A data acquisition method and system based on digital metrology technology

Abstract

The present application relates to the technical field of industrial automation control, and more particularly to a data acquisition method and system based on digital metrology technology, wherein the method sequentially performs the following steps: step 1: performing sensor dynamic calibration; step 2: constructing a phase compensation clock tree; step 3: establishing a temperature-humidity-vibration three-factor coupled error compensation model, determining the interaction weight of each factor by orthogonal test method, and updating the compensation coefficient online by using the recursive least square method with a forgetting factor; step 4: generating transmission priority according to the product of data change rate and health index, dynamically allocating transmission bandwidth based on network quality evaluation value calculated by delay change rate and packet loss rate, and implementing preamble marked compression encoding for high priority data. This method not only improves the accuracy of the system, but also optimizes the data processing and transmission efficiency, providing an effective solution for high-precision data acquisition in large-scale and complex environments.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial automation control, and in particular to a data acquisition method and system based on digital metrology technology. BACKGROUND

[0002] In the field of industrial automation control, especially in high-precision manufacturing, precision detection and other scenarios, multi-sensor data acquisition technology is the core basis of real-time monitoring and process control. The existing technology exposes the following key defects in actual application:

[0003] The existing synchronous acquisition scheme relies on a hardware clock distribution circuit, and uses a fixed frequency clock signal to trigger multi-channel sensor sampling. Such a scheme does not take into account the signal delay difference caused by cable impedance mismatch in long distance transmission, nor does it solve the clock jitter problem caused by electromagnetic interference. For example, in an automobile welding production line, the data timestamp deviation caused by the different clock synchronization of force sensors and vision sensors at different stations can reach milliseconds, causing the spatial matching error of the mechanical arm motion trajectory and the welding point position to exceed ±0.5mm, directly affecting the welding quality.

[0004] Traditional environmental compensation methods usually design linear compensation models for a single environmental factor (such as temperature or vibration), ignoring the multi-physical field coupling effect. Taking the pressure sensor of an injection molding machine as an example, the existing scheme only compensates for the mold temperature, but does not consider the nonlinear deformation of the sensor elastomer caused by the fluctuation of the hydraulic oil temperature, and ignores the signal modulation effect caused by high-frequency vibration. Experiments show that when the temperature changes by 10℃ and the vibration frequency exceeds 200Hz, the residual error of the traditional compensation method can reach 2.8 times the nominal accuracy, causing the product thickness uniformity to be out of tolerance.

[0005] In summary, the existing technical solutions cannot meet the stringent requirements of intelligent manufacturing for data acquisition systems, and there is an urgent need for an innovative technical solution that can systematically solve the above problems. SUMMARY

[0006] Based on the above purpose, the present application provides a data acquisition method and system based on digital metrology technology, wherein a data acquisition method based on digital metrology technology performs the following steps in order:

[0007] Step 1: Perform sensor dynamic calibration, continuously collect temperature drift curves, response time variation rates and zero point offsets in sensor historical working data, calculate the variation coefficients of each parameter to generate a real-time health index, and dynamically adjust the sensor reference parameters according to the correlation between the real-time health index and the real-time detected environmental temperature and humidity;

[0008] Step 2: Construct a phase compensation clock tree to generate branch clock signals with forward prediction and feedback correction mechanisms based on the master controller clock, collect physical quantity signals and corresponding sensor shell temperature gradient, power supply voltage fluctuation extreme environment parameters and vibration spectrum characteristics under the control of a synchronous clock;

[0009] Step 3: Establish a temperature-humidity-vibration three-factor coupling error compensation model, determine the interaction weight of each factor by orthogonal test method, and update the compensation coefficient online by using the recursive least squares method with forgetting factor;

[0010] Step 4: Generate transmission priority according to the product of data change rate and health index, dynamically allocate transmission bandwidth based on network quality evaluation value calculated by delay change rate and packet loss rate, and implement compression coding of preamble marking for high priority data.

[0011] Correspondingly, the embodiment of the application also provides a data acquisition system based on digital metrology technology, which is used to run any one of the data acquisition methods based on digital metrology technology provided by the embodiment of the application, and comprises:

[0012] The sensor state evaluation module comprises:

[0013] The temperature drift analysis unit is electrically connected with the sensor shell temperature probe, and is used to collect and store the temperature-zero offset corresponding relationship;

[0014] The response time monitoring unit is connected with the standard signal generator at the input end and connected with the timer at the output end, and is used to calculate the response time change rate;

[0015] The health degree calculation unit is connected with the temperature drift analysis unit, the response time monitoring unit and the zero point calibration unit at the input end respectively, and is connected with the parameter adjustment module through the data bus at the output end;

