High-temperature sensor control method for severe environment monitoring

Abstract

The invention provides a high-temperature sensor control method for severe environment monitoring, and relates to the technical field of high-temperature sensor control, and the method comprises the steps: analyzing extreme environment parameters according to an application environment, and carrying out the time domain and frequency domain feature analysis of environment monitoring data; performing cumulative influence analysis on the high-temperature sensor, and determining cumulative damage assessment information of the high-temperature sensor; performing compensation analysis, and determining compensation information; and matching cooling control parameters according to the compensation node and the compensation data, determining a cooling control strategy, and carrying out cooling control on the high-temperature sensor. The technical problem that in the prior art, due to the fact that an extreme high-temperature environment can cause long-term damage to the sensor, the performance of the sensor is degraded, and then the measurement precision is affected can be solved, temperature fluctuation is reduced through cooling control, stable operation of the sensor in the extreme environment is guaranteed, and the measurement precision is improved. And the measurement precision of the high-temperature sensor in an extreme high-temperature environment is improved.

Description

Technical Field

[0001] The present application relates to the technical field of high temperature sensor control, and in particular to a high temperature sensor control method for monitoring harsh environments. Background Art

[0002] In the fields of industry, scientific research, etc., high-temperature sensors are increasingly used, especially in monitoring in harsh environments, including extreme conditions such as high temperature, high pressure, corrosive gases or liquids. However, in extremely high temperature environments, the performance and reliability of high-temperature sensors face severe challenges. The materials inside the sensor, such as resistors and capacitors, are very sensitive to temperature changes. When the temperature exceeds the design range, the electrical properties of the material may change, causing the measurement accuracy of the sensor to be affected. Temperature changes can cause the output signal of the sensor to deviate from the true value, that is, drift, reduce the measurement accuracy of the sensor, and affect the reliability of its monitoring results. In addition, high temperature may also cause thermal fatigue and expansion of the materials inside the sensor, causing the sensor to overheat and be damaged, thereby affecting its long-term stability and life.

[0003] In summary, there are technical problems in the prior art that the extreme high temperature environment may cause long-term damage to the sensor, causing sensor drift and cumulative damage in the high temperature environment, resulting in sensor performance degradation, thereby affecting the measurement accuracy. Summary of the invention

[0004] The purpose of this application is to provide a high-temperature sensor control method for harsh environment monitoring, in order to solve the technical problems in the prior art that the extreme high temperature environment may cause long-term damage to the sensor, causing sensor drift and cumulative damage in the high temperature environment, resulting in sensor performance degradation, thereby affecting the measurement accuracy.

[0005] In view of the above problems, the present application provides a high-temperature sensor control method for harsh environment monitoring, wherein the high-temperature sensor control method for harsh environment monitoring includes: parsing extreme environmental parameters according to the application environment, acquiring environmental monitoring data, and performing time domain and frequency domain feature analysis on the environmental monitoring data, wherein the environmental monitoring data is monitoring data collected for the extreme environmental parameters; performing cumulative impact analysis on the high-temperature sensor according to the time domain and frequency domain characteristics to determine the cumulative damage assessment information of the high-temperature sensor; performing compensation analysis based on the cumulative damage assessment information to determine compensation information, wherein the compensation information includes compensation nodes and compensation data; matching cooling control parameters according to the compensation nodes and compensation data to determine a cooling control strategy, and performing cooling control of the high-temperature sensor according to the cooling control strategy.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: By analyzing the extreme environmental parameters according to the application environment, environmental monitoring data is obtained, and the environmental monitoring data is analyzed in the time domain and frequency domain, wherein the environmental monitoring data is the monitoring data collected for the extreme environmental parameters; the high temperature sensor is analyzed for cumulative impact according to the time domain and frequency domain characteristics, and the cumulative damage assessment information of the high temperature sensor is determined; compensation analysis is performed according to the cumulative damage assessment information to determine compensation information, and the compensation information includes compensation nodes and compensation data; cooling control parameters are matched according to the compensation nodes and compensation data to determine the cooling control strategy, and the high temperature sensor is cooled according to the cooling control strategy. In other words, the performance of the high temperature sensor in a harsh environment is evaluated by analyzing the extreme environmental parameters and the characteristics of the monitoring data, and the cooling control of the sensor is optimized by combining the compensation analysis and the cooling control strategy to ensure its stable operation in an extreme environment and improve the measurement accuracy of the sensor.

[0007] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0009] Figure 1 This is a flow chart of a high temperature sensor control method for harsh environment monitoring in this application; Figure 2 This is a flow chart of time domain and frequency domain feature analysis in the high temperature sensor control method for harsh environment monitoring in this application. DETAILED DESCRIPTION

[0010] This application provides a high-temperature sensor control method for harsh environment monitoring, which solves the technical problem in the prior art that the extreme high temperature environment will cause long-term damage to the sensor, causing sensor drift and cumulative damage in the high temperature environment, resulting in sensor performance degradation, thereby affecting the measurement accuracy. By analyzing the extreme environmental parameters and the characteristics of the monitoring data, the performance of the high-temperature sensor in the harsh environment is evaluated, and the cooling control of the sensor is optimized by combining compensation analysis and cooling control strategy to ensure its stable operation in extreme environments and improve the measurement accuracy of the sensor.

