A metal gasket size measurement error analysis system and method

Abstract

The present invention discloses a metal gasket size measurement error analysis system and method, which specifically relates to the technical field of metal gasket size measurement, and is used to solve the problem of inaccurate metal gasket size measurement. The present invention collects environmental, equipment and material data in the metal gasket size measurement process through sensors, and performs error detection, constructs an error classification matrix with the detected error data, and constructs an error characteristic vector according to the impact intensity, time characteristics and error correlation, performs correlation analysis and error classification of the error data, realizes real-time monitoring of error changes, ensures that error changes in the measurement process are accurately judged, and then effectively improves the accuracy of error analysis through systematic classification and analysis of error data, and then analyzes the fluctuation overlap information generated in the prediction process to generate different signals, and adjusts the measurement error analysis strategy according to these signals, so as to better adapt to error changes in different measurement environments.

Description

Technical Field

[0001] The present invention relates to the technical field of metal gasket size measurement, and more specifically, to a metal gasket size measurement error analysis system and method. Background Art

[0002] Metal gaskets are widely used in machinery, electronics, automobiles and other fields to achieve precision sealing and structural support between equipment. Since metal gaskets have extremely high precision requirements during use, their size measurement becomes a key link to ensure their performance.

[0003] Deficiencies in the existing technology: When measuring the size of metal gaskets, static error detection and correction methods are often relied upon, which cannot effectively identify and analyze the changing trend of errors over time. This limitation makes it impossible for the system to predict the cumulative effect of errors in advance, especially during long-term operation. It is impossible to capture the long-term impact of factors such as equipment sensor drift and ambient temperature fluctuations on errors. Therefore, when the error gradually accumulates to a certain extent, the relevant system will react, which may have affected the accuracy of the measurement results, resulting in the inability to perform effective preventive adjustments and error corrections during the measurement process, affecting the overall stability and accuracy of the measurement. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a metal gasket size measurement error analysis system and method to solve the problem of inaccurate metal gasket size measurement error analysis in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for analyzing metal gasket size measurement errors comprises the following steps:

[0007] The environment, equipment and material data of the metal gasket size measurement process are collected through sensors, and the collected data are error tested. The error data obtained after the test are stored in the error management database and an error classification matrix is ​​constructed.

[0008] According to the impact intensity, time characteristics and error correlation, the error characteristic vector is constructed and correlation analysis and error classification are performed. When error data is generated during the measurement process, the error change during the measurement process is judged according to the error classification results to determine the cumulative effect of the error change.

[0009] Identify the dominant error source based on the error changes and cumulative effects of the error data, predict and verify the error trend, and obtain the fluctuation overlap information generated during the error trend prediction and verification process;

[0010] The fluctuation overlap information is analyzed and different signals are generated, and the measurement error analysis strategy is adjusted according to the generated different signals.

[0011] In a preferred embodiment, the environment, equipment, and material data during the metal gasket size measurement process are collected by sensors, and error detection is performed on the collected data, the error data obtained after the detection is stored in an error management database, and an error classification matrix is ​​constructed;

[0012] According to the impact intensity, time characteristics and error correlation, the error characteristic vector is constructed and correlation analysis and error classification are performed. When error data is generated during the measurement process, the error change during the measurement process is judged according to the error classification results to determine the cumulative effect of the error change.

[0013] Identify the dominant error source based on the error changes and cumulative effects of the error data, predict and verify the error trend, and obtain the fluctuation overlap information generated during the error trend prediction and verification process;

[0014] The fluctuation overlap information is analyzed and different signals are generated, and the measurement error analysis strategy is adjusted according to the generated different signals.

[0015] In a preferred embodiment, the environment, equipment, and material data during the metal gasket size measurement process are collected by sensors, and error detection is performed on the collected data. The error data obtained after the detection is stored in the error management database and an error classification matrix is ​​constructed. The specific process is as follows:

[0016] Environmental data includes data collected by monitoring temperature, humidity, and airflow;

[0017] The equipment data includes the data collected by monitoring the displacement sensor and the data collected by monitoring the laser ranging device through the optical path sensor;

[0018] Material data refers to the data of material property testing, including thermal expansion, deformation and oxidation data of metal gaskets;

[0019] All collected environment, equipment, and material data are tested for errors, and the errors are summarized as error data in the error management database;

[0020] The error data obtained after the detection are time synchronized, the error data includes environmental error data, equipment error data and material property error data, and a global error state matrix is ​​constructed;

[0021] The error data in the global error state matrix are mapped to different error categories according to the data type and then arranged structurally.

[0022] In a preferred embodiment, an error characteristic vector is constructed according to the impact intensity, time characteristics and error correlation, and correlation analysis and error classification are performed. The specific process is as follows:

[0023] Conduct multi-dimensional characteristic evaluation on error data, analyze from three dimensions: impact intensity, time characteristics, and error correlation, and establish error characteristic vector;

[0024] The impact strength of the error represents the absolute deviation of the measurement result, which is quantified in a standardized way. The time characteristic is determined according to the set time length, and the correlation is determined by the high-order cross mutual information function.

[0025] The dimensions in the error characteristic vector are weighted, and the weight of each dimension is calculated using a fuzzy logic function to determine the priority of the error and classify the error.

