Diagnostic method and system for radiation monitoring equipment of nuclear power plant, electronic equipment and computer readable storage medium

Abstract

The invention provides a diagnosis method and system for radiation monitoring equipment of a nuclear power plant, electronic equipment and a computer readable storage medium. The method is applied to an upper computer, a nuclear island or a conventional island is divided into a plurality of monitoring areas, and the method comprises the following steps: intensively collecting multi-source underlying operation parameters, including electrical parameters, original radiation characteristic parameters and equipment state parameters, of radiation monitoring equipment in the areas through a shared communication bus; abnormal diagnosis is carried out on the parameters, early warning information is generated, and the comprehensive fault risk of the equipment is analyzed and predicted based on weighted fusion; and finally, generating a comprehensive diagnosis report in combination with the early warning information and the risk level. According to the invention, through district acquisition and multi-parameter fusion analysis, real-time monitoring of the operation state of the equipment, effective identification of key abnormity and comprehensive evaluation of fault risks are realized, and technical support is provided for turning from planned maintenance to predictive maintenance.

Description

Technical Field

[0001] This invention relates to the field of nuclear power technology, and in particular to diagnostic methods, systems, electronic devices, and computer-readable storage media for radiation monitoring equipment in nuclear power plants. Background Technology

[0002] The Nuclear Power Plant Radiation Monitoring System (KRT) uses radiation monitoring instruments to monitor radiation levels at the boundaries of radioactive areas and along the plant's processes. KRT radiation measurement data is sent to the main control room. Alarm thresholds are set for these data; when a specific threshold is reached, an audible and visual alarm is generated in the main control room, alerting plant operators to respond. Its main functions include: preventing high-dose exposure to nuclear power plant workers; preventing radiation exposure to residents (the public) around the plant; monitoring the integrity and effectiveness of the radiation barrier; and activating automatic safety measures to protect workers or the public. The instrument display values ​​are the top-level data, representing the final values ​​presented to the user, i.e., radiation dose rate (e.g., μSv / h) and alarm status. Current technology only tracks these top-level final values, creating data blind spots, making it impossible to identify early faults and hindering proactive predictive maintenance. Summary of the Invention

[0003] The technical problem to be solved by this invention is to provide a diagnostic method, system, electronic device, and computer-readable storage medium for radiation monitoring equipment in nuclear power plants, addressing the data blind spots caused by existing technologies that only monitor the final displayed value.

[0004] The technical solution adopted by this invention to solve its technical problem is: a diagnostic method for constructing radiation monitoring equipment in nuclear power plants, comprising the following steps: Step S1: Obtain multi-source underlying operating parameters for the corresponding area from multiple radiation monitoring devices. The physical area of ​​the nuclear island or conventional island is pre-divided into multiple monitoring zones, and each monitoring zone is equipped with multiple radiation monitoring devices connected in series via a shared communication bus. The multi-source underlying operating parameters include electrical parameters, raw radiation characteristic parameters, and device status parameters. Step S2: Perform anomaly diagnosis based on the electrical parameters, the original radiation characteristic parameters, and the equipment status parameters, and generate anomaly warning information based on the anomaly diagnosis results; Step S3: Perform a comprehensive weighted fusion analysis based on the electrical parameters, the original radiation characteristic parameters, and the equipment status parameters to predict the overall equipment failure risk; Step S4: Based on the abnormal warning information and the comprehensive fault risk of the equipment, generate and output a comprehensive diagnostic report.

[0005] In one embodiment, the electrical parameters include the real-time value of the ionization chamber high voltage, the nominal high voltage, and the real-time value of the digital circuit power supply voltage; the original radiation characteristic parameters include the real-time value of the radiation reference peak and the calibration reference peak; the device status parameters include the probe life clock signal; and step S2 includes: Calculate the absolute deviation between the real-time value of the high voltage in the ionization chamber and the nominal high voltage, and determine whether the absolute deviation exceeds a first preset threshold and continues for a first time. If the condition is met, generate and output a high voltage anomaly warning. And / or, calculate the standard deviation of the real-time value of the power supply voltage of the digital circuit within a preset time period, determine whether the standard deviation exceeds a second preset threshold, and if it does, generate and output a power supply fluctuation warning. And / or, calculate the absolute difference between the real-time value of the radiation reference peak and the calibrated reference peak, determine whether the absolute difference exceeds a third preset threshold, and if it does, generate and output a reference peak drift warning. And / or, determine whether the time when the probe's life clock signal stops refreshing exceeds a second preset time; if it does, generate and output a device operation abnormality warning.

