A measurement error compensation control method for high temperature sensor

Abstract

The present application provides a method for controlling the measurement error compensation of a high-temperature sensor, and relates to the technical field of error compensation for high-temperature sensors, including: collecting the high-temperature sensor layout information of the engine; optimizing the layout of the first high-temperature sensor layout array; measuring the temperature of the engine; setting a multi-feature heat conduction factor; performing heat conduction error compensation on the first result of the engine temperature measurement; loading the sensor state data stream corresponding to the second result of the engine temperature measurement, and combining the sensor abnormality temperature measurement compensation model to perform measurement error compensation on the second result of the engine temperature measurement. The present application can solve the technical problem in the prior art that the engine temperature monitoring is not comprehensive enough due to the fixed layout of the high-temperature sensor and the lack of adaptability to the dynamic temperature distribution, and achieve the technical goal of dynamically optimizing the layout of the high-temperature sensor and accurately compensating for the temperature measurement error, thereby achieving the technical effect of improving the comprehensiveness of the monitoring of the key temperature areas of the engine.

Description

Technical Field

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

[0002] With the development of engine technology, high temperature sensors play a vital role in the operation of the engine. These high temperature sensors are used to monitor the temperature of key engine components to ensure that they operate within a safe range and avoid failure or damage caused by overheating. However, the existing high temperature sensor layout and measurement methods still have many problems in practical applications.

[0003] At present, the layout of high-temperature sensors in the existing technology is often relatively fixed, and the complexity of temperature distribution under different working conditions of the engine is not fully considered. Under different engine loads, speeds and other operating conditions, the temperature distribution will change significantly, and fixed sensors cannot fully and accurately reflect these dynamic changes. Especially in complex engine structures, the temperature in certain areas may change dramatically, and these key areas may not be covered by sensors, resulting in blind spots in temperature measurement. In addition, existing sensor layout schemes often fail to fully consider the temperature correlation between sensors, resulting in small differences in temperature changes measured by sensors in some adjacent areas, and redundancy in sensor layout. This not only wastes hardware resources, but also increases the complexity of data processing.

[0004] In summary, the prior art has technical problems such as insufficient monitoring of key temperature areas and redundant sensor layout due to the fixed layout of high-temperature sensors and lack of adaptability to dynamic temperature distribution, which further affects the comprehensiveness and accuracy of engine temperature monitoring and the efficiency of data processing. Summary of the invention

[0005] The purpose of this application is to provide a measurement error compensation control method for a high-temperature sensor, so as to solve the technical problems in the prior art that the high-temperature sensors are fixedly arranged and lack adaptability to dynamic temperature distribution, resulting in insufficient monitoring of key temperature areas and redundant sensor arrangement, which further affects the comprehensiveness and accuracy of engine temperature monitoring and the efficiency of data processing.

[0006] In view of the above problems, the present application provides a measurement error compensation control method for a high-temperature sensor, comprising: collecting high-temperature sensor layout information of an engine to obtain a first high-temperature sensor layout array; based on a neighborhood temperature correlation threshold and a temperature gradient threshold, in combination with a predetermined control scheme of the engine, optimizing the layout of the first high-temperature sensor layout array to obtain a second high-temperature sensor layout array, wherein the predetermined control scheme includes predetermined control parameters of the engine; controlling the engine based on the predetermined control scheme, and measuring the temperature of the engine according to the second high-temperature sensor layout array to obtain a first engine temperature measurement result; setting a multi-feature thermal conductivity factor, wherein the multi-feature thermal conductivity factor includes a thermal conductivity material, a thermal conductivity environment, and a thermal conductivity contact parameter; based on a thermal conductivity temperature measurement influence threshold, performing thermal conduction error compensation on the first engine temperature measurement result according to the multi-feature thermal conductivity factor to obtain a second engine temperature measurement result; loading a sensor state data stream corresponding to each temperature information in the second engine temperature measurement result, and performing measurement error compensation on the second engine temperature measurement result in combination with a sensor abnormality temperature measurement compensation model to generate a third engine temperature measurement result.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] By collecting the high-temperature sensor layout information of the engine, a first high-temperature sensor layout array is obtained; based on the neighborhood temperature correlation threshold and the temperature gradient threshold, in combination with the predetermined control scheme of the engine, the layout of the first high-temperature sensor layout array is optimized to obtain a second high-temperature sensor layout array, wherein the predetermined control scheme includes the predetermined control parameters of the engine; based on the predetermined control scheme, the engine is controlled, and the temperature of the engine is measured according to the second high-temperature sensor layout array to obtain a first engine temperature measurement result; a multi-feature heat conduction factor is set, wherein the multi-feature heat conduction factor includes heat conduction material, heat conduction environment and heat conduction contact parameter; based on the heat conduction temperature measurement influence threshold, the first engine temperature measurement result is compensated for heat conduction error according to the multi-feature heat conduction factor to obtain a second engine temperature measurement result; the sensor state data stream corresponding to each temperature information in the second engine temperature measurement result is loaded, and the measurement error of the second engine temperature measurement result is compensated in combination with the sensor abnormality temperature measurement compensation model to generate a third engine temperature measurement result. That is to say, by collecting The high-temperature sensor layout information of the engine is collected to construct an initial sensor layout array; then, based on the correlation between adjacent temperatures and the gradient of temperature changes, combined with the predetermined control scheme of the engine, the initial array is optimized to generate a more reasonable sensor layout array; the engine is operated according to the predetermined control scheme, and the temperature is measured through the optimized sensor array to obtain the first round of temperature measurement results; then, a variety of factors affecting heat conduction, such as materials, environment and contact conditions, are set, which will affect the accurate measurement of temperature. Through the set heat conduction error compensation threshold, the first temperature measurement result is corrected by using a variety of factors affecting heat conduction to obtain a more accurate second round of temperature measurement results; finally, combined with the sensor status data corresponding to each temperature information, the second round of temperature measurement results are further corrected by the sensor abnormality compensation model, and finally a more accurate third round of temperature measurement results are generated, and finally the technical goals of dynamically optimizing the high-temperature sensor layout and accurately compensating for temperature measurement errors are achieved, and the technical effects of improving the comprehensiveness of engine key temperature area monitoring, improving temperature measurement accuracy and reducing data processing redundancy are achieved.

