A single-chip control method and device for piezoelectric ceramics

Abstract

The present invention discloses a monolithic control method and device for piezoelectric ceramics, relating to the field of monolithic control technology. The method comprises: interacting with the production nodes of a target production line, collecting assembly plans for assembly components, and obtaining a positioning deviation tolerance bandwidth; using a visual positioning device to obtain a positioning deviation set and a positioning offset trend direction; optimizing the positioning deviation to obtain a positioning deviation optimization record set; fitting the positioning deviation optimization record set with a control response factor to generate a control response factor; searching the positioning deviation optimization record to obtain an interference fitting factor; correcting the control response factor to obtain a control feedback factor, and moving the positioning stage. The present invention solves the technical problem 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. The present invention achieves the technical effect of realizing high-precision and intelligent positioning control, improving production efficiency and product quality.

Description

Technical Field

[0001] The present invention relates to the technical field of single-chip control, and in particular to a single-chip control method and device for piezoelectric ceramics. Background Art

[0002] Modern industrial production and scientific research are increasingly demanding on precision and performance. Piezoelectric ceramics, due to their unique electrostrictive properties, are widely used in fields such as precision positioning, micromanipulation, and nanotechnology. However, traditional piezoelectric ceramic control methods have numerous limitations. Previous control methods often lack sufficient precision to meet the increasingly demanding production and research needs. For example, in high-precision operations such as semiconductor manufacturing and optical instrument debugging, even slight positioning deviations can lead to reduced product quality or inaccurate experimental results.

[0003] The existing technology has technical problems such as low control method accuracy and lack of intelligence, which leads to deviations in product assembly and thus reduces product quality. Summary of the Invention

[0004] The present application provides a single-chip control method and device for piezoelectric ceramics, which is used to solve the technical problems in the prior art of low control accuracy and lack of intelligence, which leads to deviations in product assembly and thus reduces product quality.

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

[0006] A first aspect of the present application provides a method for controlling a single piece of piezoelectric ceramic, the method comprising:

[0007] K production nodes of an interactive target production line, wherein the K production nodes include K piezoelectric ceramic drivers and K piezoelectric ceramics; collecting K assembly plans of assembly components at the K production nodes, searching the K assembly plans with positioning deviation as an index, and obtaining K positioning deviation tolerance bandwidths; using a visual positioning device to identify position deviations of the positioning stages of the K production nodes in a preset analysis window, and obtaining K positioning deviation sets and K positioning deviation trend directions; and feeding back the K positioning deviation sets and the K positioning deviation trend directions to the K piezoelectric ceramic drivers. Perform 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 fitter for analysis, and 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 stage.

[0008] A second aspect of the present application provides a monolithic piezoelectric ceramic control device, the device comprising:

[0009] A generation node interaction module is used to interact with K production nodes of the target production line, wherein the K production nodes include K piezoelectric ceramic drivers and K piezoelectric ceramics; a positioning deviation tolerance bandwidth acquisition module is used to collect K assembly plans of assembly components at the K production nodes, and retrieve the K assembly plans with positioning deviation as an index to obtain K positioning deviation tolerance bandwidths; a position deviation identification module is used to use a visual positioning device to identify the position deviation of the positioning platform of the K production nodes in a preset analysis window to obtain K positioning deviation sets and K positioning deviation trend directions; a 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 ceramics. The driver performs positioning deviation optimization to 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, 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, the control feedback factor acquisition module is used to correct the K control response factors using the K interference fitting factors to obtain K control feedback factors; a positioning stage movement module, the positioning stage movement module is used to transmit the K control feedback factors to the K piezoelectric ceramic drivers, and drive the K piezoelectric ceramics to perform positioning stage movement.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] The system interacts with K production nodes of the target production line; collects K assembly plans for assembly components at these K production nodes, retrieves these plans, and obtains K positioning deviation tolerance bandwidths; identifies position deviations on the positioning stages of the K production nodes, obtaining K positioning deviation sets and K positioning deviation trend directions; feeds these K positioning deviation sets and K positioning deviation trend directions back to K piezoelectric ceramic drivers for positioning deviation optimization, obtaining K positioning deviation optimization record sets; performs control response factor fitting on the K positioning deviation optimization record sets to generate K control response factors; retrieves these K positioning deviation optimization records according to a preset interference factor set, inputs the retrieval results into an interference fitter for analysis, and obtains K interference fitting factors; uses the K interference fitting factors to correct the K control response factors, obtaining K control feedback factors; and transmits the K control feedback factors to K piezoelectric ceramic drivers, which drive the K piezoelectric ceramics to move the positioning stage. This achieves the technical effect of achieving high-precision and intelligent positioning control, improving production efficiency and product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 A schematic flow chart of a method for controlling a single piece of piezoelectric ceramic provided in an embodiment of the present application;

[0014] Figure 2 A schematic structural diagram of a piezoelectric ceramic monolithic control device provided in an embodiment of the present application.

