Spare part purchase reserve strategy generation method and device, terminal equipment and storage medium

Abstract

The invention is suitable for the technical field of equipment management, and provides a spare part purchase reserve strategy generation method and device, terminal equipment and a storage medium, and the method comprises the steps: obtaining spare part code data of a target spare part; if the target spare part is the equipment-level spare part containing the function position data, obtaining a first purchase reserve strategy of the target spare part according to spare part code data of the target spare part and corresponding first function position data, and outputting the first purchase reserve strategy; if the target spare part is the part-level spare part containing the function position data, obtaining a second purchase reserve strategy of the target spare part according to spare part code data of the target spare part and corresponding second function position data, and outputting the second purchase reserve strategy; and if the target spare part is the spare part which does not contain the function position data, obtaining and outputting a third purchase reserve strategy of the target spare part according to the spare part code data of the target spare part. The embodiment of the invention can effectively improve the accuracy of the spare part purchase reserve strategy of the new unit.

Description

Technical Field

[0001] The present application belongs to the technical field of equipment management, and in particular, relates to a method, device, terminal device and storage medium for generating a spare parts procurement reserve strategy. Background Art

[0002] Spare parts for nuclear power plants are not only necessities for equipment maintenance and repair, but also an important guarantee for ensuring the safe, reliable and efficient operation of nuclear power plants. Therefore, it is crucial to formulate a suitable spare parts procurement and reserve strategy for nuclear power plants.

[0003] At present, for some mature units with sufficient spare parts historical usage data, their spare parts procurement and reserve strategies can be set according to the spare parts historical usage data. However, for some new units without spare parts historical usage data, the procurement and reserve strategies can only be set based on manual experience and some general strategy setting principles. The accuracy is low and it is easy to have problems such as spare parts shortages or inventory backlogs. Summary of the invention

[0004] In view of this, an embodiment of the present application provides a method, apparatus, terminal device and storage medium for generating a spare parts procurement reserve strategy to solve the problem of low accuracy of the spare parts procurement reserve strategy for new units in the prior art.

[0005] A first aspect of an embodiment of the present application provides a method for generating a spare parts procurement reserve strategy, the method comprising:

[0006] Obtain spare part coding data of a target spare part;

[0007] If the target spare part is an equipment-level spare part including functional location data, obtaining and outputting a first procurement reserve strategy for the target spare part according to the spare part coding data of the target spare part and the corresponding first functional location data;

[0008] If the target spare part is a component-level spare part including functional location data, obtaining and outputting a second procurement reserve strategy for the target spare part according to the spare part coding data of the target spare part and the corresponding second functional location data;

[0009] If the target spare part is a spare part that does not include functional location data, a third procurement reserve strategy for the target spare part is obtained and output according to the spare part coding data of the target spare part.

[0010] A second aspect of an embodiment of the present application provides a spare parts procurement reserve strategy generation device, the spare parts procurement reserve strategy generation device comprising:

[0011] A data acquisition module, used to acquire spare part coding data of a target spare part;

[0012] A first strategy generating module, configured to obtain and output a first purchasing reserve strategy for the target spare part according to the spare part coding data and the corresponding first functional location data of the target spare part if the target spare part is a device-level spare part including functional location data;

[0013] A second strategy generating module is used to obtain and output a second procurement reserve strategy for the target spare part according to the spare part coding data and the corresponding second functional location data of the target spare part if the target spare part is a component-level spare part containing functional location data;

[0014] The third strategy generating module is used to obtain and output the third procurement reserve strategy of the target spare part according to the spare part coding data of the target spare part if the target spare part is a spare part that does not contain functional location data.

[0015] A third aspect of an embodiment of the present application provides a terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method for generating a spare parts procurement reserve strategy as described in the first aspect of the embodiment of the present application are implemented.

[0016] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the spare parts procurement reserve strategy generation method as described in the first aspect of the embodiment of the present application.

[0017] The first aspect of the embodiment of the present application provides a method for generating a spare parts procurement and reserve strategy, which obtains the spare parts coding data of the target spare parts; if the target spare parts are equipment-level spare parts containing functional location data, then according to the spare parts coding data of the target spare parts and the corresponding first functional location data, obtains and outputs the first procurement and reserve strategy for the target spare parts; if the target spare parts are component-level spare parts containing functional location data, then according to the spare parts coding data of the target spare parts and the corresponding second functional location data, obtains and outputs the second procurement and reserve strategy for the target spare parts; if the target spare parts are spare parts that do not contain functional location data, then according to the spare parts coding data of the target spare parts, obtains and outputs the third procurement and reserve strategy for the target spare parts, thereby effectively improving the accuracy of the spare parts procurement and reserve strategy for the new unit.

[0018] 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

[0019] 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.

[0020] Figure 1 This is a first flow chart of the method for generating a spare parts procurement reserve strategy provided in an embodiment of the present application;

[0021] Figure 2 It is a schematic diagram of the correspondence between a functional location BOM and a parent-child BOM provided in an embodiment of the present application;

[0022] Figure 3 It is a schematic diagram of the relationship between the spare part parent-child code and the corresponding functional position provided in an embodiment of the present application;

[0023] Figure 4 This is a second flow chart of the spare parts procurement reserve strategy generation method provided in an embodiment of the present application;

[0024] Figure 5 This is a third flow chart of the spare parts procurement reserve strategy generation method provided in the embodiment of the present application;

[0025] Figure 6 This is a fourth flow chart of the spare parts procurement reserve strategy generation method provided in the embodiment of the present application;

[0026] Figure 7 This is a schematic diagram of the classification of spare parts types provided in an embodiment of the present application;

[0027] Figure 8 This is a fifth flow chart of the spare parts procurement reserve strategy generation method provided in the embodiment of the present application;

[0028] Fig. 9 This is a sixth flow chart of the spare parts procurement reserve strategy generation method provided in the embodiment of the present application;

[0029] Fig.10 This is a schematic diagram of the failure probability distribution of spare parts of different categories at a single functional position provided in an embodiment of the present application;

[0030] Fig.11 This is a seventh flow chart of the spare parts procurement reserve strategy generation method provided in the embodiment of the present application;

[0031] Fig.12 is a schematic diagram of a neural network model library provided in an embodiment of the present application;

[0032] Fig.13It is a structural schematic diagram of a neural network model provided in an embodiment of the present application;

[0033] Fig.14 It is a structural schematic diagram of a spare parts procurement reserve strategy generating device provided in an embodiment of the present application;

[0034] Fig.15 It is a structural diagram of a spare parts procurement reserve strategy generation system provided in an embodiment of the present application;

[0035] Fig.16 It is a structural diagram of the terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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. "Multiple" means "two" or "more than two".

[0040] At present, for some new units without historical spare parts procurement data, the purchase and reserve strategy can only be set by relying on manual experience and some general strategy setting principles. Among them, the general strategy setting principles include but are not limited to applying a fixed value function (the reserve quantity of a certain type of spare parts is a fixed value), a fixed ratio function (a certain type of spare parts is configured with a reserve quantity according to a certain percentage), a fixed upper limit function (a certain type of spare parts is configured with a reserve quantity according to a certain percentage and an upper limit is also given), and a fixed upper and lower limit function (a certain type of spare parts is configured with a reserve quantity according to a certain percentage and an upper and lower limit are also given); since this method requires manual sorting of a large amount of basic spare parts information and needs to set some function parameters based on manual experience, it has low accuracy and is prone to problems such as spare parts out of stock or inventory backlogs.