[0009] 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

[0010] 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.

[0011] Figure 1 A flow chart of a measurement error compensation control method for a high temperature sensor of the present application;

[0012] Figure 2 This is a schematic diagram of a flow chart of obtaining a second high-temperature sensor arrangement array in a measurement error compensation control method of a high-temperature sensor of the present application. DETAILED DESCRIPTION

[0013] This application provides a measurement error compensation control method for a high-temperature sensor, which solves the technical problem in the prior art that the high-temperature sensor is fixedly arranged and lacks adaptability to dynamic temperature distribution, resulting in insufficient monitoring of key temperature areas and redundant sensor arrangement, which further affects the comprehensiveness and accuracy of engine temperature monitoring and the efficiency of data processing. The technical goal of dynamically optimizing the arrangement of high-temperature sensors and accurately compensating for temperature measurement errors is achieved, achieving the technical effects of improving the comprehensiveness of engine key temperature area monitoring, improving temperature measurement accuracy, and reducing data processing redundancy.

[0014] 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.

[0015] Please see attached Figure 1 The present application provides a measurement error compensation control method for a high temperature sensor, which specifically includes the following steps:

[0016] Step 1: Collect the high temperature sensor layout information of the engine to obtain a first high temperature sensor layout array.

[0017] Specifically, after collecting the layout information of the installed high temperature sensors on the engine, an initial high temperature sensor layout array is obtained as the first high temperature sensor layout array. The first high temperature sensor layout array is recorded according to the current sensor installation position of the engine, so that the specific position and arrangement of each sensor are clearly known, thereby providing a basis for subsequent optimization and compensation work.

[0018] Step 2: Based on the neighborhood temperature correlation threshold and the temperature gradient threshold, combined with the predetermined control scheme of the engine, the first high-temperature sensor layout array is optimized to obtain a second high-temperature sensor layout array, wherein the predetermined control scheme includes predetermined control parameters of the engine.

[0019] Specifically, the neighborhood temperature correlation threshold is a criterion for judging whether the temperature change between adjacent high-temperature sensors is obvious enough. If the temperature difference between adjacent high-temperature sensors is small, it means that the adjacent high-temperature sensors are too dense and can be optimized. The temperature gradient threshold refers to the rate of change of temperature in spatial position. When the temperature changes too fast, a more precise high-temperature sensor layout is required to capture the change. Combining the predetermined control scheme of the engine means optimizing the layout of high-temperature sensors according to the operating strategy of the engine under different working conditions. The predetermined control scheme includes predetermined control parameters of the engine, such as the operation mode of the engine under specific operating conditions, to ensure that the optimized high-temperature sensor array can match the actual working conditions of the engine. For example, under high-speed operation, the temperature change may be more drastic, so more high-temperature sensors are needed to accurately measure these changes. The second high-temperature sensor layout array is obtained by optimizing the layout, so that the distribution of sensors is more reasonable, covering key areas, and avoiding redundancy.

[0020] Step three: Control the engine based on the predetermined control scheme, and measure the temperature of the engine according to the second high-temperature sensor layout array to obtain a first engine temperature measurement result.

[0021] Specifically, the predetermined control scheme refers to control according to the set operating parameters when the engine is running, such as the operation mode under different loads or speeds, to ensure that the engine operates according to the design requirements and effectively manage the operating status of the engine. At the same time, the temperature of the engine is measured based on the second high-temperature sensor array, and the data fed back by each high-temperature sensor is collected to obtain the temperature information of the engine, that is, the first result of the engine temperature measurement, which reflects the real-time temperature distribution of the engine under the current control scheme.

[0022] Step 4: Setting a multi-feature heat conduction factor, wherein the multi-feature heat conduction factor includes a heat conduction material, a heat conduction environment, and a heat conduction contact parameter.

[0023] Specifically, setting a multi-feature heat conduction factor means considering multiple key factors that affect the heat conduction process for subsequent error compensation. First, heat conduction materials refer to substances involved in heat transfer, such as metals, ceramics and other materials. The thermal conductivity of heat conduction materials will directly affect the speed and effect of temperature transfer. Secondly, the heat conduction environment refers to the external conditions under which heat conduction occurs, such as temperature, humidity, and air pressure, which affect the efficiency of heat transfer. Finally, the heat conduction contact parameters refer to the contact conditions during the conduction process, such as contact area and contact pressure. Changes in heat conduction contact parameters will affect heat conduction. For example, when transferring heat between two metal blocks, the size of the contact surface and the difference in material will affect the speed and effect of heat conduction. Therefore, multi-feature heat conduction factors work together to more accurately describe and control the heat transfer process.