[0016] The clock synchronization module comprises:

[0017] The phase prediction subunit is directly connected with the master controller clock source, and is provided with an internal autoregressive model calculation unit;

[0018] The feedback correction subunit is connected with at least three historical clock offset registers at the input end, and is connected with the clock frequency divider through the digital-to-analog converter at the output end;

[0019] The branch clock generator is connected with each sensor trigger interface through the isolation circuit at the output end;

[0020] The environment coupling compensation module comprises:

[0021] The multi-parameter acquisition unit comprises a temperature sensor array, a vibration accelerometer and a voltage monitoring circuit, and is connected with the master controller through a parallel data interface;

[0022] weight calculation engine, input end is connected with orthogonal test database and real-time environment parameter cache, output end is connected with compensation matrix updating unit;

[0023] online compensation executor, data input end is connected with sensor signal channel, control end is connected with the compensation matrix updating unit;

[0024] data transmission control module, comprising:

[0025] priority calculation unit, input end is connected with data change rate analyzer and health degree evaluation unit respectively, internal integration dynamic weight distributor;

[0026] bandwidth adaptive unit, comprising network quality monitoring circuit and compression encoding selector, the network quality monitoring circuit obtains link state in real time through physical layer interface;

[0027] redundancy control unit, output end is connected with at least three independent network interfaces, input end is connected with priority label generator.

[0028] The beneficial effects of the present application are:

[0029] The dynamic calibration of the sensor ensures the accuracy of the measurement, the phase compensation clock tree guarantees the synchronization of data acquisition, and the error compensation model dynamically corrects the influence of environmental changes on data. Finally, the data transmission priority and bandwidth dynamic allocation strategy ensure the effective use of network resources, avoiding the problem of packet loss or delay in the case of large amount of data. Overall, this method not only improves the accuracy of the system, but also optimizes the data processing and transmission efficiency, providing an effective solution for high-precision data acquisition in large-scale and complex environments. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only a part of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0031] Fig. 1 The step flow chart of the method of the present application is shown in the figure.

[0032] Fig. 2 The step flow chart of the orthogonal test method in step 3 of the method of the present application is shown in the figure.

[0033] Fig. 3 The structure block diagram of the system of the present application is shown in the figure. DETAILED DESCRIPTION

[0034] The application will be described in detail below with reference to the drawings and specific embodiments. It should be noted here that in order to make the embodiments more detailed, the following embodiments are the best, preferred embodiments, and other alternative ways can also be implemented by those skilled in the art for some known technologies; and the drawings are only used to describe the embodiments more specifically, and are not intended to specifically limit the application.

[0035] Please refer to Figs. 1-3 The embodiment of the application provides a data acquisition method based on digital metrology technology, wherein step 1 is to perform dynamic calibration of the sensor. In this process, the temperature drift curve, response time change rate and zero point offset of the sensor are continuously collected, and a real-time health index is generated by calculating the coefficient of variation of each parameter. The health index is a quantitative evaluation of the state of the sensor, which combines the real-time detected environmental temperature and humidity data, and dynamically adjusts the reference parameters of the sensor through the correlation with the health index. This step can correct the deviation of the sensor in real time, ensure the accuracy of the sensor under various environmental conditions, and reduce the measurement error caused by changes in environmental factors.

[0036] In step 2, a phase compensation clock tree is constructed to ensure the synchronous acquisition of physical quantity signals and environmental parameter data. Based on the master controller clock, a branch clock signal with forward prediction and feedback correction mechanism is generated, so as to accurately synchronize the sampling time of each data channel. Clock synchronization is a key link in data acquisition, especially in a multi-sensor system, where time consistency is crucial. Through this clock synchronization technology, the data timing problem caused by clock deviation can be eliminated, and the accuracy of the collected data can be improved.

[0037] Step 3 is to establish an error compensation model coupled with temperature, humidity and vibration. The orthogonal test method is used to determine the interaction weight of each factor, and the recursive least squares method with forgetting factor is used to update the compensation coefficients online. Through this step, the error compensation model can be updated in real time according to the environmental changes, ensuring that the measurement results of the sensor remain high precision under the influence of multiple environmental factors such as temperature, humidity and vibration. This error compensation mechanism plays an important role in improving the long-term stability of the system and the accuracy of the data.

[0038] The last step is to generate data transmission priority according to the product of data change rate and health index, and dynamically allocate transmission bandwidth based on network quality evaluation value (including delay change rate and packet loss rate). High priority data will be compressed and encoded with preamble marking, so as to optimize the utilization of network resources and ensure the timely transmission of important data. This strategy can effectively deal with network congestion and ensure the stability and real-time performance of the system in a multi-task environment.

