Sensor testing system and method
By introducing modules such as data preprocessing, feature extraction and machine learning model training in sensor testing systems, the problems of test accuracy, time and cost in traditional sensor testing systems are solved, and efficient and intelligent sensor testing is achieved.
Patent Information
- Application Number
- CN202311153030.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-09-08
- Publication Date
- 2025-03-11
AI Technical Summary
Traditional sensor testing systems have problems such as limited accuracy of test results, long test time, complex operation and high cost.
A sensor testing system is designed, including modules such as data acquisition, data preprocessing, feature extraction, feature selection, machine learning model training and result analysis, and build models through machine learning algorithms to predict sensor performance indicators or perform abnormal detection.
It improves test accuracy, shortens test time, reduces test costs, and realizes intelligent analysis and evaluation, with good scalability.
Smart Images
Figure CN119598091A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of testing systems, and in particular to a sensor testing system and method. Background Art
[0002] Sensor testing systems are used to evaluate and verify the performance and accuracy of sensors. Traditional sensor testing systems usually require complex equipment and expertise, and the testing process is lengthy and expensive.
[0003] These systems have the following drawbacks:
[0004] The accuracy of the test results is limited, the test time is long, the operation is complicated, and the cost is high.
[0005] To solve the above problems, the present application proposes a sensor testing system and method. Summary of the invention
[0006] (I) Purpose of the invention
[0007] In order to solve the technical problems existing in the background technology, the present invention proposes a sensor testing system and method, which has the advantages of improving test accuracy; improving test efficiency; reducing test costs; and realizing intelligent analysis and evaluation.
[0008] (II) Technical solution
[0009] To solve the above problems, the present invention provides a sensor testing system, comprising:
[0010] The data acquisition module is used to collect raw data from the sensor to be tested. The raw data may include the output signal of the sensor or the perceived environmental parameters.
[0011] The data preprocessing module is used to preprocess and filter the collected raw data to remove noise and interference and improve data quality;
[0012] Feature extraction module, used to extract representative features from preprocessed data;
[0013] These features can be statistics related to sensor performance, frequency domain features, or time domain features.
[0014] Feature selection module, which is used to select features based on their relevance and importance, thereby reducing the dimensionality of features and improving the accuracy and efficiency of the model;
[0015] The machine learning model module is used for machine learning algorithms to build a model, take the selected features as input, and train the model to predict the performance indicators of the sensor or perform anomaly detection;
[0016] The result analysis module is used to analyze the output results of the machine learning model and evaluate the performance and accuracy of the sensor. A series of evaluation indicators can be used to measure the difference between the predicted results and the actual values.
[0017] A method for testing a sensor system, comprising the following steps:
[0018] S1. Data Collection
[0019] Get raw data from the sensor to be tested;
[0020] S2. Data preprocessing
[0021] Filter and denoise the raw data to improve data quality. You can use low-pass filters, high-pass filters, or other filtering techniques.
[0022] S3. Feature extraction
[0023] Extract representative features from the preprocessed data;
[0024] S4. Feature selection
[0025] Select the most representative features based on their relevance and importance. You can use correlation coefficient, information gain, and principal component analysis feature selection algorithms.
[0026] S5. Machine Learning Model Training
[0027] Build a model using the selected features and a suitable machine learning algorithm (such as support vector machine, random forest, neural network) and train it using labeled data.
[0028] S6. Results Analysis
[0029] Analyze and evaluate the output of the model, compare the difference between the predicted results and the actual values, and calculate evaluation metrics.
[0030] Preferably, in S3, a statistical method (such as mean, variance), a frequency domain analysis (such as Fourier transform) or a time domain analysis (such as autocorrelation function) method may be used.
[0031] Preferably, in S6, the evaluation indicators include mean square error, precision and recall.
[0032] Preferably, feature selection algorithm - correlation coefficient:
[0033] The correlation coefficient is used to measure the degree of linear correlation between two variables, ranging from -1 to 1; the formula is as follows:
[0034] r=(n∑xy-(∑x)(∑y)) / sqrt((n∑x^2-(∑x)^2)(n∑y^2-(∑y)^2))
[0035] Where n is the number of samples, x and y are the values of two variables, and ∑ represents the sum.