[0024] Step 5: Based on the heat conduction temperature measurement influence threshold, the heat conduction error compensation is performed on the first engine temperature measurement result according to the multi-characteristic heat conduction factors to obtain the second engine temperature measurement result.

[0025] Specifically, the heat conduction temperature measurement influence threshold is a standard for judging whether the temperature change in the heat conduction process has a significant impact on the measurement result. When the temperature change exceeds the heat conduction temperature measurement influence threshold, it indicates that error compensation is required. Next, the multi-feature heat conduction factor refers to multiple factors that affect the heat conduction process, including the thermal conductivity of the material, the contact area, and the temperature of the environment, which determine the transfer of temperature in the engine. For example, different metal materials will have different conduction temperatures due to differences in thermal conductivity. Performing heat conduction error compensation on the first result of engine temperature measurement based on the multi-feature heat conduction factor means adjusting the initial measurement result according to different heat conduction conditions to eliminate errors caused by factors such as materials and environment, and finally obtaining the second result of engine temperature measurement, that is, more accurate temperature data after correction. That is, when measuring temperature, consider the impact of different materials on heat transfer, and adjust the measurement value to obtain more accurate results.

[0026] Step six: Load the sensor state data stream corresponding to each temperature information in the second engine temperature measurement result, and perform measurement error compensation on the second engine temperature measurement result in combination with the sensor abnormality temperature measurement compensation model to generate a third engine temperature measurement result.

[0027] Specifically, the sensor status data stream corresponding to each temperature information in the second result of the engine temperature measurement is loaded, that is, the temperature measurement result of the engine is associated with the operating status data of each high-temperature sensor. The sensor status data stream provides real-time feedback on whether the sensor has any abnormalities during the measurement process, so that problems can be discovered at any time. Then, it is processed in combination with the sensor abnormality temperature measurement compensation model. The sensor abnormality temperature measurement compensation model is used to analyze and compensate for the measurement errors caused by sensor abnormalities. It can be adjusted according to the sensor status data to eliminate the deviation of the temperature measurement caused by the abnormal sensor. Finally, the third result of the engine temperature measurement is generated, which represents the more accurate measurement data after correction to ensure that the final data reflects the real situation.

[0028] The measurement error compensation control method of a high-temperature sensor can achieve the technical goals of dynamically optimizing the layout of high-temperature sensors and accurately compensating for temperature measurement errors, thereby achieving the technical effects of improving the comprehensiveness of monitoring of key temperature areas of the engine, improving temperature measurement accuracy, and reducing data processing redundancy.

[0029] Further, if Figure 2 As shown, this application also includes:

[0030] Based on the basic engine data of the engine, an engine simulation model is constructed; based on the predetermined control scheme, the temperature characteristics of the engine are predicted according to the engine simulation model to generate a predicted temperature distribution model; based on the neighborhood temperature correlation threshold, the first high-temperature sensor layout array is identified for layout redundancy according to the predicted temperature distribution model to obtain a sensor layout redundancy identification result; based on the temperature gradient threshold, the first high-temperature sensor layout array is identified for layout blind spots according to the predicted temperature distribution model to obtain a sensor layout blind spot identification result; based on the sensor layout redundancy identification result and the sensor layout blind spot identification result, the first high-temperature sensor layout array is adjusted to generate the second high-temperature sensor layout array.

[0031] Specifically, engine basic data refers to the structure and operating parameters of the engine, such as the engine's material, size, and working conditions, etc. Based on the engine's basic data, an engine simulation model is constructed to accurately simulate the engine's working state under different working conditions.

[0032] Next, the predetermined control scheme includes predetermined control parameters of the engine, such as engine speed, intake air temperature, intake air pressure, etc. The temperature distribution under different operating conditions is simulated according to the predetermined control scheme, and the temperature characteristics of the engine are predicted using the constructed engine simulation model to determine the hot spots and temperature change areas of the engine, thereby generating a predicted temperature distribution model.

[0033] Then, based on the neighborhood temperature correlation threshold, the first high temperature sensor layout array is redundantly identified by predicting the temperature distribution model. The neighborhood temperature correlation threshold can be used to determine whether the temperature change at adjacent positions is significant enough and whether the sensors are too dense, that is, whether there is redundant layout. If the temperature difference measured by adjacent high temperature sensors is very small, multiple high temperature sensors may be redundant. For example, the neighborhood temperature correlation threshold is obtained by a technician in this field according to the actual situation.

[0034] Next, based on the temperature gradient threshold, the blind area of ​​the first high temperature sensor array is identified by predicting the temperature distribution model. The temperature gradient threshold refers to the threshold of the rate at which the temperature changes with position. When the temperature change rate of a certain area exceeds the temperature gradient threshold, and the area is not covered by a high temperature sensor, it is considered that there is a measurement blind area in the area and the temperature of the area cannot be accurately measured.

[0035] Finally, based on the sensor layout redundancy identification results and blind spot identification results, the first high-temperature sensor layout array is adjusted to optimize the layout of the high-temperature sensors to ensure that the high-temperature sensors are neither too dense nor missed, and the second high-temperature sensor layout array is generated to ensure that each area is covered without wasting resources due to redundant layout.

[0036] A predictive temperature distribution model is built based on the basic data of the engine. The temperature distribution is predicted using the model. Thresholds are used to identify redundancy and blind spots. Finally, sensor layout is optimized to ensure that the high-temperature sensor on the engine can accurately capture key temperature changes while avoiding waste of resources.

