Comprehensive resource measuring and calculating method, device and equipment for product maintenance and storage medium

Abstract

The invention discloses a comprehensive resource measurement and calculation method and device for product maintenance, equipment and a storage medium. The invention discloses a comprehensive resource measuring and calculating method for product maintenance, and the method comprises the steps: obtaining a first maximum number of times of fault maintenance of a single product in a guarantee period for a set type of product; obtaining a second maximum number of times of preventive maintenance executed by the single product in the guarantee period; aiming at each preventive maintenance frequency in the second maximum frequency, determining a first probability group of a single product corresponding to each preventive maintenance frequency in each fault maintenance period in the first maximum frequency; determining first probability distribution data of preventive maintenance executed by the batch products based on the first probability group; obtaining second probability distribution data of failure maintenance execution of the batch products; determining comprehensive maintenance resource information based on the first probability distribution data and the second probability distribution data; and the target resource quantity is determined based on the expected satisfaction probability and the comprehensive maintenance resource information, so that the accuracy of comprehensive resource measurement and calculation of product maintenance is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a comprehensive resource calculation method, device, equipment and storage medium for product maintenance. Background Art

[0002] During the use of the product, in order to ensure the normal use of the product, two types of repairs will be performed on the product: maintenance during operation and corrective repair after a failure occurs. Preventive maintenance and regular maintenance belong to maintenance during operation, and replacement repairs belong to corrective repairs after a failure occurs.

[0003] In the prior art, the total maintenance cost of a batch of products is usually calculated by multiplying the total maintenance cost of a single product by the number of products. This method is often based on the subjective experience of maintenance managers, resulting in low accuracy in the total maintenance cost of batch products. Summary of the Invention

[0004] The present invention provides a method, device, equipment and storage medium for comprehensive resource calculation of product maintenance, so as to improve the accuracy of comprehensive resource calculation of product maintenance.

[0005] According to one aspect of the present invention, a method for calculating comprehensive resources for product maintenance is provided, the method comprising:

[0006] For a product of a set type, obtain a first maximum number of failure repairs performed on a single product during the warranty period; and obtain a second maximum number of preventive maintenance performed on a single product during the warranty period;

[0007] Determining, for each preventive maintenance frequency within the second maximum frequency, a first probability group for each preventive maintenance frequency corresponding to a single product during each fault repair period within the first maximum frequency; and determining, based on the first probability group, first probability distribution data for executing preventive maintenance on a batch of products of a set type;

[0008] Obtaining second probability distribution data of performing fault repair on batch products of a set type;

[0009] Determining comprehensive maintenance resource information based on the first probability distribution data of preventive maintenance and the second probability distribution data of breakdown maintenance, wherein the comprehensive maintenance resource information includes comprehensive probability data and comprehensive resource amounts corresponding to each combination of preventive maintenance and breakdown maintenance, wherein the comprehensive resource amounts represent the amount of resources consumed by performing the preventive maintenance and breakdown maintenance corresponding to the combination of maintenance times;

[0010] The target resource amount of the product is determined based on the expected satisfaction probability corresponding to the set type of product and the comprehensive maintenance resource information.

[0011] According to another aspect of the present invention, a device for calculating comprehensive resources for product maintenance is provided, the device comprising:

[0012] A first maximum number and a second maximum number acquisition module is configured to acquire, for a set type of product, a first maximum number of times a single product can be repaired during the warranty period; and a second maximum number of times a single product can be repaired during the warranty period.

[0013] a first probability distribution data determination module configured to determine, for each preventive maintenance frequency within the second maximum frequency, a first probability group for each preventive maintenance frequency corresponding to a single product during each fault repair period within the first maximum frequency; and determine, based on the first probability group, first probability distribution data for executing preventive maintenance on a batch of products of a set type;

[0014] A second probability distribution data acquisition module is used to acquire second probability distribution data of batch products of a set type that are to be repaired for a fault;

[0015] a comprehensive maintenance resource information determination module, configured to determine comprehensive maintenance resource information based on first probability distribution data for preventive maintenance and second probability distribution data for breakdown maintenance, wherein the comprehensive maintenance resource information includes comprehensive probability data and comprehensive resource amounts corresponding to each combination of preventive maintenance and breakdown maintenance times, wherein the comprehensive resource amounts represent the amount of resources consumed by performing the preventive maintenance and breakdown maintenance corresponding to the combination of maintenance times;

[0016] The target resource quantity determination module is used to determine the target resource quantity of the product based on the expected satisfaction probability corresponding to the set type of product and the comprehensive maintenance resource information.

[0017] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0018] at least one processor; and

[0019] a memory communicatively connected to at least one processor; wherein,

[0020] The memory stores a computer program that can be executed by at least one processor. The computer program is executed by at least one processor so that the at least one processor can execute the comprehensive resource estimation method for product maintenance according to any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for enabling a processor to implement the comprehensive resource estimation method for product maintenance according to any embodiment of the present invention when executed.

[0022] The technical solution of the embodiment of the present invention provides data support for the analysis and processing of subsequent tasks for products of a set type by obtaining the first maximum number of times a single product performs fault repairs within the warranty period; and obtaining the second maximum number of times a single product performs preventive maintenance within the warranty period, thereby ensuring that various tasks are executed efficiently and accurately; for each preventive maintenance number within the second maximum number, determining a first probability group of each preventive maintenance number corresponding to a single product during each fault repair period within the first maximum number; determining first probability distribution data of preventive maintenance performed on batch products of a set type based on the first probability group, introducing preventive maintenance during each fault repair period, which is beneficial to improving the accuracy of the first probability distribution data, and further beneficial to improving the accuracy of comprehensive resource measurement for product maintenance; obtaining the first maximum number of times a batch product of a set type performs fault repairs The second probability distribution data provides data support for the comprehensive resource calculation of product maintenance; the comprehensive maintenance resource information is determined based on the first probability distribution data of preventive maintenance and the second probability distribution data of fault maintenance, and the comprehensive maintenance resource information includes the comprehensive probability data and comprehensive resource quantity corresponding to each combination of preventive maintenance and fault maintenance. The comprehensive resource quantity represents the amount of resources consumed by the preventive maintenance and fault maintenance corresponding to the combination of maintenance times, which improves the accuracy of the determination of the comprehensive maintenance resource information, and thus helps to improve the accuracy of the comprehensive resource calculation of product maintenance; the target resource quantity of the product is determined based on the expected satisfaction probability and comprehensive maintenance resource information corresponding to the set type of product, which realizes the accurate determination of the target resource quantity, solves the problem of low accuracy of the comprehensive resource calculation of product maintenance, and improves the accuracy of the comprehensive resource calculation of product maintenance.

[0023] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 This is a flow chart of a comprehensive resource calculation method for product maintenance provided in Example 1 of the present invention;

[0026] Figure 2 This is a flow chart of a comprehensive resource calculation method for product maintenance provided by the second embodiment of the present invention;

[0027] Figure 3 This is a schematic diagram of the structure of a comprehensive resource calculation device for product maintenance provided by the third embodiment of the present invention;

[0028] Figure 4 This is a structural diagram of an electronic device provided in Example 4 of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] Example 1

[0032] Figure 1 This is a flow chart of a method for calculating comprehensive resources for product maintenance provided by the first embodiment of the present invention. This embodiment is applicable to situations where comprehensive resources for product maintenance are calculated. This method can be executed by a device for calculating comprehensive resources for product maintenance. The device for calculating comprehensive resources for product maintenance can be implemented in the form of hardware and / or software. The device for calculating comprehensive resources for product maintenance can be configured in an electronic device provided by the embodiment of the present invention. The electronic device can be a server, a computer, or a mobile terminal, for example, a mobile terminal can be a mobile phone, a tablet computer, etc. Figure 1 As shown, the method includes:

[0033] S110. For a product of a set type, obtain a first maximum number of times that a single product can be repaired during the warranty period; and obtain a second maximum number of times that a single product can be repaired during the warranty period.

