A method and system for error self-correction of intelligent temperature instruments for nuclear power plants

Abstract

This application proposes an error self-correction method and system for intelligent temperature instruments used in nuclear power plants. The intelligent temperature instrument includes a platinum resistance temperature sensor and an intelligent temperature transmitter. The intelligent temperature transmitter has a multi-channel analog-to-digital conversion module and a standard resistor connected to a second channel of the analog-to-digital conversion module. The method includes: measuring the measured resistance value of the standard resistor, and then determining a first correction parameter based on the measured resistance value; measuring the initial resistance value of the platinum resistance temperature sensor; calculating the cumulative irradiation dose based on the operating time and irradiation dose rate; inputting the cumulative irradiation dose, irradiation dose rate, operating time, and number of thermal cycles into an irradiation-thermal cycling joint aging model to obtain a second correction parameter; and calculating the measured temperature of the intelligent temperature instrument based on the corrected resistance value. The technical solution proposed in this application achieves online, real-time error self-correction without manual intervention, significantly improving the long-term stability and measurement reliability of the instrument.

Description

Technical Field

[0001] This application relates to the field of nuclear power instrumentation and control technology, and in particular to an error self-correction method and system for intelligent temperature instruments used in nuclear power plants. Background Technology

[0002] Nuclear power plants require extremely high accuracy in monitoring temperature parameters, especially in critical equipment such as reactor coolant systems, main pump bearings, and steam generators. Platinum resistance thermometers are widely used as the primary sensors, in conjunction with intelligent temperature transmitters to form temperature monitoring instruments. However, under the unique conditions of strong radiation, high temperatures, and long-term continuous operation in nuclear power plants, these instruments face two major challenges:

[0003] 1) Sensor error: Under gamma ray irradiation, the crystal structure of the platinum resistance element is damaged, which causes irreversible drift in its resistance-temperature characteristic (RT curve) (i.e., irradiation drift); at the same time, under long-term thermal cycling conditions, the stress change in the platinum resistance wire leads to thermal cycling aging, causing zero-point drift and sensitivity changes.

[0004] 2) Transmitter error: Temperature transmitters located in irradiated environments experience parameter degradation in their internal electronic components, such as analog-to-digital converters, reference voltage sources, and signal conditioning circuits, under the influence of irradiation and high temperatures. This leads to gain and offset errors in the signal acquisition and processing stages.

[0005] Traditional maintenance methods often involve periodic manual calibration, where instruments are removed and sent to the metrology lab for calibration during reactor shutdowns. This approach suffers from drawbacks such as delays (errors cannot be detected promptly), high maintenance costs, and uncontrollable instrument accuracy during the calibration interval, failing to eliminate the risks posed by dynamic drift to nuclear safety-related parameter measurements. Therefore, there is an urgent need to develop an online, real-time, and manual error self-correction method to address the long-term stability issues of platinum resistance temperature instruments in the harsh environment of nuclear power plants. Summary of the Invention

[0006] This application provides an error self-correction method and system for intelligent temperature instruments used in nuclear power plants, which at least solves the technical problem that platinum resistance temperature instruments suffer from decreased measurement accuracy due to sensor irradiation drift, thermal cycling aging, and transmitter circuit parameter degradation in the harsh environment of nuclear power plants, and that existing periodic manual calibration methods cannot achieve online, real-time error compensation.

[0007] The first aspect of this application proposes an error self-correction method for a smart temperature instrument used in nuclear power plants. The smart temperature instrument includes a platinum resistance temperature sensor and a smart temperature transmitter. The smart temperature transmitter includes a multi-channel analog-to-digital conversion module and a standard resistor connected to a second channel of the analog-to-digital conversion module. The method includes: The intelligent temperature transmitter switches the analog-to-digital converter module to the second channel to measure the resistance value of the standard resistor, and then determines the first correction parameter based on the measured resistance value; The intelligent temperature transmitter switches the analog-to-digital conversion module to the first channel to measure the initial resistance value of the platinum resistance temperature sensor; The system acquires the running time of the intelligent temperature transmitter, the number of thermal cycles generated based on changes in ambient temperature, and the preset irradiation dose rate, and calculates the cumulative irradiation dose based on the running time and irradiation dose rate. The cumulative irradiation dose, irradiation dose rate, running time and number of thermal cycles are input into the pre-trained irradiation-thermal cycling joint aging model to obtain the second correction parameter. The initial resistance value of the platinum resistance temperature sensor is corrected using the first correction parameter and the second correction parameter to obtain the corrected resistance value, and the measured temperature of the smart temperature instrument is calculated based on the corrected resistance value.

