Treatment method for rejected spare parts and computer equipment

Abstract

The invention is suitable for the technical field of computers, and provides an obsolete spare part processing method and computer equipment, and the method comprises the steps: obtaining the cumulative demand quantity distribution associated with the specified cumulative probability of obsolete spare parts; based on the accumulated demand quantity distribution, determining a predicted stockout time point of the rejected spare parts; obtaining a predicted disposal duration of the rejected spare parts, and determining a target disposal intervention time point of the rejected spare parts based on the predicted stockout time point and the predicted disposal duration; and based on the relationship between the target disposal intervention time point and the current time point, executing a target disposal operation for the rejected spare parts. In this way, the disposal accuracy of the rejected spare parts can be improved.

Description

Technical Field

[0001] The present application belongs to the field of computer technology, and in particular, relates to a method for processing obsolete spare parts and computer equipment. Background Art

[0002] Obsolete spare parts refer to spare parts that are necessary for the operation of nuclear power plants but are no longer produced by the original manufacturers. For spare parts that are still produced by the original manufacturers but cannot be purchased normally due to certain reasons, they will also be included in the management of obsolete spare parts. For nuclear power obsolete spare parts, a rolling forecast of demand plan is carried out, that is, based on the inventory, regular orders, reservations, historical requisition volume and other data of each spare part, it is predicted whether there will be a purchase demand for spare parts every year (month) in the next N years (months); if there is no purchase demand in the next N years (months), demand forecasts are regularly conducted every year (month) for all spare parts in the obsolete system that are in the "demand plan rolling" node. For spare parts that can continue to be purchased (for example, through the surplus market purchase, that is, although the product has been discontinued, the original product can still be purchased from other power plants, enterprises or sales markets to meet on-site needs), the corresponding number of spare parts will continue to be purchased based on the results of the demand forecast. For spare parts with clear demand in the next N years (months) (the current inventory quantity cannot meet future demand), the spare parts guarantee problem can be solved through item substitution, engineering transformation, research and development (including commercialization and 3D printing), etc.

[0003] In the current forecasting plan for the demand for obsolete spare parts, the historical average use value of obsolete spare parts is calculated and used as the future annual demand for spare parts to predict whether there will be a demand for spare parts in the future. For example, the number of spare parts used in the past five years was 8, 2, 0, 10, and 5, respectively. After calculation, it can be obtained that the average annual use is 5, that is, the predicted annual demand for the spare part in the future is 5. If the current inventory of spare parts is greater than or equal to 15, there will be no demand for purchase in the next three years, and there is no need to carry out obsolete disposal for the time being. If the current inventory of spare parts is less than 15, there will be a demand for purchase in the next three years, and obsolete disposal will be required.

[0004] The current forecasting methods for demand for obsolete spare parts either directly use the historical average usage data of spare parts to predict the future demand for spare parts, but fail to effectively utilize the characteristics of the planned spare parts demand determination time. The time point for intervention in the disposal of obsolete products is too early or too late, resulting in low accuracy in the disposal of obsolete spare parts. Summary of the invention

[0005] In view of this, an embodiment of the present application provides a method for processing obsolete spare parts and a computer device, which can improve the accuracy of handling obsolete spare parts.

[0006] A first aspect of an embodiment of the present application provides a method for processing obsolete spare parts, comprising:

[0007] Obtaining a cumulative demand quantity distribution associated with a specified cumulative probability of obsolete spare parts, wherein the cumulative demand quantity distribution is used to indicate a relationship between a predicted time point for the obsolete spare parts and the cumulative demand quantity;

[0008] Determining a predicted out-of-stock time point of the obsolete spare parts based on the cumulative demand quantity distribution;

[0009] Obtaining the predicted disposal time of the obsolete spare parts, and determining a target disposal intervention time point for the obsolete spare parts based on the predicted out-of-stock time point and the predicted disposal time period;

[0010] Based on the relationship between the target disposal intervention time point and the current time point, a target disposal operation for the obsolete spare part is performed.

[0011] In an implementation of the first aspect, obtaining the cumulative demand quantity distribution associated with the specified cumulative probability of obsolete spare parts includes:

[0012] Determine the demand distribution that the obsolete spare parts are adapted to each demand type, where the demand distribution is used to indicate a mapping relationship between the demand quantity and the probability of use of the obsolete spare parts at any predicted use time point;

[0013] Summarizing the demand distributions to obtain the total demand distribution at the predicted collection time point;

[0014] Based on the total demand distribution of each of the predicted collection time points, determining a plurality of parameter groups corresponding to the specified cumulative probability, the parameter groups including the predicted collection time points and the cumulative demand quantities at the predicted collection time points;

[0015] The multiple parameter groups are fitted to obtain the cumulative demand quantity distribution associated with the specified cumulative probability of obsolete spare parts.

[0016] In an implementation of the first aspect, the demand type is a first plan type, and determining the demand distribution that the obsolete spare parts are adapted to each demand type includes:

[0017] Obtaining the planned required quantity of the obsolete spare parts adapted to the first plan type in each maintenance project to be carried out, wherein the maintenance project is performed by one or more adjacent maintenance activities;

[0018] Acquire a first probability parameter group corresponding to each maintenance item, wherein the first probability parameter group includes an execution probability of the item, a postponement probability of the item, and a cancellation probability of the item;

[0019] For each maintenance activity, determining multiple possible demand quantities corresponding to the maintenance activity; wherein the possible demand quantity of a maintenance activity other than the first maintenance activity is determined based on the possible demand quantities of all previous maintenance activities;

[0020] Based on each of the first probability parameter groups and a plurality of possible demand quantities for each of the maintenance activities, a first demand distribution adapted to the obsolete spare parts and the collection time points for each of the maintenance activities is determined.

[0021] In an implementation of the first aspect, the demand type is a second plan type, and determining the demand distribution that the obsolete spare parts are adapted to each demand type includes:

[0022] Determining the potential number of the obsolete spare parts adapted to the second plan type in each maintenance project to be carried out, wherein the maintenance project is performed by one or more adjacent maintenance activities;

[0023] Acquire a quantity parameter group corresponding to each maintenance item, wherein the quantity parameter group includes a planned collection quantity, a potential collection quantity, and a historical collection quantity peak value;

[0024] Acquire a second probability parameter group corresponding to each maintenance item, wherein the second probability parameter group includes an execution probability of the item, a postponed execution probability of the item, and a cancellation probability of the item;

[0025] Obtaining a collection probability parameter group corresponding to each maintenance item and each quantity parameter;

[0026] Based on the quantity parameter group, the second probability parameter group and the collection probability parameter group of each of the maintenance items, a second demand distribution adapted to the collection time point of the obsolete spare parts and each of the maintenance activities is determined.

[0027] In an implementation of the first aspect, determining the potential number of obsolete spare parts adapted to the second plan type in each maintenance project includes:

[0028] Determining the ratio of the number of times of use to the number of times of calls;

[0029] If the ratio is less than the probability threshold of use or the number of calls for the maintenance project is zero, the required quantity of the obsolete spare parts that is mounted in the standard package of the maintenance project and is adapted to the second plan type is used as the potential use quantity;

[0030] If the ratio reaches the probability threshold of use, the potential use quantity is determined based on the actual use quantity of the obsolete spare parts in the targeted maintenance project, the total number of uses and a preset correction factor, and the correction factor is determined based on the importance identification of the obsolete spare parts and the unit price of the obsolete spare parts.

[0031] In an implementation of the first aspect, the demand type is a sudden type, and determining the demand distribution that the obsolete spare parts are adapted to each demand type includes:

[0032] Based on the historical burst collection data of the obsolete spare parts, determining the collection demand characteristics of the obsolete spare parts, wherein the collection demand characteristics include the collection mean and collection standard deviation of the obsolete spare parts;

[0033] Based on the usage demand characteristics, a third demand distribution adapted to the burst type is selected from a uniform distribution model and at least one spare parts demand function included in a spare parts demand function model library.

[0034] In an implementation of the first aspect, obtaining the predicted disposal time of the obsolete spare parts includes:

[0035] Determine the spare parts disposal time corresponding to each importance identifier based on the spare parts type and the percentage of spare parts items of the obsolete spare parts, wherein the percentage of spare parts items is determined based on the disposal difficulty of the obsolete spare parts and the average level of disposal difficulty corresponding to the spare parts type of the obsolete spare parts;

[0036] Determine the target importance identification corresponding to the replacement items of the obsolete spare parts;

[0037] Based on the target importance identifier and the disposal time of each spare part, the disposal time of the substitute item is determined, and the disposal time of the substitute item is used as the predicted disposal time of the obsolete spare part.

[0038] In an implementation of the first aspect, determining a target disposal intervention time point for the obsolete spare parts based on the predicted out-of-stock time point and the predicted disposal duration includes:

[0039] Obtaining the predicted procurement time of the obsolete spare parts and the waiting time for indicating the acceptance warehousing and work order collection time of the obsolete spare parts;

[0040] A weighted sum is taken of the predicted procurement time, the predicted disposal time of obsolete spare parts, and the waiting time to obtain a predicted processing time;

[0041] The predicted out-of-stock time point and the predicted processing time are subtracted to obtain an initial processing intervention time point for the obsolete spare parts;

[0042] Based on the relationship between the initial disposal intervention time point and the current time point, the initial disposal intervention time point is corrected to obtain the target disposal intervention time point of the obsolete spare part.

[0043] In an implementation of the first aspect, obtaining the predicted procurement time of the obsolete spare parts includes:

[0044] Determine the procurement time of the replacement item based on the manufacturer, type and importance of the replacement item of the obsolete spare part;

[0045] Obtaining a first weight of the procurement time of the replacement item and a second weight of the initial procurement time of the original item of the obsolete spare part;

[0046] Based on the first weight and the second weight, a weighted sum is taken of the procurement time of the substitute item and the initial procurement time to obtain a predicted procurement time of the obsolete spare part.

[0047] A second aspect of an embodiment of the present application provides a device for processing obsolete spare parts, comprising:

[0048] An acquisition module, used to acquire a cumulative demand quantity distribution associated with a specified cumulative probability of obsolete spare parts, wherein the cumulative demand quantity distribution is used to indicate a relationship between a predicted time point for the obsolete spare parts and a cumulative demand quantity at the predicted time point;

[0049] A first determination module is used to determine a predicted out-of-stock time point of the obsolete spare parts based on the cumulative demand quantity distribution;

[0050] A second determination module is used to obtain the predicted disposal time of the obsolete spare parts, and determine a target disposal intervention time point for the obsolete spare parts based on the predicted out-of-stock time point and the predicted disposal time period;

[0051] An execution module is used to execute a target disposal operation for the obsolete spare part based on the relationship between the target disposal intervention time point and the current time point.

[0052] A fifth aspect of an embodiment of the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect when executing the computer program.

[0053] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0054] In the first aspect of the embodiments of the present application, by obtaining the cumulative demand quantity distribution associated with the specified cumulative probability of obsolete spare parts, the out-of-stock time point of spare parts can be accurately predicted. Based on the predicted out-of-stock time point and the predicted disposal duration determined by the cumulative demand quantity distribution, the target disposal intervention time point can be comprehensively evaluated and determined, thereby improving the accuracy of determining the target disposal intervention time point. Finally, based on the relationship between the target disposal intervention time point and the current time point, the corresponding spare parts disposal operation is performed, which can effectively avoid the waste of resources or supply risks caused by premature or late disposal, and ensure the timely disposal of obsolete spare parts.

[0055] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be 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 any creative work.