[0011] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0012] For examples, please see the attached Figure 1 The present application provides a high temperature sensor control method for harsh environment monitoring, wherein the high temperature sensor control method for harsh environment monitoring specifically includes the following steps: Step 1: Analyze extreme environmental parameters according to the application environment, obtain environmental monitoring data, and perform time domain and frequency domain feature analysis on the environmental monitoring data, wherein the environmental monitoring data is monitoring data collected for the extreme environmental parameters.

[0013] Specifically, select data sets of abnormal conditions from historical monitoring data, including abnormal monitoring accuracy and sensor damage. Define abnormal monitoring accuracy and sensor damage as top events, which are the main issues for analysis. Decompose top events into multiple more specific underlying events, which are the direct causes of top events. Analyze the relationship between each underlying event and the working state of the sensor, and how environmental parameters affect these underlying events. Determine which environmental parameters have the greatest impact on the working state of the sensor, and find out the critical value range of these parameters. Collect environmental parameters in the application environment and their possible monitoring data ranges. Use key parameters and their value ranges as screening conditions to screen out extreme environmental parameters from the application environment parameters. Obtain environmental monitoring data for the screened extreme environmental parameters. The data obtained by monitoring specific environmental parameters usually include parameters such as temperature, humidity, and pressure. Arrange the environmental monitoring data into a continuous time series data set in chronological order to obtain a fluctuation curve of environmental parameters over time.

[0014] According to the segmentation threshold of the extreme environmental parameter, a marking line is set on the fluctuation curve. The fluctuation curve is segmented using the marking line to determine the time domain starting point of the extreme environmental parameter, and the time domain segment to be analyzed is determined in combination with the current time node. Each time domain segment is analyzed in detail, including the calculation of extreme values, mean values, rate of change, and duration. According to the segmentation threshold of the extreme environmental parameter, a frequency segmentation line is set in the frequency domain. The frequency segmentation line is used to analyze the frequency domain of the fluctuation curve to determine the frequency range to be analyzed. The selected frequency domain analysis target is analyzed in detail, including characteristics such as frequency peak, main frequency component, and frequency change speed. Time domain analysis involves studying the trend of data changes over time, while frequency domain analysis focuses on the frequency components of the data. By analyzing the time domain and frequency domain characteristics, the dynamic characteristics of environmental parameters can be fully understood and the ability to predict environmental changes can be improved.

[0015] Step 2: Perform cumulative impact analysis on high-temperature sensors based on time domain and frequency domain characteristics to determine the cumulative damage assessment information of high-temperature sensors.

[0016] Specifically, an impact assessment model of environmental parameters and high temperature sensors is trained through historical data, and the time domain characteristics and frequency domain characteristics of environmental parameters are used as input variables to output the sensor damage impact value. A time series chain is established, which consists of multiple time period nodes. Each node represents a specific time point and contains the time domain characteristics and frequency domain characteristics of the environmental parameters collected at that time point. The impact assessment model is applied as a node operator to each time period node in the time series chain. According to the time domain characteristics and frequency domain characteristics of the environmental parameters collected at each time node, the damage impact value of the sensor at each time period node is calculated. Since the previous state will affect the next state, the state impact of each node is accumulated. According to the node time sequence relationship of the time series chain, N nodes with continuous time sequence relationship are obtained in turn. The damage impact value is calculated and the sensor state is updated for each node. The sensor state update of each node is based on the damage impact value of the previous node and the environmental parameter data of the current node. The damage impact value of each node is added to the cumulative impact value of the previous node to obtain the cumulative damage assessment information of the entire time series chain. The sensor update status is compared with the standard sensor status to obtain the cumulative damage assessment information. The cumulative damage assessment information reflects the cumulative effect of environmental impacts suffered by the sensor at multiple time points. Based on the cumulative damage assessment information, the health status of the sensor is accurately assessed, and by predicting the performance degradation of the sensor under long-term environmental conditions, measures can be taken in advance to reduce potential performance problems.

[0017] Step three: Perform compensation analysis based on the accumulated damage assessment information to determine compensation information, where the compensation information includes compensation nodes and compensation data.

[0018] Specifically, based on the accumulated damage assessment information, the key value node is identified. From this node, the sensor begins to be damaged and its performance degrades. Starting from the key value node, the data of the accumulated damaged time series nodes are collected to determine the time point when the sensor needs cooling compensation, and the required cooling compensation data is determined. The fluctuation points of the compensation data in the time series are identified and obtained, which represent the changes of the compensation data over time, including the amplitude and frequency of the fluctuation. According to the data fluctuation time series node, the compensation node is determined. The compensation node refers to the sensor state that requires cooling compensation at a specific time point. A mapping relationship is created to associate each compensation node with the corresponding compensation data to ensure that appropriate cooling measures are applied at a specific time point. Based on the above, complete compensation information is obtained, including each compensation node and the corresponding compensation data. By identifying the key value nodes and obtaining the cooling compensation requirements of the accumulated damaged time series nodes, signs of sensor performance degradation are discovered in a timely manner, and corresponding compensation measures are taken to accurately implement the cooling control strategy and improve the reliability and life of the sensor.

[0019] Step 4: Match cooling control parameters according to the compensation nodes and compensation data, determine a cooling control strategy, and perform cooling control of the high temperature sensor according to the cooling control strategy.