[0006] In one embodiment, step S3 includes: Step S3.1: Calculate the comprehensive fault risk value based on the real-time value of the ionization chamber high voltage and the nominal high voltage, the real-time value of the digital circuit power supply voltage, the real-time value of the radiation reference peak value and the calibration reference peak value, and the probe life clock signal; Step S3.2: Compare the comprehensive fault risk value with multiple preset risk thresholds within a range, and output the comprehensive fault risk level of the equipment based on the comparison results.

[0007] In one embodiment, the original radiation characteristic parameters further include the real-time count rate and the expected count rate, and step S3.1 includes: The high voltage deviation rate is calculated based on the real-time value of the ionization chamber high voltage and the nominal high voltage. Calculate the voltage fluctuation rate based on the real-time value of the power supply voltage of the digital circuit; The deviation of the count rate is calculated based on the real-time count rate and the expected count rate of the radiation monitoring channel; Based on the probe's life clock signal, calculate the life clock anomaly index; The high voltage deviation rate, the voltage fluctuation rate, the count rate deviation, and the life clock abnormality index are weighted and fused according to a preset weight ratio to obtain the comprehensive fault risk value.

[0008] In one embodiment, the method further includes: Acquire equipment lifecycle data, perform lifecycle assessment based on the equipment lifecycle data and preset lifecycle assessment rules, and obtain lifecycle prediction results; Step S4 includes: generating and outputting the comprehensive diagnostic report based on the lifespan prediction results, the abnormal early warning information, and the comprehensive equipment failure risk level.

[0009] In one embodiment, the device lifecycle data includes basic device information, and the lifecycle assessment based on the device lifecycle data and preset lifecycle assessment rules to obtain a lifecycle prediction result includes: Based on the basic equipment information and the preset equipment lifespan expectation, the lifespan assessment result of the equipment in the time dimension is calculated.

[0010] In one embodiment, the device lifecycle data includes current radiation measurements and initial device measurements. The lifecycle assessment based on the device lifecycle data and preset lifecycle assessment rules to obtain a lifecycle prediction result includes: The equipment detection efficiency evaluation result is calculated by comparing the current radiation measurement value with the initial measurement value of the equipment. The device detection efficiency evaluation result is compared with the preset device scrapping threshold to obtain the life evaluation result of the device performance dimension; The preset equipment scrapping threshold is the lowest acceptable detection efficiency value corresponding to when the equipment reaches the scrapping standard.

[0011] This application also provides a diagnostic system based on radiation monitoring equipment in a nuclear power plant, comprising a diagnostic host computer, a shared communication bus, and multiple radiation monitoring devices; wherein the physical area of ​​the nuclear island or conventional island is pre-divided into multiple monitoring zones, and within each monitoring zone, multiple radiation monitoring devices are connected in series via the shared communication bus; the diagnostic host computer is connected to the shared communication bus; the host computer includes: The data acquisition module is used to acquire multi-source underlying operating parameters of the corresponding area from multiple radiation monitoring devices, wherein the multi-source underlying operating parameters include electrical parameters, raw radiation characteristic parameters and device status parameters; An anomaly diagnosis module is used to perform anomaly diagnosis based on the electrical parameters, the original radiation characteristic parameters, and the equipment status parameters, and to generate anomaly warning information based on the anomaly diagnosis results. The risk analysis module is used to perform a comprehensive weighted fusion analysis based on the electrical parameters, the original radiation characteristic parameters, and the equipment status parameters to predict the overall equipment failure risk. The report generation module is used to generate and output a comprehensive diagnostic report based on the abnormal warning information and the overall failure risk of the equipment.

[0012] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the diagnostic method for nuclear power plant radiation monitoring equipment as described in any of the preceding claims.

[0013] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the diagnostic method for nuclear power plant radiation monitoring equipment as described in any of the preceding claims.