[0041] Therefore, an embodiment of the present application provides a method for generating a spare parts procurement and reserve strategy, which obtains spare parts coding data of a target spare part; if the target spare part is an equipment-level spare part containing functional location data, then according to the spare parts coding data of the target spare part and the corresponding first functional location data, a first procurement and reserve strategy for the target spare part is obtained and output; if the target spare part is a component-level spare part containing functional location data, then according to the spare parts coding data of the target spare part and the corresponding second functional location data, a second procurement and reserve strategy for the target spare part is obtained and output; if the target spare part is a spare part that does not contain functional location data, then according to the spare parts coding data of the target spare part, a third procurement and reserve strategy for the target spare part is obtained and output. In this way, the accuracy of the spare parts procurement and reserve strategy for the new unit can be effectively improved, and problems such as spare parts shortages or inventory backlogs can be effectively avoided.

[0042] Embodiment 1

[0043] like Figure 1 As shown, the method for generating a spare parts procurement reserve strategy provided in the embodiment of the present application includes the following steps S1 to S4:

[0044] Step S1, obtain the spare part coding data of the target spare part, and proceed to step S2 or S3 or S4.

[0045] In the application, the target spare part can be any spare part in the target unit, without limitation here; the target unit can be any unit without historical spare part usage data, including but not limited to new nuclear power units that have not started operation, without limitation here.

[0046] In applications, for ease of management, each spare part corresponds to a unique spare part coding data, and the spare part coding data complies with certain coding rules.

[0047] In the application, when obtaining the spare parts coding data of the target spare parts, it can be obtained through the spare parts management system of the target unit, or obtained from the product catalog, manual and other materials provided by the manufacturer of the target spare parts, or the spare parts coding data of the target spare parts can be queried through some spare parts coding rules. If the target spare part has a barcode or RFID tag, the spare parts coding data therein can also be obtained by scanning the barcode or RFID tag. This is just an example and is not limited to this.

[0048] In the application, based on whether the target spare part contains BOM (Bill of Materials) data and whether the target spare part belongs to a parent spare part or a child spare part, the procurement and reserve strategy of the target spare part can be obtained through the corresponding method steps in steps S2 to S4 respectively; wherein, whether the target spare part contains BOM data refers to whether the corresponding functional location data of the target spare part can be calculated through the BOM data; whether the target spare part is a parent spare part refers to whether the functional location data is directly mounted through the target spare part.

[0049] In the application, BOM data includes functional location BOM and parent-child BOM, and their corresponding relationship is as follows Figure 2 As shown in the figure, the functional location BOM means that the target spare part belongs to the equipment-level spare part, and the target spare part itself is attached with the functional location data; the parent-child BOM refers to the relationship between spare parts. Usually, the child spare part has no directly corresponding functional location data, and needs to be traced back to the parent spare part to obtain the corresponding functional location data. Figure 3 Take this as an example to explain the contents of this section:

[0050] like Figure 3As shown, spare part code 1 is directly attached to the function location data, and belongs to the equipment-level spare part (parent code), which contains 4 function location data, namely function location 1, function location 2, function location 3, and function location 4; spare part code 4, spare part code 5, and spare part code 6 are only attached to the parent code information, and are not directly attached to the function location data, and belong to the component-level spare part (sub-code), whose parent code is spare part code 1; spare part code 10 is only attached to the parent code information, and is the sub-code of spare part code 5; spare part code 11 is also only attached to the parent code information, and is the sub-code of spare part code 6; spare part code 2 is directly attached to the function location data, and belongs to the equipment-level spare part (parent code), which contains 2 function location data, namely function location 5 and function location 6; spare part code 6, spare part code 7, and spare part code 8 are only attached to the parent code information, and are not directly attached to the function location data, and belong to the component-level spare part (sub-code), whose The parent code is spare part code 2; spare part code 6 contains 2 parent codes, namely spare part code 1 and spare part code 2, so the functional location data of spare part code 6 are functional location 1, functional location 2, functional location 3, functional location 4, functional location 5, and functional location 6, that is, spare part code 6 contains the functional location data corresponding to spare part code 1 and spare part code 2 at the same time; spare part code 3 is an equipment-level spare part, but the functional location BOM data is not mounted, and the corresponding functional location quantity cannot be calculated; the parent spare part of spare part code 9 is spare part code 3. Since spare part code 3 has no corresponding functional location data, spare part code 9 also has no corresponding functional location data; the parent spare parts of spare part code 8 are spare part code 3 and spare part code 2. Since spare part code 3 has no corresponding functional location data, spare part code 8 only contains the functional location data corresponding to spare part code 2, that is, spare part code 8 only contains functional location 5 and functional location 6.

[0051] Understandably, Figure 3 Spare part code 1 and spare part code 2 contain BOM data and are parent spare parts, and the procurement reserve strategy can be obtained through the following step S2; spare part code 4, spare part code 5, spare part code 6, spare part code 7, spare part code 8, spare part code 10, and spare part code 11 contain BOM data and are not parent spare parts, and the procurement reserve strategy can be obtained through the following step S3; spare part code 3 and spare part code 9 do not have BOM data, and the procurement reserve strategy can be obtained through the following step S4.

[0052] In the application, the functional location data of the component-level spare part is the same as the functional location data of the corresponding equipment-level spare part (parent code). If a component-level spare part has multiple parent spare parts, the functional location data of the component-level spare part also includes the functional location data corresponding to multiple parent spare parts.

[0053] Step S2: If the target spare part is an equipment-level spare part including functional location data, a first procurement reserve strategy for the target spare part is obtained and output according to the spare part coding data of the target spare part and the corresponding first functional location data.

[0054] In the application, if the target spare part is an equipment-level spare part, the first functional position data corresponding to the target spare part is obtained according to the spare part coding data of the target spare part, and then the historical usage data of other mature units of the same type as the target unit to which the target spare part belongs can be obtained, and finally the expected usage quantity and specific procurement reserve data of the target spare part are obtained and output.

[0055] In one embodiment, Figure 4 As shown, step S2 specifically includes the following steps S21 to S24:

[0056] Step S21, obtaining first functional position data corresponding to the target spare part in the target unit according to the spare part coding data;

[0057] Step S22, obtaining historical usage data of the target spare parts for s units within a first preset time period, where s is a positive integer;

[0058] Step S23, obtaining a first expected usage quantity of the target spare part in the s units according to the first functional position data and the historical usage data;

[0059] Step S24: According to the first expected usage quantity of the target spare part in the s units, a first purchasing and reserve strategy for the target spare part in the target unit is obtained and output.

[0060] In the application, the first preset time period can be in units of years, months, weeks, days, etc. The first preset time period can specifically be within 3 years, within 3 months, etc. This is just an example and is not limited.

