Aerial material spare part demand prediction accuracy correction method and related equipment

Abstract

The invention discloses an aviation material spare part demand prediction accuracy correction method and related equipment, and the method comprises the steps: 1, verifying the aviation material spare part demand prediction accuracy, comparing actual demand data of aviation material spare parts with prediction data, and judging that the aviation material spare part demand prediction accuracy needs to be corrected if a difference value exceeds a preset range; 2, analyzing external factors influencing the aerial material spare part fault rate, and judging whether the external factors influence the aerial material fault rate or not; step 3, predicting spare part demand probability distribution P1 and P0 when there is external factor influence and there is no external factor influence, calculating probabilities corresponding to different demand quantities in a prediction period, summing the demand probabilities in sequence, and when the sum of the probabilities is greater than or equal to a spare part guarantee probability, executing the step 3; the corresponding aerial material spare part number z is the spare part demand number meeting the spare part guarantee probability in the prediction period. According to the invention, the accuracy of aviation material spare part demand prediction can be improved.

Description

Technical Field

[0001] The invention belongs to the technical field of aviation maintenance and support, and in particular relates to a method for correcting the accuracy of forecasting demand for aviation material spare parts and related equipment. Background Art

[0002] The demand forecasting of aviation equipment spare parts has always been the focus of the aviation material procurement department. The accuracy of the demand forecasting of aviation spare parts directly affects the cost control of aviation material procurement and the guarantee capability of aviation equipment. Once the aviation material reserves are excessive, it will not only lead to a backlog of funds, but also occupy aviation material storage resources for a long time, and increase a lot of aviation material management work; insufficient aviation material reserves will affect the equipment maintenance and guarantee work, so that the equipment integrity rate cannot be guaranteed in time.

[0003] At present, the existing technology for forecasting the demand for aviation spare parts is mainly based on the Poisson distribution model, which uses data such as the number of aircraft, flight hours, number of installed aircraft, unplanned replacement time, scrap rate, procurement cycle, repair cycle, management cycle, and spare parts guarantee probability to theoretically predict the demand for aviation spare parts in the future. This method does not consider the impact of external factors such as climate conditions on the failure rate of aviation spare parts, which sometimes results in a large deviation between the predicted results and the actual results, and cannot accurately predict the demand for aviation spare parts. Summary of the invention

[0004] The purpose of the present invention is to provide a method for correcting the accuracy of aviation material spare parts demand forecasting and related equipment, which is used to solve the problem that the forecasting method in the prior art does not take into account the influence of external factors on the failure rate of aviation material spare parts, resulting in a large deviation between the forecasted results and the actual results. The present invention can improve the accuracy of aviation material spare parts demand forecasting.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for correcting the accuracy of forecasting demand for aviation materials and spare parts comprises the following steps:

[0007] Step 1: Verify the accuracy of the forecast of demand for aviation materials and spare parts, compare the actual demand data of aviation materials and spare parts with the forecast data, and if the difference exceeds the preset range, it is determined that the accuracy of the forecast of demand for aviation materials and spare parts needs to be corrected;

[0008] Step 2: Analyze the external factors that affect the failure rate of aviation spare parts to determine whether the external factors have an impact on the failure rate of aviation spare parts; if so, execute step 3 to consider the impact of the external factors in demand forecasting; otherwise, do not consider them;

[0009] Step 3: Considering the influence of external factors, when forecasting the demand for aviation spare parts, we first need to estimate the equipment working time t with and without the influence of external factors.1 and t 0 Then, according to the mean failure interval MTBF with and without external factors, the probability distribution P of spare parts demand with and without external factors is predicted. 1 and P 0 , treat the two probability distributions as two independent events and calculate the probability of the two independent events occurring at the same time;

[0010] Considering the impact of external factors on the prediction results, the probability distribution of spare parts demand P 0 and P 1 The prediction results are corrected as follows:

[0011] P(0)=P 0 (0)×P 1 (0)

[0012] P(1)=P 0 (0)×P 1 (1)+P 0 (1)×P 1 (0)

[0013] P(2)=P 0 (0)×P 1 (2)+P 0 (1)×P 1 (1)+P 0 (2)×P 1 (0)