[0037] Furthermore, the present application also includes:

[0038] Perform temperature correlation evaluation of adjacent positions according to the predicted temperature distribution model to obtain multiple temperature correlation coefficients of adjacent positions; determine whether the multiple temperature correlation coefficients of adjacent positions are greater than / equal to the neighborhood temperature correlation threshold; if any one of the multiple temperature correlation coefficients of adjacent positions is greater than / equal to the neighborhood temperature correlation threshold, generate an identification position temperature correlation coefficient; based on the identification position temperature correlation coefficient, perform sensor mapping on the first high-temperature sensor layout array to determine the sensor layout redundancy identification result.

[0039] Specifically, the correlation between the temperatures at adjacent locations is evaluated based on the predicted temperature distribution model, and the degree of correlation between the temperatures at adjacent locations is determined by calculating the temperature change trend at adjacent locations. The temperature correlation coefficient of multiple adjacent locations refers to the numerical value of the degree of connection between the temperature changes at adjacent locations.

[0040] Next, determine whether the temperature correlation coefficient of the adjacent positions is greater than or equal to the neighborhood temperature correlation threshold, and filter out areas with similar temperature changes. If the temperature changes of adjacent positions are almost the same, it means that the temperatures between adjacent positions have a high correlation. The neighborhood temperature correlation threshold is used to judge and ensure that only adjacent positions with sufficiently high correlation are selected. The neighborhood temperature correlation threshold is obtained by a technician in this field by custom setting according to actual conditions.

[0041] If the temperature correlation coefficient of any adjacent location among multiple adjacent locations is greater than or equal to the neighborhood temperature correlation threshold, the identified location temperature correlation coefficient will be generated, indicating that the temperature change at this location is highly correlated with other adjacent locations, so this location is recorded for subsequent analysis.

[0042] Finally, based on the temperature correlation coefficient of the identification position, sensor mapping is performed on the first high-temperature sensor layout array, which means using the correlation information of the identification position to analyze the distribution of high-temperature sensors, determine whether there is redundant layout of sensors, and obtain sensor layout redundancy identification results.

[0043] The temperature correlation between adjacent locations is evaluated through a predictive temperature model, and areas with highly correlated temperature changes are screened out. The layout of high-temperature sensors is then adjusted to identify potentially redundant high-temperature sensors.

[0044] Furthermore, the present application also includes:

[0045] According to the predicted temperature distribution model, the intensity of temperature change at each position is identified to determine the temperature gradients of multiple positions; it is judged whether the temperature gradients of the multiple positions are greater than / equal to the temperature gradient threshold; if the temperature gradient of any position among the multiple temperature gradients is greater than / equal to the temperature gradient threshold, a temperature gradient of the identified position is generated; based on the temperature gradient of the identified position, the first high-temperature sensor layout array is identified for missing sensors to generate the sensor layout blind area identification result.

[0046] Specifically, the intensity of temperature change at each location is identified based on the predicted temperature distribution model, that is, the temperature gradient at each location is determined by analyzing the temperature change at each location. The temperature gradient represents the rate of change of temperature over distance in space. For example, the temperature change at each point is measured to understand how the temperature propagates in different areas.

[0047] Next, determine whether the temperature gradients at multiple locations are greater than or equal to the set temperature gradient threshold. The temperature gradient threshold is a pre-set standard, which means that the temperature changes at multiple locations must reach or exceed the temperature gradient threshold to be considered significant changes. If the temperature gradients at multiple locations are small, it means that the temperature changes are not significant and do not require special attention.

[0048] If the temperature gradient of any one of the multiple locations is greater than or equal to the temperature gradient threshold, a temperature gradient of the marked location is generated. The temperature gradient of the marked location represents a location with significant temperature changes, and an additional high temperature sensor is required for monitoring in order to accurately capture the details of the temperature changes.

[0049] Finally, the temperature gradient of the marked location refers to the location where the temperature changes more dramatically in the predicted temperature distribution model. Drastic temperature changes mean that the temperature change rate at this location is large, and the temperature may rise or fall sharply in a short period of time or in a small area. Due to the significant temperature changes, traditional sensor layout may not be able to cover or accurately capture the temperature changes in these areas.

[0050] Next, by analyzing the marked locations of these significant temperature changes, determine whether there is insufficient sensor coverage in these areas. Insufficient sensor coverage means that the current sensor array may lack sensors in important temperature change areas, resulting in an inability to accurately monitor temperature. At this time, a detailed analysis is performed to determine whether the sensors have missed these critical areas.

[0051] Once these areas of insufficient coverage are identified, the blind spot identification results of the sensor layout can be generated. Blind spots refer to areas that cannot be effectively monitored by sensors, and these areas often require special attention due to drastic temperature changes. Therefore, through blind spot identification, the parts that need to be improved in the sensor layout are found, providing a basis for further optimization. Ultimately, this identification result will be used to optimize the sensor layout to ensure that the sensor can accurately cover areas with drastic temperature changes and avoid the omission of important data. For example, in the high temperature area of ​​the engine, if the temperature changes in some parts are particularly drastic, the existing sensor layout may not be able to capture it in time, so through this process, these blind spots can be identified and optimized.

[0052] The temperature change intensity of each location is identified through the predictive temperature model, and the locations with significant temperature changes are screened out. Then, it is analyzed whether there is insufficient sensor coverage. Finally, the blind spots in the sensor layout are identified, and the sensor layout is adjusted to ensure monitoring of key areas.