[0008] Preferably, the intelligent temperature transmitter switches the analog-to-digital converter module to the second channel to measure the resistance value of the standard resistor, and then determines the first correction parameter based on the measured resistance value, including: The voltage across the standard resistor is acquired after a constant excitation current is applied. The measured resistance value of the standard resistor is calculated based on the excitation current and the voltage across the terminals; Obtain the actual resistance value of the pre-stored standard resistor; The transmitter error is obtained by comparing the measured resistance value with the actual resistance value. The transmitter error is used as the first correction parameter.

[0009] Furthermore, the training process of the irradiation-thermal cycling combined aging model includes: Test standard pieces were selected from the same batch of samples as the platinum resistance temperature sensor. The test standard was subjected to irradiation aging test and thermal cycling test. The cumulative irradiation dose, irradiation dose rate, running time, number of thermal cycles, and time-temperature integral were recorded as input features, and the resistance drift was recorded as output variable to form a drift data table. The XGBoost algorithm was used to train the drift data table to obtain the irradiation-thermal cycling combined aging model.

[0010] Furthermore, the number of thermal cycles is generated by monitoring changes in ambient temperature; When the temperature change exceeds the preset threshold and the hysteresis meets the preset conditions, it is counted as one thermal cycle and recorded cumulatively.

[0011] Furthermore, the corrected resistance value is the sum of the initial resistance value of the platinum resistance temperature sensor and the first correction parameter and the second correction parameter.

[0012] Furthermore, the calculation of the measured temperature of the smart temperature instrument based on the corrected resistance value includes: The corrected resistance value is subjected to piecewise linear fitting to obtain the measured temperature.

[0013] Furthermore, the intelligent temperature transmitter is also equipped with a microprocessor, which stores an irradiation-thermal cycling combined aging model and an error self-correction program. The standard resistor is externally encapsulated with radiation shielding.

[0014] Furthermore, the standard resistor has a resistance of 100Ω, an accuracy of ±0.01%, a temperature coefficient of ±0.02ppm / ℃, and an operating temperature range of -55℃ to 175℃.

[0015] Furthermore, the multi-channel analog-to-digital converter module is an AD-7124 series chip; The intelligent temperature transmitter is also equipped with an interface for communication with the nuclear power plant radiation monitoring system or host computer to obtain the irradiation dose rate.

[0016] A second aspect of this application provides an error self-correction system for an intelligent temperature instrument used in a nuclear power plant. The intelligent temperature instrument includes a platinum resistance temperature sensor and an intelligent temperature transmitter. The intelligent temperature transmitter includes a multi-channel analog-to-digital conversion module and a standard resistor connected to a second channel of the analog-to-digital conversion module. The system includes: The first measurement module is used to switch the analog-to-digital conversion module to the second channel using the intelligent temperature transmitter to measure the resistance value of the standard resistor, and then determine the first correction parameter based on the measured resistance value. The second measurement module is used by the intelligent temperature transmitter to switch the analog-to-digital conversion module to the first channel to measure the initial resistance value of the platinum resistance temperature sensor. The acquisition module is used to acquire the running time of the intelligent temperature transmitter, the number of thermal cycles generated according to the change of ambient temperature, and the preset irradiation dose rate, and to calculate the cumulative irradiation dose based on the running time and irradiation dose rate. The calibration module is used to input the cumulative irradiation dose, irradiation dose rate, running time and number of thermal cycles into a pre-trained irradiation-thermal cycling joint aging model to obtain the second calibration parameters; The correction module is used to correct the initial resistance value of the platinum resistance temperature sensor using the first correction parameter and the second correction parameter to obtain the corrected resistance value, and to calculate the measured temperature of the smart temperature instrument based on the corrected resistance value.