[0034] Products of a set type can be selected according to demand, including but not limited to components, parts, assemblies and equipment, and the types of products in different fields are different. For example, the above products include but are not limited to ball bearings, relays, gyroscopes, electric motors, aircraft engines, batteries, hydraulic pumps and air turbine engines, etc. In the embodiment of the present disclosure, a comprehensive resource measurement method for product maintenance is performed for each type of product to determine the target amount of resources required for maintenance of each type of product in the future time period. The number of products in a batch of products of a set type can be multiple, and the target amount of resources for products of a set type can be understood as the total amount of resources expected to be consumed for maintenance of batch products of the set type in the future time period, wherein the future time period can be the guarantee period of the product.

[0035] Repairs for designated products include breakdown repairs and preventive maintenance. Breakdown repairs can be understood as repairs performed after a product malfunction occurs during the warranty period. Preventive maintenance can be understood as restoring or improving the product's internal operating environment during the warranty period, such as by replacing lubricants or tightening loose parts, to slow down the product's aging process and thereby extend its lifespan.

[0036] The first maximum number is the maximum cumulative number of times a single product may be repaired due to a fault during the warranty period. The second maximum number is the maximum cumulative number of times a single product may be preventively repaired during the warranty period.

[0037] In some embodiments of the present invention, the first maximum number can be determined based on the historical first maximum number of a single product. For example, the average of the historical first maximum number of a single product over a preset time period can be calculated, and the average of the historical first maximum number of times can be used as the first maximum number. The second maximum number can be determined based on the historical second maximum number of a single product. For example, the average of the historical second maximum number of a single product over a preset time period can be calculated, and the average of the historical second maximum number of times can be used as the second maximum number.

[0038] By obtaining the first maximum number of fault repairs performed on a single product during the warranty period and the second maximum number of preventive maintenance performed on a single product during the warranty period, data support is provided for the analysis and processing of subsequent tasks, ensuring that each task is executed efficiently and accurately.

[0039] In some embodiments of the present invention, the first maximum number is determined based on a first satisfaction probability index of a single product and probability data corresponding to each number of fault repairs, wherein the probability data corresponding to the first maximum number of fault repairs is greater than or equal to the first satisfaction probability index.

[0040] The first satisfaction probability indicator is the minimum probability that the actual number of fault repairs performed on a single product during the warranty period consumes less than or equal to the pre-set resource amount. This first satisfaction probability indicator can be set as needed and is not restricted here. The probability data corresponding to the first maximum number of fault repairs is the probability that the actual number of fault repairs performed on a single product during the warranty period will reach the first maximum number. This probability data can be determined based on the probability density function of a single product.

[0041] In some embodiments of the present invention, when the probability data corresponding to the first maximum number of fault repairs is greater than or equal to the first probability satisfaction index, the minimum number of actually executed fault repairs corresponding to the probability data is used as the first maximum number.

[0042] For example, the failure interval a of a single product during the warranty period follows an exponential distribution Exp(a), the planned working time of a single product during the warranty period is T1, and the probability data p1 corresponding to the first maximum number of failure repairs is calculated as follows:

[0043]

[0044] Where n1 represents the first maximum number of times, and its initial value is 0. The first satisfaction probability indicator is Ps1. Starting from 0, n1 is incremented one by one. P1 is updated based on the increment of n1. The updated p1 is compared with Ps1. When p1 ≥ Ps1, the minimum number of actual fault repair executions corresponding to the updated p1 is used as the first maximum number of times.

[0045] The first maximum number is determined by the first satisfaction probability index of a single product and the probability data corresponding to each repair number of fault repairs, which improves the accuracy of determining the first maximum number and provides accurate data support for the analysis and processing of subsequent tasks.

[0046] In some embodiments of the present invention, the second maximum number is determined based on the planned working hours and preventive maintenance interval duration of the single product during the warranty period.

[0047] The planned operating hours are the total duration of normal operation for a single product during the warranty period, pre-set based on factors such as the product's design and usage scenario. When the product operates 24 hours a day, the planned operating hours are the duration corresponding to the warranty period. To prevent product failure, regular maintenance can be performed on the product. The preventive maintenance interval is the interval between scheduled maintenance sessions during the normal operation of a single product. To prevent product failure during the planned operating hours, the planned operating hours for a single product during the warranty period must be longer than the preventive maintenance interval.

[0048] In some embodiments of the present invention, the second maximum number may be determined based on the ratio between the planned working hours of a single product during the warranty period and the preventive maintenance interval duration.

[0049] For example, the planned working time of a single product during the warranty period is T1, the preventive maintenance interval is T2, and the calculation formula for the second maximum number n2 is as follows:

[0050]

[0051] in, Indicates rounding down to an integer value.

[0052] The second maximum number of times is determined by the planned working hours and preventive maintenance intervals of a single product during the warranty period, which improves the accuracy of the second maximum number determination and provides accurate data support for the analysis and processing of subsequent tasks.

[0053] S120. Determine, for each preventive maintenance frequency within the second maximum frequency, a first probability group corresponding to each preventive maintenance frequency for a single product during each fault repair period within the first maximum frequency; and determine, based on the first probability group, first probability distribution data for executing preventive maintenance on batch products of a set type.

[0054] The first probability group is a set of probabilities corresponding to different preventive maintenance times performed on a single product during each fault maintenance period, under the condition that each preventive maintenance frequency is within the second maximum frequency. The first probability group can be formed by calculating the probability data corresponding to different preventive maintenance times performed on a single product during each fault maintenance period. Optionally, before forming the first probability group from the probability data corresponding to different preventive maintenance times, the probability data corresponding to different preventive maintenance times can be normalized to facilitate subsequent analysis and processing. The first probability distribution data is a data set of probability distributions corresponding to different total preventive maintenance times when preventive maintenance is performed on a batch of products of a set type, based on the first probability group. Here, the total preventive maintenance times can be understood as the total maintenance times for all products of the set type, where the number of products in the batch of the set type is greater than or equal to 1. The first probability distribution data describes the probability distribution pattern of different total preventive maintenance times corresponding to a batch of products, considering various combinations of fault maintenance and preventive maintenance. The first probability distribution data can be determined by calculating the first probability group corresponding to the batch of products of the set type.

[0055] In some embodiments of the present invention, for each preventive maintenance number within the second maximum number, probability data of each preventive maintenance number corresponding to a single product during each fault maintenance period within the first maximum number is determined, and the probability data of each preventive maintenance number forms a first probability group. Based on the first probability group, when preventive maintenance is performed on batch products of a set type, convolution calculation is performed between the first probability groups corresponding to the batch products of the set type to obtain first probability distribution data.

[0056] For example, the probability data of the number of preventive maintenance times for a single product is Pyd j , j represents the number of preventive maintenance actually performed on a single product. The probability data of the number of preventive maintenance is normalized. The calculation formula for normalization is as follows:

[0057]

[0058] Calculate the probability data of the number of preventive maintenance times corresponding to a set type of batch product, and form the first probability group Pyd. The number of products in the set type of batch product is N, and perform convolution processing on the first probability group corresponding to N single products. The convolution calculation formula is as follows:

[0059] Py=Pyd*…*Pyd;

[0060] Among them, Pyd represents the first probability group, Py represents the first probability distribution data, and the first probability groups corresponding to N single products are convolved N-1 times to obtain the first probability distribution data Py of the batch products of the set type.

[0061] By using the number of preventive maintenance times within the second maximum number of times, a first probability group of the number of preventive maintenance times corresponding to a single product during each fault maintenance period within the first maximum number of times is determined. Based on the first probability group, first probability distribution data of preventive maintenance performed on batch products of a set type is determined. Preventive maintenance is introduced during each fault maintenance period, which is beneficial to improving the accuracy of the first probability distribution data, and further beneficial to improving the accuracy of comprehensive resource estimation for product maintenance.

[0062] S130: Obtain second probability distribution data of performing fault repair on batch products of a set type.

[0063] The second probability distribution data is a data set of probability distributions corresponding to different fault repair times for batch products of a set type during the warranty period. The second probability distribution data can be determined based on probability data of different fault repair times for a single product during the warranty period.

