Simulation experiment effectiveness evaluation index analysis method and device based on confidence

Abstract

The present application relates to a method and device for analyzing simulation experiment performance evaluation indicators based on confidence. The method includes: determining the initial threshold interval of the performance evaluation indicator and calculating the confidence based on the existing historical data and expert experience in the complex system simulation experiment scenario; judging whether the confidence of the initial threshold interval meets the standard, and if so, verifying the credibility of the performance evaluation result based on the initial threshold interval; if not, using the Bayesian posterior algorithm to iteratively update the threshold interval, and calculating the confidence of the updated threshold interval based on the inverse CDF function of the normal distribution until the confidence meets the standard, and verifying the credibility of the performance evaluation result based on the current threshold interval. This method can achieve scientific quantification of the confidence of the performance evaluation indicator and automatic updating of the indicator threshold interval based on confidence in a complex system simulation experiment scenario with limited samples and cognitive uncertainty, and can improve the credibility of the performance evaluation result.

Description

Technical Field

[0001] The present application relates to the technical field of performance evaluation, and in particular to a method and device for analyzing simulation experiment performance evaluation indicators based on confidence. Background Art

[0002] Effectiveness evaluation technology is widely used in complex system simulation experiments, equipment evaluation, and solution optimization. With the rapid development of simulation technology, simulation-based effectiveness evaluation can provide critical support for the design, development, testing, and evaluation of equipment in real-world complex system scenarios. However, existing research has largely focused on effectiveness evaluation methods and applications, lacking systematic verification of the credibility of effectiveness evaluation results. This makes it difficult to determine whether simulation experiments are accurate and effective in comprehensively evaluating equipment effectiveness.

[0003] The effectiveness evaluation results are mainly derived from the statistical calculation of effectiveness evaluation indicators. Therefore, the credibility of the effectiveness evaluation results can be evaluated by conducting a trustworthiness analysis of the reliability and accuracy of the effectiveness evaluation indicators.

[0004] However, traditional reliability analysis of performance evaluation metrics primarily uses probability to investigate uncertainty and quantify its impact on reliability. This typically requires a large amount of sample data to ensure accurate results. However, in complex system simulation experiments, obtaining large amounts of high-quality data is often costly or infeasible, reducing the credibility of performance evaluation results. Furthermore, in complex system simulation experiments, epistemic uncertainty often dominates, making traditional probabilistic statistical methods difficult to fully reflect the system's true uncertainty. Summary of the Invention

[0005] Based on this, it is necessary to provide a confidence-based simulation experiment performance evaluation index analysis method and device to address the above technical problems, break through the limitations of the traditional probability framework, and realize the scientific quantification of the confidence of performance evaluation indicators in complex system simulation experiment scenarios with limited samples and cognitive uncertainty, so as to improve the credibility of performance evaluation results.

[0006] A confidence-based simulation experiment effectiveness evaluation index analysis method, the method comprising:

[0007] Based on existing historical data and expert experience in complex system simulation experiment scenarios, determine the initial threshold range of the effectiveness evaluation index and perform confidence calculation;

[0008] Determine whether the confidence of the initial threshold interval reaches the preset confidence threshold. If so, directly adopt the performance evaluation index based on the initial threshold interval to verify the credibility of the performance evaluation result. If not, use the Bayesian posterior algorithm to iteratively update the threshold interval of the performance evaluation index, and calculate the confidence of the updated threshold interval of the performance evaluation index based on the inverse CDF function of the normal distribution until the confidence of the updated threshold interval reaches the preset confidence threshold, and adopt the performance evaluation index based on the current threshold interval to verify the credibility of the performance evaluation result.

[0009] In one embodiment, based on existing historical data and expert experience in complex system simulation experiment scenarios, determining the initial threshold range of the effectiveness evaluation indicator and performing confidence calculation includes:

[0010] According to the existing historical data and expert experience in the complex system simulation experiment scenario, the initial threshold range of the performance evaluation index is determined, which is expressed as ;in, and Respectively represent the lower and upper limits of the initial threshold interval;

[0011] According to the historical data or simulation experiment data in the complex system simulation experiment scenario, Calculate the confidence, expressed as:

[0012] ;

[0013] in, is the confidence level of the initial threshold interval, , is the mean value of the indicator in historical data or simulation experiment data, is the sample standard deviation of historical data or simulation experimental data, and , The first data, and , It is the total number of data in historical data or simulation experiment data.

