Value prediction method and device for aero-engine parts and computer equipment

Abstract

The invention relates to a value prediction method and device for aero-engine parts, computer equipment, a computer readable storage medium and a computer program product, and can be applied to the technical field of computers. The method comprises the following steps: extracting target information of a preset dimension of an aero-engine part from attribute information of the aero-engine part; based on a preset classification condition and the target information, performing classification processing on the engine parts to obtain categories of the engine parts; and predicting the value of the engine part according to the category and the target information of the engine part through a preset value prediction model. By adopting the method, the value prediction efficiency of the aero-engine parts can be improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, device, computer equipment, computer-readable storage medium and computer program product for predicting the value of aircraft engine parts. Background Art

[0002] With the rapid development of the aviation industry, the related technologies of aircraft engine parts are becoming more and more important. Among the related technologies of aircraft engine parts, the value prediction of parts is a key link. How to efficiently predict the value of aircraft engine parts has become an important research direction.

[0003] Traditional technology usually predicts the value of aircraft engine parts through manual review and evaluation; however, predicting the value of aircraft engine parts through this method requires a lot of manual processing time, resulting in low efficiency in value prediction of aircraft engine parts. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for value prediction of aircraft engine parts, which can improve the efficiency of value prediction of aircraft engine parts, in order to address the above technical problems.

[0005] In a first aspect, the present application provides a method for predicting the value of aircraft engine parts. The method comprises:

[0006] Extracting target information of a preset dimension of the aircraft engine component from the attribute information of the aircraft engine component;

[0007] Based on the preset classification conditions and the target information, the engine parts are classified to obtain the categories of the engine parts;

[0008] The value of the engine parts is predicted by a preset value prediction model according to the category of the engine parts and the target information.

[0009] In one embodiment, the classifying process of the engine parts based on the preset classification conditions and the target information to obtain the categories of the engine parts includes:

[0010] Based on a preset classification model, the engine parts are classified according to the target information to obtain an initial classification result of the engine parts; the preset classification model is constructed based on the preset classification conditions;

[0011] Screening and rejecting the initial classification results to obtain target classification results of the engine parts;

[0012] According to the target classification result, the category of the engine parts is determined.

[0013] In one embodiment, the preset classification model includes a classification model based on the life part principle, a classification model based on the sales cycle principle, a classification model based on the inquiry frequency principle, a classification model based on the scrap rate principle, and a classification model based on the configuration principle;

[0014] The classifying process of the engine parts based on the preset classification model and the target information to obtain the initial classification results of the engine parts includes:

[0015] According to the preset principle priority, the engine parts are classified according to the target information using the classification model based on the life parts principle, the classification model based on the sales cycle principle, the classification model based on the inquiry frequency principle, the classification model based on the scrap rate principle and the classification model based on the configuration principle; the preset principle priority indicates that the life parts principle is superior to the sales cycle principle, the sales cycle principle is superior to the inquiry frequency principle, the inquiry frequency principle is superior to the scrap rate principle, and the scrap rate principle is superior to the configuration principle.

[0016] In one embodiment, before predicting the value of the engine parts according to the category of the engine parts and the target information by using a preset value prediction model, the method further includes:

[0017] Determine whether the aircraft engine parts are life-limited parts;

[0018] The method of predicting the value of the engine parts according to the category of the engine parts and the target information by using a preset value prediction model includes:

[0019] In the case where the aircraft engine parts belong to the life parts, the value of the engine parts is predicted according to the category of the engine parts and the life information in the target information through a first value prediction model; the first value prediction model is a value prediction model corresponding to the life parts in the preset value prediction model.

[0020] In one embodiment, the life information includes remaining life information and total life information of the engine parts;

[0021] The method of predicting the value of the engine component according to the category of the engine component and the life information in the target information by using the first value prediction model includes:

[0022] Calculating the ratio of the remaining life information to the total life information by using the first value prediction model;

[0023] Calculating a first initial value of the engine component according to the ratio and the product attribute value in the target information;

[0024] According to the first preset value coefficient corresponding to the category of the engine component, the first initial value of the engine component is updated to obtain the value of the engine component.

[0025] In one embodiment, the predicting the value of the engine parts according to the category of the engine parts and the target information by using a preset value prediction model also includes:

[0026] In the case where the aircraft engine parts are parts other than the life parts, the value of the engine parts is predicted by a second value prediction model based on the category of the engine parts and the product attribute values ​​in the target information; the second value prediction model is a value prediction model in the preset value prediction model that corresponds to the parts other than the life parts.

[0027] In one embodiment, predicting the value of the engine parts according to the category of the engine parts and the product attribute value in the target information by using the second value prediction model includes:

[0028] Determining a second initial value of the engine component according to the product attribute value in the target information by using the second value prediction model;

[0029] The second initial value of the engine component is updated according to the second preset value coefficient corresponding to the category of the engine component to obtain the value of the engine component.

[0030] In one embodiment, extracting target information of a preset dimension of the aircraft engine component from the attribute information of the aircraft engine component includes:

[0031] Extracting sub-target information of a first dimension of the aircraft engine parts from the first attribute information of the aircraft engine parts in the inventory module;

[0032] Extracting sub-target information of a second dimension of the aircraft engine parts from the second attribute information of the aircraft engine parts in the market module;

[0033] Extracting sub-target information of the third dimension of the aircraft engine component from the third attribute information of the aircraft engine component in the maintenance module;

[0034] Extracting the sub-target information of the fourth dimension of the aircraft engine parts from the fourth attribute information of the aircraft engine parts in the product module;

[0035] The sub-target information of the first dimension, the sub-target information of the second dimension, the sub-target information of the third dimension, and the sub-target information of the fourth dimension are all used as the target information of the preset dimensions of the aircraft engine parts.

