Control system digital model verification method and system

Abstract

The invention discloses a control system digital model verification method, and relates to the technical field of digital control systems, and the method comprises the steps: S1, analyzing and obtaining a simulation model; s2, obtaining a hierarchical relationship of each index, distinguishing a qualitative index and a quantitative index, and determining weight information of each index; s3, obtaining verification data of the bottom layer verification index through a test; s4, performing data preprocessing on the verification data to obtain reference data for model verification; s5, acquiring a bottom-layer index in the indexes, and acquiring a verification value of the bottom-layer index; and S6, according to the weight information and the verification value of the bottom layer index, obtaining a model comprehensive verification evaluation result based on a weighted average comprehensive calculation formula. The invention also provides a control system digital model verification system. The method has the advantages of accuracy, stability, reliability and wide verification range.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital control system, and particularly relates to a control system digital model verification method and system. BACKGROUND

[0002] In the research and application process of the digital control system, the accuracy and reliability of the model are crucial. In actual application, due to the complexity of the system and the variable use scenarios, the digital control system model is often difficult to be directly applied, and needs to be strictly verified to ensure its accuracy and stability in actual application. However, the traditional model verification method often only focuses on a single index or the performance of a certain aspect, and lacks comprehensive evaluation of the overall performance of the system. In addition, the traditional verification method usually does not have a clear verification index system, resulting in a lack of systematicness and pertinence in the verification process. SUMMARY

[0003] In view of the defects in the prior art, the present application provides a control system digital model verification method and system.

[0004] A control system digital model verification method, comprising: S1, obtaining a simulation model according to the use scenario requirements of the digital control system model; S2, constructing a tree-like hierarchical verification index system of the simulation model, obtaining the hierarchical relationship of each index, and distinguishing qualitative indexes and quantitative indexes, and determining the weight information of each index; S3, setting the corresponding test scene and test scheme according to the index calculation requirements, and obtaining the verification data of the bottom layer verification index through the test; S4, data preprocessing is performed on the verification data to obtain the benchmark data used for model verification; S5, according to the hierarchical relationship of the verification index system, obtaining the bottom layer indexes in each index, and taking different consistency analysis methods for calculation according to whether the obtained bottom layer indexes are qualitative indexes or quantitative indexes, and obtaining the verification value of the bottom layer indexes; S6, obtaining the model comprehensive verification evaluation result based on the weighted average comprehensive calculation formula according to the weight information and the verification value of the bottom layer indexes.

[0005] Preferably, S1 comprises: analyzing the function, composition, running process and input and output interface according to the use scenario requirements of the digital control system model to obtain the simulation model.

[0006] Preferably, S2 comprises: S21, constructing a tree-like hierarchical verification index system of the simulation model to obtain the hierarchical relationship of each index; S22, when the number of indexes is 4 or 5, the weight can be determined by using the analytic hierarchy process; S23, when the index weight data can be directly given, or the number of sub-indexes is less than or equal to 3 or greater than 5, the weight can be determined by using the direct weighting method.

[0007] Preferably, the analytic hierarchy process in S22 comprises: obtaining the weight values of the sub-indicators by constructing a judgment matrix, calculating a weight vector and performing consistency checking.

[0008] Preferably, S4 comprises: S41, evaluating the verification data as dynamic data and static data; S42, pre-processing the dynamic data by adopting an average sliding filter method and a time sequence consistency processing method; and S43, pre-processing the static data by adopting a singular value elimination and a normality test.

[0009] Preferably, S5 comprises: S51, obtaining the bottom-level indicators in each indicator according to the hierarchical relationship of the verification index system; S52, when the obtained bottom-level indicators are qualitative indicators, directly giving a model comprehensive verification evaluation result according to a domain expert; S53, when the obtained bottom-level indicators are quantitative indicators, continuing to distinguish whether the indicators are static indicators or dynamic indicators; S54, for the static indicators, performing consistency analysis and calculation by adopting a classical hypothesis testing method, a non-parametric testing method, a Bayesian hypothesis testing method and an error analysis area method to obtain a verification value; and S55, for the dynamic indicators, performing consistency analysis and calculation by adopting an absolute measurement, a relative measurement, a classical spectrum analysis method and a modern spectrum analysis method to obtain a verification value.

