Single-chip control method and device for piezoelectric ceramics

Abstract

The invention discloses a single-chip control method and device for piezoelectric ceramics, and relates to the technical field of single-chip control, and the method comprises the steps: interacting production nodes of a target production line, collecting an assembly scheme of an assembly element, and obtaining a positioning deviation tolerance bandwidth; a visual positioning device is adopted to obtain a positioning deviation set and a positioning deviation trend direction; performing positioning deviation optimization to obtain a positioning deviation optimization record set; performing control response factor fitting on the positioning deviation optimization record set to generate a control response factor; retrieving the positioning deviation optimization record to obtain an interference fitting factor; and correcting the control response factor to obtain a control feedback factor, and moving the positioning table. The technical problems that in the prior art, a control method is low in precision and lacks intelligence, so that deviation occurs in product assembly, and the product quality is reduced are solved, and the technical effects that high-precision and intelligent positioning control is achieved, and the production efficiency and the product quality are improved are achieved.

Description

Technical Field

[0001] The present invention relates to the field of monolithic control technology, and particularly relates to a method and device for monolithic control of piezoelectric ceramics. Background Art

[0002] In modern industrial production and scientific research, the requirements for precision and performance are increasing day by day. Due to its unique electrostrictive characteristics, piezoelectric ceramics have been widely used in fields such as precision positioning, micro-operation, and nanotechnology. However, traditional piezoelectric ceramic control methods have many limitations. The previous control methods often lack high precision and are difficult to meet the increasingly demanding production and research requirements. For example, in high-precision operations such as semiconductor manufacturing and optical instrument debugging, subtle positioning deviations may lead to a decline in product quality or inaccurate experimental results.

[0003] The prior art has technical problems such as low precision of the control method, lack of intelligence resulting in deviation in product assembly, and thus reduction of product quality. Summary of the Invention

[0004] The present application provides a method and device for monolithic control of piezoelectric ceramics, which are used to solve the technical problems in the prior art that the control method has low precision, lack of intelligence leads to deviation in product assembly, and thus reduces product quality.

[0005] In view of the above problems, the present application provides a method and device for monolithic control of piezoelectric ceramics.

[0006] In the first aspect of the present application, a method for monolithic control of piezoelectric ceramics is provided, and the method includes: K production nodes of an interactive target production line, where the K production nodes include K piezoelectric ceramic drivers and K piezoelectric ceramics; collect K assembly schemes of the assembly components at the K production nodes, retrieve the K assembly schemes with the positioning deviation as the index to obtain K positioning deviation tolerance bandwidths; use a vision positioning device to identify the position deviation of the positioning tables at the K production nodes in a preset analysis window to obtain K positioning deviation sets and K positioning offset trend directions; feedback the K positioning deviation sets and the K positioning offset trend directions to the K piezoelectric ceramic drivers for positioning deviation optimization to obtain K positioning deviation optimization record sets; perform control response factor fitting on the K positioning deviation optimization record sets to generate K control response factors; retrieve the K positioning deviation optimization records according to a preset interference factor set, input the retrieval results into an interference fitting device for analysis to obtain K interference fitting factors; use the K interference fitting factors to correct the K control response factors to obtain K control feedback factors; transmit the K control feedback factors to the K piezoelectric ceramic drivers to drive the K piezoelectric ceramics to move the positioning tables.

[0007] In a second aspect of the present application, a single-chip control device for a piezoelectric ceramic is provided, and the device includes: A generation node interaction module, which is used to interact with K production nodes of a target production line, where the K production nodes include K piezoelectric ceramic drivers and K piezoelectric ceramics; a positioning deviation tolerance bandwidth acquisition module, which is used to collect K assembly schemes of an assembly component at the K production nodes, and retrieve the K assembly schemes with the positioning deviation as an index to obtain K positioning deviation tolerance bandwidths; a position deviation identification module, which is used to use a vision positioning device to identify the position deviation of the positioning tables of the K production nodes in a preset analysis window to obtain K positioning deviation sets and K positioning offset trend directions; a positioning deviation optimization record acquisition module, which is used to feedback the K positioning deviation sets and the K positioning offset trend directions to the K piezoelectric ceramic drivers for positioning deviation optimization to obtain K positioning deviation optimization record sets; a control response factor generation module, which is used to fit the control response factors for the K positioning deviation optimization record sets to generate K control response factors; an interference fitting factor acquisition module, which is used to retrieve the K positioning deviation optimization records according to a preset interference factor set, input the retrieval results into an interference fitting device for analysis to obtain K interference fitting factors; a control feedback factor acquisition module, which is used to correct the K control response factors by using the K interference fitting factors to obtain K control feedback factors; a positioning table movement module, which is used to transmit the K control feedback factors to the K piezoelectric ceramic drivers to drive the K piezoelectric ceramics to move the positioning table.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: K production nodes of the interactive target production line; collect K assembly schemes of the assembly components at the K production nodes, retrieve the K assembly schemes to obtain K positioning deviation tolerance bandwidths; identify the position deviations of the positioning tables at the K production nodes to obtain K positioning deviation sets and K positioning offset trend directions; feedback the K positioning deviation sets and K positioning offset trend directions to K piezoelectric ceramic drivers for positioning deviation optimization to obtain K positioning deviation optimization record sets; perform control response factor fitting on the K positioning deviation optimization record sets to generate K control response factors; retrieve the K positioning deviation optimization records according to the preset interference factor set, input the retrieval results into an interference fitting device for analysis to obtain K interference fitting factors; use the K interference fitting factors to correct the K control response factors to obtain K control feedback factors; transmit the K control feedback factors to the K piezoelectric ceramic drivers to drive the K piezoelectric ceramics to move the positioning table. It achieves the technical effects of realizing high-precision and intelligent positioning control and improving production efficiency and product quality. Description of the Drawings

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0010] Figure 1 Schematic flowchart of a single-piece control method for a piezoelectric ceramic provided by an embodiment of the present application; Figure 2 Schematic structural diagram of a single-piece control device for a piezoelectric ceramic provided by an embodiment of the present application.

