A temperature verification system and method thereof

Abstract

The present application belongs to the technical field of temperature verification, and discloses a temperature verification system and method, which realizes micron-level accurate butt joint of a sensor and a reference temperature measuring point by using micro laser positioning technology, avoids inherent errors caused by spatial misplacement, controls the internal temperature gradient of the reference source in real time through multi-micro area temperature monitoring and PID dynamic adjustment, solves the problem of mismatching of response speed of the reference source and the sensor by combining with scene-based lag compensation parameter adjustment, matches compensation coefficients based on multi-dimensional environmental data and historical data, further corrects errors caused by environmental interference and equipment characteristics, so that the verification result can meet the demand of dynamic and micro area temperature measurement, establishes a real-time interaction link of reference database, system calibration data and the data of the sensor to be tested, realizes complete coverage of the traceability link, and encrypts and packages the verification data, operator information and time stamp information to generate a unique hash value, so that the data cannot be tampered with and is traceable throughout the process.

Description

Technical Field

[0001] This invention belongs to the field of temperature verification technology, specifically a temperature verification system and method. Background Technology

[0002] Temperature calibration technology is a core support of industrial metrology systems and is widely used in high-precision fields such as aerospace, semiconductor manufacturing, and medical equipment. It verifies and corrects temperature measurement accuracy through comparison and calibration between a reference unit and the sensor under test. Existing temperature calibration technologies mainly suffer from the following technical problems:

[0003] Traditional calibration systems' reference sources (such as blackbody furnaces and constant temperature baths) can only guarantee the temperature accuracy at a single fixed point. There is a significant temperature gradient distribution within them, and the spatial misalignment between the sensor under test's probe and the reference standard's temperature measurement point will produce inherent deviations. At the same time, the temperature stabilization process of the reference source has a certain lag, which does not match the response speed of the sensor under test, resulting in distortion of dynamic temperature point calibration data. This makes it difficult to meet the actual calibration accuracy requirements of aerospace, semiconductor manufacturing, and other fields that require dynamic and micro-area temperature measurement.

[0004] The existing traceability system is a unidirectional linear structure that only records the static calibration results of each step. The impact of long-term drift and environmental interference of the reference source on the traceability accuracy is not recorded and quantified in real time. Furthermore, the verification data storage format lacks the binding of metrological characteristics, timestamps, and operational behavior to prevent the reverse tracing of the impact of reference source drift on historical verification results. Summary of the Invention

[0005] The purpose of this invention is to provide a temperature calibration system and method to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a temperature calibration system, wherein the integrated module is used for the access and basic parameter configuration of the sensor to be calibrated, and automatically identifies temperature detectors including thermocouples, platinum resistance thermometers, and fiber optic temperature sensors through two methods: pin impedance matching and signal feature extraction, while reading the sensor range and accuracy level; according to the calibration field input by the user, a preliminary scene label is marked (used for the selection of pre-calibration benchmark units), and a pre-calibration request is sent to the collaborative module; the collaborative module provides three sets of benchmark temperature point environments of 25℃, 50℃, and 100℃ according to the preliminary scene label; the integrated module collects the raw data of the sensor under test under this environment, compares it with the local historical calibration database of the same model of sensor, and fine-tunes the signal preprocessing parameters;

[0007] Based on the user-inputted verification field and the temperature point value to be verified, the sensor is labeled with a complete scene tag. The scene tag, the temperature point value to be verified, the sensor's basic parameters, preprocessing parameters, and the raw data of the sensor under test are synchronized to the collaboration module, rule base module, and self-calibration module. Finally, through the integrated micro laser positioning module, the sensor's detection end is guided to align with the reference source standard temperature measurement point. After alignment, a verification ready signal is sent to the collaboration module.

[0008] Core parameters and accuracy verification methods for miniature laser positioning modules:

[0009] The working wavelength is set to 650nm (red light band), and the positioning time is ≤2 seconds;

[0010] The accuracy test adopts the standard calibration block comparison method. Using a metal calibration block of known size (error ≤ 0.1μm), after aligning the sensor probe end with the reference point of the calibration block, the offset is observed by an optical microscope with a magnification of 1000x. The test is repeated 10 times. If the maximum offset is ≤ 10μm, the positioning is considered qualified.

[0011] Preferably, the collaborative module receives the scene label, sensor range, temperature point value to be verified, and verification ready signal output by the integrated module. Based on the temperature point value to be verified and the scene label, it automatically selects a high-temperature reference unit or a low-temperature reference unit. The reference unit is divided into multiple independent micro-regions. The temperature distribution of each micro-region is monitored in real time through the built-in temperature array sensor. The reference drift data is calculated and generated. The temperature gradient data and reference drift data are synchronized to the integrated module, the traceability closed-loop module, and the self-calibration module.

[0012] When the temperature gradient of a micro-area exceeds the threshold corresponding to the scene, the system receives the PID adjustment parameters fed back by the self-calibration module, triggering the adjustment of the micro heating element or cooling element in the corresponding micro-area; it integrates a high-speed response acquisition unit, adjusts the temperature stabilization lag compensation parameters according to the scene label, synchronously records the temperature change curve of the reference unit and the response curve of the sensor under test, and outputs the response time difference data to the rule base module and the self-calibration module.