[0014] The beneficial effects of this invention are as follows: The diagnostic method for nuclear power plant radiation monitoring equipment provided in this application, applied to a host computer, includes the following steps: Step S1: Obtaining multi-source underlying operating parameters for corresponding areas from multiple radiation monitoring devices, wherein the physical area of ​​the nuclear island or conventional island is pre-divided into multiple monitoring zones, and each monitoring zone is equipped with multiple radiation monitoring devices connected in series via a shared communication bus; the multi-source underlying operating parameters include electrical parameters, original radiation characteristic parameters, and equipment status parameters; Step S2: Performing anomaly diagnosis based on the electrical parameters, the original radiation characteristic parameters, and the equipment status parameters, and generating anomaly warning information based on the anomaly diagnosis results; Step S3: Performing comprehensive weighted fusion analysis based on the electrical parameters, the original radiation characteristic parameters, and the equipment status parameters to predict the comprehensive equipment failure risk; Step S4: Generating and outputting a comprehensive diagnostic report based on the anomaly warning information and the comprehensive equipment failure risk. This invention, by dividing the area and performing anomaly diagnosis and weighted fusion analysis on the multi-source underlying operating parameters, achieves real-time monitoring of the operating status of nuclear power plant radiation monitoring equipment, identification of key parameter anomalies, and effective assessment of the comprehensive equipment failure risk. Attached Figure Description

[0015] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a schematic diagram of the diagnostic method for radiation monitoring equipment in nuclear power plants; Figure 2 This is the topology diagram of the regionalized diagnostic network of this invention. Detailed Implementation

[0016] The present application will now be described in further detail with reference to the accompanying drawings and specific embodiments. Similar elements in different embodiments are referred to by related similar element reference numerals. In the following embodiments, many details are described to facilitate a better understanding of the present application. However, those skilled in the art will readily recognize that some features may be omitted in different situations, or may be replaced by other elements, materials, or methods. In some cases, certain operations related to the present application are not shown or described in the specification. This is to avoid obscuring the core parts of the present application with excessive description. For those skilled in the art, detailed description of these related operations is not necessary; they can fully understand the related operations based on the description in the specification and general technical knowledge in the art.

[0017] like Figure 1 As shown, Figure 1 This is a schematic diagram of the diagnostic process for radiation monitoring equipment in nuclear power plants.

[0018] This invention provides a diagnostic method for radiation monitoring equipment in nuclear power plants, the method comprising the following steps: Step S1: Obtain multi-source underlying operating parameters for the corresponding area from multiple radiation monitoring devices. The physical area of ​​the nuclear island or conventional island is pre-divided into multiple monitoring zones. Each monitoring zone is equipped with multiple radiation monitoring devices connected in series via a shared communication bus. The multi-source underlying operating parameters include electrical parameters, raw radiation characteristic parameters, and device status parameters. It should be noted that the monitoring areas are divided according to the physical location of the radiation monitoring equipment, such as reactor building areas, turbine building areas, etc. The shared communication bus can use RS485 bus or fiber optic transmission to achieve centralized data collection from multiple devices within the area. Figure 2 As shown, the area-based intelligent diagnostic network of this invention adopts a hierarchical bus structure. Its core lies in replacing traditional point-to-point cabling with a shared communication bus. The specific architecture is as follows: the top-level host computer 1 and its RS485 acquisition card 2 constitute the monitoring center; the RS485 bus 3 (or alternative single-mode fiber) extending from it serves as the communication trunk line; within each monitoring area (such as area A, area B) divided according to the physical layout of the nuclear island / conventional island, all radiation monitoring equipment channels 4 are connected in series to the shared bus of that area, forming the underlying device nodes. Specifically, the fiber optic solution requires the addition of photoelectric conversion modules at both ends of the bus, whose anti-electromagnetic interference capability ensures the reliability of data transmission in complex environments. This architecture achieves efficient and intensive acquisition of multi-channel underlying parameters, laying the physical foundation for subsequent intelligent diagnostics and fundamentally overcoming the limitations of poor scalability and difficult modification of traditional architectures.

[0019] The electrical parameters include, but are not limited to, the ionization chamber high voltage and the ±15V / +5V / +3.3V power supply voltage; the raw radiation characteristic parameters include, but are not limited to, the radiation reference peak value and the raw CPS count rate value; and the device status parameters include the probe's life clock signal. The acquisition of these underlying operating parameters overcomes the limitation of existing technologies that only monitor the final dose rate value, providing a data foundation for subsequent in-depth diagnostics.