[0010] Preferably, the weighted average comprehensive calculation formula in S6 is represented as: wherein ωi is the weight of the i-th indicator, c i is the consistency verification value of the i-th indicator, and ξ 2 is the model comprehensive verification evaluation result. i i

[0011] Preferably, the system further comprises: S7, verifying the model comprehensive verification evaluation result.

[0012] A control system digital model verification system is also provided, which is used to implement any one of the above embodiments. The system comprises: a simulation module, configured to analyze and obtain a simulation model according to a use scene requirement of a digital control system model; a system construction module, configured to construct a tree-shaped hierarchical simulation model verification index system, obtain the hierarchical relationship of each indicator, distinguish qualitative indicators and quantitative indicators, and determine the weight information of each indicator; a test module, configured to set a corresponding test scene and test scheme according to the calculation requirement of each indicator, and further configured to obtain verification data of the bottom-level verification indicators through testing; a data processing module, configured to pre-process the verification data to obtain reference data for model verification; a first calculation module, configured to obtain the bottom-level indicators in each indicator according to the hierarchical relationship of the verification index system, and further configured to calculate the verification values of the bottom-level indicators by adopting different consistency analysis methods according to whether the obtained bottom-level indicators are qualitative indicators or quantitative indicators; and a second calculation module, configured to obtain a model comprehensive verification evaluation result based on a weighted average comprehensive calculation formula according to the weight information and the verification values of the bottom-level indicators.​​

[0013] Preferably, the method further comprises a verification module for verifying the evaluation result of the model synthesis verification.

[0014] The beneficial effects of the present application are embodied in:

[0015] In the whole control system digital model verification method of the present application, by constructing a tree-like hierarchical verification index system, the present application can comprehensively and systematically evaluate various performance indicators of the digital control system model, thereby ensuring the accuracy and reliability of the model in actual application; further, in the control system digital model verification method, different simulation models can be flexibly analyzed and obtained according to different use scene requirements, so that the verification process is closer to the actual application requirements, at the same time, by distinguishing qualitative indicators and quantitative indicators and determining the weight information of each indicator, the evaluation process is more targeted; further, reasonable test scenarios and schemes are set for each verification indicator, and accurate verification data is obtained through scientific test methods and data preprocessing techniques, in addition, different consistency analysis methods are used for calculation according to different types of indicators (qualitative or quantitative), thereby improving the accuracy and reliability of the evaluation result. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed in the specific embodiments or prior art description will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, each element or part is not necessarily drawn according to the actual scale.

[0017] Figure 1 Step schematic diagram of an embodiment of the control system digital model verification method of the present application;

[0018] Figure 2 Step schematic diagram of the control system digital model verification method S2 of the present application;

[0019] Figure 3 Step schematic diagram of the control system digital model verification method S4 of the present application;

[0020] Figure 4 Step schematic diagram of the control system digital model verification method S5 of the present application;

[0021] Figure 5 Step schematic diagram of another embodiment of the control system digital model verification method of the present application. DETAILED DESCRIPTION

[0022] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations.

[0023] Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative labor are within the scope of protection of the present application.

[0024] It should be noted that: similar reference numbers and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0025] As shown in Figure 1 A control system digital model verification method is provided, including: S1, obtaining a simulation model according to the use scene requirements of the digital control system model; S2, constructing a tree-like hierarchical simulation model verification index system, obtaining the hierarchical relationship of each index, and distinguishing qualitative indexes and quantitative indexes, and determining the weight information of each index; S3, setting the corresponding test scene and test scheme according to the index calculation requirements, and obtaining the verification data of the bottom layer verification index through the test; S4, data preprocessing is performed on the verification data to obtain the reference data for model verification; S5, according to the hierarchical relationship of the verification index system, obtaining the bottom layer indexes in each index, and taking different consistency analysis methods for calculation according to whether the obtained bottom layer indexes are qualitative indexes or quantitative indexes, and obtaining the verification value of the bottom layer indexes; S6, obtaining the model comprehensive verification evaluation result based on the weighted average comprehensive calculation formula according to the weight information and the verification value of the bottom layer indexes.

