CN119130333B - Maintenance decision method and device for multi-component system based on third-order state - Google Patents
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Abstract
The application discloses a maintenance decision method of a multi-component system based on third-order states, which comprises the steps of constructing a state transition mechanism among the three states of components, obtaining first state transition information, first spare part stock quantity and first maintenance requirement quantity of each component in a current detection system, determining second state transition and state transition probability possibly occurring when the component is detected next, determining maintenance requirement probability of the component when the component is detected next, determining second maintenance requirement quantity and system maintenance requirement probability when the system is detected next, determining average cost rate in a unit detection period based on the system maintenance requirement probability and the second maintenance requirement quantity, and determining optimal detection period and optimal safety stock quantity of the system according to a genetic algorithm by taking the average cost rate as an fitness function and taking the detection period and the safety stock quantity as decision variables. The application determines a maintenance strategy for joint optimization of system maintenance and spare part inventory.
Description
Technical Field
The application relates to the technical field of equipment maintenance decision, in particular to a maintenance decision method and device of a multi-component system based on a third-order state.
Background
In order to adapt to increasingly complex application scenes and realize richer functions, equipment gradually develops towards integration, and all parts are mutually connected and mutually influenced to form a system so as to jointly complete complex and multiple tasks. In actual engineering practice, the performance of various devices is gradually degraded by the influence of materials, structural strength, or the surrounding environment, etc., and when the devices degrade to a certain extent, faults or failures occur. For example, generators, punching machines, numerical control machine tools and the like can be failed due to performance degradation, and the damage caused by equipment failure to enterprises is various, multi-level and even long-term.
The on-demand maintenance is used in a variety of situations where maintenance is required, emphasizing that preventive maintenance strategies are arranged according to the operating conditions of the system components, and can be adjusted in time to the operating conditions of the components. In the related art, for a general multi-component system, an optional maintenance can achieve a satisfactory result in a system maintenance decision consisting of two-state components (including a normal operation state and a functional failure state). However, in most cases, during degradation of the device, it exhibits three state characteristics, such as normal operating state, latent fault state, functional fault state. When a component is in a potentially faulty state, it will incur higher operating costs than in a normal operating state, with a higher risk of failure, so that the component should be kept from this state as much as possible in engineering practice. The technical scheme related to the optimization of the condition-dependent maintenance strategy of the three-state component mainly considers the situation on a single-component system, and does not consider the situation of a multi-component system.
The maintenance components require spare parts to be consumed, while spare parts inventory is not instantaneously available due to limitations in inventory costs, time to arrival, etc., and maintenance and spare parts inventory interact and are manufactured. Spare part inventory is rarely considered when making optimal maintenance decisions for a three-state component system.
Disclosure of Invention
The application aims to provide a maintenance decision method of a multi-component system based on a third-order state, comprehensively considers the maintenance characteristics and spare part characteristics of components in the multi-component system, determines a strategy scheme for combined optimization of system maintenance and spare part inventory, and effectively reduces production cost.
In a first aspect, the present application provides a method of service decision making for a multi-component system based on a third-order state, the multi-component system comprising a plurality of identical components, the method comprising:
defining states of the component including a normal working state, a potential fault state and a functional fault state, and constructing a cumulative distribution function of the component when the component is transferred between the states, wherein the component in the potential fault state has preventive maintenance requirements and the component in the functional fault state has functional maintenance requirements;
Acquiring first state transition information, first spare part stock quantity and first maintenance requirement quantity of each part in a current detection system, wherein the first maintenance requirement quantity is the part quantity of preventive maintenance requirements and the part quantity of repair maintenance requirements in the system;
For any component, based on the first state transition information and the cumulative distribution function of the component when the component transits between states, determining second state transition information and state transition probability of the component possibly occurring in the next detection, determining maintenance requirement probability of the component in the next detection according to the state transition probability, and further determining second maintenance requirement quantity and system maintenance requirement probability of the multi-component system in the next detection;
Determining a second spare part stock amount in the next detection based on the first maintenance required amount, the first spare part stock amount and a preset safety stock amount;
Determining an average total cost of repair based on the probability of system repair demand and the second number of repair demand, determining an average total cost of spare parts based on the probability of system repair demand and the second inventory of spare parts, determining an average total cost of operation based on the probability of state transition, and determining an average rate of charge per unit detection period based on the average total cost of repair, the average total cost of spare parts, the average total cost of operation, and the single detection cost;
and determining the optimal detection period and the optimal safe stock quantity of the multi-component system according to a genetic algorithm by taking the average cost rate as an fitness function and the detection period and the safe stock quantity as decision variables.
In a second aspect, the present application provides a maintenance decision device for a multi-component system based on a third-order state, the device comprising:
The state transfer module is used for defining states of the component, including a normal working state, a potential fault state and a functional fault state, and constructing an accumulated distribution function when the component is transferred between the states, wherein the component in the potential fault state has preventive maintenance requirements, and the component in the functional fault state has functional maintenance requirements;
The acquisition module is used for acquiring first state transition information, first spare part stock quantity and first maintenance requirement quantity of each part in the current detection system, wherein the first maintenance requirement quantity is the part quantity of preventive maintenance requirements and the part quantity of repair maintenance requirements in the system;
The maintenance requirement probability module is used for determining second state transition information and state transition probability of the component possibly occurring in next detection according to the first state transition information and an accumulated distribution function of the component when the component is transited between states, determining the maintenance requirement probability of the component in next detection according to the state transition probability, and further determining second maintenance requirement quantity and system maintenance requirement probability of the multi-component system in next detection;
The spare part inventory module is used for determining a second spare part inventory amount in the next detection based on the first maintenance requirement number, the first spare part inventory amount and a preset safety inventory amount;
a cost rate module for determining an average total cost of repair based on the probability of system repair demand and the second number of repair demand, determining an average total cost of spare parts based on the probability of system repair demand and the second inventory of spare parts, determining an average total cost of operation based on the probability of state transition, and determining an average cost rate within a unit detection period based on the average total cost of repair, the average total cost of spare parts, the average total cost of operation, and the single detection cost;
And the optimal model module is used for determining the optimal detection period and the optimal safety stock quantity of the multi-component system according to the genetic algorithm by taking the average cost rate as an fitness function and taking the detection period and the safety stock quantity as decision variables.
According to the application, for the maintenance decision of the same multi-component system, the three-state characteristics of the components in the system are considered, the maintenance characteristics and spare part characteristics of the components in the multi-component system are comprehensively considered, and the strategy scheme of the combined optimization of the system maintenance and spare part inventory is determined, so that the operation reliability of the multi-component system is improved, and the production cost is effectively reduced.
