Fusion method and system for dynamic measurement uncertainty evaluation and medium

Abstract

The invention discloses a fusion method for dynamic measurement uncertainty evaluation, and discloses a system with the fusion method for dynamic measurement uncertainty evaluation and a storage medium. The dynamic measurement uncertainty evaluation fusion method comprises the steps of dynamic measurement data component separation, deterministic component uncertainty evaluation, random component uncertainty evaluation and final uncertainty synthesis, more accurate uncertainty evaluation is achieved, and meanwhile data stability and some statistical characteristics do not need to be considered. And therefore, different requirements can be better met.

Description

Technical Field

[0001] The present invention relates to the technical field of dynamic uncertainty measurement, and in particular to a fusion method, system and medium for dynamic measurement uncertainty evaluation. Background Art

[0002] Measurement uncertainty is an essential part of measurement work. It is generally believed that measurement uncertainty refers to the measure of the discrete degree of the measured value under a given measurement condition. Compared with static measurement, dynamic measurement has the advantages of not being restricted by environmental conditions and being able to realize online detection, so it is widely used in high-precision detection.

[0003] However, in the dynamic measurement process, due to the movement of the measured object, the source and value characteristics of its uncertainty also change. The traditional static uncertainty assessment method can no longer fully evaluate the quality of dynamic detection results, so the concept of dynamic value uncertainty is proposed. The GUM method is a method recognized by the international community and is a relatively complete uncertainty assessment method, but it avoids the problem of dynamic value uncertainty and has certain limitations.

[0004] The Monte Carlo method mainly relies on random simulation and statistical experiments, but it needs to generate a large number of random samples and requires a large amount of calculation. Bayesian theory is a method based on probability theory, which emphasizes the full use of prior information and may rely on subjective prior assumptions. Grey theory solves the problem of data processing by dividing data into grey and white data, but in some cases, it is necessary to select parameters based on experience, which may lack reliability. It can be seen that the existing traditional uncertainty assessment methods have some defects. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a fusion method for dynamic measurement uncertainty assessment, which can better adapt to different needs and achieve more accurate uncertainty assessment, targeting the problem that the previous methods have too high data requirements and single functions.

[0006] The present invention also proposes a system having the above-mentioned fusion method for dynamic measurement uncertainty evaluation.

[0007] The fusion method for dynamic measurement uncertainty evaluation according to the first aspect of the present invention is characterized by comprising the following steps:

[0008] The original data is separated into components to obtain several IMF components and a residual component, and the energy spectrum entropy of each IMF component is calculated to determine whether the IMF component belongs to a deterministic component or a random component;

[0009] Obtaining the uncertainty of the deterministic component based on a mapping constant method;

[0010] The random component is processed by the TVAR model to obtain the parameter estimation value and estimation error, and finally the uncertainty of the random component is calculated;

[0011] The uncertainty of the deterministic component and the uncertainty of the random component are synthesized to obtain a final uncertainty.

[0012] The fusion method of dynamic measurement uncertainty evaluation according to the embodiment of the present invention has at least the following beneficial effects: the method proposed by the present invention realizes more accurate uncertainty evaluation through a fusion method of dynamic measurement uncertainty evaluation, including the steps of dynamic measurement data component separation, deterministic component uncertainty evaluation, random component uncertainty evaluation, and final uncertainty synthesis, and does not need to consider data stability and some statistical characteristics. It can thus better adapt to different needs.

[0013] According to some embodiments of the present invention, the process of determining whether the IMF component belongs to a deterministic component or a stochastic component includes:

[0014] Calculate the energy spectrum entropy of each IMF component and compare it with the energy spectrum entropy of the original data. If the energy spectrum entropy of the component is greater than the original energy spectrum entropy, it can be considered that the IMF component belongs to the random component, otherwise it is considered to be a deterministic component.

[0015] By adding up the components, the dynamic value is divided into deterministic and random components.