[0053] Furthermore, the present application also includes:

[0054] According to the first engine temperature measurement result, a first engine temperature measurement parameter is extracted; according to the multi-feature heat conduction factor, heat conduction information corresponding to the first engine temperature measurement parameter is collected to obtain first temperature measurement heat conduction data; according to the first temperature measurement heat conduction data, anomaly detection is performed to determine first temperature measurement heat conduction anomaly data; based on the heat conduction temperature measurement influence threshold, heat conduction error compensation is performed on the first engine temperature measurement parameter according to the first temperature measurement heat conduction anomaly data to obtain a first corrected temperature measurement parameter; the first corrected temperature measurement parameter is added to the second engine temperature measurement result, and heat conduction error compensation is continued to be performed on the first engine temperature measurement result according to the heat conduction temperature measurement influence threshold and the multi-feature heat conduction factor to generate the second engine temperature measurement result.

[0055] Specifically, based on the first result of the engine temperature measurement, a first parameter of the engine temperature measurement is randomly extracted for further analysis and processing to facilitate subsequent work.

[0056] Next, according to the multi-feature heat conduction factor, the heat conduction information corresponding to the first parameter of the engine temperature measurement is collected to obtain the first temperature measurement heat conduction data. The multi-feature heat conduction factor includes the thermal conductivity of the material, environmental conditions, and contact parameters. The heat conduction data related to the temperature measurement is obtained through the multi-feature heat conduction factor, which is used to describe how the temperature is transferred in the engine, that is, to analyze the influence of factors such as air and materials on temperature changes.

[0057] Then, an abnormality detection is performed based on the first temperature measurement heat conduction data to determine the first temperature measurement heat conduction abnormal data, identify abnormal conditions that occur during the heat conduction process, and determine whether there are abnormal phenomena in the temperature measurement results. If the heat conduction data at certain locations do not meet expectations, it means that there are errors in the temperature measurement.

[0058] Next, based on the heat conduction temperature measurement influence threshold, the heat conduction error compensation is performed on the first temperature measurement parameter of the engine temperature measurement according to the first temperature measurement heat conduction abnormal data to obtain the first corrected temperature measurement parameter. The heat conduction temperature measurement influence threshold is a judgment standard, indicating that when a certain abnormal data exceeds the heat conduction temperature measurement influence threshold, compensation is required. The compensation process is to correct the temperature measurement result by adjusting the error.

[0059] Subsequently, the first correction temperature measurement parameter is added to the second engine temperature measurement result, and the temperature measurement result is further compensated according to the thermal conduction temperature measurement influence threshold and the multi-feature thermal conduction factor, that is, the corrected data is integrated with the original temperature measurement result, and further error compensation is performed to generate the final engine temperature measurement second result, so as to ensure that the final temperature measurement result is more accurate.

[0060] Through error compensation, we can ensure that the data can reflect the actual situation, thereby improving the accuracy and reliability of the measurement.

[0061] Furthermore, the present application also includes:

[0062] A temperature measurement influence evaluation is performed according to the first temperature measurement heat conduction abnormality data to obtain a first heat conduction temperature measurement influence coefficient; whether the first heat conduction temperature measurement influence coefficient is greater than / equal to the heat conduction temperature measurement influence threshold is judged; if the first heat conduction temperature measurement influence coefficient is greater than / equal to the heat conduction temperature measurement influence threshold, a heat conduction error compensation channel is activated; based on the first temperature measurement heat conduction abnormality data and the first engine temperature measurement parameter, the first corrected temperature measurement parameter is output according to the heat conduction error compensation channel.

[0063] Specifically, the temperature measurement impact evaluation is performed based on the first temperature measurement heat conduction abnormal data to evaluate the impact of the abnormal data on the temperature measurement results, that is, by analyzing the role of the abnormal data in the entire heat conduction process, the degree of interference of the abnormal data on the temperature measurement results is determined, and the first heat conduction temperature measurement impact coefficient is obtained. For example, the importance of a certain erroneous data in the experiment is evaluated to determine whether remedial measures need to be taken.

[0064] Next, the first heat conduction temperature measurement influence coefficient represents the actual influence of abnormal data on the temperature measurement result. The heat conduction temperature measurement influence threshold is used to quantify the interference of abnormal data on the temperature measurement result. It is determined whether the first heat conduction temperature measurement influence coefficient is greater than or equal to the heat conduction temperature measurement influence threshold. If the first heat conduction temperature measurement influence coefficient is greater than or equal to the heat conduction temperature measurement influence threshold, it means that the abnormal data has a significant influence on the temperature measurement result, and error compensation processing is performed.

[0065] Next, if the first heat conduction temperature measurement influence coefficient is greater than or equal to the heat conduction temperature measurement influence threshold, the heat conduction error compensation channel is activated. The heat conduction error compensation channel is used to adjust and correct errors caused by abnormal data.

[0066] Finally, the first temperature measurement heat conduction abnormality data and the first temperature measurement parameter of the engine are combined, and the temperature measurement result is corrected through the compensation channel, and the corrected temperature measurement parameter is finally output. The correction parameter is a more accurate temperature result after compensation, ensuring that the temperature measurement result can reflect the actual temperature of the engine. For example, in the combination process, the first temperature measurement heat conduction abnormality data and the first temperature measurement parameter of the engine are combined by averaging calculation.

[0067] By outputting the corrected temperature measurement results, the accuracy and reliability of the final results are ensured.

