Method, device and storage medium for determining spare parts guarantee level

Abstract

The application relates to a spare part guarantee level determination method, device, equipment and storage medium. The method comprises the following steps: determining an initial guarantee level function of a target spare part according to historical consumption data and periodic inventory quantity of the target spare part in at least two historical statistical periods; determining a guarantee level correction coefficient according to the maximum consumption quantity of the target spare part in each historical statistical period, the periodic inventory quantity, the predicted consumption quantity of each candidate spare part in a next statistical period in a spare part type to which the target spare part belongs, and the maximum consumption quantity of each candidate spare part in each historical statistical period; and adjusting the initial guarantee level function according to the guarantee level correction coefficient to obtain a target guarantee level function of the target spare part. The method can improve the accuracy of the spare part guarantee level.

Description

Technical Field

[0001] This application relates to the field of nuclear power spare parts technology, and in particular to a method, apparatus, equipment and storage medium for determining spare parts availability level. Background Technology

[0002] In nuclear power plants, to ensure normal operation, maintenance personnel need to regularly inspect and maintain the equipment. During these inspections, if a damaged component is found, a new component must be retrieved from the nuclear power plant's spare parts warehouse to replace it.

[0003] Currently, spare parts availability is typically used to determine whether the quantity of spare parts in a nuclear power plant's spare parts warehouse can meet maintenance needs. However, spare parts availability is usually calculated based on the historical usage of the spare part. For spare parts with no historical usage, even if the spare parts availability meets maintenance requirements, the inventory will be zero. When a particular spare part is needed for a specific maintenance operation, situations may arise where the spare part cannot be obtained in a timely manner, affecting the normal operation of the nuclear power plant, which urgently needs to be addressed. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, equipment, and storage medium for determining spare parts availability that can improve the accuracy of spare parts availability in addressing the aforementioned technical problems.

[0005] Firstly, this application provides a method for determining the level of spare parts support. The method includes:

[0006] The initial support level function for the target spare parts is determined based on the historical requisition data and periodic inventory quantity of the target spare parts over at least two historical statistical periods.

[0007] The guarantee level correction coefficient is determined based on the maximum usage of the target spare parts in each historical statistical period, the periodic inventory quantity, the predicted usage of each candidate spare parts in the next statistical period of the spare parts type to which the target spare parts belong, and the maximum usage of each candidate spare parts in each historical statistical period.

[0008] Based on the assurance level correction coefficient, the initial assurance level function is adjusted to obtain the target assurance level function for the target spare part.

[0009] In one embodiment, the assurance level correction coefficient is determined based on the maximum usage and periodic inventory of the target spare part in each historical statistical period, as well as the predicted usage and maximum usage of each candidate spare part in each historical statistical period for the spare part of the target spare part type. This includes:

[0010] The first requisition probability distribution is determined based on the predicted requisition volume of spare parts without requisition data in the next statistical period of the spare parts type to which the target spare parts belong; wherein, spare parts without requisition data are candidate spare parts without historical requisition data in each candidate spare parts of the spare parts type.

[0011] The second usage probability distribution is determined based on the predicted usage of each candidate spare part in the next statistical period and the maximum usage of each candidate spare part in each historical statistical period.

[0012] The guarantee level correction coefficient is determined based on the maximum usage of the target spare parts in each historical statistical period, the periodic inventory quantity, the probability distribution of the first usage and the probability distribution of the second usage.

[0013] In one embodiment, the assurance level correction coefficient includes a first correction coefficient, a second correction coefficient, and a third correction coefficient; the assurance level correction coefficient is determined based on the maximum usage of the target spare part in each historical statistical period, the periodic inventory quantity, the probability distribution of the first usage quantity, and the probability distribution of the second usage quantity, including:

[0014] The first correction coefficient is determined based on the maximum usage of the target spare parts in each historical statistical period, the periodic inventory quantity, and the probability distribution of the first usage.

[0015] The probability value corresponding to the predicted consumption of zero in the first consumption probability distribution is used as the second correction coefficient.

[0016] From the second probability distribution of requisition quantity, find the probability value corresponding to the maximum requisition quantity, and use it as the third correction coefficient.

[0017] In one embodiment, a first correction coefficient is determined based on the maximum requisition quantity of the target spare part in each historical statistical period, the periodic inventory quantity, and the probability distribution of the first requisition quantity, including:

[0018] Determine the difference between the maximum usage of the target spare parts and the periodic inventory quantity in each historical statistical period;

[0019] If the quantity difference does not exceed the first threshold, the probability value corresponding to the expected quantity being zero in the first requisition probability distribution will be used as the first correction coefficient.

[0020] If the quantity difference exceeds the first threshold but does not exceed the second threshold, then the probability value corresponding to the quantity difference in the first consumption probability distribution will be used as the first correction coefficient.

[0021] If the difference in quantity exceeds the second threshold, a preset value is set for the first correction coefficient;

[0022] The first threshold is less than the second threshold.

[0023] In one embodiment, an initial availability level function for the target spare part is determined based on historical requisition data and periodic inventory quantities over at least two historical statistical periods, including:

[0024] Based on the historical usage data of the target spare parts over at least two historical statistical periods, determine at least one set of periodic statistical data for the target spare parts.

[0025] The initial support level function for the target spare parts is determined based on the periodic inventory quantity of the target spare parts and the periodic statistics of each group.

[0026] In one embodiment, the method further includes:

[0027] Determine the periodic inventory quantity of the target spare parts based on the preset inventory quantity, historical procurement cycle, and historical statistical cycle.

[0028] In one embodiment, the initial support level function for the target spare part is determined based on the periodic inventory quantity of the target spare part and the periodic statistics of each group, including:

[0029] Based on the periodic inventory quantity of the target spare parts and the periodic statistics of each group, determine the periodic support data of the target spare parts;

[0030] The initial support level function for the target spare part is determined based on the number of periodic support data points below the third threshold in the periodic support data of the target spare part, and the total number of sets of periodic statistical data.

[0031] In one embodiment, based on historical requisition data of the target spare part over at least two historical statistical periods, at least one set of periodic statistical data for the target spare part is determined, including:

[0032] Based on the historical requisition data of the target spare parts under each demand type in at least two historical statistical periods, and the corresponding weighting coefficients under each demand type, determine at least one set of periodic statistical data for the target spare parts.

[0033] In one embodiment, the method further includes:

[0034] Based on the lead time, reserve ratio, and reserve accuracy of the target spare parts, as well as the spare parts procurement cycle, determine the weight coefficient of the target spare parts under each demand type.

[0035] Secondly, this application also provides a device for determining the level of spare parts availability. The device includes:

[0036] The initial support level determination module is used to determine the initial support level function of the target spare part based on the historical requisition data and periodic inventory quantity of the target spare part in at least two historical statistical periods.