[0020] Step S2: Perform anomaly diagnosis based on electrical parameters, original radiation characteristic parameters, and equipment status parameters, and generate anomaly warning information based on the anomaly diagnosis results; In this step, it should be noted that the anomaly diagnosis employs threshold-based judgment logic. For example: when the deviation of the ionization chamber high voltage from the nominal value is detected to exceed ±5% for an extended period, a high voltage anomaly warning is generated; when the standard deviation of the calculated +5V supply voltage exceeds 0.05V within 10 minutes, a power supply fluctuation warning is generated; when the probe's lifespan clock signal stops refreshing for more than 5 minutes, an equipment operation anomaly warning is generated; when the deviation of the radiation reference peak value from the calibration value exceeds 50 channel addresses (CH), a reference peak drift warning is generated. This multi-parameter independent anomaly diagnosis enables early identification of specific equipment fault modes.

[0021] Step S3: Perform a comprehensive weighted fusion analysis based on electrical parameters, original radiation characteristic parameters, and equipment status parameters to predict the overall equipment failure risk; In this step, it should be noted that the comprehensive weighted fusion analysis employs a weighted scoring method. Specifically, a fault risk value calculation model is constructed, where the value is a weighted sum of the abnormality levels of each parameter. For example: Fault Risk Value = 0.4 × High Voltage Deviation Rate + 0.3 × Voltage Fluctuation Rate + 0.2 × CPS Count Rate Deviation + 0.1 × Lifespan Clock Abnormality Flag. When the calculated fault risk value exceeds a preset threshold (e.g., 0.7), a Level 1 warning for comprehensive equipment faults is triggered. This model, through multi-source information fusion, achieves a quantitative assessment of the overall health status of the equipment, providing a decision-making basis for predictive maintenance. The weights of each parameter can be dynamically adjusted based on equipment type and operating experience.

[0022] Step S4: Based on the abnormal early warning information and the overall equipment failure risk, generate and output a comprehensive diagnostic report.

[0023] In this step, it should be noted that the comprehensive diagnostic report not only integrates the various abnormal warning information from step S2 and the comprehensive fault risk level from step S3, but also includes equipment health status assessment based on equipment operating parameter trends, remaining life prediction (e.g., by comparing current measured values ​​with theoretical values ​​at the end of the equipment's life), and specific maintenance recommendations. Furthermore, the system can dynamically adjust the alarm thresholds of relevant monitoring channels based on the diagnostic results (e.g., appropriately relaxing dose rate alarm limits during power fluctuations) to improve the system's robustness under non-ideal equipment operating conditions. This report provides maintenance personnel with a comprehensive view of equipment status and maintenance guidance, supporting the shift from planned maintenance to predictive maintenance.

[0024] Further, the electrical parameters include the real-time value of the ionization chamber high voltage, the nominal high voltage, and the real-time value of the digital circuit power supply voltage. The original radiation characteristic parameters include the real-time value of the radiation reference peak and the calibrated reference peak. The equipment status parameters include the probe life clock signal. Step S2 includes: calculating the absolute deviation between the real-time value of the ionization chamber high voltage and the nominal high voltage, determining whether the absolute deviation exceeds a first preset threshold and lasts for a first time. If the condition is met, a high voltage abnormality warning is generated and output. And / or, calculating the standard deviation of the real-time value of the digital circuit power supply voltage within a preset time period, determining whether the standard deviation exceeds a second preset threshold. If it exceeds, a power supply fluctuation warning is generated and output. And / or, calculating the absolute difference between the real-time value of the radiation reference peak and the calibrated reference peak, determining whether the absolute difference exceeds a third preset threshold. If it exceeds, a reference peak drift warning is generated and output. And / or, determining whether the time for the probe life clock signal to stop refreshing exceeds a second preset time. If it exceeds, a device operation abnormality warning is generated and output.

