Method and system for evaluating measurement uncertainty based on digital simulation

Abstract

The application provides a kind of based on digital simulation measurement uncertainty evaluation method and system, comprising: analysis measurement object, measurement principle, measurement chain, measurement environment and the conversion relationship between original measurement data and measurement object parameter;From the conversion relationship between measurement principle, measurement chain, measurement environment and original measurement data and measurement object parameter, determine the source and distribution of measurement uncertainty;According to the source and distribution of measurement uncertainty, generate uncertainty source sample subset;According to measurement chain, establish measurement simulation model;On the basis of measurement simulation model, at least 10 6 times Monte Carlo numerical simulation test is carried out to the random call uncertainty source sample subset, and standard measurement uncertainty sample set is obtained;Standard measurement uncertainty is calculated and obtained according to standard measurement uncertainty sample set.The technical scheme of the application is used to solve the technical problem that the complex measurement system cannot be accurately evaluated in the prior art.

Description

Technical Field

[0001] This invention relates to the fields of metrology and environmental detection technology, and in particular to a method and system for evaluating measurement uncertainty based on digital simulation. Background Technology

[0002] With increasing demands for understanding the upper atmospheric environment and assessing the dispersion of detection data, uncertainty assessment of detection data has become a crucial step. Measurement uncertainty characterizes the dispersion and reliability of measurement results, indirectly reflecting the quality of the detection activity. Conventional GUM measurement uncertainty assessment can obtain the metrological characteristics of the measurement system itself or the system measurement uncertainty through traceability calibration. However, for complex measurement systems that cannot be calibrated, where the influence of each module on the measurement results is unknown, and where sensitivity coefficients are unavailable, there is currently no way to accurately assess measurement uncertainty.

[0003] The existing technology currently has the following problems: 1) There is no effective evaluation method for complex measurement systems. The measurement uncertainty is greatly affected by subjectivity. Due to conservative estimation, the evaluated measurement results are often too large and cannot reflect the results that are more in line with the actual situation; 2) Due to the measurement principle or detection distance characteristics, complex detection systems cannot be traced as a whole and the measurement uncertainty of the system cannot be determined by calibration.

[0004] 3) For complex detection systems, the units of measurement for the index values ​​(sources of measurement uncertainty) affecting the detection results in the system modules differ from the units of measurement of the results, requiring conversion using sensitivity coefficients, which are often unavailable. 4) For complex detection systems, the correlation coefficients for the index values ​​(sources of measurement uncertainty) affecting the detection results in the system modules are often unavailable for measurement uncertainty assessment, as they are often unavailable. 5) For complex detection systems, the index values ​​(sources of measurement uncertainty) affecting the detection results in the system modules interact with each other across different systems, making it impossible to obtain the uncertainty components. 6) Due to the dynamic changes of the measured object, the detection activity is not reproducible, and there is no reliable reference experience for the measurement uncertainty components, making accurate evaluation impossible. Summary of the Invention

[0005] This invention provides a measurement uncertainty assessment method and system based on digital simulation, which can solve the technical problem that existing technologies cannot accurately assess the measurement uncertainty of complex measurement systems.

[0006] According to one aspect of the present invention, a measurement uncertainty assessment method based on digital simulation is provided. The method includes: analyzing the measurement object, measurement principle, measurement chain, measurement environment, and the conversion relationship between the original measurement data and the parameters of the measurement object; determining the sources of measurement uncertainty and their distribution from the conversion relationship between the measurement principle, measurement chain, measurement environment, and the original measurement data and the parameters of the measurement object; generating a subset of uncertainty source samples based on the sources of measurement uncertainty and their distribution; establishing a measurement simulation model based on the measurement chain; and, based on the measurement simulation model, randomly calling the subset of uncertainty source samples for at least 10... 6 A Monte Carlo numerical simulation experiment was conducted to obtain a standard measurement uncertainty sample set; the standard measurement uncertainty was then calculated based on the standard measurement uncertainty sample set.

[0007] Furthermore, the analysis of the measurement object, measurement principle, measurement chain, measurement environment, and the conversion relationship between the original measurement data and the parameters of the measurement object specifically includes: clarifying the parameters of the measurement object and the units of measurement for the parameters of the measurement object; clarifying the measurement principle of the measurement object, analyzing the gap between the theoretical measurement results obtained based on the measurement principle and the actual parameters of the measured object, and clarifying the best estimated value and the distribution of the best estimated value based on the gap between the theoretical measurement results and the actual parameters of the measured object; clarifying the composition of each module of the measurement chain; clarifying the way in which the measurement environment affects the measurement results; and clarifying the conversion relationship between the original measurement data and the parameters of the measurement object.

[0008] Furthermore, determining the sources of measurement uncertainty and their distribution from the measurement principle, measurement chain, measurement environment, and conversion relationship between the original measurement data and the parameters of the measured object specifically includes: identifying the sources of measurement uncertainty affecting the measurement results from the measurement principle, measurement chain, measurement environment, and conversion relationship between the original measurement data and the parameters of the measured object; and clarifying the distribution of each source of measurement uncertainty through actual measurement, device manuals, or metrological calibration certificates.