[0017] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects: This application proposes an error self-calibration method and system for an intelligent temperature instrument used in nuclear power plants. The intelligent temperature instrument includes a platinum resistance temperature sensor and an intelligent temperature transmitter. The intelligent temperature transmitter has a multi-channel analog-to-digital conversion module and a standard resistor connected to a second channel of the analog-to-digital conversion module. The method includes: the intelligent temperature transmitter switching the analog-to-digital conversion module to the second channel to measure the resistance value of the standard resistor, and then determining a first calibration parameter based on the measured resistance value; the intelligent temperature transmitter switching the analog-to-digital conversion module to the first channel to measure the resistance value of the platinum resistance temperature sensor. The initial resistance value is determined by: acquiring the operating time of the intelligent temperature transmitter, the number of thermal cycles generated based on ambient temperature changes, and a preset irradiation dose rate; calculating the cumulative irradiation dose based on the operating time and irradiation dose rate; inputting the cumulative irradiation dose, irradiation dose rate, operating time, and number of thermal cycles into a pre-trained irradiation-thermal cycling joint aging model to obtain a second correction parameter; correcting the initial resistance value of the platinum resistance temperature sensor using the first and second correction parameters to obtain a corrected resistance value; and calculating the measured temperature of the intelligent temperature instrument based on the corrected resistance value. The technical solution proposed in this application achieves online, real-time, and manual error self-correction, significantly improving the long-term stability and measurement reliability of the instrument.

[0018] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0019] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of an error self-correction method for a smart temperature instrument used in a nuclear power plant, according to an embodiment of this application; Figure 2 This is a schematic diagram of the connection of a smart temperature instrument according to an embodiment of this application; Figure 3 This is a schematic diagram of the PT100 temperature sensor, intelligent temperature transmitter, and protective housing in an intelligent temperature instrument according to an embodiment of this application. Figure 4A detailed flowchart of an error self-calibration method for a smart temperature instrument for a nuclear power plant according to an embodiment of this application; Figure 5 This is a structural diagram of an error self-correction system for an intelligent temperature instrument used in a nuclear power plant, according to an embodiment of this application. Figure Labels PT100 temperature sensor 1, intelligent temperature transmitter 2, protective housing 3. Detailed Implementation

[0020] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0021] This application discloses an error self-calibration method and system for an intelligent temperature instrument used in nuclear power plants. The intelligent temperature instrument includes a platinum resistance temperature sensor and an intelligent temperature transmitter. The intelligent temperature transmitter has a multi-channel analog-to-digital conversion module and a standard resistor connected to a second channel of the analog-to-digital conversion module. The method includes: the intelligent temperature transmitter switching the analog-to-digital conversion module to the second channel to measure the resistance value of the standard resistor, and then determining a first calibration parameter based on the measured resistance value; the intelligent temperature transmitter switching the analog-to-digital conversion module to the first channel to measure the resistance value of the platinum resistance temperature sensor. The initial resistance value is determined by: acquiring the operating time of the intelligent temperature transmitter, the number of thermal cycles generated based on ambient temperature changes, and a preset irradiation dose rate; calculating the cumulative irradiation dose based on the operating time and irradiation dose rate; inputting the cumulative irradiation dose, irradiation dose rate, operating time, and number of thermal cycles into a pre-trained irradiation-thermal cycling joint aging model to obtain a second correction parameter; correcting the initial resistance value of the platinum resistance temperature sensor using the first and second correction parameters to obtain a corrected resistance value; and calculating the measured temperature of the intelligent temperature instrument based on the corrected resistance value. The technical solution proposed in this application achieves online, real-time, and manual error self-correction, significantly improving the long-term stability and measurement reliability of the instrument.

[0022] The following description, with reference to the accompanying drawings, illustrates an error self-correction method and system for an intelligent temperature instrument used in a nuclear power plant, according to an embodiment of this application.

