Temperature resolution measurement method and apparatus based on noise analysis

Abstract

The application discloses a temperature resolution measurement method and device based on noise analysis, and belongs to the technical field of measurement. The method comprises the following steps: obtaining historical observation data of underground fluid temperature in an observation well in a target area; determining abnormal data caused by earthquakes in the historical observation data according to the earthquake occurrence time observed by the observation well in the target area; identifying a frequency band range of the earthquake cause corresponding to the underground fluid temperature based on time-frequency conversion of the abnormal data; performing self-noise ratio measurement by using a plurality of temperature measuring instruments to be measured, so as to eliminate environmental background noise and obtain a self-noise power spectrum density curve of the temperature measuring instrument to be measured; and calculating an effective value of the self-noise of the temperature measuring instrument to be measured based on a frequency interval formed by a lower limit frequency of the self-noise power spectrum density curve of the temperature measuring instrument to be measured and an upper limit frequency of the frequency band range, and taking the effective value of the self-noise as the minimum temperature resolution of the temperature measuring instrument to be measured. The application can ensure that the measurement result is effective.

Description

Technical Field

[0001] This invention relates to the field of measurement technology, and in particular to a temperature resolution measurement method and apparatus based on noise analysis. Background Technology

[0002] Temperature is one of the most crucial physical quantities in seismic underground fluid observation. Micro-dynamic changes in well water temperature are widely considered important precursory information reflecting crustal tectonic activity and earthquake gestation. With the advancement of digital transformation in underground fluid observation, the rich tectonic information contained in high-sampling-rate water temperature observation data provides favorable conditions for identifying precursory anomalies and tracking seismic activity. However, objectively, the identification of earthquake precursors faces significant challenges due to interference from various factors such as noise within the observation system itself, human activities, and precipitation. According to JJF 1001-2011 "General Metrological Terms and Definitions," the resolution of an instrument refers to the smallest change in the measured quantity that causes a perceptible change in the corresponding reading. The instrument's self-noise characterizes the absolute threshold of the smallest physical quantity change it can detect; that is, self-noise is the instrument's true physical resolution. For seismic underground fluid temperature measurement instruments, their temperature resolution should, and must, be characterized by the equivalent input self-noise of their system in a physically essential way. Only when the energy of the externally input temperature change signal exceeds the self-noise energy of the temperature measuring instrument within a specific frequency band can the weak signal be truly detected by the system and converted into effective recorded data. If the self-noise level of the temperature measuring instrument is too high, it may mask minute precursor temperature fluctuations, thus greatly limiting the practical application value of the observational data in complex Earth science problems. Therefore, temperature resolution, as a key indicator of the ability of seismic underground fluid temperature measuring instruments to detect minute temperature changes, is crucial for accurate testing and scientific evaluation.

[0003] Currently, the methods for checking and testing the temperature resolution of earthquake underground fluid temperature measuring instruments mainly rely on the "time-domain self-verification" method in the insulation device. JJF 2246-2025 "Calibration Specification for Well Water Temperature Measuring Instruments" is one of the first national metrological technical specifications in the field of earthquake engineering, which systematically stipulates the calibration methods and technical indicators of well water temperature monitoring instruments. The test method for the minimum temperature resolution specified in this standard is as follows: First, connect a first-class standard platinum resistance thermometer to an electrical measuring instrument, and place it together with the sensor of the temperature measuring instrument in a heat-insulating device, ensuring that the midpoints of the sensing elements of both are on the same horizontal plane and the distance between them is no more than 1 cm; after sealing the heat-insulating device, calibrate the temperature measuring instrument and the electrical measuring instrument, and continuously measure and record the data for no less than 5 days at a sampling rate of 1 / 60 Hz under ambient temperature and atmospheric pressure conditions; then, extract continuous measurement data of the first-class standard platinum resistance thermometer for no less than 30 minutes, with the absolute value of the first-order difference sequence (rounded to four decimal places) not greater than 0.0001 ℃, and perform first-order difference calculation on the measurement data of the instrument under test within the same time period; if the temperature measuring instrument records continuous measurement data for no less than 30 minutes within at least one time period with the absolute value of the first-order difference sequence (rounded to four decimal places) not greater than 0.0001 ℃, then its temperature resolution is considered to be no greater than 0.0001 ℃. ℃, otherwise it is considered to be greater than 0.0001℃.

[0004] However, existing measurement methods lack theoretical basis, and the validity of the measurement results is poor. Summary of the Invention

[0005] This invention provides a method and apparatus for measuring temperature resolution based on noise analysis. The technical solution is as follows: On the one hand, a temperature resolution measurement method based on noise analysis is provided, the method comprising: Acquire historical observation data of underground fluid temperature in observation wells within the target area; the historical observation data is obtained based on measurements taken by temperature measuring instruments placed inside the observation wells. Based on the earthquake occurrence time observed by observation wells within the target area, identify the abnormal data in the historical observation data that are caused by earthquakes; Time-frequency conversion based on anomalous data is used to identify the frequency band range of earthquake causes corresponding to underground fluid temperature. Multiple temperature measuring instruments under test are compared in a high-precision constant temperature bath to eliminate common noise introduced by the environmental background, thereby obtaining the self-noise power spectral density curve of the temperature measuring instrument under test; the self-noise power spectral density curve is a two-dimensional relationship curve between power spectral density and frequency. Based on the frequency range formed by the lower frequency limit of the self-noise power spectral density curve of the temperature measuring instrument under test and the upper frequency limit of the frequency band, the effective value of the self-noise of the temperature measuring instrument under test is calculated, and this effective value of self-noise is taken as the minimum temperature resolution of the temperature measuring instrument under test.