[0037] The correction coefficient determination module is used to determine the guarantee level correction coefficient based on the maximum usage of the target spare parts in each historical statistical period, the periodic inventory quantity, the predicted usage of each candidate spare parts in the next statistical period of the spare parts type to which the target spare parts belong, and the maximum usage of each candidate spare parts in each historical statistical period.

[0038] The target assurance level determination module is used to adjust the initial assurance level function according to the assurance level correction coefficient to obtain the target assurance level function of the target spare part.

[0039] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in any of the embodiments of the first aspect described above.

[0040] Fourthly, this application also provides a computer-readable storage medium. This computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps in any of the embodiments of the first aspect described above.

[0041] Fifthly, this application also provides a computer program product. This computer program product includes a computer program that, when executed by a processor, implements the steps in any of the embodiments of the first aspect described above.

[0042] The aforementioned method, apparatus, equipment, and storage medium for determining the initial support level function of a target spare part, based on historical requisition data and periodic inventory data over at least two historical statistical periods, are highly efficient because obtaining historical requisition data and periodic inventory data for the target spare part is very convenient. Furthermore, since data from at least two historical statistical periods is obtained, the determined initial support level function is more accurate. Further, this embodiment does not directly use the initial support level function to determine the support level of the target spare part. Instead, it determines a support level correction coefficient based on the maximum requisition quantity of the target spare part in each historical statistical period, the periodic inventory quantity, the predicted requisition quantity of each candidate spare part in the next statistical period for the target spare part's spare part type, and the maximum requisition quantity of each candidate spare part in each historical statistical period. The initial support level function of the target spare part is then corrected using the support level correction coefficient to obtain the target support level function of the target spare part. Because the determination of the support level correction coefficient incorporates the maximum usage of the target spare part in each historical statistical period, the periodic inventory quantity, the predicted usage of each candidate spare part in the next statistical period within the target spare part's spare part type, and the maximum usage of each candidate spare part in each historical statistical period, an accurate spare part support level correction coefficient can be determined even if the target spare part lacks historical usage data, using the usage data of other candidate spare parts in its spare part type. Therefore, all spare parts can be used to determine a more reasonable spare part support level function in this way, thereby improving the accuracy of spare part support levels. Attached Figure Description

[0043] Figure 1 This embodiment provides an application environment diagram for a method for determining spare parts support levels.

[0044] Figure 2 A flowchart illustrating the first method for determining spare parts support level provided in this embodiment;

[0045] Figure 3 This embodiment provides a flowchart for determining the protection level correction coefficient.

[0046] Figure 4 This embodiment provides a flowchart illustrating the determination of the initial support level function for a target spare part.

[0047] Figure 5 A flowchart illustrating the second method for determining the spare parts support level provided in this embodiment;

[0048] Figure 6 This is a structural block diagram of the first spare parts support level determination device provided in this embodiment;

[0049] Figure 7 This is a structural block diagram of the device for determining the second type of spare parts support level provided in this embodiment;

[0050] Figure 8 This is a structural block diagram of the third spare parts support level determination device provided in this embodiment;

[0051] Figure 9 This is a structural block diagram of the fourth spare parts support level determination device provided in this embodiment;

[0052] Figure 10 This is an internal structural diagram of a computer device provided in this embodiment. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0054] Currently, determining spare parts availability is typically based on the annual requisition data for the past five years. The minimum inventory parameter is compared to this data; if the minimum inventory parameter is greater than or equal to the annual requisition data, the parts are considered available; otherwise, they are considered unavailable. However, this method only provides six possible availability rates: 100%, 80%, 60%, 40%, 20%, and 0%, resulting in coarse-grained calculations. Essentially, it assumes that future spare parts demand will be the same as historical requisition volume, thus using historical requisition volume to represent future demand when compared to the minimum inventory parameter. However, future spare parts demand may exceed historical requisition volume. Calculating spare parts availability solely based on historical requisition data can lead to inaccurate results, necessitating correction of the calculated availability level data.

[0055] For example, consider 1000 spare parts with a historical issuance quantity of 0 and a procurement cycle of 365 days. If the minimum inventory parameter for all spare parts is set to 0, the availability level for each spare part calculated using the current method is 100%, and the average availability level for all spare parts in this batch is also 100%. Maintenance personnel, seeing a 100% availability level, will not consider increasing the inventory of these spare parts. However, if these spare parts are needed during a maintenance process but cannot be obtained immediately, it will affect equipment maintenance.

[0056] The method for determining the spare parts support level provided in this application embodiment can be applied to, for example, Figure 1 In the application environment shown, in one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows. Figure 1 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores data related to determining spare parts availability levels. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a method for determining spare parts availability levels.

[0057] In one embodiment, such as Figure 2 As shown, a method for determining the level of spare parts support is provided, which is then applied to... Figure 1 Taking a computer device as an example, the explanation includes the following steps:

[0058] S201, determine the initial support level function for the target spare parts based on the historical requisition data and periodic inventory quantity of the target spare parts in at least two historical statistical periods.

[0059] The target spare parts can be spare components stored in a warehouse that require calculation of spare parts inventory or spare parts availability levels. For example, target spare parts can be components needed to assemble equipment in a nuclear power plant, such as temperature transmitters. The historical statistical period can be a pre-determined statistical period, such as a month or a year, and the specific statistical period can be adjusted based on actual needs. Historical requisition data can be data used to characterize the quantity or time of requisition of the target spare parts within at least two historical statistical periods. Historical requisition data can be represented in matrix or tabular form, without limitation. The periodic inventory quantity can be the inventory quantity of the target spare parts within one statistical period. The initial availability level function can be a function used to calculate the availability level of the target spare parts. It is understood that the initial availability level of the target spare parts determined based on the initial availability level function is inaccurate, and the initial availability level functions corresponding to each spare part can be different.

[0060] Optionally, in this embodiment, historical requisition data of the target spare parts within a historical time period can be obtained. This historical requisition data is then organized according to a pre-determined historical statistical period to determine the historical requisition data of the target spare parts within each historical statistical period. For example, taking a monthly historical statistical period, the obtained requisition data of the target spare parts in each month can be used as the historical requisition data corresponding to one historical statistical period. For example, taking a historical time period of 5 years and a historical statistical period of 1 year, to make the historical requisition data within the determined historical statistical period more accurate, the historical requisition data of the target spare parts for each month within the past 5 years can be obtained. Then, based on the determined monthly historical requisition data, the historical requisition data of the target spare parts for each year is statistically organized and used as the historical requisition data of the target spare parts within each historical statistical period.

[0061] It should be noted that in the process of compiling the historical requisition data for the target spare parts each year, the historical requisition data for each month within the past five years is not simply the sum of the historical requisition data for each month within that year (for example, the historical requisition data for each month within the past five years can only determine the historical requisition data for five historical statistical periods). Instead, the historical requisition data for each 12 months is used as the historical requisition data for one year. For example, the sum of the historical requisition data from January 2018 to January 2019 can be used as one historical statistical period; the sum of the historical requisition data from February 2018 to February 2019 can be used as another historical statistical period. This allows for a larger number of historical statistical periods to be determined, thus making the initial support level function for the target spare parts determined based on historical requisition data and periodic inventory quantities within at least two historical statistical periods more accurate.

