Resonance system quality detection method based on piezoelectric ceramic driving

Abstract

The invention discloses a resonance system quality detection method based on piezoelectric ceramic driving, and belongs to the technical field of resonance system quality detection, and the method comprises the steps: employing piezoelectric ceramic to drive a resonance system to enter a resonance state, collecting the real-time data of the resonance system in the vibration process, and carrying out the processing of the collected real-time data of the resonance system, determining resonance system characteristic data; and identifying and analyzing the resonance system characteristic data, determining a resonance system quality detection result of the to-be-detected sample, and performing quality management on the to-be-detected sample by a user. The problem that the quality detection effect of the resonance system is poor due to the fact that the quality of a to-be-detected sample in the resonance system cannot be effectively detected based on piezoelectric ceramic driving in the prior art is solved. According to the invention, the quality of the to-be-detected sample in the resonance system can be effectively detected based on piezoelectric ceramic driving, the quality detection effect of the resonance system can be improved, and the method can be specifically applied to the fields of micro-nano technology and precision measurement.

Description

Technical Field

[0001] The present invention relates to the technical field of resonant system quality detection, and in particular to a resonant system quality detection method based on piezoelectric ceramic drive. Background Art

[0002] When an oscillating system is subjected to periodic external forces, when the frequency of the external force is the same as or very close to the system's natural oscillation frequency, the amplitude increases sharply, which can be used to infer the mass of the object being measured. It is widely used in fields such as micro-mass detection, biosensors, and industrial online monitoring.

[0003] Chinese patent application publication number CN109357672B discloses a bidirectional optical microwave resonance system based on a circulator structure and a method for detecting angular velocity thereof. The system uses a circulator structure to achieve bidirectional optical microwave resonance through bidirectional regenerative mode locking technology. A reciprocal bidirectional optical microwave resonance system is achieved based on the non-reciprocal error elimination technology of a wide-spectrum optical interferometer. Polarization state separation technology is used to achieve dual-wavelength separation of optical signals, and perpendicular polarization states are used for opposite-direction transmission within a sensitive ring to improve the detection capability of the sensitive ring. Cavity length control technology is used to lock the microwave oscillation frequency in one direction to a high-stability standard time reference source, stabilizing the relative cavity length of the optical resonant cavity. The system is characterized by strong practicality and high measurement accuracy. However, the patent has the following drawbacks:

[0004] Existing technologies cannot effectively detect the quality of the sample to be tested in the resonant system based on piezoelectric ceramic drive, resulting in poor quality detection of the resonant system and cannot be specifically applied in the fields of micro-nano technology and precision measurement. Summary of the Invention

[0005] The purpose of the present invention is to provide a resonant system quality detection method based on piezoelectric ceramic drive, which can effectively detect the quality of the sample to be tested in the resonant system based on piezoelectric ceramic drive, improve the quality detection effect of the resonant system, and can be specifically applied in the fields of micro-nano technology and precision measurement, solving the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The quality detection method of the resonant system based on piezoelectric ceramic drive includes:

[0008] Using piezoelectric ceramics to drive the resonance system into a resonance state, collecting real-time data of the resonance system during the vibration process, and processing the collected real-time data of the resonance system to determine characteristic data of the resonance system;

[0009] Identify and analyze the characteristic data of the resonance system to determine the quality test results of the resonance system of the sample to be tested, so that users can perform quality management on the sample to be tested.

[0010] Preferably, identifying and analyzing the characteristic data of the resonance system to determine the quality test result of the resonance system of the sample to be tested includes:

[0011] According to the quality detection requirements of the resonant system based on the piezoelectric ceramic drive, a resonant system quality detection database is pre-set;

[0012] Based on the resonant system quality detection database, the resonant system characteristic data is identified and analyzed, and the loading mass that matches the resonant system characteristic data is found, thereby determining the resonant system quality detection result of the sample to be tested;

[0013] The resonance system quality test results of the sample to be tested are displayed to the user in a visual form, so that the user can perform quality management on the sample to be tested based on the resonance system quality test results of the sample to be tested.

