Preventive maintenance budget information determination method and device, storage medium and electronic equipment
By obtaining preventive maintenance information of the equipment, determining the probability group of maintenance times and the distribution of total maintenance times, and calculating the maintenance budget information, the problem of inaccurate preventive maintenance budget is solved and more accurate budget management is achieved.
Patent Information
- Application Number
- CN202510467856.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-15
- Publication Date
- 2025-07-29
AI Technical Summary
In the prior art, the budget determination of preventive maintenance lacks accuracy, resulting in equipment being scrapped due to failure and insufficient or excessive maintenance budget, and cannot be effectively controlled.
By obtaining preventive maintenance information of the target type of equipment, including maximum number of repairs, single repair costs, maintenance reliability improvement data and maintenance interval duration, determine the probability group of maintenance times of a single equipment, and calculate the probability distribution and single repair costs of the total number of repairs based on this to form maintenance budget information.
It improves the accuracy of preventive maintenance budgets, reduces the dependence on subjective experience, and ensures that the maintenance budget is more in line with actual needs.
Smart Images
Figure CN120387632A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the technical field of equipment maintenance, and in particular, to a method, device, storage medium, and electronic device for determining budget information for preventive maintenance. Background Art
[0002] Preventive maintenance is a kind of maintenance carried out to keep equipment in a specified state by checking, detecting, and preventing incipient failures, and its purpose is to prevent or control failures at an acceptable level.
[0003] In the process of implementing the present disclosure, it is found that there are at least the following technical problems in the prior art: In reality, even if preventive maintenance is carried out, the equipment may still be scrapped due to failures, and the occurrence of failures is random, resulting in uncertainty in the actual number of repairs for each equipment, which brings difficulties to determining the preventive maintenance budget.
[0004] Generally, it is determined through the subjective experience of maintenance management personnel or based on actual historical costs, and it is impossible to accurately determine the maintenance budget, resulting in situations of insufficient or excessive maintenance budgets. Summary of the Invention
[0005] The present disclosure provides a method, device, storage medium, and electronic device for determining budget information for preventive maintenance to improve the accuracy of the preventive maintenance budget.
[0006] According to one aspect of the present disclosure, there is provided a method for determining budget information for preventive maintenance, including:
[0007] Obtaining preventive maintenance information of target type equipment, where the preventive maintenance information includes the maximum number of repairs of a single equipment, the cost per repair, repair reliability improvement data, and the repair interval duration;
[0008] For a single equipment of the target type, determining a probability group of the number of repairs of the single equipment based on the repair reliability improvement data and the repair interval duration, where the probability group includes the probabilities corresponding to each number of repairs within the maximum number of repairs of the single equipment;
[0009] Determining the probability distribution of the total number of repairs of the target type equipment based on the probability group of the number of repairs of the single equipment;
[0010] Determining the maintenance budget information of the target type equipment based on the probability distribution of the total number of repairs of the target type equipment and the cost per repair.
[0011] Optionally, determining a probability group of the number of repairs for a single device based on the maintenance reliability improvement data and the maintenance interval duration includes: determining the equivalent cumulative working hours corresponding to before and after each preventive maintenance based on the maintenance reliability improvement data and the maintenance interval duration; processing the probability density distribution function of the target type of device based on the equivalent cumulative working hours corresponding to before and after each preventive maintenance to obtain the probability corresponding to each number of repairs, and the probabilities corresponding to each number of repairs within the maximum number of repairs form the probability group.
[0012] Optionally, determining the equivalent cumulative working hours corresponding to before and after each preventive maintenance based on the maintenance reliability improvement data and the maintenance interval duration includes: determining the maintenance effect data corresponding to each preventive maintenance based on the maintenance reliability improvement data, where the maintenance effect data represents the equivalent working hours reduced by the preventive maintenance; for any number of repairs i, determining the initial cumulative working hours corresponding to the number of repairs based on the maintenance interval duration, and the cumulative value of the maintenance effect data corresponding to the previous number of repairs, and determining the equivalent cumulative working hours before the i-th preventive maintenance based on the initial cumulative working hours and the cumulative value of the maintenance effect data; taking the difference between the equivalent cumulative working hours corresponding to before the i-th preventive maintenance and the maintenance effect data as the equivalent cumulative working hours after the i-th preventive maintenance.
[0013] Optionally, determining the probability distribution of the total number of repairs of the target type of device based on the probability group of the number of repairs of the single device includes: performing a preset number of convolution processes on the probability group to obtain the probability distribution of the total number of repairs, where the preset number is determined based on the number of devices of the target type of device, and the probability distribution of the total number of repairs includes the probability corresponding to any value of the total number of repairs of the target type of device, and the value of the total number of repairs is greater than or equal to zero and less than or equal to the product of the maximum number of repairs of the single device and the number of devices.
[0014] Optionally, the maintenance budget information of the target type of device includes the budget attribute value corresponding to each value of the total number of repairs, and the maintenance satisfaction probability corresponding to each value of the total number of repairs;
[0015] The budget attribute value is calculated based on the value of the total number of repairs and the single repair cost;
[0016] The maintenance satisfaction probability is the cumulative probability value corresponding to the value of the total number of repairs; the maintenance satisfaction probability represents the probability that the budget attribute value corresponding to the value of the total number of repairs meets the actual maintenance requirements.
