A method for optimizing temperature measurement with μk resolution

Abstract

The present application relates to a kind of μK grade resolution temperature measurement optimization method for spacecraft precision measurement and control temperature, comprising the following steps: S1, μK grade resolution temperature measurement system index system construction is carried out, primary index is temperature measurement resolution, secondary index is obtained by the cross effect analysis induction of system component and temperature measurement noise effect, every secondary index includes two dimensions of system component and temperature measurement noise effect;S2, according to the two dimensions of system component and temperature measurement noise effect of μK grade resolution temperature measurement system, namely influence factor calculation secondary index is calculated according to specific performance parameter;S3, temperature measurement resolution is calculated according to the square of primary index equals the square sum of all secondary indexes.The beneficial effect is suitable for μK grade resolution temperature measurement resolution analysis and performance optimization.

Description

TECHNICAL FIELD

[0001] The application relates to the field of satellite space technology, in particular to a muK-level resolution temperature measurement optimization method. BACKGROUND

[0002] Temperature measurement resolution and temperature measurement deviation are two core indicators for measuring the performance of a temperature measurement system. The temperature measurement resolution describes the resolution and identification ability of a weak temperature signal, which is affected by temperature measurement noise. The temperature measurement deviation reflects the deviation of the measurement value from the true value in an ideal case where the influence of noise is completely eliminated.

[0003] Some heat-sensitive structures on a spacecraft, such as optical devices, large antennas and large structures for interferometric measurement, have high requirements for thermal control accuracy. For example, during a gravitational wave detection mission, the weak temperature fluctuations of the inter-satellite laser interferometric core component can cause interference forces and stiffness changes in the inertial sensor, and cause changes in the optical path of the interferometer, thereby seriously interfering with the detection of gravitational waves. This type of high-precision space equipment with temperature sensitivity has reached the mK or even muK level in terms of temperature control accuracy, which poses a great challenge to the precise temperature measurement and control technology of the spacecraft.

[0004] High-resolution temperature measurement technology is the basis and important prerequisite for precise temperature control technology. The temperature measurement and control system usually requires a temperature measurement resolution that is at least one order of magnitude higher than the temperature control accuracy. For a temperature measurement system with a resolution of 0.1K, only the resolution loss caused by individual important links needs to be considered according to prior experience, while other insignificant temperature measurement resolution loss links are ignored, so that the resolution of the entire system can be better estimated or controlled. However, a muK-level resolution temperature measurement system is often composed of multiple components, and the noise strength of different frequency bands generated by each component under the action of various effects is affected by complex factors. The resolution loss (i.e. temperature measurement noise) caused by various factors occurring in each element needs to be considered and analyzed. Therefore, the temperature measurement resolution cannot be simply estimated according to prior experience, and a corresponding index system is needed to comprehensively evaluate the resolution loss of the temperature signal in each link and comprehensively optimize the system. However, so far there is no theoretical analysis method for the resolution loss of high-precision temperature measurement systems, and there is a lack of an index system that can be used for resolution index allocation, analysis, optimization and evaluation of temperature measurement systems, resulting in strong experience and uncertainty in the design and development process of high-precision temperature measurement systems.

[0005] The present application improves the muK-level resolution temperature measurement optimization method in view of the technical problem that the traditional resolution analysis method and performance evaluation method of the complex temperature measurement system with muK-level resolution is no longer applicable. SUMMARY

[0006] The purpose of the present invention is to provide a temperature measurement resolution analysis and performance optimization method suitable for μK-level resolution.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is a μK-level resolution temperature measurement optimization method for precise temperature measurement and control of spacecraft, comprising the following steps:

[0008] S1. Construct an index system for the temperature measurement system with μK resolution. The first-level index is the temperature measurement resolution. The second-level index is obtained by analyzing and summarizing the cross-effects of system components and temperature measurement noise effects. Each second-level index includes two dimensions: system components and temperature measurement noise effects.

[0009] S2. Calculate the secondary index based on the specific performance parameters of the μK-level resolution temperature measurement system in two dimensions: system components and temperature measurement noise effect, i.e., the impact factor;

[0010] S3. The temperature measurement resolution is calculated based on the square of the first-level index being equal to the sum of the squares of all second-level indexes.

[0011] Preferably, step S1 includes the following sub-steps:

[0012] S11. Draw an orthogonal table of system components and temperature measurement noise effects;

[0013] S12. Analyze and simplify the orthogonal array noise nodes and determine the secondary index content.