[0025] By introducing and continuously monitoring key underlying parameters such as high voltage, supply voltage, and reference peak, and comparing them with their respective nominal values ​​or stability requirements, early and accurate identification of specific fault modes within the equipment is achieved. For example, a continuous small deviation in high voltage may indicate early aging of the high voltage module, while a significant drift in the reference peak directly indicates changes in the detector energy gradation. These are fault signs that can be detected before the final dose rate reading becomes abnormal. The first preset threshold (e.g., ±5%), second preset threshold (e.g., 0.05V), third preset threshold (e.g., 50CH), and first and second preset times (e.g., 5 minutes) used as criteria are adjustable parameters set based on equipment characteristics, historical operating data, and engineering practice experience. This ensures the accuracy and timeliness of diagnosis, thereby effectively reducing the high false alarm rate caused by relying solely on the final measurement value.

[0026] Further, step S3 includes: step S3.1: calculate the comprehensive fault risk value based on the real-time value of the ionization chamber high voltage, the real-time value of the nominal high voltage, the real-time value of the digital circuit power supply voltage, the real-time value of the radiation reference peak and the calibration reference peak, and the probe life clock signal; step S3.2: compare the comprehensive fault risk value with multiple preset risk thresholds in intervals, and output the comprehensive fault risk level of the equipment based on the comparison results.

[0027] In this step, it should be noted that the calculation of the comprehensive fault risk value adopts a weighted fusion model, specifically: the degree of anomaly of each underlying parameter is quantified and assigned different weights before being linearly superimposed. For example, the fault risk value = 0.4 × (high voltage absolute deviation rate) + 0.3 × (supply voltage fluctuation rate) + 0.2 × (reference peak offset rate) + 0.1 × (lifetime clock anomaly flag). This model comprehensively considers key electrical, radiation, and status parameters affecting equipment reliability, and reflects the differences in the contribution of different parameters to the overall fault risk through weight allocation, overcoming the limitations of single-parameter diagnosis.

[0028] In step S3.2, multiple preset risk thresholds divide the continuous values ​​of fault risk into different risk level ranges. For example, a risk value < 0.3 is "normal," 0.3 ≤ risk value < 0.5 is "attention," 0.5 ≤ risk value < 0.7 is "warning," and a risk value ≥ 0.7 is "alarm." This hierarchical output provides an intuitive and quantitative classification of the overall health status of the equipment, offering maintenance personnel a clear basis for decision-making. This allows them to prioritize high-risk equipment based on its risk level, thereby optimizing the allocation of maintenance resources and effectively implementing predictive maintenance strategies.

[0029] Furthermore, the original radiation characteristic parameters also include the real-time count rate and the expected count rate. Step S3.1 includes: calculating the high-voltage deviation rate based on the real-time value of the ionization chamber high voltage and the nominal high voltage; calculating the voltage fluctuation rate based on the real-time value of the digital circuit power supply voltage; calculating the count rate deviation based on the real-time count rate and the expected count rate of the radiation monitoring channel; and calculating the life clock abnormality index based on the probe life clock signal. The high voltage deviation rate, voltage fluctuation rate, count rate deviation, and life clock abnormality indicators are weighted and fused according to preset weight ratios to obtain a comprehensive fault risk value.

[0030] In this step, it should be noted that the high-voltage deviation rate reflects the stability of the high voltage at the core of the ionization chamber, and its calculation is typically |real-time high voltage - nominal high voltage| / nominal high voltage. This parameter has the highest weight (e.g., 0.4) because significant deviations in the high voltage directly affect the sensitivity and accuracy of radiation detection.

[0031] Voltage fluctuation rate characterizes the power supply quality of digital circuits and can be obtained by calculating the standard deviation or coefficient of variation of the supply voltage over a specific time window. Unstable power supply can lead to abnormal operation of electronic circuits and is a potential source of failure, therefore it is given a high weight (e.g., 0.3).

[0032] The count rate deviation assesses the degree of agreement between the current radiation measurement value and the expected normal range (which can be set based on historical data or theoretical models). It can be calculated as |real-time count rate - expected count rate| / expected count rate. It directly reflects the abnormality of the measurement function and is one of the most intuitive manifestations of a fault; its weight is set to 0.2.

[0033] The life clock abnormality indicator is a status flag (e.g., 0 for normal, 1 for abnormal) and a weight index (e.g., 0.1).

[0034] Furthermore, the method also includes: acquiring equipment lifecycle data, performing lifecycle assessment based on the equipment lifecycle data and preset lifecycle assessment rules, and obtaining lifecycle prediction results; step S4 includes: generating and outputting a comprehensive diagnostic report based on the lifecycle prediction results, abnormal early warning information and the comprehensive fault risk level of the equipment.