Drawings
FIG. 1 is a first flow chart of a method for determining maintenance of a three-stage state-based multi-component system according to an embodiment of the present application;
FIG. 2 is a second flow chart of a method for determining maintenance of a three-stage state-based multi-component system according to an embodiment of the present application;
FIG. 3 is a third flow chart of a method for determining maintenance of a three-stage state-based multi-component system according to an embodiment of the present application;
FIG. 4 is a fourth flowchart of a maintenance decision method for a three-stage state-based multi-component system according to an embodiment of the present application;
FIG. 5 is a fifth flow chart of a maintenance decision method for a three-stage state-based multi-component system according to an embodiment of the present application;
FIG. 6 is a sixth flowchart of a maintenance decision method for a three-stage state-based multi-component system according to an embodiment of the present application;
FIG. 7 is a system block diagram of a maintenance decision device for a three-stage state-based multi-component system according to an embodiment of the present application;
Fig. 8 is a system block diagram of a computer device according to an embodiment of the present application.
Detailed Description
The present application will be described in detail below with reference to the specific embodiments shown in the drawings, but these embodiments are not limited to the present application, and structural, method, or functional modifications made by those skilled in the art based on these embodiments are included in the scope of the present application.
Referring to fig. 1, the method for determining maintenance of a multi-component system based on a third-order state according to the present application includes a plurality of identical components, and includes the steps of:
S101, defining states of the components including a normal working state, a potential fault state and a functional fault state, and constructing an accumulated distribution function of the components when the components are transferred between the states, wherein the components in the potential fault state have preventive maintenance requirements, and the components in the functional fault state have functional maintenance requirements;
S102, acquiring first state transition information, first spare part stock quantity and first maintenance requirement quantity of each part in a current detection system, wherein the first maintenance requirement quantity is the part quantity of preventive maintenance requirements and the part quantity of repair maintenance requirements in the system;
S103, for any component, determining second state transition information and state transition probability of the component possibly occurring in the next detection based on the first state transition information and an accumulated distribution function of the component in each state transition, determining maintenance requirement probability of the component in the next detection according to the state transition probability, and further determining second maintenance requirement quantity and system maintenance requirement probability of the multi-component system in the next detection;
s104, determining a second spare part stock quantity in the next detection based on the first maintenance required quantity, the first spare part stock quantity and a preset safety stock quantity;
S105, determining an average total maintenance cost based on the system maintenance requirement probability and the second maintenance requirement quantity, determining an average total spare part cost based on the system maintenance requirement probability and the second spare part stock quantity, determining an average total operation cost based on the state transition probability, and determining an average cost rate in a unit detection period based on the average total maintenance cost, the average total spare part cost, the average total operation cost and the single detection cost;
S106, determining the optimal detection period and the optimal safe stock quantity of the multi-component system according to a genetic algorithm by taking the average cost rate as an fitness function and the detection period and the safe stock quantity as decision variables.
According to the method, three-state characteristics of the components, namely a normal working state, a potential fault state and a functional fault state, are considered in a multi-component system comprising a plurality of identical components, a transfer mechanism of the components among the states is determined, state transfer of each component in a current detection system is obtained, state transfer probability of each component and state transfer probability of each component in the next detection are determined, maintenance requirement probability of a single component in the next detection is determined, further maintenance requirement quantity of the system in the next detection and system maintenance requirement probability of the system are determined, spare part stock quantity in the next detection is determined, and therefore average cost rate in a single detection period is determined, an optimization model of the average cost rate is constructed, therefore, the optimal detection period and the optimal safety stock quantity of the system can be determined, maintenance characteristics and spare part characteristics of the components in the multi-component system are comprehensively considered, a strategy scheme for combined optimization of system maintenance and spare part stock is determined, operation reliability of the multi-component system is improved, and production cost is effectively reduced.
In the present application, a multi-component system comprises a system of identical components, for example, a blower field comprising a plurality of structurally similar blowers, which are operated independently of one another. The multi-component system shares spare parts inventory, which can be maintained by using any spare parts as components, and only needs to consider all spare parts of the system. Because the components are similar in structure, the same spare parts are used, and only the number of components which need to be maintained in different types is considered when the maintenance requirement of the system is solved. The components of the different component systems have different structures, spare parts can only be in one-to-one correspondence, the spare part stock of each component needs to be considered separately, and the maintenance of each component needs to be considered separately.
In some embodiments, the states defining the components in the multi-component system include a normal operating state, a latent fault state, and a functional fault state, i.e., the normal operating state is s 0, the latent fault state is s 1, and the functional fault state is s 2. The latent fault state is a transition state between a normal operating state and a functional fault state, and when the component is in the latent fault state, the fault rate is significantly higher than the normal operating state. The component can continue to operate in a potentially faulty condition, but at a significantly higher cost than in a normal operating condition.
In some embodiments, the points in time at which the state transitions of the component occur from s 0 to s 2 are defined to follow an exponential distribution of parameter λ 0, and the first probability distribution density function of the points in time at which the state transitions of the component occur from s 0 to s 2 is determinedBased on a first probability distribution density functionDetermining a first cumulative distribution functionWherein the first cumulative distribution function is a cumulative distribution function of the occurrence time of the state transition of the component from s 0 to s 2, and the first probability distribution density functionAnd a first cumulative distribution functionExpressed as:
;
;
Wherein, For the state transition occurrence time of the component from s 0 to s 2,For the probability that a transition from s 0 to s 2 occurs for a component at time t, lambda 0 is an exponential distribution parameter,To at the same timeProbability of the part going from s 0 to s 2 within the time period;
defining the exponential distribution of the parameter lambda 1 for the point in time of occurrence of the state transition of the component from s 1 to s 2, determining a second probability distribution density function for the point in time of occurrence of the state transition of the component from s 1 to s 2 Based on a second probability distribution density functionDetermining a first cumulative distribution functionWherein the second cumulative distribution function is a cumulative distribution function of the occurrence time of the state transition of the component from s 1 to s 2, and the second probability distribution density functionAnd a second cumulative distribution functionExpressed as:
;
;
Wherein, For the state transition occurrence time of the component from s 1 to s 2,For the probability that a transition from s 1 to s 2 occurs for a component at time t, lambda 1 is an exponential distribution parameter,To at the same timeProbability of the part going from s 1 to s 2 within the time period;
defining the occurrence time point of the state transition of the component from s 0 to s 1 obeys the Weibull distribution with alpha as a scale parameter and beta as a shape parameter, and determining the third probability distribution density function of the occurrence time point of the state transition of the component from s 0 to s 1 Based on a third probability distribution density functionDetermining a third cumulative distribution functionWherein the third cumulative distribution function is a cumulative distribution function of the occurrence time of the state transition of the component from s 0 to s 1, and the third probability distribution density functionAnd a third cumulative distribution functionExpressed as:
;
;
Wherein, For the state transition occurrence time of the component from s 0 to s 1,For the probability of a component transition from s 0 to s 1 at time t, alpha is the scale parameter, beta is the shape parameter,To at the same timeThe probability of the part going from s 0 to s 1 within the time period.