[0014] Preferably, the μK-level resolution temperature measurement system includes a temperature measuring bridge and a signal conversion circuit. The temperature measuring bridge uses an inductive voltage divider to replace the left arm of the Whitten bridge on the basis of a conventional double-arm Whitten bridge. The temperature measuring bridge includes four components: a bridge power supply, a standard resistor, a thermistor, and an inductive voltage divider. The signal conversion circuit includes two components: an operational amplifier and an analog-to-digital converter. When the system performs temperature measurement, the temperature signal of the thermistor is converted into a voltage signal V0 through the temperature measuring bridge, and then the voltage signal V0 is amplified by the operational amplifier. Finally, the electrical signal is converted into a digital signal through the analog-to-digital converter.

[0015] Preferably, step S11: the system components include a thermistor, a standard resistor, a bridge power supply, an inductive voltage divider, an operational amplifier and an analog-to-digital converter, and the temperature measurement noise effect includes a thermal noise effect, a power supply voltage ripple effect, a 1 / f noise effect and a quantization truncation effect, where f represents the temperature measurement frequency band; the orthogonal table has the thermistor, standard resistor, bridge power supply, inductive voltage divider, operational amplifier and analog-to-digital converter of the system components as columns, and the thermal noise effect, power supply voltage ripple effect, 1 / f noise effect and quantization truncation effect of the temperature measurement noise effect as rows, and the rows are numbered first and then the columns, including 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 and 24, a total of 24 noise nodes.

[0016] Preferably, step S12:

[0017] The thermal noise effect originates from the Brownian motion of electrons and mainly acts on resistors, causing the voltage across the resistors to fluctuate around the average value. The resistors in the system are mainly the thermistors and standard resistors in the temperature measurement bridge, as well as the resistors inside the operational amplifier and analog-to-digital converter. Noise nodes 3 and 4 are ignored, and noise nodes 1, 2, 5, and 6 are retained as secondary indicators.

[0018] The first aspect of the power supply voltage ripple effect is that the ripple of the bridge power supply causes ripples with consistent phase but different amplitudes at both ends of each component, which directly causes the output voltage V0 of the temperature measurement bridge to produce ripples with the same phase, directly generating temperature measurement noise. Noise nodes 7, 8, 9, and 10 are retained as secondary indicators. The second aspect of the power supply voltage ripple effect is that the voltage divider ripple of the thermistor will cause ripple in the self-heating power of the thermistor. Due to the thermal resistance between the thermistor and the object being measured, the self-heating generated cannot be discharged in time, and there is an unexpected fluctuating temperature difference between the thermistor and the object being measured, which causes noise in the temperature measurement. Noise node 7 not only includes the power supply voltage ripple effect, but also includes the self-heating ripple sub-effect that needs to be considered separately. The operational amplifier is not significantly affected by the power supply voltage ripple effect, so noise node 11 is ignored. The performance parameters of the analog-to-digital converter product reflect the influence of various comprehensive factors including the power supply voltage ripple effect. Noise node 12 is retained as a secondary indicator.

[0019] The 1 / f noise effect is inherent in semiconductor devices. The 1 / f noise effect in ordinary resistors and inductors can be basically ignored. The system semiconductors mentioned above are widely present in operational amplifiers, analog-to-digital converters, and thermistors. Noise nodes 14, 15, and 16 are ignored, and noise nodes 13, 17, and 18 are retained as secondary indicators.

[0020] The quantization truncation effect noise generated by the analog-to-digital converter is divided into the inherent input-referred noise of the analog-to-digital converter and the quantization noise generated by rounding the digital signal output by the analog-to-digital converter. At the same time, since the digital quantization process only exists in the ADC, the input-referred noise is a comprehensive reflection of the thermal noise effect and the 1 / f noise effect. The quantization noise only exists in the analog-to-digital converter. The combined noise nodes 6, 12, and 18 are retained as secondary indicators, and the noise node 24 is retained separately as a secondary indicator. The noise nodes 19, 20, 21, 22, and 23 are ignored.

