Electric power material life cycle evaluation method and system

Abstract

The invention relates to the technical field of electric power material evaluation, and discloses an electric power material life cycle evaluation method and system, and the method comprises the steps: determining target equipment of electric power materials, obtaining the target data of the target equipment, carrying out the quality evaluation of the target equipment, and obtaining a first evaluation value of the target equipment, and carrying out operation fault analysis on the target equipment to obtain a second evaluation value of the target equipment, constructing a life prediction model of the target equipment, predicting a residual life value of the target equipment, and calculating to obtain a comprehensive value of the whole life cycle of the target equipment. According to the method, the quality evaluation and the operation fault analysis are respectively carried out, so that the evaluation is more comprehensive and accurate, the service life prediction model is constructed based on the evaluation, various factors influencing the service life of the equipment are fully considered, the prediction accuracy is greatly improved, and an electric power material economic benefit evaluation system is perfected by calculating the comprehensive value of the whole life cycle of the equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of power material evaluation, and particularly to a power material life cycle evaluation method and system. Background Art

[0002] The life cycle of power materials refers to the entire usage process of material equipment throughout its life cycle. The main goal is to ensure the quality and performance of materials throughout the life cycle, reduce failures and maintenance times, 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. Some enterprises have also carried out relevant work. Some enterprises have established equipment ledgers to record basic information, purchase prices, maintenance records, etc. of equipment. At the same time, some simple equipment status monitoring is also carried out, such as discovering abnormal appearances and abnormal operating sounds of equipment through manual inspections.

[0003] The relevant data of existing power materials are usually scattered in different systems, lacking a unified data management and integration platform, resulting in low data utilization rate, single equipment evaluation means, mostly relying on empirical judgments and simple data analysis, lacking systematic multi-dimensional evaluation means, and being difficult to comprehensively reflect the actual state of equipment. Traditional life predictions often rely on fixed formulas or empirical formulas, unable to fully consider the actual operating environment and usage conditions of equipment, resulting in inaccurate prediction results and incomplete benefit evaluations, etc. Summary of the Invention

[0004] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Simplifications or omissions may be made in this part, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this part, the abstract, and the title. However, such simplifications or omissions shall 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 power material life cycle evaluation method to solve the above problems.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a power material life cycle evaluation method, including: Determine the target equipment of the power material and obtain the target data of the target equipment; Based on the target data, conduct a quality evaluation on the target equipment to obtain a first evaluation value of the target equipment, and at the same time conduct an operating fault analysis on the target equipment to obtain a second evaluation value of the target equipment; Based on the first evaluation value and the second evaluation value, construct a life prediction model for the target device to predict the remaining health value of the target device; According to the remaining health value of the target device, conduct a second value evaluation of the remaining life cycle, and combine the life cost and the first value of the target device to calculate the comprehensive value of the entire life cycle of the target device.

[0007] As a preferred solution of the power material life cycle assessment method described in the present invention, wherein: obtaining the target data of the target device includes device basic information, device operation data, and device maintenance and failure data; The device basic information includes device ID, device name, device type, device size, device location, device material data, device detection data, device activation date, and procurement and installation cost; The device operation data includes the device operation time per cycle, device operation status, device monitored operation parameters, operation abnormal parameters, number of abnormalities, device energy consumption, and device operation records; The device maintenance and failure data includes device 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.

[0008] As a preferred solution of the power material life cycle assessment method described in the present invention, wherein: obtaining the first evaluation value of the target device includes: Obtain the device material data of the target device, extract the key material characteristics of the device to obtain the attribute values of the device material characteristics; Obtain the device detection data of the target device, and calculate through the device material detection data and the factory operation detection data to obtain the detection value of the target device; Obtain the device operation data of the target device, divide the data according to the target device maintenance cycle, and analyze and calculate through the actual operation parameters and abnormal operation parameters of each cycle of the device to obtain the performance index value of the target device; Obtain multiple second devices with the same model as the target device throughout the life cycle, screen out a set of second devices whose similarity value to the target device is higher than the similarity threshold, and calculate the quality impact factor of the target device according to the quality evaluation results of the set of second devices; Combine the attribute value, detection value, performance index value, and quality impact factor to calculate the first evaluation value of the target device.