[0062] Optionally, the periodic inventory quantity can be preset, i.e., a pre-set inventory quantity; or it can be obtained by processing the pre-set inventory quantity, without limitation. For example, when the periodic inventory quantity is obtained by processing the pre-set inventory quantity, its determination method can be: determining the periodic inventory quantity of the target spare part based on the pre-set inventory quantity of the target spare part, the historical procurement cycle, and the historical statistical cycle. Among them, the historical procurement cycle is used to characterize the unit procurement cycle of the target spare part within a historical time period. Specifically, in this embodiment, the periodic inventory quantity of the target spare part can be determined according to the pre-determined periodic inventory quantity determination method based on the pre-set inventory quantity of the target spare part, the historical procurement cycle, and the historical statistical cycle. For example, taking the value of the pre-set inventory quantity of the target spare part as x1, the historical procurement cycle as T, and the historical statistical cycle as 365 days as an example, the periodic inventory quantity of the target spare part can be determined by the following formula (1):

[0063]

[0064] In the formula, x2 is the periodic inventory quantity of the target spare part; x1 is the preset inventory quantity of the target spare part; and T is the historical procurement cycle of the target spare part.

[0065] It should be noted that when determining the periodic inventory quantity of the target spare part using the above method, the preset inventory quantity of the target spare part is based on the target spare part's corresponding historical procurement cycle. Therefore, when the historical procurement cycle is inconsistent with the historical statistical cycle set in this embodiment, the value of the preset inventory quantity will be different from the value of the periodic inventory quantity. Determining the periodic inventory quantity of the target spare part using the above method, making it match the historical statistical cycle, facilitates subsequent processing.

[0066] Optionally, in this embodiment, the historical requisition data and periodic inventory quantity of the target spare part within at least two historical statistical periods can be input into a pre-trained initial support level function determination model. The model parses and processes the received data and outputs the initial support level function of the target spare part. Another possible implementation is to use the periodic inventory quantity of the target spare part / historical requisition data * 100% as the initial support level function of the target spare part.

[0067] S202. Based on the maximum usage of the target spare parts in each historical statistical period, the periodic inventory quantity, the predicted usage of each candidate spare parts in the next statistical period of the spare parts type to which the target spare parts belong, and the maximum usage of each candidate spare parts in each historical statistical period, determine the guarantee level correction coefficient.

[0068] The maximum requisition quantity within a historical statistical period can be the historical requisition quantity corresponding to the maximum historical requisition quantity of the target spare part within each historical statistical period. Candidate spare parts can be all spare parts of the spare part type to which the target spare part belongs, including the target spare part. The predicted requisition quantity can be a pre-determined quantity predicting the requisition quantity of each candidate spare part in the next statistical period. It is understood that each historical statistical period here can be the same as at least two historical statistical periods mentioned in S201, or more. The assurance level correction coefficient is used to adjust the initial assurance level function of the target spare part. Optionally, in this embodiment, the number of assurance level correction coefficients can be one or more.

[0069] For example, in this embodiment, the method for determining the assurance level correction coefficient may be to input the maximum usage of the target spare part in each historical statistical period, the periodic inventory quantity, the predicted usage of each candidate spare part in the next statistical period of the spare part type to which the target spare part belongs, and the maximum usage of each candidate spare part in each historical statistical period into a pre-trained assurance level correction coefficient determination model. The model parses and processes the received data and outputs the assurance level correction coefficient of the target spare part.

[0070] Another possible approach is to determine the first requisition probability distribution based on the predicted requisition volume of spare parts without requisition data in the next statistical period of the spare parts type to which the target spare parts belong; to determine the second requisition probability distribution based on the predicted requisition volume of each candidate spare parts in the next statistical period of the spare parts type to which the target spare parts belong, and the maximum requisition volume of each candidate spare parts in each historical statistical period; and to determine the assurance level correction coefficient based on the maximum requisition volume of the target spare parts in each historical statistical period, the periodic inventory quantity, the first requisition probability distribution, and the second requisition probability distribution.

[0071] Among them, spare parts without requisition data are candidate spare parts in the corresponding spare parts type that have no historical requisition data; the first requisition probability distribution can be the requisition probability distribution of spare parts without requisition data in the next statistical period in the spare parts type to which the target spare part belongs, used to predict the predicted requisition quantity and corresponding probability of spare parts without requisition data in the next statistical period. The second requisition probability distribution can be the probability distribution used to statistically analyze the requisition quantity of each candidate spare part in the spare parts type to which the target spare part belongs in the next statistical period, which does not exceed the historical maximum requisition quantity.

[0072] It is understandable that the above-mentioned first and second issuance probability distributions correspond to the spare part type to which the target spare part belongs. The first and second issuance probability distributions may differ for different spare part types.

[0073] Specifically, in this embodiment, the predicted requisition quantities of spare parts without requisition data in the next statistical period of the spare part type to which the target spare part belongs can be statistically analyzed and organized, and the probability corresponding to each predicted requisition quantity can be determined to form a first requisition quantity probability distribution. Correspondingly, the predicted requisition quantities of each candidate spare part in the next statistical period of the spare part type to which the target spare part belongs, as well as the maximum requisition quantities of each candidate spare part in each historical statistical period, can be statistically analyzed and organized, and the probability that the requisition quantity of each candidate spare part in the next statistical period does not exceed the historical maximum requisition quantity can be determined to form a second requisition quantity probability distribution. Further, the maximum requisition quantity of the target spare part in each historical statistical period, the periodic inventory quantity, the first requisition quantity probability distribution, and the second requisition quantity probability distribution are input into a pre-trained support level correction coefficient determination model to determine the support level correction coefficient. Alternatively, the corresponding support level correction coefficient can be found from the first and second requisition quantity probability distributions based on the maximum requisition quantity and periodic inventory quantity of the target spare part in each historical statistical period. This makes the methods for determining the support level correction coefficient more diverse.

[0074] S203, adjust the initial safeguard level function according to the safeguard level correction coefficient to obtain the target safeguard level function of the target spare part.

[0075] Specifically, in this embodiment, the product of the protection level correction coefficient and the initial protection level function can be used as the target protection level function for the target spare part. If there are multiple protection level correction coefficients, they can be merged, and then the initial protection level function can be adjusted based on the merged protection level correction coefficient. For example, merging the protection level correction coefficients can be done by adding, subtracting, or multiplying the protection level correction coefficients.