[0026] In a preferred embodiment, when error data is generated during the measurement process, the error change during the measurement process is judged according to the error classification result, and the cumulative effect of the error change is determined. The specific process is as follows:

[0027] During the measurement process, the error characteristic vector changes of various types are monitored in real time, and the error classification results are dynamically adjusted according to the changes in the environment and equipment status;

[0028] Monitor the error changes at each level and adjust the correction strategy in different measurement scenarios;

[0029] When new measurement data is generated, the latest error classification results are used to determine whether there is a significant error change in the measurement process. The cumulative effect of the error is calculated based on the time characteristics and correlation of the error source, and the adaptive adjustment mechanism is automatically triggered.

[0030] In a preferred embodiment, the adaptive adjustment mechanism is automatically triggered according to the time characteristics of the error source and the cumulative effect of the correlation calculation error. The specific process is as follows:

[0031] Determine the cumulative effect based on the instantaneous error value, time decay function and correlation weight function of the error source;

[0032] The instantaneous error value represents the measurement error of the error source in time; the time decay function represents the decay or amplification effect of the error in time; the correlation weight function represents the degree of correlation between the error sources;

[0033] Calculate the cumulative effect and predict the error trend during the error measurement process, and make decisions based on the predicted error trend.

[0034] In a preferred embodiment, the dominant error source is identified based on the error change and cumulative effect of the error data, and the error trend is predicted and verified, and the fluctuation overlap information generated during the error trend prediction and verification process is obtained. The specific process is as follows:

[0035] Obtaining fluctuation overlap information generated during the prediction and verification of error trends, the fluctuation overlap information includes period accuracy information and error change information;

[0036] The period accuracy information includes the trend accuracy consistency index, and the error change information includes the response sensitivity dynamic index;

[0037] The obtained trend accuracy consistency index and response sensitivity dynamic index are combined to generate a measurement stability coefficient;

[0038] The trend accuracy consistency index and response sensitivity dynamic index are proportional to the measurement stability coefficient.

[0039] A metal gasket size measurement error analysis system, used to implement the above-mentioned metal gasket size measurement error analysis method, comprising:

[0040] The data error detection module is used to collect the environment, equipment and material data in the process of metal gasket size measurement through sensors, and perform error detection on the collected data, store the error data obtained after detection in the error management database and construct an error classification matrix;

[0041] The cumulative effect determination module is used to construct the error characteristic vector according to the impact intensity, time characteristics and error correlation, and to perform correlation analysis and error classification. When error data is generated during the measurement process, the error change during the measurement process is judged according to the error classification results, and the cumulative effect of the error change is determined;

[0042] The trend verification module is used to identify the dominant error source according to the error change and cumulative effect of the error data, and to predict and verify the error trend, and obtain the fluctuation overlap information generated in the process of predicting and verifying the error trend;

[0043] The strategy adjustment module is used to analyze the fluctuation overlap information and generate different signals, and adjust the measurement error analysis strategy according to the generated different signals.

[0044] Technical effects and advantages of the present invention:

[0045] The present invention collects the environment, equipment and material data in the process of measuring the size of the metal gasket through sensors, performs error detection on these data, stores the detected error data in an error management database, constructs an error classification matrix, constructs an error characteristic vector according to the impact intensity, time characteristics and error correlation, and performs correlation analysis and error classification. It can monitor the error change in real time to ensure that the error change in the measurement process is accurately judged. By classifying and analyzing the error data, the accuracy of the error analysis can be effectively improved to ensure the reliability and consistency of the measurement data.

[0046] According to the cumulative effect of error data, the dominant error source is identified, and then the error trend is predicted and verified, and the fluctuation coincidence information generated in the prediction process is obtained. Different signals are generated by analyzing the fluctuation coincidence information, and the measurement error analysis strategy is adjusted according to these signals, so as to better adapt to the error changes in different measurement environments. This invention significantly improves the efficiency and accuracy of error correction, ensures the stability of measurement results, and enhances the system's real-time response capability to error changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 The present invention is a flow chart of a method for analyzing metal gasket size measurement errors.

[0048] Figure 2 The present invention is a schematic structural diagram of a metal gasket size measurement error analysis system. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] Example 1: Figure 1 As shown, a method for analyzing metal gasket size measurement errors includes the following steps:

[0051] The environment, equipment and material data of the metal gasket size measurement process are collected through sensors, and the collected data are error tested. The error data obtained after the test are stored in the error management database and an error classification matrix is ​​constructed.

[0052] According to the impact intensity, time characteristics and error correlation, the error characteristic vector is constructed and correlation analysis and error classification are performed. When error data is generated during the measurement process, the error change during the measurement process is judged according to the error classification results to determine the cumulative effect of the error change.

[0053] Identify the dominant error source based on the error changes and cumulative effects of the error data, predict and verify the error trend, and obtain the fluctuation overlap information generated during the error trend prediction and verification process;

[0054] The fluctuation overlap information is analyzed and different signals are generated, and the measurement error analysis strategy is adjusted according to the generated different signals.