[0013] When the calculated baseline drift data exceeds the threshold corresponding to the scenario (which is consistent with the baseline drift threshold in the scenario-based threshold library of the self-calibration module), a drift warning signal is immediately sent to the traceability closed-loop module, triggering the historical data correction process of the traceability closed-loop module.

[0014] Reference unit temperature control element and response time parameters:

[0015] The high-temperature reference unit uses a PTC ceramic miniature heating element with dimensions of 5mm×5mm×1mm and a rated power of 2W. The response time from room temperature (25℃) to 1200℃ is ≤30 minutes.

[0016] The low-temperature reference unit uses a semiconductor cooling chip, model TEC1-12706, with a rated voltage of 12V and a response time of ≤45 minutes from room temperature (25℃) to -50℃. The temperature control elements are all equipped with aluminum heat sinks with a heat dissipation area of ​​10cm², ensuring long-term operational stability.

[0017] Preferably, the rule base module receives scene tags from the integrated module, raw data from the sensor under test, and temperature gradient and response time difference data from the collaborative module. Based on experimental data, it establishes a multi-dimensional correspondence rule base for scene type, temperature gradient, response time difference, environmental interference, and compensation coefficient. It collects temperature and humidity and electromagnetic field strength data through the built-in environmental monitoring unit, performs multi-dimensional matching with scene tags, temperature gradient, and response time difference data, filters the closest record in the rule base, and uses the average value of the compensation coefficient to correct the raw output value of the sensor under test.

[0018] Accuracy and sampling frequency of the environmental monitoring unit: Temperature and humidity monitoring uses SHT30 sensor with temperature accuracy ±0.1℃ and humidity accuracy ±1%RH; electromagnetic field intensity monitoring uses EMC-100 sensor with accuracy ±1μT; sampling frequency is set to 1 time / minute, and three data points are continuously recorded for each sampling and the average value is taken to ensure the stability of environmental data and avoid the impact of instantaneous interference on the matching of compensation coefficients.

[0019] After correction, the intermediate values, compensation coefficients, and matching criteria are output to the traceability closed-loop module. At the same time, the calibration feedback signal output by the self-calibration module is received, and the compensation coefficient adaptation range for the corresponding scenario in the rule base is updated.

[0020] Preferably, the traceability closed-loop module receives sensor parameters from the integrated module, benchmark drift data from the collaborative module, and verification intermediate values ​​and compensation parameters from the rule base module; it establishes a three-level metrological traceability system, creating a real-time data interaction link between the benchmark database, system benchmark unit calibration data, and the verification data of the tested sensor; and through a built-in data encryption unit, it packages the above data with the operator's identity information and verification timestamp to generate a unique metrological hash value, which is then uploaded to the blockchain node.

[0021] When the baseline drift value exceeds the threshold corresponding to the scenario, the system receives a drift warning signal from the collaborative module, automatically retrieves the baseline data to reverse the historical verification results, generates supplementary traceability records, and uploads them to the blockchain. Based on the scenario tags of the integrated module, the system generates a corresponding traceability report containing the traceability link, key verification data, spatiotemporal error curve, traceability link diagram, and blockchain query QR code, and feeds back the report generation status to the system main control unit.

[0022] Preferably, the self-calibration module uses the scene tags of the integrated module and the temperature gradient and response time difference data of the collaborative module as triggering criteria to establish a scene-based threshold library, which includes temperature gradient thresholds in the spatial dimension, response time difference thresholds in the time dimension, and reference drift thresholds, and compares the temperature gradient data of the collaborative module with the temperature gradient thresholds and the response time difference data with the response time difference thresholds in real time.

[0023] When the trigger threshold is reached, the calibration process is automatically started. When the temperature gradient threshold is triggered, the adjustment amount of the internal temperature field of the reference source is calculated and the adjustment command is sent to the coordination module. When the response time difference threshold is triggered, the dynamic response parameters of the reference unit are corrected and fed back to the coordination module. After calibration, the calibration time, parameters before and after calibration, and execution unit information are stored in the local database and uploaded to the blockchain. At the same time, a calibration feedback signal is sent to the rule base module to update the adaptation range of the compensation coefficient.

[0024] The present invention also provides a temperature calibration method. Based on the above system, after the temperature sensor to be calibrated is connected to the system port during the scenario initialization stage, the integrated module first identifies the sensor type, range, and accuracy level through pin impedance matching and signal feature extraction; then, pre-calibration is started, and pre-processing parameters, including filter frequency and amplification factor, are fine-tuned by comparing with the historical database.

[0025] Based on the user-input verification field and scene label, the scene label, sensor parameters, and preprocessing parameters are sent to the collaboration module, rule base module, and self-calibration module, respectively. Then, through the integrated micro laser positioning module, the sensor detection end is guided to align with the standard temperature measurement point of the reference unit of the collaboration module. After successful alignment, a verification ready signal is sent to the collaboration module to complete the initialization process.

[0026] Preferably, in the temperature control verification and error compensation stage, after receiving the verification ready signal and scene tag, the collaborative module selects to start the high temperature reference unit or the low temperature reference unit according to the value of the temperature point to be verified, and sends the real-time gradient data of the temperature array sensor to the self-calibration module; when the gradient exceeds the threshold, the collaborative module receives the PID adjustment parameters and triggers micro-area temperature control.

