Method and device for evaluating recovery capability of compressor station, electronic equipment and medium

Abstract

The invention provides a compressor station recovery capability evaluation method and device, electronic equipment and a medium, and relates to the field of compressor station evaluation. The method comprises the steps that the overall state of the compressor station is constructed through the states of all components in the compressor station, and multiple compressor station states are obtained; converting the transfer process of the various compressor station states into a semi-Markov process under the assumed condition that the maintenance time of each component follows non-exponential distribution; selecting a state transition set conforming to state recovery from the semi-Markov process; and solving a semi-Markov kernel of the semi-Markov process, and calculating cumulative residence time distribution of the state transition set based on the semi-Markov kernel. According to the scheme of the invention, the semi-Markov method is introduced to more accurately describe the recovery process of the compressor station after external disturbance, the state transition is closer to the actual condition of the compressor station, the accuracy and reliability of evaluation are improved, and the performance evaluation modes of the compressor station are enriched.

Description

Technical Field

[0001] The present invention relates to the field of compressor station evaluation, and in particular to a compressor station recovery capacity evaluation method, device, electronic equipment and medium. Background Art

[0002] The compressor station is the "power heart" of the long-distance pipeline. Its process flow is highly complex, involving relevant process flows such as entry, exit, cleaning, gas separation and purification. This highly complex process flow will further increase the probability of risk at the compressor station. When facing various external shocks and internal failures, the compressor station of the gas pipeline may be severely affected, resulting in system shutdown or performance degradation. In actual operation, the gas transmission system may be subject to a variety of shocks. For example, natural disasters such as earthquakes, floods, and storms can cause physical damage to the compressor station of the gas pipeline. Similarly, factors such as harsh working environment and operational errors may also cause system failures. Therefore, evaluating the recovery capacity of the compressor station of the gas pipeline under shocks is crucial to ensure the reliability and safety of the gas transmission system.

[0003] At present, the industry has adopted Markov processes to achieve modeling and analysis of the resilience of multi-state systems. For example, Gao et al. used a Markov chain-based method to predict the resilience of power grids after natural disasters. Lin and Wang used continuous-time Markov chains to simulate the resilience of multi-state community systems after earthquakes. Tan used homogeneous Markov chains to evaluate the resilience of nuclear power plants under earthquake shocks. Although continuous-time Markov chains are suitable for the exponential distribution of the life of each component of the system and the repair time after a fault, the actual compressor station system usually has a life or repair time distribution that is not an exponential distribution, resulting in inaccurate evaluation of the recovery capacity of the compressor station system, which is urgently needed to be improved. Summary of the invention

[0004] The present invention provides a method, device, electronic equipment and medium for evaluating the recovery capacity of a gas compressor station, which are used to solve the defect of inaccurate evaluation of the recovery of a gas compressor station in the prior art and realize accurate evaluation of the recovery capacity of a gas compressor station by using a semi-Markov process.

[0005] According to a first aspect of the present invention, the present invention provides a method for evaluating the recovery capacity of a compressor station, the method comprising: The overall state of the compressor station is constructed using the states of the components in the compressor station, and multiple states of the compressor station are obtained; Under the assumption that the maintenance time of each component follows a non-exponential distribution, the transfer process of the various compressor station states is converted into a semi-Markov process; Selecting a state transition set that meets state recovery requirements from the semi-Markov process; A semi-Markov kernel of the semi-Markov process is obtained, and a cumulative residence time distribution of the state transition set is calculated based on the semi-Markov kernel.

[0006] In some possible implementations, the overall state of the compressor station is constructed using the states of the components in the compressor station, and multiple compressor station states are obtained, including: Obtain the components included in the compressor station, and set three mutually exclusive states of normal, partial failure and complete failure for each component; The states of the individual components are combined to obtain the state that characterizes the entire compressor station, wherein when all components are normal, the compressor station as a whole is in a normal state, and when any component is in a partial or complete failure, the compressor station as a whole is in a failure state.