[0039] In one possible implementation, the baseline curve is generated by recording the zero-point offset at preset temperature intervals in a constant temperature environment. The key of this step is to construct the behavior pattern of the sensor at different temperatures through the correspondence between the sensor housing temperature and the zero-point offset. The baseline curve is obtained by measuring the zero-point offset at a preset constant temperature. These data are used as a reference in actual application. Through interpolation method, the expected offset at the current temperature can be obtained from the baseline curve using the housing temperature detected by the temperature sensor in real-time work. This step ensures that the zero-point offset can be accurately predicted and compensated when the temperature changes, thereby improving the measurement accuracy of the sensor under different temperature conditions.

[0040] The response time variation rate is an important parameter of the sensor response characteristic, which is used to describe the reaction speed of the sensor to the change of the input signal. In the implementation process, first, the sensor is excited by inputting a standard step signal. Then, the time difference required from 10% to 90% steady-state value is measured, and the time difference is calculated as the response time variation rate. This process is updated once every sampling period, so as to track the change of the response speed of the sensor in real time. By regularly updating the response time variation rate, the fluctuation of the response ability of the sensor can be effectively monitored and compensated, so as to ensure that the reaction speed of the sensor is stable and reliable under different working conditions.

[0041] The measurement of the zero-point offset is performed when there is no input signal, which is usually performed by collecting the output value of the sensor in the absence of input. In order to eliminate the influence of random noise, the collected data can be smoothed by using the sliding window average method. This method removes accidental noise by calculating the average value of the data in a certain time window, so as to obtain a more stable and accurate zero-point offset. The implementation of this step can effectively eliminate the influence of external interference on the output of the sensor, so as to ensure that stable zero-point data is obtained in each measurement, thereby reducing the error.

[0042] In one possible implementation, first, a compensation coefficient lookup table containing two-dimensional combinations of temperature and humidity needs to be established according to the changes of temperature and humidity. The lookup table is obtained through a large amount of experimental data or model derivation, and records the compensation coefficients under different combinations of temperature and humidity. This lookup table can reflect the influence of temperature and humidity changes on the performance of the sensor in real time, and provide basic compensation information.

[0043] In actual use, the temperature and humidity of the environment where the sensor is located are constantly changing, so it is necessary to obtain the corresponding compensation amount by looking up the data in the lookup table. By using the bilinear interpolation method, the corresponding compensation coefficient can be found in the lookup table according to the current temperature and humidity values. The bilinear interpolation method can accurately obtain the basic compensation amount under the current environmental conditions by interpolating the coordinates of temperature and humidity. This step ensures that the system can accurately calculate the appropriate compensation coefficient under different environmental conditions, thereby better correcting the measurement value of the sensor.

[0044] The health index, as a reflection of the sensor state, usually has a value between 0 and 1. In order to calibrate using the health index, the health index is first converted into a reliability factor, which reflects the health status of the sensor and the reliability of the measurement. The calculation of the reliability factor is based on the piecewise linear conversion of the preset interval of the health index, which can give greater calibration influence when the health index is high, and reduce the effect of calibration when the health index is low, avoiding excessive correction due to sensor failure or performance degradation.

[0045] The above-mentioned reliability factor is combined with the basic compensation amount, and finally the calibrated parameter is calculated by the formula. The specific calculation method is: calibrated parameter = original parameter × (1 + reliability factor × basic compensation amount). This method combines real-time environmental changes and sensor health, and can realize dynamic adjustment of the reference parameter. When the health index is high, the reliability factor is large, and the influence of the basic compensation amount on the parameter is also large, so more accurate compensation is performed; when the health index is low, the reliability factor is small, and the influence of the compensation amount on the parameter is reduced, thereby avoiding inaccurate compensation.

[0046] In one possible implementation, first, the basis for constructing the clock tree is to use the clock frequency of the pre-controller as a reference. This clock frequency is usually provided by the system master clock and serves as the reference for the entire system. According to the maximum sampling rate of the sensor required by the system, the initial frequency division coefficient is determined. The frequency division coefficient determines the frequency of the output clock, and usually in order to ensure the efficient work of the sensor, the frequency division coefficient should be accurately set according to the maximum sampling rate of the sensor to ensure the stability and efficiency of the clock signal.

[0047] In the design of clock tree, predicting the offset of the clock is a crucial step. The offset refers to the difference between the actual clock signal and the expected clock signal in consecutive clock cycles. To achieve this, an autoregressive model can be used to predict the clock offset in the next cycle. The order of the autoregressive model is determined by the number of zero crossings of the autocorrelation function of the historical offset data. The autoregressive model uses past offset data for prediction, which can more accurately estimate future clock offsets and make corresponding adjustments in advance. This method can effectively reduce the fluctuation of the clock and ensure the stability of the clock signal.