[0016] According to some embodiments of the present invention, the step of obtaining the uncertainty of the deterministic component based on a mapping constant method comprises:

[0017] Select the appropriate mapping constant order and number of mapping constants;

[0018] The mapping constant value is calculated, then the uncertainty of the mapping constant is calculated, and finally the uncertainty of the deterministic component is estimated.

[0019] According to some embodiments of the present invention, the step of processing the random component by using the TVAR model to obtain parameter estimation values ​​and estimation errors, and finally calculating the uncertainty of the random component specifically includes:

[0020] The time-varying coefficients of the TVAR model are expressed as a weighted linear combination of a set of Fourier basis functions, and the parameter estimates and estimation errors are calculated based on the least squares theory.

[0021] The model order was calculated according to the AIC criterion;

[0022] The uncertainty of the random component is calculated based on the parameter estimation value, estimation error, and model order.

[0023] According to some embodiments of the present invention, in the step of synthesizing the uncertainty of the deterministic component and the uncertainty of the random component to obtain the final uncertainty, the synthesis is performed based on a synthesis formula proposed by the American Society of Mechanical Engineers.

[0024] The fusion system for dynamic measurement uncertainty evaluation according to the second aspect of the present invention is characterized by comprising:

[0025] The component separation module can separate the original data into components to obtain several IMF components and a residual component, calculate the energy spectrum entropy of each IMF component, and determine whether the IMF component belongs to a deterministic component or a random component;

[0026] A deterministic component analysis module, capable of obtaining the uncertainty of the deterministic component based on a mapping constant method;

[0027] The random component analysis module can process the random component through the TVAR model to obtain the parameter estimation value and estimation error, and finally calculate the uncertainty of the random component;

[0028] The uncertainty synthesis module can synthesize the uncertainty of the deterministic component and the uncertainty of the random component to obtain a final uncertainty.

[0029] According to some embodiments of the present invention, the process of determining whether the IMF component belongs to a deterministic component or a stochastic component includes:

[0030] Calculate the energy spectrum entropy of each IMF component and compare it with the energy spectrum entropy of the original data. If the energy spectrum entropy of the component is greater than the original energy spectrum entropy, it can be considered that the IMF component belongs to the random component, otherwise it is considered to be a deterministic component.

[0031] By adding up the components, the dynamic value is divided into deterministic and random components.

[0032] According to some embodiments of the present invention, the deterministic component analysis module, the working process includes:

[0033] Select the appropriate mapping constant order and number of mapping constants;

[0034] The mapping constant value is calculated, then the uncertainty of the mapping constant is calculated, and finally the uncertainty of the deterministic component is estimated.

[0035] According to some embodiments of the present invention, the random component analysis module has a working process including:

[0036] The time-varying coefficients of the TVAR model are expressed as a weighted linear combination of a set of Fourier basis functions, and the parameter estimates and estimation errors are calculated based on the least squares theory.

[0037] The model order was calculated according to the AIC criterion;

[0038] The uncertainty of the random component is calculated based on the parameter estimation value, estimation error, and model order.

[0039] According to some embodiments of the present invention, the uncertainty synthesis module performs synthesis based on a synthesis formula proposed by the American Society of Mechanical Engineers.

[0040] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0042] Figure 1 A schematic diagram of the steps of a fusion method for dynamic measurement uncertainty evaluation according to an embodiment of the present invention;

[0043] Figure 2 It is a schematic diagram of decomposition results in the fusion method for dynamic measurement uncertainty assessment according to an embodiment of the present invention;

[0044] Figure 3 A schematic diagram of final uncertainty obtained by the fusion method for dynamic measurement uncertainty assessment according to an embodiment of the present invention;

[0045] Figure 4 It is a structural block diagram of a fusion system for dynamic measurement uncertainty assessment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0047] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., and orientations or positional relationships indicated are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0048] In the description of the present invention, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0049] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0050] Embodiment 1

[0051] Reference Figure 1 The embodiment of the present invention provides a fusion method for dynamic measurement uncertainty evaluation, the method comprising the following steps:

[0052] Step S100: separate the components of the original data to obtain a plurality of IMF components and a residual component, calculate the energy spectrum entropy of each IMF component, and determine whether the IMF component belongs to a deterministic component or a random component.