[0017] Optionally, the method further includes: matching based on the expected maintenance satisfaction probability in the maintenance budget information of the target type of device to determine the target budget attribute value corresponding to the expected maintenance satisfaction probability.
[0018] Optionally, the method further includes: determining an initial budget attribute value based on the historical maintenance times of the target type of device and the single - time maintenance cost, matching based on the initial budget attribute value in the maintenance budget information of the target type of device to obtain the maintenance satisfaction probability corresponding to the initial budget attribute value, and determining the measurement result of the initial budget attribute value based on the maintenance satisfaction probability corresponding to the initial budget attribute value and the expected maintenance satisfaction probability, where the measurement result indicates whether the initial budget attribute value supports the actual maintenance requirements.
[0019] According to another aspect of the present disclosure, a preventive maintenance budget information determination device is provided, including:
[0020] An information acquisition module, configured to acquire preventive maintenance information of a target type of device, where the preventive maintenance information includes the maximum number of maintenance times of a single device, the single - time maintenance cost, maintenance reliability improvement data, and maintenance interval duration;
[0021] A first probability determination module, configured to, for a single device of the target type, determine a probability group of the number of maintenance times of the single device based on the maintenance reliability improvement data and the maintenance interval duration, where the probability group includes the probabilities corresponding to each number of maintenance times within the maximum number of maintenance times of the single device;
[0022] A second probability determination module, configured to determine the probability distribution of the total number of maintenance times of the target type of device based on the probability group of the number of maintenance times of the single device;
[0023] A maintenance budget determination module, configured to determine the maintenance budget information of the target type of device based on the probability distribution of the total number of maintenance times of the target type of device and the single - time maintenance cost.
[0024] According to another aspect of the present disclosure, an electronic device is provided, where the electronic device includes:
[0025] At least one processor; and
[0026] A memory communicatively connected to the at least one processor; where
[0027] The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute the preventive maintenance budget information determination method according to any embodiment of the present disclosure.
[0028] According to another aspect of the present disclosure, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the method for determining budget information of preventive maintenance according to any embodiment of the present disclosure when executed.
[0029] The technical solution of the embodiment of the present disclosure determines the probability of a single device in the target type of device corresponding to each number of repairs, and further determines the probability distribution of multiple devices in the target type of device corresponding to each total number of repairs. The maintenance budget information of the target type of device is formed by the probability distribution of multiple devices corresponding to each total number of repairs and the single repair cost. The maintenance budget information includes the budget attribute value and the maintenance satisfaction probability corresponding to each total number of repairs of the target type of device, providing a basis for determining the maintenance budget in different maintenance requirement situations, replacing the situation of determining the maintenance budget by subjective experience, reducing the dependence on experience, and improving the accuracy of the maintenance budget.
[0030] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS
[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present disclosure. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.
[0032] Figure 1 is a flowchart of a method for determining budget information of preventive maintenance provided by an embodiment of the present disclosure;
[0033] Figure 2 is a schematic diagram showing the correspondence between the maintenance satisfaction probability and the budget attribute value provided by an embodiment of the present disclosure;
[0034] Figure 3 is a schematic structural diagram of a device for determining budget information of preventive maintenance provided by an embodiment of the present disclosure;
[0035] Figure 4 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0036] To enable those skilled in the art to better understand the solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present disclosure without creative efforts shall fall within the scope of protection of the present disclosure.
[0037] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, device or equipment comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, devices or equipment.
[0038] Figure 1 is a flowchart of a method for determining budget information for preventive maintenance provided by an embodiment of the present disclosure. This embodiment is applicable to the situation of determining the budget required for preventive maintenance of a certain type of equipment in a future period. This method can be executed by a device for determining budget information for preventive maintenance, and the device for determining budget information for preventive maintenance can be implemented in the form of hardware and / or software. The device for determining budget information for preventive maintenance can be configured in an electronic device such as a mobile terminal, a computer device, or a server. Among them, the mobile terminal includes, but is not limited to, mobile phones, tablet computers, etc. As Figure 1 shown, the method includes:
[0039] S110. Obtain preventive maintenance information of the target type of equipment, where the preventive maintenance information includes the maximum number of repairs of a single device, the cost of a single repair, the data for improving repair reliability, and the repair interval duration.
[0040] Preventive maintenance can be understood as a maintenance method that adopts measures to restore or improve the internal operating environment of the equipment, such as replacing lubricating oil, tightening loose parts, etc., to slow down the continuous aging speed of the equipment, thereby extending the service life of the equipment.
[0041] In this embodiment, the target type of equipment is equipment in production activities, such as, but not limited to, ball bearings, relays, gyroscopes, motors, aeroengines, storage batteries, hydraulic pumps, and air turbine engines, etc. It can be understood that for different production activities, the equipment can be different, and the type of equipment is not limited herein. In some embodiments, the target type of equipment includes electromechanical equipment. Here, the target type is one of the equipment types, and for each type of equipment, the budget for preventive maintenance is determined separately. The specific type and quantity of the target type of equipment are not limited herein.