[0014] In one embodiment, the confidence threshold is set to 95%. If the confidence of the threshold interval of the performance evaluation indicator is greater than or equal to 95%, it means that the probability that the threshold interval covers the actual experimental results of the performance evaluation indicator is greater than or equal to 95%, and the performance evaluation indicator based on the current threshold interval is directly adopted to verify the credibility of the performance evaluation result; otherwise, the Bayesian posterior algorithm is used to iteratively update the threshold interval of the performance evaluation indicator until the confidence of the threshold interval is greater than or equal to 95%.

[0015] In one embodiment, a Bayesian posterior algorithm is used to iteratively update the threshold interval of the performance evaluation indicator, including:

[0016] First, the prior parameters of the threshold interval are determined based on the threshold interval of the previous round of performance evaluation indicators. Then, the a priori parameters are combined with new simulation experiment data in a complex system simulation experiment scenario to calculate and obtain the posterior parameters of the threshold interval. Finally, the threshold interval updated in the previous round is updated according to the posterior parameters to obtain the threshold interval of the current round of performance evaluation indicators. Among them, when the threshold interval of the performance evaluation indicator is updated for the first time, the threshold interval updated in the previous round is the initial threshold interval.

[0017] In one embodiment, initially updating the threshold range of the performance evaluation indicator includes the following steps:

[0018] By establishing a normal prior distribution for the initial threshold interval of the performance evaluation index, the prior parameters of the threshold interval are determined; specifically, the lower limit initial value of the threshold interval is set and upper limit initial value They are and , and determine the prior parameters of the threshold interval based on the initial values ​​of the upper and lower limits; among them, the prior parameters include mean positioning and variance estimation , respectively expressed as:

[0019] ;

[0020] ;

[0021] in, is a standard normal distribution quantile, α is the significance level, and hour, ; is the expert credibility attenuation factor; and Respectively represent the lower and upper limits of the initial threshold interval;

[0022] The simulation experiment was carried out in the complex system simulation experiment scenario, and the results were obtained. n Independent simulation experimental data are used, and the posterior parameters of the threshold interval are determined based on the simulation experimental data and the prior parameters, including and , respectively expressed as:

[0023] ;

[0024] ;

[0025] in, Indicates the i Simulation experimental data; and denote the posterior mean location parameter and the posterior variance estimation parameter respectively;

[0026] Update the initial threshold interval according to the posterior parameters to obtain the updated threshold interval of the performance evaluation index , expressed as:

[0027] ;

[0028] ;

[0029] in, and Represent the updated threshold intervals lower and upper limits.

[0030] In one embodiment, calculating the confidence level of the updated threshold interval of the performance evaluation indicator based on the inverse CDF function of the normal distribution includes:

[0031] Based on the normal distribution inverse CDF function, calculate the updated threshold interval of the performance evaluation index Confidence , expressed as:

[0032] ;

[0033] in, is the inverse CDF function of the standard normal distribution.

[0034] In one embodiment, the method further includes: setting a threshold value for the number of iterative updates of the threshold interval; if the number of iterative updates exceeds the threshold value, and the confidence of the updated threshold interval still does not reach the confidence threshold value, resetting the initial threshold interval of the performance evaluation index, and re-iteratively updating the threshold interval of the performance evaluation index based on a feedback mechanism of the confidence level to the threshold interval.

[0035] A device for analyzing simulation experiment effectiveness evaluation indicators based on confidence, comprising:

[0036] The initial calculation module is used to determine the initial threshold range of the performance evaluation index and perform confidence calculation based on the existing historical data and expert experience in the complex system simulation experiment scenario;

[0037] The confidence-based iterative update module is used to determine whether the confidence of the initial threshold interval reaches the preset confidence threshold. If so, the performance evaluation index based on the initial threshold interval is directly adopted to verify the credibility of the performance evaluation result; if not, the threshold interval of the performance evaluation index is iteratively updated using the Bayesian posterior algorithm, and the confidence of the updated threshold interval of the performance evaluation index is calculated based on the inverse CDF function of the normal distribution until the confidence of the updated threshold interval reaches the preset confidence threshold, and the performance evaluation index based on the current threshold interval is adopted to verify the credibility of the performance evaluation result.

