Aerial material demand information intelligent prediction system

Abstract

The invention discloses an intelligent prediction method and system for aviation material demand information, and the method comprises the steps: recording the information of each process in an aviation material life cycle through employing an RFID tag, carrying out the cleaning and classification of collected aviation material RFID data during the intelligent prediction of the aviation material demand information, constructing the classification data of the aviation material, and carrying out the intelligent prediction of the aviation material demand information. Then, a consumption curve of each aerial material classification and an aerial material demand prediction model are established based on the aerial material classification data, and finally, each aerial material demand is intelligently predicted according to the consumption curve of each aerial material classification and the aerial material demand prediction model, so that aerial material demand prediction information is obtained.

Description

Technical Field

[0001] The present invention relates to the field of aircraft engine demand forecasting, and in particular to an aircraft material intelligent forecasting system. Background Art

[0002] Aviation materials are one of the most critical factors to ensure the flight safety and normal operation of aircraft, and are of great significance to airlines. If there is not enough aviation material guarantee, the aircraft / engine will be in an unairworthy state due to lack of parts and will be forced to stop, which will bring huge economic losses and negative social impact to the airline. Therefore, airlines must have a complete reserve of aviation materials. The guarantee rate of aviation materials directly affects the maintenance cost of aircraft engines and the capital holdings of airlines. In order to make the reserve of aviation materials have a basis, the key is to be able to reasonably and accurately predict the demand for aviation material spare parts. Intelligent prediction of the demand for aviation materials is imminent. Summary of the invention

[0003] A brief summary of one or more aspects is given below to provide a basic understanding of these aspects. This summary is not an exhaustive overview of all conceived aspects, and is neither intended to identify the key or critical elements of all aspects nor to define the scope of any or all aspects. Its only purpose is to give some concepts of one or more aspects in a simplified form as a prelude to a more detailed description that will be given later.

[0004] The purpose of the present invention is to solve the above problems and provide a method and system for intelligent prediction of aviation material demand information, which uses RFID, an Internet of Things technology, to predict aviation material demand information, thereby coordinating the procurement timing of aviation materials and ensuring a balance between the risks and costs of aviation material inventory.

[0005] The technical solution of the present invention is:

[0006] The present invention provides an intelligent prediction method for aviation material demand information, comprising the following steps:

[0007] Collect aviation material RFID data;

[0008] Clean and classify aviation material RFID data and construct aviation material classification data;

[0009] Establish consumption curves for each aviation material classification and aviation material demand forecasting models based on aviation material classification data;

[0010] According to the consumption curve of each aviation material classification and the aviation material demand forecasting model, the demand for each aviation material is intelligently predicted, so as to obtain the aviation material demand forecasting information.

[0011] According to an embodiment of the intelligent prediction method for aviation material demand information of the present invention, the intelligent prediction method for aviation material demand information adopts a customized self-learning strategy to extract features from aviation material RFID data, classifies the aviation material RFID data according to the extracted features, and then establishes corresponding consumption curves and aviation material demand prediction models for each aviation material classification based on the classified aviation material classification data.

[0012] According to an embodiment of the intelligent prediction method for aviation material demand information of the present invention, the intelligent prediction method for aviation material demand information performs self-evaluation on a customized self-learning strategy when intelligently predicting aviation material demand information, and automatically improves the aviation material demand prediction model according to the evaluation result.

[0013] According to an embodiment of the intelligent prediction method for aviation material demand information of the present invention, after obtaining the real-time aviation material demand information forecast, the aviation material consumption situation in the most recent period is compared with the aviation material demand forecast information, so as to evaluate the accuracy of the aviation material demand forecast model, and automatically improve the aviation material demand forecast model according to the evaluation result.