[0057] Figure 1 It is a schematic diagram of the process of the method for disposing obsolete spare parts provided in the embodiment of the present application;

[0058] Figure 2 It is a schematic diagram of the process of processing obsolete spare parts provided in an embodiment of the present application;

[0059] Figure 3 is an example diagram of cumulative demand quantity distribution provided in an embodiment of the present application;

[0060] Figure 4 This is an example diagram of spare parts inventory quantity distribution provided in an embodiment of the present application;

[0061] Figure 5 is another example diagram of spare parts inventory quantity distribution provided in an embodiment of the present application;

[0062] Figure 6 It is a time distribution diagram of each process of obsolete spare parts provided in the embodiment of the present application;

[0063] Figure 7 is a flowchart of disposal of obsolete spare parts provided in an embodiment of the present application;

[0064] Figure 8 is a flow chart of a method for determining cumulative demand quantity distribution provided in an embodiment of the present application;

[0065] Fig. 9 is a first demand distribution example diagram of spare parts provided in an embodiment of the present application;

[0066] Fig.10 is a second demand distribution example diagram of spare parts provided in an embodiment of the present application;

[0067] Fig.11 is a third demand distribution example diagram of spare parts provided in an embodiment of the present application;

[0068] Fig.12 is an example diagram of the total demand distribution of spare parts provided in an embodiment of the present application;

[0069] Fig.13 It is a flowchart of a method for determining a first demand distribution provided in an embodiment of the present application;

[0070] Fig.14 This is an example diagram of planned Class A demand for spare parts in overhaul of different maintenance projects provided by an embodiment of the present application;

[0071] Fig.15 is a flow chart of a method for determining a second demand distribution provided in an embodiment of the present application;

[0072] Fig.16 This is an example diagram of potential spare parts demand relationships involved in maintenance projects provided in an embodiment of the present application;

[0073] Fig.17 This is an example diagram of planned Class B demand for spare parts in overhaul of different maintenance projects provided in an embodiment of the present application;

[0074] Fig.18 It is a flowchart of a method for determining the required number of spare parts of the second plan type provided in an embodiment of the present application;

[0075] Fig.19 is a flowchart of a method for determining a third demand distribution provided in an embodiment of the present application;

[0076] Fig. 20 It is a flow chart of a method for determining a predicted disposal time of obsolete spare parts provided in an embodiment of the present application;

[0077] Fig.21 This is an example diagram of the time distribution of obsolete spare parts of different types provided in an embodiment of the present application;

[0078] Fig. 22 This is an example diagram of the time distribution of obsolete spare parts of different importance provided in an embodiment of the present application;

[0079] Fig.23 is a flow chart of a method for determining a target treatment intervention time point provided in an embodiment of the present application;

[0080] Fig.24 It is a flow chart of a method for determining a predicted procurement time for obsolete spare parts provided in an embodiment of the present application;

[0081] Fig.25 It is a distribution diagram of spare parts procurement cycles of domestic and foreign manufacturers under different combination types provided in the embodiments of the present application;

[0082] Fig.26 is a ratio distribution diagram of the replacement items provided in the embodiment of the present application and the original items from the same manufacturer;

[0083] Fig. 27 is a schematic diagram of a device for processing obsolete spare parts provided in an embodiment of the present application;

[0084] Fig.28 It is a schematic diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0085] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0086] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0087] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0088] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0089] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0090] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0091] like Figure 1 The obsolete spare parts processing process shown in the figure obtains obsolete spare parts information during the business procurement process or other channels, and after classification and sorting, it is included in the obsolete spare parts "reservoir" management. For spare parts that are transferred to the "obsolete spare parts reservoir" for the first time, the demand planning management engineer predicts whether there will be a purchase demand in the next N years (months) based on the spare parts inventory, regular orders, reservations, historical requisitions and other data. If there is a purchase demand in the next N years (months), the obsolete products will be directly disposed of; if there is no purchase demand in the next N years (months), the spare parts will be officially transferred to the obsolete spare parts "reservoir" management. For the spare parts in the "obsolete product reservoir", demand forecasts are conducted for all spare parts in the "obsolete product reservoir" regularly every year (month) (for example, in January of each year and on the first day of each month). That is, based on the spare parts inventory, regular orders, reservations, historical requisitions and other data, it is predicted whether there will be a purchasing demand in the next N years (months). If there is a purchasing demand in the next N years (months), the obsolete product disposal is carried out directly; if there is no purchasing demand in the next N years (months), the spare part will continue to be in the "obsolete product reservoir" and wait for the next spare parts demand forecast.

[0092] In application, typical disposal strategies for obsolete spare parts include strategic procurement (residual market procurement, special manufacturing, etc.), item substitution, engineering transformation, research and development (including commercialization, 3D printing), etc. In non-emergency situations, the lowest cost and lowest risk solution should be considered; in emergency situations, multiple solutions can be considered at the same time to ensure the fastest solution to the obsolete problem. In the process of handling obsolete products, in order to reduce the operating costs of nuclear power plants, the standardization and normalization of spare parts should be considered to reduce the types of spare parts and reduce inventory.

[0093] In the application, after the disposal of obsolete products is completed, new spare parts are purchased based on the spare parts procurement process of the nuclear power plant. After the purchase arrives, the spare parts go through the spare parts acceptance process and are put into storage. If there is a maintenance requirement on site, the maintenance user will collect the new spare parts and install them in the on-site equipment to ensure the on-site maintenance needs.

[0094] The method for processing obsolete spare parts provided in the embodiment of the present application determines a target disposal intervention time point for the obsolete spare parts based on the demand forecast result of the obsolete spare parts, so as to facilitate disposal of the obsolete parts from the target disposal intervention time.

[0095] Next, the method for processing obsolete spare parts provided in the embodiment of the present application is described in detail. Figure 2 As shown, a method for processing obsolete spare parts is provided, and the method is applied to computer equipment as an example for explanation, including the following steps S101 to S104. Among them:

[0096] Step S101, obtaining a cumulative demand quantity distribution associated with a specified cumulative probability of obsolete spare parts, wherein the cumulative demand quantity distribution is used to indicate a relationship between a predicted time point for obtaining the obsolete spare parts and the cumulative demand quantity.

[0097] In the application, the cumulative demand quantity distribution specifically indicates the cumulative demand quantity for obsolete spare parts at different predicted collection time points under the specified cumulative probability. The specified cumulative probability refers to the total collection probability for obsolete spare parts at any point in time. Normally, the cumulative demand quantity distribution with a specified cumulative probability of 50% is selected for subsequent calculations. If the importance of obsolete spare parts is high, the spare parts demand quantity distribution with a specified cumulative probability of 75% can be selected. For some special cases, such as projects involving the risk of "supply interruption", the spare parts demand quantity with a specified cumulative probability of 95% can be selected. At this time, it means that the probability of collecting obsolete spare parts is as large as possible, close to the requirement for collection.

[0098] In the application, the designated cumulative probability is related to the importance identification of obsolete spare parts, and the importance identification of obsolete spare parts is used to characterize obsolete spare parts of different importance. The importance identification includes a first importance identification that is lower than the importance threshold, a second importance identification that reaches the importance threshold and does not require the intervention of designated supervision, and a third importance identification that reaches the importance threshold and requires the intervention of designated supervision. Taking nuclear power spare parts as an example, the designated supervision can be nuclear supervision, which refers to equipment or components in the civilian nuclear safety equipment targets. The different disposal times of obsolete spare parts with different importance identifications are the key factors affecting the time point of disposal intervention of obsolete spare parts.

[0099] like Figure 3As shown in the figure, the corresponding relationship between the cumulative demand quantity distribution associated with different cumulative probabilities and the predicted time point of use. The horizontal axis is the predicted time point of use (in months), 0 on the horizontal axis represents the current time point, and 12 represents the predicted time point of use is the 12th month after the current month. The vertical axis is the demand quantity for obsolete spare parts at the predicted time point of use.

[0100] Step S102: determining a predicted out-of-stock time point of the obsolete spare parts based on the cumulative demand quantity distribution.

[0101] In the application, the computer device can determine the predicted collection time point when the cumulative demand quantity reaches the target demand quantity based on the distribution of the cumulative demand quantity corresponding to the specified cumulative probability. The demand quantity of the obsolete spare parts predicted at the predicted collection time point is close to or the same as the inventory quantity of the obsolete spare parts, and the predicted collection time point can be used as the predicted shortage time point of the obsolete spare parts. The target demand quantity refers to the future inventory quantity of the obsolete spare parts, and the target demand quantity is determined based on the initial inventory quantity of the obsolete spare parts and the total acceptance quantity of the obsolete spare parts.

[0102] In the application, the computer equipment first determines the future inventory distribution of obsolete spare parts based on the initial inventory quantity of obsolete spare parts (i.e., the current inventory quantity). The calculation formula for the spare parts inventory quantity is shown in Formula 1:

[0103] St t+1 =St t +Y t -L t (1)

[0104] Among them, St is the initial inventory quantity, Y t is the acceptance quantity, L t is the number of pickups, and t is the predicted pickup time point (that is, the simulation time in the simulation analysis scenario of this embodiment). The inventory quantity of obsolete spare parts is determined by the inventory quantity of the previous period, the number of pickups of the previous period, and the number of acceptance inspections of the previous period. In the forecast of demand for obsolete spare parts, the initial inventory quantity is the inventory quantity corresponding to the time point of the forecast of obsolete demand; the acceptance quantity is the purchased arrival quantity, that is, when the demand is carried out, the acceptance time and acceptance quantity are calculated based on the information of the signed purchase order.

[0105] In the application, some spare parts cannot be delivered normally even if there is a signed purchase order. Therefore, it is necessary to confirm in advance whether the signed purchase order can be delivered normally. If it can be delivered normally, the inventory quantity and the regular order quantity (the regular order quantity is the quantity of obsolete spare parts ordered in the signed purchase order) will be used for simulation analysis; if it cannot be delivered normally, only the inventory quantity will be used for simulation analysis.

[0106] For example, when conducting demand forecasting, the initial inventory quantity of obsolete spare parts is 35, and two purchase orders are signed, with the purchase quantity of each being 5, and the delivery time is 6 months and 12 months later (that is, in the 6th and 12th months of the simulation, there are 5 acceptance quantities respectively). Based on the initial inventory quantity, acceptance quantity, and collection quantity (predicted demand quantity) of obsolete spare parts, the calculated spare parts inventory quantity distribution is as follows: Figure 4 As shown. Under the same requisition quantity, if only the inventory quantity is used for forecasting, the obsolete spare parts will be out of stock in the 33rd month, that is, the predicted out-of-stock time point of the obsolete spare parts is the current time plus 33 months; if the inventory quantity plus the regular order quantity is used for forecasting, the predicted out-of-stock time point of the obsolete spare parts will be out of stock in the 44th month.

[0107] In the application, in the above formula (1), the inventory quantity of spare parts is calculated through iterative operations, that is, if you want to obtain the inventory quantity at time t+1, you need to calculate the inventory quantity at time t in advance, which requires a lot of calculations. Since the initial inventory quantity and the correct order quantity of obsolete spare parts are both known values, the total acceptance quantity within a specified future time can be calculated based on the correct order quantity, and the total collection quantity within a specified time can be calculated based on the demand quantity distribution of obsolete spare parts. Therefore, based on the initial inventory quantity, the inventory quantity at any time point can be directly calculated through formula 2.

[0108]

[0109] Among them, St0 is the initial inventory quantity, is the total number of acceptances up to the specified time. The total number of units used up to the specified time. Figure 4 The spare parts demand quantity (also called the number of requisitions) is calculated as follows: Figure 5 Therefore, the out-of-stock time of obsolete spare parts is the time when the cumulative demand quantity reaches the target demand quantity. time point.

[0110] Step S103, obtaining the predicted disposal time of the obsolete spare parts, and determining the target disposal intervention time point of the obsolete spare parts based on the predicted out-of-stock time point and the predicted disposal time period.

[0111] In the application, computer equipment needs to set a reasonable disposal intervention time point for obsolete spare parts to ensure that the spare parts purchase application can be directly triggered after the disposal of obsolete spare parts is completed, and the obsolete spare parts can meet the goal of on-site use demand after arrival. If the disposal intervention time point of obsolete spare parts is too early or too late. The disposal intervention time point of obsolete spare parts is too early, which means that after the disposal of obsolete spare parts is completed, there is no spare parts demand for a long time, and the purchase application is not triggered, or the purchase application is triggered, but the obsolete spare parts are not used for a long time after arrival. The disposal intervention time point of obsolete spare parts is too late, which means that there is an on-site use demand for obsolete spare parts, and the procurement and arrival of spare parts cannot be completed according to the on-site demand time during or after the disposal of obsolete spare parts, that is, the on-site spare parts demand cannot be met. For example, under the specified cumulative probability, for spare parts with high importance and high obsolete disposal difficulty, a conservative strategy should be used to advance the disposal intervention time point of obsolete spare parts; for spare parts with low importance and low obsolete disposal difficulty, the disposal intervention time point of obsolete spare parts can be postponed.

[0112] In the application, the time point for the disposal of obsolete spare parts is related to the predicted shortage time point of the obsolete spare parts (i.e. the time required for on-site installation and use) and the predicted disposal time of the obsolete spare parts. If the obsolete spare parts need to be ordered, it is also related to the waiting time used to indicate the acceptance storage and work order collection time, and the arrival time of the purchase order (purchase time). Figure 6 As shown in Equation 3, when the predicted out-of-stock time point of obsolete spare parts is determined, the disposal intervention time point of obsolete spare parts can be reversely determined. The calculation is shown in Equation 3.

