CN117516617A - Measurement uncertainty assessment method and system based on digital simulation - Google Patents
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Abstract
The invention provides a measurement uncertainty assessment method and a system based on digital simulation, comprising the following steps: analyzing a conversion relation between a measurement object, a measurement principle, a measurement chain, a measurement environment and original measurement data and measurement object parameters; determining measurement uncertainty sources and distribution conditions from a measurement principle, a measurement chain, a measurement environment and a conversion relation between original measurement data and measurement object parameters; generating an uncertainty source sample subset according to the uncertainty source and distribution condition; establishing a measurement simulation model according to a measurement chain; randomly invoking a subset of uncertain source samples for at least 10 based on the measurement simulation model 6 Performing a Monte Carlo numerical simulation test to obtain a standard measurement uncertainty sample set; calculating the acquisition standard according to the standard measurement uncertainty sample setUncertainty is measured. By applying the technical scheme of the invention, the technical problem that the complex measurement system cannot be evaluated in more accurate measurement uncertainty in the prior art is solved.
Description
Technical Field
The invention relates to the technical fields of metering test and environment detection, in particular to a measurement uncertainty assessment method and system based on digital simulation.
Background
With the increasing demands for high-altitude atmospheric environment cognition and evaluation of the dispersibility of the detection data, the uncertainty evaluation of the detection data becomes an important link. The measurement uncertainty may characterize the dispersion and confidence level of the measurement results, indirectly reflecting the quality of the detection activity. Conventional GUM measurement uncertainty assessment can obtain the metering characteristics of the measurement system or the measurement uncertainty of the system by a traceability calibration mode. For complex measurement systems which cannot be calibrated, the influence mode of each module on the measurement result is unknown, and the sensitivity coefficient cannot be obtained, no method for accurately evaluating the uncertainty of measurement is available at present.
The following problems currently exist in the prior art: 1) The complex measurement system is not provided with a more effective evaluation method, the measurement uncertainty is greatly influenced by the subjective view, and the result which is evaluated by the conservative estimation is larger and can not reflect the result which is more in line with the actual situation; 2) The complex detection system cannot carry out integral tracing due to the measurement principle or detection distance characteristic, and the measurement uncertainty of the system cannot be determined in a calibration mode;
3) For a complex detection system, an index value (a measurement uncertainty source) affecting a detection result in a system module is different from a measurement unit of the measurement result, and the index value is converted by a sensitivity coefficient, but the sensitivity coefficient cannot be obtained; 4) For a complex detection system, an index value (a measurement uncertainty source) affecting a detection result in a system module needs to obtain a correlation coefficient to evaluate the measurement uncertainty, but the correlation coefficient cannot be obtained; 5) For complex detection systems, index values (sources of measurement uncertainty) affecting detection results in system modules interact between different systems, and uncertainty components cannot be obtained; 6) Because the measured object dynamically changes, the detection activity cannot be reproduced, the measurement uncertainty component has no reliable reference experience, and the measurement uncertainty component cannot be accurately estimated.
Disclosure of Invention
The invention provides a measurement uncertainty evaluation method and a measurement uncertainty evaluation system based on digital simulation, which can solve the technical problem that the measurement uncertainty evaluation of a complex measurement system cannot be accurately performed in the prior art.
According to an aspect of the present invention, there is provided a measurement uncertainty evaluation method based on digital simulation, the measurement uncertainty evaluation method including: analyzing a conversion relation between a measurement object, a measurement principle, a measurement chain, a measurement environment and original measurement data and measurement object parameters; determining a measurement uncertainty source and distribution conditions of the uncertainty source from a measurement principle, a measurement chain, a measurement environment and a conversion relation between original measurement data and measurement object parameters; generating an uncertainty source sample subset according to the distribution situation of the uncertainty source; establishing a measurement simulation model according to a measurement chain; randomly invoking a subset of uncertain source samples for at least 10 based on the measurement simulation model 6 Performing a Monte Carlo numerical simulation test to obtain a standard measurement uncertainty sample set; and calculating and acquiring the uncertainty of the standard measurement according to the sample set of the uncertainty of the standard measurement.
Further, analyzing the measurement object, the measurement principle, the measurement chain, the measurement environment, and the conversion relation between the raw measurement data and the measurement object parameters specifically includes: defining a parameter of the measured object and a parameter measurement unit of the measured object; the measurement principle of the measured object is defined, the difference between a theoretical measurement result obtained based on the measurement principle and the parameters of the real measured object is analyzed, and the best estimated value and the distribution situation of the best estimated value are defined according to the difference between the theoretical measurement result and the parameters of the real measured object; the module composition of the measuring chain is definitely determined; the influence mode of the measuring environment on the measuring result is determined; and (3) defining the conversion relation between the original measurement data and the measurement object parameters.
Further, determining a measurement uncertainty source and a distribution of uncertainty sources from a measurement principle, a measurement chain, a measurement environment, and a conversion relation between raw measurement data and measurement object parameters specifically includes: determining a measurement uncertainty source influencing a measurement result from a measurement principle, a measurement chain, a measurement environment and a conversion relation between original measurement data and measurement object parameters; the distribution of the source of uncertainty of each measurement is defined by actual measurement, device specifications or metering calibration certificates.