[0048] In practical applications, the clock signal may be affected by various external factors, resulting in periodic offset. Therefore, the feedback correction stage is necessary. By collecting the actual offset of the last three clock cycles, the current clock error can be evaluated. According to the distance in time, a decreasing weight is assigned to calculate the weighted average offset. The weighted average can effectively reduce the influence of short-term noise on the correction result, pay more attention to recent offset changes, and make the correction process more accurate.

[0049] After feedback correction, the frequency division coefficient needs to be dynamically adjusted so that the clock jitter value gradually shows an exponential decay trend until the clock offset stabilizes within the preset allowed range. This process reduces clock jitter by continuously optimizing the frequency division coefficient, ensuring that the clock frequency tends to be stable and meets the accuracy requirements of the system.

[0050] In one possible implementation, to ensure strict synchronization between environmental parameter acquisition and physical quantity sampling, the rising edge of the branch clock is used as a trigger signal to start the physical quantity sampling channel and the environmental parameter acquisition channel respectively. This means that each rising edge of the branch clock controls the start of multiple channels at the same time, ensuring that the collection of physical quantities (such as temperature, vibration, etc.) and environmental parameters (such as voltage, external temperature, etc.) occurs at the same time, thereby avoiding data inconsistency caused by timing errors. Synchronous sampling helps improve the accuracy of measurements, especially when multiple environmental factors may simultaneously affect physical quantities.

[0051] Voltage fluctuations are an important factor affecting the stability of digital systems, so collecting the extreme values of supply voltage fluctuations is of great significance. In this process, a peak holding circuit is used to capture the maximum and minimum values of the voltage. The peak holding circuit can record the instantaneous peak value of the voltage in real time and maintain it for a period of time for subsequent analysis. This design can effectively avoid the interference of transient voltage fluctuations on system performance and provide accurate data for subsequent power supply stability evaluation.

[0052] The change of the shell temperature gradient has a significant impact on the performance of the device, especially in high-precision sensor applications, where temperature fluctuations can cause measurement errors. Therefore, by arranging three temperature sensors in the axial direction of the sensor, temperature measurements at different positions are obtained. Using these data, the linear rate of temperature change can be calculated, and the shell temperature gradient can be derived. This temperature measurement method, through the coordinated work of multiple sensors, can accurately capture the changes in the external environmental temperature, thereby providing reliable temperature compensation for the measurement results of physical quantities.

[0053] Vibration is a common environmental factor that affects the operation of the device, so it is essential to extract the vibration frequency spectrum characteristics. By collecting vibration signals with an acceleration sensor and performing frequency spectrum analysis on the signals using Fast Fourier Transform (FFT), the main frequency components of the vibration signals can be extracted. These main frequency components can reflect the vibration characteristics of the device, such as whether the device is experiencing abnormal vibration or is in a resonance state within a specific frequency range. Through this method, an accurate vibration environment background can be provided for physical quantity acquisition, and further analysis of the impact of vibration on sensor or device performance can be performed.

[0054] Through peak retention of voltage fluctuations, accurate calculation of temperature gradients, and extraction of vibration frequency spectrum characteristics, the entire acquisition process not only reflects the running state of the device in real time, but also effectively compensates for measurement results under environmental changes (such as voltage fluctuations, temperature changes, vibration, etc.). In this way, through synchronous sampling and detailed recording of environmental factors, the anti-interference ability of the system and the accuracy of the data can be improved, ultimately providing reliable support for high-precision digital measurement, ensuring that the system can operate stably under various environmental conditions, reducing errors and risks.

[0055] In one possible implementation, in step 3, the orthogonal test method is applied to analyze the influence of environmental factors on the output deviation of the sensor. Specifically, by setting temperature, humidity, and vibration as three test levels of low, medium, and high, a full-factor experiment is conducted. Each test level represents an environmental condition, and different combinations of temperature, humidity, and vibration can simulate various environmental situations that may be encountered in actual applications. Through the experiment, the output deviation of the sensor under each environmental combination is measured, and the influence data of each factor and its combination condition is accumulated.

[0056] After collecting experimental data, the variance analysis method is used to separate the main effect (i.e., the individual influence) of each factor and the interaction effect (i.e., the influence of the interaction between two or more factors) between them. The variance analysis method calculates the contribution of different environmental factors to the output deviation, and determines the influence degree of each factor on the sensor deviation. By assigning weights to the main effects, it can be determined which factors have the greatest impact on the output of the sensor and which are secondary.