[0014] The present invention also provides an intelligent prediction system for aviation material demand information, including a data acquisition module, a data transmission and storage module, a data cleaning module, a self-learning module and an intelligent prediction module; wherein,

[0015] The data acquisition module is used to collect aviation material RFID data and send the collected aviation material RFID data to the data transmission and storage module;

[0016] The data transmission and storage module is used to store the received aviation material RFID data and send the received aviation material RFID data to the data processing module;

[0017] The data cleaning module is used to clean and classify aviation material RFID data and construct aviation material classification data;

[0018] The self-learning module establishes the consumption curve of each aviation material classification and the aviation material demand prediction module based on the aviation material classification data;

[0019] The intelligent prediction module intelligently predicts the demand for each aviation material based on the consumption curve of each aviation material classification and the aviation material demand prediction model, thereby obtaining the aviation material demand information forecast.

[0020] According to one embodiment of the intelligent prediction system for aviation material demand information of the present invention, the intelligent prediction system for aviation material demand information adopts a customized self-learning strategy to extract features from aviation material RFID data, classifies the aviation material RFID data according to the extracted features, and then establishes corresponding consumption curves and aviation material demand prediction models for each aviation material classification based on the classified aviation material classification data.

[0021] According to an embodiment of the intelligent prediction system for aviation material demand information of the present invention, the intelligent prediction system for aviation material demand information performs self-evaluation on a customized self-learning strategy when intelligently predicting aviation material demand information, and automatically improves the aviation material demand prediction model according to the evaluation result.

[0022] According to an embodiment of the aviation material demand information intelligent prediction system of the present invention, after obtaining the real-time aviation material demand information forecast, the aviation material demand information intelligent prediction system uses the aviation material consumption in the most recent period to compare with the aviation material demand forecast information, so as to evaluate the accuracy of the aviation material demand forecast model, and automatically improve the aviation material demand forecast model according to the evaluation results.

[0023] The present invention also provides a computer readable medium storing a computer program code, wherein the computer program code implements the method as described above when executed by a processor.

[0024] The present invention also provides an intelligent prediction device for aviation material demand information, comprising:

[0025] a memory for storing instructions executable by a processor; and

[0026] A processor is used to execute the instructions to implement the method as described above.

[0027] Compared with the prior art, the present invention has the following beneficial effects: in view of the situation that the demand information of aviation materials is difficult to accurately predict, the present invention adopts RFID tags to record the information of each process in the life cycle of aviation materials. When intelligently predicting the demand information of aviation materials, the collected RFID data of aviation materials are cleaned and classified to construct aviation material classification data, and then the consumption curve of each aviation material classification and the aviation material demand prediction model are established based on the aviation material classification data. Finally, the demand of each aviation material is intelligently predicted according to the consumption curve of each aviation material classification and the aviation material demand prediction model, so as to obtain the aviation material demand prediction information. Compared with the prior art, the present invention adopts RFID, an Internet of Things technology, to predict the demand of aviation materials, fully mines the historical material delivery data of aviation materials, and uses the historical RFID data of aviation materials to accurately predict the demand of aviation materials, thereby reducing inventory backlogs, so as to provide a higher aviation material guarantee rate with less capital investment. At the same time, the present invention can coordinate the procurement time of aviation materials through the accurate prediction of aviation material demand information, and provide users with more accurate and reasonable aviation material demand prediction through the intelligent aviation material demand prediction model, so as to ensure the balance between the risk and cost of aviation material storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The above features and advantages of the present invention can be better understood after reading the detailed description of the embodiments of the present disclosure in conjunction with the following drawings. In the drawings, the components are not necessarily drawn to scale, and components with similar related properties or features may have the same or similar reference numerals.

[0029] Figure 1 It is a flow chart showing an embodiment of the method for intelligent prediction of aviation material demand information of the present invention.

[0030] Figure 2 It is a structural diagram showing an embodiment of the intelligent prediction system for aviation material demand information of the present invention. DETAILED DESCRIPTION

[0031] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. Note that the aspects described below in conjunction with the accompanying drawings and specific embodiments are only exemplary and should not be construed as limiting the scope of protection of the present invention in any way.

[0032] An embodiment of an intelligent prediction system for aviation material demand information is disclosed herein. Figure 1 This is a flow chart showing an embodiment of the intelligent prediction method for aviation material demand information of the present invention. Figure 1 ,The following is a detailed description of each step of the ,intelligent prediction method for aviation material demand information.

[0033] Step S1: Collect aviation material RFID data.