Further, the building of the measurement simulation model according to the measurement chain specifically includes: establishing a measurement simulation model based on a measurement principle of a measurement object, each module composition of a measurement chain, a measurement process and signal circulation in the measurement process, wherein the measurement principle, the measurement chain, the measurement process and the signal circulation of the measurement simulation model are consistent with actual measurement; each uncertainty source in the subset of uncertainty source samples and the corresponding uncertainty source distribution are entered into the measurement simulation model.
Further, randomly invoking the subset of uncertain source samples for at least 10 6 The method for obtaining the standard measurement uncertainty sample set by using the Monte Carlo numerical simulation test specifically comprises the following steps: based on a measurement simulation model, starting simulation, randomly generating data by each uncertain source according to the distribution of the uncertain sources recorded by each uncertain source, and acquiring a plurality of simulation result data; storing the input measured object parameters and a plurality of simulation result data in a one-to-one correspondence manner to form a sample set; and according to the sample set, calculating deviation values between each simulation result data and the recorded measurement object parameters in sequence, and forming a standard measurement uncertainty sample set based on a plurality of deviation values.
Further, calculating the standard measurement uncertainty according to the standard measurement uncertainty sample set specifically includes: and calculating standard deviations of a plurality of deviation values in the standard measurement uncertainty sample set, and taking the standard deviations of the plurality of deviation values as the standard measurement uncertainty.
According to still another aspect of the present invention, there is provided a digital simulation-based measurement uncertainty evaluation system that performs measurement uncertainty evaluation using the digital simulation-based measurement uncertainty evaluation method as described above.
Further, the digital simulation-based measurement uncertainty assessment system includes: a 5M element analysis unit for analyzing the measurement pairThe image, the measurement principle, the measurement chain, the measurement environment and the conversion relation between the original measurement data and the measurement object parameters; the uncertainty source and distribution determining unit is used for determining a measurement uncertainty source and distribution conditions of the uncertainty source from a measurement principle, a measurement chain, a measurement environment and conversion relations between original measurement data and measurement object parameters; an uncertainty source sample subset generating unit, which is used for generating an uncertainty source sample subset according to the measurement uncertainty source and the distribution situation of the uncertainty source; the measurement simulation model generation unit is used for establishing a measurement simulation model according to a measurement chain; a standard measurement uncertainty sample set generation unit for randomly invoking an uncertainty source sample subset for at least 10 6 Performing a Monte Carlo numerical simulation test to obtain a standard measurement uncertainty sample set; and the standard measurement uncertainty calculating unit is used for calculating and acquiring the standard measurement uncertainty according to the standard measurement uncertainty sample set.
By applying the technical scheme of the invention, the invention provides a measurement uncertainty evaluation method based on digital simulation. Compared with the prior art, the measurement uncertainty evaluation method based on digital simulation provided by the invention adopts the digital simulation technology and the Monte Carlo simulation technology to form a set of complete measurement uncertainty method, integrates all measurement uncertainty sources, obtains standard measurement uncertainty at one time, and can accurately and efficiently evaluate the measurement uncertainty of a complex detection system in high altitude detection.
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The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 illustrates a flow chart of a digital simulation-based measurement uncertainty assessment method provided in accordance with a specific embodiment of the present invention;
FIG. 2 is a schematic diagram of a measurement uncertainty assessment method based on digital simulation, according to an embodiment of the present invention;
FIG. 3 illustrates a schematic diagram of a measurement simulation model provided in accordance with a specific embodiment of the present invention.
Detailed Description
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
The relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise. Meanwhile, it should be understood that the sizes of the respective parts shown in the drawings are not drawn in actual scale for convenience of description. Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but should be considered part of the specification where appropriate. In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of the exemplary embodiments may have different values. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
As shown in fig. 1 and 2, according to an embodiment of the present invention, there is provided a measurement uncertainty evaluation method based on digital simulation, the measurement uncertainty evaluation method including: analyzing a conversion relation between a measurement object, a measurement principle, a measurement chain, a measurement environment and original measurement data and measurement object parameters; determining a measurement uncertainty source and distribution conditions of the uncertainty source from a measurement principle, a measurement chain, a measurement environment and a conversion relation between original measurement data and measurement object parameters; generating an uncertainty source sample subset according to the distribution situation of the uncertainty source; establishing a measurement simulation model according to a measurement chain; randomly invoking a subset of uncertain source samples for at least 10 based on the measurement simulation model 6 Performing a Monte Carlo numerical simulation test to obtain a standard measurement uncertainty sample set; and calculating and acquiring the uncertainty of the standard measurement according to the sample set of the uncertainty of the standard measurement.
