Complex equipment risk management and control method, system, equipment and medium

Abstract

The invention discloses a complex equipment risk management and control method, system and device and a medium, and relates to the technical field of complex equipment development management risk management and control. The method comprises the following steps: constructing a multi-parameter CF-GERT network model which plays a role in event success probability according to the background of an actual development project and in combination with a milestone event for examining complex equipment development nodes; basic parameters in the network activity are collected, the distribution type of each parameter variable is estimated, the product of the transfer probability between a characteristic function and nodes is used as an equivalent transfer function in a multi-parameter CF-GERT network model, and the equivalent transfer probability of an equipment development project is determined based on a signal flow graph and a mature conclusion of a Meisson formula; and performing inversion according to the property of the equivalent transfer probability to obtain transfer variable related data of each parameter which plays a role in the project success probability in the event, and performing balanced optimization on each performance index of the equipment development project. According to the method and the system, the traditional GERT network can be optimized, so that the risk management and control of complex equipment can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of risk control of complex equipment development management, and in particular to a method, system, device and medium for risk control of complex equipment. Background Art

[0002] The development and management of complex equipment has an extremely important impact on the development of a country's national economy and the formulation of national defense and military strategies. Large engineering systems, such as civil aircraft, satellites, launch vehicles, strategic missiles, etc., have complex structures and high process requirements, which pose great challenges to the successful development of products. At the same time, for special military products and aerospace high-tech products, due to the small amount of field tests and the long equipment development cycle, the manufacturing and testing stages of products are increasingly showing the characteristics of small samples. Especially for the target assessment of complex equipment such as aerospace engineering, the actual implementation should focus on the four items of performance, funding, progress and risk. Therefore, how to make the resource consumption of the project meet the standards, reasonably allocate and plan the time of each development stage, and increase the probability of project success under the premise of meeting the characteristic indicators such as the technical conditions and technical requirements of the complex equipment development project is an important issue facing the development management of complex equipment projects.

[0003] In the process of complex equipment development management, since the transfer activities between milestone events contain multiple performance parameters and some parameters have uncertain random distribution characteristics, the use of traditional GERT network algorithms for analysis will miss some information, making the assessment and optimization of project goals extremely difficult, and even causing major risks. Traditional studies on the performance and risk of GERT networks for complex equipment development projects are based on the classic GERT network (MGF-GERT) with the product of moment generating function (MGF) and transfer probability as the equivalent transfer relationship. When the transfer parameters between nodes are taken as random variables according to a certain probability distribution, the situation where the moment generating function of some distributions does not exist is not considered. In fact, when there are many parameter variables in the network activities of product development projects and some parameters obey the Cauchy distribution or lognormal distribution, the terms in the moment generating function of random variables cannot absolutely converge during the integral operation, making the expectation, that is, the moment generating function, non-existent, so the traditional GERT network calculation method fails. At this time, if the traditional GERT network algorithm is used to analyze the various parameters of the activity, some important information may be missed, resulting in a large deviation between the final evaluation results and the actual effect. At the same time, the reverse deduction of the equivalent moment generating function can only derive the basic parameters of the network, such as the equivalent probability, random variable expectation, and variance. It is difficult to obtain the probability density function, cumulative distribution function and other properties of the random variable through the inverse Fourier transform. This greatly limits the promotion and application of the GERT network in complex equipment development projects, and it is increasingly difficult to meet the strict target requirements and market needs of complex equipment. Summary of the invention

[0004] The purpose of the present invention is to provide a complex equipment risk management method, system, device and medium, which can achieve risk management of complex equipment by optimizing the traditional GERT network.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A complex equipment risk management method, comprising:

[0007] According to the background of actual development projects, combined with the milestone events for assessing the development nodes of complex equipment, a multi-parameter CF-GERT network model that affects the success probability of the event is constructed;

[0008] Collect basic parameters in network activities, estimate the distribution type of each parameter variable, use the product of characteristic function and transmission probability between nodes as the equivalent transmission function in the multi-parameter CF-GERT network model, and determine the equivalent transmission probability of equipment development projects based on the mature conclusions of signal flow graph and Mason formula; the equivalent transmission function is used to describe the transmission relationship between various variables between nodes;

[0009] According to the properties of the equivalent transfer probability, inversion is performed to obtain the transfer variable related data of various parameters that affect the project success probability in the event, and based on the related data, various performance indicators of the equipment development project are balanced and optimized to complete the management and control of the development risks of complex equipment projects; the transfer variable related data include expectation, variance, risk degree and probability density function.