[0007] In some possible implementations, under the assumption that the maintenance of each component follows a non-exponential distribution, the transfer process of the various compressor station states is converted into a semi-Markov process, including: Assume that the life distribution of each component in the compressor station is 1-e -λt , assuming that the repair time for each partial failure follows a non-exponential distribution , assuming that the repair time after a complete failure follows a non-exponential distribution And there is mutual independence between life span and repair time; use Indicates the state of the compressor station at time t, using T n Indicates the time when the nth state transition occurs, using Zn=X(T n ) represents the state of the compressor station after n state transitions, using T n+1 -T n - represents the residence time in state Zn, where It is the status that characterizes the whole compressor station; All state transition moments are regarded as regeneration points to obtain { , t≥0} represents the semi-Markov process and obtains {Z n， T n ;n=0, 1, …} represents a time-homogeneous Markov update process.

[0008] In some possible implementations, selecting a state transition set that meets state recovery requirements from the semi-Markov process includes: Traverse all transfer processes, and take the transfer process in which the current state of the compressor station is an overall failure and the state after the transfer is an overall normal state as the target transfer state; All target transition states are aggregated to obtain the state transition set.

[0009] In some possible implementations, obtaining the semi-Markov kernel of the semi-Markov process includes: The values ​​of each element in the semi-Markov kernel are calculated based on the state transition probability, life distribution and repair time distribution of each component using the following formula; ; in, represents the Markov kernel from Line The value of the column, represents the state transition probability, Indicates status Transfer to state The distribution function of the time t required.

[0010] In some possible implementations, calculating the cumulative residence time distribution of the state transition set based on the semi-Markov kernel includes: The cumulative residence time distribution was calculated according to the following formula; ; Among them, the probability of solving each state after obtaining the semi-Markov kernel, the recovery ability here means the probability of recovering to state 1, that is, the normal state, after the time is interrupted, represents the cumulative residence time distribution, Indicates time The state of the compressor station, Indicates the time to leave normal working state. It represents the collection of normal states.

[0011] In some possible implementations, the gas compressor station is used to transmit natural gas, and the components in the gas compressor station include at least a compressor part and a filtering and separation part.

[0012] According to a second aspect of the present invention, the present invention further provides a compressor station recovery capacity evaluation device, the device comprising: A state construction module is used to construct the overall state of the compressor station using the states of the components in the compressor station to obtain multiple states of the compressor station; A conversion module, used for converting the transfer process of the multiple compressor station states into a semi-Markov process under the assumption that the maintenance time of each component follows a non-exponential distribution; A selection module, used for selecting a state transition set that meets the state recovery requirement from the semi-Markov process; The calculation module is used to obtain the semi-Markov kernel of the semi-Markov process and calculate the cumulative residence time distribution of the state transition set based on the semi-Markov kernel.

[0013] According to the third aspect of the present invention, the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements any of the above-described methods for evaluating the recovery capacity of a gas compressor station.

[0014] According to a fourth aspect of the present invention, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for evaluating the recovery capacity of a compressor station.

[0015] The present invention provides a method for evaluating the recovery capacity of a compressor station. The method uses the states of components in the compressor station to represent the overall state of the compressor station, then uses a semi-Markov process to describe the overall state transition process of multiple compressor stations, then selects a state transition set that meets the state recovery, and finally obtains a semi-Markov kernel of the semi-Markov process. The semi-Markov kernel is then used to calculate the cumulative residence time distribution of the state transition set. The semi-Markov method is introduced to more accurately describe the recovery process of the compressor station after an external disturbance. The state transfer is closer to the actual situation of the compressor station, which helps to improve the accuracy and reliability of the evaluation and enriches the compressor station performance evaluation method.

[0016] In addition, a compressor station recovery capacity evaluation device, an electronic device and a non-transitory computer-readable storage medium provided by the present invention can also achieve the above-mentioned technical effects, which will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flow chart of the method for evaluating the recovery capacity of a compressor station provided by the present invention.

[0019] Figure 2 It is a typical structural schematic diagram of the natural gas compression station provided by the present invention.

[0020] Figure 3 It is a schematic diagram of the state transition relationship provided by the present invention.

[0021] Figure 4 It is a structural schematic diagram of a compressor station recovery capacity evaluation device provided by the present invention.

[0022] Figure 5 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] Combine the following Figures 1 to 5 The present invention describes a method for evaluating the recovery capacity of a gas compressor station, a device for evaluating the recovery capacity of a gas compressor station, an electronic device and a non-transitory computer-readable storage medium.