[0006] On the other hand, a temperature resolution measuring device based on noise analysis is provided, the device comprising: The acquisition unit is used to acquire historical observation data of underground fluid temperature in observation wells within the target area; the historical observation data is obtained based on measurements taken by temperature measuring instruments placed inside the observation wells. The determination unit is used to determine the abnormal data formed by the cause of earthquakes in the historical observation data based on the earthquake occurrence time observed by the observation wells in the target area; The identification unit is used for time-frequency conversion based on anomalous data to identify the frequency band range of earthquake causes corresponding to underground fluid temperature; The comparison unit is used to compare the self-noise of multiple temperature measuring instruments under test in a high-precision constant temperature bath to eliminate common noise introduced by the environmental background and obtain the self-noise power spectral density curve of the temperature measuring instrument under test; the self-noise power spectral density curve is a two-dimensional relationship curve between power spectral density and frequency. The calculation unit is used to calculate the effective value of the self-noise of the temperature measuring instrument under test based on the frequency range formed by the lower frequency limit of the self-noise power spectral density curve of the temperature measuring instrument under test and the upper frequency limit of the frequency band range, and to use the effective value of the self-noise as the minimum temperature resolution of the temperature measuring instrument under test.

[0007] On the other hand, a computer device is provided, the computer device including a memory and a processor, the memory for storing a computer program, and the processor for executing the computer program stored in the memory to implement the steps of the temperature resolution measurement method based on noise analysis described above.

[0008] On the other hand, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when the computer program is executed by a processor, it implements the steps of the temperature resolution measurement method based on noise analysis described above.

[0009] On the other hand, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the temperature resolution measurement method based on noise analysis described above.

[0010] The technical solution provided by this invention can bring at least the following beneficial effects: In this embodiment of the invention, multiple temperature measuring instruments are compared in a high-precision constant-temperature bath to separate and eliminate common noise introduced by the environmental background, obtaining the noise power spectral density curve of the instrument under test. This power spectral density curve is a two-dimensional relationship curve between power spectral density and frequency. This transforms the self-verification of traditional temperature resolution in the time domain into power spectral density self-noise separation in the frequency domain, fundamentally resolving the controversy surrounding the lack of scientific basis in traditional testing methods. Furthermore, by acquiring anomalous data related to earthquake formation, the frequency band range corresponding to the earthquake's origin in the underground fluid temperature is identified based on the anomalous data. The upper frequency limit of this frequency band range is used as the upper frequency limit for calculating the effective value of self-noise, thus ensuring that the temperature resolution measurement is strictly aligned with the needs of real earthquake precursor monitoring. Therefore, the measurement results obtained in this embodiment of the invention are more accurate and effective. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart of a temperature resolution measurement method based on noise analysis provided in an embodiment of the present invention; Figure 2 This is a comparison chart of power spectral density curves of abnormal data and normal data provided in an embodiment of the present invention; Figure 3 This is a schematic diagram showing the curve shape observation results of the data consistency of three temperature measuring instruments under test according to an embodiment of the present invention; Figure 4 This is an example diagram of background noise PSD analysis provided in an embodiment of the present invention; Figure 5 This is a structural diagram of a temperature resolution measurement device based on noise analysis provided in an embodiment of the present invention; Figure 6 This is a hardware architecture diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0014] An analysis of the method described above reveals at least the following drawbacks: (1) There is a self-proving logical paradox and a lack of rigorous scientific basis. The existing method essentially uses the temperature fluctuation data recorded by the temperature measuring instrument in the heat preservation device to prove its own minimum temperature resolution capability. It cannot determine whether the measurement result is the instrument's true physical response to the small temperature change in the constant temperature environment or the result of a flawed mechanism in the instrument design.

[0015] (2) Failure to eliminate the influence of ambient background noise. The insulation device cannot achieve absolute insulation and constant temperature. The output of the temperature measuring instrument actually includes both ambient background noise and instrument self-noise. This may lead to the ambient background noise being mistaken for instrument noise, resulting in a serious underestimation of the resolution. It may also lead to incorrect stable measurement conclusions due to the mutual cancellation between ambient temperature fluctuations and instrument dead zone.

[0016] (3) Detached from the actual physical requirements of earthquake precursor monitoring. Existing methods have not established a connection with the actual needs of earthquake monitoring. The background noise of water temperature in observation wells and the temperature anomaly data of underground fluids during earthquakes both have typical power spectral density distribution characteristics, and these characteristics are affected by differences in the geological and structural features of the observation wells and the aquifer permeability, resulting in significant regional differences. Existing methods cannot determine whether the temperature measuring instrument under test has a signal-to-noise ratio sufficient to suppress background noise within a specific frequency band of the actual earthquake precursor signal, which is a core issue.

[0017] In conclusion, existing methods cannot guarantee the validity of measurement results.

[0018] Therefore, the inventive concept of this invention lies in using multiple temperature measuring instruments to be tested simultaneously in a high-precision constant temperature bath to compare self-noise, thereby eliminating common noise introduced by the environmental background, decoupling and separating background noise and self-noise, and obtaining the noise power spectral density curve of the temperature measuring instrument under test. This power spectral density curve is a two-dimensional relationship curve between power spectral density and frequency. In this way, the self-verification of traditional temperature resolution in the time domain can be transformed into the separation of power spectral density and self-noise in the frequency domain, thus fundamentally solving the controversy of the lack of scientific basis in traditional testing methods. Furthermore, historical observation data is used to determine anomalous data of earthquake causes, and the frequency band range corresponding to the earthquake causes of underground fluid temperature is identified using the anomalous data. The upper frequency limit of the frequency band range is used as the upper frequency limit for calculating the effective value of the self-noise of the temperature measuring instrument under test, thereby linking the measurement results with the needs of real earthquake scenarios.

[0019] The specific implementation of the above concept is described below.

[0020] Please refer to Figure 1 The present invention provides a temperature resolution measurement method based on noise analysis, the method comprising: Step 100: Obtain historical observation data of underground fluid temperature in observation wells within the target area; the historical observation data is obtained based on measurements taken by temperature measuring instruments placed inside the observation wells. Step 102: Based on the earthquake occurrence time observed by the observation wells in the target area, determine the abnormal data formed by the earthquake causes in the historical observation data; Step 104: Based on the time-frequency conversion of the anomalous data, identify the frequency band range of the seismic cause corresponding to the underground fluid temperature; Step 106: Perform self-noise comparison measurement on multiple temperature measuring instruments under test in a high-precision constant temperature bath to eliminate common noise introduced by the environmental background and obtain the self-noise power spectral density curve of the temperature measuring instrument under test; the self-noise power spectral density curve is a two-dimensional relationship curve between power spectral density and frequency. Step 108: Based on the frequency range formed by the lower frequency limit of the self-noise power spectral density curve of the temperature measuring instrument under test and the upper frequency limit of the frequency band range, calculate the effective value of the self-noise of the temperature measuring instrument under test, and use the effective value of the self-noise as the minimum temperature resolution of the temperature measuring instrument under test.