By using the configuration mode, the method sets a distribution condition set of a measurement uncertainty source and a measured object parameter through digital domain modeling, obtains a sample set of simulation data by adopting a Monte Carlo simulation method, subtracts the simulation data from the set measured object to obtain a deviation sample set, and solves the standard deviation of the set to be used as a standard measurement uncertainty. Compared with the prior art, the measurement uncertainty evaluation method based on digital simulation provided by the invention adopts the digital simulation technology and the Monte Carlo simulation technology to form a set of complete measurement uncertainty method, integrates all measurement uncertainty sources, obtains standard measurement uncertainty at one time, and can accurately and efficiently evaluate the measurement uncertainty of a complex detection system in high altitude detection.
Specifically, in the invention, in order to accurately and efficiently evaluate the measurement uncertainty of a complex detection system in high altitude detection, firstly, a measurement object, a measurement principle, a measurement chain, a measurement environment and a conversion relation between original measurement data and measurement object parameters need to be analyzed. In the invention, the analysis of the conversion relations between the measurement object, the measurement principle, the measurement chain, the measurement environment and the original measurement data and the measurement object parameters specifically comprises the following steps: defining a parameter of the measured object and a parameter measurement unit of the measured object; the measurement principle of the measured object is defined, the difference between a theoretical measurement result obtained based on the measurement principle and the parameters of the real measured object is analyzed, and the best estimated value and the distribution situation of the best estimated value are defined according to the difference between the theoretical measurement result and the parameters of the real measured object; the module composition of the measuring chain is definitely determined; the influence mode of the measuring environment on the measuring result is determined; and (3) defining the conversion relation between the original measurement data and the measurement object parameters.
As a specific embodiment of the present invention, the measurement 5M elements (measurement object, measurement principle, measurement chain, measurement environment, and conversion relation between raw measurement data and measurement object parameters) are analyzed.
1.1 A measurement object parameter and a measurement object parameter measurement unit are specified. For example, assuming that the measurement target is the atmospheric temperature, the parameter measurement unit of the measurement target is K; assuming that the measurement corresponding parameter is the atmospheric wind speed, the measurement target parameter measurement unit is m/s or the like.
1.2 The measurement principle of the measured object is clarified, the difference between the theoretical measurement result obtained based on the measurement principle and the real measured object parameter is analyzed, and the best estimated value and the distribution condition of the best estimated value are clarified according to the difference between the theoretical measurement result and the real measured object parameter. For example, assuming that the measurement target parameter is an atmospheric wind speed, the measurement target is measured to be away from the atmospheric wind speed by using the sounding balloon, and the wind speed is obtained by recording the distance travelled by the sounding balloon in a set time and dividing the measured distance travelled by the sounding balloon by the time used. Assuming that under the theoretical measurement result, the influence of the wind resistance coefficient of the sounding balloon is not considered, the theoretical measurement wind speed result is 10m/s, and the real measured object parameter is 8m/s, the difference between the theoretical measurement result and the real measured object parameter is 2m/s; then, for the set theoretical measurement result, it is assumed to be 12m/s, and according to the previously obtained difference value of 2m/s, the best estimated value of the actual measured object parameter should be 10m/s. Repeating the above process, firstly, performing multiple tests to obtain a plurality of theoretical measurement results and a difference threshold range between the parameters of the real measured object, and then obtaining the optimal estimated value distribution condition according to the difference threshold range for any one set theoretical measurement result. As a specific embodiment of the invention, a plurality of experiments are carried out to obtain a plurality of theoretical measurement results and a range of a gap threshold value between the parameters of the real measured object (1.8 m/s-2.2 m/s), and then the set theoretical measurement result is assumed to be 12m/s, and the optimal estimated value distribution of the parameters of the real measured object is obtained according to the range of the gap threshold value (1.8 m/s-2.2 m/s) obtained before (9.8 m/s-10.2 m/s).
1.3 A) the individual module compositions of the chain are explicitly measured. And confirming the technical index and the distribution condition of the measurement result which are explicitly influenced by each module. The measurement chain is all hardware modules and the like involved in the signal circulation process inside the detection equipment. As a specific embodiment of the invention, assuming that the measured object parameter is the atmospheric wind speed, each module of the measuring chain comprises a sounding balloon, a GPS positioning system, a data processing system and a data transmission system, wherein the GPS positioning system is used for measuring the position of the sounding balloon and each time point, the data processing system is used for calculating and acquiring the atmospheric wind speed according to the positions recorded by the GPS system at different times, and the data transmission system is used for transmitting the atmospheric wind speed obtained by calculation of the data processing system to the ground. As yet another embodiment of the present invention, it is assumed that atmospheric density, temperature, etc. are measured, and a transmitting system, a receiving system, a subsequent optical system, a detecting system, etc. are included in the lidar measurement chain.
1.4 The way the measuring environment affects the measurement results is clarified. If the measuring environment influences the device mainly, it is embodied in the carding of the measuring chain. As a specific embodiment of the present invention, assuming that the parameter of the measured object is the atmospheric wind speed, factors for measuring the atmospheric wind speed in the measuring environment include temperature, and in the case of temperature change, the measuring result is greatly affected.