[0025] For ease of understanding, we first explain semi-Markov (SMP), semi-Markov process transition probability matrix, and SMP solution.

[0026] Semi-Markov (SMP): SMP is a state-based probabilistic model of system behavior, an extension of Markov chains. Like Markov chains, SMP represents system behavior in a directed graph of states and transitions, where a state (node) represents a given configuration of the system, and a transition (edge) is an event that causes the system configuration to change from one state to another. Each transition has an associated probability distribution that describes the amount of time until the transition occurs once the state left is entered. One requirement of SMP, similar to Markov chains, is that states must be "memoryless", meaning that the future evolution of the system depends only on the current state, and not on the path to reach that state.

[0027] Suppose the random variable Xn takes values ​​in E={0, 1, ..., K}, and the random variable Tn takes values ​​in [0, ∞) n=0, 1...n, where 0=T0≤T1≤T2... If for all n=0, 1, ..., j∈E, t>0, we have: (1); The random process (X, T) = {Xn, Tn, n = 0, 1, ...} is called a Markov update process on the state space E.

[0028] If for all i, j∈E, t>0, independent of n, then (X, T) is called homogeneous, and { , i, j∈E} is a semi-Markov model, then the following expression holds: (2); in: is the state transition probability of the Markov chain.

[0029] Y(t)=Xn, when Tn≤t≤Tn+1, {Y(T), t≥0} is called a semi-Markov process generated by the Markov update process (X, T). The state residence time of the semi-Markov process is [Tn, Tn+1], and the distribution of its length Tn+1-Tn depends only on the current state Xn and the state Xn+1 to be entered next, and the states visited successively form a Markov chain. The residence time of the state Xn at time Tn is Tn+1-Tn, and its distribution depends on the state being visited and the state Xn+1 to be visited next. The states visited successively [Xn, n≥0} form a Markov chain. Under the condition that Xn, n=0, 1, 2..., the residence time of the successive states is conditionally independent.

[0030] In a Markov process, the time spent in each state follows an exponential distribution. Due to the memorylessness of the exponential distribution, any moment t is the regeneration point of the process, which means that any moment t has Markov properties. The so-called regeneration point refers to a moment when, given the state of the process at this moment, the probability law of the future development of the process has nothing to do with the past history. In the case of a semi-Markov process, the distribution of the residence time is a general distribution. Therefore, not all moments t are regeneration points of the process, but only the state transition moments (usually random) are regeneration points, and Markov properties exist at these moments. When all When is an exponential distribution, the semi-Markov process {X(t), t≥0} becomes a Markov process. When the state space of the process has only one state, E={0}, then {Tn+1-Tn, n=0, 1, 2,) is a sequence of independent and identically distributed random variables.

[0031] About the transition probability matrix of semi-Markov process: Transition probability matrix is the probability set of the system being in state Xn at time t, assuming that the system is in state Xn at the moment of danger. Since the transition time between states is random, the system recovery path can be selected. The matrix h i (t) quantifies the randomness of the time required for state transition, and thus can explain the randomness of the system recovery path. Since the state transition is sequential, h i (t) is an upper triangular matrix. h i (t) is completely determined by the kernel matrix Definition, it describes the probability of a given process starting from state Xn and visiting state Xn within time t. The transition probability matrix can be derived from the Markov equation.

[0032] (3); Right now: (4); Can be simplified as a vector

[0033] (5); in, is a column vector, Q(t) is the matrix composed of the semi-Markov kernel, Follow the rules of matrix multiplication, but use convolution instead of multiplication.

[0034] About SMP solution: Once the SMP of the system is generated, the key metrics of interest can be solved. These metrics can be quickly solved in the Laplace domain using matrix multiplication followed by numerical Laplace transform inversion. For convenience, following convention, the notation for the Laplace transform is abbreviated as a wave (~) on the correlation matrix. The equation for the probability of the first passage time in the Laplace domain is: (6); where I is the identity matrix.