[0027] The high-speed acquisition unit is started synchronously, the lag compensation time is adjusted according to the scenario, the reference temperature curve and the response curve of the sensor under test are recorded, and the response time difference data is output to the rule base module. After receiving the scenario label, temperature gradient, response time difference and environmental data, the rule base module matches the compensation coefficient in the rule base, corrects the original output value to generate the verification intermediate value, and outputs the verification intermediate value and compensation parameters to the traceability closed loop module.

[0028] Preferably, in the traceability closed-loop and self-calibration phase, after receiving the verification intermediate value, compensation parameters, and benchmark drift data, the traceability closed-loop module packages them to generate a metrological hash value and uploads it to the blockchain node; if the benchmark drift is detected to exceed the benchmark drift threshold of the corresponding scenario in the scenario-based threshold library of the self-calibration module, the benchmark data is automatically retrieved to reverse the historical verification results and generate supplementary traceability records; at the same time, the self-calibration module continuously compares the gradient data, time difference data, and scenario threshold of the collaboration module, and starts calibration when the threshold is reached, feeding back the calibration parameters to the collaboration module and the rule base module to update the temperature control parameters and compensation coefficient range;

[0029] Finally, the system integrates the blockchain traceability report, calibration records, and verification results to generate a scenario-based final report that includes spatiotemporal error curves, traceability link diagrams, and blockchain query QR codes.

[0030] The standard for curves and link diagrams in scenario-based reports: The horizontal axis of the spatiotemporal error curve represents temperature points in ℃, with intervals of 5℃, and the vertical axis represents the error value in ℃. The accuracy is reserved to 4 decimal places. The curve is labeled with two trend lines: the error of the sensor under test and the error after compensation.

[0031] The traceability diagram is presented in a hierarchical structure: the top layer is the NIST / CNAS benchmark database, the middle layer is the system benchmark unit calibration data, and the bottom layer is the sensor calibration data under test. Each layer is connected by arrows and labeled with data interaction timestamps and hash value summaries.

[0032] The beneficial effects of this invention are as follows:

[0033] 1. This invention utilizes micro-laser positioning technology to achieve micron-level precise docking between the sensor and the reference temperature measurement point, avoiding inherent errors caused by spatial misalignment; through multi-micro-area temperature monitoring and PID dynamic adjustment, the internal temperature gradient of the reference source is controlled in real time, and combined with scenario-based hysteresis compensation parameter adjustment, the problem of mismatch between the response speed of the reference source and the sensor is solved; at the same time, based on the compensation coefficient matching of multi-dimensional environmental data and historical data, errors caused by environmental interference and equipment characteristics are further corrected, so that the calibration results can meet the stringent requirements of dynamic, micro-area temperature measurement in aerospace, semiconductor manufacturing and other fields.

[0034] 2. This invention achieves complete coverage of the traceability chain by establishing a real-time interactive link between the benchmark database, system calibration data, and the data of the tested sensors; it encrypts and packages the verification data with information such as operators and timestamps to generate a unique hash value and puts it on the chain to ensure that the data is tamper-proof and fully traceable; when the benchmark drift exceeds the threshold, the system can automatically retrieve the benchmark data to reverse and correct the historical verification results, making up for the shortcomings of the traditional traceability system that cannot trace the impact of benchmark changes, and improving the credibility of its application in fields such as medical equipment and high-end manufacturing.

[0035] 3. This invention establishes a dedicated threshold library based on different application scenarios, monitors key indicators such as temperature gradient and response time difference in real time, and automatically starts the calibration process when a threshold is triggered, dynamically adjusting temperature control parameters and compensation coefficients to reduce the frequency of manual intervention and calibration costs. At the same time, the rule library continuously optimizes the multi-dimensional compensation model through experimental data and calibration feedback. Combined with automatic sensor identification technology, it can adapt to various types of sensors such as thermocouples, platinum resistance thermometers, and fiber optic sensors, covering different scenarios such as high temperature and low temperature, improving the system's versatility and ease of use, and reducing the adaptation cost for cross-domain applications. Attached Figure Description

[0036] Figure 1 This is a flowchart of the temperature calibration system of the present invention;

[0037] Figure 2 This is a flowchart of the scenario initialization process for this invention;

[0038] Figure 3 This is a flowchart of the temperature control verification and error compensation process of the present invention;

[0039] Figure 4 This is a flowchart of the traceability closed loop and self-calibration process of this invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] like Figures 1 to 4 As shown, this embodiment of the invention provides a temperature calibration system. An integrated module is used for the precise access and basic parameter configuration of the sensor to be calibrated. Through pin impedance matching and signal feature extraction, it automatically identifies temperature detectors such as thermocouples (K / J / T type), platinum resistance thermometers (PT100 / PT1000), and fiber optic temperature sensors, while simultaneously reading the sensor's range and accuracy level. The integrated module labels the calibration field (e.g., aviation, medical) with a preliminary scenario tag (high temperature scenario / low temperature scenario) and sends a pre-calibration request to the collaborative module. The collaborative module calls the corresponding reference unit based on the preliminary scenario tag. The high temperature reference unit is used for pre-calibration in high-temperature fields such as aviation, and the low temperature reference unit is used for pre-calibration in low-temperature fields such as medical. It provides three sets of reference temperature point environments: 25℃, 50℃, and 100℃. The integrated module collects the raw data of the sensor under test in this environment, compares it with a local historical calibration database of over 300 sets of the same model sensors, fine-tunes the signal preprocessing parameters, adaptively adjusts the filtering frequency between 30~80Hz, and calibrates the amplification factor within a ±5% range.