[0064] In some embodiments of the present invention, the probability data Pxd of performing different fault repair times for a single product during the warranty period is calculated. k , the calculation formula is as follows:

[0065]

[0066] Where λ represents the average number of failure repairs for a single product during the warranty period, and is calculated as follows:

[0067]

[0068] Where i represents the actual number of repairs performed on a single product during the warranty period. Based on the probability data for each product undergoing different repairs during the warranty period, the probability data for each product undergoing different repairs during the warranty period are calculated. This probability data for each product undergoing different repairs during the warranty period is formed into an array, and convolution processing is performed on the array to obtain the second probability distribution data.

[0069] For example, the probability data of each single product undergoing different fault repair times during the warranty period is calculated separately. The probability data of each single product undergoing different fault repair times during the warranty period forms a corresponding array. The number of products in a set batch of products is N. The probability data of each single product undergoing different fault repair times during the warranty period, which forms a corresponding array, is convolved. The convolution calculation formula is as follows:

[0070] Px=Pxd*…*Pxd;

[0071] Where Pxd represents the array of probability data corresponding to the number of times a single product undergoes repair during the warranty period, and Px represents the second probability distribution data. The array of probability data corresponding to the number of times each product undergoes repair during the warranty period is convolved N-1 times to obtain the second probability distribution data.

[0072] By obtaining the second probability distribution data of fault repairs performed on batch products of a set type, data support is provided for comprehensive resource estimation of product maintenance.

[0073] S140. Determine comprehensive maintenance resource information based on the first probability distribution data of preventive maintenance and the second probability distribution data of fault maintenance. The comprehensive maintenance resource information includes comprehensive probability data and comprehensive resource amounts corresponding to each combination of preventive maintenance times and fault maintenance times. The comprehensive resource amounts represent the amount of resources consumed by performing the preventive maintenance and fault maintenance times corresponding to the combination of maintenance times.

[0074] Comprehensive maintenance resource information is used to characterize the probability information corresponding to different maintenance frequency combinations under both preventive maintenance and breakdown maintenance, as well as the resource amounts consumed by performing preventive maintenance and breakdown maintenance in each maintenance frequency combination. This resource amount can include at least one of material resources, time resources, and human resources during the maintenance process. This resource amount can be represented by a cost budget, which is not limited here. A maintenance frequency combination is data representing a combination of preventive maintenance frequency and breakdown maintenance frequency. In some embodiments of the present invention, any maintenance frequency combination includes one preventive maintenance frequency and one breakdown maintenance frequency. For example, any maintenance frequency combination is [z1, z2], where z1 represents the preventive maintenance frequency and z2 represents the breakdown maintenance frequency. Comprehensive probability data is probability data corresponding to each maintenance frequency combination for both preventive maintenance and breakdown maintenance. In some embodiments of the present invention, the comprehensive probability data corresponding to a maintenance frequency combination is determined by multiplying the first distribution probability data corresponding to the preventive maintenance frequency by the second distribution probability data corresponding to the breakdown maintenance frequency. Comprehensive resource amounts are the resource amounts consumed by performing the preventive maintenance and breakdown maintenance corresponding to each maintenance frequency combination. In some embodiments of the present invention, the comprehensive resource amount corresponding to a maintenance frequency combination is determined based on the sum of a first resource amount corresponding to the number of preventive maintenance operations and a second resource amount corresponding to the number of breakdown repair operations. The first resource amount is the resource amount consumed by performing the preventive maintenance operations in the maintenance frequency combination, and the first resource amount includes, but is not limited to, budgeted resources. The second resource amount is the resource amount consumed by performing the breakdown repair operations in the maintenance frequency combination, and the second resource amount includes, but is not limited to, budgeted resources.

[0075] In some embodiments of the present invention, for a maintenance frequency combination, the amount of resources consumed by the preventive maintenance corresponding to the preventive maintenance frequency is obtained based on the number of preventive maintenance times in the maintenance frequency combination and the amount of resources corresponding to a single preventive maintenance execution. The amount of resources consumed by the fault repair corresponding to the fault repair frequency is obtained based on the number of fault repair times in the maintenance frequency combination and the amount of resources consumed by a single fault repair execution. The comprehensive probability data for the maintenance frequency combination is determined by multiplying the first probability distribution data corresponding to the preventive maintenance times in the maintenance frequency combination and the second probability distribution data corresponding to the fault repair times in the maintenance frequency combination. The comprehensive resource amount for the maintenance frequency combination is determined based on the amount of resources consumed by the preventive maintenance corresponding to the maintenance frequency combination and the amount of resources consumed by the fault repair corresponding to the maintenance frequency combination.

[0076] For example, the maintenance frequency combination is [j, i], where j represents the number of preventive maintenance in the maintenance frequency combination, i represents the number of fault repairs in the maintenance frequency combination, and the second probability distribution data corresponding to the number of fault repairs in the maintenance frequency combination is Px i , the first probability distribution data Py corresponding to the preventive maintenance times in the maintenance times combinationj , comprehensive probability data Mp i,j The calculation formula is as follows:

[0077] Mp i,j =Px i Py j ;

[0078] The amount of resources consumed to perform a single fault repair is m1, the second amount of resources is im1, the amount of resources consumed to perform a single preventive maintenance is m2, the first amount of resources is jm2, and the comprehensive amount of resources is Mf i,j The calculation formula is as follows:

[0079] Mf i,j =im1+jm2;

[0080] Comprehensive probability data Mp i,j and comprehensive resource Mf i,j Form comprehensive maintenance resource information.

[0081] Comprehensive maintenance resource information is determined by the first probability distribution data of preventive maintenance and the second probability distribution data of fault maintenance. The comprehensive maintenance resource information includes comprehensive probability data and comprehensive resource quantities corresponding to each combination of preventive maintenance and fault maintenance. The comprehensive resource quantities represent the amount of resources consumed by performing preventive maintenance and fault maintenance corresponding to the combination of maintenance times, thereby improving the accuracy of determining the comprehensive maintenance resource information, and thus helping to improve the accuracy of comprehensive resource calculation for product maintenance.

[0082] S150: Determine the target resource amount of the product based on the expected satisfaction probability corresponding to the set type of product and the comprehensive maintenance resource information.

[0083] The expected probability of satisfaction is the minimum probability of supporting the corresponding number of actual repairs and preventive repairs for a given product type during the warranty period. The expected probability of satisfaction and the first probability of satisfaction can be the same or different, depending on your needs and are not limited here. The target resource quantity can be understood as the minimum comprehensive resource quantity required to meet the expected probability of satisfaction.

[0084] In some embodiments of the present invention, S150 includes: determining the measured satisfaction probability corresponding to each maintenance number combination based on the comprehensive probability data corresponding to each maintenance number combination; determining the minimum measured satisfaction probability that is greater than or equal to the expected satisfaction probability, and determining the comprehensive resource quantity corresponding to the matched minimum measured satisfaction probability as the target resource quantity.

[0085] Comprehensive maintenance resource information includes comprehensive probability data and comprehensive resource quantities corresponding to each maintenance frequency combination. The calculated satisfaction probability for any maintenance frequency combination can be understood as the sum of the probabilities of the actual maintenance frequency combinations that can be supported by the comprehensive resource quantity of that maintenance frequency combination. Accordingly, the calculated satisfaction probability for any maintenance frequency combination can be the sum of the comprehensive probability data corresponding to the various maintenance frequency combinations that can be supported by the comprehensive resource quantity of that maintenance frequency combination. The calculated satisfaction probability for any maintenance frequency combination can be determined based on the comprehensive resource quantity corresponding to that maintenance frequency combination, with the sum of the comprehensive probability data corresponding to all maintenance frequency combinations that do not exceed the comprehensive resource quantity of that maintenance frequency combination being used as the calculated satisfaction probability for that maintenance frequency combination.

[0086] It can be understood that the comprehensive resource quantity M1 of the maintenance number combination 1, when the comprehensive resource quantity M2 of the maintenance number combination 2 is less than M1, represents that the comprehensive resource quantity M1 can support the actual product maintenance corresponding to the maintenance number combination 2. Correspondingly, the comprehensive resource quantity M1 of the maintenance number combination 1 supports the actual product maintenance corresponding to any maintenance number combination whose comprehensive resource quantity does not exceed M1.