[0014] Preferably, the resonant system quality detection database is pre-set, including:

[0015] According to the quality detection requirements of the resonant system based on piezoelectric ceramic drive, collect historical data of the resonant system, including the historical vibration characteristics of the resonant system and the historical loading mass of the resonant system;

[0016] Analyze the correlation between the vibration characteristics of the resonant system and the loaded mass based on the historical data of the resonant system, and determine the resonant system correlation set;

[0017] The resonance system association sets are classified based on keywords, and the classified resonance system association sets are correspondingly stored in specific storage blocks to form a resonance system quality detection database.

[0018] 1. Preferably, determining the historical data of the resonant system to analyze the correlation between the vibration characteristics of the resonant system and the loaded mass includes:

[0019] Standardizing the historical resonance system vibration characteristics in the historical data to obtain historical standard vibration characteristic data;

[0020] Determine target data of the current vibration characteristic type in the historical standard vibration characteristic data, divide other vibration characteristic types according to data ranges, and obtain multiple groups of reference data of other vibration characteristic types;

[0021] Based on the target data and reference data, the correlation coefficient between the current detection vibration characteristic type and the loading mass is calculated;

[0022] The correlation between the vibration characteristics of the resonant system and the loaded mass is calculated.

[0023] Preferably, identifying and analyzing the characteristic data of the resonance system to find a loading mass that matches the characteristic data of the resonance system includes:

[0024] Comparing and analyzing the resonance system characteristic data with multiple resonance system association sets in a resonance system quality detection database one by one, and determining a resonance system association set that matches the resonance system characteristic data;

[0025] wherein, multiple resonance system association sets in the resonance system quality detection database are extracted one by one, and the extracted resonance system association sets are compared and analyzed with the resonance system characteristic data;

[0026] When the extracted resonance system association sets and the resonance system feature data have the same keywords, the extracted resonance system association sets match the resonance system feature data;

[0027] When the keywords of the resonance system association sets extracted one by one are different from the keywords of the resonance system feature data, the extracted resonance system association sets do not match the resonance system feature data;

[0028] The resonance system characteristic data is compared and analyzed according to the matched resonance system association set, and the loading mass corresponding to the resonance system characteristic data is found from the matched resonance system association set, thereby determining the resonance system quality test result of the sample to be tested.

[0029] Preferably, if the extracted resonance system association set does not match the resonance system characteristic data, the following operations are performed:

[0030] extracting the next resonance system association set from the resonance system quality detection database in order of priority, and comparing and analyzing the extracted resonance system association set with the resonance system characteristic data;

[0031] When the extracted resonance system association set and the resonance system characteristic data have the same keyword, the extracted resonance system association set matches the resonance system characteristic data;

[0032] When the extracted resonance system association set and the resonance system characteristic data have different keywords, the extracted resonance system association set does not match the resonance system characteristic data. At this time, the next resonance system association set is extracted from the resonance system quality detection database in order of priority, and the extracted resonance system association set is compared and analyzed with the resonance system characteristic data until the extracted resonance system association set and the resonance system characteristic data have the same keywords. Then the extracted resonance system association set matches the resonance system characteristic data.

[0033] Preferably, collecting real-time data of the resonance system during the vibration process of the resonance system includes:

[0034] The sample to be tested is loaded on the resonant system, and an external electric field is applied to the piezoelectric ceramic, causing the positive and negative charge centers inside the piezoelectric ceramic to shift relative to each other. The inverse piezoelectric effect is used to cause the piezoelectric ceramic to produce mechanical deformation under the action of the electric field, thereby driving the resonant system into a resonant state and causing the resonant system to vibrate.

[0035] The frequency, amplitude and phase of the resonant system during vibration are monitored in real time, and real-time data of the resonant system driven by piezoelectric ceramics is collected.

[0036] Preferably, processing the collected real-time data of the resonance system includes:

[0037] Clean the real-time data of the resonant system driven by piezoelectric ceramics to remove the noise data that is of no value to the quality detection of the resonant system;

[0038] Check the real-time data of the resonant system based on piezoelectric ceramic drive one by one, identify missing values and abnormal values in the real-time data of the resonant system based on piezoelectric ceramic drive, and process the identified missing values and abnormal values;

[0039] Evaluate the missing values and outliers in the real-time data of the resonant system based on piezoelectric ceramic drive, and determine whether the missing values and outliers in the real-time data of the resonant system based on piezoelectric ceramic drive are valuable for the quality detection of the resonant system;

[0040] If missing values and outliers in the real-time data of the resonant system driven by piezoelectric ceramics are valuable for the quality detection of the resonant system, the missing values are filled and the outliers are replaced;

[0041] If the missing values and outliers in the real-time data of the resonant system based on piezoelectric ceramic drive are of no value to the quality detection of the resonant system, the missing values and outliers are removed.

