Auxiliary decision method for spare parts quota management based on historical use of spare parts of nuclear power plant

Abstract

This invention belongs to the field of spare parts management technology, specifically relating to a spare parts quota management auxiliary decision-making method based on the historical usage of spare parts in nuclear power plants. It includes the following steps: acquiring work orders and material requisition data; classifying historical maintenance spare parts requirements based on material information and relevant imported data, including categories such as preventative replacement, corrective replacement, or no replacement; acquiring and classifying lifespan data, with data types including complete data, left-truncation data, right-truncation data, and interval data; estimating lifespan distribution parameters; importing planned maintenance spare parts requirements; and forecasting spare parts requirements. Its advantages are: by applying the method of this invention, the difficulty of spare parts quota management can be effectively reduced, decision-making efficiency and accuracy can be improved, and ultimately, while ensuring the safe operation of the unit, spare parts inventory can be kept at a minimum level, thus reducing spare parts management costs.

Description

Technical Field

[0001] This invention belongs to the field of spare parts management technology, specifically relating to a spare parts quota management auxiliary decision-making method based on the historical usage of spare parts in nuclear power plants. Background Technology

[0002] Spare parts quota management is a crucial aspect of spare parts technical management. Its purpose is to ensure a sufficient safety stock to meet the needs of unplanned spare parts replacements during on-site maintenance and to promptly remind equipment engineers to submit spare parts procurement requests. Currently, spare parts quota management in nuclear power plants is primarily based on the experience of equipment engineers, lacking relevant methodologies and decision-making support tools. Summary of the Invention

[0003] The purpose of this invention is to provide a spare parts quota management auxiliary decision-making method based on the historical use of spare parts in nuclear power plants. This method can automate the spare parts quota management auxiliary decision-making process and reduce the difficulty of spare parts quota management.

[0004] The technical solution of this invention is as follows: a spare parts quota management auxiliary decision-making method based on the historical use of spare parts in nuclear power plants, comprising the following steps:

[0005] Step 1: Obtain work order and material requisition data;

[0006] Step 2: Classify historical maintenance spare parts requirements based on material information and relevant imported data. Categories include: preventive replacement, corrective replacement, or no replacement.

[0007] Step 3: Acquisition and classification of lifetime data. Data types are divided into: complete data, left-truncated data, right-truncated data, and interval data.

[0008] Step 4: Lifetime distribution parameter estimation;

[0009] Step 5: Import planned maintenance spare parts requirements;

[0010] Step 6: Spare parts demand forecasting.

[0011] Step 1 includes retrieving relevant field information from the work order database and the material requisition database based on the material code or material name. The relevant field information includes: equipment number, equipment name, work order number, work order type, actual start time, actual end time, work content, detailed instructions, single machine quantity, material requisition type, required quantity, issued quantity, and material attributes.

[0012] Step 2 includes,

[0013] Step 21: For materials that meet the following conditions: “Material Attribute” is “Non-Consumable Parts”; “Material Requisition Type” is “Unplanned Material Requisition”, such as “QDR Material” or “Supplement Material”, then determine the status of the materials in the N positions that have been used on the equipment for the longest time as “Corrective Replacement”, and the status of the materials in the remaining positions as “Not Replaced”, where N is the “Issued Quantity”.

[0014] Step 22: For items that meet the following conditions: "Material Attribute" is "Consumable Parts" and "Work Order Type" is a preventive maintenance work order, such as "PM", then all replaced materials will be in the "Preventive Replacement" status.

[0015] Step 23: For materials that meet the following conditions: "Material Attribute" is "Consumable Parts"; "Work Order Type" is a corrective maintenance work order, such as "CM" or "DM"; and there is a "Non-Consumable Parts" requisition in the work order associated with the material requisition, then determine that the status of the materials in the N positions that have been used on the equipment for the longest time is "Preventive Replacement", and the status of the materials in the remaining positions is "Not Replaced", where N is the "Quantity Issued".

[0016] Step 24: For materials that meet the following conditions: “Material Attribute” is “Consumable Parts”; “Work Order Type” is a corrective maintenance work order, such as “CM”, “DM”, etc.; and there are no “non-consumable parts” requisitions in the work orders associated with the material requisition, then determine that the status of materials in the N positions that have been used on the equipment for the longest time is “Corrective Replacement”, and the status of materials in the remaining positions is “Not Replaced”, where N is the “Quantity Issued”.