[0015] Explanation 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 platform movement module 80. DETAILED DESCRIPTION

[0016] The present application provides a single-chip control method and device for piezoelectric ceramics, which is 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.

[0017] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative work are within the scope of protection of this application.

[0018] Example 1

[0019] like Figure 1 As shown, the present application provides a single-chip control method for piezoelectric ceramics, the method comprising:

[0020] Step S100: interacting with K production nodes of a target production line, wherein the K production nodes include K piezoelectric ceramic drivers and K piezoelectric ceramics.

[0021] Specifically, effective interactions are established with K production nodes in the target production line. This process first involves identifying K specific locations or links within the target production line that play a critical role, defining them as production nodes. Each production node is equipped with specific components, including a piezoelectric ceramic actuator and a piezoelectric ceramic. The piezoelectric ceramic actuator provides both drive and control functions, sending precise electrical signals to the piezoelectric ceramic to induce a specific physical response. Piezoelectric ceramics are materials with unique properties that enable them to rapidly and precisely change shape, size, or position in response to electrical signals from the actuator. These changes are crucial for precise positioning, fine-tuning, or executing specific actions within the production line. For example, if K is 10, this means there are 10 such production nodes in the target production line. At each node, a piezoelectric ceramic actuator and a piezoelectric ceramic work together to ensure precise operation and efficient production.

[0022] Step S200: collecting K assembly plans of assembly components at the K production nodes, searching the K assembly plans using positioning deviation as an index, and obtaining K positioning deviation tolerance bandwidths.

[0023] Specifically, K assembly schemes for assembling components on K production nodes are collected. These assembly schemes contain specific methods and parameters on how to assemble at each production node. Then, these K assembly schemes are retrieved using positioning deviation as the key index. 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. 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 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 requirements for assembly accuracy are more stringent. Clarifying the acceptable positioning deviation range for each production node helps to control the assembly accuracy more accurately.

[0024] Step S300: 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 offset trend directions.

[0025] Specifically, an advanced visual positioning device was introduced, 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, its size, shape, and position precisely calculated and planned to ensure accurate coverage of the positioning stages of K production nodes. When the visual positioning device begins operation, it continuously and rapidly captures images of the positioning stages of each production node within the preset analysis window and performs real-time analysis on the captured images. During the analysis, the captured real-time images of the positioning stages are compared and calculated with preset standard position images to accurately determine the difference between the actual and ideal positions of the positioning stages at each production node in three-dimensional space. For each production node's positioning stage, a set of multiple deviation values is generated, each reflecting the positional deviation 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 platform will be determined, such as whether it is offset in the positive or negative direction along a certain coordinate axis, or presenting a specific angular offset trend within the plane. This can accurately identify the position deviation of the positioning platform at each production node, providing an accurate data basis for subsequent adjustments and optimizations.

[0026] Step S400: Feeding back 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.

[0027] Specifically, in this step, the K previously acquired sets of positioning deviations and K positioning deviation trend directions are fed back to the corresponding K piezoelectric ceramic actuators. Position deviation optimization is performed using a particle swarm optimization algorithm. Each combination of positioning deviations and positioning deviation trend directions is considered a characteristic of a particle. Each particle represents a possible positioning deviation optimization solution. Particles have two properties: position and velocity. Position represents the current solution, while velocity determines the direction and magnitude of solution changes. The algorithm initializes a swarm of particles, randomly generating an initial set of positioning deviation optimization solutions. The performance of each particle is evaluated using a fitness function, which can be designed based on factors such as the degree of positioning deviation reduction and the stability of the adjustment. In each iteration, the particle updates its velocity and position based on its own optimal position (individual optimum) and the optimal position found by the entire particle swarm (global optimum) to find a more optimal positioning deviation optimization solution. Once the final optimized solution is obtained, the piezoelectric ceramic actuator is adjusted according to this solution. During the adjustment process, the following information is monitored in real time: changes in specific adjustment parameters, the time required for adjustment, and the immediate positioning deviation status after each adjustment. This information is recorded in detail to form a positioning deviation optimization record. Since there are K piezoelectric ceramic drivers, K such positioning deviation optimization records will eventually be obtained, forming a set of K positioning deviation optimization records, generating a precise adjustment plan, so that the positioning deviation can be corrected more accurately and the production accuracy can be improved.

[0028] Step S500: performing control response factor fitting on the K positioning deviation optimization record sets to generate K control response factors.