[0087] For the maintenance number combination 1: [z1, z2], the comprehensive resource amount corresponding to the maintenance number combination 1 is M1 = z1·m1+z2·m2. According to the comprehensive resource amount M1 corresponding to the maintenance number combination 1, determine multiple maintenance number combinations with comprehensive resource amounts less than or equal to M1, and determine the sum of the comprehensive probability data corresponding to the multiple maintenance number combinations with comprehensive resource amounts less than or equal to M1 as the calculated satisfaction probability corresponding to the maintenance number combination 1.

[0088] For example, the maintenance number combination 1 is [1,1], and the comprehensive resource amount corresponding to the maintenance number combination is M1. Based on the comprehensive resource amount M1 corresponding to the maintenance number combination, multiple comprehensive resource amounts less than or equal to M1 are determined. Based on the multiple comprehensive resource amounts less than or equal to M1, the maintenance number combinations corresponding to the multiple comprehensive resource amounts are determined to be [0,0], [0,1], [1,0], [1,1], [0,2] and [2,0] respectively. The comprehensive probability data corresponding to each maintenance number combination are d1, d2, d3, d4, d5 and d6 respectively. The calculated satisfaction probability corresponding to the maintenance number combination 1 [1,1] is d1+d2+d3+d4+d5+d6.

[0089] Determine the measured satisfaction probability corresponding to each combination of maintenance times according to the above method, compare the measured satisfaction probability corresponding to each combination of maintenance times with the expected satisfaction probability, determine the candidate measured satisfaction probability greater than the expected satisfaction probability, determine the minimum value among the candidate measured satisfaction probabilities, that is, the minimum measured satisfaction probability greater than the expected satisfaction probability, and determine the comprehensive resource quantity corresponding to the minimum measured satisfaction probability greater than the expected satisfaction probability as the target resource quantity.

[0090] By combining the two maintenance methods of breakdown repair and preventive maintenance to determine the target resource volume, the mutual complementation of the resource volumes required for breakdown repair and preventive maintenance is taken into account. Compared with separately determining the required resource volumes for breakdown repair and preventive maintenance, for example, there are surplus resources for breakdown repair but insufficient resources for preventive maintenance, resource utilization is highly flexible, the utilization rate of resources is improved, and the accuracy of comprehensive resource estimation for product maintenance is improved.

[0091] The technical solution of this embodiment provides data support for the analysis and processing of subsequent tasks for products of a set type by obtaining the first maximum number of times a single product performs fault repairs within the warranty period; and obtaining the second maximum number of times a single product performs preventive repairs within the warranty period, thereby ensuring that each task is executed efficiently and accurately; for each preventive repair number within the second maximum number, determining a first probability group of each preventive repair number corresponding to a single product during each fault repair period within the first maximum number; determining first probability distribution data of preventive repairs performed on batch products of a set type based on the first probability group, introducing preventive repairs during each fault repair period, which is beneficial to improving the accuracy of the first probability distribution data, and further beneficial to improving the accuracy of comprehensive resource estimation for product maintenance; obtaining the second probability group of preventive repairs performed on batch products of a set type, The rate distribution data provides data support for the comprehensive resource calculation of product maintenance; the comprehensive maintenance resource information is determined based on the first probability distribution data of preventive maintenance and the second probability distribution data of fault maintenance, and the comprehensive maintenance resource information includes the comprehensive probability data and comprehensive resource amount corresponding to each combination of preventive maintenance and fault maintenance. The comprehensive resource amount represents the amount of resources consumed by the preventive maintenance and fault maintenance corresponding to the combination of maintenance times, which improves the accuracy of the determination of the comprehensive maintenance resource information, and thus helps to improve the accuracy of the comprehensive resource calculation of product maintenance; the target resource amount of the product is determined based on the expected satisfaction probability and comprehensive maintenance resource information corresponding to the set type of product, which realizes the accurate determination of the target resource amount, solves the problem of low accuracy of the comprehensive resource calculation of product maintenance, and improves the accuracy of the comprehensive resource calculation of product maintenance.

[0092] Example 2

[0093] Figure 2This is a flow chart of a comprehensive resource estimation method for product maintenance provided by the second embodiment of the present invention. This embodiment is a refinement of the above embodiment. On the basis of the above embodiment, for each preventive maintenance number within the second maximum number, the first probability group corresponding to each preventive maintenance number of a single product during each fault repair within the first maximum number is determined; based on the first probability group, the first probability distribution data for performing preventive maintenance on batch products of a set type is determined and detailed. The specific implementation method can be found in the technical solution of this embodiment. Among them, the technical terms that are the same as or corresponding to the above embodiments are not repeated here. Figure 2 As shown, the method includes:

[0094] S210: For a product of a set type, obtain a first maximum number of times that a single product can be repaired during the warranty period; and obtain a second maximum number of times that a single product can be repaired during the warranty period.

[0095] S220. For any number of preventive maintenance operations, obtain an initial probability corresponding to the preventive maintenance operation, where the number of fault repairs corresponding to the initial probability is 1; adjust the number of fault repairs to determine a probability update value corresponding to each fault repair operation, where the probability update value represents the incremental contribution of the fault repair operation to the preventive maintenance probability; update the initial probability based on the probability update value corresponding to each fault repair operation until a probability update termination condition is met, thereby obtaining a first probability corresponding to the preventive maintenance operation; and form a first probability group based on the first probabilities corresponding to each preventive maintenance operation within the second maximum number.

[0096] The initial probability is the probability data corresponding to each preventive maintenance frequency, assuming the number of fault repairs is 1. The initial probabilities corresponding to different preventive maintenance frequencies can be the same or different. The probability update value is the probability data used to update the initial probability. The probability update value represents the incremental contribution of the fault repair that determines the number of fault repairs to the probability of preventive maintenance. Optionally, the probability update value is determined based on the multi-integral of the probability density function within each fault interval corresponding to the fault repair frequency. In some embodiments of the present invention, the probability update value is determined based on the multi-integral term of the probability density during each fault repair interval and an indicator function term. The multi-integral term of the probability density is the probability data of performing preventive maintenance during each fault repair interval. The indicator function term is data indicating whether the maximum number of preventive maintenance performed during the fault repair interval is consistent with the number of preventive maintenance. In some embodiments of the present invention, when the maximum number of preventive maintenance performed during the fault repair interval is consistent with the number of preventive maintenance, the indicator function term is a valid value. When the maximum number of preventive maintenance performed during the fault repair interval is inconsistent with the number of preventive maintenance, the indicator function term is an invalid value, which can be 0. In some embodiments of the present invention, when the maximum number of preventive maintenance operations performed during a fault repair interval is consistent with the preventive maintenance count, the effective value is 1. To improve the accuracy of comprehensive resource calculations for product maintenance, preventive maintenance is introduced during any fault repair to improve the accuracy of the first probability group. The probability update termination condition is a criterion for determining whether to update the initial probability. This condition can be determined based on the first maximum count. For example, the probability update termination condition can be determined when the fault repair count is less than or equal to the first maximum count. The first probability is the probability data corresponding to the preventive maintenance count obtained after updating the initial probability.

[0097] In some embodiments of the present invention, when the number of fault repairs is 1, an initial probability is determined, and a probability update termination condition is that the adjusted number of fault repairs is greater than a first maximum number. The number of fault repairs is adjusted, and a corresponding probability update value is determined based on the number of fault repairs. The initial probability is updated based on the corresponding probability update value. When the adjusted number of fault repairs is greater than the first maximum number, the probability update termination condition is met, the update of the initial probability is terminated, the first probability corresponding to the number of preventive maintenances is determined, and the first probability corresponding to the number of preventive maintenances for each single product in the batch of products during the warranty period is calculated. The first probabilities corresponding to each number of preventive maintenances for the batch products during the warranty period form a first probability group.