[0017] Step 3 includes,

[0018] Step 31: Assume the actual start time of the material replacement work order is T. i T represents the material replacement time. The work order for the last replacement of this material at the same location ended at time T0, which is the material installation time. i -T0 represents the installation and usage time of the material on the equipment, i.e., the lifespan of the material;

[0019] Step 32: If the status of the replaced material is marked as "corrective replacement" and the installation time of the material is known, then the lifetime value of the material is "complete data"; if the status of the replaced material is marked as "corrective replacement," but it can only be determined that the installation time of the material is before a certain time, then the lifetime value of the material is "right-truncated data"; if the status of the replaced material is marked as "corrective replacement," but it can only be determined that the installation time of the material is after a certain time, then the lifetime value of the material is "left-truncated data"; if the status of the replaced material is marked as "preventive replacement," and the installation time of the material is known, then the lifetime value of the material is "right-truncated data"; if the status of the replaced material is marked as "preventive replacement," but it can only be determined that the installation time of the material is before a certain time, then the lifetime value of the material is "right-truncated data"; if the status of the replaced material is marked as "preventive replacement," but it can only be determined that the installation time of the material is after a certain time, then the lifetime value of the material is "interval data."

[0020] Step 4 includes,

[0021] Step 41: Automatically select a parameter estimation method to fit the lifetime data according to preset rules, and output the best-fit model;

[0022] Step 42: Preset rules can be customized. Custom parameters include:

[0023] i. The proportion of truncated data: When the proportion of truncated data exceeds a certain value, the parameter estimation method for heavily truncated data should be selected first.

[0024] ii. Complete data volume: Parameter estimation is performed when the complete data volume exceeds a certain value; otherwise, parameter estimation is not performed.

[0025] Step 43: Parameter estimation methods include:

[0026] Parameter estimation methods for heavily truncated data and conventional parameter estimation methods;

[0027] Step 44: Parameter estimation for heavily truncated data;

[0028] Step 45: Calculation method of likelihood value LK:

[0029] Step 46: Conventional Parameter Estimation Methods

[0030] Step 47: The preset model includes:

[0031] Weibull distribution, normal distribution, log-normal distribution, gamma distribution.

[0032] Step 44 includes,

[0033] Step 441: For all truncated data, calculate the nonparametric estimate μ0 of the mean:

[0034] μ0=2μ c / (1-0.4604s+0.4943cs)

[0035] Where s is the skewness of the truncated data; μ c σ is the mean of the truncated data; c is the standard deviation of the truncated data; ρ is the proportion of the truncated data to the total sample;

[0036] Step 442: Calculate the initial value of the Weibull distribution scaling parameter η0.

[0037] η0=μ0 / Γ(1+1 / β0)

[0038] Where β0 is the shape parameter of the Weibull distribution, and Γ() is the gamma function;

[0039] Step 443: Fix η0, and maximize the likelihood value by β. * This is the result of estimating the shape parameters of the Weibull distribution;

[0040] Step 444: Fix β * η is the value that maximizes the likelihood. * This is the estimated result of the Weibull distribution scaling parameter.

[0041] Step 45 includes,

[0042] Step 451: For complete data t i Using ln (f(t) i )) Calculate the corresponding likelihood value, where f()f(t) i ) is the probability density function;

[0043] Step 452: For right-truncated data t m Using ln(1-F(t) i Calculate the corresponding likelihood value, where F() is the cumulative distribution function;

[0044] Step 453: For left-truncated data t n Using ln(F(t) i )) Calculate the corresponding likelihood value;

[0045] Step 454: For interval-truncated data (within the range of (t) m ,t n Using ln(F(t)) n )-F(t m )) Calculate the likelihood value;

[0046] Step 455: The likelihood value LK is the sum of the likelihood values ​​corresponding to all the above data.

[0047] Step 46 includes,

[0048] Step 461: Calculate the likelihood value LK;

[0049] Step 462: The parameter that makes the likelihood value LK reach its maximum value is the parameter estimation result of the preset model;

[0050] Step 463: For all preset models, when the number of parameters of the preset models is the same, the model with the largest likelihood value LK is the best fit; when the number of parameters of the preset models is inconsistent, the Akaike Information Criterion (AIC) is used for evaluation:

[0051] AIC=-2ln(L)+2m AIC=-2ln(LK)+2m

[0052] Where L is the likelihood function and m is the number of model parameters. The model with the minimum AIC value is the best fit.