[0061] In the application, it is assumed that the number of mature nuclear power units of the same type as the target unit in the same nuclear power group is s, namely Nuclear Power Plant 1, Nuclear Power Plant 2, Nuclear Power Plant 3, Nuclear Power Plant 4, ..., Nuclear Power Plant s. When obtaining the historical usage data of the target spare parts for the s units within the first preset time period, the historical usage data of the target spare parts for the s units in the past t years (for example, 3 years) can be obtained. Then, based on the first functional position data corresponding to the target spare parts and the historical usage data of the s units, the first expected usage quantity (which can be the expected annual usage quantity) of the functional position corresponding to the target spare parts can be calculated. Finally, based on the first expected usage quantity, the procurement reserve data of the target spare parts in the target unit, i.e., the first procurement reserve strategy, is obtained and output.

[0062] In application, the spare part code and functional position of the same target spare part in the target unit may be different from the spare part code and corresponding functional position in other mature units. Specifically, they can be divided into the following 8 types:

[0063] Type 1: The functional position of the target spare part in the target unit is the same as its functional position in the mature unit k, and the spare part code used is the same;

[0064] Type 2: The functional position of the target spare part in the target unit is the same as that in the mature unit k. The spare part codes used are different, but they belong to the same series of products from the same manufacturer.

[0065] Type 3: The functional position of the target spare part in the target unit is the same as that in the mature unit k, and the spare part codes used are different, but they belong to different series of products of the same manufacturer;

[0066] Type 4: The functional position of the target spare part in the target unit is the same as that in the mature unit k, but the spare part codes used are different and the products are from different manufacturers;

[0067] Type 5: The functional position of the target spare part in the target unit is different from its functional position in the mature unit k, but the system-equipment code is the same and the spare part code used is the same;

[0068] Type 6: The functional position of the target spare part in the target unit is different from that in the mature unit k, the system-equipment code is the same, the spare part codes used are different, but they belong to the same series of products from the same manufacturer;

[0069] Type 7: The functional position of the target spare part in the target unit is different from that in the mature unit k. The system-equipment code is the same, but the spare part codes used are different, but they belong to different series of products of the same manufacturer;

[0070] Type 8: The functional position of the target spare part in the target unit is different from its functional position in the mature unit k, the system-equipment code is the same, but the products are from different manufacturers.

[0071] For example, the spare part code of the target spare part is 1100000001, which is an equipment-level spare part. The corresponding functional positions are X1AHP-001 / 002 / 003 / 004 / 005-MT, X1ABP-101-MT, X1APP-101-MT, X1ACO-101-MT, X1AGM-101-MT, and X1ADG-101-MT. The corresponding functional position classification relationship is shown in Table 1.

[0072] Table 1

[0073]

[0074]

[0075] In the application, based on the first functional position data corresponding to the target spare part and the historical usage data of s units of the same type as the target unit (for example, within t time units), the first expected usage quantity of the target spare part in the s units can be calculated using the following formula (1):

[0076]

[0077] Among them, i refers to a certain type. In this example, there are 8 types, which are related to θ 1 ~θ 8 Correspondingly, θ 1 Refers to the coefficient corresponding to type 1, θ 2 Refers to the coefficient corresponding to type 2, θ 3 Refers to the coefficient corresponding to type 3, θ 4 Refers to the coefficient corresponding to type 4, θ 5 Refers to the coefficient corresponding to type 5, θ 6 Refers to the coefficient corresponding to type 6, θ 7 Refers to the coefficient corresponding to type 7, θ 8 Refers to the coefficient corresponding to type 8; t represents the value under a certain time unit; j refers to a system + equipment code in the i-th type; α i Refers to the number of system + equipment codes in the i-th type (for example, in Table 1, since type 7 corresponds to ACO-MT and AGM-MT, α 7 is 2, and the rest α i is 1); k refers to a mature unit that has been put into operation; Refers to the number of functional positions corresponding to the jth system + equipment code of the i-th type of the mature unit k that has been put into production (for example, in Table 1, since type 4 contains X1AHP-004-MT and X1AHP-005-MT functional positions, The value of is 2 (i is 4, j is 1), and the rest The value is 1 (although type 7 also contains two functional positions, X1ACO-101-MT and X1AGM-101-MT, but due to α 7 is 2, ACO-MT and AGM-MT only correspond to one functional position, so The value of is 1); It refers to the number of units k that have been put into production and the functional position corresponding to the jth system + equipment code of the i-th type that are used within a specified time. It can be understood as is The corresponding functional position's withdrawal quantity (in Table 1, is the number of units k with functional position X1AHP-001-MT to be used. is the number of APP-MTs used in the functional position of mature unit k (i.e., the number of single APP-MTs used, which is the total number of APP-MTs used in mature unit k divided by the number of functional positions of all APP-MTs); l (k) Refers to the first expected usage quantity of mature unit k.

[0078] According to the above formula, the first expected usage quantity of the target spare parts in each unit can be obtained, which is expressed as l (1) , l (2) , l (3) ,......,l (k) ,......,l (s) .

[0079] In the application, the first expected usage of s units is summarized to obtain the usage matrix, which can be specifically expressed as L = [l (1) ,l (2) ,l (3) ,...,l (s) ].

[0080] Generally speaking, the higher the similarity of spare parts, the same functional position (same installation and use environment), the greater the reference value of historical data, and the larger the corresponding coefficient value. Specifically, θ 1 ~θ 8 They are set to 1, 0.9, 0.8, 0.7, 0.85, 0.75, 0.65, and 0.55 respectively. In actual applications, they can be set according to needs. This is just an example and is not a limitation.

[0081] In one embodiment, Figure 5 As shown, step S24 specifically includes the following steps S241 to S245:

[0082] S241, establishing a demand function model library;

[0083] S242, inputting the first average expected usage quantity of the target spare part in the s units into each demand function model in the function model library;

[0084] S243, testing each demand function model to obtain corresponding test results;

[0085] S244, determining the demand function model corresponding to the verification result that meets the first preset condition as the target demand function model;

[0086] S245. According to the target demand function model, obtain and output a first purchasing reserve strategy for the target spare parts in the target unit.

[0087] In applications, when the demand characteristics of target spare parts conform to a certain probability density, we can establish a demand function model library and use methods such as the KS test (Kolmogorov-Smirnvo test) to determine whether the expected usage of the target spare parts conforms to the corresponding probability distribution, thereby selecting a suitable demand function model for the target spare parts to calculate the procurement reserve data.

[0088] In the application, the demand function model library includes multiple distributed demand function models, such as Poisson distribution demand function model, binomial distribution demand function model, normal distribution demand function model, etc. These are just examples and are not limited.

[0089] In step S243, when testing each demand function model, the KS test method can be used to calculate the KS test value corresponding to each demand function model, and determine whether the KS test value is within the test threshold (that is, the first preset condition is that the KS test value is within the test threshold, where the test threshold can be set arbitrarily according to actual conditions). If the KS test values ​​of multiple demand function models are within the test threshold, the demand function model corresponding to the optimal KS test value is selected as the target demand function model, and the purchase reserve data of the target spare parts in the target unit is calculated and output through the target demand function model.