[0068] Furthermore, the present application also includes:

[0069] Based on the engine, a global search object is set, wherein the global search object includes the engine and multiple engines of the same model corresponding to the engine; based on the global search object, temperature measurement heat conduction abnormal data records, engine temperature measurement parameter records and engine correction temperature measurement parameter records are loaded; according to the temperature measurement heat conduction abnormal data records, the engine temperature measurement parameter records and the engine correction temperature measurement parameter records, multiple heat conduction error compensation learning models are supervised and trained to generate multiple heat conduction error compensators that meet the heat conduction error compensation accuracy constraints, wherein the multiple heat conduction error compensation learning models are different from each other; a heat conduction error compensation fusion device is trained with the output data sets of the multiple heat conduction error compensators as input information and the engine correction temperature measurement parameter records as output information; the multiple heat conduction error compensators are merged as parallel independent nodes to generate a heat conduction error compensation processing layer; the heat conduction error compensation processing layer is merged with the input layer of the heat conduction error compensation fusion device to generate the heat conduction error compensation channel.

[0070] Specifically, a global search object is set based on the engine. A global search object means defining a search range in the system, which includes not only the current engine, but also multiple engines of the same model, thereby obtaining richer data by comparing engines of the same type in order to find common problems or patterns.

[0071] Next, based on the global retrieval object, the temperature measurement heat conduction abnormal data records, engine temperature measurement parameter records and engine correction temperature measurement parameter records are loaded, that is, historical data related to the engine and its temperature measurement are extracted from the current engine and multiple engines of the same model, including abnormal conditions, conventional temperature measurement data, and corrected temperature measurement data, to facilitate analysis and summary of rules.

[0072] Then, supervised training is performed on multiple heat conduction error compensation learning models based on the temperature measurement heat conduction abnormal data records, engine temperature measurement parameter records, and engine correction temperature measurement parameter records. For example, multiple heat conduction error compensation learning models include neural networks, decision trees, etc. Through supervised training, the historical data of temperature measurement heat conduction abnormal data records, engine temperature measurement parameter records, and engine correction temperature measurement parameter records are used to help multiple heat conduction error compensation learning models learn to perform error compensation in different situations. Each learning model is based on different assumptions or algorithms, so multiple heat conduction error compensation learning models are different from each other, that is, multiple algorithms are used in machine learning to solve the same problem in order to find the optimal solution.

[0073] Next, the output data sets of multiple thermal conduction error compensators are used as input information and the engine correction temperature measurement parameter records are used as output information to train the thermal conduction error compensation fusion device. This enables the thermal conduction error compensation fusion device to integrate the outputs of different thermal conduction error compensators and find commonalities between multiple experimental results, thereby drawing the most accurate conclusions.

[0074] Subsequently, multiple thermal conduction error compensators are merged as parallel independent nodes to generate a thermal conduction error compensation processing layer, that is, each thermal conduction error compensator is run in parallel as an independent node, and the merged processing layer can process multiple compensation paths at the same time, such as running multiple correction algorithms at the same time, to ensure that the processing process is more comprehensive and efficient.

[0075] Finally, the thermal conduction error compensation processing layer is merged with the input layer of the thermal conduction error compensation fuser to generate a thermal conduction error compensation channel, making the entire compensation process more accurate and automated, such as merging multiple different data processing systems into a unified process to achieve the final error correction.

[0076] Through the training and fusion of multiple error compensation models, a channel that can automatically perform error compensation is eventually generated, which can perform error correction more accurately and ensure the accuracy of temperature measurement.

[0077] Furthermore, the present application also includes:

[0078] Anomaly detection is performed according to the sensor status data stream to obtain a sensor anomaly detection result; temperature measurement impact evaluation is performed according to the sensor anomaly detection result to obtain a sensor anomaly temperature measurement impact coefficient; it is determined whether the sensor anomaly temperature measurement impact coefficient is greater than / equal to a sensor anomaly temperature measurement impact threshold; if the sensor anomaly temperature measurement impact coefficient is greater than / equal to the sensor anomaly temperature measurement impact threshold, based on the sensor anomaly detection result and according to the sensor anomaly temperature measurement compensation model, a sensor anomaly temperature measurement compensation parameter is obtained; based on the sensor anomaly temperature measurement compensation parameter, the second engine temperature measurement result is mapped and corrected to obtain a third engine temperature measurement result.

[0079] Specifically, anomaly detection is performed based on the sensor status data stream. By analyzing the real-time feedback data stream of the high-temperature sensor, it is identified whether any abnormal situation occurs during the operation of the high-temperature sensor, such as abnormal situation manifested in data instability, sensor failure, etc., and then the sensor anomaly detection result is obtained.

[0080] Next, the temperature measurement impact evaluation is performed based on the sensor abnormality detection results to assess the impact of sensor abnormality on the engine temperature measurement results. The sensor abnormality temperature measurement impact coefficient is obtained by quantifying the degree of interference of the sensor abnormality on the measurement.

[0081] Then, it is determined whether the sensor abnormal temperature measurement influence coefficient is greater than or equal to the sensor abnormal temperature measurement influence threshold. The sensor abnormal temperature measurement influence threshold is a preset standard. If the sensor abnormal temperature measurement influence coefficient is greater than or equal to the sensor abnormal temperature measurement influence threshold, it means that the abnormality of the high temperature sensor has had a significant impact on the temperature measurement result and further processing is required.

[0082] Next, if the sensing abnormal temperature measurement influence coefficient is greater than or equal to the sensing abnormal temperature measurement influence threshold, the sensing abnormal temperature measurement compensation parameter is calculated based on the sensing abnormality detection result and using the sensing abnormality temperature measurement compensation model.