[0009] Furthermore, establishing a measurement simulation model based on the measurement chain specifically includes: establishing a measurement simulation model based on the measurement principle of the measured object, the composition of each module of the measurement chain, the measurement process, and the signal flow during the measurement process. The measurement principle, measurement chain, measurement process, and signal flow of the measurement simulation model are consistent with the actual measurement. Each uncertain source in the uncertain source sample subset and its corresponding uncertain source distribution are entered into the measurement simulation model.

[0010] Furthermore, at least 10 random subsets of samples from uncertain sources are selected. 6The Monte Carlo numerical simulation experiment obtained the standard measurement uncertainty sample set by: starting the simulation based on the measurement simulation model, randomly generating data from each uncertainty source according to its own input uncertainty source distribution, and obtaining multiple simulation result data; storing the input measurement object parameters in one-to-one correspondence with the multiple simulation result data to form a sample set; and calculating the deviation values ​​between each simulation result data and the input measurement object parameters in turn based on the sample set, and forming a standard measurement uncertainty sample set based on the multiple deviation values.

[0011] Furthermore, the calculation of standard measurement uncertainty based on the standard measurement uncertainty sample set specifically includes: calculating the standard deviation of multiple deviation values ​​in the standard measurement uncertainty sample set, and using the standard deviation of multiple deviation values ​​as the standard measurement uncertainty.

[0012] According to another aspect of the present invention, a measurement uncertainty assessment system based on digital simulation is provided, wherein the measurement uncertainty assessment system based on digital simulation uses the measurement uncertainty assessment method based on digital simulation as described above to assess the measurement uncertainty.

[0013] Furthermore, the measurement uncertainty assessment system based on digital simulation includes: a 5M element analysis unit, used to analyze the measurement object, measurement principle, measurement chain, measurement environment, and the conversion relationship between the original measurement data and the parameters of the measurement object; an uncertainty source and distribution determination unit, used to determine the sources of measurement uncertainty and their distribution from the conversion relationship between the measurement principle, measurement chain, measurement environment, and the parameters of the original measurement data and the parameters of the measurement object; an uncertainty source sample subset generation unit, used to generate a subset of uncertainty sources based on the measurement uncertainty sources and their distribution; a measurement simulation model generation unit, used to establish a measurement simulation model based on the measurement chain; and a standard measurement uncertainty sample set generation unit, used to randomly call the uncertainty source sample subset for at least 10... 6 The Monte Carlo numerical simulation experiment yields a standard measurement uncertainty sample set; the standard measurement uncertainty calculation unit is used to calculate the standard measurement uncertainty based on the standard measurement uncertainty sample set.

[0014] This invention provides a method for assessing measurement uncertainty based on digital simulation. This method involves digital domain modeling, defining the distribution set of measurement uncertainty sources and the parameters of the measured object, and using Monte Carlo simulation to obtain a sample set of simulation data. The simulation data is then subtracted from the defined measured object parameters to obtain a set of deviation samples. The standard deviation of this set is then calculated as the standard measurement uncertainty. Compared with existing technologies, the measurement uncertainty assessment method based on digital simulation provided by this invention employs both digital simulation and Monte Carlo simulation techniques, forming a complete measurement uncertainty method. It integrates all sources of measurement uncertainty and obtains the standard measurement uncertainty in one step, enabling accurate and efficient assessment of the measurement uncertainty of complex detection systems during high-altitude detection. Attached Figure Description

[0015] The accompanying drawings, which form part of this specification, are provided to further illustrate embodiments of the invention and, together with the textual description, explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0016] Figure 1 A flowchart of a measurement uncertainty assessment method based on digital simulation according to a specific embodiment of the present invention is shown;

[0017] Figure 2 A schematic diagram illustrating the principle of a measurement uncertainty assessment method based on digital simulation according to a specific embodiment of the present invention is shown.

[0018] Figure 3 A schematic diagram of a measurement simulation model provided according to a specific embodiment of the present invention is shown. Detailed Implementation

[0019] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0021] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0022] like Figure 1 and Figure 2 As shown in the figure, a measurement uncertainty assessment method based on digital simulation is provided according to a specific embodiment of the present invention. This measurement uncertainty assessment method includes: analyzing the measurement object, measurement principle, measurement chain, measurement environment, and the conversion relationship between the original measurement data and the parameters of the measurement object; determining the sources of measurement uncertainty and their distribution from the conversion relationship between the measurement principle, measurement chain, measurement environment, and the original measurement data and the parameters of the measurement object; generating a subset of uncertainty source samples based on the sources of measurement uncertainty and their distribution; establishing a measurement simulation model based on the measurement chain; and, based on the measurement simulation model, randomly calling the subset of uncertainty source samples for at least 10... 6 A Monte Carlo numerical simulation experiment was conducted to obtain a standard measurement uncertainty sample set; the standard measurement uncertainty was then calculated based on the standard measurement uncertainty sample set.