[0035] Figure 1 is a flow chart of a method for evaluating the recovery capacity of a compressor station provided by an embodiment of the present invention. Figure 1 As shown, this embodiment provides a method for evaluating the recovery capacity of a compressor station. The method can be implemented through steps S101 to S104. Each step will be described in detail below: Step S101, constructing the overall state of the compressor station by using the states of the components in the compressor station, and obtaining a plurality of compressor station states.

[0036] In this embodiment, the overall state of the compressor station includes normal or faulty. Specific faults can be divided into multiple types of faults. The overall state of the compressor station is constructed by using the state of each component in the compressor station, that is, the state of each component is combined to represent the normal and various fault states of the compressor station. For example, more important components in the compressor station can be selected to represent the overall state. For example, five components are selected, and the state of each component is defined separately, and then the state of the five components is combined to represent the overall state. For example, if all five components are normal, the compressor station as a whole is normal, two components are abnormal, indicating that it is in a type of fault state, and three components are abnormal, indicating another type of fault state. It should be noted that the number of components and the number of faults in the compressor station in this embodiment are only used for illustration and should not be understood as limiting the present invention.

[0037] Step S102: Under the assumption that the maintenance time of each component follows a non-exponential distribution, the transition process of the various compressor station states is converted into a semi-Markov process.

[0038] Step S103: selecting a state transition set that meets the state recovery requirement from the semi-Markov process.

[0039] In this embodiment, the state transition set is selected from the semi-Markov process. The conditions that meet the state recovery can be pre-set, and then the state transition set that matches the set conditions can be selected from the semi-Markov process based on the adjustment of the set.

[0040] Step S104, obtaining a semi-Markov kernel of the semi-Markov process, and calculating a cumulative residence time distribution of the state transition set based on the semi-Markov kernel.

[0041] The compressor station recovery capacity evaluation method of this embodiment uses the status of the components in the compressor station to represent the overall status of the compressor station, and then uses the semi-Markov process to describe the state transition process of multiple compressor stations as a whole, then selects a state transition set that meets the state recovery, and finally uses the semi-Markov process of the semi-Markov process to calculate the cumulative residence time distribution of the state transition set. By introducing the semi-Markov method to more accurately describe the recovery process of the compressor station after external disturbance, the state transfer is closer to the actual situation of the compressor station, which helps to improve the accuracy and reliability of the evaluation and enriches the compressor station performance evaluation method.

[0042] In some possible implementations, the aforementioned step S101 constructs the overall state of the compressor station using the states of the components in the compressor station to obtain multiple compressor station states, specifically including: Obtain the components included in the compressor station, and set three mutually exclusive states of normal, partial failure and complete failure for each component; The states of the individual components are combined to obtain the state that characterizes the entire compressor station, wherein when all components are normal, the compressor station as a whole is in a normal state, and when any component is in a partial or complete failure, the compressor station as a whole is in a failure state.

[0043] The compressor station recovery capacity evaluation method of this embodiment predefines three states for the components in the compressor station, and then combines the components and the definition of the overall state of the compressor station to obtain a compressor station state that can accurately reflect the overall situation of the compressor station.

[0044] In some possible implementations, the aforementioned step S102, under the assumption that the maintenance of each component follows a non-exponential distribution, converts the transfer process of the multiple compressor station states into a semi-Markov process, including: Assume that the life distribution of each component in the compressor station is 1-e -λt , assuming that the repair time for each partial failure follows a non-exponential distribution , assuming that the repair time after a complete failure follows a non-exponential distribution And there is mutual independence between life span and repair time; use Indicates the state of the compressor station at time t, using Tn Indicates the time when the nth state transition occurs, using Zn=X(T n ) represents the state of the compressor station after n state transitions, using T n+1 -T n represents the time spent in state Zn, where It is the status that characterizes the whole compressor station; All state transition moments are regarded as regeneration points to obtain { , t≥0} represents the semi-Markov process and obtains {Z n， T n ;n=0, 1, …} represents a time-homogeneous Markov update process.

[0045] The compressor station recovery capacity evaluation method of this embodiment, through assumptions about the compressor station maintenance time and life span, combines the aforementioned multiple compressor station states to construct a semi-Markov process that can reflect the changes in each state.