[0055] In the measurement of metal gasket size, due to the influence of multiple factors such as external environment, equipment status and material properties, there will be errors in the measurement. In the process of measuring the size of metal gaskets, environmental factors (such as temperature, humidity, airflow, etc.) will directly affect the measurement accuracy, especially temperature changes may cause thermal expansion effect of metal gaskets. Therefore, it is necessary to monitor the environment, equipment, and material related parameters in detail and perform error analysis to ensure the reliability of the data. The specific steps of error detection are as follows:

[0056] Conduct temperature detection and control, that is, install multi-point temperature sensors (such as thermocouples or infrared thermometers) in the metal gasket measurement area to ensure that the entire measurement range can be covered. The data collected by the temperature sensor is transmitted to the analysis system in real time. The analysis system compares the collected temperature data with the preset temperature reference value (such as the standard laboratory temperature of 20°C). When the temperature deviates from the reference value beyond the allowable range (such as ±0.5°C), the system automatically records the temperature change and marks it as a potential environmental error. Combined with the temperature data, the possible thermal expansion coefficient of the metal gasket can also be calculated to evaluate the specific impact of temperature changes on the gasket size;

[0057] Conduct humidity monitoring and control, set humidity sensors (such as capacitive humidity sensors) at multiple points in the measurement area to ensure that the humidity changes in the entire environment can be monitored. When the humidity value exceeds the critical range of the material, mark the humidity data as environmental error and record its potential impact on the measurement accuracy;

[0058] Air flow disturbance monitoring is performed by installing air flow sensors in the measurement area to detect air flow conditions. The installed sensors can measure air flow speed and direction, and compare the air flow data with the stability requirements of the measurement area. For example, if the air flow speed exceeds 0.1m / s, it may cause slight vibrations in the measurement equipment, affecting the measurement stability. When abnormal air flow speed or unstable air flow direction is detected, the system will mark it as an environmental error;

[0059] In the dimensional measurement of metal gaskets, the stability and accuracy of the measuring equipment itself, and equipment errors (such as sensor drift, laser instability, image equipment distortion, etc.) will directly affect the accuracy of the measurement data if they are not detected and corrected in time. Therefore, it is necessary to monitor the equipment status in detail, analyze and record the errors. The specific steps are as follows:

[0060] Install a displacement monitoring unit on key sensors (such as displacement sensors) in the metal gasket size measurement process to detect changes in the sensor's output signal in real time. Install an optical path sensor to monitor the light stability of the laser ranging equipment in real time. The optical path sensor can detect changes in laser intensity and slight deviations in the optical path. When deviations occur, they are recorded as equipment error data. Use the test image calibration method to place a standard test image in the measurement area of ​​the imaging equipment to detect the resolution and image clarity of the imaging equipment.

[0061] Carry out material property error detection, evaluate the impact of material property changes of the metal gasket itself (such as thermal expansion, surface oxidation) on the measurement results, monitor the impact of material property changes of the metal gasket itself, such as thermal expansion, deformation or oxidation, on the measurement results. Thermistors and displacement sensors can be installed in the metal gasket measurement area to monitor the temperature changes and dimensional changes of the gasket in real time. If it is detected that the size of the metal gasket changes due to thermal expansion during the measurement process, this error data will be recorded and marked as a dimensional error caused by thermal expansion. All material property related data are stored in the error management database and can be archived according to metal property classification (such as thermal expansion error, surface change error) to establish an error file.

[0062] All collected environment, equipment, and material data are error-checked, and the detected error data is integrated into the error management database as error data (including sensor drift, laser optical path stability, and imaging device resolution), such as whether sensor drift is related to laser optical path instability, and how to make comprehensive adjustments to these errors;

[0063] In the process of metal gasket size measurement, error data from different sources are summarized, correlation analysis and error classification are performed. The specific steps are as follows:

[0064] All real-time monitored error data are extracted from the error management database, involving three categories: environmental error data (such as temperature, humidity, airflow, etc.), equipment error data (such as sensor drift, laser optical path stability, etc.) and material property error data (such as thermal expansion of metal gaskets, etc.), as shown below: , where E(t) represents the environmental error vector, including temperature T(t), humidity H(t) and airflow A(t); D(t) represents the device error vector, including sensor drift S(t and laser optical path stability L(t); M(t) represents the material property error, mainly including the thermal expansion of the metal gasket ΔL(t); the three error vectors represent data from different sources and are dynamically updated at time t. The data are summarized to form a global error state matrix;

[0065] The extracted error data (environment, equipment, material characteristics) are preliminarily sorted to ensure the consistency of the data in format and time series, that is, the environmental error data E(t), equipment error data D(t) and material characteristic error data M(t) are arranged according to time t to form a multi-dimensional time series matrix to ensure the synchronization of each data point at time t. If a certain type of data is missing (for example, a certain acquisition of equipment error fails), the data is supplemented by interpolation methods (such as spline interpolation);

[0066] The aggregated data is extracted from the global error state matrix and initially mapped according to the data type (environment, equipment, material characteristics). Each error type is mapped to a different error category according to the following classification criteria. The classification logic is as follows: ; Represents the mapping and classification function of a certain error variable X(t), and classifies it according to the source of the error;

[0067] Construct an error classification matrix C. According to the results of data mapping, construct an error classification matrix C, which is used to systematically store various error data and clarify the relationship between different error categories: ,in, Indicates the type of environmental error (temperature, humidity, airflow), Indicates the type of device error (sensor drift, laser stability), Indicates the type of material error (thermal expansion);