[0076] In the aforementioned method for determining the spare parts support level, the process of determining the initial support level function of the target spare parts based on historical requisition data and periodic inventory data over at least two historical statistical periods is highly efficient because the historical requisition data and periodic inventory data of the target spare parts are readily available. Furthermore, since the data obtained is from at least two historical statistical periods, the determined initial support level function is more accurate. Further, this embodiment does not directly utilize the initial support level function to determine the support level of the target spare parts. Instead, it determines a support level correction coefficient based on the maximum requisition quantity of the target spare parts in each historical statistical period, the periodic inventory quantity, the predicted requisition quantity of each candidate spare parts in the next statistical period for the spare parts type to which the target spare parts belong, and the maximum requisition quantity of each candidate spare parts in each historical statistical period. Then, the initial support level function of the target spare parts is corrected using the support level correction coefficient to obtain the target support level function of the target spare parts. Because the determination of the support level correction coefficient incorporates the maximum usage of the target spare part in each historical statistical period, the periodic inventory quantity, the predicted usage of each candidate spare part in the next statistical period within the target spare part's spare part type, and the maximum usage of each candidate spare part in each historical statistical period, an accurate spare part support level correction coefficient can be determined even if the target spare part lacks historical usage data, using the usage data of other candidate spare parts in its spare part type. Therefore, all spare parts can be used to determine a more reasonable spare part support level function in this way, thereby improving the accuracy of spare part support levels.

[0077] Furthermore, in one embodiment, to make the target protection level function adjusted by the protection level correction coefficient more accurate, the protection level correction coefficient may include a first correction coefficient, a second correction coefficient, and a third correction coefficient. For example... Figure 3 As shown, the process of determining the assurance level correction coefficient based on the maximum requisition quantity, periodic inventory quantity, probability distribution of the first requisition quantity, and probability distribution of the second requisition quantity of the target spare parts in each historical statistical period is described in detail. It may include the following steps:

[0078] S301. Determine the first correction coefficient based on the maximum usage of the target spare parts in each historical statistical period, the periodic inventory quantity, and the probability distribution of the first usage.

[0079] The first correction factor can be one of the protection level correction factors, used to adjust the initial protection level.

[0080] Optionally, all spare parts in the target spare part type are spare parts of the same category and from the same supplier as the target spare part. The same category can be classified according to the attributes of the spare parts, and each spare part corresponds to a supplier. In this embodiment, a category code can be set for the first type of spare parts. Based on the category code of the spare part type to which the target spare part belongs, the probability distribution of the first requisition quantity corresponding to the spare part type to which the target spare part belongs can be determined.

[0081] For example, spare parts types can be set as needed to include rotating machinery, pumps, valves, general machinery, chemical consumables, instruments, and electrical components. The initial category codes for each type of spare part can be set to 10000, 20000, 30000, 40000, 50000, 60000, and 70000, respectively. Further, based on the classification principles of spare parts types, the top 9 suppliers in terms of the number of spare parts codes for each type are counted, and their supplier codes are recorded as 1 to 9. If a supplier is not among the top 9 suppliers in terms of the number of spare parts codes for that type, their supplier code is recorded as 0. The initial category codes and supplier codes are combined to construct the category code. For example, in the spare parts classification diagram shown in Table 1 below, if Supplier A ranks 2nd in the number of spare parts for rotating machinery and 5th in the number of spare parts for temperature transmitters, then spare part code 100002 represents spare parts for rotating machinery supplied by Supplier A, and spare part code 601065 represents spare parts for temperature transmitters supplied by Supplier A.

[0082] Table 1: Spare Parts Classification Diagram

[0083]

[0084]

[0085] Therefore, in this embodiment, the corresponding spare part type can be determined based on the target spare part. The category code corresponding to the spare part type can be determined from the aforementioned spare part classification diagram table. Furthermore, the first requisition probability distribution corresponding to this spare part type can be determined. It is understood that the first requisition probability distribution corresponding to each spare part type can be pre-set. For example, it can be shown in Table 2 below:

[0086] Table 2: Probability Distribution of First Requisition Quantity

[0087] 100002 0.9879 0.9954 0.9972 0.9981 0.9988 0.9990 0.9991 0.9992 0.9992 0.9993 601065 0.9501 0.9859 0.9937 0.9961 0.9966 0.9971 0.9981 0.9991 0.9991 0.9995

[0088] In Table 2, J0 to J9 represent the probability distributions of the quantities to be issued in the next year as 0, [0,1], [0,2], [0,3], [0,4], [0,5], [0,6], [0,7], [0,8], and [0,9], respectively. For example, J0 for spare parts with spare parts category code 100002 represents a 98.79% probability that the quantity of spare parts with no issuance data in the next statistical period will be ≤0, and J6 represents a 99.91% probability that the quantity of spare parts with no issuance data in the next statistical period will be ≤6.

[0089] The above Table 2 can be represented in matrix form as follows: (2)

[0090]

[0091] In the formula, P is the probability distribution of the first consumption quantity; This indicates the probability that a spare part with no requisition data in the spare part type with category code i1 will have a requisition quantity ≤ 0 in the next statistical period.

[0092] Furthermore, the quantity difference between the maximum requisition quantity of the target spare part and the periodic inventory quantity in each historical statistical period is determined; if the quantity difference does not exceed the first threshold, the probability value corresponding to the expected requisition quantity of zero in the first requisition quantity probability distribution is used as the first correction coefficient; if the quantity difference exceeds the first threshold but does not exceed the second threshold, the probability value corresponding to the quantity difference in the first requisition quantity probability distribution is used as the first correction coefficient; if the quantity difference exceeds the second threshold, a preset value is set for the first correction coefficient.

[0093] The first threshold is less than the second threshold. Taking the maximum requisition quantity of the target spare part in each historical statistical period as x3 and the periodic inventory quantity as x2 as an example, the quantity difference between the maximum requisition quantity of the target spare part in each historical statistical period and the periodic inventory quantity is x2-x3. Compare the calculated quantity difference with the first threshold. If the quantity difference does not exceed the first threshold, the probability value corresponding to the expected requisition quantity of zero from the first requisition quantity probability distribution is used as the first correction coefficient. For example, if the category code of the spare part type to which the target spare part belongs is 100002, the probability value of the row corresponding to 100002 and the column corresponding to J0 can be determined from Table 2 above as the first correction coefficient. Or, the above formula (2) can be used as the first correction coefficient. The corresponding probability value is used as the first correction coefficient (i.e., 0.9879).

[0094] Continuing with the example of the spare part type code 100002, if the quantity difference exceeds the first threshold but does not exceed the second threshold, the probability value corresponding to the quantity difference in Table 2 above is used as the first correction coefficient. If the quantity difference is 3, then the probability value of the row corresponding to 100002 and the column corresponding to J3 is determined to be the first correction coefficient. Or, in the above formula (2)... The corresponding probability value is used as the first correction coefficient (i.e., 0.9981). If the difference in quantity exceeds the second threshold, a preset value (e.g., 1) is set for the first correction coefficient.