[0113] t2=t5-ΔT4-ΔT3-ΔT2 (3)

[0114] In formula 3, t2 is the calculated initial disposal intervention time point of obsolete spare parts; t5 is the predicted out-of-stock time point, that is, the expected demand time for on-site installation and use of the spare parts; ΔT4 is the waiting time used to characterize the acceptance storage and work order collection time, that is, after the spare parts are purchased and delivered, the warehouse management personnel will conduct spare parts acceptance, and then the maintenance user will collect them based on the work order reservation. Under normal circumstances, it can be set to a fixed value, such as 1 month; ΔT3 is the arrival time of the purchase order (purchase time), which is related to the manufacturer and the type of spare parts, that is, different types of spare parts provided by different suppliers have certain differences; ΔT2 is the predicted disposal time of obsolete spare parts, that is, the time from the start of processing the obsolete spare parts to the completion of spare parts selection and the time to meet the procurement conditions. Generally, this time is related to the importance and type of spare parts. ΔT1 is the time that the spare parts are in the "obsolete reservoir". Since some spare parts are disposed of as obsolete spare parts after they are first identified as obsolete spare parts, the ΔT1 time can be 0. In the embodiment of the present application, the time of ΔT1 has no direct correlation with the time point of intervention in the disposal of obsolete spare parts.

[0115] Step S104: executing a target disposal operation for the obsolete spare parts based on the relationship between the target disposal intervention time point and the current time point.

[0116] In the application, the computer device performs the corresponding target disposal operation according to the relationship between the target disposal intervention time point of the obsolete spare parts and the current time point.

[0117] For obsolete spare parts that are transferred to the "obsolete parts reservoir" for the first time, if the disposal intervention time point of the obsolete spare parts is equal to the current time point (usually the minimum granularity of time can be set to month), the obsolete product disposal work will be carried out directly; if the disposal intervention time point of the obsolete spare parts is later than the current time point, the spare parts will be formally transferred to the obsolete spare parts "reservoir" management.

[0118] For the spare parts in the "obsolete product reservoir", conduct demand forecasting for all spare parts in the "obsolete product reservoir" regularly (e.g., every month). If the target disposal intervention time point of the obsolete spare parts is earlier than or equal to the current time point, directly carry out the disposal work of the obsolete products; if the target disposal intervention time point of the obsolete spare parts is later than the current time point, the spare parts continue to be transferred to the management of the "obsolete product reservoir". Determine the scope of spare parts calculated regularly each time based on the spare part items in the "obsolete product reservoir" and the computing power of the computer equipment. If there is no computing power problem, when calculating regularly each time, the target disposal intervention time point t2 of the obsolete products can be recalculated for all spare parts in the "obsolete product reservoir"; if due to computing power reasons, it is impossible to recalculate the disposal intervention time point t2 of all spare parts in the "obsolete product reservoir" each time, the frequency of full-scale calculation can be reduced, that is, calculate the disposal intervention time of all spare parts regularly once a year. At other times, calculate the disposal intervention time point of the specified spare parts regularly every month. For example, sort the spare parts in the "obsolete product reservoir" according to the time t2, and extract the spare parts with t2 - t0 < N (N is a set value, N can be set to 12 months), and recalculate the spare parts whose disposal intervention time t2 is less than N months from the current date every month. For example, set to calculate the disposal intervention time point of the obsolete products regularly every month, where all spare parts in the "obsolete product reservoir" are calculated in the first month of each year, and some spare parts in the "obsolete product reservoir" are calculated in the remaining months of each year. When the N value is set to 12 months, the calculation process is as Figure 7 shown in steps S1041 - S1044, where

[0119] Step S1041, start the function of calculating the target disposal intervention time point.

[0120] The computer equipment judges whether the current time is the first month of the year. If it is the first month, go to the next step; if it is not the first month, go to step S1043.

[0121] Step S1042, for all spare parts in the "obsolete product reservoir", calculate the target disposal intervention time point t2 of each obsolete spare part item by item.

[0122] The specific implementation process includes: 1) calculating the intervention time point t2 for the disposal of obsolete parts of spare parts; 2) if the intervention time point for the disposal of obsolete parts of the spare part is less than or equal to the current date, directly carry out the disposal work of obsolete parts, remove this spare part from the "obsolete parts reservoir", and go to 3); if the intervention time point for the disposal of obsolete parts is greater than the current date, update the intervention time point t2 for the disposal of obsolete parts of this spare part in the "obsolete parts reservoir", and go to 3); 3) determine whether the calculation of the intervention time points for the disposal of obsolete parts of all spare parts in the "obsolete parts reservoir" is completed. If the calculation of all spare parts is not completed, select a spare part to be calculated and go to 1); if the calculation of all spare parts is completed, go to step S1044.

[0123] Step S1043: For the specified spare parts in the "obsolete parts reservoir", calculate the target intervention time t2 for the obsolete parts of each spare part item by item.

[0124] The specific implementation process is as follows: 3.1) Based on the set conditions, identify the range of spare parts to be calculated, extract the intervention time t2 (the calculated value of the previous period) of each spare part in the "obsolete parts reservoir", and calculate the difference between the intervention time point for the disposal of obsolete parts and the current date. If t2 - t0 < N is satisfied, this spare part is included in the range of spare parts to be calculated in this calculation. After determining the range of spare parts, perform subsequent calculations. 3.2) Calculate the intervention time point t2 for the disposal of obsolete parts of the spare part. 3.3) If the intervention time point for the disposal of obsolete parts of this spare part is less than or equal to the current date, directly carry out the disposal work of obsolete parts, remove this spare part from the "obsolete parts reservoir", and go to step 3.4; if the intervention time point for the disposal of obsolete parts is greater than the current date, update the intervention time t2 for the disposal of obsolete parts of this spare part in the "obsolete parts reservoir", and go to step 3.4; 3.4) Determine whether the calculation of the intervention time points for the disposal of obsolete parts of all spare parts within the specified range in the "obsolete parts reservoir" is completed. If the calculation of all spare parts within the specified range is not completed, select a spare part to be calculated and go to step 3.1; if the calculation of all spare parts within the specified range is completed, go to step S1043.

[0125] Step S1044: Based on the target intervention time point t2 for the obsolete parts of the spare parts, re - sort the spare parts in the "obsolete parts reservoir". After this batch operation is completed, wait for the next operation time point, and go to step S1041 to run again.

[0126] Exemplarily, based on Figure 2An example of the application of the obsolete spare parts processing method is given. Assuming that the current time is January 2025, the inventory quantity of a certain obsolete spare part is 10, the number of orders is 0, and the estimated disposal time ΔT2 of the obsolete spare part is 12 months; the estimated procurement time ΔT3 of the obsolete spare part is 12 months, and the estimated waiting time ΔT4 for acceptance warehousing and work order collection time is 1 month. When calculated according to the specified cumulative probability of 50%, the cumulative demand quantity (cumulative collection quantity) of the obsolete spare part is expected to exceed 10 in the 30th month after the current time point; when calculated according to the specified cumulative probability of 75%, the cumulative collection quantity of the spare part is expected to exceed 10 in the 24th month after the current time point.

[0127] If the obsolete spare parts are regular spare parts, calculated according to the cumulative probability of 50%, replenishment must be completed 30 months after the current time point, that is, the predicted out-of-stock time point is July 2027. Based on Formula 3, the initial disposal intervention time point for obsolete spare parts can be obtained as June 2025. After correction, the target disposal intervention time point for obsolete spare parts can be obtained as June 2025, which means that obsolete disposal can be temporarily suspended.

[0128] If the obsolete spare parts are important spare parts, calculated according to the cumulative probability of 75%, replenishment must be completed 24 months after the current time point, that is, the predicted out-of-stock time point t1 is January 2027. Based on Formula 3, it can be obtained that the initial disposal intervention time point t2 of the obsolete spare parts is December 2024. After correction, it can be obtained that the target disposal intervention time point of the obsolete spare parts is January 2025, that is, the disposal of obsolete products must be carried out immediately.

[0129] In this embodiment, the demand quantity of obsolete spare parts is predicted based on the probability distribution of spare parts with different demand types. The disposal time of obsolete spare parts is calculated based on the importance of spare parts, the difficulty of disposal of obsolete spare parts, the spare parts procurement cycle, and the expected guarantee rate of spare parts, so as to improve the guarantee rate of obsolete spare parts and reduce the proportion of obsolete spare parts disposed too early or too late.

[0130] In one embodiment, Figure 8 As shown, the implementation method of obtaining the cumulative demand quantity distribution in step S102 includes the following steps S201 to S203:

[0131] Step S201, determining the demand distribution of the obsolete spare parts adapted to each demand type, wherein the demand distribution is used to indicate the mapping relationship between the demand quantity of the obsolete spare parts and the probability of receipt at any predicted receipt time point.

[0132] In applications, the demand types for obsolete spare parts can be subdivided into the first planning type, namely planned (preventive) Class A demand, the first planning type, namely planned (preventive) Class B demand, and sudden type (corrective) demand. Among them, the first planning type is also called planned (preventive) Class A demand. The demand time for spare parts of this demand type is certain, and the number of obsolete spare parts used is certain. The second planning type is also called planned (preventive) Class B demand. The demand time for spare parts of this demand type is certain, and the number of obsolete spare parts used is uncertain. The sudden spare parts demand time is uncertain, and the number of obsolete spare parts used is uncertain. The sudden type is also called the corrective spare parts demand type, and both the demand time and the number of used are uncertain.

[0133] In applications, the demand for spare parts comes from the maintenance activities of nuclear power plants, and there are many types of maintenance activities. Compared with the spare parts demand forecasting method that does not consider the maintenance type, the demand forecasting method that classifies the spare parts demand according to maintenance activities is more accurate. Nuclear power maintenance activities can be divided into planned (preventive) maintenance and emergency (corrective) maintenance. Planned maintenance mainly refers to maintenance activities that are actively carried out according to the maintenance outline or corresponding maintenance plan; emergency maintenance refers to maintenance activities other than planned maintenance work. Planned maintenance usually includes demand information for spare parts of the first plan type and the second plan type. Spare parts of the first plan type are spare parts that must be replaced in maintenance activities, and spare parts of the second plan type are spare parts that are selectively replaced according to the status of on-site equipment in maintenance activities. The demand type of spare parts required for emergency type spare parts is emergency type.

[0134] In the application, the computer device determines the demand distribution of obsolete spare parts corresponding to each demand type. The demand distribution corresponding to each type is used to indicate the mapping relationship between the demand quantity and the probability of receipt of the obsolete spare parts for the demand type at any predicted receipt time point.

[0135] Step S202, summarizing the demand distributions to obtain the total demand distribution at the predicted collection time point.

[0136] In the application, the computer equipment summarizes the demand distribution corresponding to each demand type to obtain the total demand distribution compatible with various demand types. The total demand distribution represents the relationship between the predicted collection time point and the total collection quantity required for the obsolete spare parts at the predicted collection time point. The total collection quantity is the demand quantity for spare parts of all demand types. The calculation formula 4 of the total demand distribution is as follows,

[0137] P(t)=g(P1(t),P2(t),P3(t)) (4)

[0138] Among them, P1(t) is the planned Class A demand distribution of spare parts in the next t months, P2(t) is the planned Class B demand distribution of spare parts in the next t months, P3(t) is the sudden demand distribution of spare parts in the next t months, P(t) is the distribution of all demand for spare parts in the next t months, and g(x) is a method for summarizing different types of demand distributions. For example, g(x) can be a method for weighted average, median, and variance of P1(t), P2(t), and P3(t).

[0139] For example, Fig. 9 is the first demand distribution of the first plan type at different forecast time points, Fig.10 is the second demand distribution of the second plan type at different forecast time points, Fig.11 The third demand distribution of the burst type at different forecast time points is summarized as the total demand distribution of the spare part at different forecast time points based on each simulation time, such as Fig.12 When the simulation time is the third month after the current time point, the probability of the first planned type of the spare part being 0, 1, 3, and 4 is 0.009, 0.091, 0.081, and 0.819 respectively; the probability of the second planned type being 0, 5, and 8 is 0.45, 0.44, and 0.11 respectively; the probability of the sudden type being 0, 1, 2, 3, 4 (and above) is 0.6065, 0.3033, 0.0758, 0.0126, and 0.0018 respectively. After summarizing various demands, the probabilities of using 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 (and above) are 0.0009, 0.0105, 0.0147, 0.0189, 0.0998, 0.1354, 0.1079, 0.0625, 0.0366, 0.1055, 0.1367, 0.1001, 0.0715, 0.0503, 0.0291, 0.0134, 0.0044, and 0.0017 respectively. Sub-figure (a) is the total demand distribution when the simulation time is the 3rd month after the current time point (including the distribution data of the issuance from the 0th to the 3rd month), sub-figure (b) is the total demand distribution when the simulation time is the 21st month after the current time point (including the distribution data of the issuance from the 0th to the 21st month); sub-figure (c) is the total demand distribution when the simulation time is the 39th month after the current time point (including the distribution data of the issuance from the 0th to the 39th month); sub-figure (d) is the total demand distribution when the simulation time is the 60th month after the current time point (including the distribution data of the issuance from the 0th to the 60th month). Based on the method in the previous article, the total demand distribution of spare parts at different times can be calculated.