[0011] Description of the reference numerals: Generation node interaction module 10, positioning deviation tolerance bandwidth acquisition module 20, position deviation identification module 30, positioning deviation optimization record acquisition module 40, control response factor generation module 50, interference fitting factor acquisition module 60, control feedback factor acquisition module 70, positioning table movement module 80. Detailed Embodiments

[0012] The present application provides a single-piece control method and device for a piezoelectric ceramic, which are used to solve the technical problems in the prior art that the control method has low accuracy and lacks intelligence, resulting in deviations in product assembly and thus reducing product quality.

[0013] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0014] Embodiment 1 As Figure 1 shown, the present application provides a single-piece control method for piezoelectric ceramics, and the method includes: Step S100: Interact with K production nodes of the target production line, where the K production nodes include K piezoelectric ceramic drivers and K piezoelectric ceramics.

[0015] Specifically, an effective interaction connection is established with the K production nodes in the target production line. In this process, first, it is necessary to determine K specific positions or links with key roles in the target production line and define them as production nodes. Each production node is equipped with specific components, including a piezoelectric ceramic driver and a piezoelectric ceramic. Here, the piezoelectric ceramic driver has driving and control functions and can send precise electrical signals to the piezoelectric ceramic to prompt it to generate specific physical responses. The piezoelectric ceramic is a material with special properties. When it receives an electrical signal from the driver, it can quickly and accurately change its shape, size, or position. This change is of great significance for precise positioning, fine-tuning, or performing specific actions in the production line. For example, if K is equal to 10, it means that there are 10 such production nodes in the target production line. At each node, there is a piezoelectric ceramic driver and a piezoelectric ceramic that cooperate with each other to jointly play a role in the precise operation and efficient production of the production line.

[0016] Step S200: Collect K assembly schemes of the assembly components at the K production nodes, and retrieve the K assembly schemes with the positioning deviation as the index to obtain K positioning deviation tolerance bandwidths.

[0017] Specifically, collect K assembly plans for assembling components at K production nodes. These assembly plans contain specific methods and parameters on how to perform assembly at each production node. Then, using the positioning deviation as the key index, retrieve these K assembly plans. The positioning deviation refers to the difference between the actual assembly position and the ideal assembly position. Through such a retrieval process, K positioning deviation tolerance bandwidths are finally obtained. The positioning deviation tolerance bandwidth refers to the amount of positioning deviation allowed within a certain range. For example, if the positioning deviation tolerance bandwidth of a certain production node is large, it means that there is a high tolerance for the deviation of the assembly position at this node; conversely, if the tolerance bandwidth is small, the requirement for assembly accuracy is more stringent, clarifying the acceptable positioning deviation range for each production node, which helps to more accurately control the assembly accuracy.

[0018] Step S300: Use a vision positioning device to identify the position deviation of the positioning tables of the K production nodes in a preset analysis window, and obtain K positioning deviation sets and K positioning offset trend directions.

[0019] Specifically, an advanced vision positioning device is introduced. This vision positioning device is equipped with high-precision image acquisition equipment, such as high-resolution cameras and sensitive optical sensors. The preset analysis window is a specific area set in advance, and its size, shape, and position are accurately calculated and planned to ensure that it can accurately cover the positioning tables of the K production nodes. When the vision positioning device starts working, it will continuously and quickly acquire images of the positioning tables of each production node within the preset analysis window, and perform real-time analysis on the acquired images. During the analysis process, the real-time images of the positioning tables acquired are compared and calculated with the preset standard position images, so as to accurately determine the difference between the actual position and the ideal position of the positioning tables of each production node in three-dimensional space. For the positioning table of each production node, a set containing multiple deviation values will be generated, and these values respectively reflect the position deviation amounts in different directions (such as the X-axis, Y-axis, and Z-axis). At the same time, the overall trend direction of the deviation of each positioning table will also be determined, such as whether it offsets along the positive or negative direction of a certain coordinate axis, or shows a specific angular offset trend in the plane, which can accurately identify the position deviation of the positioning tables of each production node and provide an accurate data basis for subsequent adjustment and optimization.

[0020] Step S400: Feed back the K positioning deviation sets and the K positioning offset trend directions to the K piezoelectric ceramic drivers for positioning deviation optimization, and obtain K positioning deviation optimization record sets.

[0021] Specifically, in this step, first, the K positioning deviation sets and the information of the K positioning offset trend directions obtained previously are respectively fed back to the corresponding K piezoelectric ceramic actuators. The particle swarm optimization algorithm is used to optimize the positioning deviation. Each combination of a positioning deviation set and a positioning offset trend direction is regarded as the feature of a particle, and each particle represents a possible positioning deviation optimization scheme. A particle has two attributes: position and velocity. The position represents the current scheme, and the velocity determines the change direction and amplitude of the scheme. The algorithm initializes a group of particles, that is, randomly generates a set of initial positioning deviation optimization schemes. The fitness function is used to evaluate the quality of each particle. The fitness function can be designed according to factors such as the reduction degree of the positioning deviation and the stability of the adjustment. In each iteration, the particle updates its velocity and position according to its own optimal position (individual optimal) and the optimal position found by the entire particle swarm (global optimal) to search for a better positioning deviation optimization scheme. When the final optimization scheme is obtained, the piezoelectric ceramic actuator makes adjustments according to this scheme. During the adjustment process, the following contents are monitored in real time: the specific parameter changes of the adjustment, the time spent on the adjustment, the immediate positioning deviation state after each adjustment, etc., and this information is recorded in detail to form a positioning deviation optimization record. Since there are K piezoelectric ceramic actuators, finally, K such positioning deviation optimization records are obtained, forming a set of K positioning deviation optimization records, generating an accurate adjustment scheme, making the positioning deviation more accurately corrected, and improving the production accuracy.