[0020] Specifically, the compensation nodes and compensation data are matched with the cooling control parameters, including the start time of the cooling system, the flow rate of the coolant, the temperature set point, etc., to adapt to the cooling needs of the sensor in different states. A cooling control strategy is formulated according to the matched cooling control parameters, that is, how to adjust the cooling system under different conditions to meet the cooling needs of the sensor and ensure that the sensor maintains stable operation in a high temperature environment. According to the determined cooling control strategy, the cooling system is actually operated, including adjusting the operation mode of the cooling system, starting or stopping the cooling equipment, and controlling the flow rate of the coolant. For example, assuming that the compensation node and compensation data are determined, the compensation node is 1050℃, and the compensation data is to start the cooling system; matching the cooling control parameters, the start time of the cooling system: when the furnace temperature reaches 1050℃, the cooling system is started, and the stop time of the cooling system: when the furnace temperature drops to 1020℃, the cooling system is stopped. The cooling control strategy is determined, that is, when the furnace temperature reaches 1050℃, the cooling system is automatically started; when the furnace temperature drops to 1020℃, the cooling system is automatically stopped. Implement cooling control. When the furnace temperature reaches 1050℃, the cooling system is automatically started to reduce the furnace temperature. When the furnace temperature drops to 1020℃, the cooling system is automatically stopped to avoid overcooling. By matching cooling control parameters and determining cooling control strategies, the cooling system is effectively controlled to meet the cooling needs of the sensor.

[0021] Furthermore, step one of this application includes: Acquire an abnormal case set, the abnormal case set includes monitoring accuracy abnormal cases and sensor damage cases, wherein the case data includes time-series environmental monitoring data; set monitoring accuracy abnormalities and sensor damage as top events, and decompose the top events into multiple bottom-level events according to the abnormal case set; analyze each bottom-level event layer by layer from the top event downward, fit the influence relationship between the sensor working state and the environmental parameters and the value range of the environmental parameters, determine the key parameters and their corresponding value ranges, the key parameters are the value ranges corresponding to the environmental parameters whose influence relationship reaches a threshold; acquire the environmental parameters of the application environment and their monitoring data ranges; use the key parameters and their corresponding value ranges as extreme value screening conditions, screen the environmental parameters of the application environment and their monitoring data ranges, and determine the extreme value environmental parameters; use the extreme value environmental parameters as monitoring targets, and acquire the environmental monitoring data.

[0022] Specifically, the datasets of abnormal situations that occur during environmental monitoring are collected and organized, including two main types: monitoring accuracy abnormal cases and sensor damage cases. Monitoring accuracy abnormal cases refer to situations where there is a significant deviation between environmental monitoring data and actual environmental conditions due to errors in the sensor itself, interference from environmental factors, or improper data processing. Sensor damage cases refer to situations where the sensor's performance degrades or its function fails due to long-term exposure to extreme environments or other factors. Case data include time-series environmental monitoring data, that is, environmental monitoring data that changes over time. Time-series data can reflect the changing trend of environmental parameters over time. Monitoring accuracy abnormalities and sensor damage are taken as the main focus (i.e., top events), and these complex events are decomposed into smaller and more specific bottom-level events. In fault tree analysis, the top event refers to the most undesirable event, usually a system-level fault or failure. The bottom-level event is the direct cause of the top event and is the most basic element of the fault tree.

[0023] For example, the abnormal case set of a high-temperature sensor is: the monitoring accuracy abnormal case is when the deviation between the sensor reading and the actual temperature exceeds ±2°C, and the sensor damage case is when the sensor is damaged when the temperature exceeds 1000°C. By performing top event decomposition, the monitoring accuracy abnormality can be decomposed into underlying events such as sensor drift, signal interference, and data transmission errors, and the sensor damage can be decomposed into underlying events such as material aging, circuit failure, and mechanical damage.

[0024] Starting from the set top event (monitoring accuracy anomaly and sensor damage), analyze each underlying event layer by layer, involving fault tree analysis (FTA), where each underlying event is a direct or indirect cause of the top event. Analyze how the sensor's working state (such as accuracy, stability, etc.) is affected by different environmental parameters (such as temperature, humidity, chemical corrosiveness, etc.) and their value ranges, and identify the environmental parameters that have the greatest impact on the sensor's working state, that is, the key parameters. Determine the value range of these key parameters, that is, within this range, effective work can be carried out, and exceeding this range may cause system performance degradation or failure.

[0025] The application environment refers to the specific environmental conditions in which the sensor actually works, such as industrial sites, laboratories, and outdoors. It involves collecting relevant environmental parameters in a specific application environment, such as temperature, humidity, pressure, chemical corrosivity, etc., and determining the monitoring data range of these parameters. Use key parameters and their corresponding value ranges as screening conditions to screen the environmental parameters and their monitoring data ranges in the application environment, compare the actual environmental parameters and their monitoring data ranges in the application environment with the value ranges of key parameters, and determine which parameters are in extreme value states. Extreme environmental parameters refer to the extreme values ​​of environmental parameters that may cause equipment performance degradation or failure in a specific application environment. Take extreme environmental parameters as the key monitoring objects, and collect and record monitoring data related to extreme environmental parameters. By identifying and screening extreme environmental parameters, accurately monitor the conditions that may cause sensor failures, analyze underlying events to quickly diagnose the causes of sensor failures, and reduce downtime.