[0029] Specifically, K response rate sets are extracted from K positioning deviation optimization records. The response rate refers to the speed of change in the positioning deviation during the optimization process. Next, the data from these K response rate sets are organized and constructed in chronological order to generate K response rate curves. Each curve reflects the change in the response rate of the corresponding production node over time during the positioning deviation optimization process. These K response rate curves are then linearly fitted. The goal of linear fitting is to find a straight line that best represents the trend of these curves. Through the fitting process, parameters such as the slope and intercept of the line are calculated. Finally, based on the fitted parameters, a control response factor is generated for each production node. This control response factor quantitatively describes 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, thereby optimizing the control strategy and improving control accuracy and efficiency.

[0030] Step S600: searching the K positioning deviation optimization records according to a preset interference factor set, inputting the search results into an interference fitter for analysis, and obtaining K interference fitting factors.

[0031] Specifically, a preset set of interference factors is first established. This set includes various factors that affect the positioning deviation optimization records, such as changes in ambient temperature and humidity, equipment vibration, power supply fluctuations, etc. Then, according to these preset interference factors, K positioning deviation optimization records are searched. The purpose of the search is to find information and data related to the interference factors in these records. Next, the search results are input into the interference fitter, which is a tool or model specifically used to analyze the relationship between interference factors and positioning deviation optimization records. Through the analysis and processing of the interference fitter, K interference fitting factors are ultimately obtained. These interference fitting factors reflect the extent and manner in which each positioning deviation optimization record is affected by various preset interference factors, helping to more accurately predict the results of positioning deviation optimization under different interference conditions, providing a more precise basis for production planning and quality control.

[0032] Step S700: using the K interference fitting factors to correct the K control response factors to obtain K control feedback factors.

[0033] Specifically, in this step, the K interference fitting factors obtained previously 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 positioning deviation optimization record. The control response factor, on the other hand, quantifies the response characteristics of the production node during the positioning deviation optimization process. By combining the interference fitting factors with the control response factors for correction, the actual control response of the production node in the presence of actual interference can be more accurately reflected. This aims to make the control feedback factors more consistent with actual production scenarios, providing a more valuable reference for subsequent control strategy adjustments and optimization.

[0034] Step S800: transmitting the K control feedback factors to the K piezoelectric ceramic drivers to drive the K piezoelectric ceramics to move the positioning platform.

[0035] Specifically, the K control feedback factors obtained previously are regarded as loss factors and are transmitted to the corresponding K piezoelectric ceramic drivers. These control feedback factors contain key information about positioning deviations and interference effects after a series of analyses and corrections. When the piezoelectric ceramic driver receives these control feedback factors, it will drive the corresponding K positioning stages to move according to the instructions therein. The purpose of moving the positioning stage is to facilitate the accurate positioning and assembly of assembly components. By accurately transmitting the control feedback factors and driving the piezoelectric ceramic driver to move the positioning stage, high-precision assembly of components can be achieved, improving the quality and performance of the product.

[0036] In one possible implementation, step S600 further includes:

[0037] Step S610: The preset interference factor set includes electromagnetic interference, temperature and humidity change factors, noise interference and signal channel interference.

[0038] Specifically, the preset interference factor set includes: electromagnetic interference, which refers to interference caused by electromagnetic waves. In a production environment, electromagnetic fields generated by other electrical equipment, wires, wireless communications, etc. can affect the electronic components and signal transmission of the positioning system, resulting in positioning deviation; temperature and humidity changes. Fluctuations in temperature and humidity can affect the physical properties of materials, such as causing components to expand or contract, thereby changing the accuracy of positioning. At the same time, it can also affect the performance and precision of electronic components; noise interference, which can be mechanical noise, electrical noise, etc. Mechanical noise, such as vibration and friction during equipment operation, can cause slight displacement of the positioning platform. Electrical noise can interfere with the signal acquisition and transmission of the sensor, causing misjudgment of positioning deviation; signal channel interference, which involves the lines and channels of signal transmission in the positioning system. For example, poor shielding of signal cables, attenuation during signal transmission, crosstalk between multiple signals, etc., may distort the positioning-related signals or lose some information, affecting the accuracy of positioning. Taking these interference factors into consideration can more accurately control the movement of piezoelectric ceramics, reduce positioning errors caused by interference, and improve the accuracy of single-chip piezoelectric ceramic control.

[0039] In one possible implementation, step S300 further includes:

[0040] Step S310: using the visual positioning device to collect positions of the positioning platforms of the K production nodes in a preset analysis window, and obtaining a set of K positioning platform position identifiers.

[0041] Step S320: Using the standard positioning platform position as an index, the K assembly solutions are searched to generate K standard positioning platform position identifiers.