[0010] Optionally, the construction process of the multi-parameter CF-GERT network model is specifically as follows:

[0011] Defining the CF-GERT network of complex equipment development projects: If the activities (ij) in a random network only transmit probability p ij and the random variable x ij The two parameters are calculated by using the transfer function under the condition that the basic structure of the network remains unchanged. Replace the transitive relationship between activities (ij);

[0012] Based on the CF-GERT network, if the transitive relationship between two activities (ij) is composed of n different parameter variables x ij (1),x ij (2),…,x ij (n); i = 1, 2, ..., m, j = 1, 2, ..., l, n ≥ 2, and the transfer relationship network defined by n parameter variables is determined to be a multi-parameter CF-GERT network model.

[0013] Optionally, the calculation process of the equivalent transfer function is:

[0014] If the n parameter variables x between activities (ij) ij (1),x ij (2),…,x ij (n); i = 1, 2, ..., m, j = 1, 2, ..., l, n ≥ 2 are independent of each other, then the equivalent transfer function of activity (ij) is for:

[0015]

[0016] like is the equivalent transfer function of the kth path from node i to node j in the multi-parameter CF-GERT network model, k = 1, 2, …, R, R ≥ 1; is the equivalent transfer coefficient of the u-th ring in the v-order ring in the network, then the equivalent transfer function from node i to node j is:

[0017]

[0018] Where H is the characteristic formula of the multi-parameter CF-GERT network model.

[0019] Optionally, the calculation process of the equivalent transmission probability is:

[0020] In the multi-parameter CF-GERT network model, the equivalent transmission probability p from node i to node j is ij for:

[0021]

[0022] Optionally, the expected calculation process of the transfer variable is:

[0023] In the multi-parameter CF-GERT network model, the kth parameter variable x from node i to node j is ij The n-th order origin moment of (k) is:

[0024]

[0025] Where i is an imaginary number and i 2 =-1; x ij (k) is a parameter variable; is the equivalent transfer function; is the probability of equivalent transfer;

[0026] Further calculate the kth parameter variable x from node i to node j ij The expectation of (k) is:

[0027]

[0028] Optionally, the calculation formula of the variance of the transfer variable is:

[0029]

[0030] Among them, x ij (k) is the kth parameter variable from node i to node j; is the equivalent transfer function; is the equivalent transfer probability.

[0031] Optionally, the calculation formula for the risk of the transfer variable is:

[0032]

[0033] Among them, x ij (k) is the kth parameter variable from node i to node j; V[x ij (k)] is the variance of the transfer variable; E[x ij (k)] is the expectation of the transferred variable.

[0034] The present invention also provides a complex equipment risk management and control system, comprising:

[0035] The model building unit is used to construct a multi-parameter CF-GERT network model that affects the success probability of an event based on the background of the actual development project and the milestone events for assessing the development nodes of complex equipment;

[0036] A transfer relationship determination unit is used to collect basic parameters in network activities, estimate the distribution type of each parameter variable, use the product of the characteristic function and the transfer probability between nodes as the equivalent transfer function in the multi-parameter CF-GERT network model, and determine the equivalent transfer probability of the equipment development project based on the mature conclusion of the signal flow graph and the Mason formula; the equivalent transfer function is used to describe the transfer relationship of each variable between nodes;

[0037] The risk management and control unit is used to perform inversion according to the properties of the equivalent transfer probability to obtain the transfer variable related data of various parameters that affect the project success probability in the event, and to balance and optimize various performance indicators of the equipment development project according to the related data to complete the management and control of the development risk of complex equipment projects; the transfer variable related data includes expectation, variance, risk degree and probability density function.

[0038] The present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the complex equipment risk management method described above.