[0009] As a preferred solution of the power material life cycle assessment method described in the present invention, wherein: obtaining the second evaluation value of the target device includes: Obtain the maintenance failure data of the target device, where the maintenance failure data includes the number of failures, failure interval time, failure level, failure loss, failure duration, and failure repair cost, and calculate the device failure coefficient, which is obtained by calculating the failure frequency coefficient, failure severity coefficient, and failure repair difficulty coefficient; Based on the device failure coefficient and combined with the failure impact factor of the second device, calculate the second evaluation value.

[0010] As a preferred solution of the power material life cycle assessment method described in the present invention, wherein: predicting the remaining life value of the target device includes: Perform 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 with a current life cycle greater than that of the target device, generate a set of third devices, and according to the current used life cycle value of the target device, screen out the device data of the devices in the set of third devices at the used life cycle value, calculate the first evaluation value, the second evaluation value, and the remaining life value at the used life cycle, and generate a data set; Construct a life prediction model for the target device, train it using the data set, and input 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; The life prediction model is expressed as: ; Wherein, represents the remaining life value of the target device, represents the first evaluation value of the device, represents the second evaluation value of the device, , , represent regression coefficients.

[0011] As a preferred solution of the power material life cycle assessment method described in the present invention, wherein: performing a second value assessment of the remaining life cycle includes: Based on the remaining life value of the target device, determine the device benefit index, and combined with the historical benefits of the device, calculate the second value of the device, expressed as: ; Wherein, represents the expected benefit per year, represents the discount rate, represents the predicted remaining life value of the device, represents the currently used life value of the device.

[0012] As a preferred solution of the power material life cycle assessment method described in the present invention, where: calculating the comprehensive value of the entire life cycle of the target device includes: Based on the second value, according to the life cost of the target device and the first value of the device actually generated during the actual use cycle, calculate the comprehensive value of the entire life cycle of the target device, expressed as: ; Wherein, represents the first value of the device, represents the second value of the device, represents the life cost of the device.

[0013] In a second aspect, the present invention provides a power material life cycle assessment system, including: An acquisition module for determining a target device of power materials and acquiring target data of the target device; An evaluation module for performing a quality evaluation on the target device based on the target data to obtain a first evaluation value of the target device, and simultaneously performing an operation failure analysis on the target device to obtain a second evaluation value of the target device; A prediction module for constructing a life prediction model of the target device based on the first evaluation value and the second evaluation value, and predicting the remaining life value of the target device; An output module for performing a second value evaluation of the remaining life cycle according to the remaining life value of the target device, and calculating the comprehensive value of the entire life cycle of the target device in combination with the life cost and the first value of the target device.

[0014] In a third aspect, the present invention provides an electronic device, including: A memory and a 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 power material life cycle assessment method are implemented.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, the steps of the power material life cycle assessment method are implemented.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: By separately conducting quality assessment and operation fault analysis, the present invention evaluates from two dimensions of the inherent quality and the actual operation performance of the equipment, and then comprehensively obtains the evaluation result, making the evaluation more comprehensive and accurate, and avoiding misjudgment caused by a single evaluation dimension; Based on the results of quality assessment and operation fault analysis, a life prediction model is constructed, fully considering various factors affecting the equipment life. Compared with the traditional single-factor life prediction method, the accuracy of prediction is greatly improved, and it can provide a more reliable basis for the operation and maintenance and renewal of the equipment; By calculating the comprehensive value of the equipment's full life cycle, not only the various costs of the equipment are considered, but also the value during the used period and the potential value of the remaining life cycle are included, improving the economic benefit evaluation system of electric power materials, providing a scientific economic basis for the enterprise's investment decision-making and equipment management, and helping the enterprise optimize resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0018] Figure 1 It is a schematic diagram of the overall process of the power material life cycle assessment method according to an embodiment of the present invention; Figure 2 It is a schematic diagram of the structure of the power material life cycle assessment system according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be made in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0020] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0021] Second, the "one embodiment" or "embodiment" referred to herein means a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that exclude each other with other embodiments.

[0022] The present invention is described in detail in conjunction with schematic diagrams. When detailing the embodiments of the present invention, for the convenience of description, the cross-sectional views showing the device structure will be enlarged locally out of the general proportion, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention herein. In addition, in actual production, three-dimensional spatial dimensions including length, width, and depth should be included.