[0053] For component separation, the present invention adopts the method of Empirical Mode Decomposition (EMD) energy spectrum entropy, which does not need to rely on basis functions and has obvious advantages.

[0054] According to the EMD decomposition theory, the original dynamic value data can be decomposed into n IMF components and a residual component. The energy spectrum entropy of each IMF component is calculated and compared with the energy spectrum entropy of the original data. If it is greater than the original energy spectrum entropy, it can be considered that the IMF component belongs to the random component, otherwise it is considered to be a deterministic component. By accumulating the components, the dynamic value is divided into deterministic components and random components.

[0055] Step S200: Obtain the uncertainty of the deterministic component based on a mapping constant method.

[0056] For the uncertainty assessment of deterministic components, compared with Bayesian methods, the mapping constant method (MCA) does not require prior information and has no requirements for the statistical characteristics of the data, so the mapping constant method is used.

[0057] The mapping constant principle believes that the core of dynamic uncertainty assessment is to find a constant that can be measured repeatedly, establish the relationship between the constant and the dynamic value, and realize the mapping of a set of dynamic value sampling values ​​to the constant. By selecting the appropriate mapping constant order and the number of mapping constants, according to the calculation process of the mapping constant method, the mapping constant value can be calculated first, then the uncertainty of the mapping constant can be calculated, and finally the uncertainty of the deterministic component can be estimated.

[0058] Step S300: Process the random component through the TVAR model to obtain parameter estimation values ​​and estimation errors, and finally calculate the uncertainty of the random component.

[0059] For the uncertainty assessment of random components, AR models, ARMA models, etc. were often used in the past. However, considering that the dynamic values ​​in practical problems may be complex, and the TVAR model can handle random processes, the TVAR model is used for processing regardless of the stationarity and other characteristics of the data. The time-varying coefficients of the model are expressed as a weighted linear combination of a set of Fourier basis functions, and the parameter estimates and estimation errors are calculated according to the least squares theory. The model order is calculated according to the AIC criterion, and finally the uncertainty of the random component is obtained.

[0060] Step S400: synthesize the uncertainty of the deterministic component and the uncertainty of the random component to obtain a final uncertainty.

[0061] After the uncertainty of the deterministic component and the random component is estimated, uncertainty synthesis is required. Regarding the synthesis of uncertainty, in simple terms, its purpose is to synthesize the uncertainty components in a linear synthesis manner to obtain a more accurate uncertainty. The present invention believes that the synthesis formula proposed by the American Society of Mechanical Engineers (ASME) has good robustness and adopts this formula for synthesis.

[0062] Embodiment 2

[0063] Based on the fusion method for dynamic measurement uncertainty assessment proposed in the first embodiment above, this embodiment is used to verify the accuracy and practicality of the above method by performing a simulation of the model in MATLAB.

[0064] A common simple sinusoidal signal is selected and random noise is added to the signal to simulate the dynamic value. A synthetic signal y(t) is defined as: y(t) = sin(2fπt) + ε(0,0.5), where the frequency f is 20Hz and ε(0,0.5) represents a normal distribution with zero mean and variance of 0.5.

[0065] The components are separated and the deterministic components are compared with the original true values. It is found that the reconstructed deterministic components are closer to the true values. The root mean square error is used to measure the deviation between the deterministic components and the true values, and the similarity coefficient is used to measure the similarity between them. The root mean square error is 0.26 and the similarity coefficient is 0.88, which is a good effect.

[0066] The synthetic signal is decomposed using the EMD method. According to the EMD decomposition principle, it can be decomposed into multiple IMF components and a residual component. The decomposition results are as follows: Figure 2 As shown:

[0067] Next, the energy spectrum entropy of all IMF components is calculated in turn and compared with the energy spectrum entropy of the original data. The first component, the sixth component, and the seventh component constitute the deterministic component, and the remaining components constitute the random component. The uncertainty of the deterministic component and the random component is calculated separately, and finally synthesized to obtain the final uncertainty. Figure 3 shown.