[0057] On the basis of the analysis of variance, the weight of the main effect is allocated according to the contribution of each factor to the output deviation, and the weight of the interaction effect is determined according to the proportion of the square of the effect value in the total sum of squares. In this way, through the weight allocation of the main effect and the interaction effect, the influence of each factor on the sensor deviation under different environmental conditions can be scientifically evaluated, and the calibration and compensation method of the sensor can be optimized.

[0058] In order to further improve the adaptability of the system, a forgetting factor is introduced into the data acquisition method. The forgetting factor is dynamically adjusted according to the rate of change of the environmental parameters. When the rate of change of the environmental parameters exceeds a certain preset threshold, the system automatically increases the value of the forgetting factor, ignoring the influence of the past data, so as to respond more quickly to the new environmental changes. This method adjusts the forgetting factor quickly to adapt to the environmental changes, thereby ensuring the real-time and accuracy of data acquisition. Through the dynamic adjustment of the forgetting factor, the sensitivity of the system to sudden environmental changes can be ensured, and the data out-of-date or deviation caused by long-term stable environmental changes can be reduced.

[0059] In one possible implementation, in the transmission priority generation process of step 4, the data change rate is first normalized. The data change rate refers to the difference between adjacent sampling points, which can reflect the change rate of the sensor output. In order to make different data change rates comparable, the moving average of the difference between adjacent sampling points is first calculated, and then divided by the range of the sensor. The range reflects the maximum measurable range of the sensor. Through this normalization processing, the output change rate of different sensors can be converted to a unified scale, thereby effectively comparing the speed of data change.

[0060] The health index is an important indicator reflecting the health status of the sensor, which is usually related to factors such as aging and damage of the sensor. In order to make the health index more suitable for the needs of transmission priority, a nonlinear compression processing is adopted. Specifically, according to the numerical range of the health index, a segmented processing method is adopted:

[0061] When the health index is lower than the first threshold, a logarithmic transformation is used. This is because when the health is poor, the performance of the sensor decreases sharply, and the use of logarithmic transformation can enhance the sensitivity to the change of the sensor performance.

[0062] When the health index is higher than the second threshold, a square root transformation is used. In this way, the change of the higher health index can be smoothed, so that the sensor with good health will not cause too high priority due to slight fluctuations.

[0063] Through this nonlinear compression, the influence of the health index on the priority generation can be more accurately reflected, thereby avoiding the generation of too low priority for the sensor with poor health.

[0064] Finally, the priority value is generated by weighted addition of the normalized data variation rate and the transformed health index. In this process, dynamic weights are used to balance the contributions of both. The adjustment of dynamic weights is based on the current network congestion level, and is adjusted in the linear range of 0.3 to 0.7. When the network congestion is lighter, the priority may rely more on the data variation rate, while when the network congestion is heavier, the priority is more affected by the health index, so that the sensor data with better health status is preferentially transmitted.

[0065] In one possible implementation, the time delay variation rate is an indicator of network delay fluctuation. In order to accurately evaluate the time delay variation, first, by continuously sending test data packets, the round-trip time (RTT) of the data packets from sending to receiving is measured. By calculating the standard deviation of these round-trip times, the time delay variation rate can be obtained. The standard deviation reflects the fluctuation of the time delay, and a higher standard deviation means that there is a larger time delay fluctuation in the network, which may affect the stability of data transmission. In this way, the time delay variation rate can be used as a key indicator for bandwidth allocation for subsequent decision-making.

[0066] The packet loss rate directly affects the reliability of data transmission. In this method, the packet loss rate is counted by a variable window size method. Specifically, the calculation of the packet loss rate is based on the packet loss within the window, and the size of the window is variable, and the duration of the window is an integer multiple of the current data sampling period. In this way, the calculation of the packet loss rate is more flexible, and the accuracy of the evaluation can be adjusted according to different network conditions and data sampling periods. When the packet loss rate is high, the system will adjust the bandwidth allocation strategy accordingly to ensure the reliability of data transmission.

[0067] According to the statistical results of the time delay variation rate and the packet loss rate, combined with the joint evaluation value of the two, the pre-set strategy table can be queried to select the appropriate compression algorithm. This step takes into account the time delay and packet loss of the network, and the evaluation value reflects the current state of the network and provides a basis for bandwidth allocation. When the time delay-packet loss rate joint evaluation value is in a certain critical region (i.e. the network load is high or unstable), the system will start the mixed compression mode.