[0023] At the same time, in the description of the present invention, it should be noted that the orientation or positional relationship indicated by terms such as "upper, lower, inner, and outer" is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be construed as a limitation to the present invention. In addition, the terms "first, second, or third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0024] Unless otherwise clearly defined and limited in the present invention, the terms "installed, connected, and connected" should be understood in a broad sense. For example: it can be a fixed connection, a detachable connection, or an integral connection; it can also be a mechanical connection, an electrical connection, or a direct connection, and can also be indirectly connected through an intermediate medium, or can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0025] Referring to Figure 1 - Figure 2 , for an embodiment of the present invention, a method for evaluating the life cycle of electric power materials is provided. As Figure 1 shown, it includes: S100, determining the target device of the electric power material and obtaining the target data of the target device; S200, based on the target data, performing a quality assessment on the target device to obtain the first evaluation value of the target device, and at the same time performing an operation failure analysis on the target device to obtain the second evaluation value of the target device; S300, based on the first evaluation value and the second evaluation value, constructing a life prediction model of the target device to predict the remaining health value of the target device; S400, according to the remaining health value of the target device, performing a second value assessment of the remaining life cycle, and combining the life cost and the first value of the target device to calculate the comprehensive value of the entire life cycle of the target device.

[0026] In a preferred embodiment, obtaining the target data of the target device includes, but is not limited to, device basic information, device operation data, and device maintenance and fault data; The device basic information includes device ID, device name, device type, device size, device location, device material data, device detection data, device activation date, and procurement and installation costs; The device operation data includes the device operation time per cycle, device operation status, device monitored operation parameters, operation abnormal parameters, number of abnormalities, device energy consumption, and device operation records; The device maintenance and fault data includes device maintenance cycle, maintenance content, maintenance time, maintenance cost, maintenance abnormal events, fault occurrence time, fault type, fault level, number of faults, fault duration, fault interval time, fault repair cost, and fault loss.

[0027] Specifically, detailed data of various basic information of the device can be obtained from the supplier. After the device is installed and put into use, monitoring devices are set for each type of device. During use, device data is regularly obtained and recorded in the database. Specific device operation data and maintenance and fault data are extracted from the database for subsequent analysis and evaluation of the device.

[0028] It should be noted that by clarifying the target device and collecting its detailed data (device basic attributes, technical specifications, historical records, etc.), it is ensured that subsequent analysis is based on complete and accurate information. High-quality data helps to improve the reliability and accuracy of the evaluation results.

[0029] In a preferred embodiment, obtaining the first evaluation value of the target device includes: Step 1: Obtain the device material data of the target device, extract the key material characteristics of the device to obtain the attribute values of the device material characteristics; Step 2: Obtain the device detection data of the target device, and calculate through the device material detection data and the factory operation detection data to obtain the detection value of the target device; Step 3: Obtain the device operation data of the target device, divide the data according to the target device maintenance cycle, and analyze and calculate through the actual operation parameters and abnormal operation parameters of each cycle of the device to obtain the performance index value of the target device; Step 4: Obtain multiple second devices with the same model as the target device throughout the life cycle, screen out the set of second devices with a similarity value higher than the similarity threshold between the second devices and the target device, and calculate the quality impact factor of the target device according to the quality evaluation results of the set of second devices; Step 5: Combine the attribute values, detection values, performance index values, and quality impact factors to calculate the first evaluation value of the target device.

[0030] Specifically, in the above step 1, the basic data of the target device is retrieved to obtain the main material composition of the device. Each power material device is entered and archived in the database, and various materials of the device are statistically classified by category, indicating the key characteristics (such as strength, conductivity, insulation, etc.) and quality grades of each material. For example, for a power cable, the purity of the conductor material (copper or aluminum), the withstand voltage grade of the insulation material, etc. are listed. Then, weights are assigned according to the importance of each material of the device to the overall performance of the device. For example, for a generator, the weight of the winding material is relatively high because it is directly related to the generation of electrical energy, while the weight of the housing material is relatively low. Then, the quality grades of each material are scored according to the application scenario, and the total score of the device material evaluation is obtained through weighted calculation, that is, the attribute value of the device material characteristics.