[0014] P(3)=P 0 (0)×P 1 (3)+P 0 (1)×P 1 (2)+P 0 (2)×P 1 (1)+P 0 (3)×P 1 (0)

[0015] …

[0016] P(z)=ΣP(x+y)=Σ[P 0 (x)×P 1 (y)]

[0017] z=x+y, x and y are natural numbers

[0018] The probabilities corresponding to different demands within the forecast period are obtained. Under the given spare parts guarantee probability, the demand probabilities are summed up in sequence. When the sum of the probabilities is greater than or equal to the spare parts guarantee probability, the corresponding number of spare parts z is the number of spare parts demand that meets the spare parts guarantee probability within the forecast period.

[0019] Preferably, in step 1, the actual demand quantity of aviation materials is counted, and the proportion of the occurrence frequency of different demand quantities in all statistical times is calculated:

[0020]

[0021] N in the formula (k) represents the number of times the demand quantity k appears in the statistics, N represents the actual number of statistics, and P(k) represents the proportion of the demand quantity k in the statistics;

[0022] The predicted probability distribution of aviation material and spare parts demand is compared and analyzed with the statistical distribution of aviation material and spare parts demand proportion. If the absolute value of the probability corresponding to the same demand quantity of aviation materials differs by more than the preset percentage, it is considered that the theoretical prediction is inconsistent with the actual situation and that external factors have an impact on the actual demand.

[0023] Furthermore, in step 1, if the absolute value of the probability corresponding to the actual demand data of aviation materials and spare parts differs from the predicted data by more than 10%, it is determined that the accuracy of the forecast of demand for aviation materials and spare parts needs to be corrected, and there are external factors affecting the actual demand.

[0024] Preferably, in step 2, the mean time between failures (MTBF) of aviation materials when there are external factors and the mean time between failures (MTBF) of aviation materials when there are no external factors are calculated respectively. 0 When the comparison is performed and the deviation exceeds the preset threshold, it is considered that the external factor has an impact on the aviation material failure rate.

[0025] Furthermore, in step 2, the MTBF of aviation materials with external factors and the MTBF of aviation materials without external factors are 0 If the deviation exceeds 30%, it is considered that the external factor has an impact on the failure rate of aviation materials.

[0026] Preferably, in step 3, the probability distribution formula of spare parts demand is:

[0027] P(x+y)=P 0 (x)×P 1 (y)x, y are natural numbers

[0028] In the formula, P 0 (x) represents the probability that the demand is x when there is no external factor affecting it, P 1 (y) represents the probability that the demand will be y when there are external factors affecting it, and P(x+y) represents the probability that the demand will be x+y during the forecast period.

[0029] Preferably, considering the influence of multiple external factors, when forecasting the demand for aviation materials and spare parts, it is necessary to first estimate the equipment working time t when there is no influence of external factors.0 Equipment working time t when various factors affect 1 ,t 2 ,…,t k , and then according to the corresponding mean time between failures MTBF, predict the probability distribution of spare parts demand when there is no external factor influence P 0 The probability distribution of spare parts demand under the influence of various factors P 1 , P 2 ,…,P k , treat multiple probability distributions as independent events and calculate the probability of their simultaneous occurrence; calculation formula:

[0030] P(x 0 +x 1 +x 2 +…+x k )=P 0 (x 0 )×P 1 (x 1 )×P 2 (x 2 )×…×P k (x k )

[0031] x 0 ~x k All are natural numbers

[0032] In the formula, P 0 (x 0 ) indicates that the demand is x without any factor influence. 0 The probability, P 1 (x 1 ) indicates that the demand under the influence of factor 1 is x 1 The probability, P 2 (x 2 ) indicates that the demand under the influence of factor 2 is x 2 The probability, P k (x k ) indicates that the demand under the influence of factor k is x k The probability, P(x 0 +x 1 +x 2 +…+x k ) indicates that the demand during the forecast period is x 0 +x 1 +x 2 +…+x k probability;

[0033] Considering the impact of external factors on the forecast results, the forecast results are corrected using the probability distribution of spare parts demand. The method is as follows:

[0034] P(0)=P 0 (0)×P 1 (0)×P 2 (0)×…×P k (0)