[0036] In a second aspect, the present application also provides a value prediction device for aircraft engine parts. The device comprises:

[0037] An information extraction module, used to extract target information of a preset dimension of the aircraft engine component from the attribute information of the aircraft engine component;

[0038] An information processing module, used for classifying the engine parts based on preset classification conditions and the target information to obtain the categories of the engine parts;

[0039] The value prediction module is used to predict the value of the engine parts according to the category of the engine parts and the target information through a preset value prediction model.

[0040] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0041] Extracting target information of a preset dimension of the aircraft engine component from the attribute information of the aircraft engine component;

[0042] Based on the preset classification conditions and the target information, the engine parts are classified to obtain the categories of the engine parts;

[0043] The value of the engine parts is predicted by a preset value prediction model according to the category of the engine parts and the target information.

[0044] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0045] Extracting target information of a preset dimension of the aircraft engine component from the attribute information of the aircraft engine component;

[0046] Based on the preset classification conditions and the target information, the engine parts are classified to obtain the categories of the engine parts;

[0047] The value of the engine parts is predicted by a preset value prediction model according to the category of the engine parts and the target information.

[0048] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0049] Extracting target information of a preset dimension of the aircraft engine component from the attribute information of the aircraft engine component;

[0050] Based on the preset classification conditions and the target information, the engine parts are classified to obtain the categories of the engine parts;

[0051] The value of the engine parts is predicted by a preset value prediction model according to the category of the engine parts and the target information.

[0052] The above-mentioned method, device, computer equipment, computer-readable storage medium and computer program product for predicting the value of aircraft engine parts extract target information of preset dimensions of the aircraft engine parts from the attribute information of the aircraft engine parts; classify the engine parts based on preset classification conditions and the target information to obtain the category of the engine parts; and predict the value of the engine parts according to the category of the engine parts and the target information through a preset value prediction model. This solution extracts target information from the attribute information of aircraft engine parts, classifies them based on preset classification conditions, and then uses a preset value prediction model to predict the value, which is conducive to improving the accuracy and efficiency of the value prediction of aircraft engine parts, thereby facilitating the optimization of asset management and recycling of aircraft engine parts. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 A schematic diagram of a flow chart of a method for predicting the value of aircraft engine parts in one embodiment;

[0055] Figure 2 A flowchart of the steps of determining the category of engine parts in one embodiment;

[0056] Figure 3 A schematic diagram of a flow chart of a method for predicting the value of aircraft engine parts in another embodiment;

[0057] Figure 4 A structural block diagram of a value prediction device for aircraft engine parts in one embodiment;

[0058] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0060] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0061] In an exemplary embodiment, Figure 1 As shown, a method for predicting the value of aircraft engine parts is provided. This embodiment uses the method applied to a terminal as an example for illustration; it is understandable that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. Among them, the terminal can be but is not limited to various personal computers, laptops, smart phones, tablet computers, etc.; the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. In this embodiment, the method includes the following steps:

[0062] Step S101 : extracting target information of preset dimensions of the aircraft engine parts from the attribute information of the aircraft engine parts.

[0063] Step S102: classify the engine parts based on the preset classification conditions and target information to obtain the categories of the engine parts.

[0064] Step S103, predicting the value of the engine parts according to the category and target information of the engine parts through a preset value prediction model.

[0065] Among them, aircraft engine parts can be various components that constitute an aircraft engine. For example, aircraft engine parts can be blades, bearings, combustion chamber components, etc. in the engine.

[0066] The attribute information may be various data describing the characteristics of aircraft engine parts, for example, the attribute information may be the part number, serial number, airworthiness status, certificate information and remaining life of the aircraft engine parts.

[0067] Among them, the target information of the preset dimension can be key data extracted from the attribute information for subsequent classification and value prediction. For example, the target information of the preset dimension can be the remaining life, sales order date, inquiry frequency and scrap rate of aircraft engine parts.

[0068] Among them, the preset classification conditions can be a series of rules for classifying aircraft engine parts. For example, the preset classification conditions can be the life part principle, sales cycle principle, inquiry frequency principle, scrap rate principle and configuration principle.

[0069] The categories may be different levels of aircraft engine parts divided according to preset classification conditions, for example, the categories may be Class A parts, Class B parts, Class C parts, and Class D parts. Class A parts are key value parts, Class B parts are secondary value parts, Class C parts are valuable parts, and Class D parts are worthless parts.

[0070] Among them, the preset value prediction model can be a model or algorithm for calculating the value of aircraft engine parts.

[0071] Among them, the value can be the recycling value of aircraft engine parts.

[0072] Optionally, the terminal extracts target information of preset dimensions from the attribute information of aircraft engine parts, and the target information includes key data such as the remaining life of aircraft engine parts, sales order date, inquiry frequency, scrap rate, etc. The terminal classifies aircraft engine parts based on preset classification conditions, such as the life principle, sales cycle principle, inquiry frequency principle, scrap rate principle, and configuration principle, and classifies aircraft engine parts into Class A parts, Class B parts, Class C parts, or Class D parts. The terminal predicts and calculates the predicted value of aircraft engine parts based on the category and target information of aircraft engine parts through a preset value prediction model.

[0073] In the above-mentioned value prediction method for aircraft engine parts, target information of preset dimensions of aircraft engine parts is extracted from the attribute information of aircraft engine parts; based on preset classification conditions and target information, engine parts are classified and processed to obtain the category of engine parts; and the value of engine parts is predicted according to the category and target information of engine parts through a preset value prediction model. This solution extracts target information from the attribute information of aircraft engine parts, classifies them based on preset classification conditions, and then uses a preset value prediction model to perform value prediction, which is conducive to improving the accuracy and efficiency of value prediction of aircraft engine parts, thereby facilitating the optimization of asset management and recycling of aircraft engine parts.