Preventive maintenance is required when a component is detected in a potentially faulty state, and repair maintenance is required when a component is detected in a functional faulty state. In this embodiment, the state transition of the component or the state of the component is determined and obtained through periodic system detection, for example, the previous detection finds that a certain component is in a normal working state, and the component is found to be in a potential fault state in this detection, so that we can know that the state transition occurs during this time. Setting the repaired component to be restored as new, namely to be in a normal working state again, and consuming one spare part for each repair. In the embodiment, based on the operation characteristics of the equipment and the three-state characteristics of the components, a three-stage state model which accords with the actual components is established, the state transition set of the components is determined, the component degradation process can be fitted, and the degradation mechanism of the components is met, so that a basis is provided for the joint decision of subsequent system maintenance and spare part inventory.
The method comprises the steps of carrying out periodic nondestructive testing on all components in a system, dynamically adjusting the testing period of each time according to the running states of all the components and the stock quantity of spare parts, and determining the optimal testing period to be used as the next testing period based on the technical scheme of the combined decision of the system maintenance requirement and the stock quantity of spare parts in the application, so that the testing period is dynamically adjusted, thereby being capable of better reducing the production cost and having good engineering value for equipment maintenance decision.
In some embodiments, at each inspection, the maintenance activities and spare part ordering activities that need to be performed are determined based on the detected status of each part and spare part inventory. Suppose that at the kth test, the system maintenance requirement isSpare part stock is,Is expressed asThe individual parts need repair, includingIndividual components require preventive maintenance, each time a maintenance activity is performed, a maintenance preparation cost is incurredThe cost is independent of the number of parts serviced. Setting the safety stock quantity of the system as s, ordering new spare parts when the spare part stock can not meet maintenance requirements or the quantity of the remaining spare parts is smaller than the safety stock quantity, and setting the spare part arrival time as follows. The ordering cost of the single spare part is. Spare parts inventory may result in corresponding spare parts holding costs. Spare part holding cost generated by single stock spare part per unit time is. When the stock quantity of spare parts cannot fully meet the maintenance requirement, the spare parts preferentially supply the parts in the functional failure state. If the component is not maintained in time, stopping operation may be needed, and the stopping loss cost generated in unit time when the component executes the stopping operation is defined as. The strategy for setting system maintenance and safety spare part inventory is as follows:
If it is Inventory spare part quantityThe repair need is not met and spare parts are ordered immediately at the current detection time point. Faulty components that are not undergoing repair are taken out of service before the ordered spare parts arrive. Components requiring preventive maintenance are scheduled for later operation by assessing risk if at spare part arrival timeIn which the cost of continuing operation of such components is less than the cost of stopping operation, such components continue to degrade operation, otherwise such components are stopped before the ordered spare parts arrive. All deferred repairs are completed immediately upon the arrival of the spare part;
If it is All parts and remainders requiring repairComponents requiring preventive maintenance are immediately serviced. Spare parts are ordered immediately at the current detection time point. The arrangement of the remaining components requiring preventive maintenance is the same as described above. All deferred repairs are completed immediately upon the arrival of the spare part;
If it is The stock spare part quantity can simultaneously meet all maintenance requirements, and corresponding maintenance is immediately arranged for all parts needing maintenance activities. If the stock quantity of the spare parts after meeting the maintenance requirement is smaller than the safety stock quantity s, the spare parts are detected after the detection point in order to reduce the holding cost of the spare partsSpare parts are ordered at the moment, and spare part ordering activities are not performed in the rest cases.
Based on the current detection, first state transition information, first spare part stock quantity and first maintenance requirement quantity of all parts in the system are obtained, wherein the first maintenance requirement quantity is the part quantity of preventive maintenance requirements and the part quantity of repair maintenance requirements in the system.
In some embodiments, as shown in fig. 2, determining second state transition information and a state transition probability thereof that a component may occur at a next detection based on the first state transition information and a cumulative distribution function of the component when transitioning between states, includes:
If the component is currently detected in a normal state or fails and a maintenance activity is performed, determining that a second state transition may occur includes four conditions, wherein the maintenance activity includes preventive maintenance and functional maintenance:
S201, defining the condition of the component in the normal state when the component is detected in the normal state at the present time and the condition of the component in the normal state when the component is detected in the next time as a first condition, and determining a first state transition probability when the first condition occurs according to a first accumulated distribution function and a third accumulated distribution function;
S202, the current detection of the component is transferred from a normal state to a potential fault state, the situation that the component is transferred from the normal state to the potential fault state when the component is detected next time is defined as a second situation, and the second state transfer probability when the second situation occurs is determined according to a first accumulated distribution function and a second accumulated distribution function;
S203, the current detection of the component is transferred from the normal state to the functional failure state and maintained, the situation that the component is transferred from the normal state to the functional failure state in the next detection is defined as a third situation, and the third state transfer probability in the third situation is determined according to the first accumulated distribution function and the third accumulated distribution function;
S204, the component is transferred from the normal state to the potential fault state, then transferred to the functional fault state and maintained, the situation that the component is transferred from the normal state to the potential fault state and then transferred to the functional fault state when the component is detected next time is defined as a fourth situation, and the fourth state transfer probability when the fourth situation occurs is determined according to the first accumulated distribution function and the second accumulated distribution function.
In one embodiment of the present application, in the firstTime point of detection (current detection)Acquiring states of components in a systemSpare part stock isThe maintenance requirement of the system is thatI.e. representing the current detection time pointHas the following componentsThe individual parts need repair, includingIndividual components require preventive maintenance. For a single part in the next test periodThe possible state transitions are analyzed.