[0095] Taking a first threshold of 0, a second threshold of 9, and a third threshold of 1 as an example, the formula for determining the first correction coefficient can be shown in the following formula (3):

[0096]

[0097] In the formula, h1(i,j) is the first correction coefficient; x2-x3 is the difference between the maximum usage of the target spare parts and the inventory quantity in each historical statistical period; This represents the probability value corresponding to the expected requisition quantity of spare parts with no requisition data in the requisition probability distribution of the spare parts with category code i in the next statistical period when the requisition quantity is j.

[0098] In the above embodiments, the determination of the first correction coefficient is divided into three cases, with different first correction coefficients corresponding to different cases, which can further make the determined first correction coefficient more accurate.

[0099] S302, take the probability value corresponding to the predicted consumption amount being zero in the first consumption quantity probability distribution as the second correction coefficient.

[0100] The second correction factor can be one of the two correction factors for the protection level, used to adjust the initial protection level.

[0101] Specifically, continuing with the spare parts classification diagram shown in Table 1 and the first requisition probability distribution table shown in Table 2 as examples, in this embodiment, the second correction coefficient can be the probability value corresponding to the predicted requisition quantity being zero, which is directly taken from the first requisition probability distribution. The specific determination method is the same as the determination method when the quantity difference does not exceed the first threshold in the above-mentioned first correction coefficient determination method, and will not be repeated here. For example, the formula for determining the second correction coefficient can be shown in the following formula (4):

[0102]

[0103] In the formula, h2(i,j) is the second correction coefficient; For spare parts with category code i, the probability value corresponding to the expected issuance quantity of 0 in the issuance probability distribution of spare parts without issuance data in the next statistical period.

[0104] S303, find the probability value corresponding to the maximum consumption from the second consumption probability distribution, and use it as the third correction coefficient.

[0105] The third correction factor can be one of the three support level correction factors used to adjust the initial support level. Each historical statistical period can be the sum of at least two historical statistical periods mentioned in S201, or it can be longer than the sum of at least two historical statistical periods. The historical maximum usage quantity represents the maximum usage quantity of this type of spare parts within each statistical period.

[0106] In this embodiment, the probability distribution of the second requisition quantity can be predetermined, as shown in Table 3 below, which is one such probability distribution of the second requisition quantity provided in this embodiment:

[0107] Table 3: Probability Distribution of Second Requisition Quantity

[0108]

[0109]

[0110] In Table 3, K0 to K9 represent the probability distributions of the maximum historical usage of this spare part type in each historical statistical period, corresponding to 0, (0,1], (1,2], (2,3], (3,4], (4,5], (5,6], (6,7], (7,8], and (9,∞). For example, K2 for spare part category code 100002 represents that the maximum historical usage of this type of spare part is (1,2], and the probability that its usage in the next year will not exceed the maximum historical usage is 96.63%. K7 represents that the maximum historical usage of this type of spare part is (6,7], and the probability that its usage in the next year will not exceed the maximum historical usage is 93.61%.

[0111] The above Table 3 can be represented in matrix form as follows: (5)

[0112]

[0113] In the formula, Q represents the probability distribution of the second consumption amount; This indicates the probability that the usage of spare parts type i1 in the next year will not exceed the historical maximum usage.

[0114] For example, if the category code of the spare part type to which the target spare part belongs is 100002 and the historical maximum usage is 2, then the correction factor can be determined to be 0.9663 from Table 3.

[0115] The formula for determining the third correction factor can be shown in formula (6) below:

[0116]

[0117] In the formula, h3(i,k) is the third correction coefficient; The probability value that the historical maximum requisition quantity for spare parts type with category code i is less than or equal to the historical maximum requisition quantity in the next year when the historical maximum requisition quantity is k.

[0118] In the above embodiments, the protection level correction coefficients include a first correction coefficient, a second correction coefficient, and a third correction coefficient, and the determination method of each correction coefficient is specifically described, so that the determined three correction coefficients have a better correction effect on the initial protection level function, and further improve the accuracy of protection level determination.

[0119] In one embodiment, the target spare part has multiple demand types within a historical statistical period. Therefore, to facilitate the determination of the initial assurance level function for the target spare part, it is necessary to organize and statistically analyze the historical requisition data of the target spare part within at least two historical statistical periods. For example... Figure 4 As shown, in one embodiment, the initial availability level function for the target spare part is determined based on historical requisition data and periodic inventory quantity over at least two historical statistical periods, including:

[0120] S401, Based on the historical usage data of the target spare parts in at least two historical statistical periods, determine at least one set of periodic statistical data for the target spare parts.

[0121] The periodic statistics data can be the data corresponding to the target spare part within a statistical period. The periodic statistics data can be represented in tabular form or matrix form, without limitation.

[0122] In this embodiment, the target spare part can correspond to multiple demand types. Accordingly, the historical requisition data of the target spare part within at least two historical statistical periods includes the historical requisition data of the target spare part under multiple demand types. Taking the target spare part as a spare part in a nuclear power plant as an example, the target spare part can include 8 types of demand, including overhaul planned type A demand, overhaul planned type B demand, overhaul emergency demand, overhaul non-work order demand, daily planned type A demand, daily planned type B demand, daily emergency demand, and daily non-work order demand. Accordingly, the target spare part has historical requisition data for each type of demand. For example, the historical requisition data with a historical statistical period of months, represented in matrix form, can be shown in the following formula (7). In this matrix, each row represents the historical requisition data of the target spare part under one type; each column represents the historical requisition data of the target spare part within the same month.

[0123]

[0124] In the formula, R1 represents the historical requisition data of the target spare parts with a statistical period of months; This provides historical requisition data for the target spare part in month j, under type i. tn represents the earliest month in all historical statistical periods; t-1 represents the last month in all historical statistical periods; n = 12*(n1+1)+n2, where n1 is the number of years in the historical period (e.g., n1 = 5 if the historical period is 5 years); n2 is the current month (e.g., n2 = 7 if the current month is July). i = 1, 2, 3, ..., 8 represent planned overhaul A-type demand, planned overhaul B-type demand, sudden overhaul demand, non-work order overhaul demand, routine planned A-type demand, routine planned B-type demand, routine sudden demand, and routine non-work order demand, respectively.

[0125] Formula (7) above shows the historical requisition data of the target spare parts under different demand types each month. By summing the historical requisition data of the target spare parts under different demand types each month, the historical requisition data of the target spare parts for each month can be obtained, that is, the historical requisition data within the historical statistical period. For example, at least one set of periodic statistical data of the target spare parts can be determined based on the historical requisition data of the target spare parts under each demand type within at least two historical statistical periods and the corresponding weight coefficients under each demand type. Each demand type corresponds to a weight coefficient. For the target spare parts, the guarantee type of the target spare parts can be adjusted by adjusting the weight coefficients corresponding to each demand type. Specifically, in this embodiment, the historical requisition data of the target spare parts in the same month in the above formula (7) can be weighted and summed, and then the above formula (7) can be represented in the form of a matrix. Each element in the matrix represents a set of periodic statistical data of the target spare parts. For example, at least one set of periodic statistical data of the target spare parts can be shown in the following formula (8):

[0126] R2=[r t-n ,r t-n+1 ,r t-n+2 ,...,r t-1 (8)

[0127] In the formula, R2 represents the periodic statistics of the target spare part; r t-n r t-n+1 ...t-1 represents a set of periodic statistical data for the target spare parts. Among them, k i r represents the weighting coefficient corresponding to the i-th demand type. t-n+1 ...the formula for calculating t-1 and r t-n The calculation formula is similar and will not be elaborated here.