[0035] P(1)=P 0 (1)×P 1 (0)×P 2 (0)×…×P k (0)+P 0 (0)×P 1 (1)×P 2 (0)×…

[0036] ×P k (0)+P 0 (0)×P 1 (0)×P 2 (1)×…×P k (0)+…+P 0 (0)

[0037] ×P 1 (0)×P 2 (0)×…×P k (1)

[0038] …

[0039] P(x)=∑P(x 0 +x 1 +x 2 +…+x k )

[0040] =Σ[P 0 (x 0 )×P 1 (x 1 )×P 2 (x 2 )×…×P k (x k )]

[0041] x=x 0 +x 1 +x 2 +…+x k , x 0 ~x k All are natural numbers

[0042] The probabilities corresponding to different demands when considering multiple factors within the forecast period are obtained. Under the given spare parts guarantee probability, the demand probabilities are summed up in sequence. When the sum of the probabilities is greater than or equal to the spare parts guarantee probability, the corresponding number of spare parts x is the number of spare parts demand that meets the spare parts guarantee probability within the forecast period.

[0043] A device for correcting the accuracy of forecasting demand for aviation material spare parts, comprising:

[0044] a processor suitable for executing a computer program;

[0045] A computer-readable storage medium having a computer program stored therein, wherein when the computer program is executed by the processor, a method for correcting the accuracy of forecasting demand for aviation materials and spare parts as described in any one of the above items is executed.

[0046] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for correcting the accuracy of aviation material and spare parts demand forecasting as described in any one of the above items is implemented.

[0047] A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements a method for correcting the accuracy of forecasting demand for aviation materials and spare parts as described in any one of the above items.

[0048] Compared with the prior art, the present invention has the following beneficial technical effects:

[0049] The present invention provides a correction method for the accuracy of forecasting demand for aviation materials and spare parts. Compared with the traditional Poisson distribution forecasting method, the present invention adds comparative verification of actual data. For the case where the deviation of the verification result is large, a correction method considering the influence of external factors is provided. At the same time, when forecasting the demand for spare parts, the influence of external factors within the forecast period is comprehensively considered, so that the forecasting process is more scientific and precise, and the forecasting results are more accurate and reliable. The present invention can derive the probabilities corresponding to different demands within the forecasting period. Under the condition of a given spare parts guarantee probability, the demand probabilities are summed in sequence. When the sum of the probabilities is greater than or equal to the spare parts guarantee probability, the corresponding number of aviation materials and spare parts z is the number of spare parts demand that meets the spare parts guarantee probability within the forecasting period. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 The present invention is a flow chart of a correction method for the prediction accuracy of aviation material and spare parts demand. DETAILED DESCRIPTION

[0051] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0052] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0053] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0054] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, an electrical connection, or a communication; it can be a direct connection, or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0055] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may include that the first and second features are in direct contact, or may include that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, a first feature being "above", "above" and "above" a second feature includes that the first feature is directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. A first feature being "below", "below" and "below" a second feature includes that the first feature is directly above and obliquely above the second feature, or simply indicates that the first feature is lower in level than the second feature.

[0056] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0057] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0058] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0059] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0060] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0061] A method for correcting the accuracy of forecasting demand for aviation materials and spare parts of the present invention mainly comprises the following steps:

[0062] Step S100: Verify the accuracy of the forecast of aviation material and spare parts demand. Use the actual demand data of aviation material and spare parts to verify the accuracy of the forecast. Specific method:

[0063] The actual demand quantity of a certain aviation material is counted, and the proportion of the occurrence of different demand quantities in all statistical times is calculated:

[0064]

[0065] N in the formula (k) It represents the number of times the demand quantity k appears in the statistics, N represents the actual number of statistics, and P(k) represents the proportion of the demand quantity k in the statistics.

[0066] The predicted probability distribution of aviation material spare parts demand is compared and analyzed with the statistical distribution of aviation material spare parts demand. If the absolute value of the probability corresponding to the same demand quantity of a certain aviation material differs by more than 10%, it is considered that the theoretical prediction is inconsistent with reality and that external factors have an impact on the actual demand.