[0042] Preferably, processing the collected real-time data of the resonance system further includes:

[0043] Normalize the real-time data of the resonant system based on piezoelectric ceramic drive to remove the dimension difference in the real-time data of the resonant system and form standardized real-time data of the resonant system;

[0044] The real-time data of the resonant system driven by piezoelectric ceramics is subjected to feature extraction, and features valuable for the quality detection of the resonant system are extracted from the real-time data of the resonant system driven by piezoelectric ceramics, so as to determine the characteristic data of the resonant system.

[0045] Preferably, the resonance system association sets are classified based on keywords, and the classified resonance system association sets are stored in specific storage blocks to form a resonance system quality detection database, including:

[0046] Establishing a semantic extraction model based on historical keywords, and extracting semantic features in the resonance system association set based on the semantic extraction model;

[0047] Analyze the data values corresponding to the same semantic features, and generate dynamic keywords corresponding to the semantic features according to the analysis results;

[0048] Establishing a cross-modal association between the image modality and the text modality in the resonance system association set based on a multimodal model, and establishing a composite keyword based on the cross-modal association;

[0049] Based on semantic features, the dynamic keywords and compound keywords are integrated to obtain target dynamic keywords;

[0050] Establishing a matching degree identification mechanism between the resonance system association set and the target dynamic keyword; when the matching degree between the real-time updated resonance system association set and the target dynamic keyword is less than a preset matching degree, hierarchical clustering is performed on the real-time updated resonance system association set; generating new keywords based on the hierarchical clustering results; and updating the target dynamic keyword in real time based on the new keywords;

[0051] Based on classifying the target dynamic keywords according to their attributes, group dynamic keywords are obtained, and keyword associations between the group dynamic keywords are determined based on the resonance system association set. The associations of the group dynamic keywords are deeply mined based on a convolutional neural network to obtain keyword implicit associations, and a knowledge graph is constructed based on the keyword associations and the keyword implicit associations;

[0052] Key classification attributes are obtained from the knowledge graph, the resonance system association set is classified according to the key classification attributes to obtain classification results, and each key classification attribute is configured to store the corresponding storage block to form a resonance system quality detection database.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] The present invention adopts piezoelectric ceramics to drive the resonance system into a resonance state, collects real-time data of the resonance system during the vibration process of the resonance system, processes the collected real-time data of the resonance system, determines characteristic data of the resonance system, identifies and analyzes the characteristic data of the resonance system based on a resonance system quality detection database, searches for a loading mass that matches the characteristic data of the resonance system, and then determines the quality detection result of the resonance system of the sample to be tested, and displays the quality detection result of the resonance system of the sample to be tested to the user in a visual form, so that the user can perform quality management of the sample to be tested according to the quality detection result of the resonance system of the sample to be tested. The quality of the sample to be tested in the resonance system can be effectively detected based on the piezoelectric ceramic drive, the quality detection effect of the resonance system can be improved, and the specific application can be in the fields of micro-nano technology and precision measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a flow chart of the quality detection method of the resonant system based on piezoelectric ceramic drive of the present invention;

[0056] Figure 2 This is a flow chart of the present invention for identifying and analyzing the characteristic data of the resonance system and searching for the loading mass that matches the characteristic data of the resonance system. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0058] In order to solve the problem that the existing piezoelectric ceramic drive cannot effectively detect the mass of the sample to be measured in the resonant system, resulting in poor quality detection effect of the resonant system and inability to be applied in the field of micro-nano technology and precision measurement, please refer to Figure 1-Figure 2 , this embodiment provides the following technical solutions:

[0059] The quality detection method of the resonant system based on piezoelectric ceramic drive includes:

[0060] The piezoelectric ceramics are used to drive the resonant system into a resonant state, and the real-time data of the resonant system during the vibration process is collected.