[0038] The above-mentioned confidence-based simulation experiment performance evaluation index analysis method and device constructs a feedback mechanism of confidence to the threshold interval of the performance evaluation index. By combining expert experience and statistical data to quantify the confidence of the threshold interval, it can more comprehensively reflect the real uncertainty of the complex system simulation experiment scenario. When the confidence does not meet the standard, the Bayesian posterior algorithm is used to automatically update and correct the threshold interval, realizing online incremental learning. In the case of limited samples and cognitive uncertainty, it can directly determine whether the threshold interval of the confirmation indicator is reliable through automatic iterative update of the threshold interval and recalculation of the confidence. When the confidence meets the standard, the performance evaluation index based on the current threshold interval is directly adopted to verify the credibility of the performance evaluation result, thereby improving the credibility of the performance evaluation result.

[0039] Furthermore, the present application adopts Bayesian prior parameters in the automatic iterative update process of the threshold interval, which can effectively avoid excessive oscillation of the threshold interval update calculation results caused by finite samples, thereby reducing the iterative calculation cycle of the indicator threshold interval and improving the update calculation efficiency of the indicator threshold interval. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 1 is a flow chart of a method for analyzing a simulation experiment effectiveness evaluation index based on confidence in one embodiment;

[0041] Figure 2 Schematic diagram of the structure of a confidence-based simulation experiment effectiveness evaluation index analysis device in one embodiment. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0043] In one embodiment, Figure 1 As shown, a confidence-based simulation experiment effectiveness evaluation index analysis method is provided, which includes the following steps:

[0044] Step S1: Determine the initial threshold range of the performance evaluation index and perform confidence calculation based on the existing historical data and expert experience in the complex system simulation experiment scenario.

[0045] Step S2, determine whether the confidence of the initial threshold interval reaches the preset confidence threshold. If so, directly adopt the performance evaluation index based on the initial threshold interval to verify the credibility of the performance evaluation result; if not, enter step S3.

[0046] In step S3 , the threshold interval of the performance evaluation indicator is updated using a Bayesian posterior algorithm, and the confidence level of the updated threshold interval of the performance evaluation indicator is calculated based on the inverse normal distribution CDF (Cumulative Distribution Function).

[0047] In step S4, it is further determined whether the confidence level of the updated threshold interval reaches the preset confidence threshold. If so, the credibility of the performance evaluation result is verified by adopting the performance evaluation index based on the current threshold interval. If not, step S3 is repeated to iteratively update the threshold interval until the confidence level of the updated threshold interval reaches the preset confidence threshold.

[0048] In one embodiment, based on existing historical data and expert experience in complex system simulation experiment scenarios, determining the initial threshold range of the effectiveness evaluation indicator and performing confidence calculation includes:

[0049] According to the existing historical data and expert experience in the complex system simulation experiment scenario, the initial threshold range of the performance evaluation index is determined, which is expressed as ;in, and Respectively represent the lower and upper limits of the initial threshold interval;

[0050] According to the historical data or simulation experiment data in the complex system simulation experiment scenario, Calculate the confidence, expressed as:

[0051] ;

[0052] in, is the confidence level of the initial threshold interval, , is the mean value of the indicator in historical data or simulation experiment data, is the sample standard deviation of historical data or simulation experimental data, and , The first data, and , It is the total number of data in historical data or simulation experiment data.

[0053] By combining expert experience with statistical data to perform confidence quantification calculations, on the one hand, expert experience can quantify cognitive uncertainty in complex system simulation experiment scenarios (such as decision rules, blue team strategies, and other difficult-to-count factors), and statistical data (such as historical data and simulation experiment data) can quantify random uncertainty in complex system simulation experiment scenarios (such as equipment performance fluctuations and environmental noise). The combination of the two can more comprehensively reflect the true uncertainty of complex system simulation experiment scenarios and improve the reliability of confidence calculations.

[0054] In one embodiment, the confidence threshold is set to 95%. If the confidence of the threshold interval of the performance evaluation indicator is greater than or equal to 95%, it means that the probability that the threshold interval covers the actual experimental results of the performance evaluation indicator is greater than or equal to 95%, and the performance evaluation indicator based on the current threshold interval is directly adopted to verify the credibility of the performance evaluation result; otherwise, it means that the probability that the threshold interval covers the actual experimental results of the performance evaluation indicator is low, which affects the credibility of the performance evaluation indicator result, and the threshold interval of the indicator needs to be further adjusted. Specifically, the Bayesian posterior algorithm is used to iteratively update the threshold interval of the performance evaluation indicator until the confidence of the threshold interval is greater than or equal to 95%.