[0083] Finally, based on the sensor abnormal temperature measurement compensation parameter, the second engine temperature measurement result is corrected, and the temperature measurement data is mapped and corrected to obtain the final third engine temperature measurement result. The third engine temperature measurement result is the temperature measurement data after abnormal compensation processing, which can more accurately reflect the actual temperature of the engine.

[0084] Through impact assessment and judgment on whether compensation is needed, the temperature measurement results are finally corrected using compensation parameters to ensure that sensor abnormalities do not have excessive impact on the engine temperature measurement results.

[0085] In summary, the measurement error compensation control method of a high temperature sensor provided by the present application has the following technical effects:

[0086] By collecting the high-temperature sensor layout information of the engine, a first high-temperature sensor layout array is obtained; based on the neighborhood temperature correlation threshold and the temperature gradient threshold, in combination with the predetermined control scheme of the engine, the layout of the first high-temperature sensor layout array is optimized to obtain a second high-temperature sensor layout array, wherein the predetermined control scheme includes the predetermined control parameters of the engine; based on the predetermined control scheme, the engine is controlled, and the temperature of the engine is measured according to the second high-temperature sensor layout array to obtain a first engine temperature measurement result; a multi-feature heat conduction factor is set, wherein the multi-feature heat conduction factor includes heat conduction material, heat conduction environment and heat conduction contact parameter; based on the heat conduction temperature measurement influence threshold, the first engine temperature measurement result is compensated for heat conduction error according to the multi-feature heat conduction factor to obtain a second engine temperature measurement result; the sensor state data stream corresponding to each temperature information in the second engine temperature measurement result is loaded, and the measurement error of the second engine temperature measurement result is compensated in combination with the sensor abnormality temperature measurement compensation model to generate a third engine temperature measurement result. That is to say, by collecting The high-temperature sensor layout information of the engine is collected to construct an initial sensor layout array; then, based on the correlation between adjacent temperatures and the gradient of temperature changes, combined with the predetermined control scheme of the engine, the initial array is optimized to generate a more reasonable sensor layout array; the engine is operated according to the predetermined control scheme, and the temperature is measured through the optimized sensor array to obtain the first round of temperature measurement results; then, a variety of factors affecting heat conduction, such as materials, environment and contact conditions, are set, which will affect the accurate measurement of temperature. Through the set heat conduction error compensation threshold, the first temperature measurement result is corrected by using a variety of factors affecting heat conduction to obtain a more accurate second round of temperature measurement results; finally, combined with the sensor status data corresponding to each temperature information, the second round of temperature measurement results are further corrected by the sensor abnormality compensation model, and finally a more accurate third round of temperature measurement results are generated, and finally the technical goals of dynamically optimizing the high-temperature sensor layout and accurately compensating for temperature measurement errors are achieved, and the technical effects of improving the comprehensiveness of engine key temperature area monitoring, improving temperature measurement accuracy and reducing data processing redundancy are achieved.

[0087] 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.

[0088] 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 measurement error compensation control method for a high temperature sensor, characterized in that: include: Collecting high temperature sensor layout information of the engine to obtain a first high temperature sensor layout array; Based on a neighborhood temperature correlation threshold and a temperature gradient threshold, and in combination with a predetermined control scheme of the engine, optimizing the first high temperature sensor layout array to obtain a second high temperature sensor layout array, wherein the predetermined control scheme includes predetermined control parameters of the engine; Controlling the engine based on the predetermined control scheme, and measuring the temperature of the engine according to the second high-temperature sensor arrangement array to obtain a first engine temperature measurement result; Setting a multi-feature heat conduction factor, wherein the multi-feature heat conduction factor includes a heat conduction material, a heat conduction environment, and a heat conduction contact parameter; Based on the heat conduction temperature measurement influence threshold, heat conduction error compensation is performed on the first engine temperature measurement result according to the multi-characteristic heat conduction factors to obtain a second engine temperature measurement result; Loading the sensor state data stream corresponding to each temperature information in the second engine temperature measurement result, and performing measurement error compensation on the second engine temperature measurement result in combination with the sensor abnormality temperature measurement compensation model to generate a third engine temperature measurement result; Based on the neighborhood temperature correlation threshold and the temperature gradient threshold, in combination with the predetermined control scheme of the engine, the first high temperature sensor layout array is optimized to obtain a second high temperature sensor layout array, including: Building an engine simulation model based on engine basic data of the engine; Based on the predetermined control scheme, predicting the temperature characteristics of the engine according to the engine simulation model to generate a predicted temperature distribution model; Based on the neighborhood temperature correlation threshold, performing layout redundancy identification on the first high temperature sensor layout array according to the predicted temperature distribution model to obtain a sensor layout redundancy identification result; Based on the temperature gradient threshold, and according to the predicted temperature distribution model, the first high temperature sensor array is subjected to layout blind area identification to obtain a sensor layout blind area identification result; Based on the sensor arrangement redundancy identification result and the sensor arrangement blind area identification result, adjusting the first high temperature sensor arrangement array to generate the second high temperature sensor arrangement array; Based on the heat conduction temperature measurement influence threshold, heat conduction error compensation is performed on the first engine temperature measurement result according to the multi-characteristic heat conduction factors to obtain a second engine temperature measurement result, including: Extracting a first engine temperature measurement parameter according to the first engine temperature measurement result; According to the multi-characteristic heat conduction factors, heat conduction information corresponding to the first temperature measurement parameter of the engine is collected to obtain first temperature measurement heat conduction data; Performing anomaly detection based on the first temperature measurement heat conduction data to determine first temperature measurement heat conduction anomaly data; Based on the heat conduction temperature measurement influence threshold, heat conduction error compensation is performed on the first temperature measurement parameter of the engine temperature measurement according to the first temperature measurement heat conduction abnormal data to obtain a first corrected temperature measurement parameter; adding the first correction temperature measurement parameter to the second engine temperature measurement result, and continuing to perform heat conduction error compensation on the first engine temperature measurement result according to the heat conduction temperature measurement influence threshold and the multi-characteristic heat conduction factor to generate the second engine temperature measurement result; The sensor state data stream corresponding to each temperature information in the second engine temperature measurement result is loaded, and the measurement error compensation is performed on the second engine temperature measurement result in combination with the sensor abnormality temperature measurement compensation model to generate a third engine temperature measurement result, including: Perform anomaly detection according to the sensor state data stream to obtain a sensor anomaly detection result; Perform temperature measurement impact evaluation based on the sensor abnormality detection result to obtain a sensor abnormality temperature measurement impact coefficient; Determine whether the sensor abnormal temperature measurement influence coefficient is greater than / equal to the sensor abnormal temperature measurement influence threshold; If the sensing abnormal temperature measurement influence coefficient is greater than / equal to the sensing abnormal temperature measurement influence threshold, based on the sensing abnormality detection result and according to the sensing abnormality temperature measurement compensation model, obtaining a sensing abnormality temperature measurement compensation parameter; Based on the sensor abnormal temperature measurement compensation parameter, the second engine temperature measurement result is mapped and corrected to obtain the third engine temperature measurement result.