[0140] Step S203, based on the total demand distribution of each of the predicted collection time points, a plurality of parameter groups corresponding to the specified cumulative probability are determined, wherein the parameter groups include the predicted collection time points and the cumulative demand quantities at the predicted collection time points.

[0141] In the application, computer equipment can obtain multiple two-dimensional parameter groups according to the total demand distribution under the specified cumulative probability. The two-dimensional parameter groups can be regarded as discrete points in the two-dimensional coordinate system of time-cumulative demand quantity, where the horizontal axis time refers to the time when obsolete spare parts need to be collected in the future, and the vertical axis cumulative demand refers to the number of obsolete spare parts required at any future time point.

[0142] Step S204: Fit the multiple parameter groups to obtain a cumulative demand quantity distribution associated with a specified cumulative probability of obsolete spare parts.

[0143] In the application, the computer device fits the discrete points corresponding to the multiple two-dimensional parameter groups through a preset fitting method to obtain the total demand distribution P(i) of the obsolete spare parts in the next t months, and calculates the cumulative demand probability of the obsolete spare parts in the next t months, as shown in Formula 5.

[0144]

[0145] Among them, F(t) is the cumulative probability of demand for obsolete spare parts in the next t months. After obtaining the cumulative probability of demand for spare parts based on simulation time (month), it can be used to calculate the out-of-stock time point of obsolete spare parts (i.e., the predicted out-of-stock time point).

[0146] Exemplarily, the current time point is t, and the time interval is monthly, and the total demand distribution at the predicted collection time point t+3 is determined (the horizontal axis is the demand quantity or the collection quantity, and the vertical axis is the collection probability corresponding to each collection quantity). The probabilities of using spare parts when the number of parts used is 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 (and above) are 0.0009, 0.0105, 0.0147, 0.0189, 0.0998, 0.1354, 0.1079, 0.0625, 0.0366, 0.1055, 0.1367, 0.1001, 0.0715, 0.0503, 0.0291, 0.0134, 0.0044, 0.0017, respectively. Therefore, the cumulative probabilities are 0.0 009, 0.0114 (0.0009 + 0.0105), 0.0261 (0.0147 + 0.0114), 0.0450, 0.1448, 0.2802, 0.3881, 0.4507, 0.4873, 0.5927, 0.7295, 0.8296, 0.9011, 0.9514, 0.9805, 0.9939, 0.9983, 1.0000. If the selected specified cumulative probability is 25%, 50%, 75%, and 95%, the corresponding demand quantities are 5, 9, 11, and 13 respectively, that is, the minimum demand quantity value greater than the specified cumulative probability is read. For example, if a demand quantity value with a specified cumulative probability of 50% is selected, when the demand quantity is 8, the probability is 0.487, when the demand quantity is 9, the probability is 0.59, and when the demand quantity is 10, the probability is 0.72. Only when the demand quantity is 9, the requirement is met.

[0147] In this embodiment, based on the demand type, the demand for spare parts is divided into planned Class A demand, planned Class B demand, and sudden demand, and the probability distribution of each demand type is calculated and summarized as the probability distribution of the annual demand quantity for spare parts. Compared with the method of using the historical average demand value to calculate the future demand for spare parts, the method using the probability distribution uses the characteristic of the demand time of the planned spare parts demand to determine the demand, and its prediction accuracy is higher.

[0148] In one embodiment, Fig.13 As shown, when the demand type is the first plan type, the implementation of step S201 includes the following steps S301 to S303:

[0149] Step S301, obtaining the planned required quantity of the obsolete spare parts adapted to the first plan type in each maintenance project to be carried out, wherein the maintenance project is executed by one or more adjacent maintenance activities.

[0150] In the application, the demand for spare parts comes from the maintenance activities of the nuclear power plant, and each maintenance activity can carry out one or more maintenance projects. For planned maintenance, the computer equipment sorts out the overhaul plan for the corresponding round according to the planned (preventive) maintenance outline of the nuclear power plant, and then obtains at least one maintenance project to be carried out. By matching the spare parts requirements mounted in the standard package corresponding to the maintenance project, the planned demand quantity and demand time of spare parts adapted to the first plan type for the maintenance project are calculated. Spare parts can be needed in multiple maintenance activities, and the execution time of each maintenance activity is the demand time of obsolete spare parts. The planned demand quantity is determined based on the number of spare parts mounted in the standard package of each maintenance project.

[0151] For example, the preventive maintenance outline is shown in Table 1, which includes the maintenance cycle of the project, the execution status of major maintenance cycles in history, and the execution plan of major maintenance cycles in the future.

[0152] Table 1

[0153]

[0154] The maintenance project list based on the maintenance outline is shown in Table 2. The maintenance project can obtain the spare parts requirement list required for the maintenance project by calling the maintenance standard package.

[0155] Table 2

[0156] Maintenance Project Functional location Project Text Main work center cycle Overhaul rounds Task List Group Task list group number Power Plant 400058825 LA-1-23-ABP L1ABP Vibration Pipeline Weld 3C In-Service Inspection (PT) TSR 3 L117 60000880 03 5020 400058826 LA-1-23-ABP L1ABP GD1 Pipeline Fittings 6C In-Service Inspection(TM) TSR 6 L120 60000880 04 5020 400058828 LA-1-23-ABP In-service inspection of 6C sampling of welds before secondary valves of 14 instrument pipes of L1ABP low-pressure steam-water pipeline (PT) TSR 6 L120 60000880 13 5020 400058829 A-1-23-ABP Leakage detection of important SN steam traps in ABP system before overhaul MS 1 L117 NSXX0981 01 5020 400058831 LA-1-23-ABP-001MD L1ABP-001-MD four material change cycle calibration MI_C 4 L119 60020036 01 5020 400058832 A-1-23-ABP-001VL 1ABP001VLROTORK Electric head regular inspection; regular replacement of electric head battery ME_C 3 L119 60007888 01 5020 400058833 LA-1-23-ABP-002MD L1ABP-002-MD four material change cycle verification MI_C 4 L119 60020025 01 5020 400058834 LA-1-23-ABP-002VL 1ABP002VLROTORK Electric head regular inspection; regular replacement of electric head battery ME_C 3 L118 60007895 01 5020 400058835 LA-1-23-ABP-003VL 1ABP003VLROTORK Electric head regular inspection; regular replacement of electric head battery ME_C 3 L119 60007902 01 5020 400058836 LA-1-23-ABP-004VL L1ABP-004-VL Spring Loaded Bypass Valve Disassembly Inspection MSV_C 3 L119 60029009 02 5020

[0157] That is, the required quantity of a certain spare part in a maintenance project can be obtained. The spare parts requirement list of the standard package is shown in Table 3, from which the required time and required quantity of the spare parts in a maintenance project can be obtained.

[0158] Table 3

[0159] Task List Group Task list group number Spare part number Spare part description Required quantity Material Unit 60007895 01 1070502455 Lithium-manganese dioxide square battery [U9VL-J-P49*26.5*17.5mm stainless steel shell] 1 EA 60029009 01 1001058514 Extreme pressure grease [MOBILUX EP2 (16KG / barrel)] 1 KG 60029009 02 1050510216 PACKING STRIP 5MMSQ 0.2 M 60029009 02 1050510254 PACKING RING[33.95*24.05*5] 5 EA 60029009 02 1070450428 GASKET 1 EA 60029009 02 1070708590 TENSION PIN 5MM DIA.36L 1 EA

[0160] Step S302: obtaining a first probability parameter group corresponding to each maintenance item, wherein the first probability parameter group includes an execution probability of the item, a postponement probability of the item, and a cancellation probability of the item.

[0161] In the application, maintenance projects have the situations of project postponement, project equivalence, and project cancellation. Project postponement means that it is not executed in this overhaul, but executed in the next overhaul; project equivalence and project cancellation mean that it is not executed in this overhaul and is not executed in the next overhaul. By counting the historical execution of each maintenance project, the normal execution, postponed execution, and equivalent (cancellation) ratio of the maintenance project can be calculated. In practical applications, maintenance specialty can also be used as a dimension to count the normal execution, postponed execution, and equivalent (cancellation) ratio of all maintenance projects under the maintenance specialty. All maintenance projects corresponding to the maintenance specialty use the same execution, postponement, and cancellation ratio values. Based on this, the computer device obtains the first probability parameter group corresponding to each maintenance project, and the first probability parameter group includes the execution probability of the project, the postponement probability of the project, and the cancellation probability of the project. Among them, the execution probability of the project refers to the probability that the maintenance project is executed in the planned overhaul. The postponement probability of the project refers to the probability that the project is postponed to the next round of overhaul. The cancellation probability of the project means that it is not executed in this overhaul and is not executed in the next overhaul. By counting the historical execution status of each maintenance project, the normal execution, deferred execution, and equivalent (cancelled) ratio of the maintenance project can be calculated.

[0162] In the application, let the planned Class A demand quantity of spare parts in maintenance project i be n i , the probability that maintenance item i is executed in the planned maintenance activity is The probability that the project will be postponed to the next round of maintenance activities is The probability of project equivalence and cancellation is

[0163] For example, Fig.14 As shown, a certain obsolete spare part has planned Class A demand in maintenance project 1, maintenance project 2, maintenance project 3, maintenance project 4, and maintenance project 5, which are respectively executed in maintenance activity 1 (the start time of overhaul is the 3rd month after the current time), maintenance activity 3 (the start time of overhaul is the 21st month after the simulation), maintenance activity 5 (the start time of overhaul is the 39th month after the simulation), and maintenance activity 7 (the start time of overhaul is the 57th month after the simulation). Among them, maintenance project 1 and maintenance project 4 should be executed in maintenance activity 1 as planned. If the project is delayed, they will be executed in maintenance activity 3; maintenance project 2 should be executed in maintenance activity 3 as planned. If the project is delayed, they will be executed in maintenance activity 5; maintenance projects 3 and maintenance projects 5 should be executed in maintenance activity 5 as planned. If the project is delayed, they will be executed in maintenance activity 7.

[0164] Step S303, for each maintenance activity, determining a plurality of possible demand quantities corresponding to the maintenance activity; wherein the possible demand quantities of maintenance activities other than the first maintenance activity are determined based on the possible demand quantities of all previous maintenance activities.

[0165] Calculate the data of spare parts used in maintenance activity 1 for maintenance activity 1. The probability of maintenance item 1 being performed in maintenance activity 1 is That is, the probability of not performing maintenance activity 1 is It can be calculated that the probability that the quantity of maintenance item 1 used in maintenance activity 1 is 0 is The probability of using n1 is Similarly, the probability that the quantity of maintenance item 4 used in maintenance activity 1 is 0 is The probability of using n4 is Assuming that n1 is greater than n4, the probability that the number of spare parts used in maintenance activity 1 is 0 is The probability of using n4 is The probability of using n1 is The probability of using n1+n4 is

[0166] For maintenance activity 3, calculate the total usage data before and during maintenance activity 3, that is, calculate the usage data of maintenance activity 3 and maintenance activity 1. The execution probability of maintenance item 1 before and during maintenance activity 3 is The probability of cancellation is It can be calculated that the probability that the quantity of maintenance item 1 used in maintenance activity 3 and the previous maintenance activities is 0 is The probability of using n1 is Similarly, the probability that the quantity of maintenance item 2 used in maintenance activity 3 and previous maintenance activities is 0 is The probability of using n2 is The probability that the quantity of maintenance item 4 used in maintenance activity 3 and the previous maintenance activities is 0 is The probability of using n4 is Assuming that n2 is greater than n1, and n1 is greater than n4, the probability that the number of spare parts used in maintenance activity 3 and before is 0 is The probability of using n4 is The probability of using n1 is The probability of using n1+n4 is The probability of using n2 is The probability of using n2+n4 is The probability of using n1+n2 is The probability of using n1+n2+n4 is

[0167] Step S304: determining a first demand distribution that matches the obsolete spare parts with the time points of receipt of each of the maintenance activities based on each of the first probability parameter groups and a plurality of possible demand quantities of each of the maintenance activities.

[0168] In the application, the first demand distribution indicates the mapping relationship between the demand quantity and the probability of use of obsolete spare parts adapted to the first plan type. Let the planned use quantity of maintenance items 1 to 5 be N = [3, 7, 5, 1, 4] T , the probability distribution of planned execution, postponement, and equivalent (cancellation) is shown in Formula 6, then the first demand distribution of the first plan type of the spare part is as follows Fig.10 shown.