[0023] Example 1 Figure 1 This is a flowchart illustrating an error self-correction method for a smart temperature instrument used in a nuclear power plant, according to an embodiment of this application. Figure 1As shown, the intelligent temperature instrument includes a platinum resistance temperature sensor and an intelligent temperature transmitter. The intelligent temperature transmitter contains a multi-channel analog-to-digital conversion module and a standard resistor connected to a second channel of the analog-to-digital conversion module. The method includes: Step 1: The intelligent temperature transmitter switches the analog-to-digital converter module to the second channel to measure the resistance value of the standard resistor, and then determines the first correction parameter based on the measured resistance value; In this embodiment of the disclosure, step 1 specifically includes: The voltage across the standard resistor is acquired after a constant excitation current is applied. The measured resistance value of the standard resistor is calculated based on the excitation current and the voltage across the terminals; Obtain the actual resistance value of the pre-stored standard resistor; The transmitter error is obtained by comparing the measured resistance value with the actual resistance value. The transmitter error is used as the first correction parameter.

[0024] It should be noted that the intelligent temperature transmitter is also equipped with a microprocessor, which stores an irradiation-thermal cycling combined aging model and an error self-correction program. The standard resistor is externally encapsulated with radiation shielding.

[0025] The standard resistor has a resistance of 100Ω, an accuracy of ±0.01%, a temperature coefficient of ±0.02ppm / ℃, and an operating temperature range of -55℃ to 175℃.

[0026] The multi-channel analog-to-digital converter module is an AD-7124 series chip; The intelligent temperature transmitter is also equipped with an interface for communication with the nuclear power plant radiation monitoring system or host computer to obtain the irradiation dose rate.

[0027] It should be noted that, as Figure 2 As shown, a high-precision analog-to-digital converter (ADC) module with multiple input channels is used. The first channel is used as the signal input channel for the PT100 sensor, i.e., the platinum resistance temperature sensor, and the second channel is connected to a standard resistor (100Ω) with high stability and low temperature drift, i.e., R-100Ω.

[0028] This standard resistor serves as an embedded standard reference source. To reduce the change in the standard resistor value caused by cumulative irradiation, the integrated circuit is additionally irradiated and shielded.

[0029] It should be noted that a multi-dimensional error model is set up in the microprocessor (MCU) to decompose the total measurement error: ,in: The total measurement error is... This refers to the sensitivity drift error of the platinum resistance element caused by cumulative irradiation dose. The error in the zero point and linearity of the platinum resistance thermometer caused by the number of thermal cycles. This represents the measurement channel gain and offset error of the transmitter circuit under the influence of cumulative radiation dose.

[0030] Transmitter error Correction method: The microprocessor controls the switching of the analog-to-digital converter module to the second channel, using a standard reference source to obtain signal error: acquiring the reference voltage across a standard resistor. According to the preset excitation current Calculate the measured resistance obtained by the analog-to-digital converter chip. ;Measure the resistance The difference between the resistor and the standard resistor is used as the channel error for signal acquisition. .

[0031] Step 2: The intelligent temperature transmitter switches the analog-to-digital conversion module to the first channel to measure the initial resistance value of the platinum resistance temperature sensor; It should be noted that switching the analog-to-digital converter module to the first channel PT100 signal input will... As a correction parameter Substitute the resistance value into the PT100 to eliminate the transmitter's channel error.

[0032] Step 3: Obtain the running time of the intelligent temperature transmitter, the number of thermal cycles generated according to the change of ambient temperature, and the preset irradiation dose rate, and calculate the cumulative irradiation dose based on the running time and irradiation dose rate; In this embodiment of the disclosure, the number of thermal cycles is generated by monitoring changes in ambient temperature; When the temperature change exceeds the preset threshold and the hysteresis meets the preset conditions, it is counted as one thermal cycle and recorded cumulatively.