2. A measurement error compensation control method for a high temperature sensor as claimed in claim 1, characterized in that: Based on the neighborhood temperature correlation threshold, and according to the predicted temperature distribution model, the first high temperature sensor layout array is subjected to layout redundancy identification to obtain a sensor layout redundancy identification result, including: Performing an adjacent position temperature correlation evaluation based on the predicted temperature distribution model to obtain a plurality of adjacent position temperature correlation coefficients; Determine whether the temperature correlation coefficients of the plurality of adjacent positions are greater than / equal to the neighborhood temperature correlation threshold; If any one of the plurality of adjacent position temperature correlation coefficients is greater than or equal to the neighborhood temperature correlation threshold, generating a marked position temperature correlation coefficient; Based on the identification position temperature correlation coefficient, sensor mapping is performed on the first high temperature sensor layout array to determine the sensor layout redundancy identification result.

3. The measurement error compensation control method of a high temperature sensor according to claim 1, characterized in that: Based on the temperature gradient threshold, and according to the predicted temperature distribution model, the first high temperature sensor array is subjected to blind area identification to obtain a sensor blind area identification result, including: Identify the intensity of temperature changes at each location according to the predicted temperature distribution model to determine the temperature gradients at multiple locations; Determining whether the temperature gradients of the multiple positions are greater than / equal to the temperature gradient threshold; If the temperature gradient of any one of the multiple temperature gradients is greater than / equal to the temperature gradient threshold, generating a marked temperature gradient; Based on the temperature gradient of the identification position, the first high-temperature sensor layout array is identified for missing sensors to generate the sensor layout blind area identification result.

4. The measurement error compensation control method of a high temperature sensor according to claim 1, characterized in that: Based on the heat conduction temperature measurement influence threshold, heat conduction error compensation is performed on the first temperature measurement parameter of the engine according to the first temperature measurement heat conduction abnormal data to obtain a first corrected temperature measurement parameter, including: Performing a temperature measurement impact evaluation based on the first temperature measurement heat conduction abnormality data to obtain a first heat conduction temperature measurement influence coefficient; Determining whether the first heat conduction temperature measurement influence coefficient is greater than / equal to the heat conduction temperature measurement influence threshold; If the first heat conduction temperature measurement influence coefficient is greater than / equal to the heat conduction temperature measurement influence threshold, activating the heat conduction error compensation channel; Based on the first temperature measurement heat conduction abnormality data and the first temperature measurement parameter of the engine, the first corrected temperature measurement parameter is output according to the heat conduction error compensation channel.

5. A measurement error compensation control method for a high temperature sensor as claimed in claim 4, characterized in that: If the first heat conduction temperature measurement influence coefficient is greater than / equal to the heat conduction temperature measurement influence threshold, activating a heat conduction error compensation channel includes: Based on the engine, a global search object is set, wherein the global search object includes the engine and a plurality of engines of the same model corresponding to the engine; Based on the global search object, load the temperature measurement heat conduction abnormal data record, the engine temperature measurement parameter record and the engine correction temperature measurement parameter record; According to the temperature measurement heat conduction abnormal data record, the engine temperature measurement parameter record and the engine correction temperature measurement parameter record, multiple heat conduction error compensation learning models are supervised and trained to generate multiple heat conduction error compensators that meet the heat conduction error compensation accuracy constraint, wherein the multiple heat conduction error compensation learning models are different from each other; Using the output data sets of the plurality of heat conduction error compensators as input information and the engine correction temperature measurement parameter record as output information, training a heat conduction error compensation fusion device; merging the plurality of heat conduction error compensators as parallel independent nodes to generate a heat conduction error compensation processing layer; The heat conduction error compensation processing layer is merged with the input layer of the heat conduction error compensation fuser to generate the heat conduction error compensation channel.

Images