1.5 A conversion relation between the original measurement data and the measurement object parameter is clarified. As a specific embodiment of the present invention, assuming that the measured object parameter is the atmospheric wind speed, then the original measured data is the position of the sounding balloon, and the time required for the sounding balloon to walk a set distance, then uncertainty may be introduced in the process of converting from distance and time to atmospheric wind speed, and thus the inversion process is considered in modeling. As still another embodiment of the present invention, assuming that the atmospheric density, temperature, etc. are measured, when the working principle of the lidar for atmospheric detection is to use molecular backscattering and lidar equations, the detection data is the number of echo photons of the laser, and the process of inverting the number of photons to atmospheric parameters may introduce uncertainty, so the inverting process is considered in modeling.
Further, in the present invention, after analyzing the measurement object, the measurement principle, the measurement chain, the measurement environment, and the conversion relation between the raw measurement data and the measurement object parameter, the measurement uncertainty source and the distribution of the uncertainty source can be determined from the measurement principle, the measurement chain, the measurement environment, and the conversion relation between the raw measurement data and the measurement object parameter.
In the invention, determining the measurement uncertainty source and the distribution situation of the uncertainty source from the measurement principle, the measurement chain, the measurement environment and the conversion relation between the original measurement data and the measurement object parameters specifically comprises: determining a measurement uncertainty source influencing a measurement result from a measurement principle, a measurement chain, a measurement environment and a conversion relation between original measurement data and measurement object parameters; the distribution of the uncertainty sources of each measurement is defined through actual measurement or device specifications.
As a specific embodiment of the present invention, assuming that the measurement object parameter is the atmospheric wind speed, the measurement uncertainty sources include measurement principles, a measurement chain, a measurement environment, and a conversion relationship between raw measurement data and the measurement object parameter, specifically, for the measurement chain, the measurement chain includes a plurality of modules, each module may introduce an uncertainty source, for example, GPS positioning accuracy, accuracy of data transmission, and the like; as yet another embodiment of the present invention, assuming that atmospheric density, temperature, etc. are measured, indicators in the lidar that affect the measurement result include detector efficiency, laser pulse energy, etc.
After determining the source of measurement uncertainty, it is necessary to ascertain the distribution of each source of measurement uncertainty. These indices may be given by the actual measurement activities or by the device specifications or the metrology calibration certificate in general, and may be set to a uniform distribution when the distribution cannot be determined. As a specific embodiment of the present invention, assuming that the parameter of the measurement object is the atmospheric wind speed, for the measurement uncertainty source is the GPS positioning accuracy, the measurement uncertainty range of the GPS positioning accuracy can be obtained from the GPS device specification or the measurement calibration certificate; for the measurement uncertainty source is the accuracy of data transmission, the measurement uncertainty range of the data transmission can be obtained from a device specification or a metering calibration certificate of the data transmission system, and the measurement uncertainty range of the data transmission can also be obtained through actual measurement activities.
Further, after determining the measurement uncertainty source and the distribution of uncertainty sources, a subset of uncertainty source samples may be generated from the measurement uncertainty source and the distribution of uncertainty sources.
As a specific embodiment of the present invention, assume thatThe measured object parameter is the atmospheric wind speed, and at least 10 conforming to the distribution characteristics is generated sequentially aiming at the measurement principle, the measurement chain, the measurement environment and the uncertainty source and the distribution condition in the conversion relation between the original measured data and the measured object parameter 6 A subset of source samples. For example, for GPS positioning accuracy in a data chain and measurement uncertainty of positioning accuracy, a GPS positioning accuracy sample subset is established, for an uncertainty distribution range of accuracy of data transmission in the data chain, a data transmission sample subset is established, and so on, a scaling relation sample subset between the measurement principle sample subset, the measurement environment sample subset, the raw measurement data and the measurement object parameters is respectively established, finally, at least 10 conforming to the distribution characteristics is generated according to the measurement principle sample subset, the measurement environment sample subset, the data chain sample subset and the scaling relation sample subset between the raw measurement data and the measurement object parameters 6 A subset of source samples, wherein each uncertainty source sample subset comprises at least 10 6 Samples.
Further, after generating the uncertainty source sample subset according to the uncertainty source and the distribution situation of the uncertainty source, a measurement simulation model can be built according to a measurement chain. In the invention, the establishment of the measurement simulation model according to the measurement chain specifically comprises the following steps: establishing a measurement simulation model based on a measurement principle of a measurement object, each module composition of a measurement chain, a measurement process and signal circulation in the measurement process, wherein the measurement principle, the measurement chain, the measurement process and the signal circulation of the measurement simulation model are consistent with actual measurement; each uncertainty source in the subset of uncertainty source samples and the corresponding uncertainty source distribution are entered into the measurement simulation model.