[0023] This configuration provides a measurement uncertainty assessment method based on digital simulation. This method uses digital domain modeling to define the distribution set of measurement uncertainty sources and the parameters of the measured object. It then employs Monte Carlo simulation to obtain a sample set of simulation data. Subtracting the simulation data from the defined measured object parameters yields a set of deviation samples. The standard deviation of this set is then calculated as the standard measurement uncertainty. Compared to existing technologies, the measurement uncertainty assessment method based on digital simulation provided by this invention utilizes both digital simulation and Monte Carlo simulation techniques to form a complete measurement uncertainty method. It integrates all sources of measurement uncertainty and obtains the standard measurement uncertainty in one step, enabling accurate and efficient assessment of the measurement uncertainty of complex detection systems during high-altitude detection.

[0024] Specifically, in this invention, to accurately and efficiently assess the measurement uncertainty of a complex detection system during high-altitude detection, it is first necessary to analyze the measurement object, measurement principle, measurement chain, measurement environment, and the conversion relationship between the original measurement data and the parameters of the measurement object. In this invention, analyzing the measurement object, measurement principle, measurement chain, measurement environment, and the conversion relationship between the original measurement data and the parameters of the measurement object specifically includes: clarifying the parameters of the measurement object and the units of measurement for those parameters; clarifying the measurement principle of the measurement object, analyzing the gap between the theoretical measurement results obtained based on the measurement principle and the actual parameters of the measured object, and determining the best estimate and its distribution based on the gap between the theoretical measurement results and the actual parameters of the measured object; clarifying the composition of each module of the measurement chain; clarifying the way the measurement environment affects the measurement results; and clarifying the conversion relationship between the original measurement data and the parameters of the measurement object.

[0025] As a specific embodiment of the present invention, the 5M elements of measurement (measurement object, measurement principle, measurement chain, measurement environment, and the conversion relationship between the original measurement data and the parameters of the measurement object) are analyzed.

[0026] 1.1) Clearly define the parameters of the object being measured and the units of measurement for those parameters. For example, if the object being measured is atmospheric temperature, then the unit of measurement for the object being measured is K; if the corresponding parameter being measured is atmospheric wind speed, then the unit of measurement for the object being measured is m / s, etc.

[0027] 1.2) Clarify the measurement principle of the object being measured, analyze the gap between the theoretical measurement results obtained based on the measurement principle and the actual parameters of the measured object, and determine the best estimate and its distribution based on the gap. For example, assuming the measured object parameter is atmospheric wind speed, and the measurement distance is measured using a weather balloon, the wind speed is obtained by recording the distance the weather balloon flies over within a set time and dividing the measured distance by the time taken. Assuming that the theoretical measurement result, without considering the influence of the weather balloon drag coefficient, is 10 m / s, while the actual measured object parameter is 8 m / s, then the gap between the theoretical measurement result and the actual measured object parameter is 2 m / s. Therefore, for a set theoretical measurement result, assuming it is 12 m / s, based on the previously obtained gap value of 2 m / s, the best estimate of the actual measured object parameter should be 10 m / s. Repeating the above process, multiple experiments are first conducted to obtain multiple threshold ranges of difference between theoretical measurement results and the actual measured object parameters. Then, for any given theoretical measurement result, the optimal estimated value distribution can be obtained based on the threshold range of difference. As a specific embodiment of the present invention, assuming that multiple experiments are conducted and the threshold range of difference between multiple theoretical measurement results and the actual measured object parameters is (1.8 m / s to 2.2 m / s), then for a given theoretical measurement result, assuming it is 12 m / s, based on the previously obtained threshold range of difference (1.8 m / s to 2.2 m / s), the optimal estimated value distribution of the actual measured object parameters should be (9.8 m / s to 10.2 m / s).

[0028] 1.3) Define the composition of each module in the measurement chain. For each module, clarify and confirm the technical indicators and distribution that affect the measurement results. The measurement chain comprises all hardware modules involved in the signal flow process within the detection equipment. In one specific embodiment of the invention, assuming the measured parameter is atmospheric wind speed, the modules of the measurement chain include a weather balloon, a GPS positioning system, a data processing system, and a data transmission system. The GPS positioning system measures the position of the weather balloon and various time points. The data processing system calculates the atmospheric wind speed based on the position recorded by the GPS system at different times. The data transmission system transmits the atmospheric wind speed calculated by the data processing system back to the ground. In another embodiment of the invention, assuming the measurement of atmospheric density, temperature, etc., the lidar measurement chain includes a transmitting system, a receiving system, a subsequent optical system, and a detection system.

[0029] 1.4) Clarify how the measurement environment affects the measurement results. If the measurement environment primarily affects the device, this should be reflected in the measurement chain analysis. As a specific embodiment of the present invention, assuming the measured parameter is atmospheric wind speed, the factors in the measurement environment affecting the measurement of atmospheric wind speed include temperature. Temperature changes will have a significant impact on the measurement results.