[0169]

[0170] For example, Fig.10In the figure, sub-graph (a) is the first demand distribution (including the collection distribution data of the 0th to 3rd months) when the simulation time is the third month after the current time, which corresponds to the collection data of maintenance activity 1. The planned collection quantity of maintenance item 1 in maintenance activity 1 is 3, the probability of execution as planned is 0.9, the probability of postponement is 0.05, and the probability of equivalent (cancellation) is 0.05; the planned collection quantity of maintenance item 4 in maintenance activity 1 is 1, the probability of execution as planned is 0.91, the probability of postponement is 0.06, and the probability of equivalent (cancellation) is 0.03. Based on the calculation method introduced in the previous article, the probabilities of the collection quantity of 0, 1, 3, and 4 are 0.009 = (0.1 × 0.09), 0.091 = (0.91 × 0.1), 0.081 = (0.9 × 0.09), and 0.819 = (0.9 × 0.91), respectively. Sub-figure (b) is the first demand distribution when the simulation time is the 21st month after the current time (including the collection distribution data of the 0th to 21st months), and the collection data corresponding to maintenance activities 1 and 3. The planned collection quantity of maintenance project 1 in maintenance activities 1 and 3 is 3, the total execution probability is 0.95=(0.9+0.05), and the equivalent (cancellation) probability is 0.05; the planned collection quantity of maintenance project 2 in maintenance activities 1 and 3 is 7, the probability of execution as planned is 0.85, the probability of postponement is 0.1, and the equivalent (cancellation) probability is 0.05; the planned collection quantity of maintenance project 4 in maintenance activities 1 and 3 is 1, the total execution probability is 0.97=(0.91+0.06), and the equivalent (cancellation) probability is 0.03. Based on the calculation method introduced in the previous article, in the maintenance activities to be performed at the predicted collection time point (t+21), the probabilities of the following collection quantities 0, 1, 3, 4, 7, 8, 10, and 11 are 0.000225=(0.05×0.15×0.03), 0.007275, 0.004275, 0.138225, 0.001275, 0.041225, 0.024225, and 0.783275, respectively. Sub-figure (c) is the first demand distribution when the simulation time is the 39th month after the current time (including the collection distribution data from the 0th to the 39th month), which corresponds to the collection data of maintenance activity 1, maintenance activity 3, and maintenance activity 5; Sub-figure (d) is the first demand distribution when the simulation time is the 60th month after the current time (including the collection distribution data from the 0th to the 60th month), which corresponds to the collection data of maintenance activity 1, maintenance activity 3, maintenance activity 5, and maintenance activity 7. Based on the method in the previous article, the demand distribution data of spare parts at different collection time points can be calculated.

[0171] In this embodiment, by accurately obtaining the planned demand quantity that matches the specific plan type in each maintenance project, the spare parts reserve can be planned in advance based on the spare parts attachment quantity in the standard package. At the same time, taking into account the uncertainty of the maintenance project, a first probability parameter group including execution, postponement and cancellation probabilities is introduced, which enables the prediction model to more truly reflect the variables in actual operations and improves the accuracy and practicality of the prediction. For each maintenance activity, by progressively determining the possible demand quantity, especially the demand quantity of non-first maintenance activities based on the possible demand quantity of the preceding activities, this method can capture the influence relationship between maintenance activities and make demand forecasting more refined and practical. Finally, combining the probability parameter group of the maintenance project and the possible demand quantity of each maintenance activity, the first demand distribution that is adapted to the time point of receipt is obtained, which provides a clear mapping of the relationship between spare parts demand and the probability of receipt.

[0172] In one embodiment, Fig.15 As shown, when the demand type is the second plan type, the implementation of step S201 includes the following steps S401 to S405:

[0173] Step S401, determining the potential requisition quantity of the obsolete spare parts adapted to the second plan type in each maintenance project to be carried out, wherein the maintenance project is performed by one or more adjacent maintenance activities.

[0174] In the application, the potential requisition quantity is determined based on the spare parts attachment quantity in the standard package of each maintenance project and the historical requisition data associated with the maintenance project. The historical requisition data includes the total number of calls, the total requisition quantity, the total number of requisitions, and the maximum single requisition quantity.

[0175] If the potential demand quantity of the second type of obsolete spare parts in the second type of maintenance project is directly calculated by the spare parts demand quantity attached in the maintenance project standard package, the overall effect is not good, and it is necessary to conduct a comprehensive analysis in combination with the historical use records involved in the maintenance project. Fig.16For example, the potential spare parts demand involved in the maintenance project includes two parts. One part is the number of spare parts mounted in the standard package of the maintenance project, and the other part is the spare parts that have historical requisition records but are not mounted in the standard package. If only the spare parts mounted in the standard package are referred to, the demand for spare parts 4 and 5 cannot be output. Therefore, it is necessary to summarize the number of spare parts mounted in the standard package of the maintenance project and the historical requisition information, and sort out the maintenance project and the potential demand quantity of the corresponding spare parts. By sorting out the historical requisition records of the maintenance project, a maintenance project-spare parts benchmark list is established for different types of maintenance project-spare parts models to call. The benchmark list contains at least the following: maintenance project, mounting standard package number, spare part code, spare part unit price, spare part importance mark, spare part type in the standard package, spare part mounting quantity in the standard package, total call times, total requisition quantity, total requisition times, and maximum single requisition quantity. Among them, the maintenance project + spare part code is the primary key of the list.

[0176] Step S402, obtaining a quantity parameter group corresponding to each maintenance item, wherein the quantity parameter group includes a planned collection quantity, a potential collection quantity, and a historical collection quantity peak value.

[0177] In the application, since the number of planned Class B demand requisitions fluctuates, the computer device determines the quantity parameter group corresponding to each maintenance item, and the quantity parameter group includes the planned requisition quantity, the potential requisition quantity, and the historical requisition quantity peak value. The calculation formula for the potential requisition quantity of spare parts of the second plan type for each maintenance item is shown in Formula 7:

[0178]

[0179] Among them, n is the potential demand quantity of spare parts in the planned B category of a maintenance project, θ is the number of spare parts corresponding to the planned B category in the standard package of the maintenance project, w2 is the number of times the spare parts are actually used in the maintenance project. For example, if the spare part is used 4 times in this maintenance project, the number of times the spare parts are used is 4; w3 is the number of calls, that is, the number of times the maintenance project has been carried out in history. For example, if a maintenance project has been carried out 10 times in history, the number of calls is 10; δ is the probability threshold of use. When the ratio of the number of uses to the number of calls (i.e., the number of uses / the number of calls) is less than the probability threshold of use δ, or the number of calls of the maintenance project is 0, the planned B category demand of the maintenance project is the number of demands θ mounted in the standard package. When the ratio of the number of uses to the number of calls (i.e., the number of uses / the number of calls) is greater than the threshold, the number of spare parts required for the second plan type is Among them, w1 is the total usage, that is, the sum of the actual usage of the spare part in the maintenance project. For example, the spare part was used 4 times in this maintenance project, and a total of 7 pieces were used, so the total usage is 7; λ is the correction coefficient, which is related to the importance and unit price of the spare part. For example, for spare parts with high importance and low unit price, the λ value is set to 1.3, and for spare parts with non-important and high unit price, the λ value is set to 0.77; for other types of spare parts, the λ value is set to 1. If a spare part is not attached to the standard package of the maintenance project, and the historical usage of the spare part in the maintenance project meets Or if w2=0, the predicted value of the planned Class B demand quantity of the spare part in the maintenance project is 0, and the spare part does not participate in subsequent calculations.

[0180] In the application, for any maintenance item i, the quantity to be used is The number of The number of The calculation formula is shown in formula (8):

[0181]

[0182] The m value is the planned B-type demand for the spare part in m maintenance projects. The value is 0. is the calculated value of the number of withdrawals n in formula 12, is the historical maximum number of planned Class B demand for a maintenance project. If there is no historical data for the spare part, or the historical maximum number of demand is less than or equal to the calculated value n of formula 12, then The value of sets the calculated value n of formula 12, that is, The value is equal to

[0183] Step S403, obtaining a second probability parameter group corresponding to each maintenance item, wherein the second probability parameter group includes an execution probability of the item, a postponed execution probability of the item, and a cancellation probability of the item.

[0184] In the application, referring to the process of planned Class A spare parts demand quantity, there are situations of project postponement, project equivalence, and project cancellation in planned maintenance projects. Therefore, the planned Class B spare parts demand also has the probability of normal project execution, the probability of delayed project execution, and the probability of equivalent (cancellation) project, as shown in Formula 9.

[0185]

[0186] in, To maintain the normal execution probability of project i, To maintain the probability of postponing execution of project i, is the equivalent (cancellation) probability of maintenance project i.

[0187] Step S404, obtaining a collection probability parameter group corresponding to each maintenance item and each quantity parameter.

[0188] In the application, the quantity to be drawn is The number of The number of The probability of using As shown in formula 10,

[0189]

[0190] in, is the probability that the number of withdrawals is 0. If the number of withdrawals is 0, but the calculated number of withdrawals is is not 0 (usually because the spare part is included in the standard package and the quantity to be used is θ), then The value of is set to 1-δ (probability threshold of claiming); The quantity to be used is The probability that The value is equal to but The value is set to 0, that is, there is no historical maximum usage value, or the historical maximum usage value is equal to the calculated value The quantity to be used is The probability of

[0191] For example, Fig.17 For example, a spare part has planned Class B demand in maintenance project 6, maintenance project 7, maintenance project 8, and maintenance project 9, which may be executed in maintenance activity 1 (overhaul start time is the 3rd month after simulation), maintenance activity 2 (overhaul start time is the 12th month after simulation), maintenance activity 3 (overhaul start time is the 21st month after simulation), maintenance activity 4 (overhaul start time is the 30th month after simulation), maintenance activity 5 (overhaul start time is the 39th month after simulation), and maintenance activity 6 (overhaul start time is the 48th month after simulation). Among them, maintenance item 6 is planned to be executed in maintenance activity 1. If the project is delayed, it will be executed in maintenance activity 3; among them, maintenance item 7 is planned to be executed in maintenance activity 3. If the project is delayed, it will be executed in maintenance activity 5; among them, maintenance item 8 is planned to be executed in maintenance activity 2. If the project is delayed, it will be executed in maintenance activity 4; among them, maintenance item 9 is planned to be executed in maintenance activity 4. If the project is delayed, it will be executed in maintenance activity 6.

[0192] For example, based on Fig.14, set the probability of maintenance item i being executed in the maintenance activity to The probability that the project will be postponed to the next round of overhaul is The probability of project equivalence and cancellation is The planned B-type demand quantities of spare parts in maintenance project i are The distribution probability is From this, we can calculate the probability that the quantity of maintenance item 6 used in maintenance activity 1 is 0: The number of The probability of The number of The probability of For overhaul 2, calculate the total usage during maintenance activity 2 and before maintenance activity 2, that is, the usage data of maintenance activity 2 and maintenance activity 1. The usage data and usage distribution probability of maintenance item 6 during maintenance activity 2 and before are consistent with its data in maintenance activity 1; the usage data and usage distribution of maintenance item 8 during maintenance activity 2 and before are: the probability of the usage quantity being 0 is The number of The probability of The number of The probability of Summarizing maintenance items 6 and 8, the planned total use data of Class B before and after maintenance activity 2 is (assuming that and ): The probability that the quantity to be used is 0 is The number of The probability of The number of The probability of The number of The probability of The number of The probability of The number of The probability of

[0193] Step S405, based on the quantity parameter group, the second probability parameter group and the collection probability parameter group of each maintenance item, determine the second demand distribution adapted to the collection time point of each maintenance activity for the obsolete spare parts.

[0194] In the application, the second demand distribution indicates the relationship between the demand quantity and the probability of use of spare parts adapted to the second plan type. The computer device determines the second demand distribution according to the quantity parameter group of each maintenance item, the corresponding probability parameter group, and the probability of use corresponding to each demand quantity. The second demand distribution is obtained by fitting multiple two-dimensional discrete points through a preset fitting method. The horizontal axis of the corresponding two-dimensional coordinate system is the different demand quantities for spare parts at the execution time corresponding to the maintenance activity (i.e., the use time of spare parts), and the vertical axis is the probability of use corresponding to each demand quantity.

[0195] For example, let the probability distribution of maintenance items 7 to 9 being executed as planned, postponed, or equivalent (canceled) be as shown in formula 11, the planned number of spare parts to be used in maintenance items 7 to 9 be as shown in formula 12, and the probability of use be as shown in formula 13.

[0196] As shown in formula 13, the planned B-type demand distribution of the spare parts is as follows: Fig.10 shown.