As a specific embodiment of the present invention, as shown in fig. 3, a simulation model is built in the digital domain, where the measurement principle, the measurement chain and the generated measurement data are required to be identical to the actual, where the measurement principle of the simulation model is identical to the actual measurement, the respective modules of the measurement chain of the simulation model have the same composition as the modules used in the actual measurement, the specific measurement process in the simulation model, the input quantity and the environmental elements in the measurement process are identical to the actual measurement, and the specific flow and the output quantity of the signal circulation in the simulation module are identical to the actual measurement. After the building of each module of the simulation model is completed, the uncertainty sources and uncertainty distribution of all modules on the measurement chain are input, and indexes influencing the result by the measurement chain module in the simulation process are randomly generated according to the distribution rule. For example, assuming that the measurement object parameter is an atmospheric wind speed, the measurement principle sample subset, the measurement environment sample subset, the data chain sample subset, and the conversion relation sample subset between the raw measurement data and the measurement object parameter are respectively entered into the simulation model.
Further, setting parameter values of the measured object, and simulating a measurement process by using a simulation model to obtain a simulation measurement result.
Further, at least 10 is provided 6 Randomly invoking the subset of uncertain source samples for at least 10 times the measured object parameter value 6 And performing Monte Carlo numerical simulation test to obtain a standard measurement uncertainty sample set.
In the present invention, randomly invoking a subset of uncertain source samples for at least 10 6 The method for obtaining the standard measurement uncertainty sample set by using the Monte Carlo numerical simulation test specifically comprises the following steps: based on a measurement simulation model, starting simulation, randomly generating data by each uncertain source according to the distribution of the uncertain sources recorded by each uncertain source, and acquiring a plurality of simulation result data; storing the input measured object parameters and a plurality of simulation result data in a one-to-one correspondence manner to form a sample set; and according to the sample set, calculating deviation values between each simulation result data and the recorded measurement object parameters in sequence, and forming a standard measurement uncertainty sample set based on a plurality of deviation values.
As a specific embodiment of the present invention, assuming that the measurement object parameter is the atmospheric wind speed, randomly calling a measurement principle uncertainty sample from a measurement principle sample subset, randomly calling a measurement environment sample uncertainty sample from a measurement environment sample subset, randomly calling a data link sample from a data link sample subset, randomly calling a data link sample from raw measurement data at each monte carlo test Randomly calling a conversion relation sample in a conversion relation sample subset between the conversion relation sample subset and the parameter of the measured object, and carrying out a Monte Carlo numerical simulation test according to the randomly called sample to obtain a simulation result; repeating the above process until at least 10 is obtained 6 And (5) simulating results. Storing the parameters of the input measurement object in one-to-one correspondence with the simulation results to form a sample set; calculating the deviation value of the simulation result and the parameter of the input measurement object to form a deviation value sample set, wherein at least 10 is corresponding to the deviation value sample set 6 The offset values.
Further, in the present invention, after the standard measurement uncertainty sample set is obtained, the standard measurement uncertainty may be calculated and obtained from the standard measurement uncertainty sample set. In the invention, calculating and acquiring the standard measurement uncertainty according to the standard measurement uncertainty sample set specifically comprises the following steps: and calculating standard deviations of a plurality of deviation values in the standard measurement uncertainty sample set, and taking the standard deviations of the plurality of deviation values as the standard measurement uncertainty.
According to another aspect of the present invention, there is provided a digital simulation-based measurement uncertainty evaluation system that performs measurement uncertainty evaluation using the digital simulation-based measurement uncertainty evaluation method described above.
By applying the configuration mode, the system sets a distribution condition set of a measurement uncertainty source and a measured object parameter through digital domain modeling, obtains a sample set of simulation data by adopting a Monte Carlo simulation method, subtracts the simulation data from the set measured object to obtain a deviation sample set, and solves the standard deviation of the set to serve as standard measurement uncertainty. Compared with the prior art, the measurement uncertainty evaluation system based on digital simulation provided by the invention adopts a digital simulation technology and a Monte Carlo simulation technology to form a set of complete measurement uncertainty method, integrates all measurement uncertainty sources, obtains standard measurement uncertainty at one time, and can accurately and efficiently evaluate the measurement uncertainty of the complex detection system in high altitude detection.
Specifically, in order to realize measurement uncertainty assessment, the measurement uncertainty assessment system based on digital simulation comprises 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, wherein the 5M element analysis unit is used for analyzing a measurement object, a measurement principle, a measurement chain, a measurement environment and conversion relation between original measurement data and measurement object parameters, the uncertainty source and distribution determination unit is used for determining the measurement uncertainty source and distribution situation of the uncertainty source from the conversion relation between the measurement principle, the measurement chain, the measurement environment and the original measurement data and the measurement object parameters, the uncertainty source sample subset generation unit is used for generating an uncertainty source sample subset according to the measurement uncertainty source and the distribution situation of the uncertainty source, the measurement simulation model generation unit is used for establishing a measurement simulation model according to the measurement chain, and the standard measurement uncertainty sample set generation unit is used for randomly calling the uncertainty source sample subset for at least 10 6 And performing Monte Carlo numerical simulation test to obtain a standard measurement uncertainty sample set, wherein the standard measurement uncertainty calculation unit is used for calculating and obtaining standard measurement uncertainty according to the standard measurement uncertainty sample set.