[0031] In step 2, the detection values are obtained. Before the device is installed and used, the manufacturer will conduct a quality inspection on the device. According to the material inspection results and operation inspection results of the target device, the detection values of the device are evaluated. The data of each inspection result is standardized and converted into a standard score (Z - score) to make the data of different devices comparable. Weights are assigned according to the importance of each inspection data to the device quality, and then the inspection evaluation score is calculated by the method of weighted average. For example, for a power transformer, the weight of the withstand voltage test data may be relatively high because the insulation performance is a key index of the transformer.

[0032] In step 3, the performance index values are obtained. Various sensors are installed on the device to monitor the operation parameters in real time. For example, for an electric motor, current sensors, temperature sensors and vibration sensors are installed to monitor the running current, temperature and vibration of the motor respectively. Each device is set with a maintenance cycle, which can be two weeks or one month, and is set according to the actual scenario. The key operation parameters of the target device are extracted, and the key operation parameter values are regularly recorded according to the device maintenance cycle, including the normal operation data and abnormal operation data of the device. Specifically, it can record the operation status, parameters and appearance of the device, etc. The recorded parameter values are divided according to the maintenance cycle, and the performance index values of each cycle are calculated. First, the parameter deviation degree is calculated according to the actual operation data of the device, and the parameters with parameter deviation degree greater than the deviation threshold are recorded as abnormal parameters, and the abnormality degree of the abnormal parameters is calculated. Among them, the deviation threshold is set as needed. The parameter deviation degree and the abnormality degree are added to obtain the performance index value of each cycle, and then the weighted average is performed according to the performance index values of each cycle to obtain the performance index value of the target device during the running time. Exemplarily, the parameter deviation degree of the target device is expressed as: ; Wherein, represents the actual parameter value, Represents the median value of the normal range of the parameter, Represents the width value of the normal range 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 median value of the normal range is 70°C, and the width value of the normal range is 10°C, then the temperature deviation degree is 0.02.

[0033] Furthermore, according to the set deviation threshold, in this embodiment, the deviation threshold is taken as 0.1. When the deviation degree of the operating parameter exceeds the set threshold, it is determined that the parameter is an abnormal parameter. According to factors such as the type of the abnormal parameter, the magnitude of the deviation degree, and the duration of the abnormality, the severity of the abnormal situation is classified. Multiply the number of abnormalities by the abnormal severity score to obtain the abnormality degree of the parameter; Based on the parameter deviation degree and the abnormality degree, calculate the performance index value of the target device , which is expressed as: ; Wherein, is the number of operating parameters, is the number of types of abnormal situations, represents the equipment maintenance cycle, represents the equipment parameter of the parameter deviation degree, represents the equipment parameter of the abnormality degree, represents the number of divisions of the equipment maintenance cycle.

[0034] In the above step four, select the equipment of the same model as the target equipment and whose entire life cycle has been completed, that is, the equipment that has been completed from production to scrapping, and define it as the second equipment. First, define the equipment similarity index, which can include basic parameter similarity, such as rated power, voltage level, usage environment, etc.; material similarity, the type and quality of materials used in the equipment; operating condition similarity, the actual operating conditions of the equipment, such as load rate, operating duration, etc. Calculate the similarity value for each pair of the target equipment and the second equipment through the Euclidean distance method or cosine similarity. When using the Euclidean distance to measure the difference between two equipment, the smaller the distance, the higher the similarity. Or standardize the equipment parameters into vectorized data and measure the cosine value of the angle between two vectors. The value closer to 1 indicates the higher the similarity. Then, select the second equipment with a similarity value higher than the threshold to generate a second equipment set. According to the quality evaluation results of its entire life cycle, perform weighted average, and then multiply and divide by the similarity value between the two equipment to obtain the quality impact factor of the target equipment. Among them, the quality evaluation result of the second equipment can be the first evaluation value obtained based on its entire life cycle.

[0035] Calculate the first evaluation value of the target equipment Expressed as: ; Wherein, represents the attribute value of the target device material characteristics, represents the detected value of the target device, represents the performance index value of the target device, represents the quality impact factor of the target device.

[0036] It should be noted that this embodiment combines multiple dimensions such as material evaluation, inspection evaluation, and operating performance evaluation, which can comprehensively reflect the initial quality status of the device. Through the analysis of operating failures, it can reflect the actual usage of the device, and can effectively provide data support for the evaluation of the device life cycle.