[0197]

[0198]

[0199]

[0200] exist Fig.10In the figure, sub-figure (a) is the second demand distribution when the simulation time is the third month after the current time point (including the collection distribution data of the 0th to 3rd months), which corresponds to the collection data of maintenance activity 1. Based on the calculation method introduced in the previous article, the probabilities of the collection quantities of 0, 5, and 8 are 0.45 = ((1-0.88) + 0.88 × 0.375), 0.44 = (0.88 * 0.500), and 0.11 = (0.88 * 0.125), respectively. Subfigure (b) is the second demand distribution when the simulation time is the 21st month after the current time point (including the collection distribution data from months 0 to 21), and the collection data corresponding to maintenance activities 1, 2 and 3. Based on the calculation method introduced in the previous article, the possible collection quantities include 0, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, and 17, and the corresponding collection probabilities are 0.1453, 0.1008, 0.0537, 0.2203, 0.0373, 0.1604, 0.0815, 0.0589, 0.0295, 0.0593, 0.0147, 0.0218, 0.0109, and 0.0055, respectively. Sub-figure (c) is the second demand distribution when the simulation time is the 39th month after the current time point (including the collection distribution data of the 0th to 39th months), which corresponds to the collection data of maintenance activities 1, 2, 3, 4, and 5. Sub-figure (d) is the second demand distribution when the simulation time is the 60th month after the current time point (including the collection distribution data of the 0th to 60th months), which corresponds to the collection data of maintenance activities 1, 2, 3, 4, 5, and 6. Based on the method in the previous article, the demand distribution of spare parts at different times can be calculated, that is, the second demand distribution adapted to the collection time points of obsolete spare parts and each maintenance activity.

[0201] In this embodiment, by dynamically adjusting the calculation method of the potential collection quantity, not only the accuracy and practicality of the prediction are improved.

[0202] In one embodiment, Fig.18 As shown, the implementation of step S401 includes the following steps S501 to S503:

[0203] Step S501, determining the ratio of the number of times of use to the number of times of calling.

[0204] Step S502: If the ratio is less than the probability threshold of use or the number of calls for the maintenance project is zero, the required quantity of obsolete spare parts in the standard package of the maintenance project that is adapted to the second plan type is used as the potential use quantity.

[0205] Step S503, if the ratio reaches the probability threshold of use, the potential use quantity is determined based on the actual use quantity of the obsolete spare parts in the targeted maintenance project, the total number of uses and a preset correction factor, and the correction factor is determined based on the importance identification of the obsolete spare parts and the unit price of the obsolete spare parts.

[0206] In one embodiment, Fig.19 As shown, when the demand type is a sudden type, the implementation of step S201 includes the following steps S601 to S602:

[0207] Step S601, based on the historical burst collection data of the obsolete spare parts, determine the collection demand characteristics of the obsolete spare parts, the collection demand characteristics include the collection mean and collection standard deviation of the obsolete spare parts.

[0208] In the application, based on the historical burst usage data of spare parts (statisticed on a yearly or monthly basis), the usage demand characteristics of obsolete spare parts are determined. The usage demand characteristics include the usage mean and the usage standard deviation. Assuming that the usage demand characteristics of spare parts meet a certain probability density, use sample data for parameter estimation and hypothesis testing, establish a distribution function library, and use methods such as the KS test (Kolmogorov-Smirnvo test) to determine whether the spare parts usage meets the corresponding probability distribution, so as to select a suitable spare parts demand function model library.

[0209] In the application, the distribution function library can set distribution functions such as Poisson distribution, binomial distribution, and normal distribution. For each demand function model in the model library, the KS test value is calculated using the KS test method. If the KS test value is within the threshold, the demand function model corresponding to the optimal KS test value is selected and used to predict the demand for spare parts; if the KS test value is not within the threshold, the uniform distribution model is used to predict the future demand for spare parts.

[0210] Step S602: Based on the usage demand characteristics, a third demand distribution adapted to the burst type is selected from a uniform distribution model and at least one spare parts demand function included in a spare parts demand function model library.

[0211] In the application, the Poisson distribution, binomial distribution, normal distribution, and uniform distribution functions in the model library are shown in Formula 14, Formula 15, and Formula 16. In Formula 14, n is the expected number of picks, p(n) is the probability of pick, μ is the calculated monthly mean of picks, and e is the value of a natural constant. In Formula 15, n is the expected number of picks, p(n) is the probability of pick, μ is the calculated monthly mean of picks, and l is the number of functional locations where the spare part is installed on site (sudden pick demand). In Formula 16, n is the expected number of picks, p(n) is the probability of pick, μ is the calculated monthly mean of picks, σ is the calculated monthly standard deviation of picks, and π is the value of pi.

[0212]

[0213]

[0214]

[0215] When the KS test values ​​of all models in the model library are not within the threshold, the uniform distribution model can be used to predict the future demand for spare parts. The calculation formula is shown in Formula 17, where n is the expected number of pickups, p(n) is the probability of pickup, μ is the calculated monthly mean pickup, int(μ) is the rounding down operation of the monthly mean pickup, and ω is the calculated probability of pickup, as shown in Formula 18. When μ is greater than int(μ), the value of ω is 1-(u-int(μ)); when μ is greater than int(μ), the value of ω is 1. For example, if the monthly mean usage of a spare part is 0.4, using uniform distribution for prediction, the probability of its future usage value being 0 is 0.6, the probability of its usage value being 1 is 0.4, and the probability of using it for other values ​​is 0; if the monthly mean usage of the spare part is 0.7, using uniform distribution for prediction, the probability of its future usage value being 0 is 0.3, the probability of its usage value being 1 is 0.7, and the probability of using it for other values ​​is 0; if the monthly mean usage of the spare part is 1, using uniform distribution for prediction, the probability of its future usage value being 1 is 1, and the probability of using it for other values ​​is 0.

[0216]

[0217]

[0218] For example, if the sudden demand data of a spare part conforms to the Poisson distribution and the monthly demand mean is 0.5, then the probability of the monthly demand of 0, 1, 2, 3, 4 (or more) spare parts is 0.6065, 0.3033, 0.0758, 0.0126, 0.0018 respectively. The sudden demand distribution of the spare part is as follows: Fig.11 As shown. Fig.11In the figure, sub-graph (a) is the third demand distribution of the third month after the current time (including the collection distribution data of the 0th to 3rd months), and the probabilities of the collection quantity being 0, 1, 2, 3, 4, 5, 6 (and above) are 0.2231, 0.3347, 0.2510, 0.1255, 0.0471, 0.0141, and 0.0044 respectively. Sub-graph (b) is the third demand distribution of the 21st month after the current time (including the collection distribution data of the 0th to 21st months), and the probabilities of the collection quantity being 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, and 21 are 0.00003, 0.00029, 0.00152, 0.00531, 0.01395, 0.02929, 0.05125, 0.07688, 0.10090, 0.11772, 0.12361, 0.11799, 0.10325, 0.08339, 0.06254, 0.04378, 0.02873, 0.01774, 0.01035, 0.00571, 0.00300, 0.00278. Sub-figure (c) is the third demand distribution of the 39th month after the current time (including the collection distribution data of the 0th to 39th months); sub-figure (d) is the third demand distribution of the 60th month after the current time (including the collection distribution data of the 0th to 60th months). Based on the method in the previous article, the demand distribution data of spare parts at different times can be calculated, that is, the third demand distribution adapted to the sudden type. The third demand distribution indicates the relationship between the demand quantity and the collection probability of spare parts adapted to the sudden type.

[0219] In this embodiment, by utilizing these demand characteristics and selecting a third demand distribution that is adapted to the sudden type, it is possible to more accurately simulate and predict the spare parts procurement pattern under unforeseen events, thereby ensuring a rapid response in the face of emergencies and maintaining business continuity and stability.

[0220] In one embodiment, Fig. 20 As shown, the method for obtaining the predicted disposal time of the obsolete spare parts includes the following steps S701 to S703:

[0221] Step S701, based on the spare part type of the obsolete spare parts and the proportion of spare part items, determine the spare part disposal time corresponding to each importance identifier, wherein the proportion of spare part items is determined based on the disposal difficulty of the obsolete spare parts and the average disposal difficulty level corresponding to the spare part type of the obsolete spare parts.

[0222] In application, the disposal time of obsolete spare parts is related to the type of spare parts and the importance of spare parts. For spare parts types, different types of spare parts have different difficulties in carrying out obsolete disposal plans. For example, the disposal time of control valve spare parts and indicator light spare parts is significantly different. By analyzing the obsolete disposal time of a nuclear power plant, the distribution of obsolete disposal time of different types of spare parts is as follows: Fig.21 As shown in the figure, by collecting statistics on the obsolete disposal data of this type of spare parts, the obsolete disposal data are sorted according to the disposal time. The horizontal axis is the distribution of the number of spare parts, and the vertical axis is the completion time of the obsolete disposal. Fig.21 For example, at the median of 50%, the obsolete disposal time of type 2 spare parts is 7.5 months, and the obsolete disposal time of type 3 spare parts is 4.1 months. If the lower 25% value, the median 50% value, and the upper 75% value are extracted, the obsolete disposal time of type 2 spare parts is 4.2 months, 7.5 months, and 11.0 months; the obsolete disposal time of type 3 spare parts is 2.4 months, 4.1 months, and 8.1 months. The classification example of commonly used spare parts in nuclear power plants is shown in Table 4.

[0223] Table 4

[0224]

[0225] In the application, all spare parts are classified according to the categories in the table, among which rotating machinery, pumps, valves, general machinery, chemical consumables, instruments and meters, and electrical are total category data, and the rest are sub-category data. In actual applications, when the amount of sub-category data is sufficient, the sub-category spare parts disposal time distribution data is directly used. If the amount of sub-category data is insufficient, the sub-category corresponding total category spare parts disposal time distribution data can be used.

[0226] In terms of the importance of spare parts, the obsolete product disposal process for spare parts of different importance is not exactly the same. For example, spare parts with an assurance level of C1 (failure or damage directly affects the three major safety functions of the nuclear power plant; or there are RCC specification requirements; or failure directly affects the unit availability and directly trips the machine) and spare parts with an assurance level of C2 (failure indirectly affects the safety-level equipment in performing its safety functions; or failure indirectly leads to a reduction in the unit availability) used on CCM (critical sensitive equipment) equipment need to seek the opinions of relevant maintenance departments and equipment management departments when implementing obsolete product disposal plans (item replacement); for spare parts related to nuclear safety or used on nuclear safety-related equipment, the opinions of STA (nuclear safety engineers) need to be sought when implementing obsolete product disposal plans (item replacement). Due to the introduction of the consultation link, the entire obsolete product disposal time will be extended. Through the analysis of the obsolete product disposal time of a nuclear power plant, the obsolete product disposal time distribution of spare parts of different importance is as follows: Fig. 22As shown. Nuclear supervision refers to equipment or components in the civilian nuclear safety equipment target. Usually, the spare parts are related to nuclear safety or used in nuclear safety-related equipment; high importance refers to spare parts with a quality assurance level of C1 or CCM or spare parts classified as H-level spare parts; low importance refers to spare parts with low importance. Fig. 22 It can be seen that the disposal time of spare parts with high importance is longer than that of spare parts with low importance; if a spare part belongs to nuclear regulatory spare parts, its disposal time is significantly higher than that of non-nuclear regulatory spare parts.

[0227] Step S702, determining the target importance identifier corresponding to the replacement item of the obsolete spare part.

[0228] In the application, the importance identification includes a first importance identification that is lower than the importance threshold, a second importance identification that reaches the importance threshold and does not require intervention of designated supervision, and a third importance identification that reaches the importance threshold and requires intervention of designated supervision. The computer device determines the target importance identification corresponding to the replacement item of the obsolete spare part.

[0229] Step S703, based on the target importance identifier and the disposal time of each spare part, determine the disposal time of the substitute item, and use the disposal time of the substitute item as the predicted disposal time of the obsolete spare part.

[0230] In applications, computer equipment can comprehensively consider factors such as spare part type and spare part importance identification to predict the disposal time of obsolete spare parts. In theory, it can be based on the spare part type classification in Table 4 and Fig. 22 The spare parts importance classification is used to calculate the disposal time distribution of obsolete spare parts for each combination of spare parts. However, since the number of spare parts with "high importance (non-nuclear regulation)" and "high importance (nuclear regulation)" is relatively small, if compared with Figure 4 The amount of data for each combination is small, making it difficult to obtain the time distribution relationship of obsolete product disposal (e.g. Fig.21). In the same general category, the difference between "high importance (non-nuclear regulation)" and "low importance" of each sub-category, as well as the difference between "high importance (nuclear regulation)" and "high importance (non-nuclear regulation)" fluctuates less, so the spare parts importance factor can be set to a fixed value, that is, the results of each sub-category under each general category are the same. For example, in a general category in Table 4, the difference between "high importance (non-nuclear regulation)" and "low importance" is 0.9 months, and the difference between "high importance (nuclear regulation)" and "high importance (non-nuclear regulation)" is 4.8 months. For all sub-categories under this general category, the difference between "high importance (non-nuclear regulation)" and "low importance" is also 0.9 months, and the difference between "high importance (nuclear regulation)" and "high importance (non-nuclear regulation)" is also 4.8 months. Therefore, the disposal time of obsolete spare parts can be divided into three parts, and the result is the sum of the three parts.