[0042] The number of the target type of equipment can be multiple. The maintenance budget for the target type of equipment can be understood as the total estimated maintenance cost for preventive maintenance of multiple pieces of equipment of the target type in a future period. Among them, the future period can be the maintenance cycle of future preventive maintenance, such as one year or half a year, which is not limited herein. A single piece of equipment is any one of the multiple pieces of equipment of the target type.
[0043] The single - time maintenance cost can be understood as the cost of performing preventive maintenance on a single piece of equipment of the target type, and can be determined based on historical maintenance costs.
[0044] The maintenance interval duration can be understood as the time interval for a single piece of equipment of the target type to perform preventive maintenance. For example, it can be the time interval between two adjacent preventive maintenances, or the time interval between the first preventive maintenance and the activation of the equipment.
[0045] The maintenance reliability improvement data can be understood as the improvement of the reliability of the target type of equipment after each preventive maintenance. The maintenance reliability improvement data can be determined by the actual working condition data of the target type of equipment before and after preventive maintenance in the historical process. The maintenance reliability improvement data can be marked as p1, and the maintenance reliability improvement data can be a value between 0 and 1. The maintenance reliability improvement data corresponding to different preventive maintenances can be the same or different.
[0046] The maximum number of maintenance times for a single piece of equipment can be understood as the maximum number of preventive maintenance times for each piece of equipment of the target type. It can be determined based on the historical maintenance times of the target type of equipment, or determined according to the cost - effectiveness ratio of a single piece of equipment of the target type for different numbers of maintenance times.
[0047] S120. For a single piece of equipment of the target type, determine a probability group of the number of maintenance times of the single piece of equipment based on the maintenance reliability improvement data and the maintenance interval duration. The probability group includes the probabilities corresponding to each number of maintenance times within the maximum number of maintenance times of the single piece of equipment.
[0048] The maximum number of repairs for a single device of the target type is N. Correspondingly, in actual production activities, the actual number of repairs for a single device is less than or equal to the maximum number of repairs. When the actual number of repairs for a single device is any number of repairs, it corresponds to different probabilities respectively. The sum of the probabilities corresponding to the number of repairs from 0 to N is 1, and the probabilities corresponding to the number of repairs from 0 to N form a probability group. This probability group can be in the form of an array, which is not limited here.
[0049] A probability determination strategy is preset. This probability determination strategy includes probability determination methods corresponding to different numbers of repairs. Among them, when the number of repairs is zero, it corresponds to the first probability determination method. When the number of repairs is greater than zero and less than the maximum number of repairs N, it corresponds to the second probability determination method. When the number of repairs is the maximum number of repairs N, it corresponds to the third probability determination method.
[0050] When the number of repairs is zero, the first probability determination method is determined based on the maintenance interval duration and the Weibull parameters of the life distribution of the target type device. Among them, the first probability determination method can be characterized by the following formula: Among them, t0 is the maintenance interval duration, which can represent the time interval between device startup and the first preventive maintenance here. The life of the target type device generally follows the Weibull distribution, and the Weibull distribution can generally be expressed as W(a, b), where a and b are the Weibull parameters of the life distribution, a is the scale parameter of the Weibull distribution, and b is the shape parameter of the Weibull distribution.
[0051] When the number of repairs is greater than zero and less than the maximum number of repairs N, the corresponding second probability determination method can be to perform integral processing on the probability density distribution function, where the upper and lower limits of the integral processing are determined based on the equivalent cumulative working hours corresponding to before and after each preventive maintenance respectively.
[0052] Optionally, when the number of repairs is greater than zero and less than the maximum number of repairs N, the second probability determination method includes: determining the equivalent cumulative working hours corresponding to before and after each preventive maintenance based on the maintenance reliability improvement data and the maintenance interval duration; processing the probability density distribution function of the target type device based on the equivalent cumulative working hours corresponding to before and after each preventive maintenance to obtain the probability corresponding to each number of repairs.
[0053] It is understandable that for any device, the duration from device startup to the current working moment is the actual cumulative working duration. Performing preventive maintenance on the device can improve the reliability of the device. On the basis of maintaining the original device life, preventive maintenance can equivalently reduce the cumulative working duration on the basis of the actual cumulative working duration to obtain the equivalent cumulative working duration, that is, the equivalent cumulative working duration is less than the actual cumulative working duration. By setting the equivalent cumulative working duration after preventive maintenance, the improvement of the device life can be reversely characterized. Exemplarily, before preventive maintenance, the actual cumulative working duration is T, and the equivalent cumulative working duration after preventive maintenance is T - r, where r is the maintenance effect data corresponding to the preventive maintenance, and this maintenance effect data represents the equivalent working duration reduced by the preventive maintenance. In the case of multiple preventive maintenances, r here can represent the total maintenance effect data corresponding to multiple preventive maintenances. The maintenance effect data corresponding to each preventive maintenance can be the same or different, and is determined based on the maintenance reliability improvement data corresponding to the preventive maintenance.
