A quantitative assessment method for the emergence of elements in a complex system.

Abstract

This invention proposes a quantitative evaluation method for the emergent nature of elements in complex systems, comprising the following steps: conducting experiments on the level changes of pairwise combinations of experimental factors under the condition that other factor levels are fixed; identifying catalytic combinations and the overall catalytic intensity of the system based on the experimental data from the previous step; multiplying the number of catalytic combinations in the system by the overall catalytic intensity; and evaluating the overall emergent nature of the equipment system elements under different mission scenarios. This invention is geared towards equipment products composed of complex systems, comprehensively considering the combined gains on system efficiency when various subsystems and equipment elements work collaboratively in specific mission scenarios; it can be used to guide the comparison of the combined benefits between elements in different equipment product systems, support in-depth analysis of system operation patterns, and guide the optimization of equipment system element usage patterns and usage scenarios.

Description

Technical Field

[0001] This invention belongs to the field of experimental testing technology for complex systems, and specifically relates to a quantitative evaluation method for the emergence of elements in complex systems. Background Technology

[0002] With the advancement of modern science and technology and the increasing demands of engineering applications, complex equipment products integrating multiple physical processes and unit technologies, such as mechanics, electronics, hydraulics, and optics, are gradually becoming the mainstream trend. These complex equipment products are often composed of multiple complex systems articulated together, including mechanical structures, control units, and electrical equipment. High-speed trains, CNC centers, dispatching centers, aerospace vehicles, and automobiles are typical examples of equipment systems containing multiple complex systems. These equipment products are characterized not only by long development cycles, large product investments, and high quality requirements during the design process, but also by significant characteristics such as ambiguity, hierarchy, empiricism, and comprehensiveness in actual use scenarios.

[0003] The emergent nature of complex system elements refers to the degree of overall emergent effect generated during the evolution of task execution when various subsystems and equipment elements work collaboratively under different usage scenarios. Specifically, it manifests as the combined gain of system effectiveness under different task scenarios, resulting in an effect where "the whole is greater than the sum of its parts." System emergent performance directly reflects the combined benefits of factors influencing the effectiveness of equipment system elements, as well as the combined benefits of elements with the usage environment and methods, on task completion in specific scenarios.

[0004] As a crucial indicator characterizing the inherent attributes of complex system equipment, the scientific assessment of system emergentness is of great value for understanding complex systems and mastering their operational laws, and has always been highly valued by equipment product system designers, manufacturers, and users. Traditional methods for assessing system emergentness include simple linear superposition of the performance gains of individual factors, structural equation modeling based on prior knowledge, and analytic hierarchy process (AHP) based on expert experience. However, most of these methods are primarily qualitative assessments, and a task-based quantitative method for assessing the emergentness of equipment product systems has not yet been developed, focusing on the elements of complex systems. Summary of the Invention

[0005] To address the problems existing in the prior art, this application proposes a quantitative evaluation method for the emergence of elements in a complex system, comprising the following steps:

[0006] Based on the IPO model constructed when the equipment product completes a specific task, set high and low levels for each test factor, and conduct tests on the level changes of a single factor and the level changes of pairwise combinations of test factors under the condition that the levels of other factors are fixed.

[0007] Identify the catalytic combination and the overall catalytic strength of the system based on the experimental data from the previous step;

[0008] Multiply the number of catalytic combinations in the system by the overall catalytic intensity to obtain the emergent nature of the equipment system elements;

[0009] To assess the emergence of equipment system elements under different mission scenarios, the overall emergence of equipment system elements is evaluated.

[0010] Furthermore, based on the IPO model constructed when the equipment product completes a specific task, the high and low levels of each test factor are set, specifically including:

[0011] Definition 1 (Information Entropy): For a random variable X, p(x) is the prior probability, then...

[0012]

[0013] Let X be the information entropy of the random variable. The larger the information entropy, the greater the uncertainty of the random variable.

[0014] Definition 2 (Joint Entropy) For multiple random variables X1, ..., Xn, then

[0015]

[0016] Let X1, ..., Xn be the joint entropy of random variables, which supports the uncertainty of the stochastic system consisting of X1, ..., Xn.

[0017] Definition 3 (Conditional Entropy) Let X and Y be random variables. The conditional entropy H(Y|X) represents the uncertainty of random variable Y given that random variable X is known, i.e.

[0018]

[0019] Definition 4 (Information Gain): Given a random variable X, the difference in the change of information entropy of the random variable Y, i.e.

[0020] IG(Y|X)=H(Y)-H(Y|X)

[0021] This is called information gain;

[0022] Information gain has the following properties:

[0023] Property 1: If random variables X and Y are independent, then the information gain IG(Y|X) takes its minimum value.

[0024] Property 2: If the random variable Y is completely determined by X, then the information gain IG(Y|X) reaches its maximum value;

[0025] Definition 5 (Combined Benefits): The ratio of the information gain from a random variable X to the self-information (information entropy) of that random variable, i.e.

[0026]

[0027] G(X) is the information gain ratio.