[0037] In a preferred embodiment, obtaining the second evaluation value of the target device includes: Obtain the maintenance failure data of the target device. The maintenance failure data includes the number of failures, failure interval time, failure level, failure loss, failure duration, and failure repair cost, and calculate the device failure coefficient. The device failure coefficient is obtained by calculating the failure frequency coefficient, failure severity coefficient, and failure repair difficulty coefficient; Based on the device failure coefficient and combined with the failure impact factor of the second device, calculate the second evaluation value.

[0038] Specifically, first divide the number of failures during the actual operation of the target device by the total operation time to calculate the failure frequency. Assume that the expected service life of the device is , combined with the average failure interval time of the device, the failure frequency coefficient Expressed as: ; Wherein, represents the device failure frequency, represents the average failure interval time of the device; According to the failure level and failure loss, calculate the failure severity coefficient Expressed as: ; Wherein, represents the device failure level, represents the actual failure loss, represents the set maximum failure loss; According to the failure duration and failure repair cost, calculate the device failure repair difficulty coefficient Expressed as: ; Wherein, Indicates the duration of equipment failure Indicates the total operating time of the equipment Indicates the cost of fault repair Indicates the total value of the equipment

[0039] Furthermore, for the second equipment set obtained according to the similarity, based on the fault evaluation results of its entire life cycle, a weighted average is performed, and then multiplied by the similarity value between the two equipment and divided by the similarity value to obtain the fault impact factor of the target equipment. Among them, the quality evaluation result of the second equipment can be the second evaluation value obtained based on its entire life cycle

[0040] The second evaluation value of the target equipment Is expressed as: ; Wherein Indicates the fault impact factor of the target equipment

[0041] It should be noted that the evaluation value of the fault in this embodiment can be used to obtain the evaluation value of the target equipment according to the fault situation of the equipment itself and by referring to the fault occurrence situation of its similar equipment during the entire life cycle as a correction factor. Among them, the higher the evaluation value, the higher the fault risk of the equipment. During the implementation process, parameters such as parameter weights can be adjusted according to the actual situation to better adapt to specific equipment and evaluation requirements. Through the above steps, the operating faults of the target equipment can be systematically analyzed, and the fault evaluation value can be calculated for predicting the remaining life of the equipment, which not only considers the historical fault data of the equipment but also utilizes the actual parameters of similar equipment, providing a more accurate and reliable prediction result

[0042] The equipment quality evaluation of this embodiment can understand the inherent quality status of the equipment from aspects such as the design, manufacturing, and raw materials of the equipment. The first evaluation value provides a quantitative basis for judging the overall quality level of the equipment. The operating fault analysis starts from the actual operating performance of the equipment, and the second evaluation value reflects the stability and reliability of the equipment during operation. The combination of the two can comprehensively evaluate the performance and state of the equipment

[0043] In a preferred implementation manner, predicting the remaining life value of the target equipment includes: Performing data processing on the first evaluation value and the second evaluation value of the target equipment Obtain a third device that has the same model as the target device and whose current life cycle is greater than that of the target device, that is, filter out devices whose actual usage cycle is greater than the current actual usage cycle of the target device, or devices that have completed the full life cycle, generate a set of third devices, and according to the current used life cycle value of the target device, filter out the device data of the devices in the set of third devices at the used life cycle value (the device operation data and maintenance fault data before this used life cycle), calculate the first evaluation value, the second evaluation value and the remaining life value at the used life cycle of the third device, generate a data set, and divide the data set into a training set, a validation set and a test set according to a certain ratio; Construct a life prediction model for the target device, and use the data set for training. Input the first evaluation value and the second evaluation value of the processed target device into the life prediction model to predict the remaining life value of the target device; The life prediction model is expressed as: ; Wherein, represents the remaining life value of the target device, represents the first evaluation value of the device, represents the second evaluation value of the device, , , represent regression coefficients.