[0030] 1.5) Clarify the conversion relationship between the original measurement data and the parameters of the measured object. In one specific embodiment of the present invention, assuming the measured object parameter is atmospheric wind speed, the original measurement data is the position of the weather balloon and the time required for the weather balloon to travel a set distance. Since the measured object parameter is atmospheric wind speed, uncertainties may be introduced in the process of converting distance and time to atmospheric wind speed. Therefore, the inversion process must be considered in the modeling. In another embodiment of the present invention, assuming the measurement of atmospheric density, temperature, etc., when the working principle of lidar for atmospheric detection utilizes molecular backscattering and lidar equations, the detection data is the number of echo photons from the laser. The inversion process from the photon number to atmospheric parameters may introduce uncertainties. Therefore, the inversion process must be considered in the modeling.

[0031] Furthermore, in this invention, after analyzing the measurement object, measurement principle, measurement chain, measurement environment, and the conversion relationship between the original measurement data and the parameters of the measurement object, the sources of measurement uncertainty and their distribution can be determined from the measurement principle, measurement chain, measurement environment, and the conversion relationship between the original measurement data and the parameters of the measurement object.

[0032] In this invention, determining the sources of measurement uncertainty and their distribution from the measurement principle, measurement chain, measurement environment, and conversion relationship between the original measurement data and the parameters of the measured object specifically includes: determining the sources of measurement uncertainty affecting the measurement results from the measurement principle, measurement chain, measurement environment, and conversion relationship between the original measurement data and the parameters of the measured object; and clarifying the distribution of each source of measurement uncertainty through actual measurement or device specifications.

[0033] As a specific embodiment of the present invention, assuming the measured object parameter is atmospheric wind speed, the sources of measurement uncertainty include the measurement principle, the measurement chain, the measurement environment, and the conversion relationship between the original measurement data and the measured object parameter. Specifically, for the measurement chain, the measurement chain includes multiple modules, and each module may introduce sources of uncertainty, such as GPS positioning accuracy and data transmission accuracy. As another embodiment of the present invention, assuming the measurement is of atmospheric density, temperature, etc., the indicators affecting the measurement results in the lidar include detector efficiency and laser pulse energy.

[0034] After identifying the sources of measurement uncertainty, it is necessary to determine the distribution of each source. These indicators can generally be provided by actual measurement activities, device manuals, or calibration certificates. When the distribution cannot be determined, a uniform distribution can be assumed. As a specific embodiment of this invention, assuming the measured parameter is atmospheric wind speed, for a source of measurement uncertainty such as GPS positioning accuracy, the measurement uncertainty range of GPS positioning accuracy can be obtained from the GPS device manual or calibration certificate. For a source of measurement uncertainty such as the accuracy of data transmission, the measurement uncertainty range of data transmission can be obtained from the data transmission system's device manual or calibration certificate, or it can be obtained through actual measurement activities.

[0035] Furthermore, after determining the sources of measurement uncertainty and their distribution, a subset of uncertainty sources can be generated based on these sources.

[0036] As a specific embodiment of the present invention, assuming the measured parameter is atmospheric wind speed, at least 10 uncertainty sources and distributions conforming to the distribution characteristics are generated sequentially for the measurement principle, measurement chain, measurement environment, and the conversion relationship between the original measurement data and the measured parameter. 6 Source sample subsets. For example, a GPS positioning accuracy sample subset is established to address the GPS positioning accuracy and measurement uncertainty in the data link; a data transmission sample subset is established to address the uncertainty distribution range of the data transmission accuracy in the data link; and so on. Sample subsets related to measurement principles, measurement environment, and conversion relationships between raw measurement data and measurement object parameters are then constructed. Finally, based on these sample subsets, at least 10 subsets conforming to the distribution characteristics are generated. 6 Source sample subsets, wherein each uncertainty source sample subset includes at least 10 6 One sample.

[0037] Furthermore, after generating a subset of uncertainty sources based on their sources and distribution, a measurement simulation model can be established based on the measurement chain. In this invention, establishing a measurement simulation model based on the measurement chain specifically includes: establishing a measurement simulation model based on the measurement principle of the measured object, the composition of each module of the measurement chain, the measurement process, and the signal flow during the measurement process; ensuring that the measurement principle, measurement chain, measurement process, and signal flow of the measurement simulation model are consistent with the actual measurement; and inputting each uncertainty source and its corresponding distribution from the subset of uncertainty sources into the measurement simulation model.