[0022] Step S500: Fit the control response factors for the set of K positioning deviation optimization records to generate K control response factors.

[0023] Specifically, K response rate sets are extracted from the K positioning deviation optimization records. The response rate refers to the change speed of the positioning deviation during the optimization process. Then, in the order of time, the data in these K response rate sets are sorted and constructed to generate K response rate curves. Each curve reflects the change of the response rate over time during the positioning deviation optimization process of the corresponding production node. Then, linear fitting is performed on these K response rate curves. The purpose of linear fitting is to find a straight line that can best represent the trend of these curves. Through the fitting process, parameters such as the slope and intercept of the straight line are calculated. Finally, based on the parameters obtained by fitting, a control response factor is generated for each production node. This control response factor can quantitatively describe the response characteristics of the production node during the positioning deviation optimization process. Based on the control response factor, the response of each node under different control conditions can be predicted, so as to optimize the control strategy and improve the accuracy and efficiency of control.

[0024] Step S600: Retrieve the K optimized positioning deviation records according to a preset interference factor set, input the retrieval results into an interference fitting device for analysis, and obtain K interference fitting factors.

[0025] Specifically, first, there is a preset interference factor set. This set includes various factors that affect the optimized positioning deviation records, such as changes in environmental temperature and humidity, vibrations of the device, fluctuations in power supply, etc. Then, according to these preset interference factors, the K optimized positioning deviation records are retrieved. The purpose of the retrieval is to find the information and data related to the interference factors in these records. Next, the retrieval results are input into an interference fitting device. The interference fitting device is a tool or model specifically used to analyze the relationship between interference factors and the optimized positioning deviation records. Through the analysis and processing of the interference fitting device, K interference fitting factors are finally obtained. These interference fitting factors reflect the degree and manner in which each optimized positioning deviation record is affected by various preset interference factors, helping to more accurately predict the results of positioning deviation optimization under different interference conditions and providing a more accurate basis for production planning and quality control.

[0026] Step S700: Use the K interference fitting factors to correct the K control response factors to obtain K control feedback factors.

[0027] Specifically, in this step, the previously obtained K interference fitting factors are used to correct the same number of K control response factors. Each interference fitting factor reflects the degree of influence of a specific interference factor on the optimized positioning deviation record. The control response factors quantify the response characteristics of the production node during the positioning deviation optimization process. By combining and correcting the interference fitting factors with the control response factors, the true control response of the production node under actual interference can be more accurately reflected. The purpose of this is to make the control feedback factors more in line with the actual production scenario and provide a more valuable reference basis for subsequent control strategy adjustment and optimization.

[0028] Step S800: Transmit the K control feedback factors to the K piezoelectric ceramic drivers to drive the K piezoelectric ceramics to move the positioning stage.

[0029] Specifically, the previously obtained K control feedback factors are regarded as loss factors and transmitted to the corresponding K piezoelectric ceramic actuators. These control feedback factors contain key information about positioning deviation and interference effects after a series of analyses and corrections. When the piezoelectric ceramic actuators receive these control feedback factors, they will drive the corresponding K positioning tables to move according to the instructions therein. The purpose of the movement of the positioning tables is to facilitate the accurate positioning and assembly of the assembled components. By accurately transmitting the control feedback factors and driving the piezoelectric ceramic actuators to move the positioning tables, high-precision assembly of components can be achieved, improving the quality and performance of the product.

[0030] In a possible implementation manner, step S600 further includes: Step S610: The preset interference factor set includes electromagnetic interference, temperature and humidity change factors, noise interference, and signal channel interference.

[0031] Specifically, the preset interference factor set includes: electromagnetic interference, which refers to the interference caused by electromagnetic waves. In the production environment, the electromagnetic fields generated by other electrical equipment, wires, wireless communications, etc. will affect the electronic components and signal transmission of the positioning system, resulting in positioning deviation; temperature and humidity change factors, the fluctuations of temperature and humidity will affect the physical properties of materials. For example, it will cause components to expand or contract, thereby changing the positioning accuracy. At the same time, it will also affect the performance and accuracy of electronic components; noise interference, which can be mechanical noise, electrical noise, etc. Mechanical noise such as the vibration and friction sounds during equipment operation will cause small displacements of the positioning table, and electrical noise will interfere with the signal acquisition and transmission of sensors, resulting in misjudgment of positioning deviation; signal channel interference, which involves the lines and channels for signal transmission in the positioning system. For example, poor shielding of signal cables, attenuation during signal transmission, crosstalk between multiple signals, etc. may all cause the positioning-related signals to be distorted or lose some information, affecting the positioning accuracy. Considering these interference factors can more precisely control the actions of piezoelectric ceramics, reduce positioning errors caused by interference, and improve the accuracy of single-piece piezoelectric ceramic control.

[0032] In a possible implementation manner, step S300 further includes: Step S310: Use the visual positioning device to collect the positions of the positioning tables at the K production nodes in a preset analysis window respectively, and obtain a set of K positioning table position identifiers.

[0033] Step S320: Index the K assembly schemes with the standard positioning table position, and generate K standard positioning table position identifiers.

[0034] Step S330: Based on the K standard positioning table position identifiers, perform position deviation identification on the set of K positioning table position identifiers according to the K positioning deviation tolerance bandwidths, to generate the set of K positioning deviations, where the set of K positioning deviations has K sets of deviation direction identifiers, and the deviation direction identifiers include positive identifiers and negative identifiers.

[0035] Step S340: Perform trend analysis on the set of K positioning deviations to determine K positioning offset trend directions.