[0042] Step S330: Based on the K standard positioning platform position identifiers, position deviation identification is performed on the K positioning platform 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.

[0043] Step S340: performing trend analysis on the K positioning deviation sets to determine K positioning offset trend directions.

[0044] Specifically, the visual positioning device mentioned is a high-precision measurement device equipped with advanced image acquisition and processing capabilities. The preset analysis window is a specific area in space precisely defined based on production needs and process requirements. For each of the K production nodes, the visual positioning device operates sequentially in a predetermined order. When it comes time to acquire the position of the positioning stage at a specific production node, the device activates its image acquisition component. Within the preset analysis window, the visual positioning device captures a large amount of image information from multiple angles and positions. These images not only capture the overall appearance of the positioning stage but also focus on key positional features, such as specific markings, corners, and holes. Using advanced image processing algorithms, the device accurately extracts the spatial coordinates, pose, and other relevant positional parameters of these feature points. These acquired positional parameters are organized and collated according to a specific logic and format. This processing yields a clearly structured and comprehensive set of positioning stage position identifiers for each production node. This set comprehensively and accurately describes the positioning stage's position within the preset analysis window.

[0045] The standard positioning stage position is an ideal, precisely measured and rigorously defined ideal position state. It contains comprehensive positional information, including the stage's precise coordinates, orientation, and angles in space. This information serves as a benchmark for subsequent comparisons and judgments. The K assembly plans presented are a series of detailed technical documents or data sets that cover the specific configurations and arrangements of the positioning stage in different production scenarios. These plans include different component combinations, process parameters, and corresponding positioning stage position settings. Using the standard positioning stage position as an index, the system analyzes all data fields and parameters related to the positioning stage position for each assembly plan, examining numerous detailed positional features, including the positioning stage's geometric center coordinates, displacement along each axis, rotation angle, and tilt. These extracted positional features are then compared one by one with the corresponding features of the standard positioning stage position. Through this comprehensive and in-depth comparison and analysis, a highly accurate and detailed standard positioning stage position signature is generated for each assembly plan.

[0046] There are K standard positioning station location markers, which serve as benchmarks for measurement. Each standard positioning station location marker is unique and deterministic. There are also K corresponding positioning deviation tolerance bandwidths, which define the acceptable range of positioning deviation. Operations are performed on the K positioning station location marker sets, using each standard positioning station location marker as a core reference. Each specific location marker in the corresponding positioning station location marker set is compared with it. During the comparison, if a positioning station location marker is greater than the corresponding standard positioning station location marker, it is assigned a positive label. This means that in the subsequent positioning trend analysis space, its corresponding vertical coordinate will be marked as positive. Conversely, if a positioning station location marker is less than or equal to the standard positioning station location marker, it is assigned a negative label. Accordingly, in the subsequent positioning trend analysis space, its vertical coordinate will be marked as negative. Through this meticulous comparison and identification, a specific positioning deviation set is generated for each production node. Each positioning deviation set not only contains the specific deviation value but also a corresponding deviation direction identifier set.

[0047] A trend analysis is performed on the K positioning deviation sets obtained previously. Each positioning deviation set contains a series of deviation data. By arranging and comparing these data in an orderly manner, it is determined whether the deviation is gradually increasing, gradually decreasing, remaining stable, or showing a periodic trend. To determine the trend direction of the K positioning deviations, it is necessary to clarify the direction of change of the positioning platform deviation corresponding to each production node. For example, if the data in a certain positioning deviation set shows a trend of gradually increasing, then the positioning deviation trend direction is positive; if the data gradually decreases, the trend direction is negative; if the data basically fluctuates within a small range, the trend direction can be considered to be relatively stable. By clarifying the positioning deviation trend direction, the driving parameters of the piezoelectric ceramic can be adjusted more accurately, making the control of the single-chip piezoelectric ceramic more precise and improving its performance.

[0048] In one possible implementation, step S330 further includes:

[0049] Step S331: constructing K positioning trend analysis spaces according to the K positioning deviation sets, wherein the horizontal axis of the K positioning trend analysis spaces is time, the vertical axis is positioning deviation, and the K positioning trend analysis spaces include K positioning particle point sets.

[0050] Step S332: performing drift iterative analysis on the K positioning particle point sets in the K positioning trend analysis spaces to obtain K target positioning particle points.

[0051] Step S333: When the vertical coordinates of the K target positioning particle points are negative values, the K positioning offset trend directions are underfitting directions.

[0052] Step S334: When the vertical coordinates of the K target positioning particle points are positive values, the K positioning offset trend directions are overfitting directions.