[0035] In this step, it should be noted that the introduction of equipment lifecycle data for life assessment completes the closed-loop health management system, from short-term anomaly warnings to long-term life predictions.

[0036] Specifically, equipment lifecycle data mainly includes the equipment's production date, installation date, historical maintenance records, and the design life provided by the manufacturer (such as the expected lifespan of an ionization chamber probe being 8 years).

[0037] The preset lifetime assessment rules use a combination of time-based linear decay models and performance-based decay models for comprehensive judgment.

[0038] Time-based assessment: Calculate the theoretical remaining lifespan based on the ratio of the equipment's operating time to its total design lifespan.

[0039] Performance-based assessment: By monitoring the long-term degradation trends of key performance parameters (such as detection efficiency and high-voltage stability) and comparing them with the theoretical values ​​of the equipment's lower limit for scrapping, the remaining life prediction is dynamically revised. For example, for NaI detectors, their detection efficiency decreases with aging; the degree of performance degradation is assessed by analyzing the deviation between current measurements and theoretical values ​​at the end of the equipment's life.

[0040] Lifespan prediction results are typically presented in the form of "remaining lifespan (months)" or "health level (%)". Furthermore, equipment lifespan data includes basic equipment information. Lifespan assessment is performed based on the equipment lifespan data and preset lifespan assessment rules to obtain lifespan prediction results, including: lifespan assessment results calculated based on basic equipment information and preset equipment lifespan expectations, representing the equipment's time dimension.

[0041] In this step, it should be noted that the time-dimensional lifespan assessment based on the equipment's basic information provides a fundamental and necessary reference for lifespan prediction.

[0042] The core logic of this assessment method is to perform linear extrapolation based on the equipment's design life and its operational history. Specifically, basic equipment information mainly includes key time points such as the equipment's production date and installation / commissioning date. The preset equipment lifespan expectation refers to the rated design life provided by the equipment manufacturer (e.g., the recommended lifespan of a certain type of ionization chamber is 8 years). Its calculation can be simply expressed as: Remaining equipment lifespan (time dimension) = Preset equipment lifespan expectation - Equipment operational history. Where, Equipment operational history = Current date - Installation / commissioning date.

[0043] The significance and importance of this assessment result lies in providing a stable and quantifiable time benchmark for equipment life management. Although this model does not consider the actual operating load and environmental stress differences of the equipment, its calculation is simple and the results are intuitive, providing important basic data support for long-term spare parts procurement planning, asset retirement expectations, and the initial formulation of overhaul plans. The assessment results in this time dimension will corroborate the assessment results based on performance degradation, together forming a more comprehensive equipment life prediction system. Furthermore, the equipment life cycle data includes current radiation measurements and initial equipment measurements. Based on the equipment life cycle data and preset life assessment rules, life prediction results are obtained by: calculating the equipment detection efficiency assessment result by comparing the current radiation measurements with the initial equipment measurements; and comparing the equipment detection efficiency assessment result with the preset equipment scrapping threshold to obtain the equipment performance dimension life assessment result. Among them, the preset equipment scrapping threshold is the lowest acceptable detection efficiency value corresponding to the equipment reaching the scrapping standard.

[0044] In this step, it should be noted that this method introduces a lifespan assessment dimension based on the actual performance degradation of the device, providing a more accurate and objective basis for lifespan prediction.

[0045] Specifically, the equipment detection efficiency assessment result is calculated by comparing the current radiation measurement value with the baseline measurement value (obtained under a standard radiation field) when the equipment was initially put into operation. The typical calculation method is: (current measurement value / initial measurement value) × 100%. This percentage directly reflects the degree of degradation in the equipment's detection performance relative to its initial state. A preset equipment decommissioning threshold is a key technical indicator, determined based on regulatory requirements, equipment technical specifications, or operational experience. It represents the minimum acceptable detection efficiency (e.g., 70% of the initial efficiency) at which the equipment can reliably perform its monitoring functions.

[0046] The logic behind this performance-based lifetime assessment is as follows: by continuously monitoring the degradation trajectory of the device's detection efficiency and comparing it with a preset end-of-life threshold, the point at which the device's performance deteriorates to an unacceptable level can be predicted. For example, if data shows that the detection efficiency is declining linearly at a rate of 2% per year, and the current efficiency is 85% with an end-of-life threshold of 70%, then the remaining lifetime based on performance can be estimated to be approximately 7.5 years.