If the component is at the point in timeThe second state transition that may occur in a normal state or with a repair action, i.e., a preventive or repair of the component, includes the four conditions described above:
for the first case, the component is in a normal state in the current detection period, the next detection is not needed to be maintained, and the first state transition probability of the first case indicates that The method comprises the following steps:
;
Wherein, To at the same timeProbability of the part going from s 0 to s 2 within the time period; To at the same time Probability of the part going from s 0 to s 1 within the time period;
For the second case, the component makes a state transition from s 0 to s 1 in the current detection period, but no failure occurs, and the next detection requires preventive maintenance, and the second state transition probability of the second case indicates that The method comprises the following steps:
;
Wherein, Indicating the current time point of detection,The next time point of detection is indicated,Indicating the moment of occurrence of the state transition of the component from s 0 to s 1,Is part atThe probability of a transition from s 0 to s 1 occurs at a time,Is thatThe probability of transitioning from s 0 to s 2 does not occur within the time period,Is thatThe probability of transitioning from s 1 to s 2 does not occur within the time period;
for the third case, the component makes a state transition from s 0 to s 2 in the current detection period, the next detection requires repair, and a third state transition probability for the third case indicates that The method comprises the following steps:
;
Wherein, Indicating the time at which the state transition from s 0 to s 2 occurs,Representation ofThe probability of transitioning from s 0 to s 1 does not occur within the time period,Representation ofThe probability of a transition from s 0 to s 2 occurs at a time;
For the fourth case, the component makes a state transition from s 0 to s 1 in the current detection period, then makes a state transition from s 1 to s 2, the next detection requires repair, and a fourth state transition probability for the fourth case indicates that The method comprises the following steps:
;
Wherein, Indicating the time at which the state transition from s 1 to s 2 occurs,Representation ofThe probability of the transition from s 1 to s 2 occurs at that time.
In some embodiments, as shown in fig. 3, determining second state transition information and a state transition probability thereof that a component may occur at a next detection based on the first state transition information and a cumulative distribution function of the component when transitioning between states, includes:
If the part is in fault in the current detection, the maintenance is required to be delayed due to insufficient spare parts, and the maintenance is performed after the spare parts arrive, determining that the second state transition possibly occurring in the part in the time period between the spare part arrival time and the next detection comprises the following four conditions;
S301, defining the condition that the component is in a normal state in a time period as a fifth condition, and determining a fifth state transition probability in the fifth condition according to the first accumulated distribution function and the third accumulated distribution function;
s302, defining the situation that the component is in transition from a normal state to a potential fault state in a time period as a sixth situation, and determining sixth state transition probability in the sixth situation according to a first cumulative distribution function and a second cumulative distribution function;
s303, defining the condition that the component is transited from the normal state to the functional failure state in the time period as a seventh condition, and determining a seventh state transition probability in the seventh condition according to the first cumulative distribution function and the third cumulative distribution function;
S304, the situation that the component is transferred from the normal state to the potential fault state and then to the functional fault state in the time period is defined as an eighth situation, and the eighth state transfer probability in the eighth situation is determined according to the first cumulative distribution function and the second cumulative distribution function.
If the component is inWhen the spare parts are insufficient and can not be maintained in time at any time, whether the spare parts continue to operate before the spare parts arrive at the goods is determined according to the state of the parts. At this time, if the component is in a potential failure state, the risk of evaluating is selected to beShut down or continue to operate in a potentially faulty condition for a period of time, if in a functional faulty condition, must be inAnd stopping the machine in a time period. The ordered spare parts are inAnd when the goods arrive at the moment, the part is restored to the normal working state after being maintained in a delayed mode. And the part is thenThe fifth to eighth cases described above may occur during the period of time.
For the fifth case, the component isIn normal working state in time period, the next detection does not need maintenance, and the fifth state transition probability of the fifth condition showsThe method comprises the following steps:
;
Wherein, Indicating the current time point of detection,Representing the next detection time point, T representing a preset detection period,Indicating the time to the arrival of the spare part,Representation ofThe probability that the part does not transition from s 0 to s 2 within the time period,Representation ofThe probability that the part does not transition from s 0 to s 1 within the time period;
For the sixth case, the component is The state transition from s 0 to s 1 occurs within the time period, but no fault occurs, the next detection requires preventive maintenance, and the sixth state transition probability of the sixth situation indicates thatThe method comprises the following steps:
;
Wherein, Indicating the moment of occurrence of the state transition of the component from s 0 to s 1,Is part atThe probability of a transition from s 0 to s 1 occurs at a time,Representation ofThe probability that the part does not transition from s 0 to s 2 within the time period,Is thatThe probability that the part does not transition from s 1 to s 2 within the time period.
For the seventh case, the component isState transition from s 0 to s 2 occurs within a time period, and the next detection requires preventive maintenance, and a seventh state transition probability for the seventh case indicatesThe method comprises the following steps:
;
Wherein, For the state transition occurrence time of the component from s 0 to s 2,Representation ofThe probability that the part does not transition from s 0 to s 1 within the time period,Representation ofThe probability that the time of day component has transitioned from s 0 to s 2;
for the seventh case, the component is The state transition from s 0 to s 1 occurs within the time period, and then the state transition from s 1 to s 2 occurs, the next detection needs repair, and the eighth state transition probability of the eighth case indicates thatThe method comprises the following steps:
;
Wherein, Indicating the moment of occurrence of the state transition of the component from s 1 to s 2,Indicating the moment of occurrence of the state transition of the component from s 0 to s 1,Is part atThe probability of a transition from s 0 to s 1 occurs at a time,Representation ofThe probability that the time of day component will transition from s 1 to s 2,Representation ofThe probability that the part does not transition from s 0 to s 2 within the time period.
In some embodiments, based on the obtained state transition probability of the single component, the maintenance requirement probability of the single component can be determined, that is, the maintenance requirement probability of the component in the next detection is determined according to the state transition probability, including:
if the component is in a normal working state or fails and performs maintenance activities, determining a first maintenance requirement probability that the component does not need maintenance when the component is detected next based on the first state transition probability, determining a second maintenance requirement probability that the component only needs preventive maintenance when the component is detected next based on the second state probability, and determining a third maintenance requirement probability that the component only needs maintenance when the component is detected next based on the third state transition probability and the fourth state transition probability.