[0054] Optionally, determining the equivalent cumulative working duration corresponding to before and after each preventive maintenance based on the maintenance reliability improvement data and the maintenance interval duration includes: determining the maintenance effect data corresponding to each preventive maintenance based on the maintenance reliability improvement data; for any maintenance number i, determining the initial cumulative working duration corresponding to the maintenance number based on the maintenance interval duration, and the cumulative value of the maintenance effect data corresponding to the previous maintenance number, and determining the equivalent cumulative working duration before the i-th preventive maintenance based on the initial cumulative working duration and the cumulative value of the maintenance effect data; taking the difference between the equivalent cumulative working duration corresponding to before the i-th preventive maintenance and the maintenance effect data as the equivalent cumulative working duration after the i-th preventive maintenance.
[0055] For the maintenance number i of preventive maintenance, obtain the equivalent cumulative working duration before the i-th preventive maintenance. Among them, the equivalent cumulative working duration before preventive maintenance can be understood as the equivalent cumulative working duration when reaching the i-th preventive maintenance but not yet completing the i-th preventive maintenance. For i = 1, the equivalent cumulative working duration t1 can be the maintenance interval duration.
[0056] In the case of performing preventive maintenance, the reliability of the device is improved. Correspondingly, the sum of the reliability data before the i-th preventive maintenance and the maintenance reliability improvement data is equal to the reliability data after the i-th preventive maintenance. Taking the first preventive maintenance as an example, specifically, it can be represented by the following formula: Among them, t1 is the equivalent cumulative working duration before preventive maintenance; t2 is the equivalent cumulative working duration after preventive maintenance, and P1 is the maintenance reliability improvement data corresponding to the first preventive maintenance. Input the equivalent cumulative working duration before the first preventive maintenance and the maintenance reliability improvement data corresponding to the first preventive maintenance into the above formula to obtain the equivalent cumulative working duration t2 before the first preventive maintenance. t2 is less than t1, and the difference between t1 and t2 is used as the maintenance effect data corresponding to the first preventive maintenance, that is, r1 = t1 - t2 = t0 - t2. Correspondingly, t2 = t0 - r1.
[0057] Update i = i + 1, and obtain the equivalent cumulative working duration before the (i + 1)-th preventive maintenance. The equivalent cumulative working duration before the (i + 1)-th preventive maintenance is the sum of the equivalent cumulative working duration after the i-th preventive maintenance and the maintenance interval duration. Further, based on the equivalent cumulative working duration before the (i + 1)-th preventive maintenance and the maintenance reliability improvement data corresponding to the (i + 1)-th preventive maintenance, obtain the equivalent cumulative working duration after the (i + 1)-th preventive maintenance. Taking i = 2 as an example, the equivalent cumulative working duration before the second preventive maintenance is the sum of the equivalent cumulative working duration t2 before the first preventive maintenance and the maintenance interval duration t0, that is, update the equivalent cumulative working duration t1 before preventive maintenance to 2t0 - r1. Based on Obtain the equivalent cumulative working duration after the second preventive maintenance. Determine the maintenance effect data corresponding to the second preventive maintenance by the difference between the equivalent cumulative working duration before the second preventive maintenance and the equivalent cumulative working duration after the second preventive maintenance. And so on, obtain the equivalent cumulative working durations before and after preventive maintenance for each maintenance time within the maximum number of maintenance times.
[0058] For any maintenance time, perform integral processing on the probability density distribution function of the target type of equipment to obtain the probability corresponding to each maintenance time. The probability corresponding to the maintenance time i can be expressed as Among them, the upper and lower limits of the integral for integral processing are determined based on the equivalent cumulative working durations corresponding before and after each preventive maintenance. For the maintenance time i, the lower limit of the integral is the equivalent cumulative working duration before the i-th preventive maintenance, that is The upper limit of the integral is the equivalent cumulative working duration before the (i + 1)-th preventive maintenance, that is
[0059] When the maintenance time is the maximum number of maintenance times N, the third probability determination method can be determined based on the difference between the total probability and the sum of the probabilities corresponding to the maintenance times 0 - N - 1 and the value 1. Among them, the third probability determination method can be characterized by the following formula: p kis the probability corresponding to any repair number k from repair number 0 to repair number N-1.
[0060] The probability group of the repair numbers of a single device can be marked as P = [p0, p1, …, p k , …, p N .
[0061] S130. Determine the probability distribution of the total repair numbers of the target type of devices based on the probability group of the repair numbers of the single device.
[0062] For a single device, its repair number is less than or equal to the maximum repair number. For multiple devices of the target type, the number of devices can be m, where m is an integer greater than 0. The value of the total repair numbers of the target type of devices is greater than or equal to zero and less than or equal to the product mn of the maximum repair number of the single device and the number of devices. Any value of the total repair numbers of the target type of devices corresponds to a probability respectively, and the sum of the probabilities corresponding to the respective values of the total repair numbers of the target type of devices is 1.