[0046] In some possible implementations, the aforementioned step S103, selecting a state transition set that meets the state recovery requirement from the semi-Markov process, specifically includes: Traverse all transfer processes, and take the transfer process in which the current state of the compressor station is an overall failure and the state after the transfer is an overall normal state as the target transfer state; All target transition states are aggregated to obtain the state transition set.

[0047] In some possible implementations, obtaining the semi-Markov kernel of the semi-Markov process in the aforementioned step S104 specifically includes: The values ​​of each element in the semi-Markov kernel are calculated based on the state transition probability, life distribution and repair time distribution of each component using the following formula; ; in, represents the Markov kernel from Line The value of the column, represents the state transition probability, state Transfer to state The distribution function of the time t required.

[0048] In some possible implementations, the step S104 of calculating the cumulative residence time distribution of the state transition set based on the semi-Markov kernel specifically includes: The cumulative residence time distribution was calculated according to the following formula; ; in, represents the cumulative residence time distribution, Indicates time The state of the compressor station, Indicates the time to leave normal working state. It represents the collection of normal states.

[0049] In some possible implementations, the gas compressor station is used to transmit natural gas, and the components in the gas compressor station include at least a compressor part and a filtering and separation part.

[0050] In another embodiment, in order to facilitate understanding of the solution of the present invention, this embodiment provides a method for evaluating the recovery capacity of a compressor station using a typical structure of a compressor station as an evaluation object. Figure 2 The compressor station shown is mainly divided into the filter separation part (one in use and two in standby), the compressor unit part (two in use and one in standby), and the pipe section in the station, which has the functions of filtering and pressurizing. Since the main functions of the compressor station are realized by the filter separation part and the compressor part, when considering the unit failure later, the pipe section failure is temporarily not considered, and only the compressor part and the filter separation part are considered.

[0051] The filtering and separation part and the compressor part have three mutually exclusive states: normal, partial failure and failure; system failure only occurs in the following circumstances, assuming that after the system failure, the non-faulty parts no longer continue to fail, and there is a repairman in the system who repairs the faulty parts in the order in which they failed. The repaired parts are the same as new ones and are put into operation immediately.

[0052] Let's assume that the lifetime distribution of both parts is 1-e -λt , t≥0; under the condition of failure, the probability of partial failure is p i , the probability of complete failure is q i , p i ,q i ≥0, p i +q i = 1. The repair time after a partial failure Yi has a general distribution G i (t); Repair time after complete failure W i , with a general distribution H i (t), and it is assumed that the life and repair time are independent of each other.

[0053] Define the state of the system based on the state of the components, please refer to Figure 3As shown: State 0 indicates that both components are normal; State 1 indicates that component 1 is being repaired due to a partial failure, and component 2 is normal (component 2 is suspended because the system is in partial failure state 1); State 2 indicates that the start time of this state is the time when component 1 fails, and the normal state of component 2 continues until component 1 is repaired; State 3 and State 4 are similar to State 1 and State 2, as long as the positions of components 1 and 2 are swapped.

[0054] Let X(t) be the state of the system at time t, which indicates the time when the nth state transition occurs (T0=0). Let Zn=X(Tn+0), which indicates the state of the system after the nth state transition. Tn-Tn+1 indicates the time the system stays in state Zn. Since all state transition moments of the system are regeneration points, {X(t), t≥0} is a semi-Markov process, and {Zn, Tn; n=0, 1, ...} is a time-uniform Markov update process, and its semi-Markov kernel is:

[0055] The Markov kernel is written in matrix form:

[0056] In the formula, represents the probability of partial failure of component 1, represents the probability of complete failure of component 1, The probability of partial failure of component 2, represents the probability of complete failure of component 1, represents the life distribution parameter of component 1, The life distribution parameter of component 2 is expressed as, and represents the cumulative distribution function (CDF) associated with state 1 and state 2, which is used to describe the time distribution when transferring from state 1 and state 2 to other states, respectively. Represents the distribution function of the time t required to transfer from the current state 2 to any other state.

[0057] In order to solve the recovery probability after the disturbance, the recovery probability is defined as the probability of the system's cumulative stay time in the interrupted and degraded operating state from the first time the system reaches the interrupted state to the time it recovers to the fully operational state, which can be expressed as:

[0058] Where: represents the cumulative residence time distribution, Indicates time The state of the compressor station, Indicates the time to leave normal working state. It represents the collection of normal states.