[0067] Step S200: Analysis of external factors. Sort out the external factors that may affect the demand results, and calculate the MTBF of the aircraft materials when there are external factors and the MTBF of the aircraft materials when there are no external factors. 0 When the comparison is carried out and the deviation exceeds 30%, it is considered that the external factor has an impact on the failure rate of aviation materials. The impact of this factor must be considered in demand forecasting, otherwise it will not be considered.

[0068] Step S300: Correction method considering external factors. Considering the influence of external factors, when forecasting the demand for aviation materials and spare parts, it is necessary to first estimate the equipment working time t with and without the influence of external factors. 1 and t 0 Then, according to the mean failure interval MTBF with and without external factors, the probability distribution P of spare parts demand with and without external factors is predicted respectively. 1 and P 0 , treat the two probability distributions as two independent events, and calculate the probability of two independent events occurring at the same time. Calculation formula:

[0069] P(x+y)=P 0 (x)×P 1 (y)x, y are natural numbers

[0070] In the formula, P 0 (x) represents the probability that the demand is x when there is no external factor affecting it, P 1 (y) represents the probability that the demand will be y when there are external factors affecting it, and P(x+y) represents the probability that the demand will be x+y during the forecast period.

[0071] Considering the impact of external factors on the prediction results, the probability distribution of spare parts demand P 0 and P 1 The prediction results are corrected as follows:

[0072] P(0)=P 0 (0)×P 1 (0)

[0073] P(1)=P 0 (0)×P 1 (1)+P 0 (1)×P 1 (0)

[0074] P(2)=P 0 (0)×P 1 (2)+P 0 (1)×P 1 (1)+P 0 (2)×P 1 (0)

[0075] P(3)=P 0 (0)×P 1 (3)+P 0 (1)×P 1 (2)+P 0 (2)×P 1 (1)+P 0 (3)×P 1 (0)

[0076] …

[0077] P(z)=∑P(x+y)=∑[y 0 (x)×P 1 (y)]

[0078] z=x+y, x and y are natural numbers

[0079] Through the above method, the probabilities corresponding to different demand quantities within the forecast period can be obtained. Under the given spare parts guarantee probability, the demand probabilities are summed up in sequence. When the sum of the probabilities is greater than or equal to the spare parts guarantee probability, the corresponding number of spare parts z is the number of spare parts demand that meets the spare parts guarantee probability within the forecast period.

[0080] Compared with the traditional Poisson distribution prediction method, the method of the present invention adds comparative verification of actual data on the basis of its theoretical prediction. For the case where the verification result has a large deviation, a correction method considering the influence of external factors is given. At the same time, when predicting spare parts demand, the influence of external factors within the prediction period is comprehensively considered, thereby making the prediction process more scientific and precise, and the prediction results more accurate and reliable.

[0081] Embodiment 1:

[0082] A correction method for the accuracy of forecasting demand for aviation spare parts, such as Figure 1 As shown, the following steps are included:

[0083] Step S100: Verify the accuracy of the forecast of aviation material and spare parts demand. Use the actual demand data of aviation material and spare parts to verify the accuracy of the forecast. Specific method:

[0084] The actual demand quantity of a certain aviation material is counted, and the proportion of the occurrence of different demand quantities in all statistical times is calculated:

[0085]

[0086] N in the formula (k) It represents the number of times the demand quantity k appears in the statistics, N represents the actual number of statistics, and P(k) represents the proportion of the demand quantity k in the statistics.

[0087] The predicted probability distribution of aviation material spare parts demand is compared and analyzed with the statistical distribution of aviation material spare parts demand. If the absolute value of the probability corresponding to the same demand quantity of a certain aviation material differs by more than 10%, it is considered that the theoretical prediction is inconsistent with reality and that external factors have an impact on the actual demand.

[0088] Step S200: Analysis of external factors. Sort out the external factors that may affect the demand results, and calculate the MTBF of the aircraft materials when there are external factors and the MTBF of the aircraft materials when there are no external factors. 0 When the comparison is carried out and the deviation exceeds 30%, it is considered that the external factor has an impact on the failure rate of aviation materials. The impact of this factor must be considered in demand forecasting, otherwise it will not be considered.