[0053] Specifically, K sets of positioning deviations associated with a single piezoelectric ceramic are obtained, and based on these sets, K unique positioning trend analysis spaces are constructed. Setting the horizontal axis to time is crucial. The time axis is scaled to specific moments or time periods, reflecting the sequence of each measurement, adjustment, or control operation on the single piezoelectric ceramic. This allows tracking the evolution of positioning deviations over time. The vertical axis is set to positioning deviations, which directly quantify the degree of deviation of the single piezoelectric ceramic from the expected or standard position. This setting clearly identifies the magnitude and direction of the deviation. Each positioning trend analysis space contains a specific set of positioning particle points, representing the specific positioning deviation values obtained from the inspection and evaluation of the single piezoelectric ceramic at different time points. For example, eight different single piezoelectric ceramics (K=8) are being controlled. For each single piezoelectric ceramic, a unique analysis space is constructed based on its corresponding positioning deviation set. In the positioning trend analysis space for the first single-piece piezoelectric ceramic, as time passes, assuming that the positioning deviation is measured to be +0.02 mm at 9:00 AM, a positioning particle point is marked at (9:00, +0.02 mm). If the positioning deviation is measured to be -0.01 mm at 2:00 PM, another particle point is marked at (2:00 PM, -0.01 mm). Similarly, analysis spaces containing a series of positioning particle points are constructed for the other seven single-piece piezoelectric ceramics. This construction method helps to comprehensively, intuitively, and accurately understand the changes in the positioning deviation of each single-piece piezoelectric ceramic over time, providing a solid data foundation and visual analysis tools for subsequent precise control and optimization.

[0054] In the K positioning trend analysis spaces constructed for piezoelectric ceramic monolithic control, the mean shift algorithm is used to perform drift iterative analysis on these K positioning particle point sets. The mean shift algorithm calculates the density distribution of the positioning particle points and then allows the analysis points to "drift" in the direction of increasing density, gradually converging to the area with the highest density. In each positioning trend analysis space, the algorithm performs multiple iterative calculations based on the position and deviation value of the particle points. For each positioning particle point set corresponding to a single piezoelectric ceramic, the mean shift algorithm continuously adjusts the position of the analysis point, gradually bringing it closer to the center of the particle point distribution or a representative position. After a series of iterations, a point in the space that best represents the overall characteristics of the particle point set is finally determined, which is the target positioning particle point.

[0055] When the ordinates of the K target positioning particle points are negative, this means that within the positioning trends of the K single chips, their actual positions deviate from the standard positions and are smaller than the standard positions. This indicates that the current control adjustments are not sufficient to achieve the ideal position of the piezoelectric ceramic single chip. This can be understood as insufficient control or flaws in the adjustment strategy, resulting in the positional offset of the single chip failing to meet expectations. At this point, the trend direction of these K positioning offsets is determined to be underfitting. This suggests that further optimization and improvement of the control parameters, drive voltage, or other related control factors is needed to increase the adjustment force and enable the piezoelectric ceramic single chip to approach or reach the standard position.

[0056] When the ordinates of the K target positioning particle points are positive, this indicates that the actual positions of these K monolithic chips have deviated from the standard position, and the amount of the deviation exceeds the standard position. This situation means that the current control adjustment is excessive, and the effect on the piezoelectric ceramic monolithic chip exceeds the expected value, causing its position to deviate too much from the standard. Therefore, the trend direction of these K positioning offsets is determined to be overfitting. This suggests that the control strategy needs to be adjusted in the opposite direction, such as reducing the drive voltage and modifying the control parameters, to reduce the adjustment force and adjust the position of the piezoelectric ceramic monolithic chip back to near or reach the standard position. By constructing a positioning trend analysis space, performing drift iterative analysis, and determining the positive or negative ordinates of the target positioning particle points to determine the positioning offset trend direction, precise control optimization can be achieved and production quality can be improved.

[0057] In one possible implementation, step S332 further includes:

[0058] Step S3321: extract the centers of gravity of the K positioning trend analysis spaces respectively and use them as K starting particle points.

[0059] Step S3322: Construct a weight center iteration formula, wherein the weight center iteration formula is:

[0060] .

[0061] 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.

[0062] Step S3323: 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.

[0063] Step S3324: Taking the K stage positioning particle points as the starting point, perform drift iterative analysis in the K positioning trend analysis spaces until a preset number of iterations is met to obtain K target positioning particle points.

[0064] Specifically, during the control of the piezoelectric ceramic monolithic chip, the center of gravity of each of the K constructed positioning trend analysis spaces is calculated. The location of this center of gravity comprehensively considers the distribution of all positioning particle points within the space. These centers of gravity are then set as the K starting particle points. This provides a relatively representative starting position for subsequent iterative analysis, helping to more efficiently and accurately find the target positioning particle points that reflect the positioning trend.