[0021] In this embodiment of the invention, multiple temperature measuring instruments are compared in a high-precision constant-temperature bath to separate and eliminate common noise introduced by the environmental background, obtaining the noise power spectral density curve of the instrument under test. This power spectral density curve is a two-dimensional relationship curve between power spectral density and frequency. This transforms the self-verification of traditional temperature resolution in the time domain into power spectral density self-noise separation in the frequency domain, fundamentally resolving the controversy surrounding the lack of scientific basis in traditional testing methods. Furthermore, by acquiring anomalous data related to earthquake formation, the frequency band range corresponding to the earthquake's origin in the underground fluid temperature is identified based on the anomalous data. The upper frequency limit of this frequency band range is used as the upper frequency limit for calculating the effective value of self-noise, thus ensuring that the temperature resolution measurement is strictly aligned with the needs of real earthquake precursor monitoring. Therefore, the measurement results obtained in this embodiment of the invention are more accurate and effective.

[0022] The following description Figure 1 The execution method for each step is shown.

[0023] First, we will explain step 100, "obtain historical observation data of underground fluid temperature in observation wells within the target area; the historical observation data is obtained based on temperature measuring instruments placed in the observation wells," and step 102, "determine the abnormal data formed by earthquake causes in the historical observation data based on the earthquake occurrence time observed by the observation wells within the target area."

[0024] In this embodiment of the invention, there are multiple observation wells throughout the country, each equipped with a temperature measuring instrument to measure the temperature of the underground fluid within the well. Therefore, historical observation data can be obtained using these temperature measuring instruments.

[0025] This historical observation data can be observation data from many years, such as historical observation data from the past 20 years.

[0026] In one implementation, the target area can be the entire country, a specific region, or a specific observation well.

[0027] In one embodiment of the present invention, determining the abnormal data resulting from earthquake formation in the historical observation data based on the earthquake occurrence time observed by observation wells within the target area includes: The earthquake occurrence time is located from the historical observation data, and based on the observation data within the time period before and after the earthquake occurrence time, a data segment with specific temperature changes is identified, and this data segment is identified as earthquake-causing anomaly data.

[0028] The earthquake occurrence time in the target area can be obtained through official data release platforms. Based on the determined earthquake occurrence time, the earthquake's exact time is located from historical observation data. For example, if an earthquake occurred at 12:00 on January 1, 2020, then that time can be located from historical observation data. Before and / or after an earthquake, there may be significant changes in underground fluid temperature. Therefore, after locating the earthquake occurrence time, observation data from the time periods before and after the earthquake are used to determine data segments with specific temperature changes.

[0029] In one implementation, the specific temperature change includes at least: pre-earthquake anomaly changes, co-seismic changes, and post-earthquake changes.

[0030] Pre-earthquake anomalies can be identified by morphological characteristics; for example, pre-earthquake anomalies include V-shaped, "U-shaped," step-like, and pulse-like patterns. Co-seismic changes can be identified by sudden rises or falls in water temperature; for example, a sudden rise in water temperature with an increase of 0.005 to 0.976℃. Post-earthquake changes can be identified by the duration of the changes; for example, a sustained change in water temperature lasting for several days or months, several times the normal daily variation.

[0031] Furthermore, after obtaining historical observation data, the historical observation data can be filtered to remove null values ​​or data that includes interference events such as those of unknown cause, site environment, geophysical events, observation system, human interference, and natural environment.

[0032] Then, for step 104, based on the time-frequency conversion of the abnormal data, the frequency band range of the seismic cause corresponding to the underground fluid temperature is identified.

[0033] In this embodiment of the invention, after determining the anomalous data resulting from the earthquake, the time-frequency conversion based on the anomalous data to identify the frequency band range corresponding to the earthquake's cause based on the subsurface fluid temperature may include: Based on the first time scale, the preceding normal data and subsequent normal data of the abnormal data are obtained from the historical observation data; The power spectral density curves of the abnormal data and the normal data are generated using the Fourier transform method; the normal data includes the preceding normal data and the subsequent normal data. By comparing the power spectral density curves of anomalous data and normal data, the frequency band range corresponding to the seismic cause of underground fluid temperature can be obtained.

[0034] In this embodiment of the invention, the first time scale can be determined by the duration of the abnormal data.

[0035] In one implementation, the first time scale is equal to the duration of the abnormal data. The obtained preceding / subsequent normal data consists of several consecutive normal data segments within this first time scale. The amount of preceding and subsequent normal data can be the same or different. For example, if the duration of the abnormal data is 7 days, then the first time scale is 7 days. Therefore, we can obtain five 7-day segments of normal data preceding the abnormal data, and eight 7-day segments of normal data following the abnormal data.

[0036] In another implementation, the first time scale can be one day.

[0037] In this embodiment of the invention, both abnormal and normal data are time-domain data, and time-domain data is a mixture of all signals, making it difficult to distinguish between instrument self-noise and environmental noise interference. In order to separate instrument self-noise and environmental noise interference, Fourier transform can be used to decompose these mixed signals by frequency to obtain the intensity of each frequency component.

[0038] In one embodiment of the present invention, the Welch method can be used to estimate the power spectral density (PSD) of the time series. The average of the segmented and windowed periodograms is then calculated, and the final PSD estimate is: in, The total number of segments, For discrete frequency points, For the first Periodic chart of segmented data after windowing compensation.

[0039] After generating corresponding power spectral density curves for anomalous and normal data through Fourier transform, the frequency band range of earthquake causes corresponding to underground fluid temperature can be obtained by comparing the power spectral density curves of anomalous and normal data.