[0047] The technical advantage of this assessment method lies in its ability to overcome the potential biases of life assessment models that rely solely on calendar time (such as failing to consider differences in actual equipment workload and environmental stress), making life prediction results closer to the actual health status of the equipment. This performance degradation-based prediction can identify problematic equipment with excessively rapid performance degradation earlier, providing a precise time window for scheduling preventative maintenance or spare parts replacement, thereby enabling more scientific asset management and operation and maintenance decisions.

[0048] This application also provides a diagnostic system based on radiation monitoring equipment in a nuclear power plant, comprising a diagnostic host computer, a shared communication bus, and multiple radiation monitoring devices. The physical area of ​​the nuclear island or conventional island is pre-divided into multiple monitoring zones, and within each monitoring zone, multiple radiation monitoring devices are connected in series via the shared communication bus. The diagnostic host computer is connected to the shared communication bus. The host computer includes: a data acquisition module for acquiring multi-source underlying operating parameters of the corresponding area from the multiple radiation monitoring devices, wherein the multi-source underlying operating parameters include electrical parameters, raw radiation characteristic parameters, and equipment status parameters; an anomaly diagnosis module for performing anomaly diagnosis based on the electrical parameters, raw radiation characteristic parameters, and equipment status parameters, and generating anomaly warning information based on the anomaly diagnosis results; a risk analysis module for performing comprehensive weighted fusion analysis based on the electrical parameters, raw radiation characteristic parameters, and equipment status parameters to predict the comprehensive equipment failure risk; and a report generation module for generating and outputting a comprehensive diagnostic report based on the anomaly warning information and the comprehensive equipment failure risk.

[0049] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the diagnostic method for nuclear power plant radiation monitoring equipment as described above.

[0050] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a diagnostic method for a nuclear power plant radiation monitoring device as described in any of the above claims.

[0051] It is understood that the above embodiments only illustrate preferred embodiments of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can freely combine the above technical features without departing from the concept of the present invention, and can also make several modifications and improvements, all of which fall within the protection scope of the present invention. Therefore, all equivalent transformations and modifications made with respect to the scope of the claims of the present invention should fall within the scope of the claims of the present invention.

Claims

1. A diagnostic method of a nuclear power plant radiation monitoring device applied to a host computer, characterized by, The method comprises the following steps: Step S1: obtaining multiple-source underlying operation parameters of a corresponding area from multiple radiation monitoring devices respectively, wherein a physical area of a nuclear island or a conventional island is pre-divided into multiple monitoring sub-areas, and multiple radiation monitoring devices connected in series through a shared communication bus are arranged in each monitoring sub-area; the multiple-source underlying operation parameters comprise electrical parameters, original radiation characteristic parameters and device state parameters; Step S2: performing abnormality diagnosis according to the electrical parameters, the original radiation characteristic parameters and the device state parameters respectively, and generating abnormality early warning information according to the abnormality diagnosis results; Step S3: performing comprehensive weighted fusion analysis based on the electrical parameters, the original radiation characteristic parameters and the device state parameters to predict a device comprehensive failure risk; Step S4: generating and outputting a comprehensive diagnosis report based on the abnormality early warning information and the device comprehensive failure risk.

2. The method of diagnosing a nuclear power plant radiation monitoring apparatus according to claim 1, characterized by, The electrical parameters comprise a real-time value of ionization chamber high voltage, a nominal high voltage and a real-time value of digital circuit power supply voltage, the original radiation characteristic parameters comprise a radiation reference peak real-time value and a calibrated reference peak value, and the device state parameters comprise a probe life clock signal; the step S2 comprises: calculating an absolute deviation of the real-time value of the ionization chamber high voltage from the nominal high voltage, judging whether the absolute deviation exceeds a first preset threshold and lasts for a first time, and if yes, generating and outputting a high voltage abnormality early warning; and / or, calculating a standard deviation of the real-time value of the digital circuit power supply voltage within a preset time period, judging whether the standard deviation exceeds a second preset threshold, and if yes, generating and outputting a power supply fluctuation early warning; and / or, calculating an absolute difference value of the radiation reference peak real-time value from the calibrated reference peak value, judging whether the absolute difference value exceeds a third preset threshold, and if yes, generating and outputting a reference peak drift early warning; and / or, judging whether a time when the probe life clock signal stops refreshing exceeds a second preset time, and if yes, generating and outputting a device operation abnormality early warning.