[0044] Specifically, check the integrity of the data through data processing, fill in missing values. For some missing values that cannot be filled, decide whether to delete the corresponding data records according to the importance and missing ratio of the data, remove duplicate data to ensure the uniqueness of the data and avoid bias caused by duplicate data to the model, detect and process outliers. Outliers can be identified by methods such as box plots and the 3σ principle, and corrected or deleted according to the specific situation. Standardize the collected data of different dimensions and different magnitudes so that the data is 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. Further, use the training set data to train the life prediction model, and solve the regression coefficients by methods such as the least squares method to minimize the sum of the squared errors between the predicted value and the true value, and finally predict the remaining life value of the target device.

[0045] It should be noted that the life prediction model constructed in this embodiment by comprehensively considering the device quality and operation fault conditions, combined with historical data and real-time monitoring information, provides a more accurate remaining life prediction compared with single-factor prediction, helps to reasonably arrange the maintenance and update plans of the device, avoid production interruptions caused by unexpected device failures, improve the stability and reliability of the power system operation, and at the same time can optimize resource allocation and reduce the device operation and maintenance costs.

[0046] In a preferred embodiment, the second value assessment of the remaining life cycle includes: Based on the remaining health value of the target device, determine the device benefit index, and combine the historical benefits of the device to calculate the second value of the device, expressed as: ; Wherein, represents the expected benefit of the device per year, represents the discount rate, represents the predicted remaining health value of the device, represents the current used health value of the device.

[0047] Specifically, the benefit index mainly includes analyzing the actual economic benefits of the device in the past few years, finding out its average annual income, then considering the factors of the electricity market demand, and combining the device aging degree to estimate the expected benefit of the device in the future every year.

[0048] In a preferred embodiment, calculating the comprehensive value of the whole life cycle of the target device includes: Based on the second value, according to the life cost of the target device and the actual first value of the device generated during the actual use cycle, calculate the comprehensive value of the whole life cycle of the target device, expressed as: ; Wherein, represents the first value of the device, represents the second value of the device, represents the life cost of the device.

[0049] Specifically, the life cost of the device mainly includes procurement cost, installation cost, maintenance cost, repair cost, device energy consumption, etc., that is, the initial cost of purchasing the device, the cost generated during the device installation process, the cost required for regular device maintenance, the cost required for repairing the device when a failure occurs, the energy consumption during the trademark use process, and adding these costs to obtain the life cost of the device; then count the actual economic benefits generated by the target device during the used period, and according to the used years of the device and the sum of the actual income per year, obtain the first value of the device.

[0050] It should be noted that by evaluating the expected income within the remaining life cycle and combining the total life cost of the device, comprehensively measure the benefits of the device in the entire life cycle, comprehensively consider the historical performance and future expected income of the device, optimize the resource allocation, and improve the operation efficiency.

[0051] Through separate quality assessment and operation fault analysis, the present invention evaluates from two dimensions of the inherent quality and actual operation performance of the equipment, and then comprehensively obtains the evaluation result, making the evaluation more comprehensive and accurate, and avoiding misjudgment caused by a single evaluation dimension. Based on the results of quality assessment and operation fault analysis, a life prediction model is constructed, fully considering various factors affecting the equipment life. Compared with the traditional single-factor life prediction method, the accuracy of prediction is greatly improved, and it can provide a more reliable basis for the operation and maintenance and renewal of the equipment. By calculating the comprehensive value of the equipment's full life cycle, not only the various costs of the equipment are considered, but also the value during the used period and the potential value of the remaining life cycle are included, improving the economic benefit evaluation system of electric power materials, providing a scientific economic basis for the enterprise's investment decision-making and equipment management, and helping the enterprise optimize resource allocation.

[0052] The above is a schematic solution of a method for evaluating the life cycle of electric power materials in this embodiment. It should be noted that the technical solution of the electric power material life cycle evaluation system belongs to the same concept as the technical solution of the above-mentioned method for evaluating the life cycle of electric power materials. For the details not described in detail in the technical solution of the electric power material life cycle evaluation system in this embodiment, reference can be made to the description of the technical solution of the above-mentioned method for evaluating the life cycle of electric power materials.