[0038] As a specific embodiment of the present invention, such as Figure 3 As shown, a simulation model is established in the digital domain, requiring that the measurement principle, measurement chain, and generated measurement data be consistent with reality. Specifically, the measurement principle of the simulation model is the same as that of the actual measurement; the composition of each module in the measurement chain of the simulation model is the same as that used in the actual measurement; the specific measurement process in the simulation model, as well as the input quantities and environmental factors during the measurement process, are the same as those in the actual measurement; and the specific flow of signals and the output quantities in the simulation modules are the same as those in the actual measurement. After completing the construction of each module of the simulation model, the uncertainty sources and uncertainty distributions of all modules in the measurement chain are entered. During the simulation, the indicators of the measurement chain modules that affect the results will be randomly generated according to the distribution law. For example, assuming the measured object parameter is atmospheric wind speed, subsets of measurement principle samples, measurement environment samples, data chain samples, and conversion relationship samples between the original measurement data and the measured object parameter are entered into the simulation model.

[0039] Furthermore, the parameter values ​​of the object under test are set, and the measurement process is simulated using a simulation model to obtain the simulation measurement results.

[0040] Furthermore, set at least 10 6 The parameter values ​​of the tested object are randomly selected from the uncertain source sample subset for at least 10 tests. 6 The Monte Carlo numerical simulation experiment was conducted to obtain a standard measurement uncertainty sample set.

[0041] In this invention, a subset of samples from uncertain sources is randomly selected for at least 10... 6 The Monte Carlo numerical simulation experiment obtained the standard measurement uncertainty sample set by: starting the simulation based on the measurement simulation model, randomly generating data from each uncertainty source according to its own input uncertainty source distribution, and obtaining multiple simulation result data; storing the input measurement object parameters in one-to-one correspondence with the multiple simulation result data to form a sample set; and calculating the deviation values ​​between each simulation result data and the input measurement object parameters in turn based on the sample set, and forming a standard measurement uncertainty sample set based on the multiple deviation values.

[0042] As a specific embodiment of the present invention, assuming the measured object parameter is atmospheric wind speed, in each Monte Carlo experiment, an uncertain sample of the measurement principle is randomly selected from the subset of measurement principle samples, an uncertain sample of the measurement environment sample is randomly selected from the subset of measurement environment samples, a data link sample is randomly selected from the subset of data link samples, and a conversion relationship sample is randomly selected from the subset of conversion relationship samples between the original measurement data and the measured object parameter. Monte Carlo numerical simulation experiments are performed based on the randomly selected samples, and simulation results are obtained. This process is repeated until at least 10 results are obtained.6 Each simulation result is recorded. The parameters of the measured object are stored one-to-one with the simulation results to form a sample set. The deviation between the simulation results and the recorded parameters of the measured object is calculated to form a deviation value sample set, which contains at least 10 corresponding values. 6 One deviation value.

[0043] Furthermore, in this invention, after obtaining the standard measurement uncertainty sample set, the standard measurement uncertainty can be calculated and obtained based on the standard measurement uncertainty sample set. Specifically, calculating and obtaining the standard measurement uncertainty based on the standard measurement uncertainty sample set includes: calculating the standard deviation of multiple deviation values ​​in the standard measurement uncertainty sample set, and using the standard deviation of the multiple deviation values ​​as the standard measurement uncertainty.

[0044] According to another aspect of the present invention, a measurement uncertainty assessment system based on digital simulation is provided, which uses the measurement uncertainty assessment method based on digital simulation described above to assess measurement uncertainty.

[0045] This configuration provides a measurement uncertainty assessment system based on digital simulation. The system uses digital domain modeling to define the distribution set of measurement uncertainty sources and the parameters of the measured object. It then employs Monte Carlo simulation to obtain a sample set of simulation data. Subtracting the simulation data from the defined measured object parameters yields a set of deviation samples. The standard deviation of this set is then calculated as the standard measurement uncertainty. Compared to existing technologies, the measurement uncertainty assessment system based on digital simulation provided by this invention utilizes both digital simulation and Monte Carlo simulation techniques to form a complete measurement uncertainty method. It integrates all sources of measurement uncertainty and obtains the standard measurement uncertainty in one step, enabling accurate and efficient assessment of the measurement uncertainty of complex detection systems during high-altitude detection.

[0046] Specifically, in this invention, to achieve measurement uncertainty assessment, the measurement uncertainty assessment system based on digital simulation includes a 5M element analysis unit, an uncertainty source and distribution determination unit, a measurement simulation model generation unit, a standard measurement uncertainty sample set generation unit, and a standard measurement uncertainty calculation unit. The 5M element analysis unit analyzes the measurement object, measurement principle, measurement chain, measurement environment, and the conversion relationship between the original measurement data and the parameters of the measurement object. The uncertainty source and distribution determination unit determines the sources of measurement uncertainty and their distribution from the conversion relationship between the measurement principle, measurement chain, measurement environment, and the parameters of the original measurement data and the parameters of the measurement object. The uncertainty source sample subset generation unit generates a subset of uncertainty sources based on the measurement uncertainty sources and their distribution. The measurement simulation model generation unit establishes a measurement simulation model based on the measurement chain. The standard measurement uncertainty sample set generation unit randomly calls the uncertainty source sample subset for at least 10... 6 The Monte Carlo numerical simulation experiment yielded a standard measurement uncertainty sample set, and the standard measurement uncertainty calculation unit was used to calculate and obtain the standard measurement uncertainty based on the standard measurement uncertainty sample set.