[0042] The historical calibration database for local sensors of the same model must meet the following construction criteria:

[0043] The data source is the qualified verification records of the same model of sensor under different scenario labels, and is stored in categories according to temperature range (every 10℃ interval); the sample size of a single model sensor is no less than 300 groups, and it is updated once a quarter. During pre-verification, the integrated module matches the general compensation parameters of the corresponding temperature range in the historical database according to the sensor range and the pre-verification reference temperature point (25℃ / 50℃ / 100℃), and fine-tunes the preprocessing parameters.

[0044] Signal preprocessing parameter fine-tuning logic: The filtering frequency is adaptively adjusted according to the noise intensity of the original data. When the noise amplitude is >5mV, the filtering frequency is set to 30~50Hz; when the noise amplitude is ≤5mV, the filtering frequency is set to 60~80Hz. The noise amplitude is measured by the system's built-in signal acquisition card (sampling rate ≥1kHz), with a measurement frequency band of 0~1kHz. The peak-to-peak value of the original data within 100ms is taken as the noise amplitude. The amplification factor calibration is based on the average output value of the same temperature point in the historical database. If the deviation between the original output value of the sensor under test and the average value is >3%, the calibration is performed step by step within a range of ±5% until the deviation is ≤3%.

[0045] Based on the user-inputted verification field and the temperature point value to be verified, the sensor is labeled with a scene tag, such as aviation high temperature type or medical low temperature type. The scene tag, the temperature point value to be verified, the sensor's basic parameters, preprocessing parameters, and the raw data of the sensor under test are synchronized to the collaboration module, rule base module, and self-calibration module. Finally, through the integrated micro laser positioning module, the sensor detection end is guided to achieve ±10μm-level precise alignment with the reference source standard temperature measurement point. After alignment is completed, a verification ready signal is sent to the collaboration module to trigger the subsequent temperature field construction process.

[0046] The collaborative module receives the scene label, sensor range, temperature point value to be verified, and verification ready signal output by the integrated module and then starts working. Based on the temperature point value to be verified and the scene label, it automatically selects the high-temperature reference unit or the low-temperature reference unit to be put into operation. The reference unit is divided into 6 independent micro-regions, including the central region and the edge region. The built-in 32-channel micro-nano temperature array sensor monitors the temperature distribution of each micro-region in real time, calculates and generates reference drift data, and synchronizes the temperature gradient data and reference drift data to the integrated module, the traceability closed-loop module, and the self-calibration module.

[0047] Key parameters of the 32-channel micro / nano temperature array sensor:

[0048] Temperature resolution ≤ 0.001℃, response time ≤ 10ms, measurement error ± 0.002℃;

[0049] The division method of 6 independent micro-regions: Taking the center of the reference unit as the origin, divide it into 1 central micro-region (5cm in diameter) and 5 annular edge micro-regions. Each annular edge is 3cm wide and they are concentrically distributed. The distance between the centers of each micro-region is ≥4cm.

[0050] PID control parameter rules: Initial value range — proportional coefficient The integral coefficient is 2.0~5.0. The value is 0.1~0.5;

[0051] Adjustment logic: Relative deviation of micro-area temperature gradient = (Actual temperature gradient - Scene-corresponding threshold) / Scene-corresponding threshold × 100%; When the relative deviation of micro-area temperature gradient > 50%, Take 3.5~5.0, Take a value of 0.3 to 0.5; when the deviation is ≤ 50% of the threshold, Take a value of 2.0~3.4. Take a value of 0.1 to 0.29.

[0052] The high-temperature reference unit has a temperature range of 50~1200℃ and a static temperature accuracy of ±0.03℃; the low-temperature reference unit has a temperature range of -50~100℃ and a static temperature accuracy of ±0.02℃.

[0053] When the temperature gradient of a certain micro-area exceeds the corresponding threshold of the scene (0.008℃ / mm for aviation scene, 0.012℃ / mm for medical scene), the system receives PID adjustment parameters from the self-calibration module, including the proportional coefficient Kp and integral coefficient Ki, and triggers the adjustment of the micro heating or cooling element in the corresponding micro-area. The system integrates a 1kHz high-speed response acquisition unit, which adjusts the temperature stabilization lag compensation parameters (including lag time) according to the scene label. The lag time is 2s for aviation scene and 5s for medical scene. The system synchronously records the temperature change curve of the reference unit and the response curve of the sensor under test, and outputs the response time difference data to the rule base module and the self-calibration module to ensure accurate alignment of the time axis.

[0054] The rule base module receives scene labels and raw data from the tested sensors from the integrated module, as well as temperature gradient and response time difference data from the collaborative module. Based on more than 1,000 sets of experimental data, it establishes a multi-dimensional rule base for scene type, temperature gradient, response time difference, environmental interference, and compensation coefficient. A typical rule is set to a positive 0.002 when the temperature gradient is 0.005~0.008℃ / mm, the response time difference is 20~30ms, and the ambient humidity is 30~50% in the high-temperature aviation scenario.