For further understanding of the present invention, the method for evaluating measurement uncertainty based on digital simulation provided by the present invention will be described in detail with reference to fig. 1 and 2.
As shown in fig. 1 and 2, according to an embodiment of the present invention, there is provided a measurement uncertainty evaluation method based on digital simulation, which includes the following steps.
Step one, the 5M elements (measurement object, measurement principle, measurement chain, measurement environment, and conversion relation between raw measurement data and measurement object parameters) are analyzed and measured.
1.1 A measurement object parameter and a measurement object parameter measurement unit are specified. In the present embodiment, when the corresponding parameter is measured as the atmospheric wind speed, the measurement unit of the parameter to be measured is m/s or the like.
1.2 The measurement principle of the measured object is clarified, the difference between the theoretical measurement result obtained based on the measurement principle and the real measured object parameter is analyzed, and the best estimated value and the distribution condition of the best estimated value are clarified according to the difference between the theoretical measurement result and the real measured object parameter. For example, assuming that the measurement target parameter is an atmospheric wind speed, the measurement principle of the measurement target is to measure the atmospheric wind speed by using a sounding balloon, and the wind speed can be obtained by recording the distance travelled by the sounding balloon in a set time and dividing the measured distance travelled by the sounding balloon by the time. Assuming that under the theoretical measurement result, the influence of the wind resistance coefficient of the sounding balloon is not considered, the theoretical measurement wind speed result is 10m/s, and the real measured object parameter is 8m/s, the difference between the theoretical measurement result and the real measured object parameter is 2m/s; then, for the set theoretical measurement result, it is assumed to be 12m/s, and according to the previously obtained difference value of 2m/s, the best estimated value of the actual measured object parameter should be 10m/s. Repeating the above process, firstly, performing multiple tests to obtain a plurality of difference thresholds between the theoretical measurement results and the parameters of the real measured object, and then obtaining the distribution condition of the optimal estimated value according to the difference threshold for any one set theoretical measurement result. As a specific embodiment of the invention, a plurality of experiments are carried out to obtain a plurality of theoretical measurement results and a difference threshold value between the parameters of the real measured object is (1.8 m/s-2.2 m/s), then the set theoretical measurement result is assumed to be 12m/s, and the optimal estimated value distribution of the parameters of the real measured object can be obtained according to the obtained difference threshold value (1.8 m/s-2.2 m/s) which is the set theoretical measurement result (9.8 m/s-10.2 m/s).
1.3 A) the individual module compositions of the chain are explicitly measured. And confirming the technical index and the distribution condition of the measurement result which are explicitly influenced by each module. The measurement chain is all hardware modules and the like involved in the signal circulation process inside the detection equipment. In this embodiment, if the measured object parameter is an atmospheric wind speed, each module of the measuring chain includes a sounding balloon, a GPS positioning system, a data processing system and a data transmission system, where the GPS positioning system is used to measure the position of the sounding balloon and each time point, the data processing system is used to calculate and obtain the atmospheric wind speed according to the positions recorded by the GPS system at different times, and the data transmission system is used to transmit the atmospheric wind speed obtained by calculation of the data processing system back to the ground.
1.4 The way the measuring environment affects the measurement results is clarified. If the measuring environment influences the device mainly, it is embodied in the carding of the measuring chain. As a specific embodiment of the present invention, assuming that the parameter of the measured object is the atmospheric wind speed, factors for measuring the atmospheric wind speed in the measuring environment include temperature, and in the case of temperature change, the measuring result is greatly affected.
1.5 A conversion relation between the original measurement data and the measurement object parameter is clarified. In this embodiment, the measured object parameter is the atmospheric wind speed, then the original measured data is the position of the sounding balloon, and the time required for the sounding balloon to walk a set distance, and the measured object parameter is the atmospheric wind speed, then uncertainty may be introduced in the process of converting from distance and time into the atmospheric wind speed, so the inversion process is considered in modeling.
And step two, determining a measurement uncertainty source and distribution conditions of the uncertainty source from a measurement principle, a measurement chain, a measurement environment and a conversion relation between original measurement data and measurement object parameters.
In this embodiment, if the measurement object parameter is the atmospheric wind speed, the measurement uncertainty source includes a measurement principle, a measurement chain, a measurement environment, and a conversion relationship between the raw measurement data and the measurement object parameter, specifically, for the measurement chain, the measurement chain includes a plurality of modules, and each module may introduce the uncertainty source, for example, GPS positioning accuracy, accuracy of data transmission, and the like. Aiming at the fact that the measurement uncertainty source is GPS positioning precision, the measurement uncertainty range of the GPS positioning precision can be obtained from a GPS device instruction; for the measurement uncertainty source is the accuracy of data transmission, the measurement uncertainty range of the data transmission can be obtained from the device specification of the data transmission system, and the measurement uncertainty range of the data transmission can also be obtained through actual measurement activities.
And thirdly, generating an uncertainty source sample subset according to the uncertainty source and the distribution situation of the uncertainty source.