[0047] To gain a further understanding of the present invention, the following description is provided in conjunction with... Figure 1 and Figure 2 The measurement uncertainty assessment method based on digital simulation provided by this invention will be described in detail.

[0048] like Figure 1 and Figure 2 As shown, according to a specific embodiment of the present invention, a measurement uncertainty evaluation method based on digital simulation is provided, which specifically includes the following steps.

[0049] Step 1: Analyze the 5M elements of measurement (measurement object, measurement principle, measurement chain, measurement environment, and the conversion relationship between the original measurement data and the parameters of the measurement object).

[0050] 1.1) Specify the parameters of the object to be measured and the unit of measurement for those parameters. In this embodiment, the corresponding parameter to be measured is atmospheric wind speed, so the unit of measurement for the object to be measured is m / s, etc.

[0051] 1.2) Clarify the measurement principle of the object being measured, analyze the gap between the theoretical measurement results obtained based on the measurement principle and the actual parameters of the measured object, and determine the best estimate and its distribution based on the gap. For example, assuming the measured object parameter is atmospheric wind speed, and the measurement principle is to measure atmospheric wind speed using a radiosonde balloon, by recording the distance the radiosonde balloon flies over within a set time, and dividing the measured distance by the time taken, the wind speed can be obtained. Assuming that under the theoretical measurement result, without considering the influence of the radiosonde balloon's drag coefficient, the theoretical wind speed is 10 m / s, while the actual measured object parameter is 8 m / s, then the gap between the theoretical measurement result and the actual measured object parameter is 2 m / s. Therefore, for a set theoretical measurement result, assuming it is 12 m / s, based on the previously obtained gap value of 2 m / s, the best estimate of the actual measured object parameter should be 10 m / s. Repeating the above process, multiple experiments are first conducted to obtain multiple threshold differences between theoretical measurement results and the actual measured object parameters. Then, for any given theoretical measurement result, the optimal estimated value distribution can be obtained based on the threshold difference. As a specific embodiment of the present invention, assuming that multiple experiments are conducted and the threshold differences between multiple theoretical measurement results and the actual measured object parameters are (1.8 m / s to 2.2 m / s), then for a given theoretical measurement result, assuming it is 12 m / s, based on the previously obtained threshold difference of (1.8 m / s to 2.2 m / s), the optimal estimated value distribution of the actual measured object parameters should be (9.8 m / s to 10.2 m / s).

[0052] 1.3) Define the composition of each module in the measurement chain. For each module, clarify and confirm the technical indicators and distribution that affect the measurement results. The measurement chain comprises all hardware modules involved in the signal flow process within the detection equipment. In this embodiment, the measured parameter is atmospheric wind speed. Therefore, the modules of the measurement chain include a weather balloon, a GPS positioning system, a data processing system, and a data transmission system. The GPS positioning system measures the position of the weather balloon and various time points. The data processing system calculates the atmospheric wind speed based on the position recorded by the GPS system at different times. The data transmission system transmits the atmospheric wind speed calculated by the data processing system back to the ground.

[0053] 1.4) Clarify how the measurement environment affects the measurement results. If the measurement environment primarily affects the device, this should be reflected in the measurement chain analysis. As a specific embodiment of the present invention, assuming the measured parameter is atmospheric wind speed, the factors in the measurement environment affecting the measurement of atmospheric wind speed include temperature. Temperature changes will have a significant impact on the measurement results.

[0054] 1.5) Clarify the conversion relationship between the original measurement data and the parameters of the measurement object. In this embodiment, the parameter of the measurement object is atmospheric wind speed. The original measurement data are the position of the weather balloon and the time required for the weather balloon to travel a set distance. Since the parameter of the measurement object is atmospheric wind speed, uncertainty may be introduced in the process of converting distance and time into atmospheric wind speed. Therefore, the inversion process should be considered in the modeling.

[0055] Step two: Determine the sources of measurement uncertainty and their distribution from the measurement principle, measurement chain, measurement environment, and conversion relationship between the original measurement data and the parameters of the measured object.

[0056] In this embodiment, the measured parameter is atmospheric wind speed. Therefore, the sources of measurement uncertainty include the measurement principle, the measurement chain, the measurement environment, and the conversion relationship between the original measurement data and the measured parameter. Specifically, regarding the measurement chain, it comprises multiple modules, each of which may introduce sources of uncertainty, such as GPS positioning accuracy and data transmission accuracy. For GPS positioning accuracy as a source of measurement uncertainty, the measurement uncertainty range can be obtained from the GPS device's instruction manual. For data transmission accuracy as a source of measurement uncertainty, the measurement uncertainty range can be obtained from the data transmission system's instruction manual or through actual measurement activities.

[0057] Step 3: Generate a subset of uncertainty sources based on the sources of measurement uncertainty and their distribution.