[0026] In this embodiment, it should be noted that in specific applications, the content of control system digital model verification mainly involves the consistency verification between the control system digital model and the actual installation. This verification aims to ensure that the digital model can accurately reflect the performance and characteristics of the actual digital control system, so as to provide reliable prediction and simulation in actual application. The specific verification content includes the following three aspects:

[0027] Main function consistency, this part of the examination is mainly to ensure that the main function of the digital model is consistent with the function of the installed equipment, the examination project may include (but not limited to) the accuracy of various operation modes, the function implementation of the user interface, whether the data processing and output meet the installation requirements, etc.

[0028] Technical performance consistency, the verification of technical performance consistency is to ensure that the digital model is consistent with the installation in technical performance parameters, the specific examination items include: limit range consistency, which involves whether the farthest limit, the nearest limit, the highest limit, the lowest limit and the vertical shortcut are consistent with the installation; The consistency of the number of targets intercepted at the same time, verify whether the performance of the digital model when intercepting multiple targets at the same time is consistent with the installation; The consistency of the shooting interval time, check whether the shooting interval time in the digital model is the same as the installation; The consistency of the communication cycle, verify whether the communication cycle in the digital model matches the communication cycle of the installation.

[0029] Logical timing consistency, the verification of logical timing consistency is to ensure that the digital model is synchronized with the installation in logical processing and timing control, the examination items include: work timing consistency, verify whether the work timing in the digital model is consistent with the work timing of the installation; Multi-target interception timing consistency, check whether the timing control of the digital model when intercepting multiple targets is consistent with the installation; Multi-device coordinated control consistency, ensure that the logic and timing of the digital model when coordinating the control of multiple devices are consistent with the installation.

[0030] Through these detailed consistency verifications, the accuracy and reliability of the digital model can be greatly improved, so as to better simulate and predict the performance of the control system in actual application.

[0031] It should be noted that in the whole control system digital model verification method, by constructing a tree-like hierarchical verification index system, the present scheme can comprehensively and systematically evaluate various performance indicators of the digital control system model, thereby ensuring the accuracy and reliability of the model in actual application; Further, in the control system digital model verification method, different simulation models can be flexibly analyzed and obtained according to different use scene requirements, so that the verification process is closer to the actual application requirements, at the same time, by distinguishing qualitative indicators and quantitative indicators, and determining the weight information of each indicator, the evaluation process is more targeted; Further, reasonable test scenarios and schemes are set for each verification indicator, and accurate verification data is obtained through scientific test methods and data preprocessing technology, in addition, different consistency analysis methods are used for calculation according to different types of indicators (qualitative or quantitative), thereby improving the accuracy and reliability of the evaluation results.

[0032] Specifically, S1 includes: according to the use scene requirements of the digital control system model, analyzing the function, composition, running process and input and output interface to obtain the simulation model.

[0033] In this embodiment, it should be noted that the function analysis: first, a detailed understanding of the functions required to be performed by the digital control system in practical application, according to these function requirements, analysis of the model should have which corresponding function module, to ensure that the simulation model can simulate the core function of the actual system. Composition analysis: analysis of the composition of the digital control system, including the main hardware devices, software modules and their interconnection relationship, accurately reflect these components and their mutual relationship in the simulation model, to ensure the integrity and accuracy of the model. Running process analysis: in-depth understanding of the running process of the digital control system, including the data interaction between modules, control logic and decision-making process, according to these processes, design the running logic and control strategy in the simulation model, to ensure that the model can be simulated according to the process of the actual system. Input and output interface analysis: determine the input and output interface of the digital control system and the external environment, including data acquisition, instruction sending, state feedback, etc., accurately realize these interfaces in the simulation model, to ensure that the model can correctly interact with the external system. Through the above analysis, a simulation model can be constructed which can accurately reflect the main characteristics, function logic and interface specification of the digital control system. This model will be used as the basis for subsequent verification process, to verify the correctness and performance of the system design.