[0033] Step 4: Input the cumulative irradiation dose, irradiation dose rate, running time and number of thermal cycles into the pre-trained irradiation-thermal cycling combined aging model to obtain the second correction parameters; In this embodiment of the disclosure, the training process of the irradiation-thermal cycling combined aging model includes: Test standard pieces were selected from the same batch of samples as the platinum resistance temperature sensor. The test standard was subjected to irradiation aging test and thermal cycling test. The cumulative irradiation dose, irradiation dose rate, running time, number of thermal cycles, and time-temperature integral were recorded as input features, and the resistance drift was recorded as output variable to form a drift data table. The XGBoost algorithm was used to train the drift data table to obtain the irradiation-thermal cycling combined aging model.

[0034] It should be noted that sensor error , Correction method: Considering that in nuclear power plant applications, irradiation accumulation and thermal cycling are simultaneous environmental variables, making it difficult to calculate measurement errors separately, a machine learning method is used to construct an irradiation-thermal cycling joint aging model to assess sensor errors. , Self-correction.

[0035] To realize the PT100 temperature sensor , For calibration, three samples were taken from the same batch of products as test standard samples, and constant temperature irradiation aging test, constant irradiation dose rate thermal cycling test and cumulative irradiation dose thermal cycling test were carried out respectively. The cumulative irradiation dose, irradiation dose rate, aging time, number of thermal cycles and other input variables, and the resistance drift was used as the output variable to form the temperature drift data table of the batch of sensors.

[0036] The self-calibrating model was trained using the XGBoost regression model based on ensemble learning, and the cumulative radiation dose was... Logarithm of dose rate Running time Number of thermal cycles Time-temperature integral Feature importance analysis is performed on the features to form an intelligent correction model.

[0037] The intelligent correction model is written into the microprocessor, and the temperature drift value of PT100 is calculated based on the program's recorded running time and pre-written environmental irradiation dose rate, which serves as the sensor error. .Will As a correction parameter Substitute the values ​​into the resistance calculation of PT100 to eliminate the cumulative irradiation error of the sensor.

[0038] Step 5: Correct the initial resistance value of the platinum resistance temperature sensor using the first correction parameter and the second correction parameter to obtain the corrected resistance value, and calculate the measured temperature of the smart temperature instrument based on the corrected resistance value.

[0039] In this embodiment of the disclosure, the corrected resistance value is the sum of the initial resistance value of the platinum resistance temperature sensor and the first correction parameter and the second correction parameter.

[0040] Furthermore, the calculation of the measured temperature of the smart temperature instrument based on the corrected resistance value includes: The corrected resistance value is subjected to piecewise linear fitting to obtain the measured temperature.

[0041] It should be noted that the self-calibration method of intelligent temperature instruments is as follows: During the power-on initialization and periodic operation of the intelligent temperature instrument in nuclear power plants, the transmitter error is first executed according to the preset cycle. Correction, obtain correction parameters During the execution of the XGBoost intelligent correction model, correction parameters are obtained. ,Will , Substituting the values ​​into the PT100 resistance-temperature calculation model, the intelligent temperature instrument achieves online self-calibration and outputs the calibrated measured temperature. ,in: The measured temperature after calibration; For the calculated PT100 resistance value, The model coefficients are obtained through piecewise linear fitting to calculate the model coefficients.

[0042] Example 1, such as Figure 3 The diagram shows the structure of the intelligent temperature instrument, including a PT100 temperature sensor 1, an intelligent temperature transmitter 2, and a protective housing 3. The intelligent temperature transmitter 2 contains a reference circuit. The standard resistor used is a VISHAYSMR1DZ series resistor with a resistance of 100Ω, an accuracy of ±0.01%, a temperature coefficient of ±0.02ppm / ℃, and an operating temperature range of -55~175℃. The analog-to-digital converter module used is an AD-7124 series multi-channel digital-to-analog converter.

[0043] The intelligent instrument is placed in a normal irradiation environment. After powering on, press... Figure 4 The process shown is a self-calibration procedure, and the specific steps are as follows: 1) After the instrument is powered on, the intelligent transmitter executes a self-test program to confirm that each module is working properly and the sensors are correctly connected.

[0044] 2) To perform transmitter error calibration, the MCU issues a command, and the AD-7124 switches the acquisition channel to the second channel, with a constant excitation current of 500μA, to acquire the voltage across the standard resistor. The measured resistance value of the standard resistor is then calculated based on the voltage. ,Will Compared with the actual resistance value of the standard resistor Compare and calculate the correction coefficient. Then Store the data in the MCU's self-calibration program, switch the acquisition channel to the first channel, and connect the PT100 sensor.