[0068] When the network condition is poor, especially when the latency is high or the packet loss rate is large, in order to optimize the efficiency of bandwidth use, the system will select an appropriate compression algorithm according to the preset strategy. In the mixed compression mode, the system uses lossless compression for the data packet header and lossy compression for the data payload. Lossless compression ensures the integrity of the data header information, while lossy compression reduces the size of the payload by discarding a portion of the data, thereby saving bandwidth. This flexible compression strategy can ensure data transmission efficiency while reducing network burden, making it particularly suitable for environments with limited bandwidth.

[0069] In one possible implementation, the system first calculates a dynamic threshold value using the health index. The formula for calculating the dynamic threshold value is: dynamic threshold value = base threshold value × (1 + health index decay coefficient), where the base threshold value is a preset standard value, the health index reflects the health status of the sensor, and the health index decay coefficient is obtained from a preset parameter table according to the sensor type.

[0070] Base threshold value: This value represents the standard threshold value within the normal fluctuation range of sensor data.

[0071] Health index decay coefficient: It is adjusted according to the working condition of the sensor and environmental changes. For example, the sensor may degrade in performance due to environmental factors or usage time, affecting the accuracy of the data.

[0072] When the data jump value of adjacent sampling points exceeds the calculated dynamic threshold value, it indicates that the data has abnormal fluctuations, which may be caused by device failure, environmental interference, or other reasons. At this time, the system will trigger a multi-stage data repair process. The data jump value is the change amplitude between two adjacent data points, and if it exceeds the dynamic threshold value, it indicates that an abnormal situation has occurred.

[0073] The data repair process is divided into three main stages:

[0074] In the first stage, the system uses an autoregressive moving average (ARMA) model to predict the data. The ARMA model is a time series analysis method that can predict future data trends based on historical data. Under normal circumstances, the ARMA model can accurately predict data trends, thereby identifying abnormal parts of the data.

[0075] If the predicted residual (i.e., the deviation between the predicted value and the actual value) exceeds the allowed range, it indicates that the prediction result has errors, and the data may be affected by abnormal factors.

[0076] When the prediction result of the ARMA model is inaccurate, the system switches to the second stage and uses the data of the associated sensors for spatial interpolation compensation. Spatial interpolation techniques use data from other sensors related to the target sensor location to infer missing or abnormal data values. For example, if a sensor at a certain location fails, the system can use sensor data from neighboring locations for interpolation to supplement the abnormal data.

[0077] By compensating for associated sensor data, abnormal data can be repaired in space, ensuring the continuity and accuracy of the data.

[0078] Finally, the system generates an abnormal event report containing the original data, the repaired data, the environmental parameters, and the health index. The abnormal event report details the entire data repair process, including anomaly detection, repair measures, and their effects. This report provides an important basis for subsequent analysis, monitoring, and system optimization.

[0079] Correspondingly, the embodiment of the present application also provides a data acquisition system based on digital metrology technology, which is used to run the data acquisition method based on digital metrology technology according to any one of the embodiments of the present application, comprising:

[0080] The sensor state evaluation module comprises:

[0081] The temperature drift analysis unit is electrically connected with the sensor shell temperature probe and is used to collect and store the temperature-zero offset correspondence;

[0082] The response time monitoring unit is connected with the standard signal generator at the input end and is connected with the timer at the output end, and is used to calculate the response time change rate;

[0083] The health degree calculation unit is connected with the temperature drift analysis unit, the response time monitoring unit, and the zero-point calibration unit at the input end, and is connected with the parameter adjustment module through the data bus at the output end;

[0084] The clock synchronization module comprises:

[0085] The phase prediction sub-unit is directly connected with the main controller clock source and is built-in with an autoregressive model calculation unit;

[0086] The feedback correction sub-unit is connected with at least three historical clock offset registers at the input end and is connected with the clock frequency divider through the digital-to-analog converter at the output end;

[0087] The branch clock generator is connected with each sensor trigger interface through the isolation circuit at the output end;

[0088] The environmental coupling compensation module comprises:

[0089] The multi-parameter acquisition unit comprises a temperature sensor array, a vibration accelerometer and a voltage monitoring circuit, and is connected with the main controller through a parallel data interface;

[0090] The weight calculation engine is connected with the orthogonal test database and the real-time environmental parameter cache at the input end, and is connected with the compensation matrix updating unit at the output end.

[0091] The online compensation executor is connected with the sensor signal channel at the data input end, and is connected with the compensation matrix updating unit at the control end.

[0092] The data transmission control module comprises:

[0093] The priority calculation unit is connected with the data change rate analyzer and the health degree evaluation unit at the input end, and is internally integrated with a dynamic weight distributor.

[0094] The bandwidth adaptive unit comprises a network quality monitoring circuit and a compression encoding selector, and the network quality monitoring circuit obtains the link state in real time through a physical layer interface.

[0095] The redundancy control unit is connected with at least three independent network interfaces at the output end, and is connected with the priority label generator at the input end.