[0036] Specifically, the mentioned vision positioning device is a high-precision measuring device, which has advanced image acquisition and processing capabilities. The preset analysis window is a specific area precisely delimited in space according to production requirements and process requirements. For the K production nodes, the vision positioning device will work in a predetermined order. When it comes to collecting the position of the positioning table of a specific production node, the device will activate its image acquisition component. Within the preset analysis window, the vision positioning device will obtain a large amount of image information from multiple angles and positions. These images not only include the overall appearance of the positioning table, but also focus on key position feature points, such as specific marks, corners, holes, etc. Through advanced image processing algorithms, the device can accurately extract the coordinates, postures, and other relevant position parameters of these feature points in space. The collected position parameters are organized and sorted according to a certain logic and format. After such processing, for the positioning table of each production node, a set of positioning table position identifiers with a clear structure and rich content can be obtained, and this set comprehensively and accurately describes the position state of the positioning table within the preset analysis window.

[0037] The standard positioning table position is an ideal position state that has been precisely measured and strictly defined, which includes all-round position information such as the precise coordinates, postures, and angles of the positioning table in space, and this information is used as the benchmark for subsequent comparison and judgment. The K assembly schemes faced are a series of detailed technical documents or data sets, which cover various specific settings and arrangements of the positioning table in different production scenarios. These schemes include different component combinations, process flow parameters, and corresponding positioning table position settings. Retrieve by using the standard positioning table position as an index. For each assembly scheme, deeply analyze all data fields and parameters related to the positioning table position therein, and check many fine position features such as the geometric center coordinates of the positioning table, the displacement amounts on each axis, the degree of rotation angle, and the inclination degree of the plane. Then, compare the position features extracted from these assembly schemes with the corresponding features of the standard positioning table position one by one. Through such comprehensive and in-depth comparison and analysis, a highly accurate and detailed standard positioning table position identifier is generated for each assembly scheme.

[0038] There are K standard positioning station position identifiers, which are used as reference points for measurement. Each standard positioning station position identifier is unique and deterministic. At the same time, there are also corresponding K positioning deviation tolerance bandwidths, which set the range within which position deviations are acceptable. Operations are carried out on the set of K positioning station position identifiers. Taking each standard positioning station position identifier as the core reference, each specific position identifier in the corresponding positioning station position identifier set is compared with it. When comparing, if a certain positioning position identifier is greater than the corresponding standard positioning station position identifier, a positive identifier is given. This means that in the subsequent positioning trend analysis space, its corresponding ordinate will be marked as positive. On the contrary, if the positioning position identifier is less than or equal to the standard positioning station position identifier, a negative identifier is given. Correspondingly, in the subsequent positioning trend analysis space, its ordinate will be marked as negative. Through such detailed comparison and identification, a specific positioning deviation set is generated for each production node. Each positioning deviation set not only contains specific deviation values but also has a corresponding deviation direction identifier set.

[0039] Perform trend analysis on the previously obtained K positioning deviation sets. In each positioning deviation set, a series of deviation data is included. By arranging and comparing these data in an orderly manner, it is judged whether the deviation shows trends such as gradually increasing, gradually decreasing, remaining stable, or showing periodicity. Determining the K positioning offset trend directions is to clarify the change directions of the positioning station deviations corresponding to each production node. For example, if the data in a certain positioning deviation set shows a gradually increasing trend, then its positioning offset trend direction is the positive direction; if the data gradually decreases, the trend direction is the negative direction; if the data basically fluctuates within a relatively small range, the trend direction can be considered relatively stable. Clarifying the positioning offset trend directions can more accurately adjust the driving parameters of the piezoelectric ceramics, making the control of the single-piece piezoelectric ceramics more precise and improving its performance.

[0040] In a possible implementation manner, step S330 further includes: Step S331: Construct K positioning trend analysis spaces according to the K positioning deviation sets, where the horizontal axes of the K positioning trend analysis spaces are time, the vertical axes are positioning deviations, and the K positioning trend analysis spaces include K positioning particle point sets.

[0041] Step S332: Perform drift iteration analysis on the K positioning particle point sets in the K positioning trend analysis spaces to obtain K target positioning particle points.

[0042] Step S333: When the ordinates of the K target positioning particle points are negative, the K positioning offset trend directions are underfitting directions.

[0043] Step S334: When the ordinate of the K target positioning particle points is positive, the K positioning deviation trend directions are overfitting directions.

[0044] Specifically, K positioning deviation sets related to a single piezoelectric ceramic are obtained, and K unique positioning trend analysis spaces are constructed based on these sets. It is crucial to set the horizontal axis as time. The scale of the time axis can be precise to specific moments or time periods, reflecting the sequence of each measurement, adjustment, or control operation on the single piezoelectric ceramic, which enables tracking the evolution process of the positioning deviation over time. The vertical axis is set as the positioning deviation, used to directly quantify the degree of deviation of the position of the single piezoelectric ceramic from the expected or standard position. Through this setting, the magnitude and direction of the deviation can be clearly seen at a glance. Each positioning trend analysis space contains a specific set of positioning particle points, and these particle points represent the specific positioning deviation values obtained from the detection and evaluation of the single piezoelectric ceramic at different time points. Exemplarily, 8 different single piezoelectric ceramics are being controlled (K = 8). For each single piezoelectric ceramic, a dedicated analysis space is constructed based on its corresponding positioning deviation set. In the positioning trend analysis space for the first single piezoelectric ceramic, as time goes by, assuming that the positioning deviation is measured as +0.02 mm at 9 am, a positioning particle point is marked at the position (9 am, +0.02 mm). If the positioning deviation changes to -0.01 mm when measured at 2 pm, another particle point is marked at (2 pm, -0.01 mm). In the same way, analysis spaces containing a series of positioning particle points are also constructed for the other 7 single piezoelectric ceramics respectively. Such a construction method helps to comprehensively, intuitively, and precisely understand the variation law of the positioning deviation of each single piezoelectric ceramic over time, providing a solid data basis and a visual analysis tool for subsequent precise control and optimization.