3. The method of diagnosing a nuclear power plant radiation monitoring apparatus according to claim 2, characterized by, The step S3 comprises: Step S3.1: calculating a comprehensive failure risk value based on the real-time value of the ionization chamber high voltage from the nominal high voltage, the real-time value of the digital circuit power supply voltage, the radiation reference peak real-time value and the calibrated reference peak value, and the probe life clock signal; Step S3.2: performing interval comparison of the comprehensive failure risk value with multiple preset risk threshold values, and outputting a device comprehensive failure risk level according to a comparison result.

4. The method of diagnosing a nuclear power plant radiation monitoring apparatus according to claim 3, characterized by, The original radiation characteristic parameters further comprise a real-time count rate and an expected count rate, and the step S3.1 comprises: calculating a high voltage deviation rate based on the real-time value of the ionization chamber high voltage from the nominal high voltage; calculating a voltage fluctuation rate based on the real-time value of the digital circuit power supply voltage; calculating a count rate deviation degree based on the real-time count rate and the expected count rate of a radiation monitoring channel; calculating a life clock abnormality index based on the probe life clock signal; performing weighted fusion of the high voltage deviation rate, the voltage fluctuation rate, the count rate deviation degree and the life clock abnormality index according to a preset weight proportion to obtain the comprehensive failure risk value.

5. The method of diagnosing a nuclear power plant radiation monitoring apparatus according to claim 4, characterized by, The method further comprises: Obtaining device lifecycle data, performing life assessment based on the device lifecycle data and preset life assessment rules to obtain a life prediction result; The step S4 comprises: generating and outputting the comprehensive diagnosis report based on the life prediction result, the abnormal early warning information and the device comprehensive failure risk level.

6. The method of diagnosing a nuclear power plant radiation monitoring apparatus according to claim 5, characterized by, The device lifecycle data comprises device basic information, and the life assessment based on the device lifecycle data and preset life assessment rules to obtain a life prediction result comprises: Based on the device basic information and the preset device life expectancy, a life assessment result in the time dimension of the device is calculated.

7. The method of diagnosing a nuclear power plant radiation monitoring apparatus according to claim 5, characterized by, The device lifecycle data comprises current radiation measurement values and device initial measurement values, and the life assessment based on the device lifecycle data and preset life assessment rules to obtain a life prediction result comprises: By comparing the current radiation measurement values with the device initial measurement values, a device detection efficiency assessment result is calculated; The device detection efficiency assessment result is compared with a preset device retirement threshold to obtain a life assessment result in the performance dimension of the device; The preset device retirement threshold is the lowest acceptable detection efficiency value corresponding to the device reaching the retirement standard.

8. A diagnostic system based on a nuclear power plant radiation monitoring device, characterized by, A diagnostic host computer, a shared communication bus and a plurality of radiation monitoring devices; wherein the physical area of the nuclear island or the conventional island is divided into a plurality of monitoring areas in advance, and in each monitoring area, a plurality of radiation monitoring devices are connected in series through the shared communication bus; the diagnostic host computer is connected with the shared communication bus; the host computer comprises: A data acquisition module for acquiring a plurality of source bottom layer operation parameters of the corresponding area from a plurality of radiation monitoring devices respectively, wherein the plurality of source bottom layer operation parameters comprise electrical parameters, original radiation characteristic parameters and device state parameters; An abnormal diagnosis module for performing abnormal diagnosis according to the electrical parameters, the original radiation characteristic parameters and the device state parameters respectively, and generating abnormal early warning information according to the abnormal diagnosis result; A risk analysis module for performing comprehensive weighted fusion analysis based on the electrical parameters, the original radiation characteristic parameters and the device state parameters to predict the comprehensive failure risk of the device; A report generation module for generating and outputting a comprehensive diagnosis report based on the abnormal early warning information and the comprehensive failure risk of the device.

9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the diagnostic method of the nuclear power plant radiation monitoring device according to any one of claims 1-7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the diagnostic method of the nuclear power plant radiation monitoring device according to any one of claims 1-7.

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