[0096] The technical scheme provided by the embodiment of the present application has at least the following beneficial effects:

[0097] In the present application, the dynamic calibration of the sensor ensures the accuracy of the measurement, the phase compensation clock tree ensures the synchronicity of data acquisition, and the error compensation model dynamically corrects the influence of environmental changes on data. Finally, the data transmission priority and the bandwidth dynamic allocation strategy ensure the effective use of network resources, avoiding the problem of packet loss or delay in the case of large amount of data. Overall, this method not only improves the accuracy of the system, but also optimizes the data processing and transmission efficiency, providing an effective solution for high-precision data acquisition in large-scale and complex environments.

[0098] The present application covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present application. In order to make the public have a thorough understanding of the present application, specific details are described in the following preferred embodiments of the present application, and the present application can also be completely understood without the description of these details for those skilled in the art. In addition, in order to avoid unnecessary confusion to the essence of the present application, well-known methods, processes, procedures, elements and circuits, etc. are not described in detail.

[0099] The above is only the preferred embodiment of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the principle of the present application, a number of improvements and refinements can be made, and these improvements and refinements should be regarded as the protection scope of the present application.

Claims

1. A data acquisition method based on digital metrology techniques, characterized in that, Perform the following steps in sequence: Step 1: Perform dynamic calibration of the sensor. By continuously collecting temperature drift curves, response time change rate and zero offset from the sensor's historical working data, calculate the coefficient of variation of each parameter to generate a real-time health index. Based on the correlation between this real-time health index and the real-time detected ambient temperature and humidity, dynamically adjust the sensor's reference parameters. Step 2: Construct a phase-compensated clock tree, generate branch clock signals with forward prediction and feedback correction mechanisms based on the main controller clock, and collect physical quantity signals and corresponding sensor housing temperature gradient, power supply voltage fluctuation extreme environmental parameters and vibration spectrum characteristics under synchronous clock control. The process of constructing the phase-compensated clock tree in step 2 includes: Using the clock frequency of the front controller as a reference, the initial frequency division coefficient is determined based on the maximum sampling rate of the sensor to generate the branch clock; an autoregressive model is used to predict the clock offset of the next cycle, and the model order is determined based on the number of zero-crossing points of the autocorrelation function of the historical offset data; in the feedback correction stage, the actual offset of the last three clock cycles is collected, and the weighted average offset is calculated by assigning decreasing weights according to the time proximity; finally, the clock jitter value is made to decay exponentially by dynamically adjusting the frequency division coefficient until it stabilizes within the preset allowable range. Step 3: Establish an error compensation model that couples temperature, humidity and vibration, determine the interaction weights of each factor through orthogonal experiment, and update the compensation coefficients online using the recursive least squares method with a forgetting factor. The implementation of the orthogonal experimental method in step 3 includes: Three test levels (low, medium, and high) were set up for each of the three dimensions of temperature, humidity, and vibration. The sensor output deviation under each combination of conditions was measured through a full factorial experiment. The main effects and pairwise interaction effects of each factor were separated using analysis of variance. The weight of the main effect was allocated according to the contribution ratio, and the weight of the interaction effect was calculated according to the ratio of the square of the effect value to the sum of the squares of the three factors. The forgetting factor was dynamically adjusted according to the logarithmic function value of the rate of change of environmental parameters. When the rate of change exceeded the threshold, the forgetting factor was automatically increased. Step 4: Generate transmission priority based on the product of data change rate and health index, dynamically allocate transmission bandwidth based on network quality assessment value calculated based on latency change rate and packet loss rate, and implement preamble-marked compression encoding on high priority data.

2. The data acquisition method based on digital metrology technology according to claim 1, characterized in that, In step 1, the temperature drift curve is obtained by recording the correspondence between the sensor housing temperature and the zero-point offset, specifically as follows: In a constant temperature environment, the zero-point offset is recorded at a preset temperature interval to form a reference curve. In actual work, the temperature sensor detects the shell temperature in real time and interpolates the reference curve to obtain the expected offset corresponding to the current temperature. The response time change rate is calculated by measuring the time difference from 10% to 90% of the steady-state value of the output value after inputting a standard step signal to the sensor, and the change rate is updated once in each sampling period; the zero-point offset is obtained by collecting the sensor output value in the state without input and eliminating random noise by using the sliding window averaging method.

