighted average is performed based on the performance index value of each cycle to obtain the performance index value of the target device during the operating time; Acquire multiple second devices of the same model as the target device over their entire life cycle, screen out a set of second devices whose similarity values ​​between the second devices and the target device are greater than a similarity threshold, and calculate a quality impact factor of the target device based on a quality assessment result of the set of second devices, wherein a weighted average is taken based on the quality assessment results of the second devices over their entire life cycle, and the weighted average is multiplied by the similarity value between the two devices and divided by the similarity value to obtain the quality impact factor of the target device; Calculate a first evaluation value of the target device by combining the attribute value, the detection value, the performance indicator value, and the quality impact factor; Obtaining a second evaluation value of the target device includes: Obtain maintenance failure data of the target device, the maintenance failure data including the number of failures, failure interval time, failure level, failure loss, failure duration, and failure repair cost, and calculate a device failure coefficient, the device failure coefficient being obtained by calculating a failure frequency coefficient, a failure severity coefficient, and a failure repair difficulty coefficient; Calculating the second evaluation value based on the equipment failure coefficient and in combination with the failure impact factor of the second equipment; Among them, the fault frequency coefficient Expressed as: ； in, Indicates the frequency of equipment failure, Indicates the mean time between failures of the equipment; Calculate the fault severity coefficient based on the fault level and fault loss , expressed as: ； in, Indicates the equipment failure level, Indicates the actual failure loss, Indicates the set maximum failure loss; Calculate the equipment failure repair difficulty coefficient based on the failure duration and failure repair cost , expressed as: ； in, Indicates the duration of equipment failure, Indicates the total operating time of the device. represents the failure repair cost, Indicates the total value of the equipment; Predicting the remaining life value of the target device includes: performing data processing on the first evaluation value and the second evaluation value of the target device; Obtain a third device of the same model as the target device and having a current life cycle greater than that of the target device, generate a third device set, filter device data of devices in the third device set at the used life cycle value based on the current used life cycle value of the target device, calculate the first evaluation value, the second evaluation value, and the remaining life value at the used life cycle, and generate a data set; A life prediction model for the target device is constructed and trained using the data set. The processed first evaluation value and second evaluation value of the target device are input into the life prediction model to predict the remaining life value of the target device.

7. An electronic device, characterized in that: include: memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the electric power material life cycle assessment method described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium, characterized in that It stores computer-executable instructions, which, when executed by a processor, implement the steps of the electric power material life cycle assessment method described in any one of claims 1 to 5.

Images