[0098] For example, the initial probability Pyd corresponding to the number of fault repairs being 1 is j The calculation formula is as follows:

[0099]

[0100] Where j represents the number of preventive maintenance times for a single product during the warranty period. The probability update value p' includes the probability density multi-integral term and the indicator function term during each fault repair interval. The probability update value p' is calculated as follows:

[0101]

[0102] Where s represents the indicator function term. When the maximum number of preventive maintenance performed during the fault maintenance interval is consistent with the number of preventive maintenance, the indicator function term s is a valid value of 1, that is, when When , s=1, otherwise s=0; x k Represents the working time of a single product between the k-1th fault and the kth fault. Adjust the number of fault repairs i, calculate the probability update value corresponding to each fault repair number, and update the initial probability Pyd according to the probability update value corresponding to each fault repair number j , initial probability Pyd j The update calculation formula is as follows:

[0103] Pyd j =Pyd j +p', 0≤j≤n2;

[0104] The end condition of probability update is i>n1. When i>n1, the initial probability Pyd is ended. j The update of , obtains the first probability corresponding to the number of preventive maintenance times. The calculation formula of the first probability corresponding to the number of preventive maintenance times is as follows:

[0105] Pyd j =Pyd j +q', 0≤j≤n2;

[0106] The calculation formula of q' is as follows:

[0107]

[0108] Among them, s represents the indicator function term, when When , s = 1, otherwise s = 0. The first probabilities corresponding to the respective preventive maintenance times within the second maximum times are calculated respectively, and the first probabilities corresponding to the respective preventive maintenance times within the second maximum times form a first probability group.

[0109] For any number of preventive maintenance times, the initial probability corresponding to the preventive maintenance times is obtained, the number of fault repairs corresponding to the initial probability is 1, the number of fault repairs is adjusted, and the probability update value corresponding to each fault repair time is determined. The probability update value represents the incremental contribution of the fault repair for the determined fault repair times to the probability of preventive maintenance. The initial probability is updated based on the probability update value corresponding to each fault repair time until the probability update end condition is met, and the first probability corresponding to the preventive maintenance times is obtained. The first probability group is formed based on the first probabilities corresponding to each preventive maintenance time within the second maximum number, thereby achieving accurate determination of the first probability group and providing accurate data support for subsequent analysis and processing.

[0110] S230: Determine first probability distribution data for performing preventive maintenance on batch products of a set type based on the first probability group.

[0111] The first probability distribution data can be understood as a collection of probability values corresponding to different total preventive maintenance frequency counts for a set type of batch product. Assuming the number of products in the set type of batch product is N, the second maximum number of preventive maintenance frequency counts for a single product is n², and the total preventive maintenance frequency count ranges from 0 to N×n²+1. The first probability distribution data includes the probability of each value of the total preventive maintenance frequency count.

[0112] In some embodiments of the present invention, S230 includes: performing convolution processing on the first probability group a set number of times to obtain first probability distribution data, wherein the first probability distribution data includes probability data of the total number of preventive maintenance times for batch products of a set type.

[0113] The set number of times can be determined based on the number of products in a batch of a set type. For example, if the number of products in a batch of a set type is N, the set number of times is N-1. The total number of maintenance times is the sum of the number of fault repairs performed and the number of preventive maintenance times.

[0114] In some embodiments of the present invention, first probability distribution data for performing preventive maintenance on batch products of a set type is determined based on a first probability group, and the first probability group can be convolved a set number of times, and the set number of times can be determined according to the number of products in the batch products of the set type.

[0115] S240: Obtain second probability distribution data of performing fault repair on batch products of a set type.

[0116] The second probability distribution data can be understood as a collection of probability values corresponding to different total preventive maintenance times for a given batch of products. Assuming the number of products in the given batch is N, the maximum number of preventive maintenance times for a single product is n1, and the total preventive maintenance times ranges from 0 to N × n1 + 1. The second probability distribution data includes the probability of each value of the total preventive maintenance times.

[0117] In some embodiments of the present invention, S240 includes: probability data of the total number of fault repairs performed for batch products of a set type and a second probability index that satisfies the first maximum number of fault repairs; determining a second probability group of the number of fault repairs corresponding to a single product based on the updated first maximum number, performing convolution processing on the second probability group a set number of times, and obtaining second probability distribution data; wherein the set number of times is determined based on the number of products in the batch products of the set type.

[0118] Among them, the probability data and are the probability data corresponding to each different total number of fault repairs performed on batch products of a set type during the warranty period. The probability data and can be determined based on the total number of fault repairs, and different total number of fault repairs correspond to different probability data and. The second satisfaction probability indicator is the minimum probability that the total budget of the actual number of fault repairs and preventive maintenance performed on batch products of a set type during the warranty period is less than or equal to the pre-set budget. The second satisfaction probability indicator can be set according to needs and is not limited here. The first satisfaction probability indicator, the second satisfaction probability indicator, and the expected satisfaction probability can be the same or different and can be set according to needs. The second probability group is the probability set corresponding to each number of fault repairs performed on a single product during the warranty period. The second probability group can be determined based on the updated first maximum number. For example, the probability data corresponding to different numbers of fault repairs performed on a single product during the warranty period are calculated, and the probability data corresponding to different numbers of fault repairs performed are used to form the second probability group.

[0119] In some embodiments of the present invention, when the probability data of the total number of fault repairs performed on batch products of a set type and the second probability index meet pre-set conditions, the first maximum number of fault repairs is updated, the second probability group is calculated based on the updated first maximum number, the set number is determined based on the number of products in the batch products of the set type, and the second probability group is convolved the set number of times to obtain second probability distribution data.

[0120] For example, the number of products of a set type of batch product is N, the first maximum number of fault repairs is n1, and the probability data of the total number of fault repairs performed on the set type of batch product is The second satisfaction probability index is Ps2. , update the first maximum number of fault repairs i = n1 + 1, calculate the probability data of each single product performing different fault repairs during the warranty period based on the updated first maximum number, form a second probability group based on the probability data of a single product performing different fault repairs during the warranty period, and perform N-1 convolution processing on the second probability group to obtain second probability distribution data.

[0121] By setting the probability data of the total number of fault repairs for batch products of a set type and the second probability index, the first maximum number of fault repairs is updated. Based on the updated first maximum number, a second probability group of the number of fault repairs corresponding to a single product is determined. The second probability group is convolved a set number of times to obtain the second probability distribution data, which achieves accurate determination of the second probability distribution data, thereby facilitating the improvement of the accuracy of comprehensive resource calculation for product maintenance.

[0122] In some embodiments of the present invention, the first maximum number of fault repairs is updated based on the probability data of the total number of fault repairs performed for batch products of a set type and a second probability satisfaction index, including: obtaining the initial first maximum number, determining the probability data of the total number of fault repairs performed for the fault repairs based on the first maximum number; if the probability data sum is less than the second probability satisfaction index, updating the first maximum number, and judging the updated first maximum number until the probability data sum of the total number of fault repairs for the fault repairs determined based on the updated first maximum number is greater than or equal to the second probability satisfaction index, and stopping updating the first maximum number.

[0123] The probability data sum of the total number of times a batch of products of a given type undergoes maintenance can be obtained by calculating the sum of the probability data for different total numbers of times a batch of products undergoes maintenance. For example, if the total number of times a batch of products undergoes maintenance is 0, 1, 2, and 3, and the probability data corresponding to each total number of times maintenance is performed is c1, c2, c3, and c4, respectively, the sum of the probability data for each total number of times maintenance is performed is c1 + c2 + c3 + c4.

[0124] In some embodiments of the present invention, an initial first maximum number is determined based on a first satisfaction probability index of a single product and probability data corresponding to each number of fault repairs, and the probability data sum of the total number of fault repairs performed for the fault repairs is calculated based on the first maximum number. A second satisfaction probability index is pre-set, and when the probability data sum is less than the second satisfaction probability index, a recursive update operation is performed on the first maximum number, and the probability data sum of the total number of fault repairs for the corresponding fault repairs is calculated based on the updated first maximum number. Similarly, the first maximum number and the probability data sum of the total number of fault repairs for the fault repairs are continuously updated, and when the probability data sum of the total number of fault repairs for the fault repairs is greater than or equal to the second satisfaction probability index, updating of the first maximum number is stopped.