[0039] The present invention also provides a computer-readable storage medium, characterized in that it stores a computer program, and when the computer program is executed by a processor, it implements the complex equipment risk management method as described above.

[0040] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0041] The present invention discloses a method, system, equipment and medium for risk control of complex equipment. The method includes constructing a multi-parameter CF-GERT network model that acts on the success probability of the event according to the background of the actual development project and in combination with the milestone events for assessing the development nodes of complex equipment; collecting basic parameters in network activities, estimating the distribution type of each parameter variable, using the product of the characteristic function and the transfer probability between nodes as the equivalent transfer function in the multi-parameter CF-GERT network model, and determining the equivalent transfer probability of the equipment development project based on the mature conclusion of the signal flow graph and the Mason formula; inverting according to the properties of the equivalent transfer probability, obtaining the transfer variable related data of each parameter that acts on the success probability of the project in the event, and performing balanced optimization on various performance indicators of the equipment development project. The present invention can realize risk control of complex equipment by optimizing the traditional GERT network. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0043] Figure 1 It is a flow chart of the complex equipment risk management method of the present invention;

[0044] Figure 2 Schematic diagram of the transformation of the CF-GERT network activity transfer relationship in this embodiment;

[0045] Figure 3 Schematic diagram of the basic constituent units of the multi-parameter CF-GERT network in this embodiment. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] The purpose of the present invention is to provide a complex equipment risk management method, system, device and medium, which can achieve risk management of complex equipment by optimizing the traditional GERT network.

[0048] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] like Figure 1-Figure 3 As shown, the present invention provides a complex equipment risk management method, comprising:

[0050] Step 100: Based on the background of the actual development project and in combination with the milestone events for assessing the development nodes of complex equipment, a multi-parameter CF-GERT network model that affects the success probability of the event is constructed;

[0051] Step 200: Collect basic parameters in network activities, estimate the distribution type of each parameter variable, use the product of the characteristic function and the transfer probability between nodes as the equivalent transfer function in the multi-parameter CF-GERT network model, and determine the equivalent transfer probability of the equipment development project based on the mature conclusion of the signal flow graph and the Mersenne formula; the equivalent transfer function is used to describe the transfer relationship between each variable between nodes;

[0052] Step 300: Inversion is performed according to the properties of the equivalent transfer probability to obtain transfer variable related data of various parameters that affect the probability of project success in the event, and various performance indicators of the equipment development project are balanced and optimized according to the related data to complete the management and control of the development risks of complex equipment projects; the transfer variable related data includes expectation, variance, risk degree and probability density function.

[0053] Based on the above scheme, each step is further described in detail.

[0054] In step 100, a CF-GERT network and a multi-parameter CF-GERT network are defined respectively.

[0055] (1) Defining the CF-GERT network for complex equipment development projects

[0056] In a given complex equipment development project, a GERT network with the product of the characteristic function of random variables and the transmission probability between nodes as the equivalent transmission relationship is defined as the complex equipment development project CF-GERT network.

[0057] According to the properties of GERT networks, if the activities (ij) in a random network only transmit probability p ij and the random variable x ijUnder the condition that the basic structure of the network remains unchanged, the transfer relationship between the two parameters (ij) can be replaced by the transfer function

[0058] (2) Defining a multi-parameter CF-GERT network for complex equipment development projects

[0059] In the CF-GERT network of complex equipment development projects, if the transfer relationship between two development activities (ij) is composed of n different parameter variables x ij (1),x ij (2),…,x ij (n); i = 1, 2, ..., m, j = 1, 2, ..., l, n ≥ 2, and the GERT network model is defined as a multi-parameter CF-GERT network for complex equipment development projects.

[0060] Similar to generalized activity networks, self-loops and feedback links are also allowed in multi-parameter variable CF-GERT networks; by expanding self-loops, multiple self-loop feedback, feedback between different nodes, and feedback between multiple source nodes and multiple terminal nodes can be obtained. The transfer relationship in the multi-parameter variable CF-GERT network is defined by the product of the characteristic function of the random variable and the transfer probability. Therefore, the transfer form and realization probability of the n parameter variables between activities (ij) are not restricted by their own distribution, that is, the multiple parameter random variables contained in each activity can select any type of distribution.