[0063] Optionally, determining the probability distribution of the total repair numbers of the target type of devices based on the probability group of the repair numbers of the single device includes: performing convolution processing on the probability group for a preset number of times to obtain the probability distribution of the total repair numbers.
[0064] Among them, the preset number of times is determined based on the number of devices of the target type. Optionally, the preset number of times is m-1. The probability distribution of the total repair numbers includes the probability corresponding to any value of the total repair numbers of the target type of devices, that is, the probabilities corresponding to the total repair numbers from 0 to mn respectively.
[0065] The probability distribution of the total repair numbers of the target type of devices can be realized through the following formula, Q = P * P … * P, where P is the probability group of the repair numbers of a single device, * is the convolution calculation symbol, and convolution processing is performed m-1 times in total, that is, m probability groups are subjected to convolution processing. The first 1+mn items of the array Q correspond to the probabilities corresponding to the total repair numbers from 0 to mn respectively. Exemplarily, see Table 1, which is the probability distribution of the total repair numbers of the target type of devices. Taking the number of devices of the target type as 20 and the maximum repair number of a single device as 5 as an example, it shows the probability distribution corresponding to the total repair numbers of the target type of devices from 1 to 100.
[0066] Table 1
[0067]
[0068]
[0069] S140. Determine the maintenance budget information of the target type of device based on the probability distribution of the total number of maintenance times of the target type of device and the single maintenance cost.
[0070] The maintenance budget information of the target type of device includes the budget attribute value corresponding to each value of the total number of maintenance times, and the maintenance satisfaction probability corresponding to each value of the total number of maintenance times;
[0071] Among them, the budget attribute value can be understood as the cost required to complete the preventive maintenance corresponding to each total number of maintenance times. The budget attribute value corresponding to each total number of maintenance times is calculated based on the value of each total number of maintenance times and the single maintenance cost, that is, the product of the value of each total number of maintenance times and the single maintenance cost is used as the budget attribute value corresponding to each total number of maintenance times. It can be understood that the single maintenance cost can be understood as the unit cost corresponding to each preventive maintenance, and can include but not be limited to maintenance material costs, human resource costs, and time-consuming costs during the device maintenance process, etc.
[0072] The maintenance satisfaction probability represents the probability that the budget attribute value corresponding to the value of the total number of maintenance times meets the actual maintenance requirements. Taking the single maintenance cost of 100 and the total number of maintenance times of 5 as an example, the budget attribute value is 500, that is, this budget attribute value can support preventive maintenance with the total number of maintenance times less than or equal to 5. That is to say, the maintenance satisfaction probability corresponding to the budget attribute value is the sum of the probabilities corresponding to the total number of maintenance times less than or equal to 5 respectively. Correspondingly, the maintenance satisfaction probability is the cumulative probability value corresponding to the value of the total number of maintenance times; the maintenance satisfaction probability corresponding to the value i of the total number of maintenance times can be expressed as Q k is the probability corresponding to the total number of maintenance times k.
[0073] Correspondingly, the maintenance budget information of the target type of device can be expressed as where i is the value of the total number of maintenance times and M is the single maintenance cost.
[0074] Exemplarily, referring to Table 2, Table 2 shows the maintenance budget information of the target type of device. In Table 2, taking the single maintenance cost of 100, the number of devices of the target type of device being 20, and the maximum number of maintenance times of a single device being 5 as examples, it shows the budget attribute values and maintenance satisfaction probabilities corresponding to the total number of maintenance times of the target type of device being 1 - 100. Table 2 includes multiple preventive maintenance plans, each preventive maintenance plan corresponding to different total numbers of maintenance times, different budget attribute values, and different maintenance satisfaction probabilities of the target type of device. It can be understood that Table 1 and Table 2 are only examples.
[0075] Table 2
[0076]
[0077] Exemplarily, referring toFigure 2 , Figure 2 is a schematic diagram of the correspondence between the maintenance satisfaction probability and the budget attribute value provided by an embodiment of the present disclosure. According to Figure 2 , it can be seen that as the budget attribute value increases, the maintenance satisfaction probability gradually increases, and the change rate of the maintenance satisfaction probability is different.
[0078] The technical solution provided by this embodiment determines the probability of a single device in the target type of device corresponding to each maintenance time, and further determines the probability distribution of multiple devices in the target type of device corresponding to each total maintenance time. The maintenance budget information of the target type of device is formed by the probability distribution of multiple devices corresponding to each total maintenance time and the single maintenance cost. The maintenance budget information includes the budget attribute value and the maintenance satisfaction probability corresponding to each total maintenance time of the target type of device, providing a basis for determining the maintenance budget in different maintenance requirement situations, replacing the situation of determining the maintenance budget by subjective experience, reducing the dependence on experience, and improving the accuracy of the maintenance budget.
[0079] Based on the above embodiment, a target budget attribute value is determined based on the expected maintenance satisfaction probability and the maintenance budget information of the target type of device. Specifically, based on the expected maintenance satisfaction probability, a match is made in the maintenance budget information of the target type of device to determine the target budget attribute value corresponding to the expected maintenance satisfaction probability.