[0065] In the monolithic control of piezoelectric ceramics, a weight center iteration formula is constructed, and each term in this formula has a specific meaning. The coordinate value of the particle point for stage positioning represents the position of the particle point obtained after each iterative calculation; Position the particle point for the stage, which will be continuously updated during the iteration process To locate the set of particle points in the trend analysis space, it contains multiple specific particle points; Represents the first Positioning particle points; The first particle point in the set of positioning The coordinate values of the 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 implements iterative calculation of the stage positioning particle points by comprehensively considering factors such as the starting particle point and the position and number of each particle point in the positioning particle point set, thereby more accurately determining the positioning trend.

[0066] Take the K starting particle points you've obtained, each with a corresponding starting coordinate value, and K sets of positioning particle points, each with its own positioning coordinate value. Input the K starting coordinate values of these K starting particle points and the K positioning coordinate values of the corresponding K sets of positioning particle points into the previously constructed weight center iteration formula. Through the calculations and computations of this formula, a new position can be derived for each starting particle point and the corresponding set of positioning particle points, thus obtaining K stage positioning particle points.

[0067] Using the K phase-based positioning particle points obtained as starting points, the mean shift algorithm is used to perform iterative drift analysis within the K positioning trend analysis spaces. The mean shift algorithm continuously adjusts the particle point's position based on the data distribution around the phase-based positioning particle point, moving it toward a direction with higher data density. This iterative operation continues until the pre-defined number of iterations is reached. The resulting K particle points are then the K target positioning particle points that accurately reflect the positioning trend.

[0068] In one possible implementation, step S500 further includes:

[0069] Step S510: extracting K response rate sets of the K positioning deviation optimization records respectively.

[0070] Step S520: constructing curves for the K response rate sets in chronological order to generate K response rate curves.

[0071] Step S530: performing linear fitting on the K response rate curves to generate K control response factors.

[0072] Specifically, the K records of positioning deviation optimization are obtained, and from each such record, a corresponding set of response rates is extracted. The response rate here refers to the speed at which the piezoelectric ceramic driver drives the piezoelectric ceramic to move after receiving the positioning deviation instruction.

[0073] The K response rate sets extracted previously are processed in chronological order. Using time as the horizontal axis and response rate as the vertical axis, for each response rate set, the response rate values corresponding to different time points are mapped one by one in the coordinate system. Then, by connecting these marked points, a continuous curve is constructed. This operation is repeated for each response rate set, ultimately generating K response rate curves.

[0074] Linear fitting is used to process the K response rate curves constructed previously. Linear fitting is a mathematical method that aims to find a straight line that minimizes the sum of the distances between the line and the given curve data points. The least squares method is used to calculate the sum of the squares of the vertical distances between the data points and the fitted line and to find the line parameters that minimize this sum. For each response rate curve, each time point on the curve serves as the independent variable, and the corresponding response rate value serves as the dependent variable. The least squares method is then used to calculate the equation of the line that best fits the data points. This equation is typically expressed as y = ax + b, where a and b are calculated line parameters. After fitting, the resulting line parameter a can be considered a control response factor, which reflects the approximate trend and degree of change in the response rate over time. These control response factors are of great significance. A large value of a indicates a rapid increase or decrease in the response rate over time; conversely, a small value of a indicates a relatively gradual change in the response rate. By comparing these K control response factors, we can intuitively understand the differences and characteristics of the response performance of different piezoelectric ceramic monoliths, and then optimize the control strategy in a targeted manner, such as adjusting the driving voltage, the frequency of the control signal, etc., to achieve a more ideal control effect.

[0075] In one possible implementation, step S600 further includes:

[0076] Step S610: using a preset interference factor set as an index, searching the K positioning deviation optimization records to generate K positioning interference factor sets.

[0077] Step S620: Construct an interference fitter.

[0078] Step S630: Utilize the interference fitter to analyze the K positioning interference factor sets to generate the K interference fitting factors.

[0079] Specifically, a preset interference factor set is first set. This set includes electromagnetic interference, temperature and humidity change factors, noise interference, and signal channel interference. Each factor in this preset interference factor set is used as an index or keyword to conduct a comprehensive and detailed search of the K positioning deviation optimization records. During the search process, the content of each positioning deviation optimization record is carefully checked to find out 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 subsequent more in-depth analysis and processing.