[0089] Step S300: Correction method considering external factors. Considering the influence of external factors, when forecasting the demand for aviation materials and spare parts, it is necessary to first estimate the equipment working time t with and without the influence of external factors. 1 and t 0 Then, according to the mean time between failures MTBF with and without external factors, the probability distribution of spare parts demand with and without external factors is predicted. 1 and P 0 , treat the two probability distributions as two independent events, and calculate the probability of two independent events occurring at the same time. Calculation formula:

[0090] P(x+y)=P 0 (x)×P 1 (y)x, y are natural numbers

[0091] In the formula, P 0 (x) represents the probability that the demand is x when there is no external factor affecting it, P 1 (y) represents the probability that the demand will be y when there are external factors affecting it, and P(x+y) represents the probability that the demand will be x+y during the forecast period.

[0092] Considering the impact of external factors on the prediction results, the probability distribution of spare parts demand P 0 and P 1 The prediction results are corrected as follows:

[0093] P(0)=P 0 (0)×P 1 (0)

[0094] P(1)=P 0 (0)×P 1 (1)+P 0 (1)×P 1 (0)

[0095] P(2)=P 0 (0)×P 1 (2)+P 0 (1)×P 1 (1)+P 0 (2)×P 1 (0)

[0096] P(3)=P 0 (0)×P 1 (3)+P 0 (1)×P 1 (2)+P 0 (2)×P 1 (1)+P 0 (3)×P 1 (0)

[0097] …

[0098] P(z)=ΣP(x+y)=Σ[y 0 (x)×P 1 (y)]

[0099] z=x+y, x and y are natural numbers

[0100] Through the above method, the probabilities corresponding to different demand quantities within the forecast period can be obtained. Under the given spare parts guarantee probability, the demand probabilities are summed up in sequence. When the sum of the probabilities is greater than or equal to the spare parts guarantee probability, the corresponding number of spare parts z is the number of spare parts demand that meets the spare parts guarantee probability within the forecast period.

[0101] Embodiment 2:

[0102] This embodiment is optimized on the basis of Embodiment 1, taking into account the influence of multiple external factors on the prediction results, mainly focusing on step S300, as follows:

[0103] Step S300: Correction method considering external factors. Considering the influence of multiple external factors, when forecasting the demand for aviation materials and spare parts, it is necessary to first estimate the equipment working time t when there is no external factor influence. 0 Equipment working time t when various factors affect 1 ,t 2 ,…,t k , and then according to the corresponding mean time between failures MTBF, predict the probability distribution of spare parts demand when there is no external factor influence P 0 The probability distribution of spare parts demand under the influence of various factors P 1 , P 2 ,…,P k , treat multiple probability distributions as independent events and calculate the probability of their simultaneous occurrence. Calculation formula:

[0104] P(x 0 +x 1 +x 2 +…+x k )=P 0 (x 0 )×P 1 (x 1 )×P 2 (x 2 )×…×P k (x k )

[0105] x 0 ~x k All are natural numbers

[0106] In the formula, P 0 (x 0 ) indicates that the demand is x without any factor influence. 0 The probability, P 1 (x 1 ) indicates that the demand under the influence of factor 1 is x 1 The probability, P 2 (x 2 ) indicates that the demand under the influence of factor 2 is x 2 The probability, P k (x k ) indicates that the demand under the influence of factor k is x k The probability, P(x 0 +x 1 +x 2 +…+x k ) indicates that the demand during the forecast period is x 0 +x 1 +x 2 +…+x k probability.

[0107] Considering the impact of external factors on the forecast results, the forecast results are corrected using the probability distribution of spare parts demand. The method is as follows:

[0108] P(0)=P 0 (0)×P 1 (0)×P 2 (0)×…×P k (0)

[0109] P(1)=P 0 (1)×P 1 (0)×P 2 (0)×…×P k (0)+P 0 (0)×P 1 (1)×P2 (0)×…

[0110] ×P k (0)+P 0 (0)×P 1 (0)×P 2 (1)×…×P k (0)+…+P 0 (0)

[0111] ×P 1 (0)×P 2 (0)×…×P k (1)