[0090] The following explains how to calculate the purchase reserve data using the Poisson distribution demand function model, the binomial distribution demand function model, and the normal distribution demand function model:

[0091] If the Poisson distribution demand function model is selected, the calculation is performed using formula (2):

[0092]

[0093] where t 1 is the procurement cycle of the target spare parts. If the target spare parts have been purchased in mature units, the historical procurement cycle data is used directly. If there is no procurement record for the target spare parts, the average delivery cycle of the manufacturer corresponding to the target spare parts is used as its procurement cycle data; 2 is the preset value of the availability rate. If the target spare part is a spare part of high importance (such as CCM spare parts and H-level spare parts, which users believe have unacceptable consequences of spare part failure), it can be set to 0.97; if the target spare part is a spare part of low importance (spare parts with a quality assurance level of C3), it can be set to 0.85; if the target spare part is a spare part of medium importance (not a spare part of high or low importance), it can be set to 0.92;1 is the first average expected withdrawal amount, which can be obtained by formula (7); formula (2) is solved by iteratively solving the purchase reserve h value, that is, the h value is accumulated from 0 (adding 1 each time) until the calculated If the availability rate is greater than or equal to the preset availability rate, the value is used as the calculation result and assigned to the purchase reserve data r.

[0094] If the binomial distribution demand function model is selected, the calculation is performed using formula (3):

[0095]

[0096] where t 1 is the procurement cycle of the target spare parts. If the target spare parts have been purchased in mature units, the historical procurement cycle data is used directly. If there is no procurement record for the target spare parts, the average delivery cycle of the manufacturer corresponding to the target spare parts is used as its procurement cycle data. 2 is the preset value of the availability rate. If the target spare part is a spare part of high importance (such as CCM spare parts and H-level spare parts, which users believe have unacceptable consequences of spare part failure), it can be set to 0.97; if the target spare part is a spare part of low importance (spare parts with a quality assurance level of C3), it can be set to 0.85; if the target spare part is a spare part of medium importance (not a spare part of high or low importance), it can be set to 0.92; 1 is the first average expected usage quantity, which can be obtained by formula (7); m is the installation quantity of the target spare part, m! is the factorial of m, is the failure probability of the target spare part, and the calculation formula is shown in formula (4):

[0097]

[0098] The purchase reserve h value is iteratively solved by formula (3), that is, the h value is accumulated from 0 (increased by 1 each time) until the calculated If the availability rate is greater than or equal to the preset availability rate, the value is used as the calculation result and assigned to the purchase reserve data r.

[0099] If the normal distribution demand function model is selected, the calculation is performed using formula (5):

[0100]

[0101] Among them, t 1 is the procurement cycle of the target spare parts. If the target spare parts have been purchased in mature units, the historical procurement cycle data is used directly. If there is no procurement record for the target spare parts, the average delivery cycle of the manufacturer corresponding to the target spare parts is used as its procurement cycle data; 2is the preset cargo rate value. If the target spare part is a spare part of high importance (such as CCM spare parts and H-level spare parts, which users believe have unacceptable consequences of spare part failure), it can be set to 0.97; if the target spare part is a spare part of low importance (spare parts with a quality assurance level of C3), it can be set to 0.85; if the target spare part is a spare part of medium importance (not a spare part of high or low importance), it can be set to 0.92; 1 is the first average expected usage amount, which can be obtained by formula (7); u 2 For u 1 The corresponding standard deviation; the purchase reserve h value is iteratively solved by formula (5), that is, the h value is accumulated from 0 (add 1 each time) until the calculated If the availability rate is greater than or equal to the preset availability rate, the value is used as the calculation result and assigned to the purchase reserve data r.

[0102] In one embodiment, step S243 further includes:

[0103] If the inspection results corresponding to each demand function model do not meet the first preset condition, then the first procurement reserve strategy for the target spare parts in the target unit is obtained and output based on the first average expected usage quantity of the target spare parts in the s units and the corresponding standard deviation.

[0104] In the application, for each demand function model in the demand function model library, the KS test method is used to calculate the KS test value. If the expected use quantity of the target spare parts does not meet the probability distribution of all demand function models in the demand function model library, that is, the KS test value is not within the test threshold, then the purchase reserve quantity of the spare parts is calculated using formula (6), where u 1 is the first average expected amount of use, and the calculation formula is shown in formula (7); t 1 is the procurement cycle of the target spare parts. If the target spare parts have been purchased in mature units, the historical procurement cycle data is used directly. If there is no procurement record for the target spare parts, the average delivery cycle of the manufacturer corresponding to the target spare parts is used as its procurement cycle data; 1 u is the service level value of the availability rate, which can be set to 0.524. This is just an example and is not limited to this. 2 For u 1 The corresponding standard deviation is calculated as shown in formula (8).

[0105]

[0106] In application, the purchase reserve data r value calculated by the above formulas (2) to (8) needs to be corrected according to the life span of the target spare parts to avoid excessive purchase reserves that cannot be fully used within the life span, resulting in losses due to the expiration and scrapping of the target spare parts.

[0107] Specifically, the life of the target spare part can be t 2 , if the purchase reserve data r value is greater than or equal to ω times the amount of use within the life cycle (usually the coefficient ω can be set to 0.7), then If the conditions are met, the purchase reserve quantity r value is corrected to the quantity used within ω times the lifespan, that is, using the formula If the purchase reserve quantity r value is less than the quantity used within ω times the life span, there is no need to correct the purchase quantity value.

[0108] Step S3: If the target spare part is a component-level spare part including functional location data, a second procurement reserve strategy for the target spare part is obtained and output according to the spare part coding data of the target spare part and the corresponding second functional location data.

[0109] In the application, if the target spare part is a component-level spare part, the second functional position data corresponding to the target spare part is obtained based on the spare part coding data of the target spare part, and then the historical usage data of mature units of the same type as the target unit to which the target spare part belongs can be obtained, and finally the expected usage quantity and specific procurement reserve data of the target spare part are obtained and output.

[0110] In the application, if the functional location data corresponding to the component-level spare parts involves multiple layers of parent spare parts, the parent spare parts with functional location BOM data attached are found upward and summarized as the functional location data of the spare parts.

[0111] Normally, the number of functional positions and the number of requisitions corresponding to component-level spare parts are greater than those of equipment-level spare parts. In order to prevent the abnormal requisition data of some functional positions from causing distortion of the overall requisition data, the median is used instead of the average value for each type of functional position. For example, under a certain type of functional position, there are 5 functional positions in total, and the number of requisitions for each functional position in the past three years is 2, 0, 4, 50, and 6 respectively. If the average method is used, the average annual requisition of a single functional position is 4.1 ((2+0+4+50+6) / 5 / 3); if the median method is used, the average annual requisition of a single functional position is 1.3 (4 / 3). From the perspective of probability distribution, the data of a functional position with a requisition quantity of 50 may be abnormal. Therefore, the average annual requisition of a functional position calculated using the median method is more in line with the actual needs of most functional positions.

[0112] In one embodiment, Figure 6As shown, step S3 specifically includes the following steps S31 to S34:

[0113] Step S31, obtaining second functional position data corresponding to the target spare part in the target unit according to the spare part coding data;

[0114] Step S32, obtaining historical usage data of the target spare parts for s units within a second preset time period, where s is a positive integer;

[0115] Step S33, obtaining a second expected usage quantity of the target spare part in the s units according to the second functional position data and the historical usage data;

[0116] Step S34: According to the second expected usage quantity of the target spare part in the s units, a second purchasing and reserve strategy for the target spare part in the target unit is obtained and output.