[0034] As shown in Figure 2 , specifically, S2 includes: S21, constructing a tree-like hierarchical simulation model verification index system to obtain the hierarchical relationship of each index; S22, when the number of indexes is 4 or 5, the analytic hierarchy process can be used to determine the weight; S23, when the index weight data can be directly given, or the number of sub-indexes is less than or equal to 3 or more than 5, the direct weighting method can be used to determine the weight.

[0035] In this embodiment, it should be noted that in S21, the target and requirement of the verification need to be determined first, and then a multi-level verification index system is constructed according to the target and requirement; this system should be tree-like hierarchical, that is, starting from the top-level index, it is decomposed layer by layer until the specific verification index at the bottom layer; in the construction process, the meaning and calculation method of each index, as well as the hierarchical relationship and dependency relationship between the indexes need to be determined. In S22, the analytic hierarchy process (AHP) is a commonly used multi-criteria decision-making method, which is suitable for the case where the number of indexes is moderate (such as 4 or 5) and the relative importance between indexes is easy to judge; in this step, a judgment matrix needs to be constructed to determine the relative weights of the indexes by comparing the importance of each index pairwise; then, the weight values of the indexes are obtained by calculating the eigenvalues and eigenvectors of the judgment matrix; this method can consider multiple factors to ensure the scientificity and rationality of the weights. In S23, the direct weighting method is a simple and direct method for determining weights, which is suitable for the following cases: first, there is a clear source of weight data (such as expert evaluation, historical data, etc.); second, the number of sub-indexes is small (less than or equal to 3), making it relatively easy and accurate to directly assign weights; third, the number of sub-indexes is large (more than 5), and the analytic hierarchy process may be too complex or difficult to implement; in this step, according to the existing weight data or direct judgment, specific weight values are assigned to each index; it should be noted that the direct weighting method may be affected by subjective factors, so the reliability and accuracy of the weight source should be ensured.

[0036] Specifically, the analytic hierarchy process in S22 includes: constructing a judgment matrix, calculating a weight vector, and performing consistency check to obtain the weight values of each sub-index.

[0037] In this embodiment, it should be noted that the judgment matrix is constructed: first, a judgment matrix, also known as a pairwise comparison matrix, needs to be constructed. This matrix is used to represent the relative importance between indexes. Usually, we invite experts or decision-makers to make pairwise comparisons and use a scale of 1-9 to represent the importance of one index relative to another. For example, if index A is extremely important than index B, 9 can be filled in the corresponding position in the matrix, if they are equally important, 1 can be filled in, if A is slightly more important than B, 3 can be filled in, and so on.

[0038] Calculate the weight vector: The eigenvalues and eigenvectors of the judgment matrix need to be calculated. After normalization, the eigenvectors can be used as the weight vector of each index. In practical applications, to simplify the calculation, the square root method or the sum product method is often used to approximate the weight vector. Square root method: first calculate the geometric mean of each row of the judgment matrix, then normalize these geometric means to obtain the weight vector. Sum product method: first normalize each column of the judgment matrix, then add the normalized matrix by row, and finally normalize the resulting sum vector to obtain the weight vector.

[0039] Since the judgment matrix is constructed based on pairwise comparison, there may be inconsistencies. In order to evaluate the degree of inconsistency, consistency check is needed.

[0040] Through the above steps, we can use the analytic hierarchy process to obtain the weight value of each sub-index. This method combines qualitative and quantitative analysis, which helps to determine the relative importance of each factor in complex multi-criteria decision-making problems.

[0041] As shown in Figure 3 Specifically, S4 includes: S41, evaluating the verification data as dynamic data and static data; S42, preprocessing the dynamic data using the average sliding filter method and time sequence consistency processing method; S43, preprocessing the static data using the singular value elimination and normality test.