[0053] Step 5 includes,

[0054] Step 51: Based on the material code or material name, retrieve the relevant field information from the preventive maintenance plan database. The default fields include: equipment number, equipment name, planned start time, planned end time, planned work content, planned maintenance content, and required quantity.

[0055] Step 52: Based on the material information and related field data, classify the data using preset logic into two categories: preventative replacement and no replacement.

[0056] The preset logic includes:

[0057] When the demand equals the number of equipment installation locations, it is automatically classified as: preventative replacement;

[0058] When the demand does not equal the number of equipment installation locations, the information is highlighted to prompt the user to manually categorize the data.

[0059] Step 6 includes,

[0060] Step 61: Use the parameter estimation results obtained in Step 4, the planned maintenance spare parts demand classification results obtained in Step 5, and the prediction time interval as inputs to the spare parts demand prediction process;

[0061] Step 62: Alternatively, spare parts demand can be predicted directly based on the specific parameter values ​​of the service life distribution and the planned maintenance spare parts demand information.

[0062] Step 63: Provide reserve suggestions based on the input results.

[0063] The beneficial effects of this invention are as follows: Nuclear power plants determine spare parts inventory quotas manually based on experience, lacking relevant auxiliary decision-making tools, resulting in a highly subjective and inefficient decision-making process. By applying the method of this invention, the difficulty of spare parts quota management can be effectively reduced, decision-making efficiency and accuracy can be improved, and ultimately, while ensuring the safe operation of the unit, the spare parts inventory level can be kept at a minimum, thereby reducing spare parts management costs. Attached Figure Description

[0064] Figure 1 The flowchart of the spare parts quota management auxiliary decision-making method based on the historical use of spare parts in nuclear power plants provided by the present invention is shown. Detailed Implementation

[0065] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0066] This invention provides a spare parts quota management auxiliary decision-making method based on the historical usage of spare parts in nuclear power plants. It includes the automatic advancement of each step from the creation of the spare parts quota management process to the determination of spare parts inventory quotas, importing historical work orders and material requisition data, auxiliary classification and organization of spare parts life data, conversion of spare parts life data, querying and statistics of historical and planned usage information of spare parts, spare parts demand forecasting, and determination of inventory quotas.

[0067] like Figure 1 As shown, the spare parts quota management auxiliary decision-making method based on the historical use of spare parts in nuclear power plants includes the following steps:

[0068] Step 1: Obtaining work order and material requisition data

[0069] Based on the material code or material name, relevant field information is obtained from the work order database and the material requisition database. The relevant field information includes: equipment number, equipment name, work order number, work order type, actual start time, actual end time, work content, detailed instructions, single machine quantity, material requisition type, required quantity, issued quantity, and material attributes.

[0070] Step 2: Categorize historical maintenance spare parts requirements based on material information and relevant imported data. Categories include: preventative replacement, corrective replacement, or no replacement.

[0071] The specific process is as follows:

[0072] Step 21: For materials that meet the following conditions: "Material Attribute" is "Non-Consumable Parts"; "Material Requisition Type" is "Unplanned Material Requisition", such as "QDR Material" or "Supplement Material", then determine the status of the materials in the N positions that have been used on the equipment for the longest time as "Corrective Replacement", and the status of the materials in the remaining positions as "Not Replaced". N is the "Issued Quantity".

[0073] Step 22: For items that meet the following conditions: "Material Attribute" is "Consumable Part" and "Work Order Type" is a preventive maintenance work order, such as "PM", then all replaced materials will be in the "Preventive Replacement" status.

[0074] Step 23: For materials that meet the following conditions: "Material Attribute" is "Consumable Parts"; "Work Order Type" is a corrective maintenance work order, such as "CM" or "DM"; and there is a "Non-Consumable Parts" requisition in the work order associated with the material requisition, then determine that the status of materials in the N positions that have been used on the equipment for the longest time is "Preventive Replacement", and the status of materials in the remaining positions is "Not Replaced". N is the "Quantity Issued".

[0075] Step 24: For materials that meet the following conditions: "Material Attribute" is "Consumable Parts"; "Work Order Type" is a corrective maintenance work order, such as "CM" or "DM"; and there are no "Non-Consumable Parts" requisitions in the work orders associated with the material requisition, then determine that the status of materials in the N positions that have been used on the equipment for the longest time is "Corrective Replacement", and the status of materials in the remaining positions is "Not Replaced". N is the "Quantity Issued".