In the embodiment, the measured object parameter is the atmospheric wind speed, and 10 conforming to the distribution characteristics is generated sequentially according to the measurement principle, the measurement chain, the measurement environment, and the uncertainty source and distribution condition in the conversion relationship between the original measurement data and the measured object parameter 6 A subset of source samples. For example, for GPS positioning accuracy in a data chain and measurement uncertainty of positioning accuracy, a GPS positioning accuracy sample subset is established, for an uncertainty distribution range of accuracy of data transmission in the data chain, a data transmission sample subset is established, and so on, a scaling relation sample subset between a measurement principle sample subset, a measurement environment sample subset, original measurement data and measurement object parameters is respectively established, finally, 10 conforming to distribution characteristics is generated according to the measurement principle sample subset, the measurement environment sample subset, the data chain sample subset and the scaling relation sample subset between the original measurement data and the measurement object parameters 6 A subset of source samples, wherein each uncertainty source sample subset comprises 10 6 Samples.
And step four, a measurement simulation model is established according to the measurement chain. In this embodiment, a simulation model is built in the digital domain, and the measurement principle, the measurement chain and the generated measurement data are required to be identical to the actual measurement, where the measurement principle of the simulation model is identical to the actual measurement, each module of the measurement chain of the simulation model has the same composition as the module used in the actual measurement, the specific measurement process in the simulation model, the input quantity and the environmental elements in the measurement process are identical to those in the actual measurement, and the specific flow and the output quantity of the signal circulation in the simulation module are identical to those in the actual measurement. After the building of each module of the simulation model is completed, the uncertainty sources and uncertainty distribution of all modules on the measurement chain are input, and indexes influencing the result by the measurement chain module in the simulation process are randomly generated according to the distribution rule.
Step five, randomly calling the uncertaintyPerforming at least 10 on a subset of source samples 6 And performing Monte Carlo numerical simulation test to obtain a standard measurement uncertainty sample set. In the embodiment, based on a measurement simulation model, simulation is started, each uncertain source randomly generates data according to the distribution of the uncertain sources recorded by each uncertain source, and a plurality of simulation result data are obtained; storing the input measured object parameters and a plurality of simulation result data in a one-to-one correspondence manner to form a sample set; and according to the sample set, calculating deviation values between each simulation result data and the recorded measurement object parameters in sequence, and forming a standard measurement uncertainty sample set based on a plurality of deviation values.
And step six, calculating and acquiring the uncertainty of the standard measurement according to the sample set of the uncertainty of the standard measurement. In this embodiment, standard deviations of a plurality of deviation values in a standard measurement uncertainty sample set are calculated, and the standard deviations of the plurality of deviation values are used as the standard measurement uncertainty.
In summary, the invention provides a measurement uncertainty evaluation method based on digital simulation, which is characterized in that a distribution condition set of measurement uncertainty sources and measured object parameters are set through digital domain modeling, a Monte Carlo simulation method is adopted to obtain a sample set of simulation data, the simulation data and the set measured object are subtracted to obtain a deviation sample set, and standard deviation of the set is solved to be used as standard measurement uncertainty. The measurement uncertainty evaluation method provided by the invention has the following characteristics: (1) Providing a measurement uncertainty assessment method based on digital modeling; (2) The method is suitable for the situation that the whole tracing and the measurement process cannot be repeated for verification; (3) The problems of solving measurement uncertainty components, correlation coefficients, sensitivity coefficients and the like are avoided; (4) Matching the analog measurement process with the actual detection by adopting a digital modeling technology; (5) In a Monte Carlo simulation mode, pass through 10 6 And a deviation sample collection set is formed by secondary calculation, so that the reliability of the evaluation result is improved.
Compared with the prior art, the measurement uncertainty evaluation method based on digital simulation provided by the invention adopts Monte Carlo simulation input aiming at each measurement uncertainty source, and adopts a measurement model to obtain at least 10 6 Sub-simulationThe data adopts a digital simulation technology and a Monte Carlo simulation technology to form a set of complete measurement uncertainty method, integrates all measurement uncertainty sources, obtains standard measurement uncertainty at one time, can accurately and efficiently evaluate the measurement uncertainty of a complex detection system in high altitude detection, particularly relates to measurement uncertainty evaluation of complex system detection which cannot be calibrated by tracing, and is particularly suitable for the situations that influence factors cannot be decoupled, correlation coefficients among measurement uncertainty components are uncertain, and a certain measurement uncertainty source influences various devices on a measurement chain.
Spatially relative terms, such as "above … …," "above … …," "upper surface at … …," "above," and the like, may be used herein for ease of description to describe one device or feature's spatial location relative to another device or feature as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations in use or operation in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as "above" or "over" other devices or structures would then be oriented "below" or "beneath" the other devices or structures. Thus, the exemplary term "above … …" may include both orientations of "above … …" and "below … …". The device may also be positioned in other different ways (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.