[0068] The error classification matrix systematically arranges different types of errors by type, so that each type of error has a clear position in the matrix. This step is to quickly identify and call various types of error information through the structural arrangement of the matrix in subsequent error analysis;

[0069] After the error classification matrix is ​​initially constructed, the error classification matrix is ​​optimized and structurally adjusted based on the correlation of error data and the interaction between error types. For example, if a certain type of error is found to have a strong correlation with another type of error (such as temperature error and equipment drift error), the arrangement of the matrix is ​​adjusted to highlight the correlation;

[0070] The optimized matrix can more clearly show the correlation between various errors, thereby helping to identify the main sources of error and errors with strong interactions, accurately distinguish the root causes of errors, and more effectively correct errors. The structurally adjusted matrix can reflect the error change trend more promptly, provide early warning and intervention before the error accumulates to the critical value, and avoid the accumulation of errors in long-term measurements from having a significant impact on the measurement results.

[0071] Further subdividing the error data according to its impact intensity, time characteristics and error correlation can more accurately locate and analyze the error source. The steps are as follows:

[0072] Conduct multi-dimensional error characteristic evaluation on the error data, analyze the three dimensions of impact intensity, time characteristics (such as persistence or instantaneity), and error correlation, and establish the error characteristic vector according to the evaluation results of each dimension. ,in, represents the influence strength of the i-th type error, represents temporal characteristics, such as the duration or periodicity of the error, Represents the correlation with other error sources;

[0073] The impact strength of an error indicates the independent influence of a specific error source on the entire measurement system, that is, the absolute deviation of the measurement result when there is no interference from other errors. The definition of the impact strength needs to be based on the difference between the actual measured value and the standard value, usually quantified by standardization and dimensionless method. Assuming that a certain error source The original measurement value is , the reference standard value of the system is , through the ideal value set by the laboratory or the measured value after calibration, the influence intensity of the error can be defined as: ,in, is the error source at time t The actual measured value caused by

[0074] The impact strength is standardized to obtain a percentage value, which indicates the magnitude of the error's impact on the system results under the current measurement conditions. This method ensures that different types of errors can be compared and analyzed on the same scale.

[0075] The time characteristic is determined according to the set time length, that is, the error measurement in different time periods, for example, the heating stage and the metal gasket movement measurement stage;

[0076] Correlation can be measured by a high-order cross mutual information function, which is used to capture the dependencies between error sources in complex measurements, such as when there are two error sources and , the association is defined as: ,in, The error source and The joint probability density function of are the marginal probability density functions of the two error sources respectively.

[0077] By weighting each dimension in the error characteristic vector, the comprehensive error weight is calculated , which is used to determine the priority and hierarchical classification of errors. The fuzzy logic function is used to dynamically calculate the weight of each dimension to reflect the importance of each characteristic under different measurement conditions. The formula is as follows: ,in, , It is the dynamic weight coefficient in the fuzzy logic function, which is adjusted in real time according to the measurement environment and system status;

[0078] The weights are dynamically calculated through fuzzy logic functions, which can adaptively adjust the influence of each feature according to the current measurement environment and device status. Adaptive weight calculation can ensure the accuracy of error classification in different scenarios.

[0079] According to the calculated comprehensive error weight , classify the errors into primary errors or secondary errors, and introduce a third-level error (i.e., a more refined correlation error) for the secondary error. The classification criteria are as follows: , the error is classified as a first-order error, that is, a direct and significant error source. , the error is classified as a secondary error, that is, it has a strong correlation with other errors but is not directly significant. , classifying the errors into third-order errors, i.e., derivative errors that are highly dependent on changes in other errors, and is the classification threshold, which is determined through actual measurement data and historical analysis;

[0080] During the measurement process, the error characteristic vector changes of various types are monitored in real time, and the error classification results are dynamically adjusted according to the changes in the environment and equipment status. For example, if the ambient temperature fluctuates greatly and causes a significant increase in the material characteristic error, the weight of the material error is automatically increased. , and recalculate the weights Make classification adjustments;

[0081] Monitor the error changes at each level in real time and flexibly adjust the correction strategy in different measurement scenarios. The specific steps are as follows:

[0082] Extract the latest error classification data (primary error, secondary error, tertiary error) from the error management database to conduct real-time error status monitoring;

[0083] Whenever new measurement data is generated, it automatically determines whether there is a significant error change in the measurement process based on the latest error classification results. According to the characteristics of the error source (such as time characteristics, correlation, etc.), the cumulative effect of the error is calculated in real time, and the adaptive adjustment mechanism is automatically triggered;

[0084] The calculation of the cumulative effect of errors is intended to evaluate the continuous impact of the error source on the overall system over a period of time, and to reflect its cumulative effect based on factors such as the intensity and duration of the error and the correlation between the error sources. The specific steps are as follows:

[0085] The specific calculation method of the cumulative effect is to assume that the error source In time period The measurement data in , cumulative effect The calculation formula is: ,in, The error source In time period The cumulative effect within is the instantaneous error value of the error source at time t, is a time decay function, which is used to measure the decay or amplification effect of the error accumulated over time. is a correlation weight function, which is used to reflect the interaction and correlation between the error source and other error sources;

[0086] The instantaneous error value represents the measurement error of the error source at time t. This value can be the instantaneous measurement value of temperature, humidity or device drift.