[0076] Step 3: Acquisition and classification of lifetime data. Data types are categorized as: complete data, left-truncated data, right-truncated data, and interval data. This includes the following steps:

[0077] Step 31: Assume the actual start time of the material replacement work order is T. i T represents the material replacement time. The work order for the last replacement of this material at the same location ended at time T0, which is the material installation time. i -T0 represents the installation and usage time of the material on the equipment, i.e., the lifespan of the material.

[0078] Step 32: If the status of the replaced material is marked as "corrective replacement" and the installation time of the material is known, then the lifetime value of the material is "complete data"; if the status of the replaced material is marked as "corrective replacement," but it can only be determined that the installation time of the material is before a certain time, then the lifetime value of the material is "right-truncated data"; if the status of the replaced material is marked as "corrective replacement," but it can only be determined that the installation time of the material is after a certain time, then the lifetime value of the material is "left-truncated data"; if the status of the replaced material is marked as "preventive replacement," and the installation time of the material is known, then the lifetime value of the material is "right-truncated data"; if the status of the replaced material is marked as "preventive replacement," but it can only be determined that the installation time of the material is before a certain time, then the lifetime value of the material is "right-truncated data"; if the status of the replaced material is marked as "preventive replacement," but it can only be determined that the installation time of the material is after a certain time, then the lifetime value of the material is "interval data."

[0079] Step 4: Lifetime distribution parameter estimation

[0080] Step 41: Automatically select a parameter estimation method to fit the lifetime data according to preset rules, and output the best-fit model;

[0081] Step 42: Preset rules can be customized. Custom parameters include:

[0082] i. The proportion of truncated data: When the proportion of truncated data exceeds a certain value, the parameter estimation method for heavily truncated data should be selected first.

[0083] ii. Complete data volume: Parameter estimation is performed when the complete data volume exceeds a certain value; otherwise, parameter estimation is not performed.

[0084] Step 43: Parameter estimation methods include:

[0085] Parameter estimation methods for heavily truncated data and conventional parameter estimation methods;

[0086] Step 44: Parameter estimation for heavily censored data:

[0087] Step 441: For all truncated data, calculate the nonparametric estimate μ0 of the mean:

[0088] μ0=2μ c / (1-0.4604s+0.4943cs)

[0089] Where s is the skewness of the truncated data; μ c σ is the mean of the truncated data; σ is the standard deviation of the truncated data; c is the proportion of the truncated data to the total sample; ρ is the coefficient of variation of the truncated data.

[0090] Step 442: Calculate the initial value of the Weibull distribution scaling parameter η0:

[0091] η0=μ0 / Γ(1+1 / β0)

[0092] Where β0 is the shape parameter of the Weibull distribution, and Γ() is the gamma function.

[0093] Step 443: Fix η0, and maximize the likelihood value by β. * This is the result of estimating the shape parameters of the Weibull distribution.

[0094] Step 444: Fix β * η is the value that maximizes the likelihood. * This is the estimated result of the Weibull distribution scaling parameter.

[0095] Step 45: Calculation method of likelihood value LK:

[0096] Step 451: For complete data ti Using ln(f(t) i )) Calculate the corresponding likelihood value, where f()f(t) i ) is the probability density function;

[0097] Step 452: For right-truncated data t m Using ln(1-F(t) i Calculate the corresponding likelihood value, where F() is the cumulative distribution function;

[0098] Step 453: For left-truncated data t n Using ln(F(t) i )) Calculate the corresponding likelihood value;

[0099] Step 454: For interval-truncated data (within the range of (t) m ,t n Using ln(F(t)) n )-F(t m )) Calculate the likelihood value.

[0100] Step 455: The likelihood value LK is the sum of the likelihood values ​​corresponding to all the above data.

[0101] Step 46: Conventional Parameter Estimation Methods

[0102] Step 461: Calculate the likelihood value LK;

[0103] Step 462: Find the parameter that makes the likelihood value LK reach its maximum value. This parameter is the parameter estimation result of the preset model.

[0104] Step 463: For all preset models, when the number of parameters in the preset models is the same, the model with the largest likelihood value LK is the best fit. When the number of parameters in the preset models is inconsistent, the Akaike Information Criterion (AIC) is used for evaluation:

[0105] AIC=-2ln(L)+2m AIC=-2ln(LK)+2m

[0106] Where L is the likelihood function and m is the number of model parameters. The model with the minimum AIC value is the best fit.