[0036] Preferably, the machine learning algorithm - support vector machine:
[0037] SVM is a commonly used supervised learning algorithm for classification and regression analysis. In sensor testing systems, SVM can be used for sensor performance prediction or anomaly detection.
[0038] The above technical solution of the present invention has the following beneficial technical effects:
[0039] Improve test accuracy: By adopting advanced data processing technology and machine learning algorithms, the characteristics of sensors can be accurately extracted and the performance indicators of sensors can be predicted more accurately. The accuracy of sensor test results will be greatly improved.
[0040] Improve test efficiency: Traditional sensor testing methods usually take a lot of time and manpower, while the use of machine learning methods can achieve an efficient and automated testing process. The rapid processing of data, feature extraction, and high efficiency of model training can greatly shorten the time of sensor testing.
[0041] Reduce testing costs: Traditional sensor testing systems usually require expensive equipment and expertise. The use of machine learning-based testing methods can reduce testing costs, reduce dependence on specialized equipment, and save human resources.
[0042] Improve system scalability: The system adopts a modular design, and the data flow is transmitted through clear connection relationships between modules. This modular design makes the system have good scalability and can be customized and expanded according to different sensor types and test requirements.
[0043] Intelligent analysis and evaluation: The application of machine learning models enables the sensor test system to perform intelligent data analysis and prediction. The system can automatically identify abnormal behavior of sensors and quickly evaluate the performance of sensors. This intelligent analysis and evaluation helps to quickly detect problems and optimize sensor design. BRIEF DESCRIPTION OF THE DRAWINGS
[0044] Figure 1 This is a structural schematic diagram of a sensor testing system proposed by the present invention. DETAILED DESCRIPTION
[0045] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.
[0046] like Figure 1 As shown, a sensor testing system proposed by the present invention comprises:
[0047] The data acquisition module is used to collect raw data from the sensor to be tested. The raw data may include the output signal of the sensor or the perceived environmental parameters.
[0048] Includes sensor interface circuits, analog-to-digital converters (ADCs) or other signal acquisition devices. This module acquires data from the sensor and transmits it to subsequent processing modules;
[0049] The data preprocessing module is used to preprocess and filter the collected raw data to remove noise and interference and improve data quality;
[0050] Feature extraction module, used to extract representative features from preprocessed data;
[0051] These features can be statistics related to sensor performance, frequency domain features, or time domain features.
[0052] Feature selection module, which is used to select features based on their relevance and importance, thereby reducing the dimensionality of features and improving the accuracy and efficiency of the model;
[0053] The machine learning model module is used for machine learning algorithms to build a model, take the selected features as input, and train the model to predict the performance indicators of the sensor or perform anomaly detection;
[0054] The result analysis module is used to analyze the output results of the machine learning model and evaluate the performance and accuracy of the sensor. A series of evaluation indicators can be used to measure the difference between the predicted results and the actual values.
[0055] A method for testing a sensor system, comprising the following steps:
[0056] S1. Data Collection
[0057] Get raw data from the sensor to be tested;
[0058] S2. Data preprocessing
[0059] Filter and denoise the raw data to improve data quality. You can use low-pass filters, high-pass filters, or other filtering techniques.
[0060] S3. Feature extraction
[0061] Extract representative features from the preprocessed data;
[0062] S4. Feature selection
[0063] Select the most representative features based on their relevance and importance. You can use correlation coefficient, information gain, and principal component analysis feature selection algorithms.
[0064] S5. Machine Learning Model Training
[0065] Build a model using the selected features and a suitable machine learning algorithm (such as support vector machine, random forest, neural network) and train it using labeled data.
[0066] S6. Results Analysis
[0067] Analyze and evaluate the output of the model, compare the difference between the predicted results and the actual values, and calculate evaluation metrics.
[0068] In an optional embodiment, in S3, a statistical method (such as mean, variance), a frequency domain analysis (such as Fourier transform) or a time domain analysis (such as autocorrelation function) method may be used.
[0069] Specifically, in S6, the evaluation indicators include mean square error, precision and recall.
[0070] In an optional embodiment, feature selection algorithm - correlation coefficient:
[0071] The correlation coefficient is used to measure the degree of linear correlation between two variables, ranging from -1 to 1; the formula is as follows:
[0072] r=(n∑xy-(∑x)(∑y)) / sqrt((n∑x^2-(∑x)^2)(n∑y^2-(∑y)^2))
[0073] Where n is the number of samples, x and y are the values of two variables, and ∑ represents the sum.