[0061] In step 200, the equivalent transfer function and the equivalent transfer probability are calculated as follows:

[0062] (1) Calculation of equivalent transfer function

[0063] In the multi-parameter CF-GERT network of complex equipment development projects, if the n parameter variables x between activities (ij) ij (1),x ij (2),…,x ij (n); i = 1, 2, ..., m, j = 1, 2, ..., l, n ≥ 2 are independent of each other, then the equivalent transfer function of activity (ij) is for:

[0064]

[0065] like is the equivalent transfer function of the kth path from node i to node j in the multi-parameter variable CF-GERT network, k = 1, 2, …, R, R ≥ 1. is the equivalent transfer coefficient of the u-th ring in the v-order ring in the network, H is called the characteristic formula of the multi-parameter variable CF-GERT network, then the equivalent transfer function from node i to node j is:

[0066]

[0067] (2) Calculation of equivalent transmission probability

[0068] In the independent multi-parameter CF-GERT network of complex equipment development projects, the equivalent transmission probability from node to node is:

[0069]

[0070] In step 300, the calculation of the relevant data of each transfer function is as follows:

[0071] (1) Calculation of the mean of the transfer variable

[0072] In an independent multi-parameter variable CF-GERT network, the kth parameter variable x from node i to node j is ij The n-th order origin moment of (k) is:

[0073]

[0074] Where i is an imaginary number and i 2 =-1.

[0075] In an independent multi-parameter variable CF-GERT network, the kth parameter variable x from node i to node j is ij The expectation of (k) is:

[0076]

[0077] (2) Calculation of the variance of the transfer variable

[0078] In an independent multi-parameter variable CF-GERT network, the kth parameter variable x from node i to node j is ij The variance of (k) is:

[0079]

[0080] (3) Calculation of transfer variable risk

[0081] In a multi-parameter variable CF-GERT network, the kth parameter variable x from node i to node j is ij The relative risk of (k) can be expressed as:

[0082]

[0083] In summary, the present invention proposes a complex equipment risk management technology based on a multi-parameter CF-GERT network model by constructing a CF-GERT (Characteristic Function based GERT) network with the product of the characteristic function (CF) of a random variable and the transfer probability as the equivalent transfer relationship, and extending it to the situation where multiple independent parameter variables are included in the network activity. Exploring the basic principles of the analytical solution algorithm of the multi-parameter CF-GERT network model can extend the theoretical basis of the GERT network to a certain extent and expand its practical application scope. Analyzing the probability of success of an event under the joint action of multiple characteristic parameters can dig out more valuable information and provide technical support for risk management in the process of complex equipment development and management.

[0084] Therefore, according to the background of the actual development project, the present invention combines the milestone events of the complex equipment development nodes to construct a multi-parameter variable CF-GERT network that affects the success probability of the event; collects the basic parameters in the network activities, estimates the distribution type of the variables, uses the product of the characteristic function and the transmission probability between the nodes as the W parameter to describe the transmission relationship between the variables between the nodes, and draws on the mature conclusions of the signal flow graph and the Mersenne formula to determine the equivalent transmission probability of the entire equipment development project; according to the properties of the equivalent characteristic function, the expectation, variance, risk, etc. of each parameter that affects the success probability of the project are derived from the event, and according to the results obtained, the equipment development project is analyzed and optimized to ensure the balance of various performance indicators and control the project development risk. For example, for the development progress, when the project risk is high, the average development cycle can be appropriately extended to reduce the variance of the development cycle. , Control the project development risk.

[0085] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0086] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A complex equipment risk management method, characterized in that: include: According to the background of actual development projects, combined with the milestone events for assessing the development nodes of complex equipment, a multi-parameter CF-GERT network model that affects the success probability of the event is constructed; Collect basic parameters in network activities, estimate the distribution type of each parameter variable, use the product of characteristic function and transmission probability between nodes as the equivalent transmission function in the multi-parameter CF-GERT network model, and determine the equivalent transmission probability of equipment development projects based on the mature conclusions of signal flow graph and Mason formula; the equivalent transmission function is used to describe the transmission relationship between various variables between nodes; According to the properties of the equivalent transfer probability, inversion is performed to obtain the transfer variable related data of various parameters that affect the project success probability in the event, and based on the related data, various performance indicators of the equipment development project are balanced and optimized to complete the management and control of the development risks of complex equipment projects; the transfer variable related data include expectation, variance, risk degree and probability density function.