[0040] Plotting the power spectral density curves of the generated abnormal data and normal data on the same coordinate system yields a comparison chart of the power spectral density curves of the abnormal and normal data. (See [link to comparison chart] for details.) Figure 2 . Figure 2 In the diagram, the red curve represents the power spectral density curve of the abnormal data, while the multiple gray curves represent the power spectral density curves of multiple normal data segments. For example, in the example above, the preceding normal data could include five consecutive 7-day normal data segments, and the subsequent normal data could include eight consecutive 7-day normal data segments, resulting in 13 consecutive 7-day normal data segments. Plotting the power spectral density curve for each of these 7-day consecutive normal data segments yields the corresponding 13 gray curves.

[0041] In this embodiment of the invention, normal data represents the background noise field of the observation well in the target area, while abnormal data is the superposition of the background noise field and the seismic origin signal field. Therefore, by comparing the differences in energy distribution between the two in the frequency domain, the frequency band range of the seismic event injection can be extracted from the mixed signal.

[0042] Specifically, the comparison of the power spectral density curves of anomalous data and normal data to obtain the frequency band range corresponding to the seismic origin of underground fluid temperature includes: The separation portions of the power spectral density curves of the abnormal data and the power spectral density curves of the normal data are determined, and the frequency band range is determined based on the frequency of the separation portions.

[0043] Please refer to the separation point. Figure 2 At 0.228 mHz, in the region less than 0.228 mHz, the power spectrum curve of the abnormal data is separated from the multiple power spectral density curves of the normal data. In the region greater than 0.228 mHz, the power spectrum curve of the abnormal data is located between the multiple power spectral density curves of the normal data. Therefore, based on the frequency of this separation part, the frequency band range can be determined to be less than 0.228 mHz.

[0044] Next, for step 106, multiple temperature measuring instruments under test are compared in a high-precision constant temperature bath to eliminate common noise introduced by the environmental background and obtain the self-noise power spectral density curve of the temperature measuring instruments under test.

[0045] In this embodiment of the invention, the self-noise of the temperature measuring instrument under test is still converted from the time domain to the frequency domain, and the resulting self-noise power spectral density curve is a two-dimensional relationship curve between power spectral density and frequency.

[0046] In one implementation, the self-noise ratio can be measured using a two- or three-instrument method. The core idea of ​​the experiment is to take advantage of the fact that the environmental signals recorded by multiple instruments are highly coherent (identical), while the noise of each instrument itself is incoherent (different). By performing coherent / cross-correlation analysis in the frequency domain, the common environmental signals are eliminated, thereby separating the noise of the instruments themselves.

[0047] Specifically, two or three identical temperature measuring instruments are placed in the same environment, such as a high-precision thermostatic bath, and they measure the same physical quantity, namely temperature. Because minute temperature fluctuations in the thermostatic bath are recorded simultaneously and at the same amplitude by all instruments, this signal is highly coherent in the frequency domain. The internal electronic noise and quantization errors of each instrument are random and independent, and this noise is independent of each other in the frequency domain.

[0048] Taking two instruments as an example, the outputs of each instrument are: X = S + NA and Y = S + NB. Here, S represents the shared environmental signal, i.e., the common noise introduced by the environmental background, while NA and NB represent their respective independent self-noise. By calculating the power spectral density and cross-power spectral density recorded by the two instruments, the key parameter of coherence function can be obtained. The value of the coherence function is between 0 and 1, reflecting the degree of correlation between the two signals at a specific frequency. Specifically, at frequencies where the environmental signal dominates, the coherence function value approaches 1, while at frequencies where instrument self-noise dominates, the coherence function value approaches 0.

[0049] The instrument's self-noise power spectrum can be calculated using the coherence function and the instrument's power spectrum. The core formula is: P N1N1 = P XX × (1 - γ²); Among them, P N1N1 P represents the instrument's self-noise power spectrum. XX Let γ be the power spectrum of the instrument, and γ² be the coherence function.

[0050] (1 - γ²) represents the proportion of incoherent components in the signal, and the total power spectrum P XX Multiplying by this incoherent ratio yields the instrument's own noise power spectrum, and the coherent part γ² of the environmental noise is successfully eliminated.

[0051] In this way, the self-noise power spectral density curve of the temperature measuring instrument under test can be obtained.

[0052] The following sections explain the calculation methods for coherence function and self-noise separation in the two-stage and three-stage methods, respectively.

[0053] Two-instrument method: Let the self-power spectrum estimates of the raw signals from two identical temperature measuring instruments be as follows: and The cross-power spectrum is estimated as follows: The coherence function of the two is calculated as follows: Assuming that the ambient noise is perfectly correlated in both instruments, while the instrument self-noise is uncorrelated, the self-noise power spectra of the two instruments can be estimated as follows: in, , These are the self-noise power spectra of the two instruments, respectively.

[0054] Three-instrument method: Let the self-power spectrum estimates of three identical temperature measuring instruments be as follows: , and The cross-power spectrum is estimated as follows: , and Assuming that the noise of the three instruments is uncorrelated with each other and uncorrelated with the common input signal, the contribution of the common input signal to the power spectrum of the three instruments can be eliminated by cross-spectral amplitude information.

[0055] Self-noise power spectrum of instrument 1 Estimate using the following formula: Similarly, the self-noise power spectrum of instrument 2 The self-noise power spectrum of instrument 3 They are respectively: in, This represents the amplitude of the cross-power spectrum. When there are significant differences in the amplitude responses of the three instruments, the power spectrum must first be normalized to ensure the accuracy of the calculation results.

[0056] It should be noted that before conducting experiments using the two- or three-unit method, a data consistency assessment of this model of temperature measuring instrument can be performed to further improve the accuracy of the calculation results.

[0057] Specifically, the data consistency assessment method can be as follows: Two or three identical temperature measuring instruments to be tested are compared in an observation well for a certain period of time (e.g., no less than 3 months). Statistical methods such as curve morphology observation and the intraclass correlation coefficient (ICC) method are used to judge data consistency. For example, an ICC ≥ 0.8 can be used as a quantitative pass criterion. By comparing and verifying the two methods, the objectivity and reliability of the judgment results are ensured, achieving a comprehensive assessment of the data consistency of multiple temperature measuring instruments to be tested. Please refer to Table 1 below. Figure 3 This is an example of a consistency assessment using the three-stage method.