[0080] The interference fitter constructed is a multilayer perceptron neural network model. The model structure is first determined. Assuming there are 20 features for localizing interference factors, the input layer is configured with 20 neurons to receive these feature data. Two hidden layers are configured: the first hidden layer has 100 neurons, and the second hidden layer has 50 neurons. Both hidden layers use the rectified linear unit (ReLU) as the activation function, ensuring that the output of the neuron maintains a linear relationship for positive inputs and a zero output for negative inputs. This prevents the vanishing gradient problem and facilitates neural network learning. The output layer has one neuron. The choice of activation function depends on the specific fitting task and can be a linear function. The following steps are involved in the neural network training algorithm: initialization, which assigns random initial values to the weights of all connections between neurons in the neural network. Forward propagation, which feeds the localization interference data into the input layer. After weight calculation and processing at the input layer, the data is passed to the first hidden layer. In the hidden layer, each neuron processes the weighted sum of its inputs through an activation function to produce its output. This output then serves as the input for the next layer, and so on, until the final prediction result is obtained at the output layer. Loss is calculated by comparing the output layer's predictions with the actual true values. Mean squared error (MSE) is used to measure the difference between the predicted and true values, serving as the loss. MSE is calculated by first calculating the difference between the predicted and true values for each sample, then averaging the squared differences. Backpropagation begins at the output layer and calculates the gradient of each neuron's weight based on the loss. This gradient indicates the direction and magnitude of the weight adjustment needed to reduce the loss. This gradient is then propagated layer by layer from the output layer to the input layer, calculating the weight gradient for each neuron. Weight updates are performed using the stochastic gradient descent algorithm. The weight adjustment is calculated by multiplying the learning rate by the weight gradient, which is then subtracted from the current weight to update the weight. This forward propagation, loss calculation, backpropagation, and weight update process is repeated until the loss is sufficiently small or the predetermined number of training cycles is reached. At this point, the neural network has learned the relationship between localizing interference factors and the final fitting result, thereby constructing a interference fitter that can effectively handle interference factors.

[0081] Once the interference fitter is successfully constructed, it is used to conduct an in-depth analysis of the K sets of positioning interference factors previously obtained. Each of these K sets of positioning interference factors is fed into the interference fitter one by one. The interference fitter processes and calculates each input set of positioning interference factors based on the relationship pattern between the interference factors and the fitting results it has already learned. After calculation and analysis, the interference fitter generates a corresponding interference fitting factor for each set of positioning interference factors. By optimizing the recording of positioning deviations and in-depth analysis of interference factors, we can more accurately understand the factors affecting positioning accuracy, thereby achieving more precise control of the piezoelectric ceramic actuator and improving positioning accuracy.

[0082] Example 2

[0083] Based on the same inventive concept as the monolithic control method of a piezoelectric ceramic in the above embodiment, Figure 2 As shown, the present application provides a monolithic control device for piezoelectric ceramics. The device and method embodiments in the present application are based on the same inventive concept. The device includes:

[0084] A node interaction module 10 is generated, and the node interaction module 10 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.

[0085] The positioning deviation tolerance bandwidth acquisition module 20 is used to collect K assembly plans of assembly components at the K production nodes, and retrieve the K assembly plans using positioning deviation as an index to obtain K positioning deviation tolerance bandwidths.

[0086] The position deviation identification module 30 is used to use a visual positioning device to identify the position deviation of the positioning platforms of the K production nodes in a preset analysis window, and obtain K positioning deviation sets and K positioning offset trend directions.

[0087] The positioning deviation optimization record acquisition module 40 is used to feed back the K positioning deviation sets and the K positioning offset trend directions to the K piezoelectric ceramic drivers to optimize the positioning deviation and obtain K positioning deviation optimization record sets.

[0088] The control response factor generation module 50 is used to perform control response factor fitting on the K positioning deviation optimization record sets to generate K control response factors.

[0089] The interference fitting factor acquisition module 60 is used to search the K positioning deviation optimization records according to a preset interference factor set, input the search results into the interference fitter for analysis, and obtain K interference fitting factors.

[0090] The control feedback factor acquisition module 70 is configured to modify the K control response factors using the K interference fitting factors to obtain K control feedback factors.

[0091] The positioning stage moving module 80 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.

[0092] Furthermore, the interference fitting factor acquisition module 60 further includes:

[0093] The preset interference factor set includes electromagnetic interference, temperature and humidity change factors, noise interference and signal channel interference.

[0094] Furthermore, the position deviation identification module 30 further includes:

[0095] The positioning platform position identification acquisition unit uses the visual positioning device to respectively collect positions of the positioning platforms of the K production nodes in a preset analysis window to obtain a set of K positioning platform position identifications.

[0096] A standard positioning platform position identifier generating unit is used to retrieve the K assembly solutions using the standard positioning platform position as an index, and generate K standard positioning platform position identifiers.

[0097] A positioning deviation set generation unit is configured to perform position deviation identification on the K positioning platform position identifier sets based on the K standard positioning platform position identifiers and in accordance with 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.