[0117] In the application, the second preset time period can be in units of years, months, weeks, days, etc. The second preset time period can specifically be within 3 years, within 3 months, etc. This is just an example and is not limited.

[0118] In the application, it is assumed that the number of mature nuclear power units of the same type as the target unit in the same nuclear power group is s, namely Nuclear Power Plant 1, Nuclear Power Plant 2, Nuclear Power Plant 3, Nuclear Power Plant 4, ..., Nuclear Power Plant s. When obtaining the historical usage data of the target spare parts for the s units within the first preset time period, the historical usage data of the target spare parts for the s units in the past t years (for example, 3 years) can be obtained. Then, based on the second functional position data corresponding to the target spare parts and the historical usage data of the s units, the second expected usage quantity (which can be the expected annual usage quantity) of the functional position corresponding to the target spare parts can be calculated. Finally, based on the second expected usage quantity, the procurement reserve data of the target spare parts in the target unit, i.e., the second procurement reserve strategy, is obtained and output.

[0119] In application, examples of categories of commonly used spare parts for nuclear power plants are as follows: Figure 7 As shown, rotating machinery, pumps, valves, general machinery, chemical consumables, instruments and meters, and electrical products are total category data, and the rest are sub-category data.

[0120] In the application, if the target spare part is a component-level spare part, the spare part code and functional position of the same target spare part in the target unit may be different from the spare part code and corresponding functional position in other mature units. Specifically, it can be divided into the following 6 types:

[0121] Type 1: The functional position of the target spare part in the target unit is the same as its functional position in the mature unit k, and the spare part code used is the same;

[0122] Type 2: The functional position of the target spare part in the target unit is the same as that in the mature unit k. The spare part codes used are different, but they belong to the same category of products from the same manufacturer.

[0123] Type 3: The functional position of the target spare part in the target unit is the same as that in the mature unit k, but the spare part codes used are different, and the products are of the same category from different manufacturers;

[0124] Type 4: The functional position of the target spare part in the target unit is different from its functional position in the mature unit k, but the system-equipment code is the same and the spare part code used is the same;

[0125] Type 5: The functional position of the target spare part in the target unit is different from that in the mature unit k, the system-equipment code is the same, the spare part codes used are different, but they belong to the same category of products from the same manufacturer;

[0126] Type 6: The functional position of the target spare part in the target unit is different from its functional position in the mature unit k, the system-equipment code is the same, and the products are of the same category from different manufacturers.

[0127] For example, the spare part code of the target spare part is 1100000101, which is a component-level spare part, and the corresponding functional positions are X1AHP-001 / 002 / 003 / 004 / 005-MO, X1ABP-101-MO, X1APP-101-PO, X1ACO-101-PO, X1AGM-101-MO, and X1ADG-101-MO. Since different equipment will use the same type of parts (for example, pumps and motors will use gasket spare parts), the functional position of 1100000101 includes both PO and MO equipment. The classification relationship of the functional position corresponding to the spare part is shown in Table 2.

[0128] Table 2

[0129]

[0130] In the application, based on the second functional position data corresponding to the target spare part and the historical usage data of s units of the same type as the target unit, the second expected usage quantity of the target spare part in the s units can be calculated using the following formula (9):

[0131]

[0132] Among them, i refers to a certain type. In this example, there are 6 types, which are related to θ 1 ~θ 6 Correspondingly, θ 1Refers to the coefficient corresponding to type 1, θ 2 Refers to the coefficient corresponding to type 2, θ 3 Refers to the coefficient corresponding to type 3, θ 4 Refers to the coefficient corresponding to type 4, θ 5 Refers to the coefficient corresponding to type 5, θ 6 Refers to the coefficient corresponding to type 6; j refers to a system + equipment code in the i-th type; α i Refers to the number of system + equipment codes in the i-th type (for example, in Table 2, since type 4 corresponds to ABP-MO, APP-MO, and type 5 corresponds to ACO-PO, AGM-MO, α 4 and α 5 is 2, and the rest α i is 1; K refers to a mature unit that has been put into operation); Refers to the number of functional positions corresponding to the jth system + equipment code of the i-th type of the mature unit k that has been put into production. In Table 2, since type 1 contains the X1AHP-001-MO and X1AHP-002-MO functional positions, The value of is 2 (i is 1, j is 1); similarly is 2, and the rest The value is 1 (although type 4 also contains two functional positions, X1ABP-101-MO and X1APP-101-PO, due to α 4 is 2, ABP-MO and APP-PO each correspond to only one functional position, so The value of is 1); Refers to the average annual usage (median) of a single functional position corresponding to the jth system + equipment code of the i-th type of mature unit k that has been put into production; l (k) Refers to the second expected usage quantity of mature unit k.

[0133] According to the above formula (9), the second expected usage quantity of the target spare parts in each unit s can be obtained, which is expressed as l (1) , l (2) , l (3) ,......,l (k) ,......,l (s) .

[0134] In the application, the second expected usage of s units is summarized to obtain the usage matrix, which can be specifically expressed as L = [l (1) ,l (2) ,l (3) ,...,l (s) ].

[0135] In applications, θ 1 ~θ 6They are set to 1, 0.8, 0.7, 0.85, 0.65, and 0.55 respectively. Other values ​​can also be set according to actual needs. This is just an example and is not limited.

[0136] In the application, the method for obtaining the purchase reserve data of the target spare parts in the target unit according to the second expected requisition quantity can be referred to the relevant description in the above step S24, which will not be repeated here.

[0137] Step S4: If the target spare part is a spare part that does not include functional location data, a third procurement reserve strategy for the target spare part is obtained according to the spare part coding data of the target spare part and outputted.

[0138] In the application, for the target spare parts that do not contain functional location data, there is no need to distinguish whether they are equipment-level spare parts or component-level spare parts. Since the target spare parts have no functional location data, the historical requisition data of mature units cannot be linked through the functional location information. However, the third procurement reserve strategy for the target spare parts can be obtained and output through the spare parts coding data of the target spare parts.

[0139] In application, if the target spare part has been used on other mature units, it is a common code spare part; if the target spare part has not been used on other mature units, it is a unique code spare part; the following explains the situations in which the target spare part is a common code spare part and a unique code spare part respectively.

[0140] In one embodiment, Figure 8 As shown, step S4 specifically includes the following steps S41 to S43:

[0141] Step S41: if the target spare part is a common code spare part, obtain the historical usage data and the corresponding functional position quantity of the target spare part for s units within a third preset time period, where s is a positive integer;

[0142] Step S42: obtaining a third expected usage quantity of the target spare part in the s units according to the historical usage data and the corresponding functional position quantity;

[0143] Step S43: According to the third expected usage quantity, a third purchasing reserve strategy for the target spare parts in the target unit is obtained and output.

[0144] In the application, the third preset time period can be in units of years, months, weeks, days, etc. The third preset time period can specifically be within 3 years, within 3 months, etc. This is just an example and is not limited.