[0058] In this embodiment, the measured parameter is atmospheric wind speed. Uncertainty sources and distributions conforming to the distribution characteristics are generated sequentially, addressing the measurement principle, measurement chain, measurement environment, and the conversion relationship between the original measurement data and the measured parameter. 6 Source sample subsets. For example, to address the GPS positioning accuracy in the data link and the measurement uncertainty of positioning accuracy, a GPS positioning accuracy sample subset is established; to address the uncertain distribution range of the data transmission accuracy in the data link, a data transmission sample subset is established, and so on. Subsequent subsets are constructed for measurement principle, measurement environment, and conversion relationships between raw measurement data and measurement object parameters. Finally, based on these subsets, a set of 10 samples conforming to the distribution characteristics is generated. 6 Source sample subsets, wherein each uncertainty source sample subset includes 10 6 One sample.

[0059] Step four: Establish a measurement simulation model based on the measurement chain. In this embodiment, a simulation model is established in the digital domain, requiring that the measurement principle, measurement chain, and generated measurement data be consistent with reality. Specifically, the measurement principle of the simulation model is the same as that of the actual measurement; the composition of each module of the measurement chain in the simulation model is the same as that used in the actual measurement; the specific measurement process in the simulation model, as well as the input quantities and environmental factors in the measurement process, are the same as those in the actual measurement; and the specific flow of signal transitions and output quantities in the simulation modules are the same as those in the actual measurement. After completing the construction of each module of the simulation model, the uncertainty sources and uncertainty distributions of all modules on the measurement chain are recorded. During the simulation process, the indicators of the measurement chain modules that affect the results will be randomly generated according to the distribution law.

[0060] Step 5: Randomly select the subset of samples from uncertain sources for at least 10... 6 A Monte Carlo numerical simulation experiment was conducted to obtain a standard measurement uncertainty sample set. In this embodiment, simulation was started based on a measurement simulation model. Data was randomly generated by each uncertainty source according to its own input uncertainty source distribution to obtain multiple simulation result data. The input measurement object parameters were stored one-to-one with the multiple simulation result data to form a sample set. Based on the sample set, the deviation values ​​between each simulation result data and the input measurement object parameters were calculated in sequence, and a standard measurement uncertainty sample set was formed based on multiple deviation values.

[0061] Step six: Calculate the standard measurement uncertainty based on the standard measurement uncertainty sample set. In this embodiment, the standard deviation of multiple deviation values ​​in the standard measurement uncertainty sample set is calculated, and the standard deviation of multiple deviation values ​​is used as the standard measurement uncertainty.

[0062] In summary, this invention provides a measurement uncertainty assessment method based on digital simulation. This method uses digital domain modeling to set the distribution set of measurement uncertainty sources and the parameters of the measured object. It employs Monte Carlo simulation to obtain a sample set of simulation data. The simulation data is subtracted from the set measured object to obtain a set of deviation samples. The standard deviation of this set is then calculated as the standard measurement uncertainty. The measurement uncertainty assessment method provided by this invention has the following characteristics: (1) It provides a measurement uncertainty assessment method based on digital modeling; (2) It is applicable to situations where overall traceability is impossible and the measurement process cannot be reproduced and verified; (3) It avoids the difficulties of solving measurement uncertainty components, correlation coefficients, and sensitivity coefficients; (4) It uses digital modeling technology to make the simulated measurement process match the actual detection; (5) It uses Monte Carlo simulation, after 10... 6 This calculation generates a set of deviation samples, improving the reliability of the evaluation results.

[0063] Compared with existing technologies, the measurement uncertainty assessment method based on digital simulation provided by this invention uses Monte Carlo simulation input for each source of measurement uncertainty and obtains at least 10... 6 This simulation data employs digital simulation and Monte Carlo simulation techniques to form a complete measurement uncertainty method. It integrates all sources of measurement uncertainty and obtains the standard measurement uncertainty in one go. It can accurately and efficiently evaluate the measurement uncertainty of complex detection systems during high-altitude detection, especially for the measurement uncertainty evaluation of complex system detection that cannot be traced and calibrated. It is particularly suitable for situations where influencing factors cannot be decoupled, the correlation coefficient between measurement uncertainty components is uncertain, and a certain source of measurement uncertainty affects multiple devices in the measurement chain.

[0064] For ease of description, spatial relative terms such as "above," "on top of," "on the upper surface of," "above," etc., are used herein to describe the spatial positional relationship of a device or feature as shown in the figures to other devices or features. It should be understood that spatial relative terms are intended to encompass different orientations in use or operation beyond the orientation of the device as described in the figures. For example, if the device in the figures were inverted, a device described as "above" or "on top of" other devices or structures would subsequently be positioned as "below" or "under" other devices or structures. Thus, the exemplary term "above" can include both "above" and "below." The device may also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatial relative descriptions used herein will be interpreted accordingly.