In a specific embodiment of the present application, if the component is currently detected to be in a normal working state or fails and performs maintenance activities, that is, the first to fourth cases described above, the maintenance requirement probabilities of the individual components are respectively analyzed for the four cases. The components of the same component number M multi-component system are numbered and are each denoted asIf partIn the normal working state of the current detection period or after maintenance activities, the component is at the next detection time pointFirst maintenance requirement probability that maintenance may not be requiredExpressed as:
;
If the component is The state transition from s 0 to s 1 occurs during the current test cycle, but no failure occurs, the next test requires preventive maintenance, and the component is at the next test time pointSecond maintenance requirement probability of only one preventive maintenanceRepresented as;
;
If the component is The state transition from s 0 to s 2 or from s 0 to s 1 occurs during the current test cycle, after which the state transition from s 1 to s 2 occurs, the next test requires repair, and the component is at the next test time pointThird dimension repair demand probability of only one-time repairExpressed as:
。
in some embodiments, based on the obtained state transition probability of the single component, the maintenance requirement probability of the single component can be determined, that is, the maintenance requirement probability of the component in the next detection is determined according to the state transition probability, including:
If the part is in failure in the current detection, the part needs to be maintained in a delayed manner due to insufficient spare parts and is maintained after the spare parts arrive in stock, fourth maintenance requirement probability that the part does not need to be maintained in the next detection is determined based on the fifth state transition probability, fifth maintenance requirement probability that the part only needs to be maintained in a preventive manner in the next detection is determined based on the sixth state probability, and sixth maintenance requirement probability that the part only needs to be maintained in a preventive manner in the next detection is determined based on the seventh state transition probability and the eighth state transition probability.
In one embodiment of the application, if the component isWhen the spare parts are insufficient at any time and cannot be maintained, the ordered spare parts are arranged in the following wayAnd when the goods arrive at the moment, the part is restored to the normal working state after being maintained in a delayed mode. And the part is thenThe fifth to eighth cases described above may occur in a period of time, and the maintenance demand probabilities of the individual components are analyzed for the four cases, respectively. The components of the same component number M multi-component system are numbered and are each denoted asIf the component is inIn normal working state in a time period, partsAt the next detection time pointFourth maintenance need probability that maintenance may not be neededExpressed as:
;
If the component is in State transition from s 0 to s 1 occurs within a period of time, but no failure occurs, the next test requires preventive maintenance, partThe next detection time pointFifth probability of need for preventive maintenance only onceExpressed as:
;
If the component is The state transition from s 0 to s 2 or from s 0 to s 1 occurs in the current detection cycle, and then the state transition from s 1 to s 2 occurs, and the next detection requires repair, partAt the next detection time pointSixth repair demand probability for one-time repair onlyExpressed as:
。
in some embodiments, in calculating the maintenance requirement probability of the multi-component system, the multi-component system is divided into two subsystems, one of the components is taken as one subsystem, the other component is taken as the other subsystem, and the maintenance requirement probability of the multi-component system is a superposition of the maintenance requirement probabilities of the subsystems, namely:
Dividing a multi-component system with the same component number M into a first subsystem formed by any component and a second subsystem formed by the rest M-1 components, and calculating to obtain the maintenance requirement probability of the first subsystem according to the first maintenance requirement probability, the second maintenance requirement probability, the third maintenance requirement probability, the fourth maintenance requirement probability, the fifth maintenance requirement probability and the sixth maintenance requirement probability;
Dividing the second subsystem into a third subsystem formed by any part and a fourth subsystem formed by the rest M-2 parts, and determining to obtain the maintenance requirement probability of the third subsystem;
and the like, obtaining a second maintenance requirement number of the multi-component system in next detection by recursion, and obtaining a first system maintenance requirement probability of no maintenance, a system maintenance requirement probability of m preventive maintenance and l repair maintenance, wherein the second maintenance requirement number comprises a preventive maintenance component number m and a repair maintenance component number l.
Multi-component system for component number M with maintenance requirements of componentsIs required and partsThe combination of the repair requirements of the multi-component system of the component number M-1, from which the repair requirement probability of the multi-component system of the component number M is derived, wherein the repair requirement probability of the single component can be determined from the above description, the repair requirement of the multi-component system of the component number M-1 can be decomposed by the same method intoMaintenance requirements and components of a multipart system of component number M-2Is required for maintenance. All possible repair requirements and repair requirement probabilities for the multi-component system of the component number M are successively recursively derived.
In one embodiment of the application, the next detection time of the multi-component system is definedIs toI.e. the number of parts requiring preventive maintenance isThe number of parts to be repaired isDefinition of a multiple component System from M identical componentsThe composition of the composite material comprises the components,
When (when)When the multi-component system is inProbability of system maintenance requirement that maintenance is not required at any timeThe method comprises the following steps:
;
Wherein, Is composed ofThe multi-component system is composed ofThe probability that no repair is required at the moment,Is a componentAt the position ofProbability of no need for maintenance at the moment;
When (when) In the case of a multi-component system, this is doneProbability of maintenance need for secondary preventative maintenanceThe method comprises the following steps:
;
Wherein, Is composed ofThe multi-component system is composed ofTime of day needsThe probability of a secondary preventative maintenance is that,Is composed ofThe composed multi-component system requiresThe probability of a secondary preventative maintenance is that,Is a componentAt the position ofThe probability that only one preventive maintenance is required at a time,Representing an exponential function, if condition A is establishedOtherwise;
When (when)When the multi-component system is inTime of dayProbability of repair need for secondary repairability repairThe method comprises the following steps:
;
Wherein, Is composed ofThe multi-component system is composed ofTime of day needsThe probability of a secondary repair service is that,Is composed ofThe multi-component system is composed ofTime of day needsThe probability of a secondary repair service is that,Is a componentAt the position ofProbability that repair is only needed once at any time;
When (when) When the multi-component system is inTime of daySecondary restorative repairProbability of maintenance need for secondary preventative maintenanceThe method comprises the following steps:
;
Wherein, Is composed ofThe multi-component system is composed ofTime of day needsSecondary restorative repairThe probability of a secondary preventative maintenance is that,Is composed ofThe multi-component system is composed ofTime of day needsSecondary restorative repairThe probability of a secondary preventative maintenance is that,Is composed ofThe multi-component system is composed ofTime of day needsSecondary restorative repairProbability of secondary preventative maintenance.
In the case where the number of system components is known, the probability of a state transition of a single component that may occur in one detection cycle is analyzed, and the obtained probability is substituted into the expression of the above-described multi-component system maintenance requirement, thereby obtaining the system maintenance requirement probability at the next detection.