[0055] The data collection conditions for 1000 sets of experiments were as follows: ambient temperature 20~25℃ (fluctuation ≤±1℃), relative humidity 30%~60% (fluctuation ≤±5%), electromagnetic field strength ≤100μT; sensor models covered thermocouples (K / J / T type), platinum resistance thermometers (PT100 / PT1000), and fiber optic temperature sensors, with a sample size of ≥300 sets for each type; each experiment was repeated 3 times, and the average value was taken as the valid data.

[0056] Multi-dimensional matching criteria: The similarity between real-time data and rule base records is calculated using the Euclidean distance method. The Euclidean distance formula is as follows:

[0057] ,

[0058] Where G is the temperature gradient, T is the response time difference, H is the ambient humidity, and E is the electromagnetic field strength, the three records with the smallest d values ​​are selected as the closest records.

[0059] The built-in environmental monitoring unit collects temperature, humidity, and electromagnetic field strength data. Combined with scene tags, temperature gradients, and response time difference data, it performs multi-dimensional matching, selects the three closest sets of records in the rule base, and takes the average of the compensation coefficients to correct the original output value of the sensor under test.

[0060] After the correction is completed, the intermediate value of the verification, the compensation coefficient and the matching basis are output to the traceability closed-loop module. At the same time, the calibration feedback signal output by the self-calibration module updates the compensation coefficient threshold range of the corresponding scenario in the rule base.

[0061] Correction formula for intermediate values ​​in the test:

[0062] ,

[0063] In the formula: This represents the intermediate value of the calibration, and the sensor output value after correction by the compensation coefficient.

[0064] This indicates the original output value of the sensor under test, which is acquired by the integrated module at a reference temperature point in the environment.

[0065] This represents the average compensation coefficient, which is obtained by taking the arithmetic mean of the compensation coefficients from the three closest records matched across multiple dimensions by the rule base module.

[0066] The traceability closed-loop module receives sensor parameters (including calculation basis) from the integrated module, benchmark drift data (automatically collected once per hour) from the collaborative module, and verification intermediate values ​​and compensation parameters from the rule base module; it establishes a three-level metrological traceability system, creating a real-time data interaction link between benchmark databases such as NIST and CNAS, system benchmark unit calibration data, and verification data of the tested sensors; through a built-in data encryption unit, it packages the above data with operator identity information and verification timestamps to generate a unique metrological hash value, which is then uploaded to the blockchain node;

[0067] Interaction process of the three-level metrological traceability system:

[0068] Establish a real-time data exchange link between the benchmark database, system benchmark unit calibration data, and the test sensor verification data (key data is synchronized in real time during the verification process); the system benchmark unit sends periodic calibration data, including temperature value and drift amount, to the NIST / CNAS benchmark database once every 24 hours, and the database returns the verification result within 10 minutes of receiving it; if the deviation between the system benchmark unit calibration data and the standard value returned by the NIST / CNAS benchmark database is ≤ ±0.005℃, the calibration is valid; if the deviation is > ±0.005℃, the system automatically starts the benchmark unit to recalibrate.

[0069] Blockchain node parameters: A consortium blockchain is used, and the nodes include inspection agencies, regulatory departments, and equipment manufacturers. The hash value generation algorithm is SHA-256. After the data is uploaded, a block is generated every 10 minutes. The block contains the hash value of the previous block to ensure that it cannot be tampered with.

[0070] When the baseline drift value exceeds the corresponding threshold for the scenario (±0.02℃ for aviation scenario and ±0.015℃ for semiconductor scenario), the system receives a drift warning signal from the collaborative module, automatically retrieves the baseline data to reverse the historical verification results, generates supplementary traceability records, and uploads them to the blockchain. Based on the scenario tags of the integrated module, the system generates a corresponding traceability report containing the traceability link and key verification data. The aviation scenario includes a dynamic temperature point verification curve, and the medical scenario adds a 24-hour stability verification page. The system then feeds back the report generation status to the main control unit, triggering subsequent processes.

[0071] The self-calibration module uses the scene tags of the integrated module and the temperature gradient and response time difference data of the collaborative module as triggering criteria to establish a scene-based threshold library, which includes temperature gradient thresholds in the spatial dimension, response time difference thresholds in the temporal dimension, and reference drift thresholds. In the aerospace scenario, the spatial threshold is set to 0.008℃ / mm and the time threshold is set to 30ms; in the medical device scenario, the spatial threshold is set to 0.012℃ / mm and the time threshold is set to 40ms. The module compares the temperature gradient data of the collaborative module with the temperature gradient threshold and the response time difference data with the response time difference threshold in real time.

[0072] When the trigger threshold is reached, the calibration process is automatically started. When the temperature gradient threshold is triggered, the adjustment amount of the internal temperature field of the reference source is calculated, and the micro heating / cooling array adjustment command is sent to the coordination module. When the response time difference threshold is triggered, the dynamic response parameters of the reference unit are corrected and fed back to the coordination module. After calibration, the calibration time, parameters before and after calibration, and execution unit information are stored in the local database and uploaded to the blockchain. At the same time, a calibration feedback signal is sent to the rule base module to update the adaptation range of the compensation coefficient, forming a stable closed loop of monitoring, calibration, and feedback.