[0045] In the K positioning trend analysis spaces constructed for the control of single piezoelectric ceramics, the mean shift algorithm is used to perform drift iterative analysis on these K sets of positioning particle points. The mean shift algorithm calculates the density distribution of the positioning particle points and then makes the analysis points "drift" along the direction of increasing density, gradually converging to the region with the maximum density. In each positioning trend analysis space, the algorithm performs multiple iterative calculations based on the positions and deviation values of the particle points. For each set of positioning particle points corresponding to a single piezoelectric ceramic, the mean shift algorithm continuously adjusts the position of the analysis point to gradually approach the center or representative position of the particle point distribution. After a series of iterations, a point that best represents the overall characteristics of the set of particle points in this space is finally determined, that is, the target positioning particle point.

[0046] When the vertical coordinates of the determined K target positioning particle points are negative, it means that in the positioning trends of the K single piezoelectric ceramic plates, their actual positions deviate from the standard positions and are less than the standard positions. This situation indicates that the current control adjustment is not sufficient and the single piezoelectric ceramic plates have not reached the ideal position state. It can be understood that the control force is insufficient or there are defects in the adjustment strategy, resulting in the position deviation of the single plates not meeting the expectations. At this time, the trend directions of these K positioning deviations are determined as underfitting directions. This prompts the need to further optimize and improve the control parameters, driving voltage, or other relevant control factors to increase the adjustment force and enable the single piezoelectric ceramic plates to be closer to or reach the standard positions.

[0047] When the vertical coordinates of the determined K target positioning particle points are positive, it indicates that the actual positions of these K single plates deviate from the standard positions and the deviation amount exceeds the standard positions. This situation means that the current control adjustment is excessive, the effect on the single piezoelectric ceramic plates exceeds the expectations, and their positions deviate too much from the standard. Thus, the trend directions of these K positioning deviations are determined as overfitting directions. This prompts the need to make reverse adjustments to the control strategy, such as reducing the driving voltage, modifying the control parameters, etc., to reduce the adjustment force and callback the positions of the single piezoelectric ceramic plates to be closer to or reach the standard positions. By constructing the positioning trend analysis space, performing drift iteration analysis, and judging the positive and negative of the vertical coordinates of the target positioning particle points to determine the positioning deviation trend directions, precise control optimization and production quality improvement can be achieved.

[0048] In a possible implementation manner, step S332 further includes: Step S3321: Extract the centroids of the K positioning trend analysis spaces respectively and use them as K starting particle points.

[0049] Step S3322: Construct a weight center iteration formula, where the weight center iteration formula is: .

[0050] Where, is the coordinate value of the stage positioning particle point, is the stage positioning particle point, is the set of positioning particle points in the positioning trend analysis space, is the i-th positioning particle point in the set of positioning particle points, is the coordinate value of the i-th positioning particle point in the set of positioning particle points, is the starting particle point, is the coordinate value of the starting particle point.

[0051] Step S3323: Input the K starting coordinate values of the K starting particle points and the K sets of positioning coordinate values of the K sets of positioning particle points into the weight center iteration formula to obtain K stage positioning particle points.

[0052] Step S3324: Starting from the K stage positioning particle points, perform drift iteration analysis in the K positioning trend analysis spaces until the preset number of iterations is satisfied to obtain K target positioning particle points.

[0053] Specifically, during the process of controlling a single piezoelectric ceramic sheet, for the K constructed positioning trend analysis spaces, calculate the centroid of each space respectively. The position of this centroid comprehensively considers the distribution of all positioning particle points within the space. Then, set the positions of these centroids as the K starting particle points. The purpose of doing this is to provide a relatively representative starting position for subsequent iteration analysis, which helps to more efficiently and accurately find the target positioning particle points that can reflect the positioning trend.

[0054] In the control of a single piezoelectric ceramic sheet, a weight center iteration formula is constructed, and each term in this formula has a specific meaning. is the coordinate value of the stage positioning particle point, representing the position of the particle point obtained after each iteration calculation; is the stage positioning particle point, which will be continuously updated during the iteration process is the set of positioning particle points in the positioning trend analysis space, which contains multiple specific particle points; represents the th positioning particle point in the set of positioning particle points; the th coordinate value of the positioning particle point in the set of positioning particle points; is the starting particle point, which is the starting position of the iteration; is the coordinate value of the starting particle point. This formula realizes the iterative calculation of the stage positioning particle point by comprehensively considering factors such as the starting particle point, the positions and quantities of each particle point in the set of positioning particle points, so as to more accurately determine the positioning trend.

[0055] For the K obtained starting particle points, each starting particle point has a corresponding starting coordinate value, and there are also K sets of positioning particle points, and each set also has its own positioning coordinate values. Input the K starting coordinate values of these K starting particle points and the K sets of positioning coordinate values of the corresponding K sets of positioning particle points into the previously constructed weight center iteration formula. Through the operation and calculation of the formula, a new position can be obtained for each starting particle point and the corresponding set of positioning particle points, that is, K stage positioning particle points are obtained.

[0056] Take the obtained K stage positioning particle points as starting points, and in the K positioning trend analysis spaces, use the mean shift algorithm to perform drift iteration analysis. The mean shift algorithm will continuously adjust the positions of the particle points according to the data distribution around the stage positioning particle points, making them move towards the direction with higher data density. Continue such iterative operations until the preset number of iterations is reached. At this time, the K particle points obtained are the K target positioning particle points that can accurately reflect the positioning trend.

[0057] In a possible implementation manner, step S500 further includes: Step S510: Respectively extract the K response rate sets of the K positioning deviation optimization records.

[0058] Step S520: Construct curves for the K response rate sets in chronological order to generate K response rate curves.

[0059] Step S530: Perform linear fitting on the K response rate curves to generate K control response factors.