[0125] For example, an initial first maximum number n1 is obtained, and the probability data of each total number of fault repairs for performing fault repairs is determined based on the initial first maximum number n1. Preset the second probability of satisfaction index Ps2, compare and the value of Ps2, when When the first maximum number is updated to n1+1, the probability data of the total number of fault repairs after the update is calculated according to the updated first maximum number, and the probability data of the total number of fault repairs after the update is compared with the probability data of the total number of fault repairs after the update. and the value of Ps2, when When , stop updating the first maximum number of times.

[0126] By combining the total number of fault repairs for batch products of a set type and the second probability index of satisfaction, the first maximum number is updated to avoid the randomness of fault repairs for a single product, thereby improving the accuracy of the first maximum number and providing data support for the analysis and processing of subsequent tasks, ensuring that each task can be executed efficiently and accurately.

[0127] S250. Determine comprehensive maintenance resource information based on the first probability distribution data of preventive maintenance and the second probability distribution data of fault maintenance. The comprehensive maintenance resource information includes comprehensive probability data and comprehensive resource amounts corresponding to each combination of preventive maintenance times and fault maintenance times. The comprehensive resource amounts represent the amounts of resources consumed by performing the preventive maintenance and fault maintenance times corresponding to the combination of maintenance times.

[0128] S260: Determine the target resource amount of the product based on the expected satisfaction probability corresponding to the set type of product and the comprehensive maintenance resource information.

[0129] For example, assume that the number of batch products of a given type is 10, the failure interval a of a single product during the warranty period is 150 hours, the planned operating hours T1 of a single product during the warranty period is 800 hours, the resource consumption m1 of performing a single failure repair is 1000, the resource consumption m2 of performing a single preventive maintenance is 180, and the first and second probability of satisfaction Ps1 and Ps2 are the same as the expected probability of satisfaction, both 0.9. Based on the first probability of satisfaction Ps1, the first maximum number of failure repairs n1 for a single product during the warranty period is calculated. When n1 = 3, the probability data p1 corresponding to the first maximum number of failure repairs is calculated to be 0.9399. If p1 ≥ Ps1 for the first time, the first maximum number of failure repairs n1 for a single product during the warranty period is 3. Based on the planned operating hours T1 and the failure interval a of a single product during the warranty period, the second maximum number of preventive maintenance n2 for a single product during the warranty period is calculated, where n2 = 5.

[0130] Based on the preventive maintenance times j within the second maximum number of times, a first probability group Pyd is determined for each preventive maintenance times corresponding to a single product during each fault maintenance period within the first maximum number of times n1. The first probability group Pyd includes preventive maintenance times 0, 1, 2, 3, 4, and 5 for each product during each fault maintenance period within the first maximum number of times n1. The probability data corresponding to each preventive maintenance times are Pyd0 = 0.0032, Pyd1 = 0.0099, Pyd2 = 0.0205, Pyd3 = 0.1127, Pyd4 = 0.4731, and Pyd5 = 0.3806, respectively. The first probability group Pyd is convolved based on the number of products in a batch of a set type to obtain first probability distribution data Py. The first probability distribution data Py includes Nn2 + 1 data. See Table 1, which is a result diagram of the first probability distribution data provided by an embodiment of the present invention. The subscript j represents the number of preventive maintenance times performed, 0 ≤ j ≤ Nn2, and Py j Represents the probability data corresponding to performing preventive maintenance j times for batch products of a set type.

[0131] Table 1

[0132]

[0133]

[0134] Obtain the initial first maximum number n1, and determine the probability data of each total fault repair number for performing fault repair based on the first maximum number and Compare the probability data of each total number of failure repairs performed and and the value of the second probability index Ps2, when When n1=n1+1 is updated, the probability data of the total number of fault repairs after the update is calculated according to the updated first maximum number i, and the probability data of the total number of fault repairs after the update is compared with the probability data of the total number of fault repairs after the update. and the value of Ps2, when When the first maximum number n1 is stopped, the first maximum number n1 is 20, and the first maximum number n1 is stopped. The first maximum number at this time is recorded as n x .

[0135] According to the first maximum number n after update x Determine a second probability group Pxd for the number of fault repairs for a single product, perform convolution processing on the second probability group a set number of times, and obtain second probability distribution data Px. The second probability distribution data Px includes 200 data, see Table 2, which is a result diagram of the second probability distribution data provided by an embodiment of the present invention, wherein the subscript i represents the number of fault repairs performed.

[0136] Table 2

[0137]

[0138]

[0139]

[0140] The second resource quantity, im1, is calculated based on the resource quantity m1 consumed by performing a single fault repair. The first resource quantity, jm2, is calculated based on the resource quantity m2 consumed by performing a single preventive maintenance. The comprehensive resource quantity, im1 + jm2, is calculated based on the first resource quantity jm2 and the second resource quantity im1. The comprehensive resource quantity is compared with the expected satisfaction probability corresponding to each combination of repair times. The calculated satisfaction probability corresponding to each combination of repair times is determined. Among the calculated satisfaction probabilities, the minimum calculated satisfaction probability that is greater than or equal to the expected satisfaction probability is determined. The comprehensive resource quantity corresponding to the matched minimum calculated satisfaction probability is determined as the target resource quantity. See Table 3, which shows a partial result of comprehensive probability data and corresponding comprehensive resource quantities provided by an embodiment of the present invention.

[0141] Table 3

[0142] Maintenance cost budget Satisfaction rate 23920 0.702 24560 0.754 25280 0.803 26100 0.855 27100 0.902 28740 0.953

[0143] It is understood that the values in Table 1, Table 2 and Table 3 are merely examples.

[0144] The satisfaction rate represents the comprehensive probability data corresponding to each combination of maintenance times, and the maintenance cost budget represents the comprehensive resource volume corresponding to the comprehensive probability data. The expected satisfaction probability is 0.9, and the measured satisfaction probabilities include 0.902 and 0.953. The minimum measured satisfaction probability greater than or equal to the expected satisfaction probability is 0.902, and the comprehensive resource volume corresponding to the minimum measured satisfaction probability of 0.902 is 27,100. Using the comprehensive resource volume of 27,100 corresponding to the minimum measured satisfaction probability of 0.902 as the target resource volume for the product allows for precise determination of the target resource volume and improves the accuracy of comprehensive resource calculation for product maintenance.

[0145] The technical solution of this embodiment is to obtain, for products of a set type, a first maximum number of fault repairs performed on a single product during the warranty period, and a second maximum number of preventive maintenance performed on a single product during the warranty period, thereby providing data support for the analysis and processing of subsequent tasks and ensuring that each task can be performed efficiently and accurately; for any preventive maintenance number, obtain an initial probability corresponding to the preventive maintenance number, the number of fault repairs corresponding to the initial probability is 1, adjust the number of fault repairs, determine a probability update value corresponding to each fault repair number, the probability update value represents the incremental contribution of the fault repair for the determined fault repair number to the preventive maintenance probability, update the initial probability based on the probability update value corresponding to each fault repair number until the probability update end condition is met, obtain a first probability corresponding to the preventive maintenance number, form a first probability group based on the first probabilities corresponding to each preventive maintenance number within the second maximum number, thereby achieving accurate determination of the first probability group and providing accurate data support for subsequent analysis and processing; determine the first probability of preventive maintenance for batch products of a set type based on the first probability group Distribution data, preventive maintenance is introduced during each fault repair, which is conducive to improving the accuracy of the first probability distribution data, and thus is conducive to improving the accuracy of the comprehensive resource calculation of product maintenance; obtaining the second probability distribution data of batch products of the set type performing fault repair, provides data support for the comprehensive resource calculation of product maintenance; based on the first probability distribution data of preventive maintenance and the second probability distribution data of fault repair, comprehensive maintenance resource information is determined, and the comprehensive maintenance resource information includes comprehensive probability data and comprehensive resource quantities corresponding to each combination of preventive maintenance and fault repair. The comprehensive resource quantity represents the amount of resources consumed by preventive maintenance and fault repair corresponding to the combination of maintenance times, which improves the accuracy of the determination of comprehensive maintenance resource information, and thus is conducive to improving the accuracy of the comprehensive resource calculation of product maintenance; based on the expected satisfaction probability and comprehensive maintenance resource information corresponding to the set type of product, the target resource quantity of the product is determined, the target resource quantity is accurately determined, the problem of low accuracy of comprehensive resource calculation of product maintenance is solved, and the accuracy of comprehensive resource calculation of product maintenance is improved.