In some embodiments, determining the second stock of spare parts at the next inspection based on the status of each component in the system currently inspected, the first maintenance requirement number, the first stock of spare parts, and the preset safety stock includes:
defining a first spare part inventory as First maintenance required quantityComponent count for preventive maintenance requirements in a systemAnd number of parts required for repairThe stock quantity of the safety spare parts is s;
If it is After the maintenance activities are carried out, all the components are in a normal working state, and the spare part stock is not less than the safe spare part stock after the maintenance requirements are met, and the spare part ordering activities are not triggered, so that the second spare part stock in the next detection is carried out;
If it isAfter the maintenance activities are carried out, all the components are in a normal working state, and the spare part stock quantity is smaller than the safety spare part stock quantity after the maintenance requirements are met, the spare part ordering activities are triggered, the delayed maintenance is carried out after the spare parts arrive, and the safety spare part stock quantity is restored after the spare part stock quantity is maintained, so that the second spare part stock quantity in the next detection is obtained;
If it isThere isThe spare parts cannot be maintained immediately due to insufficient spare parts, the spare part ordering activity is triggered, the delayed maintenance is carried out after the spare parts arrive, and the safe spare part stock is restored after the spare part stock is maintained, so that the second spare part stock in the next detection is realized。
The total cost generated during the inspection period is determined based on the average total maintenance cost, the average total spare part cost, the average total running cost, and the single inspection cost, thereby determining the average rate of charges per inspection period.
In some embodiments, as shown in fig. 4, determining the average total cost of repair based on the probability of system repair demand and the second number of repair demands includes:
s401, determining average maintenance preparation cost in a current detection period based on the probability of system maintenance requirement and maintenance preparation cost generated by single maintenance;
S402, determining average maintenance activity cost in a current detection period based on the probability of system maintenance requirements, the cost of one-time preventive maintenance of the component, the cost of one-time repair maintenance of the component, the second spare part inventory and the second maintenance requirement quantity;
S403, determining average shutdown loss cost in the current detection period based on the system maintenance requirement probability, shutdown cost generated by shutdown in unit time of a single component, spare part arrival time, second spare part inventory and second maintenance requirement quantity;
s404, determining the average total maintenance cost in the current detection period based on the average maintenance preparation cost, the average maintenance activity cost and the average shutdown loss cost.
In one embodiment of the application, the cost of maintenance preparation is averagedAn expected value indicating maintenance preparation cost in one detection period, and the next detectionAt the moment, if there is no maintenance requirement, i.eIn this case, no maintenance preparation cost is generated. If all maintenance requirements are completed in time, that isAnd is also provided withWhen the components with maintenance requirements are simultaneously maintained at the detection points, only one maintenance preparation cost is generated. If the maintenance is delayed due to insufficient spare parts in the period, namelyAnd is also provided withDelayed repair would then increase the cost of repair preparation by one, thus the average cost of repair preparation over the current inspection periodExpressed as:
;
Wherein, Representing a multi-component system inTime of daySecondary restorative repairProbability of maintenance need for secondary preventative maintenance,Represents the maintenance preparation cost generated by a single maintenance, which is independent of the number of parts to be maintained.
Average repair activity costs refer to the total cost of repair occurring at the inspection point in time and delayed repair. The cost of one-time preventive maintenance of the components isThe cost for performing one-time repair and maintenance is that. In general, the difficulty of maintaining a component in a functional failure is far greater than the difficulty of maintaining a component in a potentially failed state, thus providing for. If no maintenance requirement occurs at the detection time, namelyWhen the maintenance is carried out, no maintenance activity cost is generated. If all maintenance requirements can be completed in time, that isAnd is also provided withWhen the maintenance cost is. If the repair is delayed due to insufficient spare parts in the cycle, the delayed preventive repair is considered as a repair if it is continued to be operated, so the average repair activity cost in the current inspection cycle is expressed as:
;
Wherein, Indicating the cost of performing a preventive maintenance of the component,Indicating the cost of performing a repair one-time repair of the component,Representing a second inventory of spare parts,Representing the cost expectations incurred by continued operation of the components maintained in the s 1 state due to insufficient inventory of spare parts, including the cost of operation of the components and the cost of repair at the time of spare part arrival,Representing the cost expectations of components that are shutdown due to insufficient inventory of spare parts, including the cost of shutdown of the components and the cost of preventive maintenance when the spare parts are in stock,Indicating the number of components that require preventive maintenance,Indicating the number of parts that require repair.
The average downtime loss cost refers to the cost of waiting for maintenance when the component to be maintained is stopped due to insufficient spare parts. The shutdown cost generated by the shutdown of a single component per unit time is. When (when)In the time-course of which the first and second contact surfaces,A component in a functional failure state is shut down and is waiting,Evaluating whether a risk selection for a component in a potentially faulty state is to be waited for by downtime, which is the delivery time of the spare partWhen (1)In the time-course of which the first and second contact surfaces,Evaluating whether a risk selection for a component in a potentially faulty state is to be waited for by downtime, which is the delivery time of the spare partThus average downtime loss cost over the current detection periodExpressed as:
;
Wherein, Representing the downtime costs incurred by a downtime per unit of time of a single component,Representing the time of arrival of the spare part;
the average maintenance total cost comprises three parts, namely average maintenance preparation cost, average maintenance activity cost, average shutdown loss cost and average maintenance total cost in the current detection period Expressed as:
。
In some embodiments, as shown in fig. 5, determining the average total cost of the spare parts based on the probability of repair need and the second spare part inventory includes:
S501, determining average spare part ordering cost in a current detection period based on system maintenance requirement probability, ordering cost of single spare part, second spare part stock quantity, preset safety stock quantity and second maintenance requirement quantity;
S502, determining average spare part holding cost in a current detection period based on the probability of system maintenance requirement, the time for arrival of the spare part, the spare part holding cost generated in unit time of a single stock spare part, the second spare part stock quantity, the preset safety stock quantity and the second maintenance requirement quantity;
S503, determining the average total cost of the spare parts in the current detection period based on the average spare part ordering cost and the average spare part holding cost.
In one embodiment of the application, the average spare part order cost is a measure of the expected value of the spare part order cost over a period. Only if the spare part status satisfiesWhen spare parts are ordered, the ordering cost is further generated. The ordering cost of the single spare part is. If the spare parts do not meet all the requirements, the order quantity should contain an out-of-stock quantity for delayed maintenanceAnd a newly replenished safety stock. If the spare parts can meet all the requirements, the spare parts are required to be leftCan be added to the safety stock on the basis of (i) orderSpare parts. Thus average spare part ordering cost over the current detection periodExpressed as:
;
Wherein, Representing the cost of ordering a single spare part,Indicating the amount of security stock,Representing a second inventory of spare parts.