[0107] Step 47: The preset model includes:

[0108] Weibull distribution, normal distribution, log-normal distribution, gamma distribution;

[0109] Step 5: Import planned maintenance spare parts requirements

[0110] Step 51: Based on the material code or material name, retrieve the relevant field information from the preventive maintenance plan database. The default fields include: equipment number, equipment name, planned start time, planned end time, planned work content, planned maintenance content, and required quantity.

[0111] Step 52: Classify the data according to the material information and related field data using preset logic. The categories are: preventive replacement and no replacement.

[0112] The preset logic includes:

[0113] When the demand equals the number of equipment installation locations, it is automatically classified as: preventative replacement;

[0114] When the demand does not equal the number of equipment installation locations, the information is highlighted to prompt the user to manually categorize the data.

[0115] Step 6: Spare Parts Demand Forecast

[0116] Step 61: Use the parameter estimation results obtained in Step 4, the planned maintenance spare parts demand classification results obtained in Step 5, and the prediction time interval as inputs to the spare parts demand prediction process;

[0117] Step 62: Alternatively, spare parts demand can be predicted directly based on the specific parameter values ​​of the service life distribution and the planned maintenance spare parts demand information.

[0118] Step 63: Provide reserve suggestions based on the input results.

Claims

1. A spare parts quota management auxiliary decision-making method based on the historical use of spare parts in nuclear power plants, characterized in that, Includes the following steps: Step 1: Obtain work order and material requisition data; Step 2: Classify historical maintenance spare parts requirements based on material information and relevant imported data. Categories include: preventive replacement, corrective replacement, or no replacement. Step 3: Acquisition and classification of lifetime data. Data types are divided into: complete data, left-truncated data, right-truncated data, and interval data. Step 4: Lifetime distribution parameter estimation; Step 4 includes, Step 41: Automatically select a parameter estimation method to fit the lifetime data according to preset rules, and output the best-fit model; Step 42: Preset rules can be customized. Custom parameters include: i. The proportion of truncated data: When the proportion of truncated data exceeds a certain value, the parameter estimation method for heavily truncated data is selected; ii. Complete data volume: Parameter estimation is performed when the complete data volume exceeds a certain value; otherwise, parameter estimation is not performed. Step 43: Parameter estimation methods include: Parameter estimation methods for heavily truncated data and conventional parameter estimation methods; Step 44: Parameter estimation for heavily truncated data; Step 44 includes, Step 441: For all truncated data, calculate the nonparametric estimate of the mean. ; ， in, The skewness of the truncated data; The mean of the truncated data; This represents the proportion of truncated data to the total sample. Step 442: Calculate the Weibull distribution scaling parameters The initial value; ， in, Let be the shape parameter of the Weibull distribution. It is a gamma function; Step 443: Fix The one that maximizes the likelihood value This is the result of estimating the shape parameters of the Weibull distribution; Step 444: Fix When the likelihood value is maximized This is the estimation result of the Weibull distribution scaling parameter; Step 45: Calculation method of likelihood value LK; Step 46: Conventional parameter estimation methods; Step 47: The preset model includes: Weibull distribution, normal distribution, log-normal distribution, gamma distribution; Step 5: Import planned maintenance spare parts requirements; Step 6: Spare parts demand forecasting.

2. The spare parts quota management auxiliary decision-making method based on the historical use of spare parts in nuclear power plants as described in claim 1, characterized in that: Step 1 includes retrieving relevant field information from the work order database and the material requisition database based on the material code or material name. The relevant field information includes: equipment number, equipment name, work order number, work order type, actual start time, actual end time, work content, detailed instructions, single machine quantity, material requisition type, required quantity, issued quantity, and material attributes.

3. The spare parts quota management auxiliary decision-making method based on the historical use of spare parts in nuclear power plants as described in claim 1, characterized in that: Step 2 includes, Step 21: For materials that meet the following conditions: "Material Attribute" is "Non-Consumable Parts"; "Material Requisition Type" is "Unplanned Material Requisition", including "QDR Materials" or "Supplementary Materials", then determine the material that has been used on the equipment for the longest time. The status of the supplies in one location is "corrective replacement", while the status of the supplies in the other locations is "not replaced". This refers to the "quantity distributed"; Step 22: For items that meet the following conditions: "Material Attribute" is "Consumable Parts" and "Work Order Type" is a preventive maintenance work order, such as "PM", then all replaced materials will be in the "Preventive Replacement" status. Step 23: For items that meet the following conditions: "Material Attribute" is "Consumable Part"; "Work Order Type" is a corrective maintenance work order, including "CM" and "DM"; and there is a "Non-Consumable Part" requisition in the work order associated with the material requisition, then determine the item that has been used on the equipment for the longest time. The supplies in one location are in the "precautionary replacement" status, while the supplies in the other locations are in the "not replaced" status. This refers to the "quantity distributed"; Step 24: For items that meet the following conditions: "Material Attribute" is "Consumable Part"; "Work Order Type" is a corrective maintenance work order, including "CM" and "DM"; and there are no "Non-Consumable Part" requisitions in the work orders associated with the material requisition, then determine the item that has been used on the equipment for the longest time. The status of the supplies in one location is "corrective replacement", while the status of the supplies in the other locations is "not replaced". This refers to the "distribution volume".