[0028] Information gain and information gain ratio can be used as indicators to measure the importance of random variables. The higher the information gain or information gain ratio, the more important the random variable is.

[0029] Furthermore, identifying the catalytic combination and the overall catalytic strength of the system specifically includes:

[0030] For example, during the execution of a certain task, by analyzing the response indicators and factor indicators of each stage of the task, an IPO model of the equipment system in the scenario of executing this task is formed. (Where Y is the task completion degree output by the model, and X1, X2, X3, and X4 are the factor indicators required to execute this task, respectively).

[0031] Assuming other conditions and the levels of influencing factors X3 and X4 remain unchanged, we only consider the impact of influencing factors X1 and X2 on the task completion degree Y. The levels of influencing factors X1 and X2 are coded, and the highest level designed for influencing factors X1 and X2 using the equipment system is 2, and the lowest level is 1.

[0032] First, calculate the average gain of influencing factor X1 on task completion: with other factors remaining constant, the performance of influencing factor X1 improves from a low level to a high level, resulting in the following average increment on task completion:

[0033]

[0034] in,

[0035] H(Y|X1=2)=Y(X1=2,X2=2)-Y(X1=2,X2=1)

[0036] H(Y|X1=1)=Y(X1=1,X2=2)-Y(X1=1,X2=1)

[0037] Secondly, calculate the average gain of influencing factor X2 on task completion: when other factors remain unchanged, an increase in influencing factor X2 from a low level to a high level results in an average increment on task completion of...

[0038]

[0039] in,

[0040] H(Y|X2=2)=Y(X1=2,X2=2)-Y(X1=1,X2=2)

[0041] H(Y|X2=1)=Y(X1=2, X2=2)-Y(X1=1, X2=2).

[0042] Furthermore, the emergence of computational equipment system elements specifically includes: the information gain and combined benefits on task completion when both influencing factor X1 and influencing factor X2 simultaneously increase in level.

[0043]

[0044] Right now:

[0045] or:

[0046] Compared with the prior art, the advantages of this invention are as follows:

[0047] This invention targets equipment products based on complex systems, comprehensively considering the combined gains on system effectiveness when various subsystems and equipment elements work collaboratively in specific mission scenarios. It aims to examine the combined gains on system effectiveness generated by the collaborative operation of various subsystems and components of existing equipment products. It can be used to guide comparisons of the combined benefits between elements of different equipment product systems, support in-depth analysis of system operation patterns, and guide the optimization of the combination of usage patterns and usage scenarios of equipment system elements. Attached Figure Description

[0048] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0049] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described below with reference to the accompanying drawings.

[0050] like Figure 1 As shown, taking a scenario where a complex system performs a certain task as an example, the quantitative evaluation method for the emergence of elements in a complex system proposed in this application has the following specific steps:

[0051] Step 1: Screening the emergence combinations of system influencing factors for equipment products based on information entropy theory;

[0052] Definition 1 (Information Entropy): For a random variable X, p(x) is the prior probability, then...

[0053]

[0054] H(X) is the information entropy of the random variable X. The larger the information entropy, the greater the uncertainty of the random variable.

[0055] Definition 2 (Joint Entropy) For multiple random variables X1, ..., Xn, then

[0056]

[0057] H(X1, ..., X) n Let X1, ..., Xn be the joint entropy of random variables, which supports the measurement of the uncertainty of the stochastic system composed of X1, ..., Xn;

[0058] Definition 3 (Conditional Entropy) Let X and Y be random variables. The conditional entropy H(Y|X) represents the uncertainty of random variable Y given that random variable X is known, i.e.

[0059]

[0060] Definition 4 (Information Gain): Given a random variable X, the difference in the change of information entropy of the random variable Y, i.e.

[0061] IG(Y|X)=H(Y)-H(Y|X)

[0062] This is called information gain;

[0063] Information gain has the following properties:

[0064] Property 1: If random variables X and Y are independent, then the information gain IG(Y|X) takes its minimum value.

[0065] Property 2: If the random variable Y is completely determined by X, then the information gain IG(Y|X) reaches its maximum value;

[0066] Definition 5 (Combined Benefits): The ratio of the information gain from a random variable X to the self-information (information entropy) of that random variable, i.e.

[0067]

[0068] G(X) is the information gain ratio.

[0069] Information gain and information gain ratio can be used as indicators to measure the importance of random variables. The larger the information gain or information gain ratio, the more important the random variable is.

[0070] The second step is to assess the combined benefits of the overall influencing factors of the system based on the impact of all elements of the equipment system on the system effectiveness.

[0071] Based on the results obtained in the first step, the evaluation method for the combined benefits of influencing factors on the equipment system is as follows:

[0072] For example, during the execution of a certain task, by analyzing the response indicators and factor indicators of each stage of the task, an IPO model of the equipment system in the scenario of executing this task is formed. (Where Y is the task completion degree output by the model, and X1, X2, X3, and X4 are the factor indicators required to execute this task, respectively).