[0145] In the application, the target spare part belongs to the common code spare part, and the third expected usage quantity of the target spare part in s units can be obtained by the following formula (10):

[0146]

[0147] Where Δm is the average number of functional positions of the spare parts category corresponding to the spare parts code. For details of spare parts categories, see Figure 7 ;ω 1 The coefficient of setting, ω 1 ×Δm is the estimated target spare parts installation quantity. If the target spare parts installation quantity can be obtained, the actual installation quantity value is used instead of ω 1 ×Δm value; statistics on the use data of the spare part code in the same type of mature unit k in commercial operation within t years, the total number of uses is n (k) ; The number of functional locations is m (k) If there is no functional position quantity data for the target spare part in the mature unit k, the average functional position quantity of the spare part category corresponding to the spare part code is used instead of the functional position quantity m. (k) value.

[0148] According to the above formula (10), the third expected usage of the target component in s units can be obtained, which is expressed as l (1) , l (2) , l (3) ,......,l (k) ,......,l (s) .

[0149] In the application, the third expected usage of s units is summarized to obtain the usage matrix, which can be specifically expressed as L = [l (1) ,l (2) ,l (3) ,...,l (s) ].

[0150] Finally, according to the third expected usage quantity, the method of obtaining the procurement reserve data of the target spare parts in the target unit, that is, the third procurement reserve strategy, can be found in the relevant description in the above step S24, which will not be repeated here.

[0151] In one embodiment, Fig. 9 As shown, step S4 specifically includes the following steps S44 to S49:

[0152] Step S44: if the target spare part is a unique-code spare part, basic information of the target spare part is obtained according to the spare part coding data of the target spare part;

[0153] Step S45: acquiring historical usage data and corresponding functional position quantity of the target spare parts for s units within a third preset time period according to the basic information of the target spare parts;

[0154] Step S46: Summarize the historical requisition data and the corresponding functional position quantity according to preset rules;

[0155] Step S47: when the second preset condition is met, the failure probability of the target spare part is obtained according to the aggregated historical use data and the corresponding functional position quantity;

[0156] Step S48: obtaining a third expected usage quantity of the target spare part according to the failure probability;

[0157] Step S49: According to the third expected usage quantity, a third purchasing reserve strategy for the target spare parts in the target unit is obtained and output.

[0158] In the application, the basic information of the target spare part includes but is not limited to the manufacturer, model series and category information of the target spare part.

[0159] In the application, step S46 refers to calculating the number of spare parts codes of other mature units in the order of "same category + same manufacturer + same series, same category + same manufacturer, same category" (in order to increase the number of samples, the statistics are no longer calculated according to a single mature unit, but the historical usage data of all mature units of the same type are summarized).

[0160] In the application, step S47 means that if under the conditions of "same category + same manufacturer + same series", the number of spare parts codes of other mature units is greater than or equal to the set threshold, the spare parts code under the conditions of "same category + same manufacturer + same series" is used to calculate the failure probability; if it is less than the set threshold, the number of spare parts codes of other units under the conditions of "same category + same manufacturer" is calculated. If it is greater than or equal to the set threshold, the spare parts code under the conditions of "same category + same manufacturer" is used to calculate the failure probability, otherwise the spare parts code under the "same category" condition is used to calculate the failure probability.

[0161] Generally speaking, the spare parts selected by the "same category + same manufacturer + same series" condition are more relevant to the target spare parts than the spare parts selected by the "same category" condition, and the reference data is stronger. However, if the number of selected spare parts codes is too small, it is difficult to calculate the spare parts failure probability distribution relationship based on a small amount of data. By balancing the relationship between data volume and effectiveness, the threshold can be set to 50, that is, the sample data of the spare parts needs to meet the condition of greater than or equal to 50. For example, the spare parts category of a spare part code is battery (70301), the manufacturer is A, and the manufacturer's product series is X series. For the same type of unit, the number of spare parts codes selected by the "same category + same manufacturer + same series" condition is 30, the number of spare parts codes selected by the "same category + same manufacturer" condition is 70, and the number of spare parts codes selected by the "same category" condition is 1100. If the threshold is set to 50, the 70 spare parts code sample data selected by the "same category + same manufacturer" condition will be used to calculate the failure probability.

[0162] In the application, assuming that the number of samples of mature spare parts of the same type selected based on a certain condition is d, the calculation formula of the failure probability p value of the target spare part at a single functional position is shown in (11):

[0163]

[0164] Among them, n is the total number of target spare parts issued in all mature units of the same type within the third preset time period, m is the total number of functional positions of the target spare parts in all mature units of the same type within the third preset time period, and t represents the specific value of the third preset time period. For example, if the third preset time period is 3 years, the failure probability calculated by this formula is the annual failure probability; if the third preset time period is 3 months, the failure probability calculated by this formula is the monthly failure probability, and so on.

[0165] The failure probability of each functional position of all spare parts is sorted from small to large, and the failure probability distribution matrix P is constructed, which is specifically expressed as P = [p 1 ,p 2 ,p 3 ,…,p d ].

[0166] Based on the set percentile value, through the formula f(q) = p int(q×d) The corresponding failure probability value is obtained, where q is the set percentile value, and int(x) is the rounding operation function. For example, when q is set to 0.5, the failure probability value corresponding to the median of the matrix P can be obtained.

[0167] In the application, the calculation formula of the third expected amount of use is shown in (12):

[0168] l=ω1 ×Δm×f(q) (12)

[0169] Where Δm is the average number of functional positions of the spare parts category corresponding to the spare parts code. For details of spare parts categories, see Figure 7 ;ω 1 is the coefficient set, ω 1 ×Δm is the estimated number of spare parts installed. If the number of spare parts installed is available, the actual number of installed is used instead of ω. 1 ×Δm value.

[0170] Normally, the q value can be set to 0.5. If the failure probability of the spare part is expected to be higher than the average level, the q value can be set to 0.6 or 0.75. If the failure probability of the spare part is expected to be lower than the average level, the q value can be set to 0.4 or 0.25.

[0171] like Fig.10 The figure shows the failure probability values ​​of single functional positions of 10 spare parts categories at values ​​of 25%, 40%, 50%, 60% and 75% respectively.

[0172] According to the above formula (12), the third expected usage quantity of the target spare part can be obtained.

[0173] In the application, the method for obtaining the purchase reserve data of the target spare parts in the target unit according to the third expected requisition quantity can refer to the relevant description in the above step S24.

[0174] Since the third expected usage quantity calculated in formula (12) has only one value, it is impossible to directly use methods such as KS test to select a suitable spare parts demand function model. However, when calculating the third expected usage quantity of the target spare parts, d spare parts samples of mature units of the same type are used. For each sample data, its historical usage data is used, and the KS test and other methods are applied to calculate its corresponding spare parts demand function model, so as to select the demand function model with the largest proportion, which is used to calculate the purchase reserve value r of the spare parts.

[0175] For example, the number of spare parts codes selected by the "same category + same manufacturer" condition for a certain spare part is 70. The KS test method is used for calculation for all 70 spare parts, of which 40% choose Poisson distribution, 10% choose binomial distribution, 20% choose normal distribution, and 30% have KS test values ​​that are not within the threshold. Since Poisson distribution accounts for the highest proportion, the spare part uses the Poisson distribution function to calculate its procurement reserve data.