[0074] In an exemplary embodiment, referring to Figure 2 Based on the preset classification conditions and target information, the engine parts are classified and processed to obtain the categories of engine parts, which specifically include the following contents:

[0075] Step S201, based on a preset classification model, classify the engine parts according to the target information to obtain an initial classification result of the engine parts; the preset classification model is constructed based on preset classification conditions;

[0076] Step S202, screening and rejecting the initial classification results to obtain target classification results of engine parts;

[0077] Step S203, determining the category of the engine parts according to the target classification result.

[0078] Among them, the preset classification model can be an algorithm or rule set for preliminary classification of aircraft engine parts. For example, the preset classification model can be a decision tree model constructed based on the life parts principle, sales cycle principle, inquiry frequency principle, scrap rate principle and configuration principle.

[0079] Among them, the initial classification result can be the result obtained after preliminary classification of aircraft engine parts through a preset classification model. For example, the initial classification result can be the preliminary classification of aircraft engine parts into Class A parts, Class B parts, Class C parts or Class D parts.

[0080] Among them, the screening and rejection processing can be a process of further optimizing and adjusting the initial classification results. For example, the screening and rejection processing can include correcting the initial classification results based on factors such as market activity, installed capacity, and number of inquiries.

[0081] The target classification result may be the final classification result obtained after screening and rejection processing, for example, the target classification result may be the final category of aircraft engine parts.

[0082] Optionally, the terminal constructs a classification model according to preset classification conditions, and the model includes the life part principle, sales cycle principle, inquiry frequency principle, scrap rate principle and configuration principle. The terminal uses the model to process the target information of aircraft engine parts and obtains the initial classification result. The terminal screens and excludes the initial classification results, including market activity calculation, installed quantity comparison and inquiry number verification, to obtain the target classification result. The terminal determines the final category of aircraft engine parts according to the target classification result.

[0083] The technical solution provided in this embodiment performs preliminary classification through a preset classification model, and screens and rejects the initial classification results, which is beneficial to improving the accuracy of aircraft engine parts classification, thereby facilitating more accurate assessment of parts value and optimizing asset management and recycling strategies.

[0084] In an exemplary embodiment, based on a preset classification model, engine parts are classified according to target information to obtain initial classification results of the engine parts, which specifically include the following contents: according to preset principle priorities, a classification model based on the life parts principle, a classification model based on the sales cycle principle, a classification model based on the inquiry frequency principle, a classification model based on the scrap rate principle and a classification model based on the configuration principle are used to classify engine parts according to the target information to obtain initial classification results; the preset principle priorities indicate that the life parts principle is better than the sales cycle principle, the sales cycle principle is better than the inquiry frequency principle, the inquiry frequency principle is better than the scrap rate principle, and the scrap rate principle is better than the configuration principle.

[0085] Among them, the preset classification models include a classification model based on the life parts principle, a classification model based on the sales cycle principle, a classification model based on the inquiry frequency principle, a classification model based on the scrap rate principle and a classification model based on the configuration principle.

[0086] Among them, the classification model based on the life parts principle can be a model for classification according to the remaining life of aircraft engine parts. For example, the classification model based on the life parts principle can position the remaining life of engine parts of a preset model in the range of 9000-11000 as Class A parts.

[0087] Among them, the classification model based on the sales cycle principle can be a model that classifies according to the sales cycle of aircraft engine parts. For example, the classification model based on the sales cycle principle can position parts with a sales cycle of less than 5 months as Class A parts.

[0088] Among them, the classification model based on the inquiry frequency principle can be a model that classifies according to the market activity of aircraft engine parts. For example, the classification model based on the inquiry frequency principle can calculate the market activity of parts divided by the installed quantity, and compare it with the transaction coefficient for classification.

[0089] Among them, the classification model based on the scrap rate principle can be a model that classifies aircraft engine parts according to the scrap rate during the maintenance process. For example, the classification model based on the scrap rate principle can position parts with a scrap rate greater than 80% as Class A parts.

[0090] Among them, the classification model based on the configuration principle can be a model that classifies according to the configuration update and iteration information of aircraft engine parts. For example, the classification model based on the configuration principle can classify parts that have updated configurations according to technical documents as Class A parts.

[0091] Among them, the preset principle priority can be the priority order of various classification principles when applied. For example, the preset principle priority can be that the life part principle takes precedence over the sales cycle principle, the sales cycle principle takes precedence over the inquiry frequency principle, the inquiry frequency principle takes precedence over the scrap rate principle, and the scrap rate principle takes precedence over the configuration principle.

[0092] Optionally, the terminal classifies aircraft engine parts according to the preset principle priority, and applies the classification model based on the life part principle, the classification model based on the sales cycle principle, the classification model based on the inquiry frequency principle, the classification model based on the scrap rate principle, and the classification model based on the configuration principle in turn. The terminal first uses the classification model based on the life part principle for classification. If no result can be obtained, it continues to use the classification model based on the sales cycle principle, and so on, until the initial classification result is obtained or all models have been applied. The terminal will make judgments based on the corresponding classification model and target information in each step, and ensure the accuracy and consistency of classification according to the preset principle priority.

[0093] For example, the terminal receives target information of an aircraft engine component, including remaining life, sales cycle, inquiry frequency, scrap rate and configuration information. The terminal first applies the classification model based on the life part principle to determine whether the remaining life of the component is within a specific range. If the conditions are not met, the terminal continues to apply the classification model based on the sales cycle principle to calculate the sales cycle and make a judgment. If no result can be obtained, the terminal applies the classification model based on the inquiry frequency principle, the classification model based on the scrap rate principle and the classification model based on the configuration principle in turn until the initial classification result of the aircraft engine component is obtained.

[0094] The technical solution provided in this embodiment, through the combination of multiple classification models and the application of preset priorities, is conducive to comprehensively considering the various characteristics of aircraft engine parts for classification, thereby facilitating improving the accuracy of classification and achieving accurate prediction of the value of parts.