[0080] Match the expected maintenance satisfaction probability in the maintenance budget information of the target type of device to determine the minimum maintenance satisfaction probability greater than or equal to the expected maintenance satisfaction probability, and determine the budget attribute value corresponding to the matched minimum maintenance satisfaction probability as the target budget attribute value. For example, if the expected maintenance satisfaction probability is 0.85, 0.882 is obtained as the minimum maintenance satisfaction probability greater than 0.85 in Table 2. Correspondingly, the target budget attribute value corresponding to 0.882 is 8400.
[0081] Based on the above embodiment, an initial budget attribute value is determined based on the historical maintenance times of the target type of device and the single maintenance cost. A match is made in the maintenance budget information of the target type of device based on the initial budget attribute value to obtain the maintenance satisfaction probability corresponding to the initial budget attribute value. The measurement result of the initial budget attribute value is determined based on the maintenance satisfaction probability corresponding to the initial budget attribute value and the expected maintenance satisfaction probability. The measurement result characterizes whether the initial budget attribute value supports the actual maintenance requirement.
[0082] The historical maintenance times of the target type of device can be the average maintenance times or the maximum maintenance times in the historical maintenance cycle (for example, it can be one year or half a year, etc.). The initial budget attribute value is determined by the product of the historical maintenance times and the single maintenance cost. The initial budget attribute value is matched in the maintenance budget information of the target type of device to determine the maintenance satisfaction probability corresponding to the successfully matched budget attribute value. The maintenance satisfaction probability corresponding to the initial budget attribute value is compared with the expected maintenance satisfaction probability. If the maintenance satisfaction probability corresponding to the initial budget attribute value is less than the expected maintenance satisfaction probability, it indicates that the initial budget attribute value does not support the actual maintenance requirements and there is a risk of budget shortage; if the maintenance satisfaction probability corresponding to the initial budget attribute value is greater than, it indicates that the initial budget attribute value supports the actual maintenance requirements. Further, the difference between the maintenance satisfaction probability corresponding to the initial budget attribute value and the expected maintenance satisfaction probability is determined. If the above difference is greater than the set threshold, it indicates that there is a risk of budget surplus for the initial budget attribute value.
[0083] The technical solution of this embodiment provides a data basis for the relevant processing of the maintenance budget by determining the maintenance budget information of the target type of device, determining the maintenance budget of the target type of device through the maintenance budget information of the target type of device, or judging the maintenance budget of the target type of device, thereby improving the accuracy of the maintenance budget.
[0084] Figure 3 It is a schematic structural diagram of a device for determining the budget information of preventive maintenance provided by an embodiment of the present disclosure. As Figure 3 shown, the device includes:
[0085] An information acquisition module 210, configured to acquire preventive maintenance information of the target type of device, where the preventive maintenance information includes the maximum maintenance times, single maintenance cost, maintenance reliability improvement data, and maintenance interval duration of a single device;
[0086] A first probability determination module 220, configured to determine a probability group of the maintenance times of a single device of the target type based on the maintenance reliability improvement data and the maintenance interval duration, where the probability group includes the probabilities corresponding to each maintenance time within the maximum maintenance times of the single device;
[0087] A second probability determination module 230, configured to determine the probability distribution of the total maintenance times of the target type of device based on the probability group of the maintenance times of the single device;
[0088] A maintenance budget determination module 240, configured to determine the maintenance budget information of the target type of device based on the probability distribution of the total maintenance times of the target type of device and the single maintenance cost.
[0089] The technical solution of this embodiment determines the probability of each maintenance time corresponding to a single device in the target type of device, and further determines the probability distribution of each total maintenance time corresponding to multiple devices in the target type of device. The maintenance budget information of the target type of device is formed by the probability distribution of each total maintenance time corresponding to multiple devices and the single maintenance cost. The maintenance budget information includes the budget attribute value and the maintenance satisfaction probability corresponding to each total maintenance time of the target type of device, providing a basis for determining the maintenance budget in different maintenance requirement situations, replacing the situation of determining the maintenance budget by subjective experience, reducing the dependence on experience, and improving the accuracy of the maintenance budget.
[0090] Based on the above embodiment, optionally, the first probability determination module 220 is configured to: determine the equivalent cumulative working hours corresponding to before and after each preventive maintenance based on the maintenance reliability improvement data and the maintenance interval duration; process the probability density distribution function of the target type of device based on the equivalent cumulative working hours corresponding to before and after each preventive maintenance, and obtain the probability corresponding to each maintenance time. The probabilities corresponding to each maintenance time within the maximum maintenance time form the probability group.
[0091] Optionally, the first probability determination module 220 is further configured to: determine the maintenance effect data corresponding to each preventive maintenance based on the maintenance reliability improvement data, where the maintenance effect data represents the equivalent working hours reduced by the preventive maintenance; for any maintenance time i, determine the initial cumulative working hours corresponding to the maintenance time based on the maintenance interval duration, and the cumulative value of the maintenance effect data corresponding to the previous maintenance time, and determine the equivalent cumulative working hours before the i-th preventive maintenance based on the initial cumulative working hours and the cumulative value of the maintenance effect data; use the difference between the equivalent cumulative working hours corresponding to before the i-th preventive maintenance and the maintenance effect data as the equivalent cumulative working hours after the i-th preventive maintenance.