[0074] Machine Learning Algorithm — Support Vector Machine:
[0075] SVM is a commonly used supervised learning algorithm for classification and regression analysis. In sensor testing systems, SVM can be used for sensor performance prediction or anomaly detection.
[0076] The specific mathematical formulas and algorithm steps are beyond the scope of this answer. You can refer to relevant machine learning textbooks or literature for in-depth study.
[0077] In the present invention, the test accuracy is improved: by adopting advanced data processing technology and machine learning algorithms, the characteristics of the sensor can be accurately extracted and the performance indicators of the sensor can be predicted more accurately, and the accuracy of the sensor test results will be greatly improved.
[0078] Improve test efficiency: Traditional sensor testing methods usually take a lot of time and manpower, while the use of machine learning methods can achieve an efficient and automated testing process. The rapid processing of data, feature extraction, and high efficiency of model training can greatly shorten the time of sensor testing.
[0079] Reduce testing costs: Traditional sensor testing systems usually require expensive equipment and expertise. The use of machine learning-based testing methods can reduce testing costs, reduce dependence on specialized equipment, and save human resources.
[0080] Improve system scalability: The system adopts a modular design, and the data flow is transmitted through clear connection relationships between modules. This modular design makes the system have good scalability and can be customized and expanded according to different sensor types and test requirements.
[0081] Intelligent analysis and evaluation: The application of machine learning models enables the sensor test system to perform intelligent data analysis and prediction. The system can automatically identify abnormal behavior of sensors and quickly evaluate the performance of sensors. This intelligent analysis and evaluation helps to quickly detect problems and optimize sensor design.
[0082] It should be understood that the above specific embodiments of the present invention are only used to illustrate or explain the principles of the present invention, and do not constitute a limitation of the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. In addition, the appended claims of the present invention are intended to cover all changes and modifications that fall within the scope and boundaries of the appended claims, or the equivalent forms of such scope and boundaries.
Claims
1. A sensor testing system, characterized in that: include: A data acquisition module, used for collecting raw data from the sensor to be tested; The data preprocessing module is used to preprocess and filter the collected raw data to remove noise and interference and improve data quality; Feature extraction module, used to extract representative features from preprocessed data; Feature selection module, which is used to select features based on their relevance and importance, thereby reducing the dimensionality of features and improving the accuracy and efficiency of the model; The machine learning model module is used for machine learning algorithms to build a model, take the selected features as input, and train the model to predict the performance indicators of the sensor or perform anomaly detection; The result analysis module is used to analyze the output results of the machine learning model and evaluate the performance and accuracy of the sensor.
2. A method for testing a sensor system, characterized in that: The following steps are involved: S1. Data Collection Get raw data from the sensor to be tested; S2. Data preprocessing Filter and denoise the original data to improve data quality; S3. Feature extraction Extract representative features from the preprocessed data; S4. Feature selection According to the relevance and importance of features, the most representative features are selected, using correlation coefficient, information gain, and principal component analysis feature selection algorithms; S5. Machine Learning Model Training Build a model using the selected features and a suitable machine learning algorithm, and train it using labeled data; S6. Results Analysis Analyze and evaluate the output of the model, compare the difference between the predicted results and the actual values, and calculate evaluation metrics.
3. A method for a sensor testing system according to claim 2, characterized in that: In S3, statistical methods, frequency domain analysis or time domain analysis methods can be used.
4. A method for testing a sensor system according to claim 3, characterized in that: In S6, the evaluation indicators include mean square error, precision and recall.
5. The method of a sensor testing system according to claim 2, characterized in that: Feature selection algorithm - correlation coefficient: The correlation coefficient is used to measure the degree of linear correlation between two variables, ranging from -1 to 1; The formula is as follows: r=(n∑xy-(∑x)(∑y)) / sqrt((n∑x^2-(∑x)^2)(n∑y^2-(∑y)^2))where, n is the number of samples, x and y are the values of two variables, and ∑ represents the sum.
6. The method of a sensor testing system according to claim 5, characterized in that: Machine Learning Algorithm — Support Vector Machine: Used for classification and regression analysis, in sensor test systems, to predict sensor performance or detect anomalies.