[0095] In an exemplary embodiment, before predicting the value of engine parts according to the category and target information of engine parts through a preset value prediction model, the following contents are also included: judging whether aircraft engine parts are life parts; predicting the value of engine parts according to the category and target information of engine parts through a preset value prediction model, specifically including the following contents: in the case that aircraft engine parts are life parts, predicting the value of engine parts according to the category and life information in the target information of engine parts through a first value prediction model; the first value prediction model is a value prediction model corresponding to life parts in the preset value prediction model.

[0096] Among them, life parts can be aircraft engine parts with specific service life limits specified by the engine manufacturer. For example, life parts can only be operated within the specified service life period and must be replaced after the end of their service life.

[0097] Among them, the first value prediction model can be a model specifically used to predict the value of life parts. For example, the first value prediction model can be a model that calculates the value based on the remaining life of the life parts, the total life value and the manufacturer's new parts catalog price.

[0098] The life information may be data related to the service life of aircraft engine parts. For example, the life information may include the remaining life of the parts, the total life value specified for the corresponding parts, etc.

[0099] Among them, the preset value prediction model can be a series of models used to predict the value of aircraft engine parts. For example, the preset value prediction model can include a first value prediction model for life parts and other value prediction models for non-life parts.

[0100] Optionally, the terminal first determines whether the aircraft engine component to be evaluated is a life-long component. If it is determined to be a life-long component, the terminal calls the first value prediction model, which is specifically used to predict the value of life-long components. The terminal extracts the life information from the target information, combines the category of the component (such as Class A, Class B, etc.), and inputs it into the first value prediction model. The first value prediction model calculates the predicted value of the life-long component based on these input information.

[0101] The technical solution provided in this embodiment helps to more accurately evaluate the value of life parts by distinguishing between life parts and non-life parts and applying a special value prediction model for life parts, thereby helping to improve the accuracy and efficiency of the overall value prediction of aircraft engine parts.

[0102] In an exemplary embodiment, the life information includes the remaining life information and total life information of the engine parts; the value of the engine parts is predicted according to the category of the engine parts and the life information in the target information through the first value prediction model, which specifically includes the following contents: the ratio of the remaining life information to the total life information is calculated through the first value prediction model; the first initial value of the engine parts is calculated according to the ratio and the product attribute value in the target information; the first initial value of the engine parts is updated according to the first preset value coefficient corresponding to the category of the engine parts to obtain the value of the engine parts.

[0103] The remaining life information may describe the time or number of times an aircraft engine component can continue to be used.

[0104] The total life information may describe the total usable time or number of times an aircraft engine component can be used from new to scrapped.

[0105] The product attribute value may be data describing the characteristics of aircraft engine parts. For example, the product attribute value may include the original new parts catalog price of the parts.

[0106] The first initial value may be a value preliminarily calculated based on the remaining life ratio and the product attribute value. For example, the first initial value may be the product of the remaining life ratio and the original new parts catalog price.

[0107] Among them, the first preset value coefficient can be an adjustment factor set according to the category of aircraft engine parts. For example, the first preset value coefficient can be the percentage set for Class A, Class B, Class C and Class D parts respectively, such as 95% for Class A parts, 85% for Class B parts, 50% for Class C parts, and 0 for Class D parts.

[0108] Optionally, the terminal obtains life information of the engine parts, including remaining life information and total life information. The terminal calculates the ratio of the remaining life information to the total life information through the first value prediction model (for example, the remaining life is divided by the total life to obtain the ratio). The terminal calculates the first initial value of the engine parts based on the calculated ratio and the product attribute value in the target information. The terminal updates the first initial value of the engine parts (for example, multiplying) according to the first preset value coefficient corresponding to the category of the engine parts (for example, the part is classified as Class A, and the corresponding first preset value coefficient is 95%) to obtain the final value of the engine parts.

[0109] The technical solution provided in this embodiment helps to accurately reflect the actual usage status and value of engine parts by calculating the ratio of remaining life to total life, combining product attribute values ​​and value coefficients corresponding to categories, thereby helping to improve the accuracy and objectivity of value prediction.

[0110] In an exemplary embodiment, the value of engine parts is predicted based on the category and target information of the engine parts through a preset value prediction model, and also includes the following content: in the case where aircraft engine parts are parts other than life parts, the value of the engine parts is predicted based on the category and product attribute values ​​in the target information of the engine parts through a second value prediction model; the second value prediction model is a value prediction model corresponding to parts other than life parts in the preset value prediction model.

[0111] Among them, parts other than life parts may be aircraft engine parts that are not subject to specific service life restrictions. For example, parts other than life parts may be parts that do not need to be compulsorily replaced within a fixed service life period.

[0112] The second value prediction model may be a model specifically used to predict the value of non-lifetime parts. For example, the second value prediction model may be a model that calculates the value based on the category of parts and product attribute values.

[0113] The product attribute value may be data describing the characteristics of aircraft engine parts. For example, the product attribute value may include the manufacturer's new parts catalog price of the parts.

[0114] The preset value prediction model may include a first value prediction model for service life parts and a second value prediction model for non-service life parts.

[0115] Optionally, after the terminal determines that the aircraft engine component to be evaluated is not a life-endurance component, the second value prediction model is called. The terminal extracts product attribute values ​​from the target information, including information such as the manufacturer's new component catalog price of the component. The terminal inputs the category of the component (such as Class A, Class B, etc.) and the extracted product attribute value into the second value prediction model, and the second value prediction model calculates the predicted value of the non-life-endurance component based on these input information.

[0116] The technical solution provided in this embodiment, by applying a special second value prediction model for non-life parts and combining the component category and product attribute value for calculation, is conducive to more accurate assessment of the value of non-life parts, thereby facilitating improving the efficiency and accuracy of the overall value prediction of aircraft engine parts.