[0231] Among them, the first part is the disposal time distribution value of the low-importance spare parts of the sub-category (if the data volume of low-importance spare parts of a sub-category is small, the distribution value data of the low-importance spare parts of the corresponding total category will be used); the second part is the difference between "high importance (non-nuclear regulation)" and "low importance" of the total category. If the spare parts to be calculated are low-importance spare parts, the value of this part is 0; the third part is the difference between "high importance (nuclear regulation)" and "high importance (non-nuclear regulation)" of the total category. If the spare parts to be calculated are non-nuclear regulation spare parts, the value of this part is 0.

[0232] Based on the above content, the calculation formula for the disposal time of obsolete spare parts is shown in Formula 20:

[0233] ΔT2=f1(θ1,θ2)+f2(θ1,θ3)+f3(θ1,θ3) (20)

[0234] Among them, ΔT2 is the predicted disposal time of obsolete spare parts; θ1 is the sub-category index of spare parts (as shown in Table 4, if the amount of data corresponding to the sub-category is small, it will automatically switch to the data corresponding to the total category); θ2 is the proportion of spare parts items in the disposal time, and the default value is 50%. The user can adjust the proportion configuration value as needed. If it is judged by manual experience that the disposal difficulty of the spare part is greater than the average level of the category, the θ2 value can be increased, and the disposal time of obsolete products will be longer. If it is judged by manual experience that the disposal difficulty of the spare part is lower than the average level of the category, the θ2 value can be reduced, and the disposal time of obsolete products will be shorter. f1(θ1,θ2) is the disposal time of low-importance spare parts of the category calculated based on the spare part category index and the proportion of spare parts items. θ3 is the importance mark of spare parts, f2(θ1,θ3) is the difference between "high importance (non-nuclear regulation)" and "low importance" of the total category corresponding to the spare parts sub-category index θ1. If the spare parts to be calculated are spare parts of low importance, the value of this part is 0. f3(θ1,θ3) is the difference between "high importance (nuclear regulation)" and "high importance (non-nuclear regulation)" of the total category corresponding to the spare parts sub-category index θ1. If the spare parts to be calculated are spare parts of non-nuclear regulation, the value of this part is 0.

[0235] In this embodiment, the corresponding disposal time can be finely allocated to spare parts of different importance levels through the type of spare parts, the proportion of the number of items and the disposal difficulty, so as to ensure the rational allocation of resources and avoid the inefficiency or waste of resources that may be caused by a "one-size-fits-all" disposal strategy.

[0236] In one embodiment, Fig.23 As shown, the implementation of step S105 includes the following steps S801 to S804:

[0237] Step S801, obtaining the predicted procurement time of the obsolete spare parts and the waiting time for indicating the acceptance warehousing and work order collection time of the obsolete spare parts.

[0238] Step S802, performing weighted summation on the predicted procurement time, the predicted handling time and the waiting time to obtain the predicted processing time.

[0239] In the application, the computer equipment obtains the predicted procurement time ΔT3 of obsolete spare parts, the predicted disposal time ΔT2 of obsolete spare parts, the waiting time ΔT4 for indicating the acceptance warehousing and work order collection time, and the weights corresponding to ΔT3, ΔT2, and ΔT4, and performs weighted summation based on the corresponding weights to determine the predicted processing time α2ΔT2+α3ΔT3+α4ΔT4 for obsolete spare parts. If α2=α3=α4=1, then the predicted processing time = ΔT2+ΔT3+ΔT4.

[0240] Step S802, calculating the difference between the predicted out-of-stock time point and the predicted processing duration to obtain an initial processing intervention time point for the obsolete spare parts.

[0241] In the application, the computer device determines the difference between the predicted out-of-stock time point t5 and the predicted processing time α2ΔT2+α3ΔT3+α4ΔT4, and uses the difference as the initial disposal intervention time point t2 for obsolete spare parts.

[0242] Step S803: Based on the relationship between the initial disposal intervention time point and the current time point, the initial disposal intervention time point is corrected to obtain the target disposal intervention time point of the obsolete spare part.

[0243] In the application, since the initial disposal intervention time point t2 of the obsolete spare parts is earlier than the current time point t0, the computer device compares the early and late relationship between the initial disposal intervention time point t2 and the current time point, and corrects the initial disposal intervention time point. The calculation formula is shown in Formula 21.

[0244]

[0245] Based on the above correction formula, if the initial disposal intervention time point t2 is earlier than or equal to the current date, the target disposal intervention time point t2 of the obsolete spare parts is equal to the current time point t0; if the initial disposal intervention time point t2 is later than the current date t0, the target disposal intervention time point t2 of the obsolete spare parts is equal to the initial disposal intervention time point t2 of the obsolete spare parts. For example, if the current time is June 2024, for a certain obsolete spare part, if the calculated initial disposal intervention time point t2 is November 2023, the target disposal intervention time point t2 of the obsolete spare parts should be corrected to June 2024; if the calculated initial disposal intervention time point t2 is November 2024, the target disposal intervention time point t2 of the obsolete spare parts is determined to be November 2024.

[0246] In this embodiment, by comprehensively considering the predicted procurement time, predicted disposal time, and the waiting time for acceptance warehousing and work order collection, and performing weighted summation, the predicted processing time obtained can fully reflect the entire cycle of spare parts from procurement to disposal. This helps to more accurately estimate the availability and disposal efficiency of spare parts, and reduce resource waste or delays caused by improper time estimation. The acquisition of the initial disposal intervention time point can provide a preliminary time benchmark for the disposal of spare parts. The target disposal intervention time point obtained by correction based on the relationship between the initial disposal intervention time point and the current time point can effectively improve the accuracy of the target disposal intervention time.

[0247] In one embodiment, Fig.24As shown, the method for obtaining the predicted procurement time of the obsolete spare parts includes the following steps S901 to S903:

[0248] Step S901, determining the procurement time of the substitute item based on the manufacturer, type of spare parts, and importance identifier of the substitute item of the obsolete spare part.

[0249] In the application, the spare parts to be purchased after the disposal of obsolete spare parts are referred to as replacement items. The spare parts procurement cycle includes the time for purchasing spare parts inquiry, signing the purchase order, and the manufacturer's production, manufacturing, and transportation time. The spare parts procurement cycle is related to the manufacturer of the spare parts replacement item, the spare parts type, and the importance of the spare parts.

[0250] Generally speaking, the procurement cycle of spare parts produced by foreign manufacturers is longer than that of spare parts produced by domestic manufacturers. The procurement cycles of different types of spare parts are also different. For example, the average procurement cycle of control valve spare parts is longer than that of thermometer spare parts. There are also certain differences in the procurement cycles of spare parts of different importance. For example, some C1 spare parts need to be supervised during the production process, and nuclear regulatory spare parts also need to be inspected. These factors will increase the procurement cycle of such spare parts. Based on the manufacturer of spare parts, type of spare parts, and importance of spare parts, the procurement cycle data of spare parts is counted, and the data with the median number of spare parts accounting for 50% is selected. The distribution results are as follows: Fig.25 As shown. Combination 1 refers to spare parts of "type 1, low importance", combination 2 refers to spare parts of "type 1, high importance (non-nuclear regulation)", combination 3 refers to spare parts of "type 1, high importance (nuclear regulation)", combination 4 refers to spare parts of "type 2, low importance", combination 5 refers to spare parts of "type 2, high importance (non-nuclear regulation)", combination 6 refers to spare parts of "type 2, high importance (nuclear regulation)", combination 7 refers to spare parts of "type 3, low importance", combination 8 refers to spare parts of "type 3, high importance (non-nuclear regulation)", and combination 9 refers to spare parts of "type 3, high importance (nuclear regulation)". Therefore, you can refer to Fig.25 The calculation method is based on the spare parts sub-category index θ1 (if the amount of data corresponding to the sub-category is small, it will automatically switch to the data corresponding to the total category), the spare parts importance identification θ3, and the domestic or foreign manufacturer identification θ4. The spare parts procurement cycle is calculated and the calculated spare parts procurement cycle value is set to f4(θ1,θ3,θ4).

[0251] Step S902, obtaining a first weight of the procurement time of the replacement item and a second weight of the initial procurement time of the original item of the obsolete spare part.

[0252] In the application, in order to improve the accuracy of the predicted spare parts procurement cycle, when calculating the procurement cycle of the replacement items of obsolete spare parts, the procurement cycle of the spare parts before the disposal of obsolete products (referred to as the original items) can also be referred to. For example, a nuclear power plant uses a pressure gauge spare part (original item) produced by a certain manufacturer. Since the spare part is discontinued and upgraded, it is included in the obsolete spare parts management of the nuclear power plant. If the original upgraded product (replacement item) of the spare part is selected for replacement during the disposal of the obsolete product, the procurement cycle of the replacement item is likely to be close to the procurement cycle of the original item; if the original manufacturer's product of the spare part is not selected during the disposal of the obsolete product, that is, it is replaced with the product of the new manufacturer, then the procurement cycle of the replacement item has no direct correlation with the procurement cycle of the original item. At this time, the procurement cycle of the replacement item can be calculated based on f4 (θ1, θ3, θ4).

[0253] In summary, the calculation method for the procurement cycle of obsolete spare parts is shown in Formula 22:

[0254] ΔT3=k1×p1+k2×f4(θ1, θ3, θ4) (22)

[0255] Among them, f4(θ1,θ3,θ4) is the spare parts procurement cycle calculated based on the spare parts subcategory index θ1, the spare parts importance identifier θ3, and the domestic or foreign manufacturer identifier θ4; p1 is the original item procurement cycle value of the obsolete spare parts; k1 and k2 are coefficients, that is, the value range is between [0,1], and the sum of the two is 1. By determining the value of k1, the value of k2 can be determined. According to the statistical distribution of the ratio of the replacement items and the original items from the same manufacturer in the obsolete disposal plan, the k1 coefficient value is calculated.

[0256] Step S903: Based on the first weight and the second weight, a weighted sum is taken of the procurement time of the substitute item and the initial procurement time to obtain a predicted procurement time of the obsolete spare part.

[0257] In applications, such as Fig.26As shown, 20 manufacturers of original items in the obsolete product disposal plan were selected, and the proportion of substitute items and original items from the same manufacturer was calculated. Taking manufacturers No. 4 and No. 10 as examples, manufacturer No. 4 had a total of 162 original items, of which 85 substitute items still selected No. 4 as the manufacturer, that is, the manufacturer consistency ratio was 52%. If the manufacturer's spare parts are subsequently subjected to obsolete product disposal, the k1 coefficient value can be set to 0.52 when predicting the procurement cycle of obsolete spare parts; manufacturer No. 10 had a total of 79 original items, of which 29 substitute items still selected No. 10 as the manufacturer, that is, the manufacturer consistency ratio was 37%. If the manufacturer's spare parts are subsequently subjected to obsolete product disposal, the k1 coefficient value can be set to 0.37 when predicting the procurement cycle of obsolete spare parts. If the amount of original item data of a manufacturer is too small (for example, less than 30 items), the average value of all manufacturers can be used instead of the manufacturer's data (such as Fig.26 The dotted line in the figure).

[0258] In this embodiment, the procurement time can be predicted more specifically through the manufacturer of the alternative item, the type of spare parts and the importance identification of the spare parts, reflecting the impact of different factors on the procurement cycle, thereby improving the prediction accuracy of the procurement time.

[0259] The embodiment of the present application also provides a device for processing obsolete spare parts, which is used to execute the steps in the above obsolete spare parts processing method embodiment. The obsolete spare parts processing device can be a virtual device (Virtual Appliance) in a computer device, which is run by a processor of the computer device, or it can be the computer device itself.

[0260] like Fig. 27 As shown, the obsolete spare parts processing device 400 provided in the embodiment of the present application includes:

[0261] The acquisition module 410 is used to acquire the cumulative demand quantity distribution associated with the specified cumulative probability of the obsolete product, and the cumulative demand quantity distribution is used to indicate the relationship between the predicted collection time point and the cumulative demand quantity for the obsolete spare parts.

[0262] The first determination module 420 is used to determine the predicted out-of-stock time point of the obsolete spare parts based on the cumulative demand quantity distribution.

[0263] The second determination module 430 is used to obtain the predicted disposal time of the obsolete spare parts, and determine the target disposal intervention time point for the obsolete spare parts based on the predicted out-of-stock time point and the predicted disposal time period.

[0264] The execution module 440 is used to execute the target disposal operation for the obsolete spare part based on the relationship between the target disposal intervention time point and the current time point.

[0265] In one embodiment, the acquisition module is also used to determine the demand distribution of the obsolete spare parts adapted to each demand type, the demand distribution is used to indicate the mapping relationship between the demand quantity and the probability of receipt for the obsolete spare parts at any predicted receipt time point; the demand distributions are summarized to obtain the total demand distribution of the obsolete spare parts corresponding to the predicted receipt time point; based on the total demand distribution of each predicted receipt time point, multiple parameter groups corresponding to the specified cumulative probability are determined, the parameter groups include the predicted receipt time point and the cumulative demand quantity at the predicted receipt time point; the multiple parameter groups are fitted to obtain the cumulative demand quantity distribution associated with the specified cumulative probability of the obsolete spare parts.