[0061] In this embodiment, collecting real-time data of the resonant system during the vibration process includes:

[0062] The sample to be tested is loaded on the resonant system, and an external electric field is applied to the piezoelectric ceramic, causing the positive and negative charge centers inside the piezoelectric ceramic to shift relative to each other. The inverse piezoelectric effect is used to cause the piezoelectric ceramic to produce mechanical deformation under the action of the electric field, thereby driving the resonant system into a resonant state and causing the resonant system to vibrate.

[0063] The frequency, amplitude and phase of the resonant system during vibration are monitored in real time, and real-time data of the resonant system driven by piezoelectric ceramics is collected to facilitate subsequent quality testing of the sample to be tested.

[0064] The collected real-time data of the resonance system is processed to determine the characteristic data of the resonance system.

[0065] In this embodiment, the collected real-time data of the resonance system is processed, including:

[0066] Clean the real-time data of the resonant system driven by piezoelectric ceramics to remove the noise data that is of no value to the quality detection of the resonant system;

[0067] Check the real-time data of the resonant system based on piezoelectric ceramic drive one by one, identify missing values and abnormal values in the real-time data of the resonant system based on piezoelectric ceramic drive, and process the identified missing values and abnormal values;

[0068] Evaluate the missing values and outliers in the real-time data of the resonant system based on piezoelectric ceramic drive, and determine whether the missing values and outliers in the real-time data of the resonant system based on piezoelectric ceramic drive are valuable for the quality detection of the resonant system;

[0069] If missing values and outliers in the real-time data of the resonant system driven by piezoelectric ceramics are valuable for the quality detection of the resonant system, the missing values are filled and the outliers are replaced;

[0070] If the missing values and outliers in the real-time data of the resonant system based on piezoelectric ceramic drive are of no value to the quality detection of the resonant system, the missing values and outliers are removed.

[0071] In this embodiment, processing the collected real-time data of the resonance system further includes:

[0072] Normalize the real-time data of the resonant system based on piezoelectric ceramic drive to remove the dimension difference in the real-time data of the resonant system and form standardized real-time data of the resonant system;

[0073] The real-time data of the resonant system driven by piezoelectric ceramics is subjected to feature extraction, and features valuable for the quality detection of the resonant system are extracted from the real-time data of the resonant system driven by piezoelectric ceramics, so as to determine the characteristic data of the resonant system.

[0074] Identify and analyze the characteristic data of the resonance system to determine the quality test results of the resonance system of the sample to be tested, so that users can perform quality management on the sample to be tested.

[0075] In this embodiment, identifying and analyzing the resonance system characteristic data to determine the resonance system quality test result of the sample to be tested includes:

[0076] According to the quality detection requirements of the resonant system based on the piezoelectric ceramic drive, a resonant system quality detection database is pre-set;

[0077] Among them, historical data of the resonant system is collected, including the historical vibration characteristics of the resonant system and the historical loading mass of the resonant system; the correlation between the vibration characteristics of the resonant system and the loading mass is analyzed according to the historical data of the resonant system to determine the resonant system association set; the resonant system association set is classified based on keywords, and the classified resonant system association sets are stored in specific storage blocks to form a resonant system quality detection database.

[0078] In one embodiment, determining the correlation between the vibration characteristics of the resonant system and the loading mass by analyzing the historical data of the resonant system includes:

[0079] Standardizing the historical resonance system vibration characteristics in the historical data to obtain historical standard vibration characteristic data;

[0080] Determine target data of the current vibration characteristic type in the historical standard vibration characteristic data, divide other vibration characteristic types according to data ranges, and obtain multiple groups of reference data of other vibration characteristic types;

[0081] Based on the target data and reference data, the correlation coefficient between the current detection vibration characteristic type and the loading mass is calculated;

[0082]

[0083] Among them, R AP represents the correlation coefficient between the current detection vibration characteristic type A and the loading mass P, n represents the number of detection vibration characteristic types, m represents the number of reference data groups, r Aij It represents the partial correlation coefficient between the currently detected vibration characteristic type A and the i-th vibration characteristic type in the j-th group of reference data, and its value is (-1, 1). represents the average loading mass corresponding to the currently detected vibration characteristic type A in the jth group of reference data, and P represents the value of the loading mass corresponding to the currently detected vibration characteristic type A;

[0084] The correlation between the vibration characteristics of the resonant system and the loaded mass is calculated;

[0085] The calculation formula for the correlation F between the vibration characteristics of the current resonant system and the loaded mass P is as follows:

[0086]

[0087] Among them, R ωP represents the correlation coefficient between the i-th detected vibration characteristic type and the loading mass P, R minP Indicates the minimum correlation coefficient between all detected vibration characteristics and the loading mass P, R maxP Indicates the maximum correlation coefficient with the load mass P among all detected vibration characteristic types.