[0092] Based on the above embodiment, optionally, the second probability determination module 230 is configured to: perform a convolution process on the probability group for a preset number of times to obtain the probability distribution of the total maintenance time, where the preset number of times is determined based on the number of devices of the target type of device. The probability distribution of the total maintenance time includes the probability corresponding to any value of the total maintenance time of the target type of device, and the value of the total maintenance time is greater than or equal to zero and less than or equal to the product of the maximum maintenance time of the single device and the number of devices.
[0093] Based on the above embodiment, optionally, the maintenance budget information of the target type of device includes the budget attribute value corresponding to each value of the total maintenance time, and the maintenance satisfaction probability corresponding to each value of the total maintenance time;
[0094] The budget attribute value is calculated based on the value of the total number of repairs and the cost of each repair;
[0095] The repair satisfaction probability is the cumulative probability value corresponding to the value of the total number of repairs; the repair satisfaction probability represents the probability that the budget attribute value corresponding to the value of the total number of repairs meets the actual repair requirements.
[0096] Based on the above embodiments, optionally, the repair budget determination module 240 is further configured to: match based on the expected repair satisfaction probability in the repair budget information of the target type of device, and determine the target budget attribute value corresponding to the expected repair satisfaction probability.
[0097] Optionally, the repair budget determination module 240 is further configured to: determine an initial budget attribute value based on the historical number of repairs and the cost of each repair of the target type of device, match the initial budget attribute value in the repair budget information of the target type of device, obtain the repair satisfaction probability corresponding to the initial budget attribute value, and determine the measurement result of the initial budget attribute value based on the repair satisfaction probability corresponding to the initial budget attribute value and the expected repair satisfaction probability, where the measurement result represents whether the initial budget attribute value supports the actual repair requirements.
[0098] The preventive maintenance budget information determination device provided by the embodiments of the present disclosure can execute the preventive maintenance budget information determination method provided by any embodiment of the present disclosure, and has corresponding functional modules and beneficial effects for executing the method.
[0099] Figure 4 It is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure. 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 can also represent various forms of mobile devices, such as personal digital processors, 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 only examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.
[0100] Such as Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory communicatively connected to the at least one processor 11, such as read-only memory (ROM) 12, random access memory (RAM) 13, etc. Among them, the memory stores computer programs executable by the at least one processor. The processor 11 can execute various appropriate actions and processes according to the computer programs stored in the read-only memory (ROM) 12 or the computer programs loaded from the storage unit 18 into the random access memory (RAM) 13. In the random access memory (RAM) 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the read-only memory (ROM) 12, and the random access memory (RAM) 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.
[0101] Multiple components in the electronic device 10 are connected to the 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 disk, an optical disc, 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 through a computer network such as the Internet and / or various telecommunication networks.
[0102] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include but are not limited to a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the method for determining budget information for preventive maintenance.
[0103] In some embodiments, the method for determining budget information for preventive maintenance can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto 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 method for determining budget information for preventive maintenance described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the method for determining budget information for preventive maintenance in any other appropriate way (for example, by means of firmware).
[0104] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard parts (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 interpretable on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.
[0105] A computer program for implementing the budget information determination method for preventive maintenance of the present disclosure 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 apparatus, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine, or entirely on the remote machine or server.
[0106] Embodiments of the present disclosure also provide a computer-readable storage medium storing computer instructions for causing a processor to execute a method for determining budget information for preventive maintenance, the method including:
[0107] Obtaining preventive maintenance information of target type devices, where the preventive maintenance information includes the maximum number of repairs of a single device, the cost of a single repair, data for improving repair reliability, and the repair interval duration; for a single device of the target type, determining a probability group of the number of repairs of the single device based on the data for improving repair reliability and the repair interval duration, where the probability group includes probabilities corresponding to each number of repairs within the maximum number of repairs of the single device; determining the probability distribution of the total number of repairs of the target type devices based on the probability group of the number of repairs of the single device; and determining the repair budget information of the target type devices based on the probability distribution of the total number of repairs of the target type devices and the cost of a single repair.
[0108] In the context of this disclosure, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAN), a read-only memory (RON), an erasable programmable read-only memory (EPRON or flash memory), an optical fiber, a portable compact disk read-only memory (CD-RON), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
[0109] To provide for 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 a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide for interaction with the user; for example, 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, speech input, or tactile input).
[0110] The systems and techniques described herein can be implemented in a computing system that includes backend components (such as, for example, a data server), or a computing system that includes middleware components (such as, for example, an application server), or a computing system that includes frontend components (such as, for example, a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (such as, for example, a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.
[0111] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host device in a cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.
[0112] It should be understood that various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of this disclosure can be achieved, and no limitation is imposed herein.