4. The spare parts quota management auxiliary decision-making method based on the historical use of spare parts in nuclear power plants as described in claim 1, characterized in that: Step 3 includes, Step 31: Assume the actual start time of the material replacement work order is... This refers to the material replacement time; the end time of the last work order for replacing this material at the same location is... That is, the installation time of the materials. The lifespan of the material refers to the time it is installed and used on the equipment. Step 32: If the status of the replaced material is marked as "corrective replacement" and the installation time of the material is known, then the lifetime value of the material is "complete data"; if the status of the replaced material is marked as "corrective replacement," but it can only be determined that the installation time of the material is before a certain time, then the lifetime value of the material is "right-truncated data"; if the status of the replaced material is marked as "corrective replacement," but it can only be determined that the installation time of the material is after a certain time, then the lifetime value of the material is "left-truncated data"; if the status of the replaced material is marked as "preventive replacement," and the installation time of the material is known, then the lifetime value of the material is "right-truncated data"; if the status of the replaced material is marked as "preventive replacement," but it can only be determined that the installation time of the material is before a certain time, then the lifetime value of the material is "right-truncated data"; if the status of the replaced material is marked as "preventive replacement," but it can only be determined that the installation time of the material is after a certain time, then the lifetime value of the material is "interval data." 5. The spare parts quota management auxiliary decision-making method based on the historical use of spare parts in nuclear power plants as described in claim 1, characterized in that: Step 45 includes, Step 451: For complete data ,use Calculate the corresponding likelihood value, where f () represents the probability density function; Step 452: For right-truncated data ,use Calculate the corresponding likelihood value, where The cumulative distribution function; Step 453: For left-hand truncated data ,use Calculate the corresponding likelihood value; Step 454: For interval-truncated data, the value range is... ,use Calculate the likelihood value; Step 455: The likelihood value LK is the sum of the likelihood values ​​corresponding to all data from steps 451 to 454.

6. The spare parts quota management auxiliary decision-making method based on the historical use of spare parts in nuclear power plants as described in claim 1, characterized in that: Step 46 includes, Step 461: Calculate the likelihood value LK; Step 462: The parameter that makes the likelihood value LK reach its maximum value is the parameter estimation result of the preset model; Step 463: For all preset models, when the number of parameters of the preset models is the same, the model with the largest likelihood value LK is the best fit; when the number of parameters of the preset models is inconsistent, the Akaike Information Criterion is used for evaluation. ， Where m is the number of model parameters, and the model with the minimum AIC value is the best fit.

7. The spare parts quota management auxiliary decision-making method based on the historical use of spare parts in nuclear power plants as described in claim 1, characterized in that: Step 5 includes, Step 51: Based on the material code or material name, retrieve the relevant field information from the preventive maintenance plan database. The default fields include: equipment number, equipment name, planned start time, planned end time, planned work content, planned maintenance content, and required quantity. Step 52: Based on the material information and related field data, classify the data using preset logic into two categories: preventative replacement and no replacement. The preset logic includes: When the demand equals the number of equipment installation locations, it is automatically classified as: preventative replacement; When the demand does not equal the number of equipment installation locations, the information is highlighted to prompt the user to manually categorize the data.

8. The spare parts quota management auxiliary decision-making method based on the historical use of spare parts in nuclear power plants as described in claim 1, characterized in that: Step 6 includes, Step 61: Use the parameter estimation results obtained in Step 4, the planned maintenance spare parts demand classification results obtained in Step 5, and the prediction time interval as inputs to the spare parts demand prediction process; Step 62: Alternatively, spare parts demand can be predicted directly based on the specific parameter values ​​of the service life distribution and the planned maintenance spare parts demand information. Step 63: Provide reserve suggestions based on the input results.

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