[0068] The dynamic measurement uncertainty is further calculated according to the method of the present invention and compared with other methods.

[0069] Compared with the method using only mapping constants, the average value of uncertainty at each point is used as the evaluation index. The evaluation index estimated by using only mapping constant method is 0.7183, and the evaluation index using the improved uncertainty evaluation method is 0.6864.

[0070] It can be seen that the average uncertainty evaluated by the two methods is close to the standard deviation of the actual random noise, indicating that the methods are effective. However, the uncertainty of the improved uncertainty evaluation method is smaller, proving that the evaluation of the improved method is better than the mapping constant method alone.

[0071] Compared with the least squares method, the least squares method has a poor fitting effect when the model function changes rapidly, and it is difficult to select the appropriate basis function and fitting number. However, there is no fitting problem for the mapping constant (or the fitting value is the value of the Maclaurin expansion), so this problem can be solved by adjusting the expansion order or sampling frequency.

[0072] Compared with the grey theory method, the grey model has a better fitting effect but requires repeated measurement data. The more repeated measurement data, the better the fitting effect. The method proposed in the present invention does not require the use of repeated data, and the effect is not much different from the grey theory in accuracy.

[0073] The method proposed in the present invention is suitable for uncertainty assessment of any dynamic value and is not limited by data stability and other statistical characteristics. It can effectively solve the limitations of some existing assessment methods and is very meaningful for improving the quality of measurement data.

[0074] Embodiment 3

[0075] Based on the first and second embodiments, another embodiment of the present application provides a fusion system for dynamic measurement uncertainty assessment, such as Figure 4 As shown, the system 20 includes:

[0076] The component separation module 201 can separate the components of the original data to obtain a number of IMF components and a residual component, calculate the energy spectrum entropy of each IMF component, and determine whether the IMF component belongs to a deterministic component or a random component;

[0077] A deterministic component analysis module 202, capable of obtaining the uncertainty of the deterministic component based on a mapping constant method;

[0078] The random component analysis module 203 can process the random component through the TVAR model to obtain parameter estimation values ​​and estimation errors, and finally calculate the uncertainty of the random component;

[0079] The uncertainty synthesis module 204 can synthesize the uncertainty of the deterministic component and the uncertainty of the random component to obtain a final uncertainty.

[0080] Furthermore, the process of determining whether the IMF component belongs to a deterministic component or a stochastic component includes:

[0081] Calculate the energy spectrum entropy of each IMF component and compare it with the energy spectrum entropy of the original data. If the energy spectrum entropy of the component is greater than the original energy spectrum entropy, it can be considered that the IMF component belongs to the random component, otherwise it is considered to be a deterministic component.

[0082] By adding up the components, the dynamic value is divided into deterministic and random components.

[0083] Furthermore, the deterministic component analysis module 202 includes the following working process:

[0084] Select the appropriate mapping constant order and number of mapping constants;

[0085] The mapping constant value is calculated, then the uncertainty of the mapping constant is calculated, and finally the uncertainty of the deterministic component is estimated.

[0086] Furthermore, the random component analysis module 203 includes the following working process:

[0087] The time-varying coefficients of the TVAR model are expressed as a weighted linear combination of a set of Fourier basis functions, and the parameter estimates and estimation errors are calculated based on the least squares theory.

[0088] The model order was calculated according to the AIC criterion;

[0089] The uncertainty of the random component is calculated based on the parameter estimation value, estimation error, and model order.

[0090] Furthermore, the uncertainty synthesis module 204 performs synthesis based on a synthesis formula proposed by the American Society of Mechanical Engineers.

[0091] Another embodiment of the present application provides a computer-readable storage medium storing computer-executable instructions for executing the above-mentioned Figure 1 The fusion method for dynamic measurement uncertainty evaluation is shown.