[0073] Assuming other conditions and the levels of influencing factors X3 and X4 remain unchanged, we only consider the impact of influencing factors X1 and X2 on the task completion degree Y. The levels of influencing factors X1 and X2 are coded, and the highest level designed for influencing factors X1 and X2 using the equipment system is 2, and the lowest level is 1.

[0074] First, calculate the average gain of influencing factor X1 on task completion: with other factors remaining constant, the performance of influencing factor X1 improves from a low level to a high level, resulting in the following average increment on task completion:

[0075]

[0076] in,

[0077] H(Y|X1=2)=Y(X1=2,X2=2)-Y(X1=2,X2=1)

[0078] H(Y|X1=1)=Y(X1=1,X2=2)-Y(X1=1,X2=1)

[0079] Secondly, calculate the average gain of influencing factor X2 on task completion: when other factors remain unchanged, an increase in influencing factor X2 from a low level to a high level results in an average increment on task completion of...

[0080]

[0081] in,

[0082] H(Y|X2=2)=Y(X1=2,X2=2)-Y(X1=1,X2=2)

[0083] H(Y|X2=1)=Y(X1=2,X2=2)-Y(X1=1,X2=2);

[0084] The third step is to quantitatively assess the emergent nature of the equipment system based on the number of emerging combinations of influencing factors of the equipment system and the overall combined benefits of influencing factors of the equipment system.

[0085] Calculate the information gain and combined benefit on task completion when both influencing factor X1 and influencing factor X2 increase simultaneously:

[0086] IG 12 =H(YX1=2)-H(YX1=1)

[0087] =H(YX2=2)-H(YX2=1)

[0088] Right now or

[0089] The fourth step is to assess the rationality of the configuration of system elements under different task scenarios, and evaluate the overall rationality of the configuration of system elements.

[0090] In summary, these are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. All equivalent changes and modifications made in accordance with the scope of the present invention and the contents of the specification are within the scope of the present invention.

Claims

1. A quantitative evaluation method for the emergent nature of elements in a complex system, characterized in that, Includes the following steps: Based on the IPO model constructed when the equipment product completes a specific task, set high and low levels for each test factor, and conduct tests on the level changes of a single factor and the level changes of pairwise combinations of test factors under the condition that the levels of other factors are fixed. Identify the catalytic combination and the overall catalytic strength of the system based on the experimental data from the previous step; Multiply the number of catalytic combinations in the system by the overall catalytic intensity to obtain the emergent nature of the equipment system elements; To assess the overall emergence of equipment system elements under different mission scenarios; Based on the IPO model constructed when the equipment product completes a specific task, the high and low levels of each test factor are set, specifically including: Definition 1 Information Entropy: For a random variable X, p(x) is the prior probability, then Let X be the information entropy of the random variable. The larger the information entropy, the greater the uncertainty of the random variable. Definition 2 Joint Entropy: For multiple random variables X1, ..., Xn, then Let X1, ..., Xn be the joint entropy, which supports the measurement of the uncertainty of the stochastic system consisting of X1, ..., Xn; Definition 3: Conditional Entropy: Let X and Y be random variables. The conditional entropy H(Y|X) represents the uncertainty of random variable Y given that random variable X is known. Definition 4: Information Gain: Given a random variable X, the difference in the change of information entropy of the random variable Y, i.e. This is called information gain; Information gain has the following properties: Property 1: If random variables X and Y are independent, then the information gain... Take the minimum value. Property 2: If the random variable Y is completely determined by X, then the information gain... Take the maximum value; Definition 5: Combinatorial benefit: The ratio of the information gain from a random variable X to the self-information of that random variable, i.e. Information gain ratio; Information gain and information gain ratio are indicators for measuring the importance of random variables. The higher the information gain or information gain ratio, the more important the random variable is.

2. The quantitative evaluation method for the emergence of elements in a complex system according to claim 1, characterized in that, Identifying the catalytic combination and the overall catalytic strength of the system specifically includes: During the execution of a certain task, the response indicators and factor indicators of each stage of the task were analyzed to form an IPO model of the equipment system in the scenario of executing this task. Y is the task completion degree output by the model, and X1 and X2 are the factor indicators required to execute this task, respectively. Assuming other conditions and influencing factors remain unchanged, only the impact of influencing factors X1 and X2 on the task completion degree Y is considered; the levels of influencing factors X1 and X2 are coded, and the highest level of influencing factors X1 and X2 designed using the equipment system is 2, and the lowest level is 1. First, calculate the average gain of influencing factor X1 on task completion: with other factors remaining constant, the performance of influencing factor X1 improves from a low level to a high level, resulting in the following average increment on task completion: in, Secondly, calculate the average gain of influencing factor X2 on task completion: when other factors remain unchanged, an increase in influencing factor X2 from a low level to a high level results in an average increment on task completion of... in, 。 3. The quantitative evaluation method for the emergence of elements in a complex system according to claim 2, characterized in that, The emergence of computational equipment system elements specifically includes: calculating the information gain and combined benefits on task completion when both influencing factor X1 and influencing factor X2 simultaneously increase in level. ； ； or: .

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