[0065] Furthermore, it should be noted that the use of terms such as "first" and "second" to define components is merely for the purpose of distinguishing the corresponding components. Unless otherwise stated, the above terms have no special meaning and therefore should not be construed as limiting the scope of protection of this invention.

[0066] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for evaluating measurement uncertainty based on digital simulation, characterized in that, The measurement uncertainty evaluation method includes: Analyze the measurement object, measurement principle, measurement chain, measurement environment, and the conversion relationship between the raw measurement data and the parameters of the measurement object; The sources of measurement uncertainty and their distribution are determined from the measurement principle, the measurement chain, the measurement environment, and the conversion relationship between the original measurement data and the parameters of the measured object. A subset of uncertainty sources is generated based on the sources of measurement uncertainty and their distribution. Establish a measurement simulation model based on the measurement chain; Based on the measurement simulation model, at least 10 samples from uncertain sources are randomly selected for testing. 6 The Monte Carlo numerical simulation experiment was conducted to obtain a standard measurement uncertainty sample set. The standard measurement uncertainty is calculated based on the aforementioned standard measurement uncertainty sample set. The analysis of the conversion relationships between the measurement object, measurement principle, measurement chain, measurement environment, and raw measurement data and the parameters of the measurement object specifically includes: Clearly define the parameters of the object being measured and the units of measurement for those parameters; Clarify the measurement principle of the measured object, analyze the gap between the theoretical measurement results obtained based on the measurement principle and the actual measured object parameters, and determine the best estimated value and the distribution of the best estimated value based on the gap between the theoretical measurement results and the actual measured object parameters; Clearly define the components of each module in the measurement chain; Clearly define how the measurement environment affects the measurement results; Clarify the conversion relationship between the original measurement data and the parameters of the measured object. Determine the sources of measurement uncertainty and their distribution from the measurement principle, the measurement chain, the measurement environment, and the conversion relationship between the original measurement data and the parameters of the measured object. Specifically, this includes: The sources of measurement uncertainty affecting the measurement results are determined from the measurement principle, the measurement chain, the measurement environment, and the conversion relationship between the original measurement data and the parameters of the measured object. The distribution of each source of measurement uncertainty is determined through actual measurements, device manuals, or metrological calibration certificates. A measurement simulation model is then established based on the measurement chain, specifically including: A measurement simulation model is established based on the measurement principle of the measured object, the composition of each module of the measurement chain, the measurement process, and the signal flow during the measurement process. The measurement principle, measurement chain, measurement process, and signal flow of the measurement simulation model are consistent with the actual measurement. Each uncertain source in the uncertain source sample subset and its corresponding uncertain source distribution are entered into the measurement simulation model.

2. The measurement uncertainty assessment method based on digital simulation according to claim 1, characterized in that, Randomly select the subset of samples from uncertain sources for at least 10... 6 The Monte Carlo numerical simulation experiment yielded a standard measurement uncertainty sample set, specifically including: Simulation begins based on the measurement simulation model, and data is randomly generated from each of the uncertain sources according to their respective input uncertain source distributions to obtain multiple simulation result data. The input measurement object parameters are stored one-to-one with multiple simulation result data to form a sample set; Based on the sample set, the deviation values ​​between each simulation result data and the entered measurement object parameters are calculated sequentially, and a standard measurement uncertainty sample set is formed based on multiple deviation values.

3. The measurement uncertainty assessment method based on digital simulation according to claim 2, characterized in that, The calculation of standard measurement uncertainty based on the standard measurement uncertainty sample set specifically includes: calculating the standard deviation of multiple deviation values ​​in the standard measurement uncertainty sample set, and using the standard deviation of multiple deviation values ​​as the standard measurement uncertainty.

4. A measurement uncertainty evaluation system based on digital simulation, characterized in that, The measurement uncertainty assessment system based on digital simulation uses the measurement uncertainty assessment method based on digital simulation as described in any one of claims 1 to 3 to assess measurement uncertainty.

5. The measurement uncertainty assessment system based on digital simulation according to claim 4, characterized in that, The measurement uncertainty assessment system based on digital simulation includes: The 5M element analysis unit is used to analyze the measurement object, measurement principle, measurement chain, measurement environment, and the conversion relationship between the original measurement data and the parameters of the measurement object. An uncertainty source and distribution determination unit is used to determine the sources of measurement uncertainty and the distribution of these sources from the measurement principle, the measurement chain, the measurement environment, and the conversion relationship between the original measurement data and the parameters of the measurement object. An uncertain source sample subset generation unit is used to generate an uncertain source sample subset based on the measurement uncertainty sources and their distribution. A measurement simulation model generation unit is used to establish a measurement simulation model based on a measurement chain. A standard measurement uncertainty sample set generation unit is used to randomly call the subset of uncertainty sources for at least 10... 6 The Monte Carlo numerical simulation experiment was conducted to obtain a standard measurement uncertainty sample set. A standard measurement uncertainty calculation unit is used to calculate and obtain the standard measurement uncertainty based on the standard measurement uncertainty sample set.

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