[0117] In an exemplary embodiment, the value of engine parts is predicted based on the category of engine parts and product attribute values ​​in the target information through a second value prediction model, which specifically includes the following contents: determining the second initial value of engine parts based on the product attribute values ​​in the target information through the second value prediction model; updating the second initial value of engine parts based on a second preset value coefficient corresponding to the category of engine parts to obtain the value of engine parts.

[0118] The second initial value may be the value of non-life-span aircraft engine parts preliminarily calculated based on product attribute values.

[0119] Among them, the second preset value coefficient can be an adjustment factor set according to the category of non-life-span aircraft engine parts. For example, the second preset value coefficient can be the percentage set for Class A, Class B, Class C and Class D parts respectively, such as 40% for Class A parts, 30% for Class B parts, 25% for Class C parts, and 10% or 0 for Class D parts.

[0120] Optionally, the terminal determines the second initial value of the engine parts according to the product attribute value in the target information through the second value prediction model (for example, the product attribute value is used as the second initial value of the engine parts). The terminal obtains the category information of the engine parts, and updates the second initial value according to the second preset value coefficient corresponding to the category (for example, the part is classified as Class A, and the corresponding second preset value coefficient is 40%). The terminal uses the result of the update as the final value of the engine parts.

[0121] The technical solution provided in this embodiment is conducive to accurately reflecting the value and usage status of non-life engine parts by combining product attribute values ​​and value coefficients corresponding to categories, thereby helping to improve the accuracy and objectivity of value prediction.

[0122] In an exemplary embodiment, target information of a preset dimension of aircraft engine parts is extracted from the attribute information of aircraft engine parts, specifically including the following contents: sub-target information of a first dimension of aircraft engine parts is extracted from the first attribute information of aircraft engine parts in the inventory module; sub-target information of a second dimension of aircraft engine parts is extracted from the second attribute information of aircraft engine parts in the market module; sub-target information of a third dimension of aircraft engine parts is extracted from the third attribute information of aircraft engine parts in the maintenance module; sub-target information of a fourth dimension of aircraft engine parts is extracted from the fourth attribute information of aircraft engine parts in the product module; and the sub-target information of the first dimension, the sub-target information of the second dimension, the sub-target information of the third dimension and the sub-target information of the fourth dimension are all used as target information of a preset dimension of aircraft engine parts.

[0123] The inventory module may be a database module for storing inventory information of aircraft engine parts. For example, the inventory module may include information such as part number, serial number, and airworthiness status of the parts.

[0124] The market module may be a database module that stores information related to the aircraft engine parts market. For example, the market module may include information such as the sales order date and inquiry frequency of the parts.

[0125] The maintenance module may be a database module that stores information related to the maintenance of aircraft engine parts. For example, the maintenance module may include information such as the historical replacement configuration and scrap rate of parts.

[0126] Among them, the product module can be a database module that stores information related to aircraft engine parts and components. For example, the product module can include information such as the installed quantity of parts and components, the manufacturer's new parts catalog price, and the full life value of life parts.

[0127] Among them, the sub-target information of the first dimension may be the aircraft engine parts information extracted from the inventory module. For example, the sub-target information of the first dimension may include the part number, remaining life, etc. of the parts.

[0128] Among them, the sub-target information of the second dimension may be the aircraft engine parts information extracted from the market module. For example, the sub-target information of the second dimension may include the sales order date, inquiry frequency, etc. of the parts.

[0129] Among them, the sub-target information of the third dimension may be the aircraft engine parts information extracted from the maintenance module. For example, the sub-target information of the third dimension may include the historical replacement configuration and scrap rate of the parts.

[0130] Among them, the sub-target information of the fourth dimension can be the aviation engine parts information extracted from the product module. For example, the sub-target information of the fourth dimension can include the installed quantity of parts, the manufacturer's new parts catalog price, and the full life value of life parts.

[0131] Optionally, the terminal accesses the inventory module to extract sub-target information of the first dimension, such as the part number and remaining life of the component. The terminal accesses the market module to extract sub-target information of the second dimension, such as the sales order date and inquiry frequency. The terminal accesses the maintenance module to extract sub-target information of the third dimension, such as historical replacement configuration and scrap rate. The terminal accesses the product module to extract sub-target information of the fourth dimension, such as the number of installations and the manufacturer's new parts catalog price. The terminal integrates the sub-target information of these four dimensions to form complete target information of aircraft engine parts.

[0132] The technical solution provided in this embodiment is conducive to comprehensively collecting relevant data of aircraft engine parts by extracting and integrating multi-dimensional sub-target information from four different modules, thereby facilitating improving the accuracy and efficiency of subsequent classification and value prediction.

[0133] The following is an application example to illustrate the value prediction method for aircraft engine parts provided in this application. This application example uses the method applied to a terminal (system) as an example.

[0134] refer to Figure 3 , parts information is uploaded to the system server, and the system extracts data from the server through the inventory module, market module, maintenance module and product module. The system processes the extracted information in accordance with the calculation principles, among which the life part principle is better than the sales cycle principle, the sales cycle principle is better than the inquiry frequency principle, the inquiry frequency principle is better than the scrap rate principle, and the scrap rate principle is better than the configuration principle. The calculation results are obtained, screened and excluded, and manually reviewed. After the review is passed, the classification results of parts ABCD are obtained, and the system calculates the value of ABCD parts respectively through the set formula.

[0135] definition:

[0136] 1. The types of parts are divided into ABCD categories. A-type parts are key value parts, with extremely high market circulation rate or extremely high scrap rate; B-type parts are secondary value parts, with relatively high market circulation rate or relatively high scrap rate; C-type parts are valuable parts, with fair market circulation rate or average scrap rate; D-type parts are worthless parts, with low market circulation rate or low scrap rate. All parts except those in categories ABC are D-type parts.