[0146] Example 3

[0147] Figure 3 This is a schematic diagram of the structure of a comprehensive resource calculation device for product maintenance provided by the third embodiment of the present invention. Figure 3 As shown, the device includes:

[0148] The first maximum number and second maximum number acquisition module 310 is configured to acquire, for a product of a specified type, a first maximum number of times that a single product can undergo fault repairs during the warranty period; and a second maximum number of times that a single product can undergo preventive maintenance during the warranty period.

[0149] The first probability distribution data determining module 320 is configured to determine, for each preventive maintenance frequency within the second maximum frequency, a first probability group for each preventive maintenance frequency corresponding to each single product during each fault repair period within the first maximum frequency; and determine, based on the first probability group, first probability distribution data for executing preventive maintenance on a batch of products of a set type;

[0150] The second probability distribution data acquisition module 330 is used to obtain second probability distribution data of the batch products of a set type that are to be repaired for a fault;

[0151] Comprehensive maintenance resource information determination module 340 is configured to determine comprehensive maintenance resource information based on the first probability distribution data of preventive maintenance and the second probability distribution data of fault maintenance. The comprehensive maintenance resource information includes comprehensive probability data and comprehensive resource amounts corresponding to each combination of preventive maintenance and fault maintenance times. The comprehensive resource amounts represent the amount of resources consumed by performing the preventive maintenance and fault maintenance corresponding to the combination of maintenance times.

[0152] The target resource quantity determination module 350 is configured to determine the target resource quantity of a product based on the expected satisfaction probability corresponding to a set type of product and the comprehensive maintenance resource information.

[0153] The technical solution of this embodiment provides data support for the analysis and processing of subsequent tasks by obtaining the first maximum number of times a single product performs fault repairs during the warranty period; and obtaining the second maximum number of times a single product performs preventive maintenance during the warranty period, thereby ensuring that each task can be performed efficiently and accurately; for each preventive maintenance number within the second maximum number, a first probability group corresponding to each preventive maintenance number of a single product during each fault repair period within the first maximum number is determined; based on the first probability group, first probability distribution data of preventive maintenance performed on batch products of a set type is determined, and preventive maintenance is introduced during each fault repair period, which is conducive to improving the accuracy of the first probability distribution data, and further conducive to improving the accuracy of comprehensive resource measurement for product maintenance; second probability distribution data of fault repair performed on batch products of a set type is obtained , which provides data support for the comprehensive resource estimation of product maintenance; the comprehensive maintenance resource information is determined based on the first probability distribution data of preventive maintenance and the second probability distribution data of fault maintenance, and the comprehensive maintenance resource information includes the comprehensive probability data and comprehensive resource amount corresponding to each maintenance number combination of preventive maintenance and fault maintenance, and the comprehensive resource amount represents the amount of resources consumed by the preventive maintenance and fault maintenance corresponding to the maintenance number combination, which improves the accuracy of the determination of the comprehensive maintenance resource information, and thus helps to improve the accuracy of the comprehensive resource estimation of product maintenance; the target resource amount of the product is determined based on the expected satisfaction probability and comprehensive maintenance resource information corresponding to the set type of product, which realizes the accurate determination of the target resource amount, solves the problem of low accuracy of the comprehensive resource estimation of product maintenance, and improves the accuracy of the comprehensive resource estimation of product maintenance.

[0154] Based on the above embodiment, optionally, the first maximum number and second maximum number acquisition module 310 is also used to: determine the first maximum number based on the first satisfaction probability index of a single product and the probability data corresponding to each repair number of fault repairs, wherein the probability data corresponding to the first maximum number of fault repairs is greater than or equal to the first satisfaction probability index.

[0155] Optionally, the first maximum number and second maximum number obtaining module 310 is further configured to: determine the second maximum number based on the planned working hours and preventive maintenance interval duration of a single product during the warranty period.

[0156] Optionally, the first probability distribution data determination module 320 is also used to: for any number of preventive maintenance times, obtain the initial probability corresponding to the preventive maintenance times, the number of fault repairs corresponding to the initial probability is 1; adjust the number of fault repairs, determine the probability update value corresponding to each fault repair number, the probability update value represents the probability incremental contribution of the fault repair of the determined fault repair number to the preventive maintenance; update the initial probability based on the probability update value corresponding to each fault repair number until the probability update end condition is met, and obtain the first probability corresponding to the preventive maintenance times; form a first probability group based on the first probability corresponding to each preventive maintenance number within the second maximum number.

[0157] Optionally, the first probability distribution data determination module 320 is further used to: determine the probability update value based on the probability density multi-integral term and the indicator function term during each fault repair interval, wherein, when the maximum number of preventive maintenance performed during the fault repair interval is consistent with the number of preventive maintenance, the indicator function term is a valid value.

[0158] Optionally, the first probability distribution data determination module 320 is further used to: perform convolution processing on the first probability group a set number of times to obtain first probability distribution data, wherein the first probability distribution data includes probability data of the total number of maintenance times for preventive maintenance performed on batch products of a set type.

[0159] Optionally, the second probability distribution data acquisition module 330 is also used to: update the first maximum number of fault repairs based on the probability data of the total number of fault repairs performed for batch products of a set type and the second probability index; determine the second probability group of the number of fault repairs corresponding to a single product based on the updated first maximum number, and perform convolution processing on the second probability group a set number of times to obtain second probability distribution data; wherein the set number of times is determined based on the number of products in the batch products of the set type.

[0160] Optionally, the second probability distribution data acquisition module 330 is also used to: obtain the initial first maximum number of times, determine the probability data sum of the total fault repair times of the fault repairs performed based on the first maximum number of times; if the probability data sum is less than the second probability satisfaction index, update the first maximum number of times, and judge the updated first maximum number of times until the probability data sum of the total fault repair times of the fault repairs determined based on the updated first maximum number of times is greater than or equal to the second probability satisfaction index, and stop updating the first maximum number of times.

[0161] Optionally, any combination of maintenance times includes a preventive maintenance time and a breakdown maintenance time.

[0162] Optionally, the comprehensive maintenance resource information determination module 340 is also used to: determine the comprehensive probability data corresponding to the maintenance number combination based on the product of the first distribution probability data corresponding to the preventive maintenance number and the second distribution probability data corresponding to the fault repair number; and determine the comprehensive resource quantity corresponding to the maintenance number combination based on the sum of the first resource quantity corresponding to the preventive maintenance number and the second resource quantity corresponding to the fault repair number.

[0163] Optionally, the target resource quantity determination module 350 is also used to: determine the measured satisfaction probability corresponding to each maintenance number combination based on the comprehensive probability data corresponding to each maintenance number combination; determine the minimum measured satisfaction probability that is greater than or equal to the expected satisfaction probability, and determine the comprehensive resource quantity corresponding to the matched minimum measured satisfaction probability as the target resource quantity.

[0164] The comprehensive resource calculation device for product maintenance provided by the embodiment of the present invention can execute the comprehensive resource calculation method for product maintenance provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0165] Example 4

[0166] Figure 4 1 is a structural diagram of an electronic device provided in Embodiment 4 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0167] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the random access memory (RAM) 13. The processor 11, the read-only memory (ROM) 12, and the random access memory (RAM) 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0168] Various components in the electronic device 10 are connected to an input / output (I / O) interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0169] Processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any other suitable processors, controllers, microcontrollers, etc. Processor 11 executes the various methods and processes described above, such as the comprehensive resource estimation method for product maintenance.