[0045] 3) Perform sensor error correction; the MCU obtains the ambient radiation dose rate from the host computer. Simultaneously, it calculates the cumulative irradiation record through its own running time record. .Will , Input the intelligent correction model in the MCU to obtain the correction coefficients. Subsequently It will be stored in the MCU's self-calibration program.

[0046] 4) Start running normally and use Perform self-calibration on the measurement results, repeating steps 2 and 3 every 24 hours to update the calibration coefficients in the self-calibration program.

[0047] By introducing a self-calibration program, the measurement accuracy of the smart instrument was reduced from ±0.5℃ to ±0.1℃. After four cycles of continuous operation (7×24h), the accuracy remained unchanged, ensuring the long-term stability of the smart instrument.

[0048] Example 2: Place the smart temperature instrument used in Example 1 in an irradiated environment with temperature variations, and periodically adjust the temperature. After power-on, it still operates as follows: Figure 3 The process shown performs self-calibration.

[0049] When performing sensor error correction, the MCU obtains the ambient radiation dose rate from the host computer. In conjunction with changes in ambient temperature, the number of times the temperature change exceeds ±30℃ is recorded as the number of thermal cycles. Simultaneously, it calculates the cumulative irradiation record through its own running time record. .Will , Input the intelligent correction model in the MCU to obtain the correction coefficients. Subsequently It will be stored in the MCU's self-calibration program.

[0050] 4) Start running normally and use Perform self-calibration on the measurement results, repeating steps 2 and 3 every 24 hours to update the calibration coefficients in the self-calibration program.

[0051] With continuous operation for 7×24h as one cycle, the accuracy did not decrease significantly after 4 cycles, remaining within ±0.2℃, which verified the long-term stability of the intelligent instrument under complex working conditions.

[0052] The error self-correction method for intelligent temperature instruments used in nuclear power plants proposed in this embodiment has the following beneficial effects: 1) Achieve end-to-end online self-calibration: This invention not only corrects the transmitter error, but also achieves online error compensation for the platinum resistance sensor body for the first time in the harsh environment of nuclear power plants through embedded reference sources and aging models, solving the problem of sensors being unable to be calibrated online.

[0053] 2) Reduced operation and maintenance costs: The elimination of frequent manual disassembly and offline calibration transforms the maintenance mode of the instrumentation and control system from post-correction to online self-calibration, significantly reducing the operation and maintenance costs of nuclear power plants.

[0054] 3) Improve measurement reliability: Through multi-source data fusion and dynamic compensation, the impact of irradiation accumulation and thermal cycling aging on measurement accuracy is eliminated, ensuring that the instrument maintains high accuracy throughout the entire refueling cycle and even the entire life cycle, thereby enhancing the reliability of monitoring nuclear safety-related parameters.

[0055] In summary, the error self-correction method for intelligent temperature instruments used in nuclear power plants proposed in this embodiment achieves online, real-time error self-correction without manual intervention, significantly improving the long-term stability and measurement reliability of the instrument.

[0056] Example 2 Figure 5 This is a structural diagram of an error self-correction system for an intelligent temperature instrument used in a nuclear power plant, according to an embodiment of this application. Figure 5 As shown, the intelligent temperature instrument includes a platinum resistance temperature sensor and an intelligent temperature transmitter. The intelligent temperature transmitter contains a multi-channel analog-to-digital conversion module and a standard resistor connected to the second channel of the analog-to-digital conversion module. The system includes: The first measurement module 100 is used to switch the analog-to-digital conversion module to the second channel using the intelligent temperature transmitter to measure the resistance value of the standard resistor, and then determine the first correction parameter based on the measured resistance value. It should be noted that the intelligent temperature transmitter is also equipped with a microprocessor, which stores an irradiation-thermal cycling combined aging model and an error self-correction program. The standard resistor is externally encapsulated with radiation shielding.