[0060] Specifically, for the obtained K records regarding positioning deviation optimization, from each such record, respectively extract the corresponding response rate sets. Here, the response rate refers to the speed performance of the piezoelectric ceramic actuator driving the piezoelectric ceramic when a positioning deviation instruction is received.

[0061] For the previously extracted K response rate sets, process them in chronological order. Using time as the abscissa and the response rate as the ordinate, for each response rate set, mark the response rate values corresponding to different time points one by one in the coordinate system, and then, by connecting these marked points, construct a continuous curve. Such an operation is performed once for each response rate set, and finally K response rate curves are generated.

[0062] For the K response rate curves constructed and generated previously, a linear fitting method is used for processing. Linear fitting is a mathematical method aimed at finding a straight line such that the sum of the distances between this straight line and the given curve data points is minimized. The least squares method is selected. By calculating the sum of the squares of the vertical distances between the data points and the fitting straight line, and finding the straight line parameters that minimize this sum of squares. For each response rate curve, each time point on the curve is used as the independent variable, and the corresponding response rate value is used as the dependent variable. Then, the least squares method is used to calculate the straight line equation that best fits these data points, usually expressed in the form of y = ax + b, where a and b are the straight line parameters obtained through calculation. After the fitting is completed, the obtained straight line parameter a can be regarded as a control response factor, which can reflect the general change trend and degree of the response rate over time. These control response factors are of great significance. If the value of a is large, it indicates that the response rate increases or decreases relatively rapidly over time; conversely, if the value of a is small, it means that the change in the response rate is relatively gentle. By comparing these K control response factors, the differences and characteristics in the response performance of different piezoelectric ceramic monoliths can be intuitively understood, and then the control strategy can be optimized accordingly, such as adjusting the driving voltage, the frequency of the control signal, etc., to achieve a more ideal control effect.

[0063] In a possible implementation manner, step S600 further includes: Step S610: Using the preset interference factor set as an index, retrieve the K positioning deviation optimization records to generate K positioning interference factor sets.

[0064] Step S620: Construct an interference fitting device.

[0065] Step S630: Use the interference fitting device to analyze the K positioning interference factor sets to generate the K interference fitting factors.

[0066] Specifically, a preset interference factor set is first set, which includes electromagnetic interference, temperature and humidity change factors, noise interference, and signal channel interference. Using each factor in this preset interference factor set as an index or keyword, a comprehensive and detailed retrieval is performed on the K positioning deviation optimization records owned. During the retrieval process, carefully check the content of each positioning deviation optimization record to find the information related to the factors in the preset interference factor set. Similarly, the same operation is performed on the remaining K - 1 positioning deviation optimization records, and finally K corresponding positioning interference factor sets are generated. In this way, the interference factors involved in each positioning deviation optimization record are clearly sorted out and summarized, providing a basis for further in-depth analysis and processing.

[0067] The constructed interference fitting model is a multi-layer perceptron neural network model. First, the model structure is determined. Assuming that there are 20 features for the positioning interference factors, 20 neurons are set in the input layer to receive these feature data. Two hidden layers are set. 100 neurons are arranged in the first hidden layer and 50 neurons are arranged in the second hidden layer. The rectified linear unit (ReLU) is used as the activation function for both of these two hidden layers, enabling the neurons to maintain a linear output when the input is positive and output 0 when the input is negative. This can avoid the problem of gradient disappearance and contribute to the learning of the neural network. 1 neuron is set in the output layer, and the choice of the activation function depends on the specific fitting task and can be a linear function. Next are the algorithm steps for training this neural network: Initialization, assign random initial values to the weights of the connections between all neurons in the neural network; Forward propagation, input the data of the positioning interference factors into the input layer. After the data is calculated and processed by the weights in the input layer, it is transmitted to the first hidden layer. In the hidden layer, each neuron processes the weighted sum of the input through the activation function to obtain the output of the neuron. This output is then used as the input for the next layer, and so on, until the final prediction result is obtained in the output layer. Calculate the loss, compare the prediction result obtained in the output layer with the actual true value. The mean squared error (MSE) is used to measure the gap between the predicted value and the true value as the loss value. The calculation of the mean squared error is to first calculate the difference between the predicted value and the true value for each sample, and then square these differences and take the average. Backward propagation, starting from the output layer, calculate the gradient of each neuron's weight according to the loss value. This gradient indicates the direction and magnitude of the weight adjustment required to reduce the loss, and then propagates layer by layer from the output layer to the input layer to calculate the weight gradient of each layer of neurons. Weight update, use the stochastic gradient descent algorithm to update the weights. Multiply the learning rate by the gradient of the weights to obtain the adjustment amount of the weights, and then subtract this adjustment amount from the current weights to achieve the weight update. Continuously repeat the above processes of forward propagation, calculating the loss, backward propagation, and weight update until the loss value is small enough or a predetermined number of training times is reached. At this time, the neural network has learned the relationship between the positioning interference factors and the final fitting result, thus constructing an interference fitting model that can effectively handle the interference factors.

[0068] After successfully constructing the interference fitting device, the next step is to use it to deeply analyze the previously obtained set of K positioning interference factors. The set of these K positioning interference factors is input into the interference fitting device one by one. The interference fitting device will process and calculate each input set of positioning interference factors according to the relationship pattern between the interference factors and the fitting results that it has learned internally. Through the operation and analysis of the interference fitting device, a corresponding interference fitting factor is generated for each set of positioning interference factors. By optimizing the record of positioning deviation and deeply analyzing the interference factors, the factors affecting the positioning accuracy can be understood more accurately, so as to achieve more precise control of the piezoelectric ceramic actuator and improve the positioning accuracy.

[0069] Embodiment 2 Based on the same inventive concept as the single-piece control method of a piezoelectric ceramic in the foregoing embodiment, as Figure 2 shown, the present application provides a single-piece control device for a piezoelectric ceramic. The device in the embodiment of the present application and the method embodiment are based on the same inventive concept. Among them, the device includes: A generation node interaction module 10, which is used to interact with K production nodes of the target production line. Among them, the K production nodes include K piezoelectric ceramic actuators and K piezoelectric ceramics.