[0112] …

[0113] P(x)=∑P(x 0 +x 1 +x 2 +…+x k )

[0114] =∑[P 0 (x 0 )×P 1 (x 1 )×P 2 (x 2 )×…×P k (x k )]

[0115] x=x 0 +x 1 +x 2 +…+x k , x 0 ~x k All are natural numbers

[0116] Through the above method, we can obtain the probabilities corresponding to different demands when considering multiple factors within the forecast period. Under the given spare parts guarantee probability, the demand probabilities are summed up in sequence. When the sum of the probabilities is greater than or equal to the spare parts guarantee probability, the corresponding number of aviation spare parts x is the number of spare parts demand that meets the spare parts guarantee probability within the forecast period.

[0117] The following are device embodiments of the present invention, which can be used to perform method embodiments of the present invention. For details not disclosed in the device embodiments, please refer to the method embodiments of the present invention.

[0118] In another embodiment of the present invention, a computer device is provided, the computer device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a method for correcting the accuracy of forecasting demand for aviation materials and spare parts.

[0119] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the method for correcting the accuracy of aviation material and spare parts demand forecasting in the above embodiment.

[0120] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0121] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0122] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0123] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0124] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

[0125] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0126] In addition, it should be understood that although this specification is described in accordance with the implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation modes that can be understood by those skilled in the art. The above content is only to illustrate the technical idea of ​​the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution according to the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for correcting the accuracy of forecasting demand for aviation materials and spare parts, characterized in that: The following steps are included: Step 1: Verify the accuracy of the forecast of demand for aviation materials and spare parts, compare the actual demand data of aviation materials and spare parts with the forecast data, and if the difference exceeds the preset range, it is determined that the accuracy of the forecast of demand for aviation materials and spare parts needs to be corrected; Step 2: Analyze the external factors that affect the failure rate of aviation spare parts to determine whether the external factors have an impact on the failure rate of aviation spare parts; if so, execute step 3 to consider the impact of the external factors in demand forecasting; otherwise, do not consider them; Step 3: Considering the influence of external factors, when forecasting the demand for aviation spare parts, we first need to estimate the equipment working time t1 and t0 with and without the influence of external factors, and then, based on the mean time between failures MTBF with and without the influence of external factors, respectively forecast the probability distributions P1 and P0 of spare parts demand with and without the influence of external factors. Take the two probability distributions as two independent events and calculate the probability of the two independent events occurring at the same time. Considering the impact of external factors on the prediction results, the prediction results are corrected using the spare parts demand probability distribution P0 and P1, specifically: P(0)=P0(0)×P1(0) P(1)=P0(0)×P1(1)+P0(1)×P1(0) P(2)=P0(0)×P1(2)+P0(1)×P1(1)+P0(2)×P1(0) P(3)=P0(0)×P1(3)+P0(1)×P1(2)+P0(2)×P1(1)+P0(3)×P1(0) … P(z)=∑P(x+y)=∑[P0(x)×P1(y)] z=x+y, x and y are natural numbers The probabilities corresponding to different demands within the forecast period are obtained. Under the given spare parts guarantee probability, the demand probabilities are summed up in sequence. When the sum of the probabilities is greater than or equal to the spare parts guarantee probability, the corresponding number of spare parts z is the number of spare parts demand that meets the spare parts guarantee probability within the forecast period.

2. A method for correcting the accuracy of forecasting demand for aviation materials and spare parts according to claim 1, characterized in that: In step 1, the actual demand quantity of aviation materials is counted, and the proportion of the occurrence of different demand quantities in all statistical times is calculated: N in the formula (k) represents the number of times the demand quantity k appears in the statistics, N represents the actual number of statistics, and P(k) represents the proportion of the demand quantity k in the statistics; The predicted probability distribution of aviation material and spare parts demand is compared and analyzed with the statistical distribution of aviation material and spare parts demand proportion. If the absolute value of the probability corresponding to the same demand quantity of aviation materials differs by more than the preset percentage, it is considered that the theoretical prediction is inconsistent with the actual situation and that external factors have an impact on the actual demand.