[0026] Further, as attached Figure 2 As shown, the application also includes the following steps: Integrate the environmental monitoring time series data according to the monitoring time series relationship of the environmental monitoring data; fit the fluctuation curve of the environmental parameters according to the environmental monitoring time series data; set the time series identification mark line with the segmentation threshold of the extreme environmental parameter, use the time series identification mark line to segment the fluctuation curve of the environmental parameter to obtain the time domain starting point, and determine the time domain analysis target according to the time domain starting point and the current time node; perform extreme value, mean value, change rate, and duration analysis and fitting of each time domain segment of the time domain analysis target to obtain time domain characteristics; set the frequency segmentation line according to the segmentation threshold of the extreme environmental parameter, use the frequency segmentation line to segment the fluctuation curve of the environmental parameter to determine the frequency domain analysis target; perform frequency peak, main frequency component, and frequency change rate fitting on the frequency domain analysis target to obtain the frequency domain characteristics.

[0027] Specifically, according to the relationship between the environmental monitoring data and time, the collected environmental monitoring data are sorted and merged in chronological order to form a coherent data set. Mathematical models (such as polynomial fitting, exponential smoothing, etc.) are used to process the time series data to obtain the fluctuation curve of environmental parameters over time. A specific threshold is set according to the extreme environmental parameters as the marking line for identifying the time series data. The segmentation threshold refers to the critical value that divides the environmental parameter fluctuation curve into different parts. The time series identification marking line is a mark drawn on the time series data based on these thresholds, which is used to identify and segment important change points in the data. The fluctuation curve of the environmental parameters is divided into different parts by the set marking line, so as to facilitate a deeper analysis of the characteristics and laws of each part. Through segmentation, the starting point of each part is determined, that is, the time domain starting point, which usually marks an important change point on the environmental parameter fluctuation curve, which is related to a specific environmental event or state change. According to the time domain starting point and the current time node, the target of the time domain analysis is determined, and a specific time range is selected to focus on analyzing the changes in environmental parameters within the time period. The time domain analysis target refers to the specific time range of the analysis, usually from the time domain starting point to the current time node.

[0028] The time domain analysis target is divided into several time domain segments. Each time domain segment represents a specific time period, which contains a set of continuous environmental parameter data. In each time domain segment, find the maximum and minimum values ​​of the environmental parameters, that is, the extreme values. Calculate the average value, change rate (the speed at which the parameter changes over time) and duration (the duration of a specific state or event) in each time domain segment. According to the extreme value, mean value, change rate and duration, the environmental parameters of each time domain segment are feature fitted to obtain the time domain features, which comprehensively reflect the overall changes of the environmental parameters in each time domain segment.

[0029] The frequency segmentation line is set according to the segmentation threshold of the extreme environmental parameter to identify and segment different frequency components in the data. The fluctuation curve of the environmental parameter is segmented using the set frequency segmentation line, and the continuous time series data is divided into multiple parts to facilitate the analysis of the different frequency components represented by each part. According to the segmented frequency components, the target of the frequency domain analysis is determined, and a specific frequency range or component is selected to focus on analyzing the characteristics of the frequency segment. In each frequency domain segment, the frequency corresponding to the maximum amplitude in the environmental parameter fluctuation curve, that is, the frequency peak, is found. The dominant frequency component in each frequency domain segment is identified and analyzed. The main frequency component refers to the frequency with the largest energy contribution in a specific frequency domain segment. The speed of change of the frequency component in each frequency domain segment over time is calculated. The frequency change speed reflects the trend of the frequency component of the environmental parameter fluctuation curve over time. The environmental parameters of each frequency domain segment are feature fitted to obtain the frequency domain characteristics, which comprehensively reflects the frequency characteristics of the environmental parameters in each frequency domain segment. By integrating and fitting time series data, the changing trends of environmental parameters over time are determined. Time domain and frequency domain analysis provide a comprehensive understanding of the changes in environmental parameters, including time change characteristics and frequency change characteristics, which helps to identify the change patterns of environmental parameters.

[0030] Further, step 2 of this application includes: An impact assessment model of environmental parameters and high temperature sensors is established, wherein the input variables of the impact assessment model are the time domain characteristics and frequency domain characteristics of the environmental parameters, and the output result is the sensor damage impact value; a time series chain is established, wherein the time series chain includes multiple time period nodes, and the time period nodes have the time domain characteristics and frequency domain characteristics of the environmental parameters collected at the time nodes, and the impact assessment model is fitted to the time period nodes as a node operator, and the damage impact value is calculated through the node operator according to the time domain characteristics and frequency domain characteristics of the environmental parameters collected at the time nodes; according to the node timing relationship of the time series chain, continuous cumulative calculation is performed according to the damaged impact value of the node to obtain the cumulative damage assessment information.