[0055] Specifically, a Bayesian posterior algorithm is used to iteratively update the threshold interval of the performance evaluation index, including: first, determining the prior parameters of the threshold interval based on the threshold interval of the previous round of update of the performance evaluation index, then calculating based on the prior parameters combined with new simulation experiment data in a complex system simulation experiment scenario to obtain the posterior parameters of the threshold interval, and finally updating the threshold interval of the previous round of update based on the posterior parameters to obtain the threshold interval of the current round of update of the performance evaluation index; wherein, when the threshold interval of the performance evaluation index is updated for the first time, the threshold interval of the previous round of update is the initial threshold interval.

[0056] The initial update of the threshold range of the performance evaluation indicator includes the following steps:

[0057] First, a normal prior distribution is established for the initial threshold interval of the performance evaluation index to determine the prior parameters of the threshold interval; specifically, the lower limit initial value of the threshold interval is set and upper limit initial value They are and , and determine the prior parameters of the threshold interval based on the initial values ​​of the upper and lower limits; among them, the prior parameters include mean positioning and variance estimation , respectively expressed as:

[0058] ;

[0059] ;

[0060] in, is a standard normal distribution quantile, α is the significance level, and hour, ; is the expert credibility attenuation factor, with an empirical value of 1.2-1.5; and Respectively represent the lower and upper limits of the initial threshold interval;

[0061] Then, a simulation experiment is conducted in a complex system simulation experiment scenario, and the n Independent simulation experimental data are used, and the posterior parameters of the threshold interval are determined based on the simulation experimental data and the prior parameters, including and , respectively expressed as:

[0062] ;

[0063] ;

[0064] in, Indicates the i Simulation experimental data; and denote the posterior mean location parameter and the posterior variance estimation parameter respectively;

[0065] Finally, the initial threshold interval is updated according to the posterior parameters to obtain the updated threshold interval of the performance evaluation index , expressed as:

[0066] ;

[0067] ;

[0068] in, and Represent the updated threshold intervals lower and upper limits.

[0069] Based on this, the updated threshold interval of the performance evaluation index is calculated based on the inverse CDF function of the normal distribution. Confidence , expressed as:

[0070] ;

[0071] in, is the inverse CDF function of the standard normal distribution.

[0072] Specifically, if , then the confidence of the current threshold interval meets the requirements, and the threshold interval of the performance evaluation index can be adjusted according to Determine and adopt the performance evaluation index based on the threshold interval to verify the credibility of the performance evaluation results. , then the confidence of the current threshold interval cannot meet the requirements, and it is necessary to Then iterate and update until the confidence .

[0073] Furthermore, in order to prevent the confidence level after the threshold interval is adjusted from always failing to meet the requirements, the method also includes: setting a threshold for the number of iterative updates of the threshold interval. If the number of iterative updates exceeds the threshold, and the confidence level of the updated threshold interval still does not reach the confidence threshold, the initial threshold interval of the performance evaluation index is reset, and based on the feedback mechanism of the confidence level on the threshold interval, the threshold interval of the performance evaluation index is iteratively updated again.

[0074] Furthermore, this method can iteratively update the threshold intervals of single or multiple performance evaluation indicators based on confidence, including performance evaluation indicators such as maneuvering distance, range, equipment utilization rate, and anti-interference capability in complex system simulation experiment scenarios.

[0075] In summary, the present application provides a confidence-based simulation experiment performance evaluation index analysis method, which quantifies the confidence of the threshold interval by combining expert experience and statistical data, and can more comprehensively reflect the real uncertainty of complex system simulation experiment scenarios. When the confidence does not meet the standard, the Bayesian posterior algorithm is used to automatically update and correct the threshold interval, realizing online incremental learning. In the case of limited samples and cognitive uncertainty, it can directly determine whether the threshold interval of the confirmation indicator is reliable through automatic iterative update of the threshold interval and recalculation of the confidence. When the confidence meets the standard, the performance evaluation index based on the current threshold interval is directly adopted to verify the credibility of the performance evaluation result, thereby improving the credibility of the performance evaluation result.