[0021] Preferably, step S12 determines the nine secondary indicators as thermistor-thermal noise temperature measurement noise Tn1, thermistor-self-heating ripple temperature measurement noise Tn2, thermistor-1 / f temperature measurement noise Tn3, standard resistor-thermal noise temperature measurement noise Tn4, bridge-power supply voltage ripple temperature measurement noise Tn5, operational amplifier-thermal noise temperature measurement noise Tn6, operational amplifier-1 / f temperature measurement noise Tn7, ADC-reduced temperature measurement noise Tn8, ADC-quantization truncation temperature measurement noise Tn9;

[0022] Step S3: Temperature measurement resolution level 1 index S T The relationship between the above secondary indicators is

[0023] Preferably, step S2 includes the following sub-steps:

[0024] S21. Establish the correlation formula between 9 secondary indicators and impact factors;

[0025] S22. Input the specific performance parameters of the μK-level resolution temperature measurement system, substitute them into the correlation formula, and perform analysis and targeted optimization based on the indicator system to improve the temperature measurement resolution.

[0026] Preferably, step S21:

[0027] Thermistor - thermal noise temperature measurement noise Tn1, the calculation formula is Among them, V n1 The thermal noise voltage of the thermistor Vn1(T,ω)=4k B TR(T), S b The sensitivity of the temperature bridge output temperature to the thermistor voltage k b is the Boltzmann constant, T is the thermistor temperature, ω is the angular frequency of the noise, Tn2 is the standard resistance, R(T) is the thermistor resistance, and β is the temperature characteristic constant of the thermistor;

[0028] Thermistor - self-heating ripple temperature measurement noise Tn2, the calculation formula is Among them, r zr S is the thermal resistance between the temperature measuring core of the thermistor group and the temperature measuring object,Pv The power fluctuation of the thermistor caused by the voltage ripple S PR The power fluctuation caused by the thermistor's own resistance change S v is the voltage ripple of the bridge power supply, S T is the temperature fluctuation of the thermistor itself, R0 is the resistance value of the thermistor at a given reference temperature T0, η RT is the voltage divider ratio η of the thermistor RT =R(T) / (R(T)+R2);

[0029] Thermistor -1 / f temperature measurement noise Tn3, the empirical correlation formula is Among them, V R is the voltage across the thermistor, M1 and M2 are constants determined by the thermistor's own characteristics;

[0030] Standard resistance-thermal noise temperature measurement noise Tn4, the calculation formula is Where Vn2(T,ω)=4k B TR2;

[0031] The bridge-power supply voltage ripple temperature measurement noise Tn5 is calculated as follows: Among them, v b is the output voltage of the temperature measuring bridge, η L η is the ratio of the number of turns of the coil at the output and input of the inductive voltage divider L =N out / N in ;

[0032] Operational amplifier - thermal noise temperature measurement noise Tn6, the calculation formula is Among them, e i,1 (T,ω) is the thermal noise of the current K i and ω ci is the current noise parameter of the operational amplifier chip, e v,1 (T,ω) is the thermal noise of the voltage e v,1 2 (T,ω)=K v 2 , K v and ω cv is the voltage noise parameter of the operational amplifier chip;

[0033] The operational amplifier's -1 / f temperature measurement noise, Tn7, is calculated as: Among them, e i,f (T,ω) is the current 1 / f noise e v,f (T,ω) is the voltage 1 / f noise

[0034] ADC-reduced temperature measurement noise Tn8, the calculation formula is Among them, s ADC is the least significant bit, N is the number of bits of the analog-to-digital converter, f s is the sampling frequency, V FS is the full-scale voltage;

[0035] ADC-quantization truncation temperature measurement noise Tn9, the calculation formula is

[0036] A μK-level resolution temperature measurement optimization method of the present invention has the following beneficial effects: 1. The present invention proposes a method for constructing an index system of a high-resolution temperature measurement system, selects secondary indicators of temperature measurement resolution by drawing and simplifying the temperature measurement noise node table, and establishes a quantitative correlation between the secondary indicators and the influencing factors, thereby decomposing the temperature measurement resolution indicator into multiple parallel independent modules, and each module is quantifiable, predictable, and adjustable, thereby realizing a comprehensive evaluation and analysis system for the high-resolution temperature measurement system; 2. The present invention fills the gap in the field. By establishing an index system, the resolution index of the system temperature measurement can be disassembled into each specific component and effect of the system operation, and can be further associated with specific system performance parameters; the present invention can clearly and intuitively reflect the resolution performance of the temperature measurement system in different noise frequency bands, establish a scientific and accurate evaluation system, and at the same time predict the impact of changes in the performance of each system on the temperature measurement resolution; 3. The index system construction and application method of the present invention can be used to guide the design, optimization and improvement of spacecraft high-resolution temperature measurement systems.

Brief Description of the Drawings

[0037] Figure 1 The diagram is a schematic diagram of a temperature measurement bridge circuit for a μK-level resolution temperature measurement system.