[0034] In this embodiment, in order to realize the intelligent prediction of the demand information of aviation materials, RFID technology is adopted. According to the use environment of aviation materials, different RFID tags are configured for each aviation material when the aviation materials are tagged out of the factory, and the relevant attribute information of the aviation materials is written into the RFID tags, and the relevant attribute information is continuously supplemented during the life cycle of the aviation materials. In this way, by reading the RFID tags of aviation materials, the attribute information of each process of the entire life cycle of the aviation materials, that is, the aviation material RFID data, can be obtained, and the read aviation material RFID data can be stored. In this way, the attribute information of each process of the entire life cycle of aviation materials can be monitored and queried through a visual interface, and the intelligent prediction of aviation material demand information can be realized by mining and analyzing the stored aviation material RFID data.

[0035] Step S2: Clean and classify the aviation material RFID data to construct aviation material classification data.

[0036] In this embodiment, before mining and analyzing the aviation material RFID data, the aviation material RFID data needs to be classified. A customized self-learning strategy is adopted to further pre-process the historical material delivery data through cloud platform, big data, data mining, data cleaning, self-learning and other means, and unknown features with important utilization value are extracted from the massive aviation material RFID data. Then, the aviation material RFID data is classified according to the extracted features to form classification information based on important features or rules. Finally, the corresponding consumption curve and aviation material demand forecasting model are established for each aviation material classification according to the classified aviation material classification data, and the consumption curve of each aviation material classification and the aviation material demand forecasting model are used to predict and analyze the demand information of the corresponding aviation material.

[0037] Step S3: Establish consumption curves for each aviation material classification and an aviation material demand prediction model based on the aviation material classification data.

[0038] Step S4: Intelligently predict the demand for each aviation material based on the consumption curve of each aviation material classification and the aviation material demand prediction model, so as to obtain aviation material demand prediction information.

[0039] In this embodiment, in the process of intelligently predicting the demand information of aviation materials by using the consumption curve of each aviation material classification and the aviation material demand prediction model, the correctness or excellence of the self-learning strategy is evaluated according to the prediction results, and then the parameters of the aviation material demand prediction model are automatically modified according to the evaluation results, and the aviation material demand prediction model is automatically selected or merged and spliced, so as to improve the prediction accuracy of the intelligent prediction system of aviation material demand information and realize the intelligent prediction of the intelligent prediction system of aviation material demand information. At the same time, in this embodiment, the aviation material RFID data can also be analyzed by historical RFID records and self-learning strategies, so as to propose the optimal aviation material procurement plan for different periods and different inventory bases.

[0040] In addition, in this embodiment, after obtaining real-time aviation material demand information using the consumption curve of each aviation material classification and the aviation material demand forecasting model, the aviation material consumption in the most recent period can be compared with the aviation material demand forecasting information to evaluate whether the prediction results of the aviation material demand forecasting model are consistent with the actual aviation material consumption, thereby evaluating the accuracy of the aviation material demand forecasting model. At the same time, in this embodiment, the forecasting strategy in the aviation material demand forecasting model can be automatically adjusted and improved according to the evaluation results, so that the predicted results of the demand forecasting model are closer to the actual consumption, thereby improving the accuracy of the demand forecasting model.

[0041] The present invention also discloses an embodiment of an intelligent prediction system for aviation material demand information. Figure 2 FIG. 1 is a diagram showing an architecture of an embodiment of an intelligent prediction system for aviation material demand information of the present invention. Figure 2As shown, in this embodiment, the intelligent prediction system for aviation material demand information includes a data acquisition module, a data transmission and storage module, a data cleaning module, a self-learning module and an intelligent prediction module. Among them, the data acquisition module is used to collect aviation material RFID data and send the collected aviation material RFID data to the data transmission and storage module. The data transmission and storage module is used to store the received aviation material RFID data and send the received aviation material RFID data to the data processing module. The data cleaning module is used to clean and classify the aviation material RFID data and construct aviation material classification data. The self-learning module establishes the consumption curve of each aviation material classification and the aviation material demand prediction module based on the aviation material classification data. The intelligent prediction module intelligently predicts the demand for each aviation material based on the consumption curve of each aviation material classification and the aviation material demand prediction model, thereby obtaining the aviation material demand information prediction.