[0076] In one embodiment, Figure 2 As shown, a device for analyzing simulation experiment effectiveness evaluation indicators based on confidence is provided, comprising:

[0077] The initial calculation module 201 is used to determine the initial threshold range of the effectiveness evaluation index and perform confidence calculation based on the existing historical data and expert experience in the complex system simulation experiment scenario.

[0078] The confidence-based iterative update module 202 is used to determine whether the confidence of the initial threshold interval reaches a preset confidence threshold. If so, the performance evaluation index based on the initial threshold interval is directly adopted to verify the credibility of the performance evaluation result; if not, the threshold interval of the performance evaluation index is iteratively updated using the Bayesian posterior algorithm, and the confidence of the updated threshold interval of the performance evaluation index is calculated based on the inverse CDF function of the normal distribution until the confidence of the updated threshold interval reaches the preset confidence threshold, and the performance evaluation index based on the current threshold interval is adopted to verify the credibility of the performance evaluation result.

[0079] Regarding the specific definition of the confidence-based simulation experiment effectiveness evaluation index analysis device, please refer to the definition of the confidence-based simulation experiment effectiveness evaluation index analysis method above, which will not be repeated here. The various modules in the above-mentioned confidence-based simulation experiment effectiveness evaluation index analysis device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0080] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0081] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A confidence-based simulation experiment effectiveness evaluation index analysis method, characterized in that: The method comprises: Based on existing historical data and expert experience in complex system simulation experiment scenarios, determine the initial threshold range of the effectiveness evaluation index and perform confidence calculation; Determine whether the confidence level of the initial threshold interval reaches a preset confidence threshold. If so, directly adopt the performance evaluation index based on the initial threshold interval to verify the credibility of the performance evaluation result. If not, iterate the threshold interval of the performance evaluation index using the Bayesian posterior algorithm, and calculate the confidence level of the updated threshold interval of the performance evaluation index based on the normal distribution inverse CDF function until the confidence level of the updated threshold interval reaches the preset confidence threshold, and adopt the performance evaluation index based on the current threshold interval to verify the credibility of the performance evaluation result. Wherein, the threshold interval of the performance evaluation index is iteratively updated by adopting the Bayesian posterior algorithm, including: firstly determining the prior parameters of the threshold interval according to the threshold interval of the previous round of update of the performance evaluation index, then calculating according to the prior parameters in combination with new simulation experiment data in the complex system simulation experiment scenario to obtain the posterior parameters of the threshold interval, and finally updating the threshold interval of the previous round of update according to the posterior parameters to obtain the threshold interval of the current round of update of the performance evaluation index; wherein, when the threshold interval of the performance evaluation index is updated for the first time, the threshold interval of the previous round of update is the initial threshold interval; The initial update of the threshold range of the performance evaluation indicator includes the following steps: By establishing a normal prior distribution for the initial threshold interval of the performance evaluation index, the prior parameters of the threshold interval are determined; specifically, the lower limit initial value of the threshold interval is set and upper limit initial value They are and , and determine the prior parameters of the threshold interval based on the initial values ​​of the upper and lower limits; wherein the prior parameters include the mean positioning and variance estimation , respectively expressed as: ； ； in, is a standard normal distribution quantile, α is the significance level, and hour, ; is the expert credibility attenuation factor; and Respectively represent the lower and upper limits of the initial threshold interval; The simulation experiment was carried out in the complex system simulation experiment scenario, and the results were obtained. n Independent simulation experimental data are obtained, and calculations are performed based on the simulation experimental data and the prior parameters to determine the posterior parameters of the threshold interval, including and , respectively expressed as: ； ； in, Indicates the i Simulation experimental data; and denote the posterior mean location parameter and the posterior variance estimation parameter respectively; The initial threshold interval is updated according to the posterior parameter to obtain the updated threshold interval of the performance evaluation index , expressed as: ； ； in, and Represent the updated threshold intervals lower and upper limits.

2. The method according to claim 1, characterized in that Based on existing historical data and expert experience in complex system simulation experiment scenarios, determine the initial threshold range of the effectiveness evaluation indicator and perform confidence calculation, including: According to the existing historical data and expert experience in the complex system simulation experiment scenario, the initial threshold range of the performance evaluation index is determined, which is expressed as ;in, and Respectively represent the lower and upper limits of the initial threshold interval; According to the historical data or simulation experiment data in the complex system simulation experiment scenario, Calculate the confidence, expressed as: ； in, is the confidence level of the initial threshold interval, , is the mean value of the indicator in historical data or simulation experiment data, is the sample standard deviation of historical data or simulation experimental data, and , The first data, and , It is the total number of data in historical data or simulation experiment data.