[0038] Figure 2 The present invention is a schematic diagram of a signal conversion circuit of a temperature measurement system with μK resolution.

[0039] Figure 3 This is a step-by-step diagram of a μK-level resolution temperature measurement optimization method.

[0040] Figure 4 It is a schematic diagram of the thermistor installation composition and self-heating accumulation of a μK-level resolution temperature measurement system.

[0041] Figure 5 It is a schematic diagram of the index system of high-resolution temperature measurement system and an optimization method for temperature measurement with μK resolution.

[0042] Figure 6 This is a schematic diagram of the temperature measurement resolution in different frequency bands under the current system configuration.

[0043] Figure 7 This is a schematic diagram of the secondary indicator value of temperature measurement resolution in the 0.001Hz frequency band.

[0044] Figure 8 It is a schematic diagram of the secondary indicator value of temperature measurement resolution in the 1Hz frequency band. [Specific implementation method]

[0045] The present invention will be further described below in conjunction with embodiments and with reference to the accompanying drawings.

[0046] Example

[0047] This embodiment implements a μK-level resolution temperature measurement optimization method.

[0048] The method in this embodiment constructs an indicator system for a μK-level resolution temperature measurement system, clarifies the specific components and effects that cause resolution loss in the temperature measurement system, illustrates the coupling relationship between system components and between effects, analyzes in detail the influence of various specific indicators on the overall temperature measurement resolution, and clearly demonstrates the proportion of resolution loss in each link, thus guiding the design, evaluation, and optimization of spacecraft high-resolution temperature measurement systems.

[0049] This embodiment method is a method for constructing and applying an indicator system for a high-resolution temperature measurement system. The specific technical features are:

[0050] 1. This temperature measurement index system is designed for a μK-level resolution temperature measurement system. In order to reduce temperature measurement noise, the left arm of the Whittons bridge is replaced with an inductive voltage divider.

[0051] 2. Its first-level indicator is temperature measurement resolution, and its second-level indicators are obtained by analyzing and summarizing the cross-effects of system components and temperature measurement noise effects. Each second-level indicator includes two dimensions: system components and temperature measurement noise effects.

[0052] 3. The system component dimension of the secondary indicators includes but is not limited to thermistors, standard resistors, bridge power supplies, inductive voltage dividers, operational amplifiers, and ADC modules;

[0053] 4. The noise effect dimension of the secondary indicator includes but is not limited to thermal noise effect (i.e. Johnson noise effect), power supply voltage ripple effect, 1 / f noise effect and quantization truncation effect;

[0054] 5. Secondary indicators are calculated based on the specific performance parameters of the system (i.e., impact factors);

[0055] 6. The square of the first-level index is equal to the sum of the squares of all second-level indexes.

[0056] Figure 1The diagram is a schematic diagram of a temperature measurement bridge circuit for a μK-level resolution temperature measurement system. Figure 2 This is a schematic diagram of the signal conversion circuit of a μK-level resolution temperature measurement system. Figure 1 、 Figure 2 As shown, the high-resolution temperature measurement system corresponding to the application case of the index system exemplified by the method of this embodiment includes a high-resolution temperature measurement bridge and a signal conversion part; wherein, the temperature measurement bridge includes four components: a bridge power supply, a standard resistor, a thermistor and an inductive voltage divider, and the signal conversion part includes two components: a differential amplifier and an ADC (i.e., an analog-to-digital converter). In order to reduce the noise caused by the resistor, the temperature measurement bridge is optimized and improved on the basis of the conventional double-arm Huitongs bridge, and an inductive voltage divider is used to replace the left arm of the Huitongs bridge. When measuring temperature, the temperature signal of the thermistor is first converted into a voltage signal V0 through the temperature measurement bridge, and then the voltage signal is amplified by the operational amplifier, and finally the electrical signal is converted into a digital signal through the ADC.

[0057] Figure 3 This is a step-by-step diagram of the optimization method for μK-level resolution temperature measurement. Figure 3 As shown, the specific implementation process of the temperature measurement system of this embodiment constructing and applying the temperature measurement index system to optimize temperature measurement includes the following steps:

[0058] (1) Draw an orthogonal table of system components and noise effects;

[0059] The temperature measurement noise of this embodiment's temperature measurement system stems from various effects affecting various components (the bridge power supply, standard resistor, thermistor, inductive voltage divider, operational amplifier, and ADC). These effects primarily include thermal noise, power supply voltage ripple, 1 / f noise, and quantization truncation.