[0070] A positioning deviation tolerance bandwidth acquisition module 20, which is used to collect K assembly schemes of the assembly components at the K production nodes, and retrieve the K assembly schemes with the positioning deviation as the index to obtain K positioning deviation tolerance bandwidths.

[0071] A position deviation identification module 30, which is used to use a vision positioning device to identify the position deviation of the positioning tables at the K production nodes in a preset analysis window to obtain K sets of positioning deviations and K positioning offset trend directions.

[0072] A positioning deviation optimization record acquisition module 40, which is used to feedback the K sets of positioning deviations and the K positioning offset trend directions to the K piezoelectric ceramic actuators for positioning deviation optimization to obtain K sets of positioning deviation optimization records.

[0073] A control response factor generation module 50, which is used to perform control response factor fitting on the K sets of positioning deviation optimization records to generate K control response factors.

[0074] Interference fitting factor acquisition module 60, which is configured to retrieve the K positioning deviation optimization records according to a preset interference factor set, input the retrieval results into an interference fitting device for analysis, and obtain K interference fitting factors.

[0075] Control feedback factor acquisition module 70, which is configured to correct the K control response factors by using the K interference fitting factors to obtain K control feedback factors.

[0076] Positioning table movement module 80, which is configured to transmit the K control feedback factors to the K piezoelectric ceramic drivers to drive the K piezoelectric ceramics to move the positioning table.

[0077] Furthermore, the interference fitting factor acquisition module 60 further includes: The preset interference factor set includes electromagnetic interference, temperature and humidity change factors, noise interference, and signal channel interference.

[0078] Furthermore, the position deviation identification module 30 further includes: Positioning table position identification acquisition unit, which uses the vision positioning device to respectively collect the positions of the positioning tables of the K production nodes in a preset analysis window to obtain K positioning table position identification sets.

[0079] Standard positioning table position identification generation unit, which is configured to retrieve the K assembly schemes by using the standard positioning table position as an index to generate K standard positioning table position identifications.

[0080] Position deviation set generation unit, which is configured to identify the position deviations of the K positioning table position identification sets according to the K positioning deviation tolerance bandwidths based on the K standard positioning table position identifications to generate the K position deviation sets, where the K position deviation sets have K deviation direction identification sets, and the deviation direction identifications include positive identifications and negative identifications.

[0081] Position offset trend direction determination unit, which is configured to perform trend analysis on the K position deviation sets to determine K position offset trend directions.

[0082] Furthermore, the position deviation set generation unit further includes: Positioning trend analysis space construction unit, which is used to construct K positioning trend analysis spaces according to the K positioning deviation sets. Among them, the horizontal axis of the K positioning trend analysis spaces is time, the vertical axis is the positioning deviation, and the K positioning trend analysis spaces include K positioning particle point sets.

[0083] Target positioning particle point acquisition unit, which is used to perform drift iteration analysis on the K positioning particle point sets in the K positioning trend analysis spaces to obtain K target positioning particle points.

[0084] When the vertical coordinates of the K target positioning particle points are negative values, the K positioning offset trend directions are underfitting directions.

[0085] When the vertical coordinates of the K target positioning particle points are positive values, the K positioning offset trend directions are overfitting directions.

[0086] Further, the target positioning particle point acquisition unit further includes: Starting particle point acquisition unit, which is used to extract the centroids of the K positioning trend analysis spaces respectively and use them as K starting particle points.

[0087] Weight center iteration formula construction unit, which is used to construct a weight center iteration formula. Among them, the weight center iteration formula is: .

[0088] Among them, is the coordinate value of the stage positioning particle point, is the stage positioning particle point, is the positioning particle point set in the positioning trend analysis space, is the i-th positioning particle point in the positioning particle point set, is the coordinate value of the i-th positioning particle point in the positioning particle point set, is the starting particle point, is the coordinate value of the starting particle point.

[0089] Stage positioning particle point acquisition unit, which is used to input the K starting coordinate values of the K starting particle points and the K positioning coordinate value sets of the K positioning particle point sets into the weight center iteration formula to obtain K stage positioning particle points.

[0090] A drift iteration analysis unit, which is used to start from the K-stage positioned particle points and perform drift iteration analysis in the K positioning trend analysis spaces until a preset number of iterations is satisfied, obtaining K target positioned particle points.

[0091] Further, the control response factor generation module 50 further includes: A response rate set acquisition unit, which is used to respectively extract K response rate sets of the K positioning deviation optimization records.

[0092] A response rate curve generation unit, which is used to construct curves for the K response rate sets in chronological order to generate K response rate curves.

[0093] A response control factor generation unit, which is used to perform linear fitting on the K response rate curves to generate K control response factors.

[0094] Further, the interference fitting factor acquisition module 60 further includes: A positioning interference factor set generation unit, which is used to retrieve the K positioning deviation optimization records with a preset interference factor set as an index to generate K positioning interference factor sets.

[0095] An interference fitting device construction unit, which is used to construct an interference fitting device.

[0096] An interference fitting factor generation unit, which is used to analyze the K positioning interference factor sets by using the interference fitting device to generate the K interference fitting factors.

[0097] It should be noted that the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above describes specific embodiments of this specification. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0098] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0099] This specification and the accompanying drawings are merely exemplary illustrations of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, provided that these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications therein.