In addition, the terms "first", "second", etc. are used to define the components, and are only for convenience of distinguishing the corresponding components, and the terms have no special meaning unless otherwise stated, and therefore should not be construed as limiting the scope of the present invention.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A method for measuring uncertainty assessment based on digital simulation, characterized in that the method for measuring uncertainty assessment comprises the following steps:
analyzing a conversion relation between a measurement object, a measurement principle, a measurement chain, a measurement environment and original measurement data and measurement object parameters;
determining a measurement uncertainty source and a distribution situation of the uncertainty source from the measurement principle, the measurement chain, the measurement environment and a conversion relation between the original measurement data and a measurement object parameter;
Generating an uncertainty source sample subset according to the distribution situation of the uncertainty source;
establishing a measurement simulation model according to a measurement chain;
randomly invoking the subset of uncertain source samples for at least 10 based on the measurement simulation model 6 Performing a Monte Carlo numerical simulation test to obtain a standard measurement uncertainty sample set;
and calculating and acquiring the standard measurement uncertainty according to the standard measurement uncertainty sample set.
2. The method for evaluating measurement uncertainty based on digital simulation according to claim 1, wherein analyzing the measurement object, the measurement principle, the measurement chain, the measurement environment, and the conversion relation between the raw measurement data and the measurement object parameters specifically comprises:
defining a parameter of the measured object and a parameter measurement unit of the measured object;
the measurement principle of the measured object is defined, the difference between a theoretical measurement result obtained based on the measurement principle and the parameters of the real measured object is analyzed, and the best estimated value and the distribution situation of the best estimated value are defined according to the difference between the theoretical measurement result and the parameters of the real measured object;
the module composition of the measuring chain is definitely determined;
The influence mode of the measuring environment on the measuring result is determined;
and (3) defining the conversion relation between the original measurement data and the measurement object parameters.
3. The method for evaluating measurement uncertainty based on digital simulation according to claim 2, wherein determining a measurement uncertainty source and a distribution of uncertainty sources from the measurement principle, the measurement chain, the measurement environment, and a scaling relationship between the raw measurement data and a measurement object parameter specifically comprises:
determining a measurement uncertainty source affecting a measurement result from the measurement principle, the measurement chain, the measurement environment and a conversion relation between the original measurement data and a measurement object parameter;
the distribution of the source of uncertainty of each measurement is defined by actual measurement, device specifications or metering calibration certificates.
4. The method for evaluating measurement uncertainty based on digital simulation according to claim 3, wherein the step of building the measurement simulation model based on the measurement chain specifically comprises:
establishing a measurement simulation model based on a measurement principle of a measurement object, each module composition of a measurement chain, a measurement process and signal circulation in the measurement process, wherein the measurement principle, the measurement chain, the measurement process and the signal circulation of the measurement simulation model are consistent with actual measurement;
Each uncertain source in the subset of uncertain source samples and corresponding uncertain source distribution are entered into the measurement simulation model.
5. The digital simulation based measurement uncertainty rating method of claim 4, wherein randomly invoking said subset of uncertainty source samples is performed for at least 10 6 The method for obtaining the standard measurement uncertainty sample set by using the Monte Carlo numerical simulation test specifically comprises the following steps:
based on the measurement simulation model, starting simulation, randomly generating data by each uncertain source according to the distribution of the uncertain sources recorded by each uncertain source, and obtaining a plurality of simulation result data;
storing the input measured object parameters and a plurality of simulation result data in one-to-one correspondence to form a sample set;
and according to the sample set, calculating deviation values between each simulation result data and the input measurement object parameters in sequence, and forming a standard measurement uncertainty sample set based on a plurality of deviation values.
6. The method for evaluating measurement uncertainty based on digital simulation according to claim 5, wherein calculating the standard measurement uncertainty from the standard measurement uncertainty sample set comprises: and calculating standard deviations of a plurality of deviation values in the standard measurement uncertainty sample set, and taking the standard deviations of the deviation values as standard measurement uncertainty.
7. A digital simulation-based measurement uncertainty evaluation system, wherein the digital simulation-based measurement uncertainty evaluation system performs measurement uncertainty evaluation using the digital simulation-based measurement uncertainty evaluation method according to claims 1 to 6.
8. The digital simulation based measurement uncertainty rating system of claim 7, wherein the digital simulation based measurement uncertainty rating system comprises:
the 5M element analysis unit is used for analyzing a measuring object, a measuring principle, a measuring chain, a measuring environment and conversion relations between original measuring data and measuring object parameters;
an uncertainty source and distribution determining unit, configured to determine a measurement uncertainty source and a distribution situation of the uncertainty source from the measurement principle, the measurement chain, the measurement environment, and a conversion relationship between the raw measurement data and a measurement object parameter;
an uncertainty source sample subset generating unit for generating an uncertainty source sample subset according to the measured uncertainty source and the distribution situation of the uncertainty source;
The measurement simulation model generation unit is used for establishing a measurement simulation model according to a measurement chain;
a standard measurement uncertainty sample set generation unit for randomly invoking the uncertainty source sample subset for at least 10 6 Performing a Monte Carlo numerical simulation test to obtain a standard measurement uncertainty sample set;
and the standard measurement uncertainty calculation unit is used for calculating and acquiring standard measurement uncertainty according to the standard measurement uncertainty sample set.
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