[0113] The above specific embodiments do not constitute a limitation on the protection scope of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of this disclosure shall be included within the protection scope of this disclosure.
Claims
1. A method for determining budget information for preventive maintenance, characterized in that, Including: Obtain preventive maintenance information of target type devices, where the preventive maintenance information includes the maximum number of repairs for a single device, the cost of each repair, data on the improvement of repair reliability, and the repair interval duration; For a single device of the target type, determine a probability group of the number of repairs for the single device based on the data on the improvement of repair reliability and the repair interval duration, where the probability group includes the probabilities corresponding to each number of repairs within the maximum number of repairs for the single device; Determine the probability distribution of the total number of repairs for the target type devices based on the probability group of the number of repairs for the single device; Determine the repair budget information for the target type devices based on the probability distribution of the total number of repairs for the target type devices and the cost of each repair.
2. The method according to claim 1, characterized in that Determine a probability group of the number of repairs for a single device based on the data on the improvement of repair reliability and the repair interval duration, including: Determine the equivalent cumulative working hours corresponding to before and after each preventive maintenance based on the data on the improvement of repair reliability and the repair interval duration; Process the probability density distribution function of the target type devices based on the equivalent cumulative working hours corresponding to before and after each preventive maintenance to obtain the probability corresponding to each number of repairs, and the probabilities corresponding to each number of repairs within the maximum number of repairs form the probability group.
3. The method according to claim 1, characterized in that Determine the equivalent cumulative working hours corresponding to before and after each preventive maintenance based on the data on the improvement of repair reliability and the repair interval duration, including: Determine the repair effect data corresponding to each preventive maintenance based on the data on the improvement of repair reliability, where the repair effect data represents the equivalent working hours reduced by the preventive maintenance; For any number of repairs i, determine the initial cumulative working hours corresponding to the number of repairs based on the repair interval duration, and the cumulative value of the repair effect data corresponding to the previous number of repairs, and determine the equivalent cumulative working hours before the i-th preventive maintenance based on the initial cumulative working hours and the cumulative value of the repair effect data; Use the difference between the equivalent cumulative working hours corresponding to before the i-th preventive maintenance and the repair effect data as the equivalent cumulative working hours after the i-th preventive maintenance.
4. The method according to claim 1, wherein Determine the probability distribution of the total number of repairs for the target type devices based on the probability group of the number of repairs for the single device, including: Perform convolution processing on the probability group a preset number of times to obtain the probability distribution of the total number of repairs, where the preset number is determined based on the number of devices of the target type, and the probability distribution of the total number of repairs includes the probability corresponding to any value of the total number of repairs for the target type devices, and the value of the total number of repairs is greater than or equal to zero and less than or equal to the product of the maximum number of repairs for a single device and the number of devices; 5. The method according to claim 1, wherein The repair budget information for the target type devices includes the budget attribute value corresponding to each value of the total number of repairs, and the repair satisfaction probability corresponding to each value of the total number of repairs; The budget attribute value is calculated based on the value of the total number of repairs and the cost of each repair; The maintenance satisfaction probability is the probability cumulative value corresponding to the value of the total number of maintenance times; the maintenance satisfaction probability represents the probability that the budget attribute value corresponding to the value of the total number of maintenance times meets the actual maintenance requirements.
6. The method according to claim 5, characterized in that The method further includes: Based on the expected maintenance satisfaction probability, match in the maintenance budget information of the target type of equipment to determine the target budget attribute value corresponding to the expected maintenance satisfaction probability.
7. The method according to claim 5, characterized in that, The method further includes: Determine the initial budget attribute value based on the historical number of maintenance times of the target type of equipment and the single maintenance cost, match the initial budget attribute value in the maintenance budget information of the target type of equipment, obtain the maintenance satisfaction probability corresponding to the initial budget attribute value, and determine the measurement result of the initial budget attribute value based on the maintenance satisfaction probability corresponding to the initial budget attribute value and the expected maintenance satisfaction probability. The measurement result represents whether the initial budget attribute value supports the actual maintenance requirements.
8. A device for determining budget information for preventive maintenance, characterized in that, It includes: An information acquisition module, configured to acquire preventive maintenance information of a target type of equipment, where the preventive maintenance information includes the maximum number of maintenance times of a single equipment, the single maintenance cost, the maintenance reliability improvement data, and the maintenance interval duration; A first probability determination module, configured to, for a single equipment of the target type, determine a probability group of the number of maintenance times of the single equipment based on the maintenance reliability improvement data and the maintenance interval duration, where the probability group includes the probabilities corresponding to each number of maintenance times within the maximum number of maintenance times of the single equipment; A second probability determination module, configured to determine the probability distribution of the total number of maintenance times of the target type of equipment based on the probability group of the number of maintenance times of the single equipment; A maintenance budget determination module, configured to determine the maintenance budget information of the target type of equipment based on the probability distribution of the total number of maintenance times of the target type of equipment and the single maintenance cost.
9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor can execute the method for determining the budget information of preventive maintenance according to any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method for determining the budget information of preventive maintenance according to any one of claims 1-7 is implemented.