[0087] The time decay function represents the decay or amplification effect of the error over time. The cumulative effect of different error sources may decay over time (such as certain environmental errors) or amplify (such as the accumulation of equipment failures). is the time attenuation coefficient, determined according to experimental data, if , error attenuation; if , error amplification, for example, for environmental errors that gradually dissipate, such as short-term temperature fluctuations, you can choose Gradually weaken the error effect;

[0088] The correlation weight function indicates the degree of correlation between an error source and other error sources. If an error source is strongly affected by other errors during the accumulation process, this function is needed to adjust the cumulative effect. The correlation weight function can be based on the error correlation matrix definition: ,in Indicates the error source and The correlation at time t (calculated using high-order cross mutual information or other nonlinear metrics) is that if an error source is strongly correlated with other error sources, the correlation weight increases, and the calculation result of the cumulative effect also increases;

[0089] The cumulative effect formula is as follows: Assume that in the time period [0, 10], the instantaneous value of an environmental error (such as temperature change) is 2sin(t) (the temperature fluctuates periodically during this period), and the attenuation coefficient is , and the relevance weight , the cumulative effect is: , in practical applications, the specific value of the cumulative effect can be obtained by numerical integration (such as trapezoidal method or Simpson method);

[0090] The calculation formula of the error accumulation effect takes into account the instantaneous error, time decay and correlation factors, and can accurately evaluate the overall impact of the error within a certain period of time;

[0091] In a complex measurement environment, different error sources may exist simultaneously and interact with each other. The calculation of cumulative effects can help identify which type of error has the greatest impact on the system in the long term, so as to prioritize the development of correction plans for these key error sources. For example, metal gaskets will be affected by multiple factors such as the environment, equipment, and material properties during use. These factors will have a cumulative effect on the measurement results in the long term. By calculating the cumulative effect of errors, risks that may lead to deviations in measurement results can be identified in advance, ensuring that measurement accuracy is maintained during long-term operation.

[0092] The calculation of the cumulative effect during the error measurement process can be used to predict future error trends and help make corresponding correction or maintenance decisions in advance, so as to intervene before the error impact expands. For example, if the cumulative effect shows that the device drift error gradually accumulates to a critical value, calibration can be performed before the device becomes inaccurate to avoid sudden changes in the measurement results.

[0093] The collected historical data is time-seriesized to ensure the consistency and continuity of all data points in the time dimension. The cumulative effect is calculated to evaluate the changes and accumulation of these data in a specific time period, and then the potential dominant error sources are identified. After completing the cumulative effect evaluation of historical data, future trend speculation is carried out, and the error trend is predicted and verified in combination with real-time monitoring data;

[0094] Obtain the fluctuation overlap information generated during the prediction and verification of the error trend to determine the accurate situation of the measurement error. The fluctuation overlap information includes period accuracy information and error change information.

[0095] The periodic accuracy information includes the trend accuracy consistency index and is calibrated as TPCI, and the error change information includes the response sensitivity dynamic index and is calibrated as DRSI;

[0096] The trend accuracy consistency index is used to measure the consistency between the actual error trend and the benchmark error trend. It evaluates the accuracy of the error trend by comparing the difference between the actual error and the benchmark error at a time point. The specific functions are as follows:

[0097] Trend Precision Consistency Index TPCI can detect the stability and accuracy of the system under different measurement environments by calculating the deviation between the error trend and the benchmark trend. This is particularly important for systems that require long-term monitoring and measurement accuracy, such as metal gasket size measurement systems. If the TPCI value is high, it means that the error trend of the system at each time point is very consistent with the benchmark trend, indicating that the system is stable and accurate. On the contrary, a low TPCI value indicates that the system has a large deviation in tracking the error trend and may need to be corrected or adjusted.

[0098] The trend accuracy consistency index can help the system identify abnormal fluctuations or deviations in the error trend. If the trend accuracy consistency index decreases significantly in certain time periods, the system can quickly identify this trend deviation and issue an early warning, which helps to take timely measures to adjust the system and prevent the impact of the error from expanding.

[0099] The logic for obtaining the trend accuracy consistency index is as follows:

[0100] Set the length of the time window to T and obtain the reference error value at the i-th time point The actual measurement error value at the i-th time point , calculate the trend fluctuation difference value at the i-th time point, the calculation expression is: , where N is the number of data points in the time window. The error trend value in the time window is calculated using the following expression: , calculate the trend precision consistency index TPCI: , where max is the maximum value function.

[0101] It should be noted that the reference error value may be a baseline value obtained through long-term observation and data analysis, and the size of the time window is set according to actual needs.

[0102] The Response Sensitivity Dynamic Index (DRSI) measures the system's response speed and adjustment effect to error changes. It evaluates the efficiency and effectiveness of adjustments after detecting error changes, and reflects the system's sensitivity to errors and real-time correction capabilities. If the system can respond quickly and reduce error changes, the value of the Response Sensitivity Dynamic Index is large, indicating that the system has strong correction capabilities and response speed. A high value indicates that the system is highly sensitive to errors and can accurately identify subtle changes in errors and make adjustments. A low value indicates that the system may be slow to respond or ineffective in correcting errors when dealing with error changes.