[0098] A positioning offset trend direction determining unit is configured to perform trend analysis on the K positioning deviation sets to determine K positioning offset trend directions.

[0099] Furthermore, the positioning deviation set generating unit further includes:

[0100] A positioning trend analysis space construction unit is used to construct K positioning trend analysis spaces based on the K positioning deviation sets, wherein the horizontal axis of the K positioning trend analysis spaces is time, the vertical axis is positioning deviation, and the K positioning trend analysis spaces include K positioning particle point sets.

[0101] The target positioning particle point acquisition unit is used to perform drift iterative analysis on the K positioning particle point sets in the K positioning trend analysis spaces to obtain K target positioning particle points.

[0102] When the ordinates of the K target positioning particle points are negative values, the K positioning offset trend directions are underfitting directions.

[0103] When the ordinates of the K target positioning particle points are positive values, the K positioning offset trend directions are overfitting directions.

[0104] Furthermore, the target positioning particle point acquisition unit further includes:

[0105] The starting particle point acquisition unit is used to extract the centers of gravity of the K positioning trend analysis spaces respectively and use them as K starting particle points.

[0106] The weight center iteration formula construction unit is used to construct a weight center iteration formula, wherein the weight center iteration formula is:

[0107] .

[0108] 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.

[0109] The stage positioning particle point acquisition unit 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.

[0110] The drift iterative analysis unit is used to perform drift iterative analysis in the K positioning trend analysis spaces with the K stage positioning particle points as the starting point until a preset number of iterations is met to obtain K target positioning particle points.

[0111] Furthermore, the control response factor generation module 50 further includes:

[0112] A response rate set acquisition unit is used to respectively extract K response rate sets of the K positioning deviation optimization records.

[0113] A response rate curve generating unit is used to construct a curve for the K response rate sets in chronological order to generate K response rate curves.

[0114] A response control factor generating unit is used to perform linear fitting on the K response rate curves to generate K control response factors.

[0115] Furthermore, the interference fitting factor acquisition module 60 further includes:

[0116] A positioning interference factor set generation unit is configured to use a preset interference factor set as an index to search the K positioning deviation optimization records and generate K positioning interference factor sets.

[0117] An interference fitter construction unit is used to construct an interference fitter.

[0118] An interference fitting factor generating unit is configured to analyze the K positioning interference factor sets using the interference fitter to generate the K interference fitting factors.

[0119] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0120] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0121] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A method for controlling a single chip of a piezoelectric ceramic, characterized in that: The method is applied to a single-chip control device, the device being communicatively connected to K piezoelectric ceramic drivers, and 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 the assembly components at the K production nodes, search the K assembly plans using positioning deviation as an index, 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 offset trend directions; Feeding back 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; 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; Correcting the K control response factors using the K interference fitting factors to obtain K control feedback factors; Transmitting the K control feedback factors to the K piezoelectric ceramic drivers to drive the K piezoelectric ceramics to move the positioning stage; The step of performing control response factor fitting on the K positioning deviation optimization record sets to generate K control response factors includes: Extracting K response rate sets of the K positioning deviation optimization records respectively; Constructing a curve for the K response rate sets in chronological order to generate K response rate curves; Performing linear fitting on the K response rate curves to generate K response control factors.

2. The method according to claim 1, wherein 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, wherein The K positioning deviation trend directions include: Using the visual positioning device to collect the positions of the positioning platforms of the K production nodes in a preset analysis window, and 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 platform position identifiers as a reference, position deviation identification is performed on the K positioning platform 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; Perform trend analysis on the K positioning deviation sets to determine K positioning offset trend directions.

4. The method according to claim 3, wherein 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 iterative analysis on the K positioning particle point sets in the K positioning trend analysis spaces to obtain K target positioning particle points; When the vertical coordinates 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 offset trend directions are overfitting directions.

5. The method according to claim 4, wherein 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 space until a preset number of iterations is met, thereby obtaining K target positioning particle points.

6. The method according to claim 1, wherein 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.

7. A piezoelectric ceramic monolithic control device, characterized in that: The device is used to implement the single-chip control method of a piezoelectric ceramic according to any one of claims 1 to 6, and the device comprises: A 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 plans of assembly components at the K production nodes, search the K assembly plans using positioning deviation as an index, and obtain K positioning deviation tolerance bandwidths; A position deviation identification module, configured to use a visual positioning device to identify position deviations of the positioning platforms of the K production nodes in a preset analysis window, 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 being used to feed back the K positioning deviation sets and the K positioning offset trend directions to the K piezoelectric ceramic drivers for positioning deviation optimization, thereby obtaining K positioning deviation optimization record sets; A control response factor generation module, configured 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 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, configured 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.

Images