[0088] In this embodiment, the vibration characteristics of the resonance system include resonance frequency, damping ratio, amplitude, quality factor, etc.

[0089] In this embodiment, the partial correlation coefficient between the currently detected vibration characteristic type A and the i-th vibration characteristic type in the j-th set of reference data is used to represent the correlation between A and the i-th vibration characteristic type.

[0090] In this embodiment, when calculating the correlation coefficient between the currently detected vibration characteristic type and the loaded mass, the partial correlation coefficient is considered to represent the correlation between A and the i-th vibration characteristic type, and the corresponding average loaded mass of the currently detected vibration characteristic type A in the j-th group of reference data is considered in order to eliminate the influence of other vibration characteristic types on the calculation results, and to consider the relationship between the average loaded mass and the current loaded mass, eliminate randomness, and ensure the accuracy of the calculation results.

[0091] In this embodiment, when calculating the correlation between the vibration characteristics of the resonant system and the loaded mass, the minimum correlation coefficient and the maximum correlation coefficient with the loaded mass P in all detected vibration characteristic types are added to eliminate the influence of large fluctuations in the correlation coefficient and ensure the accuracy of the correlation calculation.

[0092] In this embodiment, multiple groups of reference data of other vibration characteristic types are obtained. For example, the target data is a resonant frequency of 10kHz. The damping ratio of the first group of reference data is less than 0.01, the amplitude is within the AB range, and the quality factor is within the CD range; the damping ratio of the second group of reference data is less than 0.01, the amplitude is within the AB range, and the quality factor is within the DF range; for actual situations, for example, the correlation coefficient between the resonant frequency of 10kHz and the resonant frequency of 10kHz to the loaded mass P under the first group of reference data is not large, but the correlation coefficient between the resonant frequency of 10kHz to the loaded mass P under the first group of reference data is larger; the correlation coefficient indicates that the resonant frequency has a greater influence on the determination of the loaded mass.

[0093] The beneficial effect of the above design scheme is that it solves the core problem of multi-vibration characteristic coupling interference correlation calculation in traditional methods through systematic optimization of data preprocessing, multivariable control and correlation calculation, provides a high-precision and explainable quantitative basis for the quality detection of resonant systems, and significantly improves the reliability of quality detection.

[0094] Based on the resonant system quality detection database, the resonant system characteristic data is identified and analyzed, and the loading mass that matches the resonant system characteristic data is found, thereby determining the resonant system quality detection result of the sample to be tested;

[0095] The resonance system characteristic data is compared and analyzed one by one with a plurality of resonance system association sets in a resonance system quality detection database to determine a resonance system association set that matches the resonance system characteristic data;

[0096] Extracting multiple resonance system association sets from the resonance system quality detection database one by one, and comparing and analyzing the extracted resonance system association sets with the resonance system characteristic data;

[0097] When the extracted resonance system association sets and the resonance system feature data have the same keywords, the extracted resonance system association sets match the resonance system feature data;

[0098] When the keywords of the extracted resonance system association sets and the resonance system feature data are different, the extracted resonance system association sets do not match the resonance system feature data. At this time, the next resonance system association set is extracted from the resonance system quality detection database in order of priority, and the extracted resonance system association set is compared and analyzed with the resonance system feature data.

[0099] When the extracted resonance system association set is identical to the keyword of the resonance system characteristic data, the extracted resonance system association set matches the resonance system characteristic data; when the extracted resonance system association set is different from the keyword of the resonance system characteristic data, the extracted resonance system association set does not match the resonance system characteristic data, and then the next resonance system association set is continuously extracted from the resonance system quality detection database in order of priority, and the extracted resonance system association set is compared and analyzed with the resonance system characteristic data until the extracted resonance system association set is identical to the keyword of the resonance system characteristic data, and the extracted resonance system association set matches the resonance system characteristic data;

[0100] The resonance system characteristic data is compared and analyzed according to the matched resonance system association set, and the loading mass corresponding to the resonance system characteristic data is found from the matched resonance system association set to determine the resonance system quality test result of the sample to be tested, and the resonance system quality test result of the sample to be tested is displayed to the user in a visual form, so that the user can perform quality management of the sample to be tested based on the resonance system quality test result of the sample to be tested.