[0103] In the measurement error analysis system, the performance of the equipment and the environmental conditions will directly affect the error change of the system. The response sensitivity dynamic index can measure the equipment's ability to respond to environmental changes and ensure that the equipment can quickly adapt to the fluctuations of external conditions. Especially for the measurement of metal gasket dimensions that require precise control, a high value of the response sensitivity dynamic index can reflect the system's efficient interactive ability.

[0104] The logic for obtaining the response sensitivity dynamic index is as follows:

[0105] Set the time period length to ST, obtain real-time error data through sensors and measuring equipment, and at each time point t, the system records the error value before correction and the corrected error value , the adjustment is calculated by the difference between the two error measurements: ;

[0106] Get the actual deviation value E(t) of the metal gasket size that can be measured at a specific time t, and calculate the time interval The change in the internal error value is taken as the variation: , and calculate the error change rate of the measurement process. The calculation expression is: , the response sensitivity dynamic index is calculated, and the calculation expression is: .

[0107] The cycle accuracy information and error change information are combined to generate the measurement stability coefficient, that is, the trend accuracy consistency index TPCI and response sensitivity dynamic index Combined generation of measurement stability coefficient, measurement stability coefficient The expression is: , where , is the preset proportionality coefficient of the trend accuracy consistency index and the response sensitivity dynamic index, and , Both are greater than 0.

[0108] The specific method of jointly generating the measurement stability coefficient may involve multiple algorithms and models, which depends on the actual situation and application requirements. The present embodiment can use a weighted summation method to combine the trend accuracy consistency index and the response sensitivity dynamic index to generate a comprehensive measurement stability coefficient. This measurement stability coefficient can be used as an input parameter for the trend prediction analysis of the metal gasket size measurement error, and is used to predict the accuracy of the measurement error.

[0109] It should be noted that the size of the preset proportional coefficient is to quantify each parameter to obtain a specific numerical value. In order to facilitate subsequent comparison, the size of the coefficient depends on the amount of sample data and the preliminary setting of the corresponding preset proportional coefficient for each group of sample data by technical personnel in this field. It is not unique, as long as it does not affect the proportional relationship between the parameter and the quantized numerical value. For example, the trend accuracy consistency index is proportional to the measurement stability coefficient. The trend accuracy consistency index and the response sensitivity dynamic index are normalized to have the same dimension and range. This can be achieved by subtracting the mean from the original data and dividing it by the standard deviation, or mapping the data to the range of [0, 1].

[0110] The larger the trend accuracy consistency index and the larger the response sensitivity dynamic index, the larger the measurement stability coefficient generated by the combination, indicating that a high stability is maintained in the long-term measurement error analysis process. Not only is the performance consistent when tracking errors, but it can also respond quickly when dealing with error changes in real time, which can reduce the impact of errors on measurement results.

[0111] The smaller the trend accuracy consistency index and the smaller the response sensitivity dynamic index, the smaller the measurement stability coefficient generated by the combination, indicating that the metal gasket size measurement accuracy is poor, the error control ability is weak, the correction response is slow, the overall measurement process is unstable, and it is easily affected by external factors. This requires improving the error tracking and correction strategy to improve its measurement accuracy and stability;

[0112] Comparing the generated measurement stability coefficient with a preset measurement threshold value to generate an error measurement stability signal and an error measurement fluctuation signal;

[0113] After obtaining the measurement stability coefficient, the measurement stability coefficient is compared with the measurement threshold;

[0114] If the measurement stability coefficient is greater than or equal to the measurement threshold, an error measurement stability signal is generated, which indicates that the measurement accuracy and responsiveness have reached the preset standard, and the predicted measurement error can be stably maintained during the metal gasket size measurement error analysis process. At this time, the error fluctuation during the measurement and analysis process is small, and the error trend can be accurately tracked. The measurement result has a high degree of credibility and is suitable for important analysis and decision-making, without the need for additional measurement error analysis strategy adjustment;

[0115] If the measurement stability coefficient is less than the measurement threshold, an error measurement fluctuation signal is generated, which indicates that there is a significant error fluctuation in the current measurement. The measurement result may be interfered by external factors or the error changes rapidly and cannot be effectively controlled. This volatility will lead to a decrease in the accuracy of the measurement data, a rapid change in the error or a large amplitude. The current correction measures cannot effectively cope with the error fluctuation, resulting in the error failing to stabilize within the allowable range. The existing adjustment measures may not be sufficient to maintain an accurate measurement state and need to be adjusted in a timely manner.

[0116] After the error measurement fluctuation signal is generated, it is necessary to respond quickly and adjust the measurement correction measures, that is, analyze the source of error fluctuations, and quickly locate the specific cause of error fluctuations based on real-time monitoring data, focusing on factors that may cause fluctuations such as temperature, humidity, equipment drift, or changes in material properties. Classify the errors by source (such as environmental errors, equipment errors, and material errors), and evaluate the contribution and impact of each type of error on the measurement results. By analyzing the rate of change of the error (such as the speed and magnitude of the change), determine whether the error will continue to expand or fluctuate in the short term;

[0117] Adjust the frequency of measurement data acquisition according to the error change rate. If the error fluctuates quickly, increase the sampling frequency to ensure that the error changes are captured in real time, so that timely adjustments can be made. Shorten the calibration cycle to make the calibration frequency more compatible with the error changes, and timely reduce the cumulative effect of the error. For parameters with large error fluctuations (such as temperature and equipment calibration), start a dynamic calibration strategy so that the measuring equipment can self-adjust according to the real-time changes in the error.