[0137] 2. The interception data calculation time period specified for ABCD parts is: Class A parts are equal to or less than 5 months; Class B parts are equal to or less than 10 months; Class C parts are less than or equal to 20 months; Class D parts are greater than 20 months. The above time periods are set as rolling cycles and are regularly intercepted by the server.

[0138] 3. Customer level scoring. The system screens existing customers on the server according to the manually entered conditions, and the classification levels are divided into Class I customers, Class II customers, Class III customers, and Class IV customers. Customer classification conditions: 1) Class I customers: airlines, maintenance units, and internal maintenance departments of companies; 2) Class II customers: have order records within 10 rolling months and the cumulative amount reaches 100,000 or more; 3) have order records within 20 rolling months; 4) Class IV customers: distributors other than Class I, II, and III customers. Scoring standard: Class I customers are 1 point; Class II customers are 0.4 points; Class III customers are 0.2 points; Class IV customers are 0.1 points.

[0139] 4. Market activity. Reference data to help determine the current market circulation level of parts and components. The formula is: single customer level score × total number of historical inquiries for a single part number. The time period for calling data in the market activity calculation process is consistent with the calculation period defined for ABCD-type parts.

[0140] 5. Transaction coefficient. The server extracts the number of inquiries for the sold parts before the date of the sales order, and counts the number of inquiries within the specified calculation period for ABCD parts. The formula is: number of inquiries ÷ number of transactions = average value.

[0141] step:

[0142] 1. The information of the disassembled engine parts is entered into the system, including but not limited to: part number, serial number, serial number of the engine from which it comes, airworthiness status of the parts, date and type of the certificate accompanying the cargo, and remaining life of the parts (only for life parts).

[0143] 2. The system extracts information from the inventory module, market module, maintenance module, and product module respectively.

[0144] 1) The information required by the inventory module includes: the part number of the component to be calculated and the remaining life of the component (if any);

[0145] 2) The information required by the market module includes: the sales order date of the corresponding inventory parts and the frequency of customer inquiries for the part number;

[0146] 3) The information required by the maintenance module includes: the part number configuration of the historical replacement parts of the engine sent to the factory for maintenance, and the scrap rate of the parts sent to the factory for maintenance;

[0147] 4) The information required for the product module includes: the installed quantity of the component, the manufacturer's new parts catalog price, and the full life value of the life parts.

[0148] 3. The system processes the extracted information in accordance with the calculation principles, which are as follows:

[0149] 1) Life span principle.

[0150] Preset model engine: The remaining life of the parts to be calculated is within the range of 9000-11000, which is positioned as Class A parts; the remaining life is within the range of 8000-9000 or 11000-15000, which is positioned as Class B parts; the remaining life is outside the above defined range and is greater than 2000, which is positioned as Class C parts; the remaining life is less than 2000, which is positioned as Class D parts.

[0151] Another preset model engine: the remaining life of the parts to be calculated is positioned as Class A parts if it is within the range of 9000-12000; the remaining life is positioned as Class B parts if it is within the range of 7000-9000 or 12000-13000; the remaining life is outside the above defined range and is greater than 3000, it is positioned as Class C parts, and the remaining life is less than 3000, it is positioned as Class D parts.

[0152] 2) Sales cycle principle. The formula is: order date of the parts to be calculated - issuance date of the airworthiness certificate of the parts = difference days. Parts with a waiting time of 5 months or so are classified as Class A parts; parts with a waiting time of less than 10 months are classified as Class B parts; parts with a waiting time of less than 20 months are classified as Class C parts; and parts with a waiting time of more than 20 months are classified as Class D parts.

[0153] 3) Inquiry frequency principle. Within the specified data calculation period for ABCD parts (i.e. 5 months, 10 months, 20 months), the formula is: market activity of the part number to be calculated ÷ installed quantity ≧ transaction coefficient.

[0154] 4) Scrap rate principle. The parts to be calculated have a scrap rate of >80% in the repair shop, which is a Class A part; a scrap rate range of 60%-80% is a Class B part; a scrap rate range of 30%-60% is a Class C part; and a scrap rate of <20% is a Class D part.

[0155] 5) Configuration principle. Engineers enter the updated information of component configuration into the system based on the issued technical documents. The system records the component information and lists it in Class A parts.

[0156] 6) The priority order of the above principles is: life principle > sales cycle principle > inquiry frequency principle > scrap rate principle > configuration principle.

[0157] 7) The above principles are all calculated in a step-by-step manner, that is, data is calculated first to obtain Class A parts, and Class B parts are calculated on the premise of excluding Class A parts. After obtaining Class B parts, Class C parts are calculated on the premise of excluding Class A and Class B parts.

[0158] 4. Screening and rejection of calculation results.

[0159] The result of dividing market activity by installed capacity in the inquiry frequency principle needs to be further excluded and screened.

[0160] The exclusion principles are as follows:

[0161] 1) The number of installed units is in the range of 2-10, and the calculated result is greater than 15.

[0162] 2) The installed quantity is greater than 10 and the calculated result is greater than 20.

[0163] 3) The installed quantity is greater than 1 and the calculated result is greater than or equal to 10.

[0164] The list of parts numbers obtained through exclusion is manually reviewed to determine whether to exclude the ABC classification candidates.

[0165] The screening principles are as follows:

[0166] 1) The number of inquiries is greater than 2 but the market activity is less than 2.

[0167] 2) The manufacturer's new parts catalog price is less than 1000 and the installed quantity is less than 5.

[0168] The selected parts are directly filtered and not classified as ABC parts.

[0169] 5. The system calculates the value of ABCD components based on the set percentage.

[0170] Calculation formula for parts other than life parts:

[0171] 1) Value of Class A parts: Catalog price of original engine parts × 40% = value of parts;

[0172] 2) Value of Category B parts: Catalog price of original engine parts × 30% = value of parts;

[0173] 3) Value of Category C parts: Catalog price of original engine parts × 25% = value of parts;

[0174] 4) Value of Category D parts: catalog price of original engine new parts × 10% or 0; if the status of the parts is judged to be scrapped, it will be directly 0.