[0101] In one embodiment, the resonance system association sets are classified based on keywords, and the classified resonance system association sets are stored in specific storage blocks to form a resonance system quality detection database, including:

[0102] Establishing a semantic extraction model based on historical keywords, and extracting semantic features in the resonance system association set based on the semantic extraction model;

[0103] Analyze the data values corresponding to the same semantic features, and generate dynamic keywords corresponding to the semantic features according to the analysis results;

[0104] Establishing a cross-modal association between the image modality and the text modality in the resonance system association set based on a multimodal model, and establishing a composite keyword based on the cross-modal association;

[0105] Based on semantic features, the dynamic keywords and compound keywords are integrated to obtain target dynamic keywords;

[0106] Establishing a matching degree identification mechanism between the resonance system association set and the target dynamic keyword; when the matching degree between the real-time updated resonance system association set and the target dynamic keyword is less than a preset matching degree, hierarchical clustering is performed on the real-time updated resonance system association set; generating new keywords based on the hierarchical clustering results; and updating the target dynamic keyword in real time based on the new keywords;

[0107] Based on classifying the target dynamic keywords according to their attributes, group dynamic keywords are obtained, and keyword associations between the group dynamic keywords are determined based on the resonance system association set. The associations of the group dynamic keywords are deeply mined based on a convolutional neural network to obtain keyword implicit associations, and a knowledge graph is constructed based on the keyword associations and the keyword implicit associations;

[0108] Key classification attributes are obtained from the knowledge graph, the resonance system association set is classified according to the key classification attributes to obtain classification results, and each key classification attribute is configured to store the corresponding storage block to form a resonance system quality detection database.

[0109] In this embodiment, the greater the association with other keywords in the knowledge graph, the greater the probability of being a key classification attribute. For example, if the quality interval within the range AB corresponds to more vibration characteristics, the data associated with the quality interval within the range AB can be stored in one storage block.

[0110] In this embodiment, the traditional keyword classification is static, with manually set labels and lacks adaptability, resulting in insufficient accuracy and flexibility of the classification.

[0111] In this embodiment, the dynamic keywords include professional terms such as “frequency drift trend” and “damping mutation mode”.

[0112] In this embodiment, the compound keyword is, for example, high frequency attenuation-macro mass load. The purpose of establishing the compound keyword is to enrich the content of the keyword and improve the classification accuracy.

[0113] In this embodiment, when the matching degree between the real-time updated resonance system association set and the target dynamic keyword is less than the preset matching degree, the real-time updated resonance system association set is hierarchically clustered, and new keywords are generated according to the hierarchical clustering results. The purpose is to ensure a high degree of adaptability between the association set and the keyword, avoid classification errors caused by traditional tags not covering new scenarios, and thus provide a basis for accurate classification.

[0114] The beneficial effects of this design are as follows: the semantic extraction model and dynamic keyword generation solve the problems of traditional static tag classification and improve the adaptability and accuracy of classification. The multimodal model establishes composite keywords, enriching keyword content and improving classification accuracy. Matching degree identification and hierarchical clustering update keywords, ensuring the adaptability of keywords to data. The knowledge graph mines implicit associations, helping to identify key classification attributes and optimize storage structures. This fundamentally solves the problems of traditional static tag classification, such as rigidity, single information, and poor adaptability. It significantly improves the accuracy and efficiency of resonant system quality testing and the intelligent level of data management, providing highly reliable technical support for scenarios such as industrial online testing and laboratory precision measurement.

[0115] In summary, based on the resonant system quality detection database, the resonant system characteristic data is identified and analyzed, and the loading mass that matches the resonant system characteristic data is found, and then the resonant system quality detection result of the sample to be tested is determined, and the resonant system quality detection result of the sample to be tested is displayed to the user in a visual form, so that the user can manage the quality of the sample to be tested according to the resonant system quality detection result of the sample to be tested. The quality of the sample to be tested in the resonant system can be effectively detected based on the piezoelectric ceramic drive, which can improve the resonant system quality detection effect and can be specifically applied in the fields of micro-nano technology and precision measurement.