[0042] Specifically, in this embodiment, in order to realize the intelligent prediction of aviation material demand information, RFID technology is adopted. According to the use environment of aviation materials, different RFID tags are configured for each aviation material when the aviation material is tagged out of the factory, and the relevant attribute information of the aviation material is written into the RFID tag, and the relevant attribute information is continuously supplemented during the life cycle of the aviation material. In this way, by reading the RFID tag of the aviation material, the attribute information of each process of the entire life cycle of the aviation material, that is, the aviation material RFID data, can be obtained, and the read aviation material RFID data can be stored. In this way, the attribute information of each process of the entire life cycle of the aviation material can be monitored and queried through the visual interface, and the intelligent prediction of aviation material demand information can be realized by mining and analyzing the stored aviation material RFID data.

[0043] In this embodiment, before mining and analyzing the aviation material RFID data, the aviation material RFID data needs to be classified. A customized self-learning strategy is adopted to further pre-process the historical material delivery data through cloud platform, big data, data mining, data cleaning, self-learning and other means, and unknown features with important utilization value are extracted from the massive aviation material RFID data. Then, the aviation material RFID data is classified according to the extracted features to form classification information based on important features or rules. Finally, the corresponding consumption curve and aviation material demand forecasting model are established for each aviation material classification according to the classified aviation material classification data, and the consumption curve of each aviation material classification and the aviation material demand forecasting model are used to predict and analyze the demand information of the corresponding aviation material.

[0044] In this embodiment, in the process of intelligently predicting the demand information of aviation materials by using the consumption curve of each aviation material classification and the aviation material demand prediction model, the correctness or excellence of the self-learning strategy is evaluated according to the prediction results, and then the parameters of the aviation material demand prediction model are automatically modified according to the evaluation results, and the aviation material demand prediction model is automatically selected or merged and spliced, so as to improve the prediction accuracy of the intelligent prediction system of aviation material demand information and realize the intelligent prediction of the intelligent prediction system of aviation material demand information. At the same time, in this embodiment, the aviation material RFID data can also be analyzed by historical RFID records and self-learning strategies, so as to propose the optimal aviation material procurement plan for different periods and different inventory bases.

[0045] In addition, in this embodiment, after obtaining real-time aviation material demand information using the consumption curve of each aviation material classification and the aviation material demand forecasting model, the aviation material consumption in the most recent period can be compared with the aviation material demand forecasting information to evaluate whether the prediction results of the aviation material demand forecasting model are consistent with the actual aviation material consumption, thereby evaluating the accuracy of the aviation material demand forecasting model. At the same time, in this embodiment, the forecasting strategy in the aviation material demand forecasting model can be automatically adjusted and improved according to the evaluation results, so that the predicted results of the demand forecasting model are closer to the actual consumption, thereby improving the accuracy of the demand forecasting model.

[0046] The present specification also provides a computer-readable medium storing a computer program code, which, when executed by a processor, implements the above-mentioned intelligent prediction method for aviation material demand information.

[0047] This specification also provides an intelligent prediction device for aviation material demand information, including an instruction memory for storing instructions executable by a processor, and a processor for executing instructions in the instruction memory to implement the intelligent prediction method for aviation material demand information as described above.

[0048] The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein, but should be granted the widest scope consistent with the principles and novel features disclosed herein.

[0049] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or a combination of the two. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps are generally described above in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. The technician may implement the described functionality in different ways for each specific application, but such implementation decisions should not be interpreted as resulting in a departure from the scope of the present invention.

[0050] The various illustrative logic blocks, modules, and circuits described in conjunction with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in cooperation with a DSP core, or any other such configuration.

[0051] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software module may reside in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read and write information from / to the storage medium. In an alternative, a storage medium may be integrated into a processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and the storage medium may reside in a user terminal as discrete components.