[0170] In some embodiments, the comprehensive resource estimation method for product maintenance can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the read-only memory (ROM) 12 and / or the communication unit 19. When the computer program is loaded into the random access memory (RAM) 13 and executed by the processor 11, one or more steps of the comprehensive resource estimation method for product maintenance described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the comprehensive resource estimation method for product maintenance in any other appropriate manner (for example, by means of firmware).

[0171] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0172] The computer programs for implementing the comprehensive resource estimation method for product maintenance of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0173] An embodiment of the present invention further provides a computer-readable storage medium storing computer instructions, the computer instructions being used to cause a processor to execute a method for calculating comprehensive resources for product maintenance, the method comprising:

[0174] For products of a set type, obtain the first maximum number of times a single product performs fault repairs during the warranty period; and obtain the second maximum number of times a single product performs preventive maintenance during the warranty period; for each preventive maintenance number within the second maximum number, determine a first probability group for each preventive maintenance number corresponding to the single product during each fault repair period within the first maximum number; determine first probability distribution data for batch products of the set type performing preventive maintenance based on the first probability group; obtain second probability distribution data for batch products of the set type performing fault repairs; determine comprehensive maintenance resource information based on the first probability distribution data for preventive maintenance and the second probability distribution data for fault repairs, the comprehensive maintenance resource information including comprehensive probability data and comprehensive resource amounts corresponding to each combination of preventive maintenance and fault repairs, the comprehensive resource amounts representing the amount of resources consumed for performing preventive maintenance and fault repairs corresponding to the combination of maintenance numbers; determine the target resource amount of the product based on the expected satisfaction probability corresponding to the product of the set type and the comprehensive maintenance resource information.

[0175] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0176] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0177] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0178] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0179] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0180] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A comprehensive resource calculation method for product maintenance, characterized in that: include: For a product of a specified type, obtain the maximum number of times a single product can be repaired during the warranty period. and, obtaining the second maximum number of preventive maintenance operations performed on a single product during the stated coverage period; Determining, for each preventive maintenance frequency within the second maximum frequency, a first probability group for each preventive maintenance frequency corresponding to a single product during each fault repair period within the first maximum frequency; and determining, based on the first probability group, first probability distribution data for performing preventive maintenance on a batch of products of the set type; Obtaining second probability distribution data of performing fault repair on batch products of the set type; Determining comprehensive maintenance resource information based on the first probability distribution data of the preventive maintenance and the second probability distribution data of the fault maintenance, wherein the comprehensive maintenance resource information includes comprehensive probability data and comprehensive resource amounts corresponding to each combination of the preventive maintenance and the fault maintenance, wherein the comprehensive resource amounts represent the amount of resources consumed by performing the preventive maintenance and the fault maintenance corresponding to the combination of the maintenance times; The target resource amount of the product is determined based on the expected satisfaction probability corresponding to the product of the set type and the comprehensive maintenance resource information.

2. The method according to claim 1, characterized in that The first maximum number is determined based on a first satisfaction probability index of the single product and probability data corresponding to each repair number of the fault repair, wherein the probability data corresponding to the first maximum number of fault repairs is greater than or equal to the first satisfaction probability index; The second maximum number is determined based on the planned working hours and preventive maintenance interval duration of the single product during the warranty period.

3. The method according to claim 1, characterized in that The determining, for each preventive maintenance frequency within the second maximum frequency, a first probability group of preventive maintenance frequencies corresponding to a single product during each fault repair period within the first maximum frequency includes: For any of the preventive maintenance times, obtaining an initial probability corresponding to the preventive maintenance times, where the number of fault repairs corresponding to the initial probability is 1; Adjusting the number of fault repairs and determining a probability update value corresponding to each of the number of fault repairs, wherein the probability update value represents a probability increment contribution of the fault repair with the number of fault repairs to the preventive maintenance; Updating the initial probability based on the probability update value corresponding to each of the fault repair times until a probability update end condition is satisfied, thereby obtaining a first probability corresponding to the preventive maintenance times; The first probability group is formed based on first probabilities corresponding to each of the preventive maintenance times within the second maximum number.

4. The method according to claim 3, characterized in that The probability update value is determined based on a probability density multi-integral term and an indicator function term during each fault repair interval, wherein the indicator function term is a valid value when the maximum number of preventive maintenance performed during the fault repair interval is consistent with the number of preventive maintenance.

5. The method according to claim 1, wherein The determining, based on the first probability group, first probability distribution data for performing preventive maintenance on batch products of the set type includes: Performing a convolution process on the first probability group a set number of times to obtain first probability distribution data, wherein the first probability distribution data includes probability data of the total number of preventive maintenance times for batch products of the set type; And, obtaining second probability distribution data of performing fault repair on batch products of the set type, including: updating the first maximum number of fault repairs based on probability data of each total number of fault repairs performed on batch products of a set type and a second satisfaction probability index; Determining a second probability group of the number of repairs for each fault corresponding to a single product based on the updated first maximum number, and performing convolution processing on the second probability group a set number of times to obtain the second probability distribution data; The set number of times is determined based on the quantity of batch products of the set type.

6. The method according to claim 5, characterized in that The updating of the first maximum number of fault repairs based on the probability data of the total number of fault repairs performed on batch products of a set type and the second satisfaction probability index includes: Obtaining an initial first maximum number of times, and determining a sum of probability data of total fault repair times for performing fault repair based on the first maximum number of times; If the sum of the probability data is less than the second satisfaction probability index, the first maximum number is updated, and the updated first maximum number is judged until the sum of the probability data of the total fault repair times of the fault repairs determined based on the updated first maximum number is greater than or equal to the second satisfaction probability index, and then the updating of the first maximum number is stopped.

7. The method according to claim 1, characterized in that Any combination of maintenance times includes a preventive maintenance time and a breakdown maintenance time. The comprehensive probability data corresponding to the maintenance time combination is determined based on the product of the first distribution probability data corresponding to the preventive maintenance time and the second distribution probability data corresponding to the breakdown maintenance time. The comprehensive resource amount corresponding to the maintenance time combination is determined based on the sum of the first resource amount corresponding to the preventive maintenance time and the second resource amount corresponding to the breakdown maintenance time. Determining a target resource amount for the product based on the expected satisfaction probability corresponding to the product of the set type and the maintenance resource information includes: Determining the calculated satisfaction probability corresponding to each combination of maintenance times based on the comprehensive probability data corresponding to each combination of maintenance times; Determine a minimum calculated satisfaction probability that is greater than or equal to the expected satisfaction probability, and determine the comprehensive resource quantity corresponding to the matched minimum calculated satisfaction probability as the target resource quantity.

8. A comprehensive resource calculation device for product maintenance, characterized in that: include: A first maximum number and a second maximum number obtaining module is used to obtain, for a set type of product, a first maximum number of times a single product can be repaired during the warranty period; and, obtaining the second maximum number of preventive maintenance operations performed on a single product during the stated coverage period; a first probability distribution data determining module configured to determine, for each preventive maintenance frequency within the second maximum frequency, a first probability group for each preventive maintenance frequency corresponding to a single product during each fault repair period within the first maximum frequency; and determine, based on the first probability group, first probability distribution data for executing preventive maintenance on a batch of products of the set type; A second probability distribution data acquisition module is used to obtain second probability distribution data of the batch products of the set type that are to be repaired for the fault; a comprehensive maintenance resource information determination module, configured to determine comprehensive maintenance resource information based on the first probability distribution data of the preventive maintenance and the second probability distribution data of the fault maintenance, wherein the comprehensive maintenance resource information includes comprehensive probability data and comprehensive resource amounts corresponding to each combination of the preventive maintenance and the fault maintenance, wherein the comprehensive resource amounts represent the amount of resources consumed by performing the preventive maintenance and the fault maintenance corresponding to the combination of the maintenance times; The target resource quantity determination module is used to determine the target resource quantity of the product based on the expected satisfaction probability corresponding to the set type of product and the comprehensive maintenance resource information.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the comprehensive resource estimation method for product maintenance according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the comprehensive resource estimation method for product maintenance according to any one of claims 1 to 7 when executed.

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