[0058] Table 1: Consistency Assessment Results of Data from the Three-Platform Method In Table 1, PT-2 is the model of the temperature measuring instrument to be tested. Among the analysis variables, 804, 805 and 806 are the numbers of the three temperature measuring instruments to be tested. The analysis is performed on any two temperature measuring instruments and all three temperature measuring instruments to be tested, respectively. The results of the single metric ICC are analyzed and confidence intervals are attached.

[0059] Figure 3 This is a schematic diagram showing the curve shape observation results of the data consistency of the three temperature measuring instruments under test.

[0060] Finally, for step 108, based on the frequency range formed by the lower frequency limit of the self-noise power spectral density curve of the temperature measuring instrument under test and the upper frequency limit of the frequency band range, the effective value of the self-noise of the temperature measuring instrument under test is calculated, and this effective value of self-noise is used as the minimum temperature resolution capability of the temperature measuring instrument under test.

[0061] In this embodiment of the invention, in order to obtain an accurate effective value of self-noise, the entire frequency range cannot be directly used as the calculation range for the effective value of self-noise. Instead, a reasonable and scientific frequency range needs to be selected.

[0062] In this embodiment of the invention, the frequency range formed by the lower frequency limit of the self-noise power spectral density curve of the temperature measuring instrument under test and the upper frequency limit of the frequency band range can be used as the calculation range for calculating the effective value of the self-noise of the temperature measuring instrument under test.

[0063] The lower frequency limit of the power spectral density curve of the temperature measuring instrument under test is chosen as the lower limit of the calculation range for the effective value of self-noise. The rationale is to avoid unreliable low-frequency regions. Specifically, the lower frequency limit of the power spectral density curve, i.e., the lowest frequency that can be analyzed, is determined by the length of the data used in the calculation; the longer the data length, the lower the lowest frequency that can be extracted. Below this lower frequency limit, the calculation results are statistically unstable because the calculation window cannot contain a complete fluctuation period. Therefore, this lower limit is the lowest frequency boundary where the self-noise of the temperature measuring instrument under test can be effectively evaluated during testing. Excluding frequencies below this lower frequency limit ensures that the calculated effective value of self-noise (RMS) is based on reliable data.

[0064] The reason for choosing the upper frequency limit of the frequency band as the upper limit of the interval for calculating the effective value of self-noise is to align with the high-frequency boundary of the target signal. Specifically, the seismic origin frequency band of interest in this embodiment of the invention is a specific frequency band, such as the aforementioned band below 0.228 MHz. The upper limit of this specific frequency band represents the highest frequency boundary where earthquake precursor signals may exist. In order to accurately evaluate the signal-to-noise ratio of the instrument when detecting earthquake precursors, the total energy of the instrument's self-noise across all frequency bands that may interfere with the seismic signal must be calculated. Therefore, the upper limit must extend at least to the highest point of this specific frequency band to ensure that no instrument noise that may mask high-frequency precursor signals is missed.

[0065] If the upper limit of the calculation is set too high, the calculation results will be contaminated by a large amount of irrelevant high-frequency circuit noise, leading to an overestimation of the instrument's effective self-noise value and failing to accurately reflect the instrument's potential in observing low-frequency precursor signals of earthquakes. If the lower limit of the calculation is extended to extremely low frequencies, the instrument's low-frequency drift may be erroneously amplified due to unreliable calculations and the influence of long-term crustal drift. Therefore, in this embodiment of the invention, the frequency range formed by the lower limit of the self-noise power spectral density curve of the temperature measuring instrument under test and the upper limit of the frequency band is used as the calculation range for calculating the effective self-noise value of the temperature measuring instrument under test. This is equivalent to constructing a dedicated evaluation window for earthquake precursor monitoring tasks. The lower limit ensures the data quality of the calculation results, while the upper limit ensures the physical relevance of the calculation results. In summary, involving this frequency range not only ensures that the calculated effective self-noise value can accurately reflect whether the instrument has sufficient detection capability in the earthquake precursor frequency band that truly needs attention, but also avoids the problem of bringing irrelevant high-frequency noise or unreliable low-frequency drift into the calculation results.

[0066] Furthermore, before performing precise calculations of the RMS self-noise, it is necessary to confirm whether the instrument physically possesses the potential to observe earthquake precursor signals. If the instrument's own noise level is already higher than the background noise of its monitoring environment, then no matter how accurately its RMS self-noise is calculated, it will be impossible to distinguish earthquake precursor signals in actual observation. This is because the signal is already submerged by the environment itself before it reaches the instrument.

[0067] Based on this, in one embodiment of the present invention, after obtaining historical observation data of underground fluid temperature in observation wells within the target area, the method further includes: constructing a background noise model using the historical observation data.

[0068] In one implementation, the background noise model can be constructed as follows: Based on the historical observation data, multiple background noise power spectral density curves are generated using the fast Fourier transform method; the background noise power spectral density curves are two-dimensional curves showing the relationship between power spectral density and frequency; the multiple background noise power spectral density curves are generated one-to-one by dividing the historical observation data according to a second time scale. Based on the minimum and maximum power spectral density values ​​at each frequency point in the background noise power spectral density curve, the reference points for the lower and upper boundaries of the envelope are determined. Based on the reference point, a smooth envelope is generated; the lower envelope and upper envelope are used to characterize the low-noise model and high-noise model of the background noise, respectively.

[0069] According to the fundamental properties of Fourier transform, frequency resolution is determined by data length. Since the energy of earthquake precursor signals is typically concentrated in the extremely low frequency band, while daily or monthly data can cover this range, annual data can provide a lower frequency limit, thus enabling the observation of background changes over a longer period. Furthermore, because the temperature changes of underground fluids are strongly influenced by external periodicity, their typical period is an annual cycle. Therefore, preferably, the second time scale is an annual scale. In this way, the generated background noise power spectral density curve contains a complete annual cycle, accurately reflecting the low-frequency peak of the background noise. Moreover, using an annual time scale results in a moderate amount of data to be processed, facilitating batch processing and statistical analysis.