Average spare part holding cost an expected value of spare part holding cost in one detection period. Spare part holding cost generated by single stock spare part per unit time is. If it isThe order of spare parts is not required, stock spare parts amount isHolding time is T, ifThe stock quantity of the maintenance spare parts isHold time is T, due toOrder spare parts at any time, spare parts are inTime arrives, so that the ordered spare parts do not generate holding cost in the detection period, ifSpare part stock quantity is 0 after spare part maintenance, inOrder spare parts at moment, wherein the ordered spare parts are as followsSpare parts are inThe time arrives, the stock quantity of the spare parts for delayed maintenance is s, and the holding time isThus average spare part holding cost in current detection periodExpressed as:
;
Wherein, Representing the cost of ordering a single spare part, T representing the detection period,Representing spare part holding costs generated per unit time of a single stock spare part;
The average total cost of spare parts is an expected value of the total cost related to spare parts in one detection period, including average spare parts ordering cost and average spare parts holding cost, and the average total cost of spare parts in the current detection period Expressed as:
。
in some embodiments, as shown in fig. 6, determining the average total cost of operation based on the state transition probabilities includes:
S601, defining the cost generated by operating in a normal state in unit time of a single component as a first cost, the cost generated by operating in a potential fault state in unit time of the single component as a second cost, and the cost generated by operating in a functional fault state in unit time of the single component as a third cost;
S602, if the component is detected to be in a normal state or fails and performs maintenance activities next time, determining a first expected running time of the component in the normal state next time based on the first accumulated distribution function, the third accumulated distribution function and the first state transition probability, determining a second expected running time of the component in the potential failure state next time based on the first accumulated distribution function, and determining a third expected running time of the component in the functional failure state next time based on the first accumulated distribution function;
s603, if the component is detected to not meet the maintenance requirement next time and is stopped within the time for getting the spare part to be freighted, determining fourth expected operation time of the component in a normal state next time based on the first accumulated distribution function, the second accumulated distribution function, the third accumulated distribution function and the fifth state transition probability, determining fourth expected operation time of the component in a potential fault state next time based on the first accumulated distribution function and the second accumulated distribution function, and determining sixth expected operation time of the component in a functional fault state next time based on the first accumulated distribution function and the third accumulated distribution function;
S604, if the component is detected to not meet the maintenance requirement next time and is operated in a potential fault state in the spare part arrival time, determining a seventh expected operation time of the component in a normal state next time based on the first accumulated distribution function, the second accumulated distribution function and the third accumulated distribution function, determining an eighth expected operation time of the component in the potential fault state next time based on the first accumulated distribution function, the second accumulated distribution function and the spare part arrival time, and determining a ninth expected operation time of the component in the functional fault state next time based on the first accumulated distribution function and the second accumulated distribution function;
S605, determining an average total cost of operation based on the first cost, the second cost, the third cost, the first expected operation time, the second expected operation time, the third expected operation time, the fourth expected operation time, the fifth expected operation time, the sixth expected operation time, the seventh expected operation time, the eighth expected operation time, and the ninth expected operation time.
In one embodiment of the present application, the average total cost of operation is the expected value of the total cost of operation in one test period, i.e., the cost of operation that results from the component being in three states s 0、s1、s2. Defining the cost generated by operating a single component in a normal state in unit time as a first costThe cost generated by operating in a potential fault state in a single component per unit time is the second costThe cost generated by the operation of the single component in the functional failure state in unit time is the third cost,. Due to the next detection periodThe state transition mechanism in the system is not changed, and the analysis can be performed by adopting the same method as the aboveThe situation and probability that may occur during the time period.
Specifically, if the component is detected to be in a normal state or fails and is subjected to maintenance activities next time, determining the next detection time period of the component based on the first cumulative distribution function, the third cumulative distribution function and the first state transition probabilityFirst expected runtime in normal stateExpressed as:
;
Wherein T represents the detection period, Indicating the next time point of detection,The next time point of detection is indicated,Indicating the moment of occurrence of the state transition of the component from s 0 to s 1,A first state transition probability is represented and,Is part atThe probability of a transition from s 0 to s 1 occurs at a time,Is thatThe probability that the part does not transition from s 0 to s 2 within the time period,Is thatThe probability that the part does not transition from s 1 to s 2 within the time period,Indicating the moment of occurrence of the state transition of the component from s 0 to s 2,Representation ofThe probability that the part does not transition from s 0 to s 1 within the time period,Representation ofThe probability that the time of day component will transition from s 0 to s 2,Indicating the moment of occurrence of the state transition of the component from s 1 to s 2,Representation ofThe probability that the time of day component will transition from s 1 to s 2.
Determining a second expected run time of the component at a next detection of the potentially faulty state based on the first cumulative distribution function, the second expected run timeExpressed as:
;
determining a third expected operating time of the component at a next detection of the functional failure state based on the first cumulative distribution function Expressed as:
;
If the next detection of the component does not meet the maintenance requirement and stops in the time of the spare part arrival, determining a fourth expected running time of the component in a normal state in the next detection based on the first cumulative distribution function, the second cumulative distribution function, the third cumulative distribution function and the fifth state transition probability The method comprises the following steps:
;
Wherein, Indicating the moment of occurrence of the state transition of the component from s 0 to s 1,A fifth state transition probability is indicated,Is thatThe probability that the part does not transition from s 0 to s 2 within the time period,Is thatThe probability that the part does not transition from s 1 to s 2 within the time period,Indicating the moment of occurrence of the state transition of the component from s 0 to s 2,Representation ofThe probability that the part does not transition from s 0 to s 1 within the time period.
Determining a fifth expected run time for the component to detect a potential fault condition next time based on the first cumulative distribution function and the second cumulative distribution functionThe method comprises the following steps:
;
Wherein, Is thatThe probability that the part does not transition from s 0 to s 2 within the time period,Is thatThe probability that the part does not transition from s 1 to s 2 within the time period.
Determining a sixth expected run time for the component to be in a functional failure state at a next detection based on the first cumulative distribution function and the third cumulative distribution functionThe method comprises the following steps:
。
if the next detection of the component does not meet the maintenance requirement and is operated in a potential fault state in the time of the arrival of the spare part, determining a seventh expected operation time of the component in a normal state in the next detection based on the first cumulative distribution function, the second cumulative distribution function and the third cumulative distribution function The method comprises the following steps:
;
If the next detection of the component does not meet the maintenance requirement and operates in the potential fault state in the spare part arrival time, determining an eighth expected operation time of the component in the potential fault state in the next detection based on the first cumulative distribution function, the second cumulative distribution function and the spare part arrival time The method comprises the following steps:
;
if the next detection of the component does not meet the maintenance requirement and is operated in the potential fault state in the time of the spare part arrival, determining a ninth expected operation time of the component in the functional fault state in the next detection based on the first accumulated distribution function and the second accumulated distribution function The method comprises the following steps:
;
Average total cost of operation Expressed as:
;
Wherein, A first cost is indicated and a second cost is indicated,A second cost is indicated as such,Representing a third cost.