Claims

1. A monolithic control method of piezoelectric ceramics, characterized in that: The method is applied to a single-chip control device, the device is communicatively connected with K piezoelectric ceramic drivers, and the method comprises: K production nodes of an interactive target production line, wherein the K production nodes include K piezoelectric ceramic drivers and K piezoelectric ceramics; Collect K assembly plans of assembly components at the K production nodes, use positioning deviation as an index, search the K assembly plans, and obtain K positioning deviation tolerance bandwidths; Using a visual positioning device to identify position deviations of the positioning platforms of the K production nodes in a preset analysis window, and obtaining K positioning deviation sets and K positioning deviation trend directions; Feeding back the K positioning deviation sets and the K positioning offset trend directions to the K piezoelectric ceramic drivers to optimize the positioning deviations, and obtaining K positioning deviation optimization record sets; Performing control response factor fitting on the K positioning deviation optimization record sets to generate K control response factors; Retrieving the K positioning deviation optimization records according to a preset interference factor set, inputting the retrieval results into an interference fitter for analysis, and obtaining K interference fitting factors; Using the K interference fitting factors to correct the K control response factors, to obtain K control feedback factors; The K control feedback factors are transmitted to the K piezoelectric ceramic drivers to drive the K piezoelectric ceramics to move the positioning stage.

2. The method according to claim 1, characterized in that The preset interference factor set includes electromagnetic interference, temperature and humidity change factors, noise interference and signal channel interference.

3. The method according to claim 1, characterized in that The K positioning deviation trend directions include: Using the visual positioning device to collect positions of the positioning platforms of the K production nodes in a preset analysis window, respectively, to obtain a set of K positioning platform position identifiers; Using the standard positioning platform position as an index, the K assembly solutions are searched to generate K standard positioning platform position identifiers; Taking the K standard positioning station position identifiers as a reference, performing position deviation identification on the K positioning station position identifier sets according to the K positioning deviation tolerance bandwidths to generate the K positioning deviation sets, wherein the K positioning deviation sets have K deviation direction identifier sets, and the deviation direction identifiers include positive identifiers and negative identifiers; A trend analysis is performed on the K positioning deviation sets to determine K positioning deviation trend directions.

4. The method according to claim 3, characterized in that Performing position deviation identification on the K positioning platform position identification sets to generate the K positioning deviation sets includes: Constructing K positioning trend analysis spaces according to the K positioning deviation sets, wherein the abscissa axis of the K positioning trend analysis spaces is time, the ordinate axis is positioning deviation, and the K positioning trend analysis spaces include K positioning particle point sets; Performing drift iteration analysis on the K positioning particle point sets in the K positioning trend analysis spaces to obtain K target positioning particle points; When the ordinates of the K target positioning particle points are negative, the K positioning offset trend directions are underfitting directions; When the ordinates of the K target positioning particle points are positive values, the K positioning deviation trend directions are overfitting directions.

5. The method according to claim 4, characterized in that Obtaining K target positioning particle points includes: Extracting the centers of gravity of the K positioning trend analysis spaces respectively and using them as K starting particle points; Construct a weight center iteration formula, wherein the weight center iteration formula is: ； in, is the coordinate value of the particle point for stage positioning, Position the particle point for the stage, To locate the set of particle points in the trend analysis space, is the i-th positioning particle point in the positioning particle point set, is the coordinate value of the i-th positioning particle point in the positioning particle point set, is the starting particle point, is the coordinate value of the starting particle point; Inputting the K starting coordinate values ​​of the K starting particle points and the K positioning coordinate value sets of the K positioning particle point sets into the weight center iteration formula to obtain K stage positioning particle points; Taking the K stage positioning particle points as the starting point, drift iterative analysis is performed in the K positioning trend analysis spaces until a preset number of iterations is met to obtain K target positioning particle points.

6. The method according to claim 1, characterized in that The control response factor fitting is performed on the K positioning deviation optimization record sets to generate K control response factors, including: Respectively extracting K response rate sets of the K positioning deviation optimization records; Constructing a curve for the K response rate sets in chronological order to generate K response rate curves; Linear fitting is performed on the K response rate curves to generate K response control factors.

7. The method according to claim 1, characterized in that The search results are input into the interference fitter for analysis, and K interference fitting factors are obtained, including: Using a preset interference factor set as an index, searching the K positioning deviation optimization records to generate K positioning interference factor sets; Construct a disturbance fitter; The interference fitter is used to analyze the K positioning interference factor sets to generate the K interference fitting factors.

8. A piezoelectric ceramic monolithic control device, characterized in that: The device is used to implement a single-chip control method for piezoelectric ceramics according to any one of claims 1 to 7, and the device comprises: A generation node interaction module, wherein the generation node interaction module is used to interact with K production nodes of a target production line, wherein the K production nodes include K piezoelectric ceramic drivers and K piezoelectric ceramics; A positioning deviation tolerance bandwidth acquisition module, wherein the positioning deviation tolerance bandwidth acquisition module is used to collect K assembly schemes of assembly components at the K production nodes, retrieve the K assembly schemes using the positioning deviation as an index, and obtain K positioning deviation tolerance bandwidths; A position deviation identification module, wherein the position deviation identification module is used to identify the position deviation of the positioning platforms of the K production nodes in a preset analysis window using a visual positioning device, and obtain K positioning deviation sets and K positioning deviation trend directions; A positioning deviation optimization record acquisition module, the positioning deviation optimization record acquisition module is used to feed back the K positioning deviation sets and the K positioning deviation trend directions to the K piezoelectric ceramic drivers to optimize the positioning deviation, and obtain K positioning deviation optimization record sets; A control response factor generation module, the control response factor generation module is used to perform control response factor fitting on the K positioning deviation optimization record sets to generate K control response factors; An interference fitting factor acquisition module, wherein the interference fitting factor acquisition module is used to retrieve the K positioning deviation optimization records according to a preset interference factor set, input the retrieval results into an interference fitter for analysis, and obtain K interference fitting factors; A control feedback factor acquisition module, wherein the control feedback factor acquisition module is used to modify the K control response factors using the K interference fitting factors to obtain K control feedback factors; A positioning stage moving module is used to transmit the K control feedback factors to the K piezoelectric ceramic drivers to drive the K piezoelectric ceramics to move the positioning stage.

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