[0175] The above percentages are set based on the average ratio of the used parts price and the historical maintenance cost of the parts specified by the engine maintenance manufacturer. For example, for 40% of Class A parts, the used parts price of the maintenance manufacturer is generally: the new parts catalog price issued by the engine manufacturer × 70%. The average ratio of the historical maintenance cost of parts calculated by the system is the new parts catalog price issued by the engine manufacturer × 30%, so the percentage coefficient of Class A parts is set to 40%. The calculation of Class BCD parts is a step-by-step reduction.

[0176] The calculation formula of life parts:

[0177] 1) Value of Class A parts: (remaining life ÷ total life value specified for the corresponding parts × manufacturer's new parts catalog price) × 95% = part value;

[0178] 2) Value of Class B parts: (remaining life ÷ total life value specified for the corresponding parts × manufacturer's new parts catalog price) × 85% = part value;

[0179] 3) Value of Class C parts: (remaining life ÷ total life value specified for the corresponding parts × manufacturer's new parts catalog price) × 50% = part value;

[0180] 4) Value of Class D items: equal to 0.

[0181] Among them, the number of installed units: the number of each component that must be installed in the engine as specified in the original engine manual. The original engine factory new parts catalog price: the original engine factory will regularly issue new engine parts catalog prices every year. Life parts: the engine manufacturer stipulates that each model of engine has a specific part number of life parts. Such parts can only be operated within the life period and must be replaced after the life period. Scrap status: When a part is found to have defects during the disassembly or airworthiness restoration repair work and is determined by the engineer or manufacturer to be unrepairable, it is in scrap status.

[0182] The technical solution provided in this application example can improve accuracy: by processing a large amount of information through the server, the accuracy of product classification can be improved; data-driven decision-making: by analyzing big data, understanding market trends and behaviors, thus helping to make better decisions; improving efficiency: the automated product classification system can save a lot of manpower and time, enabling resources to be concentrated on more valuable work; contributing information to front-end procurement: by analyzing sales data and data generated by the maintenance process, it can provide important reference information for the front-end procurement of major assets; inventory management: it can help better manage inventory, predict internal and external demand for products, and reduce inventory backlogs and out-of-stock situations; risk management: through the process of data analysis, potential risks can be discovered in advance, such as changes in market demand, supply chain problems, and out-of-stock situations caused by configuration changes, so that corresponding measures can be taken to reduce risks; improving the accuracy and efficiency of value prediction of aircraft engine parts.

[0183] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0184] Based on the same inventive concept, the embodiment of the present application also provides a device for predicting the value of an aircraft engine component for implementing the above-mentioned method for predicting the value of an aircraft engine component. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of the value prediction device for one or more aircraft engine components provided below can refer to the limitations of the above-mentioned method for predicting the value of an aircraft engine component, and will not be repeated here.

[0185] In an exemplary embodiment, Figure 4 As shown, a value prediction device for aircraft engine parts is provided. The value prediction device 400 for aircraft engine parts may include:

[0186] The information extraction module 401 is used to extract target information of a preset dimension of the aircraft engine parts from the attribute information of the aircraft engine parts;

[0187] The information processing module 402 is used to classify the engine parts based on the preset classification conditions and target information to obtain the categories of the engine parts;

[0188] The value prediction module 403 is used to predict the value of the engine parts according to the category and target information of the engine parts through a preset value prediction model.

[0189] In an exemplary embodiment, the information processing module 402 is also used to classify engine parts according to target information based on a preset classification model to obtain initial classification results of the engine parts; the preset classification model is constructed based on preset classification conditions; the initial classification results are screened and rejected to obtain target classification results of the engine parts; and the category of the engine parts is determined based on the target classification results.

[0190] In an exemplary embodiment, the preset classification models include a classification model based on the life parts principle, a classification model based on the sales cycle principle, a classification model based on the inquiry frequency principle, a classification model based on the scrap rate principle and a classification model based on the configuration principle; the information processing module 402 is also used to classify the engine parts according to the target information according to the preset principle priority, using the classification model based on the life parts principle, the classification model based on the sales cycle principle, the classification model based on the inquiry frequency principle, the classification model based on the scrap rate principle and the classification model based on the configuration principle; the preset principle priority indicates that the life parts principle is better than the sales cycle principle, the sales cycle principle is better than the inquiry frequency principle, the inquiry frequency principle is better than the scrap rate principle, and the scrap rate principle is better than the configuration principle.

[0191] In an exemplary embodiment, the device 400 also includes: a component judgment module, used to judge whether the aircraft engine component is a life-span part; a value prediction module 403, which is also used to predict the value of the engine component by using a first value prediction model when the aircraft engine component is a life-span part, according to the category of the engine component and the life information in the target information; the first value prediction model is a value prediction model corresponding to the life-span part in the preset value prediction model.

[0192] In an exemplary embodiment, the life information includes the remaining life information and the total life information of the engine parts; the value prediction module 403 is also used to calculate the ratio of the remaining life information to the total life information through a first value prediction model; calculate the first initial value of the engine parts according to the ratio and the product attribute value in the target information; update the first initial value of the engine parts according to the first preset value coefficient corresponding to the category of the engine parts to obtain the value of the engine parts.

[0193] In an exemplary embodiment, the value prediction module 403 is also used to predict the value of the aircraft engine parts by using a second value prediction model according to the category of the engine parts and the product attribute values ​​in the target information when the aircraft engine parts are parts other than life parts; the second value prediction model is a value prediction model corresponding to parts other than life parts in the preset value prediction model.