[0073] Post-calibration verification standards: The temperature gradient must be ≤80% of the scene threshold, for example, ≤0.0064℃ / mm for aviation scenes and ≤0.0096℃ / mm for medical scenes; the response time difference must be ≤80% of the scene threshold, for example, ≤24ms for aviation scenes and ≤32ms for medical scenes; the verification duration must be ≥5 minutes, during which data is collected once every 10 seconds. The peak-to-peak fluctuation of 30 consecutive data collections must be ≤0.001℃ (temperature gradient) and ≤1ms (response time difference) to be considered as qualified calibration.

[0074] Formula for calculating temperature field adjustment:

[0075] ,

[0076] In the formula: This indicates the adjustment amount of the internal temperature field of the reference source, used to guide the adjustment of the micro heating / cooling elements in the collaborative module;

[0077] This represents the proportional coefficient, a PID control parameter fed back by the self-calibration module, used for rapid response to temperature gradient deviations;

[0078] This represents the integral coefficient, which is the PID control parameter fed back by the self-calibration module and is used to eliminate long-term accumulated temperature gradient deviations.

[0079] This represents the actual temperature gradient monitored by the collaborative module, in °C / mm, and is calculated by a 32-channel micro-nano temperature array sensor.

[0080] This represents the scenario-specific temperature gradient threshold, in °C / mm, derived from the scenario-specific threshold library of the self-calibration module, such as 0.008°C / mm for aviation scenarios and 0.012°C / mm for medical scenarios.

[0081] The time integral term representing the temperature gradient deviation, and the integration interval. τ To emerge from the deviation ( τ =0) to adjustment start ( Duration , The value range is 0~5s.

[0082] In the formula for calculating temperature field adjustment Contextualized value selection: Aerospace scenario: =4.0~5.0, =0.4~0.5; Medical equipment scenario: =3.0~4.0, =0.2~0.3; Industrial control scenarios: =2.5~3.5, =0.15~0.25.

[0083] This invention also provides a temperature calibration method. Based on the above system, during the scenario initialization phase, after the temperature sensor to be calibrated is connected to the system port, the integrated module first identifies the sensor type, range, and accuracy level through pin impedance matching and signal feature extraction; then, a 30-second pre-calibration is started, and the preprocessing parameters such as the filter frequency and amplification factor are finely adjusted by comparing with the historical database.

[0084] The data acquisition frequency for the 30-second pre-calibration is as follows: 10 output values ​​of the sensor under test are acquired per second, for a total of 300 data points. The first 10 unstable data points are removed, and the average value of the remaining 290 data points is taken as the raw data for the pre-calibration.

[0085] 1kHz high-speed acquisition unit parameters: Data storage format is CSV, including timestamp, reference temperature value, sensor output value, data transmission protocol is TCP / IP, transmission rate is ≥1Mbps, ensuring no data packet loss (packet loss rate ≤0.01%).

[0086] Based on the user-input verification field and scene label, the scene label, sensor parameters, and preprocessing parameters are sent to the collaboration module, rule base module, and self-calibration module, respectively. Then, through the integrated micro laser positioning module, the sensor detection end is guided to achieve ±10μm-level alignment with the standard temperature measurement point of the reference unit of the collaboration module. After successful alignment, a verification ready signal is sent to the collaboration module to complete the initialization process and wait to enter the temperature field construction stage.

[0087] In the temperature control verification and error compensation stage, after receiving the verification ready signal and scene label, the collaborative module selects the high temperature reference unit or the low temperature reference unit to start according to the value of the temperature point to be verified, and sends the real-time gradient data of the 32-channel micro-nano temperature array sensor to the self-calibration module. When the self-calibration module detects that the gradient exceeds the threshold, the collaborative module receives the PID adjustment parameters and triggers micro-area temperature control.

[0088] The 1kHz high-speed acquisition unit is started simultaneously, the hysteresis compensation time is adjusted according to the scenario, the reference temperature curve and the response curve of the sensor under test are recorded, and the response time difference data is output to the rule base module. After receiving the scenario label, temperature gradient, response time difference and environmental data, the rule base module matches the compensation coefficient in the rule base, corrects the original output value to generate the verification intermediate value, and outputs the verification intermediate value and compensation parameters to the traceability closed-loop module to complete the core verification process.

[0089] In the traceability closed-loop and self-calibration phases, the traceability closed-loop module receives the verification intermediate value, compensation parameters, and benchmark drift data, packages them to generate a metrological hash value, and uploads it to the blockchain node. If the benchmark drift is detected to exceed the benchmark drift threshold of the corresponding scenario in the scenario-based threshold library of the self-calibration module, the benchmark data is automatically retrieved to correct the historical verification results in reverse and generate supplementary traceability records. At the same time, the self-calibration module continuously compares the gradient data, time difference data, and scenario thresholds of the collaboration module. When the threshold is reached, calibration is initiated, and the calibration parameters are fed back to the collaboration module and the rule base module to update the temperature control parameters and compensation coefficient range.