[0042] In this embodiment, it should be noted that in S41, the verification data is classified into dynamic data and static data; dynamic data refers to data that changes over time, such as temperature, humidity, pressure, and other real-time monitoring data; static data refers to data that does not change or changes little over time, such as device parameters, geographic location information, etc. In S42, the average sliding filter method: this method is used to smooth the noise and fluctuations in dynamic data, which takes a sliding window on each data point and calculates the average value of the data in the window to replace the original data. In this way, random errors and noise in the data can be reduced; time series consistency processing method: time series data should be continuous in time, but sometimes due to various reasons (such as sensor failure, data transmission error, etc.), there may be outliers or jumps in the data, and time series consistency processing aims to detect and correct these abnormalities to ensure the continuity and consistency of the data in time series. In S43, outlier rejection: static data may contain outliers (also known as outliers) due to measurement errors, input errors, or other reasons, which can seriously affect data analysis and model accuracy. Therefore, it is necessary to identify and reject these outliers through statistical methods (such as IQR rule, Z-score, etc.); normality test: many statistical analysis and models assume that data is normally distributed, so it is important to perform a normality test on static data. If the data does not conform to the normal distribution, it may need to be transformed or use non-parametric statistical methods, and normality test can be performed through graphical methods (such as Q-Q plot, histogram) or statistical quantities (such as Shapiro-Wilk test, Kolmogorov-Smirnov test).

[0043] As shown in Figure 4 , specifically, S5 includes: S51, according to the hierarchical relationship of the verification index system, obtaining the bottom-level indicators in each indicator; S52, when the obtained bottom-level indicators are qualitative indicators, directly giving the model comprehensive verification and evaluation results according to the field experts; S53, when the obtained bottom-level indicators are quantitative indicators, continue to distinguish whether the indicators are static indicators or dynamic indicators; S54, for static indicators, adopt classical hypothesis testing method, non-parametric testing method, Bayesian hypothesis testing method and error analysis area method for consistency analysis and calculation to obtain verification values; S55, for dynamic indicators, adopt absolute measurement, relative measurement, classical spectrum analysis method and modern spectrum analysis method for consistency analysis and calculation to obtain verification values.

[0044] In this embodiment, it should be noted that in S51, it is necessary to traverse the hierarchical relationship of the verification indicator system to find the bottom-level indicators. These bottom-level indicators are specific indicators actually used for verification, and they no longer contain sub-indicators. In S52, if the bottom-level indicators obtained are qualitative (such as the quality of certain performance indicators, the reliability of the system, etc.), then these indicators may be difficult to measure through quantitative data. In this case, the knowledge and experience of domain experts can be relied upon to directly give the comprehensive verification and evaluation results of the model. In S53, for quantitative indicators, it is necessary to further distinguish whether they are static or dynamic. Static indicators refer to indicators that do not change or change little over time, while dynamic indicators are indicators that change over time. In S54, classic hypothesis testing methods, such as the t-test and F-test, are used to test whether there is a significant difference between sample data and a known value or between two sample data. Non-parametric testing methods, such as the Mann-Whitney U test and the Kruskal-Wallis test, are applicable when data do not conform to a normal distribution or when the population distribution is unknown. Bayesian hypothesis testing combines prior information and sample data to perform hypothesis testing, which is particularly applicable to small sample sizes. Error analysis area method evaluates model consistency by analyzing the error area between predicted and actual values. In S55, absolute measurement directly compares the absolute difference between the actual and predicted values ​​of dynamic indicators. Relative measurement compares the relative difference between the actual and predicted values ​​of dynamic indicators, such as percentage error. Classical spectral analysis converts time series data from the time domain to the frequency domain using methods such as Fourier transform, and analyzes the frequency characteristics of the data to evaluate model consistency. Modern spectral analysis uses more advanced signal processing techniques, such as wavelet transform and Hilbert-Huang transform, to analyze the consistency of dynamic indicators.