[0116] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0117] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for detecting the quality of a resonant system based on a piezoelectric ceramic drive, characterized in that: include: Using piezoelectric ceramics to drive the resonance system into a resonance state, collecting real-time data of the resonance system during the vibration process, and processing the collected real-time data of the resonance system to determine characteristic data of the resonance system; Identify and analyze the characteristic data of the resonance system to determine the quality test results of the resonance system of the sample to be tested, so that users can perform quality management on the sample to be tested.

2. The method for detecting the quality of a resonant system based on piezoelectric ceramic drive according to claim 1, wherein: Identify and analyze the resonance system characteristic data to determine the resonance system quality test results of the sample to be tested, including: According to the quality detection requirements of the resonant system based on the piezoelectric ceramic drive, a resonant system quality detection database is pre-set; Based on the resonant system quality detection database, the resonant system characteristic data is identified and analyzed, and the loading mass that matches the resonant system characteristic data is found, thereby determining the resonant system quality detection result of the sample to be tested; The resonant system quality test results of the samples to be tested are displayed to the user in a visual form, so that the user can perform quality management on the samples to be tested based on the resonant system quality test results of the samples to be tested (it is recommended to identify and analyze the characteristic data of the resonant system and then determine the resonant system quality test results of the samples to be tested for algorithm mining).

3. The method for detecting the quality of a resonant system based on piezoelectric ceramic drive according to claim 2, wherein: Pre-set resonant system quality detection database, including: According to the quality detection requirements of the resonant system based on piezoelectric ceramic drive, collect historical data of the resonant system, including the historical vibration characteristics of the resonant system and the historical loading mass of the resonant system; Analyze the correlation between the vibration characteristics of the resonant system and the loaded mass based on the historical data of the resonant system, and determine the resonant system correlation set; The resonance system association sets are classified based on keywords, and the classified resonance system association sets are stored in specific storage blocks to form a resonance system quality detection database (it is recommended to perform algorithm mining on the set resonance system quality detection database).

4. The method for detecting the quality of a resonant system based on piezoelectric ceramic drive according to claim 3, wherein: Determine the correlation between the vibration characteristics of the resonant system and the loading mass by analyzing the historical data of the resonant system, including: Standardizing the historical resonance system vibration characteristics in the historical data to obtain historical standard vibration characteristic data; Determine target data of the current vibration characteristic type in the historical standard vibration characteristic data, divide other vibration characteristic types according to data ranges, and obtain multiple groups of reference data of other vibration characteristic types; Based on the target data and reference data, the correlation coefficient between the current detection vibration characteristic type and the loading mass is calculated; The correlation between the vibration characteristics of the resonant system and the loaded mass is calculated.

5. The method for detecting the quality of a resonant system based on piezoelectric ceramic drive according to claim 3, wherein: Identify and analyze the characteristic data of the resonant system and find the loading mass that matches the characteristic data of the resonant system, including: Comparing and analyzing the resonance system characteristic data with multiple resonance system association sets in a resonance system quality detection database one by one, and determining a resonance system association set that matches the resonance system characteristic data; wherein, multiple resonance system association sets in the resonance system quality detection database are extracted one by one, and the extracted resonance system association sets are compared and analyzed with the resonance system characteristic data; When the extracted resonance system association sets and the resonance system feature data have the same keywords, the extracted resonance system association sets match the resonance system feature data; When the keywords of the resonance system association sets extracted one by one are different from the keywords of the resonance system feature data, the extracted resonance system association sets do not match the resonance system feature data; The resonance system characteristic data is compared and analyzed according to the matched resonance system association set, and the loading mass corresponding to the resonance system characteristic data is found from the matched resonance system association set, thereby determining the resonance system quality test result of the sample to be tested.

6. The method for detecting the quality of a resonant system based on piezoelectric ceramic drive according to claim 5, wherein: The extracted resonance system association set does not match the resonance system feature data. Perform the following operations: extracting the next resonance system association set from the resonance system quality detection database in order of priority, and comparing and analyzing the extracted resonance system association set with the resonance system characteristic data; When the extracted resonance system association set and the resonance system characteristic data have the same keyword, the extracted resonance system association set matches the resonance system characteristic data; When the extracted resonance system association set and the resonance system characteristic data have different keywords, the extracted resonance system association set does not match the resonance system characteristic data. At this time, the next resonance system association set is extracted from the resonance system quality detection database in order of priority, and the extracted resonance system association set is compared and analyzed with the resonance system characteristic data until the extracted resonance system association set and the resonance system characteristic data have the same keywords. Then the extracted resonance system association set matches the resonance system characteristic data.