[0053] In this embodiment, the electric power material life cycle evaluation system, as Figure 2 shown, includes: An acquisition module, configured to determine the target equipment of the electric power material and acquire the target data of the target equipment; An evaluation module, configured to perform quality evaluation on the target equipment based on the target data to obtain the first evaluation value of the target equipment, and at the same time perform operation fault analysis on the target equipment to obtain the second evaluation value of the target equipment; A prediction module, configured to construct a life prediction model of the target equipment based on the first evaluation value and the second evaluation value, and predict the remaining life value of the target equipment; An output module, configured to perform a second value evaluation of the remaining life cycle according to the remaining life value of the target equipment, and calculate the comprehensive value of the full life cycle of the target equipment in combination with the life cost and the first value of the target equipment.

[0054] This embodiment also provides an electronic device applicable to the situation of evaluating the life cycle of electric power materials, including: A memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for evaluating the life cycle of electric power materials proposed in the above embodiment.

[0055] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the method for realizing the life cycle assessment of electric power materials proposed in the above embodiment.

[0056] The storage medium proposed in this embodiment and the method for realizing the life cycle assessment of electric power materials proposed in the above embodiment belong to the same inventive concept. For the technical details not described in detail in this embodiment, reference can be made to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0057] From the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software and necessary general-purpose hardware. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part 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 floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disc of a computer, etc., including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of the various embodiments of the present invention.

[0058] 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 them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A method for evaluating the life cycle of electric power materials, characterized in that: include: Determine the target equipment of the electric power material, and obtain the target data of 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, construct a life prediction model for the target device to predict the remaining life value of the target device; According to the remaining life value of the target device, a second value assessment of the remaining life cycle is performed, 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.

2. The 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 power material life cycle assessment method according to claim 1 or 2, characterized in that: Obtaining a first evaluation value of the target device includes: Acquire 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; Obtaining equipment test data of the target equipment, and calculating through equipment material test data and factory operation test data to obtain a test value of the target equipment; Acquire the equipment operation data of the target equipment, divide the data according to the maintenance cycle of the target equipment, analyze and calculate the actual operation parameters and abnormal operation parameters of the equipment in each cycle, and obtain the performance index value of the target equipment; Acquire multiple second devices of the same model as the target device in their entire life cycle, screen out a set of second devices whose similarity values ​​between the second devices and the target device are higher than a similarity threshold, and calculate a quality impact factor of the target device according to a quality evaluation result of the set of second devices; 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.

4. The method for evaluating the life cycle of electric power materials according to claim 3, characterized in that: Obtaining a second evaluation value of the target device includes: Obtain maintenance fault data of the target device, the maintenance fault data including the number of faults, fault interval time, fault level, fault loss, fault duration and fault repair cost, and calculate the equipment fault coefficient, the equipment fault coefficient is obtained by calculating the fault frequency coefficient, the fault severity coefficient and the fault repair difficulty coefficient; The second evaluation value is calculated based on the equipment failure coefficient and in combination with the failure impact factor of the second equipment.

5. The method for evaluating the life cycle of electric power materials according to claim 4, characterized in that: 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; Acquire 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 out device data of devices in the third device set at the used life cycle value according to 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; Constructing a life prediction model for the target device and using the data set for training, 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; The life prediction model is expressed as: ； in, Indicates the remaining health of the target device. Indicates the first evaluation value of the device, Indicates the second evaluation value of the device, , , represents the regression coefficient.

6. The method for evaluating the life cycle of electric power materials according to claim 1 or 5, characterized in that: Conducting a second value assessment of the remaining life cycle includes: Based on the remaining life value 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, represents the expected annual benefit, represents the discount rate, Indicates the device's predicted remaining health value. Indicates the current life value of the device.

7. The method for evaluating the life cycle of electric power materials according to claim 6, characterized in that: The comprehensive value of the target equipment over its entire life cycle is calculated to include: Based on the second value, according to the life cost of the target device and the first value of the device actually generated during the actual use period, the comprehensive value of the target device over its entire life cycle is calculated and expressed as: ； in, Indicates the first value of the equipment, Indicates the second value of the device, Indicates the life cost of the equipment.

8. An electric power material life cycle assessment system, used in the electric power material life cycle assessment method according to any one of claims 1 to 7, characterized in that: include, An acquisition module, used to determine a target device of electric power materials 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 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; The output module is used to perform a second value evaluation 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.

9. 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 7 are implemented.

10. A computer-readable storage medium, characterized in that: It stores computer executable instructions, which, when executed by a processor, can implement the steps of the electric power material life cycle assessment method described in any one of claims 1 to 7.

Images