[0052] In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented as a computer program product in software, each function may be stored on or transmitted by a computer-readable medium as one or more instructions or codes. Computer-readable media include both computer storage media and communication media, including any medium that facilitates the transfer of a computer program from one place to another. Storage media may be any available medium that can be accessed by a computer. As an example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer. Any connection is also properly referred to as a computer-readable medium. For example, if the software is transmitted from a website, a server, or other remote source using a coaxial cable, a fiber optic cable, a twisted pair, a digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwaves, the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwaves are included in the definition of the medium. Disk and disc as used herein include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, wherein disk often reproduces data magnetically, while disc reproduces data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

Claims

1. An intelligent prediction method for aviation material demand information, characterized in that: The following steps are involved: Collect aviation material RFID data; Clean and classify aviation material RFID data and construct aviation material classification data; Establish consumption curves for each aviation material classification and aviation material demand forecasting models based on aviation material classification data; According to the consumption curve of each aviation material classification and the aviation material demand forecasting model, the demand for each aviation material is intelligently predicted to obtain aviation material demand forecast information.

2. The intelligent prediction method for aviation material demand information according to claim 1 is characterized in that: The method for intelligently predicting aviation material demand information adopts a customized self-learning strategy to extract features from aviation material RFID data, classifies the aviation material RFID data according to the extracted features, and then establishes corresponding consumption curves and aviation material demand prediction models for each aviation material classification based on the classified aviation material classification data.

3. The intelligent prediction method for aviation material demand information according to claim 2 is characterized in that: The aviation material demand information intelligent prediction method performs self-evaluation on a customized self-learning strategy when intelligently predicting aviation material demand information, and automatically improves the aviation material demand prediction model according to the evaluation result.

4. The intelligent prediction method for aviation material demand information according to claim 1, characterized in that: After obtaining the real-time forecast of aviation material demand information, the aviation material demand information intelligent forecasting method compares the aviation material consumption in the most recent period with the aviation material demand forecast information, thereby evaluating the accuracy of the aviation material demand forecasting model, and automatically improving the aviation material demand forecasting model according to the evaluation result.

5. An intelligent prediction system for aviation material demand information, characterized in that: It includes data acquisition module, data transmission and storage module, data cleaning module, self-learning module and intelligent prediction module; among them, The data acquisition module is used to collect aviation material RFID data and send the collected aviation material RFID data to the data transmission and storage module; The data transmission and storage module is used to store the received aviation material RFID data and send the received aviation material RFID data to the data processing module; The data cleaning module is used to clean and classify aviation material RFID data and construct aviation material classification data; The self-learning module establishes the consumption curve of each aviation material classification and the aviation material demand prediction module based on the aviation material classification data; The intelligent prediction module intelligently predicts the demand for each aviation material based on the consumption curve of each aviation material classification and the aviation material demand prediction model, thereby obtaining the aviation material demand information forecast.

6. The intelligent prediction system for aviation material demand information according to claim 5, characterized in that: The aviation material demand information intelligent prediction system adopts a customized self-learning strategy to extract features from aviation material RFID data, classifies the aviation material RFID data according to the extracted features, and then establishes corresponding consumption curves and aviation material demand prediction models for each aviation material classification based on the classified aviation material classification data.

7. The intelligent prediction system for aviation material demand information according to claim 6, characterized in that: When intelligently predicting the aviation material demand information, the aviation material demand information intelligent prediction system performs self-evaluation on the customized self-learning strategy, and automatically improves the aviation material demand prediction model according to the evaluation result.

8. The intelligent forecasting system for aviation material demand information according to claim 5, characterized in that: After obtaining the real-time aviation material demand information forecast, the aviation material demand information intelligent forecasting system compares the aviation material consumption in the most recent period with the aviation material demand forecast information, thereby evaluating the accuracy of the aviation material demand forecasting model, and automatically improving the aviation material demand forecasting model based on the evaluation results.

9. A computer readable medium storing computer program code, characterized in that: The computer program code implements the method according to any one of claims 1 to 4 when executed by a processor.

10. An intelligent prediction device for aviation material demand information, characterized in that: include: a memory for storing instructions executable by a processor; as well as A processor, configured to execute the instructions to implement the method according to any one of claims 1 to 4.

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