7. The method for detecting the quality of a resonant system based on piezoelectric ceramic drive according to claim 1, wherein: Collect real-time data of the resonant system during vibration, including: The sample to be tested is loaded on the resonant system, and an external electric field is applied to the piezoelectric ceramic, causing the positive and negative charge centers inside the piezoelectric ceramic to shift relative to each other. The inverse piezoelectric effect is used to cause the piezoelectric ceramic to produce mechanical deformation under the action of the electric field, thereby driving the resonant system into a resonant state and causing the resonant system to vibrate. The frequency, amplitude and phase of the resonant system during vibration are monitored in real time, and real-time data of the resonant system driven by piezoelectric ceramics is collected.

8. The method for detecting the quality of a resonant system based on piezoelectric ceramic drive according to claim 1, wherein: Processing of the collected real-time data of the resonant system, including: Clean the real-time data of the resonant system driven by piezoelectric ceramics to remove the noise data that is of no value to the quality detection of the resonant system; Check the real-time data of the resonant system based on piezoelectric ceramic drive one by one, identify missing values and abnormal values in the real-time data of the resonant system based on piezoelectric ceramic drive, and process the identified missing values and abnormal values; Evaluate the missing values and outliers in the real-time data of the resonant system based on piezoelectric ceramic drive, and determine whether the missing values and outliers in the real-time data of the resonant system based on piezoelectric ceramic drive are valuable for the quality detection of the resonant system; If missing values and outliers in the real-time data of the resonant system driven by piezoelectric ceramics are valuable for the quality detection of the resonant system, the missing values are filled and the outliers are replaced; If the missing values and outliers in the real-time data of the resonant system based on piezoelectric ceramic drive are of no value to the quality detection of the resonant system, the missing values and outliers are removed.

9. The method for detecting the quality of a resonant system based on piezoelectric ceramic drive according to claim 1, wherein: Processing the collected real-time data of the resonant system also includes: Normalize the real-time data of the resonant system based on piezoelectric ceramic drive to remove the dimension difference in the real-time data of the resonant system and form standardized real-time data of the resonant system; The real-time data of the resonant system driven by piezoelectric ceramics is subjected to feature extraction, and features valuable for the quality detection of the resonant system are extracted from the real-time data of the resonant system driven by piezoelectric ceramics, so as to determine the characteristic data of the resonant system.

10. The method for detecting the quality of a resonant system based on piezoelectric ceramic drive according to claim 3, wherein: The resonance system association sets are classified based on keywords, and the classified resonance system association sets are stored in specific storage blocks to form a resonance system quality detection database, including: Establishing a semantic extraction model based on historical keywords, and extracting semantic features in the resonance system association set based on the semantic extraction model; Analyze the data values corresponding to the same semantic features, and generate dynamic keywords corresponding to the semantic features according to the analysis results; Establishing a cross-modal association between the image modality and the text modality in the resonance system association set based on a multimodal model, and establishing a composite keyword based on the cross-modal association; Based on semantic features, the dynamic keywords and compound keywords are integrated to obtain target dynamic keywords; Establishing a matching degree identification mechanism between the resonance system association set and the target dynamic keyword; when the matching degree between the real-time updated resonance system association set and the target dynamic keyword is less than a preset matching degree, hierarchical clustering is performed on the real-time updated resonance system association set; generating new keywords based on the hierarchical clustering results; and updating the target dynamic keyword in real time based on the new keywords; Based on classifying the target dynamic keywords according to their attributes, group dynamic keywords are obtained, and keyword associations between the group dynamic keywords are determined based on the resonance system association set. The associations of the group dynamic keywords are deeply mined based on a convolutional neural network to obtain keyword implicit associations, and a knowledge graph is constructed based on the keyword associations and the keyword implicit associations; Key classification attributes are obtained from the knowledge graph, the resonance system association set is classified according to the key classification attributes to obtain classification results, and each key classification attribute is configured to store the corresponding storage block to form a resonance system quality detection database.

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