[0194] In an exemplary embodiment, the value prediction module 403 is also used to determine the second initial value of the engine parts according to the product attribute value in the target information through a second value prediction model; and to update the second initial value of the engine parts according to the second preset value coefficient corresponding to the category of the engine parts to obtain the value of the engine parts.

[0195] In an exemplary embodiment, the information extraction module 401 is also used to extract sub-target information of the first dimension of aircraft engine parts from the first attribute information of aircraft engine parts in the inventory module; extract sub-target information of the second dimension of aircraft engine parts from the second attribute information of aircraft engine parts in the market module; extract sub-target information of the third dimension of aircraft engine parts from the third attribute information of aircraft engine parts in the maintenance module; extract sub-target information of the fourth dimension of aircraft engine parts from the fourth attribute information of aircraft engine parts in the product module; and use the sub-target information of the first dimension, the sub-target information of the second dimension, the sub-target information of the third dimension, and the sub-target information of the fourth dimension as target information of preset dimensions of aircraft engine parts.

[0196] Each module in the above-mentioned value prediction device for aircraft engine parts can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0197] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a value prediction method for aircraft engine parts is realized. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0198] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0199] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0200] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0201] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0202] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0203] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0204] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for predicting the value of aircraft engine parts, characterized in that: The method comprises: Extracting target information of a preset dimension of the aircraft engine component from the attribute information of the aircraft engine component; Based on the preset classification conditions and the target information, the engine parts are classified to obtain the categories of the engine parts; The value of the engine parts is predicted by a preset value prediction model according to the category of the engine parts and the target information.

2. The method according to claim 1, characterized in that The classifying process of the engine parts based on the preset classification conditions and the target information to obtain the categories of the engine parts includes: Based on a preset classification model, the engine parts are classified according to the target information to obtain an initial classification result of the engine parts; the preset classification model is constructed based on the preset classification conditions; Screening and rejecting the initial classification results to obtain target classification results of the engine parts; According to the target classification result, the category of the engine parts is determined.

3. The method according to claim 2, characterized in that The preset classification models include a classification model based on the life parts principle, a classification model based on the sales cycle principle, a classification model based on the inquiry frequency principle, a classification model based on the scrap rate principle and a classification model based on the configuration principle; The classifying process of the engine parts based on the preset classification model and the target information to obtain the initial classification results of the engine parts includes: According to the preset principle priority, the engine parts are classified according to the target information using the classification model based on the life parts principle, the classification model based on the sales cycle principle, the classification model based on the inquiry frequency principle, the classification model based on the scrap rate principle and the classification model based on the configuration principle to obtain the initial classification result; The preset principle priority indicates that the life parts principle is superior to the sales cycle principle, the sales cycle principle is superior to the inquiry frequency principle, the inquiry frequency principle is superior to the scrap rate principle, and the scrap rate principle is superior to the configuration principle.

4. The method according to claim 1, characterized in that: Before predicting the value of the engine parts according to the category of the engine parts and the target information through a preset value prediction model, the method further includes: Determine whether the aircraft engine parts are life-limited parts; The method of predicting the value of the engine parts according to the category of the engine parts and the target information by using a preset value prediction model includes: In the case where the aircraft engine parts belong to the life parts, the value of the engine parts is predicted according to the category of the engine parts and the life information in the target information through a first value prediction model; the first value prediction model is a value prediction model corresponding to the life parts in the preset value prediction model.

5. The method according to claim 4, characterized in that The life information includes the remaining life information and total life information of the engine parts; The method of predicting the value of the engine component according to the category of the engine component and the life information in the target information by using the first value prediction model includes: Calculating the ratio of the remaining life information to the total life information by using the first value prediction model; Calculating a first initial value of the engine component according to the ratio and the product attribute value in the target information; According to the first preset value coefficient corresponding to the category of the engine component, the first initial value of the engine component is updated to obtain the value of the engine component.

6. The method according to claim 4, characterized in that The method of predicting the value of the engine parts according to the category of the engine parts and the target information by using a preset value prediction model also includes: In the case where the aircraft engine parts are parts other than the life parts, the value of the engine parts is predicted by a second value prediction model based on the category of the engine parts and the product attribute values ​​in the target information; the second value prediction model is a value prediction model in the preset value prediction model that corresponds to the parts other than the life parts.

7. The method according to claim 6, characterized in that The method of predicting the value of the engine parts according to the category of the engine parts and the product attribute value in the target information by using the second value prediction model includes: Determining a second initial value of the engine component according to the product attribute value in the target information by using the second value prediction model; The second initial value of the engine component is updated according to the second preset value coefficient corresponding to the category of the engine component to obtain the value of the engine component.

8. The method according to any one of claims 1 to 7, characterized in that: The step of extracting target information of a preset dimension of the aircraft engine component from the attribute information of the aircraft engine component comprises: Extracting sub-target information of a first dimension of the aircraft engine parts from the first attribute information of the aircraft engine parts in the inventory module; Extracting sub-target information of a second dimension of the aircraft engine parts from the second attribute information of the aircraft engine parts in the market module; Extracting sub-target information of the third dimension of the aircraft engine component from the third attribute information of the aircraft engine component in the maintenance module; Extracting the sub-target information of the fourth dimension of the aircraft engine parts from the fourth attribute information of the aircraft engine parts in the product module; The sub-target information of the first dimension, the sub-target information of the second dimension, the sub-target information of the third dimension, and the sub-target information of the fourth dimension are all used as the target information of the preset dimensions of the aircraft engine parts.

9. A value prediction device for aircraft engine parts, characterized in that: The device comprises: An information extraction module, used to extract target information of a preset dimension of the aircraft engine component from the attribute information of the aircraft engine component; An information processing module, used to classify the engine parts based on preset classification conditions and the target information to obtain the categories of the engine parts; The value prediction module is used to predict the value of the engine parts according to the category of the engine parts and the target information through a preset value prediction model.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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