[0092] The device embodiments described above are only illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0093] It will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed method above may be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that may be used to store desired information and may be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0094] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the above-mentioned implementation mode. Technical personnel familiar with the field can also make various equivalent deformations or substitutions without violating the spirit of the present application. These equivalent deformations or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. A fusion method for dynamic measurement uncertainty assessment, It is characterized in that The following steps are involved: The original data is separated into components to obtain several IMF components and a residual component, and the energy spectrum entropy of each IMF component is calculated to determine whether the IMF component belongs to a deterministic component or a random component; Obtaining the uncertainty of the deterministic component based on a mapping constant method; The random component is processed by the TVAR model to obtain the parameter estimation value and estimation error, and finally the uncertainty of the random component is calculated; The uncertainty of the deterministic component and the uncertainty of the random component are synthesized to obtain a final uncertainty.

2. The method according to claim 1, It is characterized in that The process of determining whether the IMF component belongs to a deterministic component or a stochastic component includes: Calculate the energy spectrum entropy of each IMF component and compare it with the energy spectrum entropy of the original data. If the energy spectrum entropy of the component is greater than the original energy spectrum entropy, it can be considered that the IMF component belongs to the random component, otherwise it is considered to be a deterministic component. By adding up the components, the dynamic value is divided into deterministic and random components.

3. The method according to claim 1, It is characterized in that The step of obtaining the uncertainty of the deterministic component based on the mapping constant method comprises: Select the appropriate mapping constant order and number of mapping constants; The mapping constant value is calculated, then the uncertainty of the mapping constant is calculated, and finally the uncertainty of the deterministic component is estimated.

4. The method according to claim 1, It is characterized in that The step of processing the random component by the TVAR model to obtain parameter estimation values ​​and estimation errors, and finally calculating the uncertainty of the random component specifically includes: The time-varying coefficients of the TVAR model are expressed as a weighted linear combination of a set of Fourier basis functions, and the parameter estimates and estimation errors are calculated based on the least squares theory. The model order was calculated according to the AIC criterion; The uncertainty of the random component is calculated based on the parameter estimation value, estimation error, and model order.

5. The method according to claim 1, It is characterized in that In the step of synthesizing the uncertainty of the deterministic component and the uncertainty of the random component to obtain the final uncertainty, the synthesis is performed based on a synthesis formula proposed by the American Society of Mechanical Engineers.

6. A fusion system for dynamic measurement uncertainty assessment, It is characterized in that include: The component separation module can separate the original data into components to obtain several IMF components and a residual component, calculate the energy spectrum entropy of each IMF component, and determine whether the IMF component belongs to a deterministic component or a random component; A deterministic component analysis module, capable of obtaining the uncertainty of the deterministic component based on a mapping constant method; The random component analysis module can process the random component through the TVAR model to obtain the parameter estimation value and estimation error, and finally calculate the uncertainty of the random component; The uncertainty synthesis module can synthesize the uncertainty of the deterministic component and the uncertainty of the random component to obtain a final uncertainty.

7. The system according to claim 6, It is characterized in that The process of determining whether the IMF component belongs to a deterministic component or a stochastic component includes: Calculate the energy spectrum entropy of each IMF component and compare it with the energy spectrum entropy of the original data. If the energy spectrum entropy of the component is greater than the original energy spectrum entropy, it can be considered that the IMF component belongs to the random component, otherwise it is considered to be a deterministic component. By adding up the components, the dynamic value is divided into deterministic and random components.

8. The system according to claim 6, It is characterized in that The deterministic component analysis module includes the following working process: Select the appropriate mapping constant order and number of mapping constants; The mapping constant value is calculated, then the uncertainty of the mapping constant is calculated, and finally the uncertainty of the deterministic component is estimated.

9. The system according to claim 6, It is characterized in that The random component analysis module, the working process includes: The time-varying coefficients of the TVAR model are expressed as a weighted linear combination of a set of Fourier basis functions, and the parameter estimates and estimation errors are calculated based on the least squares theory. The model order was calculated according to the AIC criterion; The uncertainty of the random component is calculated based on the parameter estimation value, estimation error, and model order.

10. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the method according to any one of claims 1 to 5.

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