[0090] Finally, the system integrates the blockchain traceability report, calibration records, and verification results to generate a scenario-based final report that includes spatiotemporal error curves, traceability link diagrams, and blockchain query QR codes, completing the entire process of a single temperature verification.

[0091] It should be noted that, in this document, relational terms such as "first" and "second" 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 process, method, article, or apparatus.

[0092] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A temperature verification system, characterized by, It includes an integrated module, a collaborative module, a rule base module, a traceability closed-loop module, and a self-calibration module; The integrated module identifies the temperature detector and reads basic parameters, collects raw data and fine-tunes signal preprocessing parameters; it labels the sensor with scene tags, guides the sensor probe end to the reference source standard temperature measurement point through the integrated micro laser positioning module to achieve ±10μm level precise alignment, and then outputs a verification ready signal; The collaborative module selects a reference unit based on temperature points and scene labels, divides the reference unit into multiple independent micro-regions, and monitors the temperature gradient of each micro-region in real time through a built-in temperature array sensor. When the temperature gradient of a micro-region exceeds the threshold corresponding to the scene, it receives PID adjustment parameters from the self-calibration module for dynamic adjustment. Adjust the lag compensation parameters according to the scenario, synchronously record the curve and output the time difference data; The rule base module establishes a multi-dimensional correspondence rule base, collects environmental data and matches records in the rule base to correct sensor output values; Output the verification data to the traceability closed-loop module; The traceability closed-loop module builds a three-level measurement traceability system, encrypts and packages the data and puts it on the blockchain, corrects historical data when the threshold is exceeded, and generates corresponding traceability reports according to the scenario. The self-calibration module establishes a scenario-based threshold library, compares monitoring data in real time, and initiates calibration when the threshold is reached, feeding back parameters to the relevant modules.

2. A temperature verification system according to claim 1, wherein, The integrated module automatically identifies the temperature detector by pin impedance matching and signal feature extraction, reads the sensor range and accuracy level, marks the preliminary scene label according to the verification field input by the user, and sends a pre-verification request to the collaborative module. The collaborative module provides three sets of reference temperature point environments based on the initial scene labels. The integrated module collects the raw data of the sensor under test under the three sets of reference temperature point environments of 25℃, 50℃, and 100℃, and compares them with the historical verification database to fine-tune the signal preprocessing parameters. Based on the user-inputted verification field and the temperature point value to be verified, complete scene labels are added and the scene labels, temperature point values ​​to be verified, sensor basic parameters, preprocessing parameters, and raw data of the sensor under test are synchronized to the collaboration module, rule base module, and self-calibration module. Finally, the integrated micro laser positioning module guides the sensor probe end to align with the reference source standard temperature measurement point. After alignment, a verification ready signal is sent to the collaboration module.

3. A temperature verification system according to claim 2, wherein, The collaborative module receives the scene label, sensor range, temperature point value to be verified, and verification ready signal output by the integrated module, and automatically selects the high temperature reference unit or the low temperature reference unit according to the temperature point value to be verified and the scene label. The high-temperature reference unit refers to a reference temperature generating unit with a temperature adjustment range of 50~1200℃, using PTC ceramic micro heating elements as temperature control elements, and a static temperature accuracy of ±0.03℃. The low-temperature reference unit refers to a reference temperature generating unit with a temperature adjustment range of -50~100℃, using a TEC1-12706 type semiconductor refrigeration chip as the temperature control element, and a static temperature accuracy of ±0.02℃. The reference unit is divided into multiple independent micro-regions. The temperature distribution of each micro-region is monitored in real time by the built-in temperature array sensor. The reference drift data is calculated and generated. The temperature gradient data and reference drift data are synchronized to the integrated module, the traceability closed-loop module and the self-calibration module. When the temperature gradient of a micro-area exceeds the threshold corresponding to the scene, the self-calibration module receives feedback adjustment parameters, triggering the temperature control component of the corresponding micro-area to adjust; the high-speed response acquisition unit is integrated to adjust the temperature stabilization hysteresis compensation parameters according to the scene label, and synchronously records the temperature change curve of the reference unit and the response curve of the sensor under test, and outputs the response time difference data; when the reference drift data exceeds the threshold corresponding to the scene, a drift warning signal is issued.

4. A temperature verification system according to claim 3, wherein, The rule base module receives scene labels and raw data from the sensor under test from the integrated module, as well as temperature gradient and response time difference data from the collaborative module. Based on experimental data, it establishes a multi-dimensional correspondence rule base for scene type, temperature gradient, response time difference, environmental interference, and compensation coefficient. It collects environmental interference data, performs multi-dimensional matching with scene labels, temperature gradient, and response time difference data, selects the three sets of records with the smallest Euclidean distance as the closest records, determines the compensation coefficient, and corrects the raw output value of the sensor under test. After correction, the intermediate values, compensation coefficients, and matching criteria are output to the traceability closed-loop module. At the same time, the calibration feedback signal output by the self-calibration module is received, and the compensation coefficient adaptation range for the corresponding scenario in the rule base is updated.