[0059] The compressor station recovery capacity evaluation method of this embodiment uses semi-Markov to establish the transfer relationship between various states and construct a kernel matrix. By establishing the kernel matrix and combining the application of Laplace transform, the recovery capacity of the system under disturbance can be quantitatively calculated, which provides a new idea for quantitative analysis of the recovery capacity of natural gas transmission stations.

[0060] The compressor station recovery capacity evaluation device provided by the present invention is described below. The compressor station recovery capacity evaluation device described below and the compressor station recovery capacity evaluation method described above can be referenced to each other.

[0061] Please refer to Figure 4 As shown, this embodiment provides a compressor station recovery capacity evaluation device, specifically, the device includes: a state construction module 410, a conversion module 420, a selection module 430 and a calculation module 440, and the above modules are described in detail below: A state construction module 410 is used to construct the overall state of the compressor station using the states of the components in the compressor station to obtain multiple states of the compressor station; A conversion module 420, configured to convert the transfer process of the plurality of compressor station states into a semi-Markov process under the assumption that the maintenance time of each component follows a non-exponential distribution; A selection module 430, configured to select a state transition set that meets the state recovery requirement from the semi-Markov process; The calculation module 440 is used to obtain the semi-Markov kernel of the semi-Markov process, and calculate the cumulative residence time distribution of the state transition set based on the semi-Markov kernel.

[0062] The compressor station recovery capacity evaluation device of this embodiment uses the status of the components in the compressor station to represent the overall status of the compressor station, and then uses the semi-Markov process to describe the state transition process of multiple compressor stations as a whole, then selects a state transition set that meets the state recovery, and finally obtains the semi-Markov kernel of the semi-Markov process, and then uses the semi-Markov kernel to calculate the cumulative residence time distribution of the state transition set. By introducing the semi-Markov method to more accurately describe the recovery process of the compressor station after external disturbance, the state transfer is closer to the actual situation of the compressor station, which helps to improve the accuracy and reliability of the evaluation and enriches the compressor station performance evaluation method.

[0063] It should be noted that each module in the above compressor station recovery capacity evaluation device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above modules can be embedded in or independent of a processor in an electronic device in the form of hardware, or can be stored in a memory in the electronic device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0064] Figure 5 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 5 As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530 and a communication bus 540, wherein the processor 510, the communication interface 520 and the memory 530 communicate with each other through the communication bus 540. The processor 510 may call the logic instructions in the memory 530 to execute the compressor station recovery capacity evaluation method, which includes: constructing the overall state of the compressor station using the state of each component in the compressor station to obtain multiple compressor station states; under the assumption that the maintenance time of each component follows a non-exponential distribution, converting the transition process of the multiple compressor station states into a semi-Markov process; selecting a state transition set that meets the state recovery from the semi-Markov process; obtaining the semi-Markov kernel of the semi-Markov process, and calculating the cumulative residence time distribution of the state transition set based on the semi-Markov kernel.

[0065] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0066] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the compressor station recovery capacity evaluation method provided by the above-mentioned methods, and the method includes: constructing the overall state of the compressor station using the state of each component in the compressor station to obtain a plurality of compressor station states; under the assumption that the maintenance time of each component follows a non-exponential distribution, converting the transition process of the plurality of compressor station states into a semi-Markov process; selecting a state transition set that meets the state recovery from the semi-Markov process; obtaining the semi-Markov kernel of the semi-Markov process, and calculating the cumulative residence time distribution of the state transition set based on the semi-Markov kernel.

[0067] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the compressor station recovery capacity evaluation method provided by the above-mentioned methods, the method comprising: constructing the overall state of the compressor station using the states of each component in the compressor station to obtain a plurality of compressor station states; under the assumption that the maintenance time of each component follows a non-exponential distribution, converting the transition process of the plurality of compressor station states into a semi-Markov process; selecting a state transition set that meets state recovery from the semi-Markov process; obtaining the semi-Markov kernel of the semi-Markov process, and calculating the cumulative residence time distribution of the state transition set based on the semi-Markov kernel.