3. A method for correcting the accuracy of forecasting demand for aviation materials and spare parts according to claim 2, characterized in that: In step 1, if the absolute value of the probability corresponding to the actual demand data of aviation materials and spare parts differs from the predicted data by more than 10%, it is judged that the accuracy of the demand forecast of aviation materials and spare parts needs to be corrected, and there are external factors affecting the actual demand.

4. The method for correcting the accuracy of forecasting demand for aviation materials and spare parts according to claim 1, characterized in that: In step 2, the mean time between failures (MTBF) of aviation materials when there are external factors affecting them is calculated and compared with the mean time between failures (MTBF0) of aviation materials when there are no external factors affecting them. If the deviation exceeds a preset threshold, it is considered that the external factors have an impact on the failure rate of aviation materials.

5. A method for correcting the accuracy of forecasting demand for aviation materials and spare parts according to claim 4, characterized in that: In step 2, if the deviation between the mean time between failures MTBF of aviation materials when there are external factors and the mean time between failures MTBF0 of aviation materials when there are no external factors is greater than 30%, it is considered that the external factors have an impact on the failure rate of aviation materials.

6. The method for correcting the accuracy of forecasting demand for aviation materials and spare parts according to claim 1, characterized in that: In step 3, the probability distribution formula of spare parts demand is: P(x+y)=P0(x)×P1(y)x and y are natural numbers In the formula, P0(x) represents the probability that the demand is x when there are no external factors affecting it, P1(y) represents the probability that the demand is y when there are external factors affecting it, and P(x+y) represents the probability that the demand is x+y during the forecast period.

7. The method for correcting the accuracy of forecasting demand for aviation materials and spare parts according to claim 1, characterized in that: Considering the influence of multiple external factors, when forecasting the demand for aviation spare parts, it is necessary to first estimate the equipment working time t0 when there is no external factor influence and the equipment working time t1, t2, ..., t k Then, according to the corresponding mean time between failures MTBF, the probability distribution of spare parts demand P0 when there is no external factor influence and the probability distribution of spare parts demand P1, P2, ..., P when each factor influences are predicted. k , treat multiple probability distributions as independent events and calculate the probability of their simultaneous occurrence; calculation formula: P(x0+x1+x2+…+x k )=P0(x0)×P1(x1)×P2(x2)×…×P k (x k ) x0~x k All are natural numbers In the formula, P0(x0) represents the probability that the demand is x0 without the influence of factors, P1(x1) represents the probability that the demand is x1 under the influence of factor 1, P2(x2) represents the probability that the demand is x2 under the influence of factor 2, and P k (x k ) indicates that the demand under the influence of factor k is x k The probability of P(x0+x1+x2+…+x k ) indicates that the demand in the forecast period is x0+x1+x2+…+x k probability; Considering the impact of external factors on the forecast results, the forecast results are corrected using the probability distribution of spare parts demand. The method is as follows: P(0)=P0(0)×P1(0)×P2(0)×…×P k (0) P(1)=P0(1)×P1(0)×P2(0)×…×P k (0)+P0(0)×P1(1)×P2(0)×… ×P k (0)+P0(0)×P1(0)×P2(1)×…×P k (0)+…+P0(0) ×P1(0)×P2(0)×…×P k (1) … P(x)=ΣP(x0+x1+x2+…+x k ) =Σ[P0(x0)×P1(x1)×P2(x2)×…×P k (x k )] x=x0+x1+x2+…+x k , x0~x k All are natural numbers The probabilities corresponding to different demands when considering multiple factors within the forecast period are obtained. Under the given spare parts guarantee probability, the demand probabilities are summed up in sequence. When the sum of the probabilities is greater than or equal to the spare parts guarantee probability, the corresponding number of spare parts x is the number of spare parts demand that meets the spare parts guarantee probability within the forecast period.

8. A device for correcting the accuracy of forecasting demand for aviation materials and spare parts, characterized in that: include: a processor suitable for executing a computer program; A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the method for correcting the accuracy of aviation material and spare parts demand forecasting as described in any one of claims 1 to 7 is executed.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for correcting the accuracy of forecasting demand for aviation materials and spare parts as described in any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method for correcting the accuracy of forecasting demand for aviation materials and spare parts as claimed in any one of claims 1 to 7 is implemented.

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