[0045] Specifically, the weighted average comprehensive calculation formula in S6 is expressed as: Among them, ω i is the weight of indicator i, c i is the consistency verification value of indicator i, and ξ2 is the comprehensive verification evaluation result of the model.

[0046] like Figure 5 As shown, specifically, it also includes: S7, verifying the comprehensive verification and evaluation results of the model.

[0047] In this embodiment, it is necessary to check the data source and accuracy: verify whether the data used in the evaluation process is accurate, complete, and confirm the reliability of the data source, check whether the data preprocessing step is correctly performed to ensure data quality. Compare the results of different validation methods: if multiple validation methods are used in the evaluation process (such as Holdout test, cross-validation, bootstrap method, etc.), the evaluation results obtained by these methods should be compared to see if they are consistent or similar, analyze the differences between the results of different validation methods, and explore the possible reasons. Check the correctness of the index calculation: recalculate the evaluation indexes such as accuracy, precision, etc. to ensure the accuracy of the calculation process, use different tools or libraries for calculation to verify the stability of the results. Invite experts to review: invite experts in the field to review the evaluation results to obtain more comprehensive feedback and suggestions, and make necessary adjustments and optimization to the model according to the opinions of the expert review.

[0048] A control system digital model verification system is also provided, which is used to implement the control system digital model verification method according to any one of claims 1 to 8, and comprises: a simulation module configured to analyze and obtain a simulation model according to a use scene requirement of a digital control system model; a system construction module configured to construct a tree-like hierarchical simulation model verification index system, obtain a hierarchical relationship of each index, distinguish between qualitative indexes and quantitative indexes, and determine index weight information; a test module configured to set a corresponding test scene and test scheme according to a requirement of each index calculation, and further configured to obtain verification data of a bottom layer verification index through a test; a data processing module configured to perform data preprocessing on the verification data to obtain baseline data for model verification; a first calculation module configured to obtain a bottom layer index in each index according to a hierarchical relationship of the verification index system, and further configured to adopt different consistency analysis methods for calculation according to whether the obtained bottom layer index is a qualitative index or a quantitative index, and obtain a verification value of the bottom layer index; and a second calculation module configured to obtain a model comprehensive verification evaluation result based on a weighted average comprehensive calculation formula according to the weight information and the verification value of the bottom layer index.

[0049] Specifically, the system further comprises a verification module configured to verify the model comprehensive verification evaluation result.

[0050] In this embodiment, it is necessary to check the data source and accuracy: verify whether the data used in the evaluation process is accurate, complete, and confirm the reliability of the data source, check whether the data preprocessing step is correctly performed to ensure data quality. Compare the results of different validation methods: if multiple validation methods are used in the evaluation process (such as Holdout test, cross-validation, bootstrap method, etc.), the evaluation results obtained by these methods should be compared to see if they are consistent or similar, analyze the differences between the results of different validation methods, and explore the possible reasons. Check the correctness of the index calculation: recalculate the evaluation indexes such as accuracy, precision, etc. to ensure the accuracy of the calculation process, use different tools or libraries for calculation to verify the stability of the results. Invite experts to review: invite experts in the field to review the evaluation results to obtain more comprehensive feedback and suggestions, and make necessary adjustments and optimization to the model according to the opinions of the expert review.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can still be modified, or some or all of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should be covered in the scope of the claims and the description of the present application.

Claims

1. A control system digital model verification method, characterized in that: include: S1. Analyze and obtain the simulation model according to the usage scenario requirements of the digital control system model; S2. Construct a tree-like hierarchical verification index system for the simulation model, obtain the hierarchical relationship of each index, distinguish between qualitative and quantitative indicators, and determine the weight information of each indicator; S3. Set up corresponding test scenarios and test plans according to the calculation requirements of each indicator, and obtain verification data of the underlying verification indicators through experiments; S4. Perform data preprocessing on the verification data to obtain benchmark data for model verification; S5. Obtain the underlying indicators of each indicator according to the hierarchical relationship of the verification indicator system. Depending on whether the underlying indicators are qualitative or quantitative, different consistency analysis methods are used to calculate and obtain the verification values ​​of the underlying indicators. S6. According to the weight information and the verification value of the underlying indicators, the comprehensive verification and evaluation results of the model are obtained based on the weighted average comprehensive calculation formula.