[0068] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0069] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the recovery capacity of a compressor station, characterized in that: The method comprises: The overall state of the compressor station is constructed using the states of the components in the compressor station, and multiple states of the compressor station are obtained; Under the assumption that the maintenance time of each component follows a non-exponential distribution, the transfer process of the various compressor station states is converted into a semi-Markov process; Selecting a state transition set that meets state recovery requirements from the semi-Markov process; A semi-Markov kernel of the semi-Markov process is obtained, and a cumulative residence time distribution of the state transition set is calculated based on the semi-Markov kernel.

2. The method for evaluating the recovery capacity of a compressor station according to claim 1, characterized in that: The overall state of the compressor station is constructed using the states of each component in the compressor station, and multiple compressor station states are obtained, including: Obtain the components included in the compressor station, and set three mutually exclusive states of normal, partial failure and complete failure for each component; The states of the individual components are combined to obtain the state that characterizes the entire compressor station, wherein when all components are normal, the compressor station as a whole is in a normal state, and when any component is in a partial or complete failure, the compressor station as a whole is in a failure state.

3. The method for evaluating the recovery capacity of a compressor station according to claim 2, characterized in that: Under the assumption that the maintenance of each component follows a non-exponential distribution, the transfer process of the various compressor station states is converted into a semi-Markov process, including: Assume that the life distribution of each component in the compressor station is 1-e -λt , assuming that the repair time for each partial failure follows a non-exponential distribution , assuming that the repair time after a complete failure follows a non-exponential distribution And there is mutual independence between life span and repair time; use Indicates the state of the compressor station at time t, using T n Indicates the time when the nth state transition occurs, using Zn=X(T n ) represents the state of the compressor station after n state transitions, using T n+1 -T n - represents the residence time in state Zn, where It is the status that characterizes the whole compressor station; All state transition moments are regarded as regeneration points to obtain { , t≥0} represents the semi-Markov process and obtains {Z n， T n ;n=0, 1, …} represents a time-homogeneous Markov update process.

4. The method for evaluating the recovery capacity of a compressor station according to claim 2, characterized in that: A state transition set that meets the state recovery requirement is selected from the semi-Markov process, including: Traverse all transfer processes, and take the transfer process in which the current state of the compressor station is an overall failure and the state after the transfer is an overall normal state as the target transfer state; All target transition states are aggregated to obtain the state transition set.

5. The method for evaluating the recovery capacity of a compressor station according to claim 3, characterized in that: Obtaining the semi-Markov kernel of the semi-Markov process includes: The values ​​of each element in the semi-Markov kernel are calculated based on the state transition probability, life distribution and repair time distribution of each component. ； in, represents the Markov kernel from Line The value of the column, represents the state transition probability, Indicates status Transfer to state The distribution function of the time t required.

6. The method for evaluating the recovery capacity of a compressor station according to claim 1, characterized in that: Calculating the cumulative residence time distribution of the state transition set based on the semi-Markov kernel includes: The cumulative residence time distribution was calculated according to the following formula; ； Among them, the probability of solving each state after obtaining the semi-Markov kernel, the recovery ability here means the probability of recovering to state 1, that is, the normal state, after the time is interrupted, represents the cumulative residence time distribution, Indicates time The state of the compressor station, Indicates the time to leave normal working state. It represents the collection of normal states.

7. The gas station recovery capacity evaluation method according to claim 1, characterized in that: The gas compressor station is used for transmitting natural gas, and the components in the gas compressor station at least include a compressor part and a filtering and separating part.

8. A compressor station recovery capacity evaluation device, characterized in that: The device comprises: A state construction module is used to construct the overall state of the compressor station using the states of the components in the compressor station, and obtain multiple states of the compressor station; A conversion module, used for converting the transfer process of the multiple compressor station states into a semi-Markov process under the assumption that the maintenance time of each component follows a non-exponential distribution; A selection module, used for selecting a state transition set that meets the state recovery requirement from the semi-Markov process; The calculation module is used to obtain the semi-Markov kernel of the semi-Markov process and calculate the cumulative residence time distribution of the state transition set based on the semi-Markov kernel.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the compressor station recovery capacity evaluation method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for evaluating the recovery capacity of a compressor station as claimed in any one of claims 1 to 7 is implemented.

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