[0060] Based on the above information, an orthogonal table of system component-noise effects is plotted below. This table includes 4 x 6 = 24 noise nodes, all of which are potential sources of temperature measurement noise. However, the noise impact of some of these nodes can be eliminated through preliminary analysis, reducing the redundancy of the indicator system.

[0061] Table 1 Noise node table

[0062]

[0063] (2) Analyze and simplify the noise node table to determine the secondary index content;

[0064] Thermal noise originates from the Brownian motion of electrons and primarily affects resistors, causing the voltage across them to fluctuate around their average value. The resistors in a temperature measurement system primarily include the thermistor and standard resistor in the bridge, as well as the internal resistors of the amplifier and ADC modules. Therefore, nodes 3 and 4 are omitted from the noise node table, while nodes 1, 2, 5, and 6 are retained as secondary indicators.

[0065] Power supply voltage ripple refers to the phenomenon in which voltage ripple in the system power supply causes ripple in the component voltage divider, resulting in noise. This power supply voltage ripple primarily occurs within the bridge circuit, causing ripple in the bridge output voltage V0, which in turn induces temperature measurement noise. The noise effect of power supply voltage ripple occurs in two ways. Firstly, the ripple in the bridge power supply causes ripple at both ends of each component, with consistent phase but varying amplitudes. This directly causes ripple in the bridge output voltage V0, which in turn directly generates temperature measurement noise. Secondly, the ripple in the thermistor voltage divider causes ripple in the thermistor's self-heating power. Figure 4 This is a diagram of the thermal installation composition and self-heating accumulation of a μK-level resolution temperature measurement system. Figure 4 As shown, due to the thermal resistance between the thermistor and the object being measured, the generated self-heating cannot be dissipated in time, resulting in an unexpected temperature difference between the thermistor and the object being measured, which causes noise in the temperature measurement. The first aspect of noise depends primarily on the voltage divider of the bridge circuit, and noise nodes 7, 8, 9, and 10 need to be retained in the noise node table. The second aspect of noise also depends on the thermistor's self-heating and its heat dissipation. Therefore, noise node 7 not only includes the power supply voltage ripple effect, but also includes the sub-effect of self-heating ripple, which needs to be considered separately. In addition, the power supply voltage ripple effect is not significant in amplifiers and ADCs, so it does not need to be considered separately. However, given that the ADC product performance parameters reflect the influence of various comprehensive factors including the power supply voltage ripple effect, this indicator system chooses to ignore node 11 and retain node 12.

[0066] 1 / f noise is inherent in semiconductor devices, while the 1 / f effect in common resistors and inductors is largely negligible. In temperature measurement systems, semiconductors are prevalent in operational amplifiers and ADC modules, and in the form of thermistors in temperature measurement bridges. Therefore, noise nodes 14, 15, and 16 can be ignored, retaining noise nodes 13, 17, and 18 as secondary indicators.

[0067] The primary function of the ADC (analog-to-digital converter) module is to convert analog signals into digital signals. The noise it generates can be divided into the ADC's inherent input-referred noise and the quantization noise caused by rounding its output digital signal. The input-referred noise is a combination of effects such as thermal noise and 1 / f noise, and can be easily estimated from the product manual. Therefore, the combined noise nodes 6, 12, and 18 are retained in the indicator system, while 24 is retained separately as a secondary indicator. Furthermore, since the digital quantization process only occurs within the ADC, the noise nodes 19, 20, 21, 22, and 23 are ignored in the indicator system.

[0068] Based on the above analysis, the noise node table is simplified as shown in the following table. The nine secondary indicators can be determined as follows: thermistor-thermal noise temperature measurement noise Tn1, thermistor-self-heating ripple temperature measurement noise Tn2, thermistor-1 / f temperature measurement noise Tn3, standard resistor-thermal noise temperature measurement noise Tn4, bridge-power supply voltage ripple temperature measurement noise Tn5, operational amplifier-thermal noise temperature measurement noise Tn6, operational amplifier-1 / f temperature measurement noise Tn7, ADC-reduced temperature measurement noise Tn8, ADC-quantization truncation temperature measurement noise Tn9. Temperature measurement system resolution index S T The relationship between the above secondary indicators is

[0069] Table 2 Secondary indicators and their physical connotations

[0070]