2. The complex equipment risk management method according to claim 1, characterized in that: The construction process of the multi-parameter CF-GERT network model is specifically as follows: Defining the CF-GERT network of complex equipment development projects: If the activities (ij) in a random network only transmit probability p ij and the random variable x ij The two parameters are calculated by using the transfer function under the condition that the basic structure of the network remains unchanged. Replace the transitive relationship between activities (ij); Based on the CF-GERT network, if the transitive relationship between two activities (ij) is composed of n different parameter variables x ij (1), x ij (2), …, x ij (n); i = 1, 2, ..., m, j = 1, 2, ..., l, n ≥ 2, and the transfer relationship network defined by n parameter variables is determined to be a multi-parameter CF-GERT network model.

3. The complex equipment risk management method according to claim 1, characterized in that: The calculation process of the equivalent transfer function is: If the n parameter variables x between activities (ij) ij (1), x ij (2), …, x ij (n); i = 1, 2, ..., m, j = 1, 2, ..., l, n ≥ 2 are independent of each other, then the equivalent transfer function of activity (ij) is for: like is the equivalent transfer function of the kth path from node i to node j in the multi-parameter CF-GERT network model, k = 1, 2, …, R, R ≥ 1; is the equivalent transfer coefficient of the u-th ring in the v-order ring in the network, then the equivalent transfer function from node i to node j is: Where H is the characteristic formula of the multi-parameter CF-GERT network model.

4. The complex equipment risk management method according to claim 3 is characterized in that: The calculation process of the equivalent transmission probability is: In the multi-parameter CF-GERT network model, the equivalent transmission probability p from node i to node j is ij for:

5. The complex equipment risk management method according to claim 1, characterized in that: The expected calculation process of the transfer variable is: In the multi-parameter CF-GERT network model, the kth parameter variable x from node i to node j is ij The n-th order origin moment of (k) is: Where i is an imaginary number and i 2 =-1; x ij (k) is a parameter variable; is the equivalent transfer function; is the probability of equivalent transfer; Further calculate the kth parameter variable x from node i to node j ij The expectation of (k) is:

6. The complex equipment risk management method according to claim 1, characterized in that: The calculation formula of the variance of the transfer variable is: Among them, x ij (k) is the kth parameter variable from node i to node j; is the equivalent transfer function; is the equivalent transfer probability.

7. The complex equipment risk management method according to claim 1, characterized in that: The calculation formula of the risk of the transfer variable is: Among them, x ij (k) is the kth parameter variable from node i to node j; V[x ij (k)] is the variance of the transfer variable; E[x ij (k)] is the expectation of the transferred variable.

8. A complex equipment risk management and control system, characterized in that: include: The model building unit is used to construct a multi-parameter CF-GERT network model that affects the success probability of an event based on the background of the actual development project and the milestone events for assessing the development nodes of complex equipment; A transfer relationship determination unit is used to collect basic parameters in network activities, estimate the distribution type of each parameter variable, use the product of the characteristic function and the transfer probability between nodes as the equivalent transfer function in the multi-parameter CF-GERT network model, and determine the equivalent transfer probability of the equipment development project based on the mature conclusion of the signal flow graph and the Mason formula; the equivalent transfer function is used to describe the transfer relationship of each variable between nodes; The risk management and control unit is used to perform inversion according to the properties of the equivalent transfer probability to obtain the transfer variable related data of various parameters that affect the project success probability in the event, and to balance and optimize various performance indicators of the equipment development project according to the related data to complete the management and control of the development risk of complex equipment projects; the transfer variable related data includes expectation, variance, risk degree and probability density function.

9. An electronic device, characterized in that: It includes a memory and a processor, the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the complex equipment risk management method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: It stores a computer program, which, when executed by a processor, implements the complex equipment risk management method as described in any one of claims 1 to 7.

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