[0057] The standard resistor has a resistance of 100Ω, an accuracy of ±0.01%, a temperature coefficient of ±0.02ppm / ℃, and an operating temperature range of -55℃ to 175℃.

[0058] The multi-channel analog-to-digital converter module is an AD-7124 series chip; The intelligent temperature transmitter is also equipped with an interface for communication with the nuclear power plant radiation monitoring system or host computer to obtain the irradiation dose rate.

[0059] The second measurement module 200 is used by the intelligent temperature transmitter to switch the analog-to-digital conversion module to the first channel to measure the initial resistance value of the platinum resistance temperature sensor. The acquisition module 300 is used to acquire the running time of the intelligent temperature transmitter, the number of thermal cycles generated according to the change of ambient temperature, and the preset irradiation dose rate, and to calculate the cumulative irradiation dose based on the running time and the irradiation dose rate. The calibration module 400 is used to input the cumulative irradiation dose, irradiation dose rate, running time and number of thermal cycles into a pre-trained irradiation-thermal cycling joint aging model to obtain the second calibration parameter. It should be noted that the training process of the irradiation-thermal cycling combined aging model includes: Test standard pieces were selected from the same batch of samples as the platinum resistance temperature sensor. The test standard was subjected to irradiation aging test and thermal cycling test. The cumulative irradiation dose, irradiation dose rate, running time, number of thermal cycles, and time-temperature integral were recorded as input features, and the resistance drift was recorded as output variable to form a drift data table. The XGBoost algorithm was used to train the drift data table to obtain the irradiation-thermal cycling combined aging model.

[0060] It should be noted that the number of thermal cycles is generated by monitoring changes in ambient temperature; When the temperature change exceeds the preset threshold and the hysteresis meets the preset conditions, it is counted as one thermal cycle and recorded cumulatively.

[0061] The correction module 500 is used to correct the initial resistance value of the platinum resistance temperature sensor using the first correction parameter and the second correction parameter to obtain the corrected resistance value, and to calculate the measured temperature of the smart temperature instrument based on the corrected resistance value.

[0062] It should be noted that the corrected resistance value is the sum of the initial resistance value of the platinum resistance temperature sensor and the first correction parameter and the second correction parameter.

[0063] In this embodiment of the disclosure, the first measurement module 100 is further configured to: The voltage across the standard resistor is acquired after a constant excitation current is applied. The measured resistance value of the standard resistor is calculated based on the excitation current and the voltage across the terminals; Obtain the actual resistance value of the pre-stored standard resistor; The transmitter error is obtained by comparing the measured resistance value with the actual resistance value. The transmitter error is used as the first correction parameter.

[0064] In this embodiment of the disclosure, the correction module 500 is further configured to: The corrected resistance value is subjected to piecewise linear fitting to obtain the measured temperature.

[0065] In summary, the error self-correction system for intelligent temperature instruments used in nuclear power plants proposed in this embodiment achieves online, real-time error self-correction without manual intervention, significantly improving the long-term stability and measurement reliability of the instrument.

[0066] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0067] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0068] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for error self-correction of a smart temperature instrument for nuclear power plants, wherein the smart temperature instrument includes a platinum resistance temperature sensor and a smart temperature transmitter, the smart temperature transmitter comprising a multi-channel analog-to-digital conversion module and a standard resistor connected to a second channel of the analog-to-digital conversion module, characterized in that, The method includes: The intelligent temperature transmitter switches the analog-to-digital converter module to the second channel to measure the resistance value of the standard resistor, and then determines the first correction parameter based on the measured resistance value; The intelligent temperature transmitter switches the analog-to-digital conversion module to the first channel to measure the initial resistance value of the platinum resistance temperature sensor; The system acquires the running time of the intelligent temperature transmitter, the number of thermal cycles generated based on changes in ambient temperature, and the preset irradiation dose rate, and calculates the cumulative irradiation dose based on the running time and irradiation dose rate. The cumulative irradiation dose, irradiation dose rate, running time and number of thermal cycles are input into the pre-trained irradiation-thermal cycling joint aging model to obtain the second correction parameter. The initial resistance value of the platinum resistance temperature sensor is corrected using the first correction parameter and the second correction parameter to obtain the corrected resistance value, and the measured temperature of the smart temperature instrument is calculated based on the corrected resistance value.