[0118] After adjustment and calibration, monitor the error changes in real time to evaluate the effectiveness of the correction measures. If the correction effect is not good, continue to optimize the correction strategy and make feedback adjustments based on the monitoring data to ensure that the system can adapt to the changing measurement environment and conditions. For example, when the ambient temperature or humidity continues to fluctuate, automatically adjust the sensitivity of the sensor to cope with such changes.

[0119] It should be noted that the thresholds involved in the embodiments can be determined according to specific scenarios and requirements.

[0120] The present invention collects the environment, equipment and material data in the process of measuring the size of the metal gasket through sensors, performs error detection on these data, stores the detected error data in an error management database, constructs an error classification matrix, constructs an error characteristic vector according to the impact intensity, time characteristics and error correlation, and performs correlation analysis and error classification. It can monitor the error change in real time to ensure that the error change in the measurement process is accurately judged. By classifying and analyzing the error data, the accuracy of the error analysis can be effectively improved to ensure the reliability and consistency of the measurement data.

[0121] According to the cumulative effect of error data, the dominant error source is identified, and then the error trend is predicted and verified, and the fluctuation coincidence information generated in the prediction process is obtained. Different signals are generated by analyzing the fluctuation coincidence information, and the measurement error analysis strategy is adjusted according to these signals, so as to better adapt to the error changes in different measurement environments. This invention significantly improves the efficiency and accuracy of error correction, ensures the stability of measurement results, and enhances the system's real-time response capability to error changes.

[0122] Embodiment 2: A metal gasket size measurement error analysis system, such as Figure 2 As shown, specifically including:

[0123] The data error detection module is used to collect the environment, equipment and material data in the process of metal gasket size measurement through sensors, and perform error detection on the collected data, store the error data obtained after detection in the error management database and construct an error classification matrix;

[0124] The cumulative effect determination module is used to construct the error characteristic vector according to the impact intensity, time characteristics and error correlation, and to perform correlation analysis and error classification. When error data is generated during the measurement process, the error change during the measurement process is judged according to the error classification results, and the cumulative effect of the error change is determined;

[0125] The trend verification module is used to identify the dominant error source according to the error change and cumulative effect of the error data, and to predict and verify the error trend, and obtain the fluctuation overlap information generated in the process of predicting and verifying the error trend;

[0126] The strategy adjustment module is used to analyze the fluctuation overlap information and generate different signals, and adjust the measurement error analysis strategy according to the generated different signals.

[0127] The above formulas are all dimensionless and calculated numerically. Specific dimension removal can be achieved by various means such as standardization, which will not be elaborated here. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0128] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, an ATA hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state ATA hard disk.