[0071] (3) Figure 5 This is a μK-level resolution temperature measurement optimization method and a high-resolution temperature measurement system indicator system diagram. Figure 5 As shown, the relationship between the secondary indicators and the impact factors is established;

[0072] (3.1) Thermistor - Thermal Noise Temperature Measurement Noise Tn1

[0073] The calculation method is

[0074] Among them, V n1 is the thermal noise voltage of the thermistor, S b is the sensitivity of the bridge output temperature to the thermistor voltage. Its calculation formulas are

[0075] Vn1(T,ω)=4k B TR(T) (3)

[0076]

[0077] Among them, k bis the Boltzmann constant, T is the thermistor temperature, ω is the angular frequency of the noise, R2 is the standard bridge resistance, R(T) is the thermistor resistance, and β is the temperature characteristic constant of the thermistor.

[0078] (3.2) Thermistor - Self-heating ripple temperature noise Tn2

[0079] The calculation method is

[0080] Among them, r zr S is the thermal resistance between the thermistor core and the temperature measurement object. Pv is the power fluctuation of the thermistor due to the voltage ripple, and S PR It is the power fluctuation caused by the change of thermistor's own resistance.

[0081] The calculation methods are

[0082]

[0083] Among them S v is the voltage ripple of the bridge power supply, S T is the temperature fluctuation of the thermistor itself. R0 is the resistance value of the thermistor at a given reference temperature T0, η RT is the voltage divider ratio of the thermistor. The calculation formula is

[0084] η RT =R(T) / (R(T)+R2) (8)

[0085] (3.3) Thermistor-1 / f temperature measurement noise Tn3

[0086] Tn3 generally needs to be estimated in combination with specific experimental data. Based on the experimental data, the empirical correlation formula obtained in this embodiment is:

[0087]

[0088] Among them, V R is the voltage across the thermistor, and M1 and M2 are constants determined by the thermistor's own characteristics. According to the test results, their values ​​are 1.52×10 -9 Hz 1 / 2 and 0.01Hz.

[0089] (3.4) Standard resistance-thermal noise temperature measurement noise Tn4

[0090] The calculation method is

[0091] Where Vn2(T,ω)=4k B TR2(11)

[0092] (3.5) Bridge - Power supply voltage ripple temperature noise Tn5

[0093] The voltage ripple across the thermally sensitive and inductive voltage divider will affect the bridge output voltage v b . The resulting temperature noise is

[0094]

[0095] where η L is the turns ratio of the inductive voltage divider output to input, i.e.

[0096] η L = N out / N in (13)

[0097] (3.6) Operational amplifier - Thermal noise temperature noise Tn6

[0098] The calculation is

[0099] where e i,1 (T, ω) is the thermal noise of the current

[0100]

[0101] where K i and ω ci are the amplifier chip current noise parameters.

[0102] e v,1 (T, ω) is the thermal noise of the voltage, calculated as

[0103] e v,1 2 (T, ω) = K v 2 (16)

[0104] where K v and ω cv are the amplifier chip voltage noise parameters.

[0105] (3.7) Operational amplifier - 1 / f temperature noise Tn7

[0106] The calculation is

[0107] where e i,f (T, ω) is the current 1 / f noise, calculated as

[0108]

[0109] e v,f (T, ω) is the voltage 1 / f noise, calculated as

[0110]

[0111] (3.8)ADC-reduced temperature noise Tn8

[0112] The calculation method is

[0113] Where s ADC is the least significant digit, N is the ADC digit, f s is the sampling frequency, V FS is the full-scale voltage.

[0114] (3.9)ADC-quantization truncation temperature noise Tn9

[0115] The calculation method is as follows

[0116] Combining the above contents, we can obtain a high-resolution temperature measurement index system.

[0117] (4) Input system parameters, analyze and optimize according to the indicator system

[0118] It is planned to build a μK-level high-resolution temperature measurement system so that the temperature measurement resolution of the temperature measurement system can meet S T <2μK / Hz 1 / 2 , and maximize the high-frequency temperature measurement resolution. The temperature of the measurement object and the measurement environment is approximately 25°C. The preliminary plan selected temperature measurement system components, whose parameters are shown in the table below.

[0119] Table 3 System component selection

[0120]

[0121] Figure 6 This is a schematic diagram of the temperature measurement resolution in different frequency bands under the current system configuration. Figure 6 As shown, the parameters are substituted into the formula to calculate the secondary index and temperature measurement resolution at different noise frequencies. It can be obtained that within the range of 0.001Hz-1Hz, the temperature measurement resolution of the current temperature measurement system changes as shown in the figure. It can be seen that within the frequency range of 0.1Hz-1Hz, the temperature measurement resolution of the system can reach S T <2μK / Hz 1 / 2 However, in the range of 0.01Hz-0.001Hz, the resolution of the temperature measurement system exceeds the specification limit.