[0070] For each observation well, several years of historical observation data can be obtained. Therefore, a background noise power spectral density curve can be plotted for each year's historical observation data for each well in the same coordinate system. Thus, the coordinate system can include several background noise power spectral density curves corresponding to several years. To ensure the background noise model covers the target area, the background noise power spectral density curves corresponding to the historical observation data of multiple observation wells in the target area can also be plotted in the same coordinate system. For example, if one observation well includes 20 years of historical observation data, and the target area includes 10 observation wells, then 200 background noise power spectral density curves can be obtained in the same coordinate system. Please refer to [link to relevant documentation]. Figure 4 This is an example of PSD analysis for background noise. Figure 4 The example uses 87 years of historical observation data. Figure 4 The blue solid line represents the background noise power spectral density curve, the red solid line represents the high-noise model, and the green solid line represents the low-noise model. The blue and orange dashed lines represent the self-noise power spectral density curves of instruments 2518-877 and 2810-859, respectively. The pink area represents the frequency range formed by the lower frequency limit and the upper frequency limit of the self-noise power spectral density curve of the temperature measuring instrument under test, which is 0.002-0.228 mHz. This frequency range is used as the calibration band for subsequent calculation of the effective value of self-noise.

[0071] When determining the reference point, one implementation method is to directly select the maximum and minimum values ​​as reference points for each frequency, and then smooth the process to generate the envelope.

[0072] In another implementation, to eliminate the influence of abnormal interference values, instead of directly using the maximum and minimum values ​​at each frequency as the envelope reference points, the maximum and minimum values ​​are contracted inwards, and the contracted values ​​are used as the reference points. This inward movement refers to moving from the extreme points towards the internal interval formed by the maximum and minimum values; that is, for the maximum value, the value is taken in the direction of decreasing value, and for the minimum value, the value is taken in the direction of increasing value.

[0073] Among them, for the maximum value V max Reference point P max P is calculated as follows: max =V max -| V max |×(1- ),in, For a specified ratio, and 0 < <1.

[0074] For the minimum value V min Reference point P min P is calculated as follows: min =V min +| V min |×(1- ).

[0075] By moving the reference point towards the inner interval, we can avoid directly using the extreme values, which would cause the envelope to be too conservative or affected by isolated outliers, making the generated low-noise and high-noise models more robust and representative.

[0076] In this embodiment of the invention, the lower envelope and upper envelope are used to characterize the low-noise model and high-noise model of the background noise, respectively. Please refer to [link / reference]. Figure 4 The red curve corresponds to the upper envelope, representing a high-noise model, while the green curve corresponds to the lower envelope, representing a low-noise model.

[0077] After constructing the background noise model, before calculating the effective value of the self-noise of the temperature measuring instrument under test, the method further includes: using the background noise model to determine whether the temperature measuring instrument under test has the ability to distinguish earthquake precursor signals; if it is determined that the temperature measuring instrument under test has the ability to distinguish earthquake precursor signals, then the calculation of the effective value of the self-noise of the temperature measuring instrument under test is performed.

[0078] Specifically, the step of using the background noise model to determine whether the temperature measuring instrument under test has the ability to distinguish earthquake precursor signals includes: Determine whether the self-noise power spectral density curve of the temperature measuring instrument under test is lower than the lower envelope corresponding to the low-noise model within the specified frequency range. If so, the temperature measuring instrument under test is determined to have the ability to distinguish earthquake precursor signals. Please continue reading. Figure 4The self-noise power spectral density curve of the temperature measuring instrument under test is lower than the lower envelope corresponding to the low-noise model within the frequency range, i.e., the pink area. Therefore, the temperature measuring instrument under test has the ability to distinguish earthquake precursor signals.

[0079] Since the low-noise model represents the quietest and most stable state of the environment within the observation well, it serves as the lowest interference baseline for instrument observations. This means that the background noise of the observation well can never be lower than the energy level represented by this curve. Therefore, by using whether the self-noise power spectral density curve of the temperature measuring instrument under test is lower than the lower envelope corresponding to the low-noise model, we can determine whether the instrument has the ability to distinguish earthquake precursor signals. This ensures that the instrument's self-noise will not become a more serious source of interference than the environment itself, thus ensuring that the instrument can capture any seismic signals that might penetrate the environmental background.

[0080] In addition, for earthquake precursor signals to be reliably recorded, two conditions must be met simultaneously: Condition A is a physical condition: the energy of the seismic signal S must be greater than the environmental background noise E, i.e., S > E; otherwise, the seismic signal will be submerged by environmental fluctuations before reaching the instrument. Condition B is an instrument condition: the energy of the seismic signal S must be greater than the instrument's self-noise N, i.e., S > N; otherwise, the instrument's own jitter will mask the input seismic signal.

[0081] Assume the instrument's self-noise power spectral density curve N is lower than that of the low-noise model E throughout the entire curve. min That is, N < E min Given condition A, S > E, because the ambient background noise always satisfies E ≥ E. min Therefore, we can conclude that: S>E≥E min Since S > N, it is necessary that S > N.

[0082] If S > N always holds true, it means that as long as there exists a precursory earthquake signal with energy strong enough to change the environment and overcome background noise, its energy must be greater than the instrument's self-noise. Therefore, the instrument possesses the physical prerequisites to capture this signal.

[0083] If the instrument's self-noise is higher than that of the low-noise model, i.e., N > E min When the environment is extremely quiet, E≈E min The instrument output may primarily consist of self-noise N. In this case, even a weak real signal mixed in may be masked by the instrument noise, resulting in a missed detection; or the fluctuations recorded by the instrument may simply be self-noise, but may be mistakenly identified as environmental signals, leading to false detections.

[0084] Therefore, ensuring that the instrument's self-noise is lower than that of the low-noise model ensures that when the environment is quietest, the fluctuations in the output of the instrument mainly reflect the real physical processes of the environment, rather than its own false jitter.

[0085] Please continue reading Figure 4 , Figure 4 In the diagram, the blue dashed line and the orange dashed line represent the self-noise power spectral density curves corresponding to the two temperature measuring instruments under test.