[0129] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0130] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0131] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0132] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0133] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0134] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for analyzing metal gasket size measurement errors, characterized in that: The steps include: The environment, equipment and material data of the metal gasket size measurement process are collected through sensors, and the collected data are error tested. The error data obtained after the test are stored in the error management database and an error classification matrix is ​​constructed. According to the impact intensity, time characteristics and error correlation, the error characteristic vector is constructed and correlation analysis and error classification are performed. When error data is generated during the measurement process, the error change during the measurement process is judged according to the error classification results to determine the cumulative effect of the error change. The impact strength of the error represents the absolute deviation of the measurement result, which is quantified by a standardized method; Error correlation is a source of error Correlation with other error sources; The temporal characteristics refer to the duration or periodicity of the error; Identify the dominant error source based on the error changes and cumulative effects of the error data, predict and verify the error trend, and obtain the fluctuation overlap information generated during the error trend prediction and verification process; Analyze the fluctuation coincidence information and generate different signals, and adjust the measurement error analysis strategy according to the generated different signals; The environment, equipment, and material data of the metal gasket size measurement process are collected through sensors, and the collected data are error detected. The error data obtained after the detection is stored in the error management database and an error classification matrix is ​​constructed. The specific process is as follows: Environmental data includes data collected by monitoring temperature, humidity, and airflow; The equipment data includes the data collected by monitoring the displacement sensor and the data collected by monitoring the laser ranging device through the optical path sensor; Material data refers to the data of material property testing, including thermal expansion, deformation and oxidation data of metal gaskets; All collected environment, equipment, and material data are tested for errors, and the errors are summarized as error data in the error management database; The error data obtained after the detection are time synchronized, the error data includes environmental error data, equipment error data and material property error data, and a global error state matrix is ​​constructed; Mapping the error data in the global error state matrix to different error categories according to data types for structural arrangement; When error data is generated during the measurement process, the error change during the measurement process is judged according to the error classification result, and the cumulative effect of the error change is determined. The specific process is as follows: During the measurement process, the error characteristic vector changes of various types are monitored in real time, and the error classification results are dynamically adjusted according to the changes in the environment and equipment status; Monitor the error changes at each level and adjust the correction strategy in different measurement scenarios; When new measurement data is generated, the latest error classification result is used to determine whether there is a significant error change in the measurement process. When the error classification result is a first-level error, that is, there is a significant error change, the cumulative effect of the error is calculated based on the time characteristics and correlation of the error source, and the adaptive adjustment mechanism is automatically triggered; According to the time characteristics and correlation of the error source, the cumulative effect of the error is calculated and the adaptive adjustment mechanism is automatically triggered. The specific process is as follows: Determine the cumulative effect based on the instantaneous error value of the error source, the time decay function, and the correlation weight function; The instantaneous error value represents the measurement error of the error source at time t; the time decay function represents the decay or amplification effect of the error over time; the correlation weight function represents the degree of correlation between the error sources; Calculate the cumulative effect and predict the error trend during the error measurement process, and make decisions based on the predicted error trend; Among them, the dominant error source is identified according to the error change and cumulative effect of the error data, and the error trend is predicted and verified, and the fluctuation coincidence information generated in the process of error trend prediction and verification is obtained. The specific process is as follows: Obtaining fluctuation overlap information generated during the prediction and verification of error trends, the fluctuation overlap information includes period accuracy information and error change information; The period accuracy information includes the trend accuracy consistency index, and the error change information includes the response sensitivity dynamic index; The logic for obtaining the trend accuracy consistency index is as follows: Set the length of the time window to T and obtain the reference error value at the i-th time point The actual measurement error value at the i-th time point , calculate the trend fluctuation difference value at the i-th time point, the calculation expression is: , where N is the number of data points in the time window. The error trend value in the time window is calculated using the following expression: , calculate the trend precision consistency index TPCI: , where max is the maximum value function; The logic for obtaining the response sensitivity dynamic index is as follows: Set the time period length to ST, obtain real-time error data through sensors and measuring equipment, and at each time point t, the system records the error value before correction and the corrected error value , the adjustment is calculated by the difference between the two error measurements: ; Get the actual deviation value E(t) of the metal gasket size measurement at time t, and calculate the deviation value E(t) at the time interval The change in the internal error value is taken as the variation: , and calculate the error change rate of the measurement process. The calculation expression is: , the response sensitivity dynamic index is calculated, and the calculation expression is: ; The obtained trend accuracy consistency index and response sensitivity dynamic index are combined to generate the measurement stability coefficient; the trend accuracy consistency index and response sensitivity dynamic index are proportional to the measurement stability coefficient.

2. A metal gasket size measurement error analysis method according to claim 1, characterized in that: According to the impact intensity, time characteristics and error correlation, the error characteristic vector is constructed and correlation analysis and error classification are performed. The specific process is as follows: Conduct multi-dimensional characteristic evaluation on error data, analyze from three dimensions: impact intensity, time characteristics, and error correlation, and establish error characteristic vector; The temporal characteristics are determined by the set time length, and the correlation is determined by the high-order cross-mutual information function; Relevance is defined as: ,in, The error source and The joint probability density function of are the marginal probability density functions of the two error sources respectively; The dimensions in the error characteristic vector are weighted, and the fuzzy logic function is used to calculate the weight of each dimension, determine the priority of the error, and classify the error. The specific formula is as follows: ,in, , It is the dynamic weight coefficient in the fuzzy logic function, which is adjusted in real time according to the measurement environment and system status; represents the influence strength of the i-th type error, Represents the time characteristic, Represents the correlation with other error sources; according to the comprehensive error weight Classify errors as first-order, second-order, or third-order errors.

3. A metal gasket size measurement error analysis method according to claim 1, characterized in that: The fluctuation coincidence information is analyzed and different signals are generated. The measurement error analysis strategy is adjusted according to the generated different signals. The specific steps are as follows: The generated measurement stability coefficient is compared with the set measurement threshold; if the measurement stability coefficient is greater than or equal to the measurement threshold, an error measurement stability signal is generated, and no additional measurement error analysis strategy adjustment is required; if the measurement stability coefficient is less than the measurement threshold, an error measurement fluctuation signal is generated, and the measurement correction measures need to be adjusted.

4. A metal gasket size measurement error analysis system, used to implement a metal gasket size measurement error analysis method according to any one of claims 1 to 3, characterized in that: include: The data error detection module is used to collect the environment, equipment and material data in the process of metal gasket size measurement through sensors, and perform error detection on the collected data, store the error data obtained after detection in the error management database and construct an error classification matrix; The cumulative effect determination module is used to construct the error characteristic vector according to the impact intensity, time characteristics and error correlation, and to perform correlation analysis and error classification. When error data is generated during the measurement process, the error change during the measurement process is judged according to the error classification results, and the cumulative effect of the error change is determined; The trend verification module is used to identify the dominant error source according to the error change and cumulative effect of the error data, and to predict and verify the error trend, and obtain the fluctuation overlap information generated in the process of predicting and verifying the error trend; A strategy adjustment module is used to analyze the fluctuation coincidence information and generate different signals, and adjust the measurement error analysis strategy according to the generated different signals; Environmental data includes data collected by monitoring temperature, humidity, and airflow; The equipment data includes the data collected by monitoring the displacement sensor and the data collected by monitoring the laser ranging device through the optical path sensor; The material data is the data of material property detection, including the thermal expansion, deformation and oxidation data of the metal gasket.

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