[0122] Figure 7 This is a diagram of the secondary index value of temperature measurement resolution within the 0.001Hz frequency band. Figure 7As shown in the figure, the index system is expanded at the frequency band f = 0.001Hz, and its secondary indicators are shown in the figure. It can be seen that the main factors affecting resolution at this time are the operational amplifier 1 / f noise Tn7 and the thermistor 1 / f noise Tn3. Therefore, to improve the temperature measurement resolution of the system under low-frequency conditions, the following targeted measures can be taken based on the calculation formulas for Tn3 and Tn7:

[0123] 1. Choose an operational amplifier with lower noise parameters;

[0124] 2. Add a phase-locked frequency modulation device before the noise amplifier to modulate the weak voltage signal collected by the bridge to a higher frequency band to prevent the temperature signal from being submerged by the 1 / f noise of the operational amplifier;

[0125] 3. Choose a thermistor with lower 1 / f noise, or appropriately reduce the bridge supply voltage;

[0126] 4. Use post-processing to filter out noise.

[0127] Figure 8 This is a diagram of the secondary index value of temperature measurement resolution within the 1Hz frequency band. Figure 8 As shown in the figure, the index system is expanded at the frequency band of f = 1 Hz, and its secondary indicators are shown in the figure. It can be seen that the main factor affecting resolution loss at this time is the bridge-power supply voltage ripple noise Tn5, and the secondary factor is the ADC module's reduced input noise Tn8. Based on the calculation formulas for Tn5 and Tn8, the temperature measurement resolution in the high-frequency band can be improved by the following measures:

[0128] 1. The voltage value of the bridge power supply can be appropriately increased;

[0129] 2. Choose a power supply with smaller voltage ripple;

[0130] 3. The turns ratio of the inductive voltage divider coil can be adjusted to make it closer to the thermistor voltage divider ratio;

[0131] 4. Use post-processing to filter out noise;

[0132] 5. ADC module selection optimization.

[0133] Those skilled in the art will appreciate that all or part of the steps for implementing the above embodiments may be accomplished by hardware, or may be accomplished by a program instructing the relevant hardware, and the program may be stored in a computer-readable storage medium, wherein the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0134] The above is only a preferred embodiment of the present invention. It should be pointed out that ordinary technicians in this technical field can make several improvements and supplements without departing from the principles of the present invention. These improvements and supplements should also be regarded as the scope of protection of the present invention.

Claims

1. A μK-level resolution temperature measurement optimization method for precise temperature measurement and control of spacecraft, characterized by The following steps are involved: S1. Construct an index system for the temperature measurement system with μK resolution. The first-level index is the temperature measurement resolution. The second-level index is obtained by analyzing and summarizing the cross-effects of system components and temperature measurement noise effects. Each second-level index includes two dimensions: system components and temperature measurement noise effects. S2. Calculate the secondary index based on the specific performance parameters of the μK-level resolution temperature measurement system in two dimensions: system components and temperature measurement noise effect, i.e., the impact factor; S3. The temperature measurement resolution is calculated based on the square of the first-level index being equal to the sum of the squares of all second-level indexes.

2. A μK-level resolution temperature measurement optimization method according to claim 1, characterized in that Step S1 includes the following sub-steps: S11. Draw an orthogonal table of system components and temperature measurement noise effects; S12. Analyze and simplify the orthogonal array noise nodes and determine the secondary index content.

3. The μK-level resolution temperature measurement optimization method according to claim 2, characterized in that: The μK-level resolution temperature measurement system includes a temperature measuring bridge and a signal conversion circuit. The temperature measuring bridge uses an inductive voltage divider to replace the left arm of a conventional double-arm Whitten bridge. The temperature measuring bridge includes four components: a bridge power supply, a standard resistor, a thermistor, and an inductive voltage divider. The signal conversion circuit includes two components: an operational amplifier and an analog-to-digital converter. When the system performs temperature measurement, the temperature signal of the thermistor is converted into a voltage signal V0 through the temperature measuring bridge, the voltage signal V0 is then amplified by the operational amplifier, and finally the electrical signal is converted into a digital signal by the analog-to-digital converter.