[0086] In this embodiment of the invention, given the power spectral density, it is possible to calculate from... to The RMS value within the frequency range, the effective value of the self-noise of the temperature measuring instrument under test. It can be calculated using the following formula: The unit of the effective value of self-noise is degrees Celsius, PSD ( f ) represents frequency f The power spectral density at that location.

[0087] Please refer to Table 2 for the calculation results of the effective value (RMS) of self-noise.

[0088] Table 2: Calculation results of the effective value (RMS) of self-noise Table 2 shows the calculation results for the entire frequency band on the left, and the calculation results for the frequency range formed by the lower frequency limit of the self-noise power spectral density curve of the temperature measuring instrument under test and the upper frequency limit of the frequency band on the right. 2518-877 and 2810-859 are the serial numbers of the two temperature measuring instruments under test. According to Table 2, the calculated effective value of self-noise differs between the full frequency band and the frequency range. Based on the above analysis, the self-noise calculated within the frequency range is more effective.

[0089] Furthermore, suitable temperature measuring instruments can be selected for the observation wells based on the embodiments of the present invention. Specifically: the background noise model is generated using historical observation data obtained from the target observation wells; After determining that the temperature measuring instrument under test has the ability to distinguish earthquake precursor signals, the method also includes: identifying the temperature measuring instrument under test as suitable for measuring the temperature of underground fluids in the target observation well.

[0090] If the self-noise power spectral density curve of the temperature measuring instrument to be tested is lower than that of the low-noise model of the target observation well within the frequency range, then the temperature measuring instrument to be tested can be determined as suitable for measuring the underground fluid temperature of the target observation well, thus providing a guarantee for the accurate observation of subsequent earthquake precursor signals.

[0091] Please refer to Figure 5This invention provides a temperature resolution measurement device based on noise analysis, the device comprising: The acquisition unit 500 is used to acquire historical observation data of underground fluid temperature in observation wells within the target area; the historical observation data is obtained based on measurements taken by temperature measuring instruments placed in the observation wells. The determining unit 502 is used to determine the abnormal data formed by the cause of earthquakes in the historical observation data based on the earthquake occurrence time observed by the observation wells in the target area; The identification unit 504 is used for time-frequency conversion based on anomalous data to identify the frequency band range of earthquake causes corresponding to underground fluid temperature; The comparison unit 506 is used to compare the self-noise of multiple temperature measuring instruments under test in a high-precision constant temperature bath to eliminate common noise introduced by the environmental background and obtain the self-noise power spectral density curve of the temperature measuring instrument under test; the self-noise power spectral density curve is a two-dimensional relationship curve between power spectral density and frequency. The calculation unit 508 is used to calculate the effective value of the self-noise of the temperature measuring instrument under test based on the frequency range formed by the lower frequency limit of the self-noise power spectral density curve of the temperature measuring instrument under test and the upper frequency limit of the frequency band range, and to use the effective value of the self-noise as the minimum temperature resolution of the temperature measuring instrument under test.

[0092] In one embodiment of the present invention, the step of determining the abnormal data of earthquake formation in the historical observation data based on the earthquake occurrence time observed by the observation wells in the target area includes: locating the earthquake occurrence time from the historical observation data, and determining a data segment with specific temperature changes based on the observation data within the time period before and after the earthquake occurrence time, and identifying the data segment as abnormal data of earthquake formation. The time-frequency conversion based on anomalous data to identify the frequency band range of earthquake causes corresponding to underground fluid temperature includes: obtaining the preceding normal data and subsequent normal data of the anomalous data from the historical observation data based on a first time scale; generating the power spectral density curves of the anomalous data and normal data using the Fourier transform method; the normal data includes the preceding normal data and the subsequent normal data; and comparing the power spectral density curves of the anomalous data and the normal data to obtain the frequency band range of earthquake causes corresponding to underground fluid temperature.

[0093] In one embodiment of the present invention, the step of comparing the power spectral density curves of anomalous data and normal data to obtain the frequency band range corresponding to the seismic cause of underground fluid temperature includes: determining the separation portion of the power spectral density curves of the anomalous data and the power spectral density curves of the normal data, and determining the frequency band range based on the frequency of the separation portion.

[0094] In one embodiment of the present invention, after acquiring historical observation data of underground fluid temperature in observation wells within the target area, the method further includes: a construction unit for constructing a background noise model using the historical observation data; Before calculating the effective value of the self-noise of the temperature measuring instrument under test, the system further includes a determination unit, which uses the background noise model to determine whether the temperature measuring instrument under test has the ability to distinguish earthquake precursor signals. If it is determined that the temperature measuring instrument under test has the ability to distinguish earthquake precursor signals, the calculation unit is triggered to perform the calculation of the effective value of the self-noise of the temperature measuring instrument under test.

[0095] In one embodiment of the present invention, the step of constructing a background noise model using the historical observation data includes: generating multiple background noise power spectral density curves based on the historical observation data using the Fast Fourier Transform method; the background noise power spectral density curves are two-dimensional curves relating power spectral density to frequency; the multiple background noise power spectral density curves are generated one-to-one by dividing the historical observation data according to a second time scale; determining reference points for the lower and upper boundaries of the envelope based on the minimum and maximum values ​​of the power spectral density at each frequency point in the background noise power spectral density curves; generating a smooth envelope based on the reference points; the lower and upper envelopes are used to characterize low-noise and high-noise models of the background noise, respectively.

[0096] In one embodiment of the present invention, the step of using the background noise model to determine whether the temperature measuring instrument under test has the ability to distinguish earthquake precursor signals includes: determining whether the self-noise power spectral density curve of the temperature measuring instrument under test is lower than the lower envelope corresponding to the low noise model in the frequency range; if so, it is determined that the temperature measuring instrument under test has the ability to distinguish earthquake precursor signals.

[0097] It should be noted that the temperature resolution measuring device based on noise analysis provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the temperature resolution measuring device based on noise analysis provided in the above embodiments and the temperature resolution measuring method based on noise analysis belong to the same concept. The specific implementation process is detailed in the method embodiments and will not be repeated here.