Average total cost in current detection periodThe method comprises the following steps:
;
Wherein, Representing the cost of a single test.
Above-mentionedThe solution of (2) is based on various possible maintenance requirements and their probability calculations, which require the solution of the state transition situations and their probabilities that may occur in a single component in a detection cycle.
An optimization model is established with minimum average cost rate as a target and detection period and safety stock quantity as decision variables. Average rate of chargeCan be expressed as:
;
Wherein, Is a function of the relationship between the cost rate and the decision variable.
In some embodiments, the maintenance requirements and inventory of spare parts for the system at the next test are obtained, a maintenance decision is made for the next test, atTime of day or time of dayOrder spare parts at a time toAnd (3) taking the lowest average cost rate in the time period as a target, and solving by using a genetic algorithm to obtain the optimal combination strategy.
Illustratively, individuals in the population are encoded using real numbers encoding, each individual exhibiting a joint strategy, i.e., a combination of detection period and safe inventory. Different individuals represent different maintenance strategies as their genotypes. After this, an initial population is produced, which is constructed by randomly producing a set of individuals (i.e., different association strategies). The population scale is set according to the complexity of a specific problem, and the population scale is set to be 50 in the embodiment, so that the population diversity is ensured. And taking the average cost rate as a fitness function, wherein the combination strategies represented by each individual are different, and adopting the corresponding combination strategy, namely the combination of the detection period and the safety stock quantity, when the fitness function of a certain individual is calculated. Substituting the detection period and the safety stock corresponding to the individual into the average cost rate model by taking the detection period and the safety stock as decision variables to obtain a specific value of the average cost rate, wherein the smaller the average cost rate is, the higher the fitness is. After the fitness function is determined, a selection link is entered. The individuals are selected to enter the next generation according to the fitness value, the selected probability of each individual is proportional to the fitness of each individual, and the individuals with high fitness are more likely to be selected. New individuals are produced by combining partial genetic information of two individuals. The goal of the crossover operation is to create new individuals with a higher fitness. The mutation operation is to introduce new genes and increase the diversity of the population, so as to avoid premature convergence. Variation randomly alters the genome of an individual. The number of detection periods and secure inventory levels corresponding to the individual may be randomly increased or decreased. The probability of variation was set to be 0.02, which is low. The iterative process is the core of the genetic algorithm, and each generation undergoes selection, crossover and mutation operations to produce a new population. In each generation, individuals with higher fitness have a greater chance of breeding, allowing better association strategies to continue. As the iteration proceeds, the average fitness of individuals in the population will gradually increase, approaching the optimal solution. When the iteration times reach 20 times, the algorithm iteration is ended, and the genotype of the optimal individual in the final generation population is the optimal combined decision, namely the combination of the optimal detection period and the safe stock quantity, so that the average cost rate is minimum.
Based on the same inventive concept, the embodiment of the application also provides a maintenance decision device of the multi-component system based on the third-order state, which is used for realizing the maintenance decision method. The implementation of the solution provided by the device is similar to that described in the above method, so the specific limitations in one or more embodiments of the device provided below may be referred to above as limitations of the maintenance decision method, and will not be repeated here.
Referring to fig. 7, the present application provides a maintenance decision device of a multi-component system based on a third-order state, comprising:
A state transition module 701, configured to define states of the component including a normal operation state, a potential failure state, and a functional failure state, and construct a cumulative distribution function of the component when transitioning between the states, where the component in the potential failure state has a preventive maintenance requirement and the component in the functional failure state has a functional maintenance requirement;
an obtaining module 702, configured to obtain first state transition information, a first spare part inventory, and a first maintenance requirement number of each component in the current detection system, where the first maintenance requirement number is a component number of a preventive maintenance requirement and a component number of a repair maintenance requirement in the system;
A maintenance requirement probability module 703, configured to determine, for any component, second state transition information and a state transition probability thereof that may occur when the component is detected next based on the first state transition information and an accumulated distribution function of the component when the component transitions between states, determine a maintenance requirement probability of the component when the component is detected next according to the state transition probability, and further determine a second maintenance requirement number and a system maintenance requirement probability thereof when the multi-component system is detected next;
a spare part inventory module 704, configured to determine a second spare part inventory amount at the next detection based on the first maintenance requirement number, the first spare part inventory amount, and a preset safety inventory amount;
A cost rate module 705 for determining an average total cost of repair based on the probability of system repair demand and the second number of repair demands, determining an average total cost of spare parts based on the probability of system repair demand and the second inventory of spare parts, determining an average total cost of operation based on the probability of state transition, and determining an average cost rate per unit detection period based on the average total cost of repair, the average total cost of spare parts, the average total cost of operation, and the single detection cost;
And the optimal model module 706 is configured to determine an optimal detection period and an optimal safety stock of the multi-component system according to a genetic algorithm by using the average cost rate as an fitness function and using the detection period and the safety stock as decision variables.
The application provides a computer device comprising a memory storing a computer program and a processor implementing the method of maintenance decision as described above when executing the computer program. Fig. 8 is a schematic hardware structure of a computer device according to an embodiment of the present application, which includes a processor 801, a communication interface 802, a memory 803, and a communication bus 804, where the processor 801, the communication interface 802, and the memory 803 complete communication with each other through the communication bus 804. The memory 803 may be used to store a computer program which may include instructions and data to implement the steps of any of the methods described above. The memory 803 may be random access memory, read only memory, non-volatile, programmable ROM, erasable PROM, electrically erasable, flash memory, optical memory, registers, and so forth. The processor 801 may be a general-purpose processor, which may be a processor that performs certain steps and/or operations by reading and executing computer programs stored in memory, which may be used in performing the steps and/or operations. The general purpose processor may be a central processing unit, an ASIC, an FPGA, or the like. Communication interface 802 may include input/output interfaces, physical interfaces, logical interfaces, and the like for enabling interconnection of devices within a network device. In implementation, the steps of the above method may be performed by integrated logic circuitry in hardware in the processor 801 or by instructions in software. The method disclosed in connection with the embodiments of the present application may be directly embodied as a hardware processor executing or may be executed by a combination of hardware and software modules in the processor.
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