[0128] Formula (8) above shows the historical requisition data of the target spare parts with a historical statistical period of months. If the historical statistical period required in this embodiment is years, then the above historical requisition data needs to be sorted and statistically analyzed to obtain the historical requisition data of the target spare parts with a historical statistical period of years. For example, the historical requisition data of the target spare parts with a historical statistical period of years (i.e., statistical data for each period) can be determined by the following formula (9):

[0129]

[0130] In the formula, R3 represents the historical requisition data of the target spare parts with a statistical period of months; and r in formula (8) t-n Taking January 2018 as an example, This can represent the period from January 2018 to January 2019; This refers to the period from February 2018 to February 2019.

[0131] Furthermore, to improve the process of determining at least one set of periodic statistical data for the target spare parts, this embodiment also provides a specific method for determining the corresponding weight coefficients for each demand type: based on the lead time, reserve ratio, and reserve accuracy of the target spare parts, as well as the spare parts procurement cycle, the weight coefficients corresponding to the target spare parts for each demand type are determined.

[0132] The spare parts procurement cycle characterizes how often a target spare part is procured, and this can be predetermined. The lead time indicates how far in advance a reservation for the target spare part needs to be made. For example, if the target spare part corresponds to a planned overhaul, a reservation work order needs to be issued 13 months in advance. If the target spare part corresponds to a routine planned demand, a reservation work order needs to be issued 11 months in advance. The reservation ratio indicates how many spare parts of the same type will be reserved within the target spare part's category. For example, for planned Category A demand, most spare parts will be reserved before a major overhaul; for planned Category B demand, only a few spare parts will be reserved in advance. The reservation accuracy rate indicates the percentage of correctly predicted reservations for the target spare part; different demand types require different reservation accuracy rates. The lead time, reservation ratio, and reservation accuracy rate of the target spare part (collectively referred to as reservation influencing factors) are all predetermined. For example, the reservation lead time, reservation ratio, and reservation accuracy for different types can be shown in Table 4 below:

[0133] Table 4: Reference Table for Factors Affecting Reserved Space

[0134]

[0135]

[0136] Different nuclear power plants have different requirements for spare parts inventory management. These differences are reflected in the types of spare parts required. For certain types of spare parts, work orders can be made in advance to reserve them, triggering procurement based on the reserved quantity. Once the spare parts arrive, they will meet the spare parts requirements for that type of demand. The factors affecting advance work order reservation are the three mentioned above: reservation lead time, reservation ratio, and reservation accuracy.

[0137] Based on the spare parts demand type of the target spare parts and in conjunction with the above-mentioned reference table of reserved influencing factors, the weight coefficient of the target spare parts under each demand type can be determined by the following formula (10).

[0138]

[0139] In the formula, k i This represents the weighting coefficient of the target spare part under each demand type; t is the spare part procurement cycle of the target spare part. This indicates the lead time for the target spare part under the i-th type of demand. This indicates the reservation ratio of the target spare parts under the i-th type of demand; This represents the reservation accuracy rate for the target spare part under the i-th type of demand.

[0140] Based on the above formula (10) and Table 4, the weight coefficient value k corresponding to the target spare part under each demand type can be determined as [0, 0.56, 0.89, 0.89, 0, 0.46, 0.89, 0.89]. When any one of the reservation lead time, reservation ratio, and reservation accuracy of the target spare part under each demand type changes, the weight coefficient value k corresponding to the target spare part under each demand type will change accordingly.

[0141] The above method is used to determine the weight coefficients of the target under each demand type, making the determination of the weight coefficients simpler and more convenient, and laying the foundation for determining the initial guarantee level function of the target spare parts.

[0142] S402, determine the initial support level function for the target spare parts based on the periodic inventory quantity of the target spare parts and the periodic statistics of each group.

[0143] Optionally, in this embodiment, the periodic inventory quantity of the target spare parts and the periodic statistical data of each group can be input into a pre-trained initial support level determination model. The model analyzes and processes the received data and outputs the initial support level function of the target spare parts.

[0144] In one embodiment, another implementation of the initial support level function for the target spare part is provided, namely, determining the periodic support data of the target spare part based on the periodic inventory quantity of the target spare part and the periodic statistical data of each group; and determining the initial support level function of the target spare part based on the number of periodic support data of the target spare part that are less than the third threshold and the total number of groups of periodic statistical data.

[0145] Among them, the periodic support data can be used to characterize the support status of the target spare parts within a historical statistical period. The third threshold can be a pre-set threshold used to determine whether the periodic support data of the target spare parts meets the requirements.

[0146] Specifically, since the periodic inventory quantity of the target spare part represents the expected inventory quantity of the target spare part within a period, and the periodic statistical data of each group of the target spare part represents the sum of the historical requisition data corresponding to a historical statistical period, the difference between the periodic inventory quantity of the target spare part and the periodic statistical data of the corresponding period can represent whether the periodic inventory quantity can meet the historical requisition requirements. That is to say, in this embodiment, the difference between the periodic inventory quantity of the target spare part and its corresponding periodic statistical data can be used as the periodic guarantee data of the target spare part. That is to say, the difference between the periodic inventory quantity of the target spare part and the periodic statistical data in the above formula (9) is used. For example, if the periodic inventory quantity of the target spare part is represented in matrix form as R4=[x2,x2,x2,x2...x2], the calculation formula of the periodic guarantee data of the target spare part can be shown in the following formula (11):

[0147]

[0148] In the formula, R5 represents the periodic support data for the target spare part;

[0149] Each element in the table can represent the support data corresponding to the target spare part within a historical statistical period.

[0150] It should be noted that in the above formula (11), if the guarantee data corresponding to the target spare part in a historical statistical period is less than 0, it proves that the periodic inventory quantity of the target spare part in the historical statistical period does not meet the guarantee requirements.

[0151] The number of periodic support data points less than 0 in the periodic support data of the target spare part is determined, and combined with the total number of sets of periodic statistical data, the initial support level function of the target spare part is determined. For example, the number of periodic support data points less than 0 and the total number of sets of periodic statistical data in the periodic support data of the target spare part can be substituted into the formula for determining the initial support level function to obtain the initial support level function of the target spare part. For example, the formula for determining the initial support level function can be as shown in formula (12):

[0152]

[0153] In the formula, g1 is the initial support level function of the target spare part; m is the number of periodic support data that are less than 0 in the periodic support data of the target spare part; n-12 is the total number of groups of periodic statistical data.