[0098] Embodiments of this application also provide a computer device, please refer to... Figure 6The computer device includes a processor and a memory, the memory storing at least one instruction, at least one program, code set or instruction set, the at least one instruction, at least one program, code set or instruction set being loaded and executed by the processor to implement the temperature resolution measurement method based on noise analysis provided in the above-described method embodiments.

[0099] Embodiments of this application also provide a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the temperature resolution measurement method based on noise analysis provided in the above-described method embodiments.

[0100] Embodiments of this application also provide a computer program product, which includes a computer program. A processor of a computer device reads the computer program from a computer-readable storage medium and executes the computer program, causing the computer device to perform any of the noise analysis-based temperature resolution measurement methods described in the above embodiments.

[0101] For ease of description, the above systems or devices are described separately as various modules or units based on their functions. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware components.

[0102] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0103] Finally, it should be noted that in this document, relational terms such as first, second, third, and fourth are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0104] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A temperature resolution measurement method based on noise analysis, characterized in that, include: Acquire historical observation data of underground fluid temperature in observation wells within the target area; The historical observation data was obtained based on measurements taken by temperature measuring instruments placed inside the observation well. Based on the earthquake occurrence time observed by observation wells within the target area, identify the abnormal data in the historical observation data that are caused by earthquakes; Time-frequency conversion based on anomalous data is used to identify the frequency band range of earthquake causes corresponding to underground fluid temperature. Multiple temperature measuring instruments under test are compared in a high-precision constant temperature bath to eliminate common noise introduced by the environmental background, thereby obtaining the self-noise power spectral density curve of the temperature measuring instrument under test; the self-noise power spectral density curve is a two-dimensional relationship curve between power spectral density and frequency. Based on the frequency range formed by the lower frequency limit of the self-noise power spectral density curve of the temperature measuring instrument under test and the upper frequency limit of the frequency band, the effective value of the self-noise of the temperature measuring instrument under test is calculated, and this effective value of self-noise is taken as the minimum temperature resolution of the temperature measuring instrument under test.

2. The method according to claim 1, characterized in that, The step of determining anomalous data related to earthquake causes in historical observation data based on the earthquake occurrence time observed by observation wells within the target area includes: The earthquake occurrence time is located from the historical observation data, and based on the observation data within the time period before and after the earthquake occurrence time, a data segment with specific temperature changes is identified, and this data segment is identified as earthquake-causing anomaly data. The time-frequency conversion based on anomalous data, used to identify the frequency band range corresponding to the seismic origin of underground fluid temperature, includes: Based on the first time scale, the preceding normal data and subsequent normal data of the abnormal data are obtained from the historical observation data; The power spectral density curves of the abnormal data and the normal data are generated using the Fourier transform method; the normal data includes the preceding normal data and the subsequent normal data. By comparing the power spectral density curves of anomalous data and normal data, the frequency band range corresponding to the seismic cause of underground fluid temperature can be obtained.

3. The method according to claim 2, characterized in that, By comparing the power spectral density curves of the anomalous data and the power spectral density curves of the normal data, the frequency band range corresponding to the seismic origin of the underground fluid temperature is obtained, including: The separation portions of the power spectral density curves of the abnormal data and the power spectral density curves of the normal data are determined, and the frequency band range is determined based on the frequency of the separation portions.

4. The method according to claim 1, characterized in that, After acquiring historical observation data of underground fluid temperature in observation wells within the target area, the method further includes: constructing a background noise model using the historical observation data; Before calculating the effective value of the self-noise of the temperature measuring instrument under test, the method further includes: using the background noise model to determine whether the temperature measuring instrument under test has the ability to distinguish earthquake precursor signals; if it is determined that the temperature measuring instrument under test has the ability to distinguish earthquake precursor signals, then the calculation of the effective value of the self-noise of the temperature measuring instrument under test is performed.

5. The method according to claim 4, characterized in that, The construction of the background noise model using the historical observation data includes: Based on the historical observation data, multiple background noise power spectral density curves are generated using the fast Fourier transform method; the background noise power spectral density curves are two-dimensional curves showing the relationship between power spectral density and frequency; the multiple background noise power spectral density curves are generated one-to-one by dividing the historical observation data according to a second time scale. Based on the minimum and maximum power spectral density values ​​at each frequency point in the background noise power spectral density curve, the reference points for the lower and upper boundaries of the envelope are determined. Based on the reference point, a smooth envelope is generated; the lower envelope and upper envelope are used to characterize the low-noise model and high-noise model of the background noise, respectively.

6. The method according to claim 5, characterized in that, The method of using the background noise model to determine whether the temperature measuring instrument under test has the ability to distinguish earthquake precursor signals includes: Determine whether the self-noise power spectral density curve of the temperature measuring instrument under test is lower than the lower envelope corresponding to the low-noise model within the frequency range. If so, it is determined that the temperature measuring instrument under test has the ability to distinguish earthquake precursor signals.

7. A temperature resolution measuring device based on noise analysis, characterized in that, The device includes: The acquisition unit is used to acquire historical observation data of underground fluid temperature in observation wells within the target area; the historical observation data is obtained based on measurements taken by temperature measuring instruments placed inside the observation wells. The determination unit is used to determine the abnormal data formed by the cause of earthquakes in the historical observation data based on the earthquake occurrence time observed by the observation wells in the target area; The identification unit is used for time-frequency conversion based on anomalous data to identify the frequency band range of earthquake causes corresponding to underground fluid temperature; The comparison unit is used to compare the self-noise of multiple temperature measuring instruments under test in a high-precision constant temperature bath to eliminate common noise introduced by the environmental background and obtain the self-noise power spectral density curve of the temperature measuring instrument under test; the self-noise power spectral density curve is a two-dimensional relationship curve between power spectral density and frequency. The calculation unit is used to calculate the effective value of the self-noise of the temperature measuring instrument under test based on the frequency range formed by the lower frequency limit of the self-noise power spectral density curve of the temperature measuring instrument under test and the upper frequency limit of the frequency band range, and to use the effective value of the self-noise as the minimum temperature resolution of the temperature measuring instrument under test.

8. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement the steps of the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1-6.

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