3. A data acquisition method based on digital metrology technology according to claim 2, characterized in that, In the step 1 of dynamically adjusting the reference parameter, firstly, a compensation coefficient lookup table containing a two-dimensional combination of temperature and humidity is established, the basic compensation amount under the current environmental condition is obtained through a bilinear interpolation method, then the health index is converted into a reliability factor in the range of 0 to 1, and finally the calibrated parameter is calculated by multiplying the original parameter by (1+reliability factor x basic compensation amount), wherein the reliability factor is segmented and linearly converted according to the preset interval in which the health index is located.

4. The data acquisition method based on digital metrology technology according to claim 1, characterized in that, In the step 2 of collecting the environmental parameters and sampling the physical quantities, strict synchronization is maintained, and the specific implementation manner is as follows: The physical quantity sampling channel and the environmental parameter collection channel are triggered at the rising edge of the branch clock; the peak value holding circuit is used to capture the maximum and minimum values of the voltage at the sampling moment for recording the extreme value of the power supply voltage fluctuation; The linear change rate of the shell temperature gradient is calculated by the measurement values of the three temperature sensors arranged in the axial direction of the sensor; the vibration frequency spectrum feature is extracted by performing fast Fourier transform on the acceleration sensor collected data.

5. The data acquisition method based on digital metrology technology according to claim 1, characterized in that, The generation process of the transmission priority in the step 4 specifically includes: The data change rate is normalized by calculating the moving average of the difference value of adjacent sampling points and dividing the sensor range; the health index is nonlinearly compressed by using a segmented processing method, and when the index is lower than a first threshold, a logarithmic transformation is used, and when the index is higher than a second threshold, a square root transformation is used; and finally, the priority value is obtained by adding the normalized change rate and the transformed health index according to a dynamic weight, and the dynamic weight is linearly adjusted between 0.3 and 0.7 according to the network congestion degree.

6. A data acquisition method based on digital metrology technology according to claim 5, characterized in that, The specific implementation of the bandwidth allocation strategy in the step 4 includes: The time delay change rate is obtained by calculating the standard deviation of the round-trip time measured by continuously sending test data packets; the packet loss rate is counted by using a variable window size method, and the window duration is an integer multiple of the current data sampling period; the joint evaluation value of the time delay and the packet loss rate is used to query a preset strategy table to select a compression algorithm, and when the evaluation value is in a critical region, a mixed compression mode is started, and the data packet header is compressed losslessly while the data payload is compressed lossily.

7. A data acquisition method based on digital metrology technology according to claim 6, characterized in that, Further including: When the data jump value of the adjacent sampling points is detected to be greater than a dynamic threshold determined by the health index, a multi-stage data repair process is started, first, the normal data trend is predicted based on an autoregressive moving average model, when the prediction residual is greater than the allowed range, the spatial interpolation compensation is performed based on the associated sensor data, finally, an abnormal event report containing the original data, the repaired data, the environmental parameters and the health index is generated, and the dynamic threshold is calculated by multiplying the basic threshold by (1+health index decay coefficient), wherein the decay coefficient is obtained from a preset parameter table according to the sensor type.

8. A data acquisition system based on digital metrology technology for carrying out the method of any one of claims 1 to 7, characterized by Including: The sensor state evaluation module includes: A temperature drift analysis unit, which is electrically connected with a sensor shell temperature probe, is used to collect and store the temperature-zero offset relationship; A response time monitoring unit, which is connected with a standard signal generator at the input end and a timer at the output end, is used to calculate the response time change rate; The health degree calculation unit is connected with the temperature drift analysis unit, the response time monitoring unit and the zero point calibration unit respectively, and is connected with the parameter adjustment module through a data bus; The clock synchronization module comprises: The phase prediction subunit is directly connected with the main controller clock source and is internally provided with an autoregressive model calculation unit; The feedback correction subunit is connected with at least three historical clock offset registers at the input end and is connected with the clock frequency divider through a digital-to-analog converter at the output end; The branch clock generator is connected with each sensor trigger interface through an isolation circuit at the output end; The environment coupling compensation module comprises: The multi-parameter acquisition unit comprises a temperature sensor array, a vibration accelerometer and a voltage monitoring circuit and is connected with the main controller through a parallel data interface; The weight calculation engine is connected with a orthogonal test database and a real-time environment parameter cache at the input end and is connected with the compensation matrix update unit at the output end; The online compensation executor is connected with a sensor signal channel at the data input end and is connected with the compensation matrix update unit at the control end; The data transmission control module comprises: The priority calculation unit is connected with a data change rate analyzer and a health degree evaluation unit at the input end and is internally integrated with a dynamic weight distributor; The bandwidth adaptation unit comprises a network quality monitoring circuit and a compression encoding selector, and the network quality monitoring circuit acquires a link state in real time through a physical layer interface; The redundancy control unit is connected with at least three independent network interfaces at the output end and is connected with a priority mark generator at the input end.

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