[0031] Specifically, a mathematical model is created to evaluate the impact of environmental parameters on the performance of high-temperature sensors. Based on previous environmental monitoring data and sensor performance data, the potential impact of changes in environmental conditions on sensor performance is predicted. Using the time domain characteristics (such as extreme values, mean values, change rates, etc.) and frequency domain characteristics (such as frequency peaks, main frequency components, etc.) of environmental parameters as input helps to reflect the change pattern and frequency characteristics of environmental parameters. The output of the model is the impact value of sensor damage, which indicates the degree of negative impact of changes in environmental parameters on sensor performance. It can be quantitative, such as the reduction in sensor life or the degree of performance degradation, or qualitative, such as whether the sensor is likely to be damaged. Use machine learning algorithms (such as regression analysis, neural networks) or physical models to establish the relationship between environmental parameter characteristics and sensor damage impact values. Divide historical data into training sets and validation sets to train and validate the model, improve the accuracy of the model, and ensure that the model can accurately predict sensor damage.

[0032] A time series chain is established. The time series chain consists of multiple time period nodes. Each node represents a specific time point and contains the time domain characteristics and frequency domain characteristics of the environmental parameters collected at that time point. The impact assessment model is applied as a node operator to each time period node in the time series chain. According to the time domain characteristics and frequency domain characteristics of the environmental parameters collected at each time node, the impact value of the sensor damage at each time period node is calculated. The node operator refers to the calculation method or model used to process each time period node in the time series chain. The damage impact value of each node in the time series chain is continuously accumulated and calculated, considering not only the damage impact at the current time point, but also the cumulative effect of the impact at all previous time points. The cumulative damage assessment information is the cumulative sum of the damage impact values ​​of all nodes in the time series chain. The damage impact value of each node is added to the cumulative impact value of the previous node to obtain the cumulative damage assessment information of the entire time series chain, which reflects the cumulative impact of environmental parameter changes on sensor performance from the beginning of the time series chain to the current time point. By establishing an impact assessment model and time series chain, real-time monitoring and evaluation of the impact of environmental parameter changes on sensor performance can help predict the remaining life and performance status of the sensor and improve the reliability and life of the sensor.

[0033] Furthermore, the present application also includes the following steps: According to the node timing relationship of the time series chain, the first node, the second node, ..., until the Nth node are obtained in sequence, wherein N is a positive integer and the nodes are in a continuous timing relationship; the damage impact value of the first node is obtained, and the sensor state corresponding to the second node is updated according to the damage impact value of the first node, wherein the sensor update state of the second node is the state information after the damage state transfer evaluation is performed on the initial standard sensor state according to the damage impact value of the first node; the damage impact value of the second node is calculated and obtained by the node operator according to the time domain characteristics and frequency domain characteristics of the environmental parameters of the second node, and the damage transfer evaluation is performed on the sensor update state of the second node using the damage impact value of the second node, and the sensor state of the third node is updated; and so on, the damage impact value calculation of the Nth node is completed, and the sensor state of the N-1th node is damaged cumulatively updated; the damage state comparison is performed between the sensor update state of the Nth node and the standard sensor state to obtain the cumulative damage assessment information.

[0034] Specifically, according to the node timing relationship of the time series chain, each node in the time series chain is obtained in turn, and according to the time order of each node in the time series chain, starting from the first node, the subsequent nodes are obtained one by one until the Nth node is obtained. Each node represents a specific time point, and is arranged in time order based on the time point of the previous node to form a time series. The first node is obtained from the time series chain, and the damage impact value of the sensor on the node is calculated, which indicates the degree of negative impact of the change of environmental parameters on the sensor performance at the first time point. The sensor state corresponding to the second node is updated using the damage impact value of the first node. The damage impact value of the first node is applied to the initial standard sensor state to simulate the state that the sensor may reach after experiencing the influence of the first node. The updated sensor state is the result of the damage state transition evaluation of the initial standard sensor state based on the damage impact value of the first node, reflecting the performance degradation or change that may occur to the sensor under the influence of the first node. The damage state transition evaluation is to predict the sensor state of the next node based on the damage impact of the previous node.

[0035] Using the node operator, according to the environmental parameter characteristics (time domain characteristics and frequency domain characteristics) of the second node, the damaged impact value of the second node is calculated, which indicates the degree of negative impact of the environmental parameter change on the sensor performance at the second time point. Using the damaged impact value of the second node to evaluate the damaged state transition of the sensor state of the second node means evaluating the degree of sensor performance degradation according to the environmental parameter changes of the second node, and predicting possible failures or performance degradation of the sensor. According to the damaged impact value of the second node and the damaged state transition evaluation result, the sensor state of the third node is updated, that is, the damaged state transition evaluation result of the second node is applied to the sensor state of the third node to reflect the continuous impact of environmental parameter changes on sensor performance.

[0036] Repeat this process, and calculate the damage impact value of each node one by one in the order of the time series chain. Calculate the damage impact value and update the sensor status for each subsequent node. The sensor status update of each node is based on the damage impact value of the previous node and the environmental parameter data of the current node. When the last node (the Nth node) is reached, the sensor status of the N-1th node is cumulatively updated. Compare the updated sensor status of the Nth node with the standard sensor status. The standard sensor status usually refers to the performance status of the sensor under ideal conditions, that is, the state without any performance degradation or damage. Through comparison, the cumulative damage assessment information of the sensor at the Nth node can be obtained, which reflects the overall performance degradation of the sensor from the beginning of monitoring to the Nth node, including the cumulative impact of all environmental parameter changes on the sensor performance. By obtaining and updating the sensor status of each node in turn, the performance changes of the sensor under environmental changes are monitored in real time. The cumulative damage assessment information reflects the cumulative damage of the sensor due to changes in environmental parameters during the entire monitoring period, which helps to predict the remaining life and performance status of the sensor.