2. The method as described in claim 1, characterized in that, The intelligent temperature transmitter switches the analog-to-digital conversion module to the second channel to measure the resistance value of the standard resistor, and then determines the first calibration parameter based on the measured resistance value, including: The voltage across the standard resistor is acquired after a constant excitation current is applied. The measured resistance value of the standard resistor is calculated based on the excitation current and the voltage across the terminals; Obtain the actual resistance value of the pre-stored standard resistor; The transmitter error is obtained by comparing the measured resistance value with the actual resistance value. The transmitter error is used as the first correction parameter.

3. The method as described in claim 2, characterized in that, The training process of the irradiation-thermal cycling combined aging model includes: Test standard pieces were selected from the same batch of samples as the platinum resistance temperature sensor. The test standard was subjected to irradiation aging test and thermal cycling test. The cumulative irradiation dose, irradiation dose rate, running time, number of thermal cycles, and time-temperature integral were recorded as input features, and the resistance drift was recorded as output variable to form a drift data table. The XGBoost algorithm was used to train the drift data table to obtain the irradiation-thermal cycling combined aging model.

4. The method as described in claim 2, characterized in that, The number of thermal cycles is generated by monitoring changes in ambient temperature. When the temperature change exceeds the preset threshold and the hysteresis meets the preset conditions, it is counted as one thermal cycle and recorded cumulatively.

5. The method as described in claim 4, characterized in that, The corrected resistance value is the sum of the initial resistance value of the platinum resistance temperature sensor and the first correction parameter and the second correction parameter.

6. The method as described in claim 5, characterized in that, The calculation of the measured temperature of the smart temperature instrument based on the corrected resistance value includes: The corrected resistance value is subjected to piecewise linear fitting to obtain the measured temperature.

7. The method as described in claim 6, characterized in that, The intelligent temperature transmitter is also equipped with a microprocessor, which stores an irradiation-thermal cycling combined aging model and an error self-correction program. The standard resistor is externally encapsulated with radiation shielding.

8. The method as described in claim 7, characterized in that, The standard resistor has a resistance of 100Ω, an accuracy of ±0.01%, a temperature coefficient of ±0.02ppm / ℃, and an operating temperature range of -55℃ to 175℃.

9. The method as described in claim 8, characterized in that, The multi-channel analog-to-digital converter module is an AD-7124 series chip; The intelligent temperature transmitter is also equipped with an interface for communication with the nuclear power plant radiation monitoring system or host computer to obtain the irradiation dose rate.

10. An error self-correction system for an intelligent temperature instrument used in a nuclear power plant, the intelligent temperature instrument comprising a platinum resistance temperature sensor and an intelligent temperature transmitter, wherein the intelligent temperature transmitter includes a multi-channel analog-to-digital conversion module and a standard resistor connected to a second channel of the analog-to-digital conversion module, characterized in that, The system includes: The first measurement module is used to switch the analog-to-digital conversion module to the second channel using the intelligent temperature transmitter to measure the resistance value of the standard resistor, and then determine the first correction parameter based on the measured resistance value. The second measurement module is used by the intelligent temperature transmitter to switch the analog-to-digital conversion module to the first channel to measure the initial resistance value of the platinum resistance temperature sensor. The acquisition module is used to acquire the running time of the intelligent temperature transmitter, the number of thermal cycles generated according to the change of ambient temperature, and the preset irradiation dose rate, and to calculate the cumulative irradiation dose based on the running time and irradiation dose rate. The calibration module is used to input the cumulative irradiation dose, irradiation dose rate, running time and number of thermal cycles into a pre-trained irradiation-thermal cycling joint aging model to obtain the second calibration parameters; The correction module is used to correct the initial resistance value of the platinum resistance temperature sensor using the first correction parameter and the second correction parameter to obtain the corrected resistance value, and to calculate the measured temperature of the smart temperature instrument based on the corrected resistance value.

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