2. The control system digital model verification method according to claim 1, characterized in that: Said S1 includes: analyzing the functions, components, operation processes and input and output interfaces of the digital control system model according to the usage scenario requirements, and obtaining a simulation model.

3. The control system digital model verification method according to claim 1, characterized in that: The S2 includes: S21. Construct a tree-like hierarchical verification index system for the simulation model and obtain the hierarchical relationship of each index; S22. When the number of indicators is 4 or 5, the weights are determined using the analytic hierarchy process; S23. When the indicator weight data can be given directly, or the number of sub-indicators is less than or equal to 3 or greater than 5, the direct weighting method is used to determine the weight.

4. The control system digital model verification method according to claim 3, characterized in that: The analytic hierarchy process in S22 includes: constructing a judgment matrix, calculating a weight vector, and performing a consistency test to obtain a weight value of each sub-indicator.

5. The control system digital model verification method according to claim 1, characterized in that: The S4 includes: S41. Evaluate the verification data into dynamic data and static data; S42, pre-processing the dynamic data using an average sliding filter method and a time series consistency processing method; S43. Preprocess the static data by removing singular values ​​and performing normality test.

6. The control system digital model verification method according to claim 1, characterized in that: The S5 includes: S51. Obtain the bottom-level indicators of each indicator according to the hierarchical relationship of the verification indicator system; S52. When the underlying indicators obtained are qualitative indicators, the comprehensive verification and evaluation results of the model are directly given according to the domain experts; S53. When the underlying indicator obtained is a quantitative indicator, continue to distinguish whether the indicator type is a static indicator or a dynamic indicator; S54. Perform consistency analysis and calculation on static indicators using the classical hypothesis testing method, nonparametric testing method, Bayesian hypothesis testing method, and error analysis area method to obtain verification values; S55. Absolute measurement, relative measurement, classical spectrum analysis and modern spectrum analysis are used to perform consistency analysis and calculation on dynamic indicators to obtain verification values.

7. The control system digital model verification method according to claim 1, characterized in that: The weighted average comprehensive calculation formula in S6 is expressed as: Among them, ω i is the weight of indicator i, c i is the consistency verification value of indicator i, and ξ2 is the comprehensive verification evaluation result of the model.

8. The control system digital model verification method according to claim 1, characterized in that: Also includes: S7. Verify the comprehensive validation and evaluation results of the model.

9. A control system digital model verification system, characterized in that: The system is used to implement the control system digital model verification method according to any one of claims 1 to 8, and the system includes: A simulation module is used to analyze and obtain a simulation model based on the usage scenario requirements of the digital control system model; The system construction module is used to construct a tree-like hierarchical verification index system for the simulation model, obtain the hierarchical relationship of each index, distinguish between qualitative and quantitative indicators, and determine the weight information of each indicator; The test module is used to set up corresponding test scenarios and test plans according to the calculation requirements of each indicator, and is also used to obtain verification data of the underlying verification indicators through experiments; A data processing module is used to perform data preprocessing on the verification data to obtain benchmark data for model verification; The first calculation module is used to obtain the underlying indicators of each indicator according to the hierarchical relationship of the verification indicator system, and is also used to adopt different consistency analysis methods to calculate the verification value of the underlying indicator according to whether the underlying indicator is a qualitative indicator or a quantitative indicator; The second calculation module is used to obtain the comprehensive verification and evaluation results of the model based on the weighted average comprehensive calculation formula according to the weight information and the verification value of the underlying indicators.

10. The control system digital model verification system according to claim 9, characterized in that: Also includes: The verification module is used to verify the comprehensive verification and evaluation results of the model.

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