[0037] Furthermore, the present application also includes the following steps: Configure multiple levels of damaged states, each level represents the health state or damage degree of the high temperature sensor; set a transition probability matrix, which is constructed based on historical experience data, and the matrix elements For the damaged state from the level Transfer to level impaired state The probability that each element satisfy , m is the number of levels of damaged state, where is the initial standard sensor state; using the transition probability matrix, according to the damaged impact value of the first node, the high temperature sensor is transferred from the initial standard sensor state to The probability of the damaged state is used to determine the sensor update state.

[0038] Specifically, a multi-level damaged state is configured to divide the health state or damage degree of the high temperature sensor into multiple levels, such as normal, slightly damaged, moderately damaged, and severely damaged. These levels are usually classified according to the degree of sensor performance degradation, and each level represents a specific state of the sensor. Based on historical experience data, a transition probability matrix is ​​constructed to represent the probability of the sensor transitioning from one damaged state to another, reflecting the probability distribution of the sensor transitioning under different damaged states. The matrix elements Identifies the probability of transitioning from a damaged state at level i to a damaged state at level j. For example, represents the probability of transitioning from the initial standard sensor state to the slightly damaged state. Each element in the matrix The property of probability must be satisfied, that is, the sum of the probabilities of all possible transitions must be equal to 1. is the initial standard sensor state, representing the ideal state of the sensor without any damage. This state is usually used as a starting point in the matrix to calculate the transition probability of other states.

[0039] According to the damaged impact value of the first node, the transition probability matrix is ​​used to calculate the transition of the high temperature sensor from the initial standard sensor state to the Probability of damaged state. Determine the updated state of the sensor at the next node based on the calculated damaged state transition probability. At the next time point, use the accumulated probability to update the state. If the current probability of the damaged state of the sensor has reached the preset threshold, indicating that the sensor has reached a certain damaged state, then this state will be used directly to update the current state of the sensor. If the current probability of the damaged state of the sensor has not reached the preset threshold, indicating that the sensor has not yet reached a certain damaged state, then the probability value will be used to update the current state of the sensor. After updating the current state of the sensor, continue to consider the probability of damaged states on subsequent nodes and accumulate these probabilities to the current state of the sensor. Over time, the probability of the damaged state of the sensor will gradually accumulate, which will affect the performance of the sensor. Through the multi-level damaged state and transition probability matrix, the health status of the sensor is evaluated and the future state transition trend is predicted.

[0040] Further, step three of this application includes: According to the cumulative damage assessment information, a key value node is identified, where the key value node is the time node when the sensor protection threshold is reached and the sensor starts to be damaged from the key value node; taking the key value node as the starting point, the cooling compensation demand of the cumulative damaged timing node is obtained, and the compensation data is determined; the data fluctuation timing node of the compensation data is obtained, the compensation node is determined according to the data fluctuation timing node mapping, a mapping association between the compensation node and the compensation data is established, and the compensation information is obtained.

[0041] Specifically, the accumulated damage assessment information is analyzed to identify the time node at which the sensor protection threshold is reached. The critical value node is the critical point where the sensor performance begins to degrade, marking the time point when the sensor may begin to suffer serious damage. The sensor protection threshold refers to the maximum allowable environmental parameter value at which the sensor can work normally without suffering serious damage. When the accumulated damage assessment information reaches or exceeds this threshold, the critical value node is identified, indicating that the sensor begins to be damaged and the performance gradually decreases. Starting from the critical value node, the data of the accumulated damaged timing nodes are collected to determine the need for cooling compensation of the sensor. The compensation demand is the cooling measure that needs to be applied to reduce or eliminate the performance degradation of the sensor caused by environmental factors. According to the cooling compensation demand of the accumulated damaged timing nodes, the required cooling compensation data is determined, that is, the specific data guiding the cooling control strategy, such as cooling rate, cooling time, etc.

[0042] Analyze the time series data corresponding to the compensation data and find the time series nodes where the data fluctuates, which reflects the timing and effect of the compensation data. These fluctuation points are related to the fluctuation of sensor performance or the change of environmental conditions and require special attention. The compensation node refers to the time point at which cooling compensation needs to be applied at a specific time point. Establish a mapping association between the compensation node and the compensation data in the form of a table, diagram or programming logic to ensure that appropriate cooling measures are applied at a specific time point. By obtaining complete compensation information, including compensation nodes and compensation data, the implementation of the cooling control strategy is guided to protect the sensor from further damage.

[0043] For example, there is a high-temperature sensor used to monitor the furnace temperature, and the cumulative damage assessment information has been established. The key value node is identified as when the furnace temperature reaches 1000℃, the sensor begins to be damaged. Starting from the furnace temperature reaching 1000℃, the data of the cumulative damaged time series nodes are collected. According to the cumulative damage assessment information, it is determined that when the furnace temperature reaches 1050℃, the cooling system needs to be started. By analyzing the time series data corresponding to the compensation data, it is found that the furnace temperature begins to drop at 1050℃. 1050℃ is determined to be the compensation node, that is, the cooling system is started at this temperature. A mapping association is established between the compensation node (1050℃) and the compensation data (starting the cooling system). The compensation information is obtained, including the compensation node (1050℃) and the compensation data (starting the cooling system). Through the identification of key value nodes and the acquisition of compensation information, the sensor damage situation is responded to in real time and corresponding cooling compensation measures are taken.