[0154] In the above embodiments, the periodic support data of the target spare parts is determined based on the periodic inventory quantity of the target spare parts and the periodic statistical data of each group. Then, the initial support level function of the target spare parts is determined based on the number of periodic support data that are less than 0 and the total number of groups of periodic statistical data, making the method of determining the initial support level function more reasonable.

[0155] The above embodiments provide a specific method for determining the initial support level function of the target spare part, which provides a basis for subsequently determining the target support level function of the target spare part.

[0156] Furthermore, based on the first, second, and third correction coefficients determined above, the initial protection level function can be adjusted using the following formula (13):

[0157]

[0158] In the formula, f(x) represents the target support level function of the target spare part; g1 is the initial support level function; h1(i,j) is the first correction coefficient; h2(i,j) is the second correction coefficient; and h3(i,k) is the third correction coefficient.

[0159] To facilitate understanding by those skilled in the art, the method for determining the above-mentioned spare parts support level is described in detail, such as... Figure 5 As shown, the method may include:

[0160] S501, based on the historical requisition data of the target spare parts under each demand type in at least two historical statistical periods, and the corresponding weight coefficients under each demand type, determine at least one set of periodic statistical data for the target spare parts.

[0161] The weighting coefficient is determined based on the lead time, reserve ratio, and reserve accuracy of the target spare parts, as well as the spare parts procurement cycle.

[0162] S502, determine the periodic inventory quantity of the target spare parts based on the preset inventory quantity, historical procurement cycle, and historical statistical cycle.

[0163] S503 determines the periodic availability data for the target spare parts based on the periodic inventory quantity of the target spare parts and the periodic statistics of each group.

[0164] S504. Based on the number of periodic support data points less than the third threshold in the periodic support data of the target spare part, and the total number of periodic statistical data sets, determine the initial support level function of the target spare part.

[0165] S505, determine the probability distribution of the first requisition quantity based on the predicted requisition quantity of spare parts without requisition data in the next statistical period of the spare parts type to which the target spare parts belong.

[0166] Among them, spare parts without requisition data are candidate spare parts in each candidate spare part category that have no historical requisition data.

[0167] S506. Based on the predicted usage of each candidate spare part in the next statistical period of the spare part type to which the target spare part belongs, and the maximum usage of each candidate spare part in each historical statistical period, determine the probability distribution of the second usage.

[0168] S507, determine the first correction coefficient based on the maximum usage of the target spare parts in each historical statistical period, the periodic inventory quantity, and the probability distribution of the first usage.

[0169] Specifically, if the quantity difference does not exceed the first threshold, the probability value corresponding to the expected quantity being zero in the first requisition probability distribution is used as the first correction coefficient; if the quantity difference exceeds the first threshold but does not exceed the second threshold, the probability value corresponding to the quantity difference in the first requisition probability distribution is used as the first correction coefficient; if the quantity difference exceeds the second threshold, a preset value is set for the first correction coefficient; wherein, the first threshold is less than the second threshold.

[0170] S508, the probability value corresponding to the predicted consumption amount being zero in the first consumption quantity probability distribution is used as the second correction coefficient.

[0171] S509, find the probability value corresponding to the maximum consumption from the second consumption probability distribution, and use it as the third correction coefficient.

[0172] S510, adjust the initial safeguard level function according to the safeguard level correction coefficient to obtain the target safeguard level function of the target spare part.

[0173] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0174] Based on the same inventive concept, this application also provides a spare parts support level determination device for implementing the spare parts support level determination method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the spare parts support level determination device provided below can be found in the limitations of the spare parts support level determination method described above, and will not be repeated here.

[0175] In one embodiment, such as Figure 6 As shown, a spare parts support level determination device 1 is provided, comprising: an initial support level determination module 10, a correction coefficient determination module 11, and a target support level determination module 12, wherein:

[0176] The initial support level determination module 10 is used to determine the initial support level function of the target spare part based on the historical requisition data and periodic inventory quantity of the target spare part in at least two historical statistical periods.

[0177] The correction coefficient determination module 11 is used to determine the guarantee level correction coefficient based on the maximum usage of the target spare parts in each historical statistical period, the periodic inventory quantity, the predicted usage of each candidate spare parts in the next statistical period of the spare parts type to which the target spare parts belong, and the maximum usage of each candidate spare parts in each historical statistical period.

[0178] The target assurance level determination module 12 is used to adjust the initial assurance level function according to the assurance level correction coefficient to obtain the target assurance level function of the target spare part.

[0179] In one embodiment, such as Figure 7 As shown, the correction coefficient determination module 11 includes a first determination unit 110, a second determination unit 111, and a correction coefficient determination unit 112. Wherein:

[0180] The first determining unit 110 is used to determine the first requisition probability distribution based on the predicted requisition quantity of spare parts without requisition data in the next statistical period of the spare parts type to which the target spare parts belong.

[0181] Among them, spare parts without requisition data are candidate spare parts in each candidate spare part category that have no historical requisition data.

[0182] The second determining unit 111 is used to determine the second usage probability distribution based on the predicted usage of each candidate spare part in the next statistical period and the maximum usage of each candidate spare part in each historical statistical period.

[0183] The correction coefficient determination unit 112 is used to determine the guarantee level correction coefficient based on the maximum usage of the target spare parts in each historical statistical period, the periodic inventory quantity, the probability distribution of the first usage and the probability distribution of the second usage.

[0184] In one embodiment, the correction coefficient determination unit 112 includes a first determination subunit, a second determination subunit, and a third determination subunit.

[0185] in:

[0186] The first determining subunit is used to determine the first correction coefficient based on the maximum usage of the target spare parts in each historical statistical period, the periodic inventory quantity, and the probability distribution of the first usage.

[0187] The second determining sub-unit is used to take the probability value corresponding to the predicted consumption amount being zero in the first consumption quantity probability distribution as the second correction coefficient.

[0188] The third determining sub-unit is used to find the probability value corresponding to the maximum consumption from the second consumption probability distribution, and serve as the third correction coefficient.

[0189] In one embodiment, the first determining subunit is specifically used to: determine the quantity difference between the maximum requisition quantity of the target spare part and the periodic inventory quantity in each historical statistical period; if the quantity difference does not exceed a first threshold, then the probability value corresponding to the expected requisition quantity being zero in the first requisition quantity probability distribution is used as a first correction coefficient; if the quantity difference exceeds the first threshold but does not exceed a second threshold, then the probability value corresponding to the quantity difference in the first requisition quantity probability distribution is used as the first correction coefficient; if the quantity difference exceeds the second threshold, then a preset value is set for the first correction coefficient; wherein, the first threshold is less than the second threshold.

[0190] In one embodiment, such as Figure 8 As shown, the initial protection level determination module 10 includes a statistical data determination unit 100 and an initial protection level determination unit 101. Wherein:

[0191] The statistical data determination unit 100 is used to determine at least one set of periodic statistical data for the target spare parts based on the historical usage data of the target spare parts in at least two historical statistical periods.