[0266] In one embodiment, the demand type is a first plan type, and the acquisition module is further used to obtain the planned demand quantity of the obsolete spare parts that is adapted to the first plan type in each maintenance project to be carried out, and the maintenance project is performed by one or more adjacent maintenance activities; obtain a first probability parameter group corresponding to each of the maintenance projects, and the first probability parameter group includes the execution probability of the project, the postponement probability of the project, and the cancellation probability of the project; for each maintenance activity, determine a plurality of possible demand quantities corresponding to the maintenance activity; wherein the possible demand quantity for non-first maintenance activities is determined based on the possible demand quantities of all previous maintenance activities; based on each of the first probability parameter groups and the plurality of possible demand quantities for each of the maintenance activities, determine a first demand distribution that is adapted to the obsolete spare parts and the time point of receipt of each of the maintenance activities.

[0267] In one embodiment, the demand type is a second plan type, and the acquisition module is further used to determine the potential requisition quantity of the obsolete spare parts that is adapted to the second plan type in each maintenance project to be carried out, and the maintenance project is performed by one or more adjacent maintenance activities; obtain a quantity parameter group corresponding to each of the maintenance projects, and the quantity parameter group includes a planned requisition quantity, a potential requisition quantity, and a historical requisition quantity peak; obtain a second probability parameter group corresponding to each of the maintenance projects, and the second probability parameter group includes a project execution probability, a project postponement probability, and a project cancellation probability; obtain a requisition probability parameter group corresponding to each of the quantity parameters for each of the maintenance projects; based on the quantity parameter group, the second probability parameter group, and the requisition probability parameter group of each of the maintenance projects, determine the second demand distribution adapted to the requisition time points of the obsolete spare parts and each of the maintenance activities.

[0268] In one embodiment, the acquisition module is also used to determine the ratio of the number of times of use to the number of calls; if the ratio is less than the use probability threshold or the number of calls for the maintenance project is zero, the demand quantity of the obsolete spare parts that is mounted in the standard package for the maintenance project and is adapted to the second plan type is used as the potential use quantity; if the ratio reaches the use probability threshold, the potential use quantity is determined based on the actual use quantity of the obsolete spare parts in the maintenance project, the total number of uses and a preset correction coefficient, and the correction coefficient is determined based on the importance identification of the obsolete spare parts and the unit price of the obsolete spare parts.

[0269] In one embodiment, the demand type is a sudden type, and the acquisition module is further used to determine the usage demand characteristics of the obsolete spare parts based on the historical sudden usage data of the obsolete spare parts, and the usage demand characteristics include the usage mean and usage standard deviation of the obsolete spare parts; based on the usage demand characteristics, a third demand distribution adapted to the sudden type is selected from a uniform distribution model and at least one spare parts demand function included in a spare parts demand function model library.

[0270] In one embodiment, the second determination module is further used to determine the spare parts disposal time corresponding to each spare part with an importance mark based on the spare parts type of the obsolete spare parts and the proportion of spare parts items, the proportion of spare parts items being determined based on the disposal difficulty of the obsolete spare parts and the average disposal difficulty level corresponding to the spare parts type of the obsolete spare parts; determine the target importance mark corresponding to the substitute item of the obsolete spare part; determine the disposal time of the substitute item based on the target importance mark and the disposal time of each spare part, and use the disposal time of the substitute item as the predicted disposal time of the obsolete spare part.

[0271] In one embodiment, the second determination module is also used to obtain the predicted procurement time of the obsolete spare parts and the waiting time for indicating the acceptance warehousing and work order collection time for the obsolete spare parts; perform weighted summation of the predicted procurement time, the predicted disposal time and the waiting time to obtain the predicted processing time; calculate the difference between the predicted out-of-stock time point and the predicted processing time to obtain the initial disposal intervention time point for the obsolete spare parts; based on the relationship between the initial disposal intervention time point and the current time point, correct the initial disposal intervention time point to obtain the target disposal intervention time point for the obsolete spare parts.

[0272] In one embodiment, the second determination module is further used to determine the procurement time of the substitute item of the obsolete spare part based on the manufacturer of the substitute item of the obsolete spare part, the spare part type, and the spare part importance identifier; obtain a first weight of the procurement time of the substitute item and a second weight of the initial procurement time of the original item of the obsolete spare part; and based on the first weight and the second weight, perform a weighted summation of the procurement time of the substitute item and the initial procurement time to obtain a predicted procurement time of the obsolete spare part.

[0273] In application, each module in the obsolete spare parts processing device can be a software program module, or can be implemented by different logic circuits integrated in a processor, or can be implemented by multiple distributed processors.

[0274] Fig.28 The structure diagram of a computer device for implementing a method for processing obsolete spare parts provided in an embodiment of the present application is shown in FIG. Fig.28 As shown, the computer device 1000 of this embodiment includes: at least one processor 1010 ( Fig.28 Only one is shown in the figure) a processor, a memory 1020, and a computer program 1030 stored in the memory 1020 and executable on the at least one processor 1010, and when the processor 1010 executes the computer program 1030, the steps in any of the above-mentioned data processing method embodiments are implemented.

[0275] The computer device 1000 may include, but is not limited to, a processor 1010 and a memory 1020. Those skilled in the art will appreciate that Fig.28 The computer device 1000 is merely an example and does not constitute a limitation on the computer device 1000 . The computer device 1000 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device 1000 may also include input and output devices, network access devices, etc.

[0276] The processor 1010 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 gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0277] In some embodiments, the memory 1020 may be an internal storage unit of the computer device 1000, such as a hard disk or memory of the computer device 1000. In other embodiments, the memory 1020 may also be an external storage device of the computer device 1000, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device 1000. Further, the memory 1020 may also include both an internal storage unit of the computer device 1000 and an external storage device. The memory 1020 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory 1020 may also be used to temporarily store data that has been output or is to be output.

[0278] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0279] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0280] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0281] An embodiment of the present application provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0282] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electric carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0283] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0284] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0285] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0286] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0287] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for processing obsolete spare parts, characterized in that: include: Obtaining a cumulative demand quantity distribution associated with a specified cumulative probability of obsolete spare parts, wherein the cumulative demand quantity distribution is used to indicate a relationship between a predicted time point for the obsolete spare parts and the cumulative demand quantity; Determining a predicted out-of-stock time point of the obsolete spare parts based on the cumulative demand quantity distribution; Obtaining the predicted disposal duration of the obsolete spare parts, and determining a target disposal intervention time point for the obsolete spare parts based on the predicted out-of-stock time point and the predicted disposal duration; Based on the relationship between the target disposal intervention time point and the current time point, a target disposal operation for the obsolete spare part is performed.

2. The method for processing obsolete spare parts according to claim 1, characterized in that: The step of obtaining the cumulative demand quantity distribution associated with the specified cumulative probability of obsolete spare parts includes: Determine the demand distribution that the obsolete spare parts are adapted to each demand type, where the demand distribution is used to indicate a mapping relationship between the demand quantity and the probability of use of the obsolete spare parts at any predicted use time point; Summarizing the demand distributions to obtain the total demand distribution at the predicted collection time point; Based on the total demand distribution of each of the predicted collection time points, determining a plurality of parameter groups corresponding to the specified cumulative probability, the parameter groups including the predicted collection time points and the cumulative demand quantities at the predicted collection time points; The multiple parameter groups are fitted to obtain the cumulative demand quantity distribution associated with the specified cumulative probability of obsolete spare parts.

3. The method for processing obsolete spare parts according to claim 2, characterized in that: The demand type is a first planning type, and determining the demand distribution that the obsolete spare parts are adapted to each demand type includes: Obtaining the planned required quantity of the obsolete spare parts adapted to the first plan type in each maintenance project to be carried out, wherein the maintenance project is performed by one or more adjacent maintenance activities; Acquire a first probability parameter group corresponding to each maintenance item, wherein the first probability parameter group includes an execution probability of the item, a postponement probability of the item, and a cancellation probability of the item; For each maintenance activity, determining multiple possible demand quantities corresponding to the maintenance activity; wherein the possible demand quantity of a maintenance activity other than the first maintenance activity is determined based on the possible demand quantities of all previous maintenance activities; Based on each of the first probability parameter groups and a plurality of possible demand quantities for each of the maintenance activities, a first demand distribution adapted to the obsolete spare parts and the collection time points for each of the maintenance activities is determined.

4. The method for processing obsolete spare parts according to claim 2, characterized in that: The demand type is the second planning type, and determining the demand distribution that the obsolete spare parts are adapted to each demand type includes: Determining the potential requisition quantity of the obsolete spare parts adapted to the second plan type in each maintenance project to be carried out, wherein the maintenance project is performed by one or more adjacent maintenance activities; Acquire a quantity parameter group corresponding to each maintenance item, wherein the quantity parameter group includes a planned collection quantity, a potential collection quantity, and a historical collection quantity peak value; Acquire a second probability parameter group corresponding to each maintenance item, wherein the second probability parameter group includes an execution probability of the item, a postponed execution probability of the item, and a cancellation probability of the item; Obtaining a collection probability parameter group corresponding to each maintenance item and each quantity parameter; Based on the quantity parameter group, the second probability parameter group and the collection probability parameter group of each of the maintenance items, a second demand distribution adapted to the collection time point of the obsolete spare parts and each of the maintenance activities is determined.

5. The method for processing obsolete spare parts according to claim 4, characterized in that: The step of determining the potential number of obsolete spare parts that are compatible with the second plan type in each maintenance project to be carried out includes: Determining the ratio of the number of times of use to the number of times of calls; If the ratio is less than the probability threshold of use or the number of calls for the maintenance project is zero, the required quantity of the obsolete spare parts that is mounted in the standard package of the maintenance project and is adapted to the second plan type is used as the potential use quantity; If the ratio reaches the probability threshold of use, the potential use quantity is determined based on the actual use quantity of the obsolete spare parts in the targeted maintenance project, the total number of uses and a preset correction factor, and the correction factor is determined based on the importance identification of the obsolete spare parts and the unit price of the obsolete spare parts.

6. The method for processing obsolete spare parts according to claim 2, characterized in that: The demand type is a sudden type, and determining the demand distribution that the obsolete spare parts are adapted to each demand type includes: Based on the historical burst collection data of the obsolete spare parts, determining the collection demand characteristics of the obsolete spare parts, wherein the collection demand characteristics include the collection mean and collection standard deviation of the obsolete spare parts; Based on the usage demand characteristics, a third demand distribution adapted to the burst type is selected from a uniform distribution model and at least one spare parts demand function included in a spare parts demand function model library.

7. The method for processing obsolete spare parts according to claim 1, characterized in that: The obtaining of the predicted disposal time of the obsolete spare parts includes: Determine the spare parts disposal time corresponding to each spare part with each importance mark based on the spare parts type and the percentage of spare parts items of the obsolete spare parts, wherein the percentage of spare parts items is determined based on the disposal difficulty of the obsolete spare parts and the average level of disposal difficulty corresponding to the spare parts type of the obsolete spare parts; Determine the target importance identification corresponding to the replacement items of the obsolete spare parts; Based on the target importance identifier and the disposal time of each spare part, the disposal time of the substitute item is determined, and the disposal time of the substitute item is used as the predicted disposal time of the obsolete spare part.

8. The method for processing obsolete spare parts according to claim 1, characterized in that: The determining of the target disposal intervention time point of the obsolete spare parts based on the predicted out-of-stock time point and the predicted disposal duration includes: Obtaining the predicted procurement time of the obsolete spare parts and the waiting time for indicating the acceptance warehousing and work order collection time of the obsolete spare parts; Taking a weighted sum of the predicted procurement time, the predicted handling time, and the waiting time to obtain a predicted processing time; The predicted out-of-stock time point and the predicted processing time are subtracted to obtain an initial disposal intervention time point for the obsolete spare parts; Based on the relationship between the initial disposal intervention time point and the current time point, the initial disposal intervention time point is corrected to obtain the target disposal intervention time point of the obsolete spare part.

9. The method for processing obsolete spare parts according to claim 8, characterized in that: The obtaining of the predicted procurement time of the obsolete spare parts includes: Determine the procurement time of the replacement item based on the manufacturer, type and importance of the replacement item of the obsolete spare part; Obtaining a first weight of the procurement time of the replacement item and a second weight of the initial procurement time of the original item of the obsolete spare part; Based on the first weight and the second weight, a weighted sum is taken of the procurement time of the substitute item and the initial procurement time to obtain a predicted procurement time of the obsolete spare part.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for processing obsolete spare parts according to any one of claims 1 to 9 is implemented.

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