[0044] Furthermore, the present application also includes the following steps: The cooling compensation requirement is calculated by the cooling requirement calculation formula, and the expression of the cooling requirement calculation formula is: , where P(t) represents the cooling demand or cooling power of the system at time t, T(t) is the actual temperature of the high temperature sensor at time t, is the target temperature, i.e., the ideal temperature maintained by the sensor; α is the adjustment coefficient used to adjust the degree of influence of temperature difference on cooling demand; β is the adjustment coefficient used to adjust the influence of temperature change rate on cooling demand; is the rate of temperature change, D(t) is the cumulative damage assessment value of the sensor at time t, and γ is the adjustment coefficient for adjusting the impact of cumulative damage on cooling demand.

[0045] Specifically, the cooling demand calculation formula is expressed as: , where P(t) represents the cooling demand or cooling power of the system at time t, which determines the required cooling system capacity to keep the sensor operating within the target temperature range; T(t) is the actual temperature of the high temperature sensor at time t, which is the current temperature state of the sensor; The target temperature is the ideal temperature maintained by the sensor, which is the temperature the sensor should maintain; is the rate of temperature change, reflecting the degree of temperature change over time. Rapid temperature changes require more cooling. D(t) is the cumulative damage assessment value of the sensor at time t, reflecting the degree of damage accumulated by the sensor due to environmental factors. The more serious the damage, the greater the cooling demand. α, β, and γ are adjustment coefficients, which are used to adjust the temperature difference (T(t) and The difference between the actual temperature of the sensor, the target temperature, the temperature change rate, and the cumulative damage assessment value are used to calculate the cooling demand. The cooling demand of the cooling system is calculated by the formula, and the influence of different factors on the cooling demand is adjusted by different parameters.

[0046] In summary, the high temperature sensor control method for harsh environment monitoring provided by the present application has the following technical effects: By analyzing the extreme environmental parameters according to the application environment, environmental monitoring data is obtained, and the environmental monitoring data is analyzed in the time domain and frequency domain, wherein the environmental monitoring data is the monitoring data collected for the extreme environmental parameters; the high temperature sensor is analyzed for cumulative impact according to the time domain and frequency domain characteristics, and the cumulative damage assessment information of the high temperature sensor is determined; compensation analysis is performed according to the cumulative damage assessment information to determine compensation information, and the compensation information includes compensation nodes and compensation data; cooling control parameters are matched according to the compensation nodes and compensation data to determine the cooling control strategy, and the high temperature sensor is cooled according to the cooling control strategy. In other words, the performance of the high temperature sensor in a harsh environment is evaluated by analyzing the extreme environmental parameters and the characteristics of the monitoring data, and the cooling control of the sensor is optimized by combining the compensation analysis and the cooling control strategy to ensure its stable operation in an extreme environment and improve the measurement accuracy of the sensor.

[0047] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0048] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A high temperature sensor control method for harsh environment monitoring, characterized in that: The high temperature sensor control method for harsh environment monitoring includes: Analyze extreme environmental parameters according to the application environment, obtain environmental monitoring data, and perform time domain and frequency domain feature analysis on the environmental monitoring data, wherein the environmental monitoring data is monitoring data collected for the extreme environmental parameters; Conduct cumulative impact analysis on high-temperature sensors based on time-domain and frequency-domain characteristics to determine cumulative damage assessment information for high-temperature sensors; Perform compensation analysis based on the accumulated damage assessment information to determine compensation information, where the compensation information includes compensation nodes and compensation data; According to the compensation node and compensation data, cooling control parameters are matched to determine a cooling control strategy, and cooling control of the high-temperature sensor is performed according to the cooling control strategy.

2. The high temperature sensor control method for harsh environment monitoring according to claim 1, characterized in that: The step of analyzing extreme environmental parameters according to the application environment and obtaining environmental monitoring data includes: Acquire an abnormal case set, wherein the abnormal case set includes monitoring accuracy abnormal cases and sensor damage cases, wherein the case data includes time series environmental monitoring data; Set monitoring accuracy anomaly and sensor damage as top events, and decompose the top events into multiple bottom-level events based on the abnormal case set; Analyze each underlying event layer by layer from the top event downward, fit the influence relationship between the sensor working state and the environmental parameters and the value range of the environmental parameters, and determine the key parameters and their corresponding value ranges, wherein the key parameters are the value ranges corresponding to the environmental parameters whose influence relationship reaches the threshold; Obtaining environmental parameters of the application environment and their monitoring data ranges; Using the key parameters and their corresponding value ranges as extreme value screening conditions, screening the environmental parameters of the application environment and their monitoring data ranges to determine the extreme value environmental parameters; The extreme environmental parameter is used as a monitoring target to obtain the environmental monitoring data.