[0192] The initial support level determination unit 101 is used to determine the initial support level function of the target spare parts based on the periodic inventory quantity of the target spare parts and the periodic statistical data of each group.

[0193] In one embodiment, such as Figure 9 As shown, the initial assurance level determination module 10 also includes an inventory quantity determination unit 102, which is used to determine the periodic inventory quantity of the target spare parts based on the preset inventory quantity of the target spare parts, the historical procurement cycle, and the historical statistical cycle.

[0194] In one embodiment, the initial protection level determination unit 101 includes a protection data determination subunit and an initial protection level determination subunit.

[0195] in:

[0196] The data assurance determination subunit is used to determine the periodic assurance data of the target spare parts based on the periodic inventory quantity of the target spare parts and the periodic statistics of each group.

[0197] The initial support level determination subunit is used to determine the initial support level function of the target spare part based on the number of periodic support data that are less than the third threshold in the periodic support data of the target spare part and the total number of groups of periodic statistics data.

[0198] In one embodiment, the statistical data determination unit 100 is specifically used to determine at least one set of periodic statistical data for the target spare parts based on the historical requisition data of the target spare parts under each demand type in at least two historical statistical periods, and the corresponding weight coefficients under each demand type.

[0199] In one embodiment, the statistical data determination unit 100 includes a weight coefficient determination subunit, which is used to determine the weight coefficient of the target spare part under each demand type based on the lead time, reserve ratio and reserve accuracy of the target spare part, as well as the spare part procurement cycle.

[0200] Each module in the aforementioned spare parts availability determination device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can invoke and execute the operations corresponding to each module.

[0201] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 10As shown, the computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for determining spare parts availability levels.

[0202] Those skilled in the art will understand that Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0203] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0204] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0205] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for determining the level of spare parts support, characterized in that, The method includes: The initial support level function of the target spare parts is determined based on the historical requisition data and periodic inventory quantity of the target spare parts in at least two historical statistical periods. Based on the predicted requisition volume of spare parts without requisition data in the next statistical period of the spare parts type to which the target spare part belongs, a first requisition volume probability distribution is determined; wherein, the spare parts without requisition data are candidate spare parts without historical requisition data among the candidate spare parts in the spare parts type; based on the predicted requisition volume of each candidate spare part in the next statistical period of the spare parts type to which the target spare part belongs, and the maximum requisition volume of each candidate spare part in each historical statistical period, a second requisition volume probability distribution is determined; based on the maximum requisition volume of the target spare part in each historical statistical period, the periodic inventory quantity, the first requisition volume probability distribution, and the second requisition volume probability distribution, a guarantee level correction coefficient is determined; The initial safeguard level function is adjusted according to the safeguard level correction coefficient to obtain the target safeguard level function of the target spare part.

2. The method according to claim 1, characterized in that, The assurance level correction coefficient includes a first correction coefficient, a second correction coefficient, and a third correction coefficient; determining the assurance level correction coefficient based on the maximum usage of the target spare parts in each historical statistical period, the periodic inventory quantity, the probability distribution of the first usage quantity, and the probability distribution of the second usage quantity includes: The first correction coefficient is determined based on the maximum usage of the target spare parts in each historical statistical period, the inventory quantity in the period, and the probability distribution of the first usage. The probability value corresponding to the predicted consumption of zero in the first consumption probability distribution is used as the second correction coefficient. From the second probability distribution of requisition quantity, find the probability value corresponding to the maximum requisition quantity, and use it as the third correction coefficient.

3. The method according to claim 2, characterized in that, The step of determining the first correction coefficient based on the maximum usage of the target spare part in each historical statistical period, the periodic inventory quantity, and the probability distribution of the first usage includes: Determine the quantity difference between the maximum usage of the target spare part in each historical statistical period and the inventory quantity in that period; If the quantity difference does not exceed the first threshold, then the probability value corresponding to the expected quantity being zero in the first requisition probability distribution is used as the first correction coefficient. If the quantity difference exceeds the first threshold but does not exceed the second threshold, then the probability value corresponding to the quantity difference in the first consumption probability distribution is used as the first correction coefficient. If the difference in quantity exceeds the second threshold, then a preset value is set for the first correction coefficient; Wherein, the first threshold is less than the second threshold.

4. The method according to claim 1, characterized in that, The step of determining the initial availability level function for the target spare part based on historical requisition data and periodic inventory quantity over at least two historical statistical periods includes: Based on the historical requisition data of the target spare parts over at least two historical statistical periods, determine at least one set of periodic statistical data for the target spare parts; Based on the periodic inventory quantity of the target spare parts and the periodic statistical data of each group, the initial guarantee level function of the target spare parts is determined.

5. The method according to claim 4, characterized in that, The method further includes: The periodic inventory quantity of the target spare part is determined based on the preset inventory quantity of the target spare part, the historical procurement cycle, and the historical statistical cycle.

6. The method according to claim 4, characterized in that, The step of determining the initial availability level function for the target spare part based on the periodic inventory quantity and periodic statistical data of each group includes: Based on the periodic inventory quantity of the target spare parts and the periodic statistics of each group, determine the periodic guarantee data of the target spare parts; The initial support level function of the target spare part is determined based on the number of periodic support data points less than the third threshold in the periodic support data of the target spare part, and the total number of groups of periodic statistical data.

7. The method according to claim 4, characterized in that, The step of determining at least one set of periodic statistical data for the target spare parts based on historical requisition data for at least two historical statistical periods includes: Based on the historical requisition data of the target spare parts under each demand type within at least two historical statistical periods, and the corresponding weighting coefficients under each demand type, at least one set of periodic statistical data for the target spare parts is determined.

8. The method according to claim 7, characterized in that, The method further includes: Based on the lead time, reservation ratio, and reservation accuracy of the target spare parts, as well as the spare parts procurement cycle, determine the weight coefficient of the target spare parts under each demand type.

9. A device for determining the level of spare parts availability, characterized in that, The device includes: The initial support level determination module is used to determine the initial support level function of the target spare part based on the historical requisition data and periodic inventory quantity of the target spare part in at least two historical statistical periods. The correction coefficient determination module is used to determine a first requisition probability distribution based on the predicted requisition quantity of spare parts without requisition data in the next statistical period of the spare parts type to which the target spare parts belong; wherein, the spare parts without requisition data are candidate spare parts without historical requisition data among the candidate spare parts in the spare parts type; determine a second requisition probability distribution based on the predicted requisition quantity of each candidate spare part in the next statistical period of the spare parts type to which the target spare parts belong, and the maximum requisition quantity of each candidate spare part in each historical statistical period; and determine a guarantee level correction coefficient based on the maximum requisition quantity of the target spare parts in each historical statistical period, the periodic inventory quantity, the first requisition probability distribution, and the second requisition probability distribution. The target assurance level determination module is used to adjust the initial assurance level function according to the assurance level correction coefficient to obtain the target assurance level function of the target spare part.

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

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

Images