4. A μK-level resolution temperature measurement optimization method according to claim 3, characterized in that Step S11: the system components include a thermistor, a standard resistor, a bridge power supply, an inductive voltage divider, an operational amplifier, and an analog-to-digital converter, and the temperature measurement noise effect includes a thermal noise effect, a power supply voltage ripple effect, a 1 / f noise effect, and a quantization truncation effect, where f represents the temperature measurement frequency band; the orthogonal table has the thermistor, standard resistor, bridge power supply, inductive voltage divider, operational amplifier, and analog-to-digital converter of the system components as columns, and the thermal noise effect, power supply voltage ripple effect, 1 / f noise effect, and quantization truncation effect of the temperature measurement noise effect as rows, and the rows are numbered first and then the columns, including 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, and 24, a total of 24 noise nodes.

5. A μK-level resolution temperature measurement optimization method according to claim 4, characterized in that Step S12: The thermal noise effect originates from the Brownian motion of electrons and mainly acts on resistors, causing the voltage across the resistors to fluctuate around the average value. The resistors in the system are mainly the thermistors and standard resistors in the temperature measurement bridge, as well as the resistors inside the operational amplifier and analog-to-digital converter. Noise nodes 3 and 4 are ignored, and noise nodes 1, 2, 5, and 6 are retained as secondary indicators. The first aspect of the power supply voltage ripple effect is that the ripple of the bridge power supply causes ripples with consistent phase but different amplitudes at both ends of each component, which directly causes the output voltage V0 of the temperature measurement bridge to produce ripples with the same phase, directly generating temperature measurement noise. Noise nodes 7, 8, 9, and 10 are retained as secondary indicators. The second aspect of the power supply voltage ripple effect is that the voltage divider ripple of the thermistor will cause ripple in the self-heating power of the thermistor. Due to the thermal resistance between the thermistor and the object being measured, the self-heating generated cannot be discharged in time, and there is an unexpected fluctuating temperature difference between the thermistor and the object being measured, which causes noise in the temperature measurement. Noise node 7 not only includes the power supply voltage ripple effect, but also includes the self-heating ripple sub-effect that needs to be considered separately. The operational amplifier is not significantly affected by the power supply voltage ripple effect, so noise node 11 is ignored. The performance parameters of the analog-to-digital converter product reflect the influence of various comprehensive factors including the power supply voltage ripple effect. Noise node 12 is retained as a secondary indicator. The 1 / f noise effect is inherent in semiconductor devices. The 1 / f noise effect in ordinary resistors and inductors can be basically ignored. The system semiconductors mentioned above are widely present in operational amplifiers, analog-to-digital converters, and thermistors. Noise nodes 14, 15, and 16 are ignored, and noise nodes 13, 17, and 18 are retained as secondary indicators. The quantization truncation effect noise generated by the analog-to-digital converter is divided into the inherent input-reduced noise of the analog-to-digital converter and the quantization noise generated by rounding the digital signal output by the analog-to-digital converter. At the same time, since the digital quantization process only exists in the ADC, the input-reduced noise is a comprehensive reflection of the thermal noise effect and the 1 / f noise effect. The quantization noise only exists in the analog-to-digital converter. The combined noise nodes 6, 12, and 18 are retained as secondary indicators, and the noise node 24 is retained separately as a secondary indicator. The noise nodes 19, 20, 21, 22, and 23 are ignored.

6. The μK-level resolution temperature measurement optimization method according to claim 5, characterized in that: Step S12 determines nine secondary indicators: thermistor-thermal noise temperature measurement noise Tn1, thermistor-self-heating ripple temperature measurement noise Tn2, thermistor-1 / f temperature measurement noise Tn3, standard resistor-thermal noise temperature measurement noise Tn4, bridge-power supply voltage ripple temperature measurement noise Tn5, operational amplifier-thermal noise temperature measurement noise Tn6, operational amplifier-1 / f temperature measurement noise Tn7, ADC-reduced noise Tn8, ADC-quantization truncation temperature measurement noise Tn9; Step S3: Temperature measurement resolution level 1 index S T The relationship between the above secondary indicators is 7. A μK-level resolution temperature measurement optimization method according to claim 6, characterized in that Step S2 includes the following sub-steps: S21. Establish the correlation formula between 9 secondary indicators and impact factors; S22. Input the specific performance parameters of the μK-level resolution temperature measurement system, substitute them into the correlation formula, and perform analysis and targeted optimization based on the indicator system to improve the temperature measurement resolution.

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