5. A temperature verification system according to claim 4, wherein, The traceability closed-loop module receives sensor parameters, reference drift data, verification intermediate values ​​and compensation parameters; it builds a three-level metrological traceability system and establishes a real-time data interaction link between the reference database, system reference unit calibration data and the verification data of the sensor under test. The built-in data encryption unit packages the relevant data, along with the operator's identity information and verification timestamp, and uploads them to the blockchain node. When the baseline drift value exceeds the threshold corresponding to the scenario, a drift warning signal is received from the collaborative module, the baseline data is retrieved to correct the historical verification results in reverse, and a supplementary traceability record is generated and uploaded to the blockchain simultaneously. Based on the scenario label, a corresponding traceability report is generated, which includes the traceability link, key verification data, spatiotemporal error curve, traceability link diagram and blockchain query QR code.

6. A temperature calibration system according to claim 5, characterized in that, The self-calibration module uses the scene tags of the integrated module and the temperature gradient and response time difference data of the collaborative module as triggering criteria to establish a scene-based threshold library, which includes temperature gradient thresholds in the spatial dimension, response time difference thresholds in the time dimension, and reference drift thresholds. It compares the temperature gradient data of the collaborative module with the temperature gradient thresholds and the response time difference data with the response time difference thresholds in real time. When the trigger threshold is reached, the calibration process is automatically started. When the temperature gradient threshold is triggered, the adjustment amount of the internal temperature field of the reference source is calculated and the adjustment command is sent to the coordination module. When the response time difference threshold is triggered, the dynamic response parameters of the reference unit are corrected and fed back to the coordination module. After calibration, the calibration time, parameters before and after calibration, and execution unit information are uploaded to the blockchain. At the same time, a calibration feedback signal is sent to the rule base module to update the adaptation range of the compensation coefficient.

7. A temperature detection method, based on the system of claim 6, characterized in that, The specific steps of this method are as follows: Scene initialization stage: The integrated module identifies and verifies the temperature sensor parameters, pre-verifies and adjusts the parameters, then labels the scene. After the sensor probe is guided by the micro laser positioning module to achieve ±10μm-level precise alignment with the reference source standard temperature measurement point, a verification ready signal is output. Temperature control verification and error compensation stage: After receiving the ready signal and scene label, the collaborative module selects the reference unit according to the temperature point to be verified, and sends the gradient data to the self-calibration module; when the threshold is exceeded, the adjustment parameter is received to trigger micro-area temperature control, the hysteresis compensation is adjusted according to the scene, the temperature and response curves are recorded and the time difference data is sent to the rule base module; the rule base module combines multiple types of data to match the compensation coefficient to correct the output value, generates the verification intermediate value and sends it to the traceability closed-loop module; Traceability closed-loop and self-calibration phase: The traceability closed-loop module packages data and uploads it to the blockchain, and corrects historical data when the threshold is exceeded; The self-calibration module compares the data, initiates calibration, and provides feedback parameters. The system integrates the data to generate a scenario-based traceability report.

8. The temperature verification method according to claim 7, characterized in that, During the scenario initialization phase, after the temperature sensor to be tested is connected to the system port, the integrated module first identifies the sensor type, range, and accuracy level through pin impedance matching and signal feature extraction. Initiate pre-calibration and fine-tune preprocessing parameters by comparing with historical databases; Based on the user-input verification field and scene label, the scene label, sensor parameters, and preprocessing parameters are sent to the collaboration module, rule base module, and self-calibration module, respectively. The integrated micro laser positioning module guides the sensor detection end to align with the standard temperature measurement point of the reference unit. After successful alignment, a verification ready signal is sent to the collaboration module.

9. A temperature calibration method according to claim 8, characterized in that, During the temperature control verification and error compensation stage, after receiving the verification ready signal and scene label, the collaborative module selects the corresponding reference unit to start according to the temperature point value to be verified, and monitors the temperature gradient of each micro-area in real time through the temperature array sensor and synchronizes it to the self-calibration module. When the gradient exceeds the threshold, the collaborative module receives the adjustment parameters from the self-calibration module and triggers the micro-area temperature control component to adjust. The high-speed acquisition unit is started synchronously, the lag compensation time is adjusted according to the scenario, the reference temperature curve and the response curve of the sensor under test are recorded, and the response time difference data is output to the rule base module. After receiving the scenario label, temperature gradient, response time difference and environmental data, the rule base module matches the compensation coefficient in the rule base, corrects the original output value, generates the verification intermediate value and outputs it to the traceability closed loop module.

10. A temperature detection method according to claim 9, characterized in that, In the traceability closed-loop and self-calibration phase, after receiving the verification intermediate value, compensation parameters and benchmark drift data, the traceability closed-loop module packages them with the operator's identity information and verification timestamp and uploads them to the blockchain node; if the benchmark drift is detected to exceed the benchmark drift threshold of the corresponding scenario, the benchmark data is automatically retrieved to correct the historical verification results in reverse, and supplementary traceability records are generated and uploaded to the chain. The self-calibration module continuously compares the gradient data, time difference data and scene threshold of the collaborative module. When the threshold is reached, calibration is initiated, and the calibration parameters are fed back to the collaborative module and the rule base module to update the temperature control parameters and compensation coefficient range. Finally, the system integrates blockchain traceability reports, calibration records, and verification results to generate a scenario-based report that includes spatiotemporal error curves, traceability link diagrams, and blockchain query QR codes.

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