3. The high temperature sensor control method for harsh environment monitoring according to claim 2, characterized in that: Perform time domain and frequency domain feature analysis on the environmental monitoring data, including: Integrate the environmental monitoring time series data according to the monitoring time series relationship of the environmental monitoring data; Fitting a fluctuation curve of an environmental parameter according to the environmental monitoring time series data; Setting a time series identification line with a segmentation threshold of the extreme environmental parameter, segmenting the fluctuation curve of the environmental parameter using the time series identification line to obtain a time domain starting point, and determining a time domain analysis target based on the time domain starting point and the current time node; Performing analysis and fitting of the extreme value, mean value, change rate, and duration of each time domain segment on the time domain analysis target to obtain time domain characteristics; Setting a frequency dividing line according to a segmentation threshold of the extreme environmental parameter, and using the frequency dividing line to segment the fluctuation curve of the environmental parameter to determine a frequency domain analysis target; The frequency domain analysis target is fitted with frequency peak, main frequency component and frequency change speed to obtain the frequency domain characteristics.

4. The high temperature sensor control method for harsh environment monitoring according to claim 3, characterized in that: The cumulative impact analysis of the high-temperature sensor based on the time domain and frequency domain characteristics to determine the cumulative damage assessment information of the high-temperature sensor includes: Establishing an impact assessment model for environmental parameters and high-temperature sensors, wherein the input variables of the impact assessment model are the time domain characteristics and frequency domain characteristics of the environmental parameters, and the output result is the sensor damage impact value; Establishing a time series chain, the time series chain including a plurality of time period nodes, the time period nodes having time domain characteristics and frequency domain characteristics of environmental parameters collected at the time nodes, fitting the impact assessment model as a node operator to the time period nodes, and calculating the damage impact value through the node operator based on the time domain characteristics and frequency domain characteristics of the environmental parameters collected at the time nodes; According to the time sequence relationship of the nodes in the time series chain, continuous cumulative calculation is performed according to the damage impact value of the node to obtain the cumulative damage assessment information.

5. The high temperature sensor control method for harsh environment monitoring according to claim 4, characterized in that: The method of performing continuous cumulative calculation according to the node time sequence relationship of the time series chain and the damage impact value of the node to obtain the cumulative damage assessment information includes: According to the time sequence relationship of the nodes in the time series chain, sequentially obtain the first node, the second node, ..., until the Nth node, where N is a positive integer and the nodes are in a continuous time sequence relationship; Obtaining a damage impact value of a first node, and updating a sensor state corresponding to a second node according to the damage impact value of the first node, wherein the updated sensor state of the second node is state information obtained by performing a damage state transition assessment on an initial standard sensor state according to the damage impact value of the first node; Calculate the damage impact value of the second node based on the time domain characteristics and frequency domain characteristics of the environmental parameters of the second node through a node operator, perform damage transfer assessment on the sensor update state of the second node using the damage impact value of the second node, and update the sensor state of the third node; By analogy, the damage impact value calculation of the Nth node is completed, and the damage cumulative update of the sensor state of the N-1th node is performed; The damage state is compared by using the sensor update state of the Nth node and the standard sensor state to obtain the accumulated damage assessment information.

6. The high temperature sensor control method for harsh environment monitoring according to claim 5, characterized in that: The updating of the sensor state corresponding to the second node according to the damage impact value of the first node includes: Configure multiple levels of damage status, each level represents the health status or damage degree of the high temperature sensor; Set a transition probability matrix, which is constructed based on historical experience data. For damaged state Transfer to level damaged state The probability that each element satisfy , m is the number of levels of damaged state, where is the initial standard sensor state; The transition probability matrix is ​​used to calculate the transition probability of the high temperature sensor from the initial standard sensor state to the damaged state according to the damaged impact value of the first node. The probability of the damaged state is used to determine the sensor update state.

7. The high temperature sensor control method for harsh environment monitoring according to claim 4, characterized in that: Compensation analysis is performed based on the accumulated damage assessment information to determine compensation information, where the compensation information includes compensation nodes and compensation data, including: Identifying a key value node based on the accumulated damage assessment information, where the key value node is a time node at which a sensor protection threshold is reached and from which the sensor begins to be damaged; Taking the key value node as a starting point, obtaining the cooling compensation requirements of the accumulated damaged timing nodes and determining the compensation data; The data fluctuation time series node of the compensation data is obtained, the compensation node is determined according to the data fluctuation time series node mapping, a mapping association between the compensation node and the compensation data is established, and the compensation information is obtained.

8. The high temperature sensor control method for harsh environment monitoring according to claim 7, characterized in that: The obtaining of the cooling compensation requirements of the accumulated damaged timing nodes includes: The cooling compensation requirement is calculated by the cooling requirement calculation formula, and the expression of the cooling requirement calculation formula is: , where P(t) represents the cooling demand or cooling power of the system at time t, T(t) is the actual temperature of the high temperature sensor at time t, is the target temperature, i.e., the ideal temperature maintained by the sensor; α is the adjustment coefficient used to adjust the degree of influence of temperature difference on cooling demand; β is the adjustment coefficient used to adjust the influence of temperature change rate on cooling demand; is the rate of temperature change, D(t) is the cumulative damage assessment value of the sensor at time t, and γ is the adjustment coefficient for adjusting the impact of cumulative damage on cooling demand.

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