3. The method according to claim 1, characterized in that The confidence threshold is set to 95%. If the confidence of the threshold interval of the performance evaluation indicator is greater than or equal to 95%, it means that the probability that the threshold interval covers the actual experimental results of the performance evaluation indicator is greater than or equal to 95%. The performance evaluation indicator based on the current threshold interval is directly adopted to verify the credibility of the performance evaluation result; otherwise, the Bayesian posterior algorithm is used to iteratively update the threshold interval of the performance evaluation indicator until the confidence of the threshold interval is greater than or equal to 95%.

4. The method according to claim 1, wherein The confidence level of the updated threshold interval of the performance evaluation index is calculated based on the inverse CDF function of the normal distribution, including: Based on the normal distribution inverse CDF function, calculate the updated threshold interval of the performance evaluation index Confidence , expressed as: ； in, is the inverse CDF function of the standard normal distribution.

5. The method according to claim 1, wherein The method further comprises: A threshold for the number of iterative updates of the threshold interval is set. If the number of iterative updates exceeds the threshold, and the confidence of the updated threshold interval still does not reach the confidence threshold, the initial threshold interval of the performance evaluation index is reset, and based on the feedback mechanism of the confidence on the threshold interval, the threshold interval of the performance evaluation index is iteratively updated again.

6. A confidence-based simulation experiment effectiveness evaluation index analysis device, characterized in that: The device comprises: The initial calculation module is used to determine the initial threshold range of the performance evaluation index and perform confidence calculation based on the existing historical data and expert experience in the complex system simulation experiment scenario; The confidence-based iterative update module is used to determine whether the confidence of the initial threshold interval reaches a preset confidence threshold. If so, the performance evaluation index based on the initial threshold interval is directly adopted to verify the credibility of the performance evaluation result. If not, the threshold interval of the performance evaluation index is iteratively updated using the Bayesian posterior algorithm, and the confidence of the updated threshold interval of the performance evaluation index is calculated based on the inverse CDF function of the normal distribution until the confidence of the updated threshold interval reaches the preset confidence threshold, and the performance evaluation index based on the current threshold interval is adopted to verify the credibility of the performance evaluation result. Wherein, the threshold interval of the performance evaluation index is iteratively updated by adopting the Bayesian posterior algorithm, including: firstly determining the prior parameters of the threshold interval according to the threshold interval of the previous round of update of the performance evaluation index, then calculating according to the prior parameters in combination with new simulation experiment data in the complex system simulation experiment scenario to obtain the posterior parameters of the threshold interval, and finally updating the threshold interval of the previous round of update according to the posterior parameters to obtain the threshold interval of the current round of update of the performance evaluation index; wherein, when the threshold interval of the performance evaluation index is updated for the first time, the threshold interval of the previous round of update is the initial threshold interval; The initial update of the threshold range of the performance evaluation indicator includes the following steps: By establishing a normal prior distribution for the initial threshold interval of the performance evaluation index, the prior parameters of the threshold interval are determined; specifically, the lower limit initial value of the threshold interval is set and upper limit initial value They are and , and determine the prior parameters of the threshold interval based on the initial values ​​of the upper and lower limits; wherein the prior parameters include the mean positioning and variance estimation , respectively expressed as: ； ； in, is a standard normal distribution quantile, α is the significance level, and hour, ; is the expert credibility attenuation factor; and Respectively represent the lower and upper limits of the initial threshold interval; The simulation experiment was carried out in the complex system simulation experiment scenario, and the results were obtained. n Independent simulation experimental data are obtained, and calculations are performed based on the simulation experimental data and the prior parameters to determine the posterior parameters of the threshold interval, including and , respectively expressed as: ； ； in, Indicates the i Simulation experimental data; and denote the posterior mean location parameter and the posterior variance estimation parameter respectively; The initial threshold interval is updated according to the posterior parameter to obtain the updated threshold interval of the performance evaluation index , expressed as: ； ； in, and Represent the updated threshold intervals lower and upper limits.

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