[0176] Since formula (2), formula (3), formula (5), and formula (6) may be used to calculate the purchase reserve data, the expected amount u 1 Value and corresponding standard deviation u2 , where u 1 The calculation formula is u 1 = l,u 2 The calculation formula is

[0177] In one embodiment, Fig.11 As shown, step S4 specifically includes the following steps S410 to S411:

[0178] Step S410: if the number of functional positions of the target spare part of the s units within the third preset time period cannot be obtained, then a corresponding neural network model is established based on the category to which the target spare part belongs;

[0179] Step S411: input relevant usage data of the target spare parts within a third preset time period into the neural network model, obtain a third procurement reserve strategy for the target spare parts and output it.

[0180] In one embodiment, the relevant collection data includes but is not limited to the average collection quantity, the maximum collection quantity, the average collection standard deviation, and the average collection times of the target spare part.

[0181] In the application, there are some target spare parts in the unit whose functional position quantity m cannot be obtained. Even if the spare parts have a requisition quantity n, the historical requisition data cannot be used. Such target spare parts need to be eliminated (because A zero division error will occur). According to statistics, about 30% of spare parts do not have functional position quantity data. If all such spare parts are eliminated, the number of cases that meet the spare parts sample quantity threshold requirement will decrease, and a large number of spare parts can only use spare parts sample data of the "same category" condition, which will affect the effectiveness of the purchase reserve quantity setting.

[0182] Therefore, a corresponding neural network model can be established for each category of spare parts, and then the relevant spare parts usage data can be input, including the average usage quantity, maximum usage quantity, average usage standard deviation, average usage times, etc. of the spare parts, and the number of functional locations of the spare parts can be predicted through the neural network model.

[0183] In the application, the neural network model may be a BP model (Back Propagation Artificial Neural Network), which is only an example and not a limitation.

[0184] In the application, after the neural network model is established, it is necessary to select appropriate training data to train it. The training set is classified according to the spare parts category, such as Figure 7 As shown in the figure, for each category of spare parts, a corresponding neural network model is established. The corresponding training set data is needed for model training. The specific neural network model library is as follows: Fig.12 In order to improve the effectiveness of neural network model training and prediction, when selecting training data, try to cover spare parts with different annual average usage, annual maximum usage, annual average usage standard deviation, annual average usage frequency, and number of functional positions.

[0185] The structure of the specific neural network model is as follows Fig.13 As shown in the figure. All data are historical data of mature units of the same type in the same group. With the commissioning of the target unit, the unit can be used as sample data for subsequent new units. Therefore, when a new unit meets the commercial operation time limit requirements, the training set data can be updated to train the neural network model.

[0186] This embodiment uses the neural network model to calculate the functional position quantity data of spare parts for spare parts without functional position data, thereby increasing the historical usage data of "valid" spare parts, increasing the matching degree between the parameter spare parts to be calculated and the spare parts in the corresponding category library, and improving the effectiveness of parameter settings.

[0187] It should be noted that the corresponding purchase reserve data r obtained by calculating the above formulas in this application is the corresponding purchase reserve strategy.

[0188] In the application, the first procurement reserve strategy, the second procurement reserve strategy, the third procurement reserve strategy, etc. of the target spare parts obtained through the above-mentioned embodiments can be output to the user end, and the user end can be a mobile phone, a tablet computer, a wearable device, an augmented reality (AR) / virtual reality (VR) device, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA) and other devices.

[0189] The spare parts procurement and reserve strategy generation method proposed in the embodiment of the present application solves the problem that there is no relevant data on spare parts for new units (target units), and the procurement and reserve strategy can only be set based on manual experience and general principles, which is prone to problems such as spare parts shortages or inventory backlogs, and improves the accuracy of spare parts procurement and reserve strategy setting.

[0190] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0191] Embodiment 2

[0192] The present application also provides a spare parts procurement reserve strategy generation device for executing the method steps in the above spare parts procurement reserve strategy generation method embodiment. The device can be a virtual appliance in a terminal device, which is run by a processor of the terminal device, or it can be the terminal device itself.

[0193] like Fig.14 As shown, the spare parts procurement reserve strategy generation device 100 provided in the embodiment of the present application includes:

[0194] The data acquisition module 101 is used to acquire the spare part coding data of the target spare part;

[0195] A first strategy generating module 102 is used to obtain and output a first purchasing reserve strategy for the target spare part according to the spare part coding data and the corresponding first functional location data of the target spare part if the target spare part is a device-level spare part including functional location data;

[0196] A second strategy generating module 103 is used to obtain and output a second procurement reserve strategy for the target spare part according to the spare part coding data and the corresponding second functional location data of the target spare part if the target spare part is a component-level spare part including functional location data;

[0197] The third strategy generating module 104 is used to obtain and output a third procurement reserve strategy for the target spare part according to the spare part coding data of the target spare part if the target spare part is a spare part that does not include functional location data.

[0198] In application, each unit in the above device may be a software program module, or may be implemented by different logic circuits integrated in a processor or independent physical components connected to a processor, or may be implemented by multiple distributed processors.

[0199] Embodiment 3

[0200] The present application also provides a spare parts procurement reserve strategy generation system. Fig.15 As shown, the method steps used to implement the above-mentioned spare parts procurement reserve strategy generation method embodiment.

[0201] The spare parts procurement reserve strategy generation system includes a spare parts reserve quantity calculation module and a basic business data acquisition module.

[0202] Among them, the basic business data acquisition module acquires the material master data, work order collection, purchase order and other information of the target spare parts through sensor collection, communication module reception or terminal equipment. It includes the group factory material master data acquisition unit, factory material master data acquisition unit, spare parts outbound unit, purchase order acquisition unit, delivery acceptance unit, inventory check unit, maintenance work order acquisition unit and function location acquisition unit.

[0203] The spare parts reserve quantity calculation module includes spare parts coding, power plant acquisition unit, spare parts classification unit, spare parts failure probability calculation unit, and spare parts reserve quantity calculation unit.

[0204] The relevant data of the target spare parts are obtained through the basic business data acquisition module, and then the relevant data are input into the spare parts reserve quantity calculation module to obtain the corresponding output data.

[0205] Embodiment 4

[0206] like Fig.16 As shown, the embodiment of the present application further provides a terminal device 200, including: at least one processor 201 ( Fig.16 Only one processor is shown in the figure), memory 202, and a computer program 203 stored in the memory 202 and executable on at least one processor 201. When the processor 201 executes the computer program 203, the steps in the above-mentioned method embodiments are implemented.

[0207] In the application, the terminal device may include, but is not limited to, a processor, a memory, Fig.16 It is only an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, such as human-computer interaction devices, input and output devices, network access devices, etc. The network access device may include a communication module for the terminal device to communicate with the user terminal.

[0208] In applications, the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. For example, the processor may be a timing controller (TCON). A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0209] In applications, the memory may be an internal storage unit of a terminal device in some embodiments, for example, a hard disk or memory of the terminal device. The memory may also be an external storage device of the terminal device in other embodiments, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the terminal device. The memory may also include both an internal storage unit of the terminal device and an external storage device. The memory is used to store operating systems, applications, boot loaders, data, and other programs, such as program codes of computer programs. The memory may also be used to temporarily store data that has been output or is to be output.

[0210] In the application, the communication module can be set to any device that can directly or indirectly perform long-distance wired or wireless communication with the user terminal according to actual needs. For example, the communication module can provide solutions for communication including wireless local area networks (WLAN) (such as Wi-Fi networks), Bluetooth, Zigbee, mobile communication networks, global navigation satellite systems (GNSS), frequency modulation (FM), near field communication technology (NFC), infrared technology (IR), etc., which are applied to network devices. The communication module may include an antenna, and the antenna may have only one array element or an antenna array including multiple array elements. The communication module can receive electromagnetic waves through the antenna, frequency modulate and filter the electromagnetic wave signal, and send the processed signal to the processor. The communication module can also receive the signal to be sent from the processor, frequency modulate and amplify it, and convert it into electromagnetic waves for radiation through the antenna.

[0211] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / modules 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.

[0212] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example for illustration. In actual applications, the above-mentioned function allocation can be completed by different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The functional modules in the embodiment can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software functional modules. In addition, the specific names of the functional 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 modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0213] The embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0214] An embodiment of the present application provides a computer program product. When the computer program product runs on a terminal device, the terminal device can implement the steps in the above-mentioned various method embodiments.

[0215] If the integrated module is implemented in the form of a software function module 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. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. 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 include at least: any entity or device capable of carrying the computer program code to the 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 disk or an optical disk.

[0216] 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.

[0217] Those of ordinary skill in the art will appreciate that the modules 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.

[0218] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules 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 modules, which can be electrical, mechanical or other forms.

[0219] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0220] 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 generating a spare parts procurement reserve strategy, characterized in that: The spare parts procurement reserve strategy generation method comprises: Obtain spare part coding data of a target spare part; If the target spare part is an equipment-level spare part including functional location data, obtaining and outputting a first procurement reserve strategy for the target spare part according to the spare part coding data of the target spare part and the corresponding first functional location data; If the target spare part is a component-level spare part including functional location data, obtaining and outputting a second procurement reserve strategy for the target spare part according to the spare part coding data of the target spare part and the corresponding second functional location data; If the target spare part is a spare part that does not include functional location data, a third procurement reserve strategy for the target spare part is obtained and output according to the spare part coding data of the target spare part.

2. The method for generating a spare parts procurement reserve strategy according to claim 1, characterized in that: If the target spare part is a device-level spare part including functional location data, obtaining and outputting a first procurement reserve strategy for the target spare part according to the spare part coding data of the target spare part and the corresponding first functional location data, including: According to the spare part coding data, obtaining first functional position data corresponding to the target spare part in the target unit; Obtain historical usage data of the target spare parts for s units within a first preset time period, where s is a positive integer; Obtaining a first expected usage quantity of the target spare part in the s units according to the first functional position data and the historical usage data; According to the first expected usage quantity of the target spare part in the s units, a first purchasing and reserve strategy for the target spare part in the target unit is obtained and output.

3. The method for generating a spare parts procurement reserve strategy according to claim 2, characterized in that: The step of obtaining and outputting the purchase reserve data of the target spare part in the target unit according to the first expected usage quantity of the target spare part in the s units includes: Establish demand function model library; Inputting the first average expected usage quantity of the target spare part in the s units into each demand function model in the function model library respectively; Testing each demand function model to obtain corresponding test results; Determining the demand function model corresponding to the verification result that meets the first preset condition as the target demand function model; According to the target demand function model, a first purchasing reserve strategy for the target spare parts in the target unit is obtained and output.

4. The method for generating a spare parts procurement reserve strategy according to claim 3, characterized in that: After testing each demand function model and obtaining corresponding test results, the method further includes: If the inspection results corresponding to each demand function model do not meet the first preset condition, then the first procurement reserve strategy for the target spare parts in the target unit is obtained and output based on the first average expected usage quantity of the target spare parts in the s units and the corresponding standard deviation.

5. The method for generating a spare parts procurement reserve strategy according to claim 1, characterized in that: If the target spare part is a component-level spare part including functional location data, obtaining and outputting a second procurement reserve strategy for the target spare part according to the spare part coding data of the target spare part and the corresponding second functional location data, including: According to the spare part coding data, obtaining second functional position data corresponding to the target spare part in the target unit; Obtain historical usage data of the target spare parts for s units within a second preset time period, where s is a positive integer; Obtaining a second expected usage quantity of the target spare part in the s units according to the second functional position data and the historical usage data; According to the second expected usage quantity of the target spare part in the s units, a second purchasing reserve strategy for the target spare part in the target unit is obtained and output.

6. The method for generating a spare parts procurement reserve strategy according to claim 1, characterized in that: If the target spare part is a spare part that does not include functional location data, obtaining and outputting a third procurement reserve strategy for the target spare part according to the spare part coding data of the target spare part includes: If the target spare part is a common code spare part, then obtain the historical usage data and corresponding functional position quantity of the target spare part for s units within a third preset time period, where s is a positive integer; According to the historical use data and the corresponding functional position quantity, the third expected use quantity of the target spare part in the s units is obtained. According to the third expected usage quantity, a third procurement reserve strategy for the target spare parts in the target unit is obtained and output.

7. The method for generating a spare parts procurement reserve strategy according to claim 6, characterized in that: If the target spare part is a spare part that does not include functional location data, obtaining and outputting a third procurement reserve strategy for the target spare part according to the spare part coding data of the target spare part, further comprising: If the target spare part is a unique spare part, then basic information of the target spare part is obtained according to the spare part coding data of the target spare part, wherein the basic information includes the manufacturer, model series and category information of the target spare part; According to the basic information of the target spare part, historical usage data and corresponding functional position quantity of the target spare part for s units within a third preset time period are obtained, where s is a positive integer; According to preset rules, the historical requisition data and the corresponding functional position quantities are summarized; When the second preset condition is met, the failure probability of the target spare part is obtained according to the summarized historical use data and the corresponding functional position quantity; According to the failure probability, obtaining a third expected usage quantity of the target spare part; According to the third expected usage quantity, a third purchasing reserve strategy for the target spare parts in the target unit is obtained and output.

8. The method for generating a spare parts procurement reserve strategy according to claim 7, characterized in that: If the target spare part is a spare part that does not include functional location data, obtaining and outputting a third procurement reserve strategy for the target spare part according to the spare part coding data of the target spare part, further comprising: If the number of functional positions of the target spare part of the s units within the third preset time period cannot be obtained, then a corresponding neural network model is established based on the category to which the target spare part belongs; Inputting relevant usage data of the target spare parts within a third preset time period into the neural network model, obtaining and outputting a third procurement reserve strategy for the target spare parts; The relevant collection data include the average collection quantity, maximum collection quantity, average collection standard deviation, and average collection times of the target spare parts.

9. A terminal 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 steps of the method for generating a spare parts procurement reserve strategy according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for generating a spare parts procurement reserve strategy according to any one of claims 1 to 8 are implemented.

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