Physical stepping focusing curve prediction method based on automatic focusing principle

Abstract

The invention belongs to the technical field of image acquisition, and discloses a physical stepping focusing curve prediction method based on an automatic focusing principle, a model does not simply depend on a test value of an actual module to carry out curve fitting, but imports a focal length parameter of module design, further provides a theoretical support framework, and has the advantages of being simple in structure and convenient to use. According to the method, the change direction of the focusing curve is limited, the influence caused by the quality deviation of the module is avoided, the original fitting curve can be accurately obtained, and the step values of the clearest images corresponding to different object distances can be predicted. According to the model, for different use distance ranges, only data of four points need to be taken for fitting, a large amount of discrete data does not need to be taken to verify the accuracy of a focusing curve, and an original fitting curve with representativeness can be rapidly and operably obtained. Only for modules with no quality problem, the same data set is subjected to fitting processing by adopting two models, and two curves with good consistency are obtained.

Description

Technical Field

[0001] The invention belongs to the technical field of image acquisition, and in particular relates to a physical step prediction focus curve method based on the auto-focus principle. Background Art

[0002] With the rapid development of biometric technology, the camera module as an image acquisition device needs to have a fast and accurate focusing function to meet the needs of fast and accurate recognition and acquisition. The fast implementation method of autofocus is the main factor affecting the recognition rate. Professionals in the field start from the optical design of zoom lenses, liquid lens zoom, VCM motor lens zoom, etc. to solve the problem of how to achieve fast autofocus. VCM motor lenses are widely used for their low cost, high accuracy and high efficiency. The VCM focus camera module consists of a lens, an image sensor, a voice coil motor base (VCM system), a PCB board, and other parts. The lens is an optical imaging system that transmits the image of the object to the image plane; the image sensor is a photoelectric conversion device that converts the optical image into an electronic signal for subsequent processing; the voice coil motor base is a micro motor for autofocus; the PCB board is a physical platform that provides a stable electrical connection for the entire module to ensure normal signal transmission between the interfaces.

[0003] The voice coil motor is mainly composed of a coil, a permanent magnet and a bracket. The coil is usually wound on a lightweight frame and can move freely along the optical axis. The permanent magnet is fixed around the lens group or lens module to provide a stable magnetic field environment. When the lens position needs to be adjusted for focusing, the control system applies a certain current to the coil. According to Ampere's law, the energized coil will be acted upon by a force in the magnetic field. The direction of this force follows the left-hand rule (or Lorentz force), and its magnitude is proportional to the current intensity. Due to the interaction between the coil and the permanent magnet, the coil will move in the direction of the force, thereby driving the lens or lens group connected to it to move. This movement step value is continuously adjustable, enabling precise focus control.

[0004] Existing technical solution: As described in claim 1 of patent CN202210291995, the first step is to install a camera on the top of the original device, and formulate a suitable standard detection chart according to the camera module pixels, field of view angle and usage distance; the second step is to select a VCM motor with a suitable stroke according to the usage distance of the original device; the third step is to test different object distances within the usage distance and the number of focusing steps for obtaining a clear image at the object distance; the fourth step is to perform curve fitting on the object distance and the number of focusing steps in S3 to obtain the original fitting curve of the object distance and the number of focusing steps; and as described in claim 4: The method for implementing automatic focus error calibration according to claim 1 is characterized in that the tool used for curve fitting in S4 is matlab, and specifically the curve fitting in matlab is used.

[0005] The existing methods need to collect a large amount of data to obtain the original fitting curve, and the process of analyzing the clearest image is cumbersome, which takes a long time for R&D testing. Moreover, it is completely dependent on the actual operating status of several modules, and is limited by the quality of the voice coil motor and the influence of the module assembly process. It will also cause large deviations in the original fitting curve of a single module of the same model, which ultimately affects the confirmation and output of the representative original fitting curve.

[0006] Through the above analysis, the problems and defects of the prior art are as follows:

[0007] The existing methods require a large amount of data to be acquired, and the process of analyzing the clearest image is cumbersome. It takes a long time for R&D testing, and is limited by the quality of the voice coil motor and the module assembly process. It will also cause large deviations in the original fitting curve of a single module of the same model, ultimately affecting the confirmation and output of the representative original fitting curve. Summary of the invention

[0008] In view of the problems existing in the prior art, the present invention provides a physical step prediction focus curve method based on the auto-focus principle.

[0009] The present invention is implemented in this way: a physical step prediction focus curve method based on the auto-focus principle includes:

[0010] Step 1, create a physical model;

[0011] Step 2, calculate k1 and k0 in sequence according to the sampling points (u1, y1) and (u3, y3);

[0012] Step 3: Verify model accuracy;

[0013] Step 4: Error precision analysis.

[0014] Further, the physical model is created:

[0015] Any optical imaging system, according to the Gaussian formula in optics

[0016] A. Establish physical model:

[0017] u*f / (uf)-u0*f / (u0-f)=k1*(y-y0)+k0

[0018] u is the object distance, with an error of 3-4%

[0019] f is the focal length of the lens, with a nominal error within 5%;

[0020] y is the step position value of the driver IC corresponding to u

[0021] y0 is the driver IC step position value corresponding to the motor starting current under u0;

[0022] u0 is the farthest object distance corresponding to y0;

[0023] k1 is the linear step-displacement ratio corresponding to the motor-driver IC, um / step, which is the linear motor stroke displacement represented by each step of the driver IC;

[0024] k0 is the linear compensation of displacement;

[0025] B. Determine the physical model parameters k1 and k0.

[0026] Furthermore, the physical model k1, k0 parameters are determined as follows:

[0027] Take 3 different u points and corresponding y points, which are 1 / 4, 1 / 2, and 3 / 4 of the effective distance range respectively.

[0028] For example, if it is 40-80cm, then choose 50, 60, 70cm.

[0029] Three groups of sampling points (u1, y1), (u2, y2), and (u3, y3) are obtained, among which (u2, y2) is used to verify the accuracy of the model.

[0030] Further, according to the sampling points (u1, y1), (u3, y3), k1 and k0 are calculated in sequence:

[0031] k1=[u3*f / (u3-f)-u1*f / (u1-f)-k0] / (y3-y1);

[0032] k0=u1*f / (u1-f)-u0*f / (u0-f)-k1*(y1-y0);

[0033] or

[0034] k0=u3*f / (u3-f)-u0*f / (u0-f)-k1*(y3-y0).

[0035] Furthermore, the verification model accuracy is:

[0036] According to the calculated k1, k0, u2 is substituted into the model to obtain y2', and the error accuracy d between verification and actual (u2, y2) is verified;

[0037] d=|y2'-y2|.

[0038] Further, the error precision analysis:

[0039] (1) Perform error processing on the step value range corresponding to the clearest image at the object distance of the sampling point to obtain a mean value;

[0040] (2) If the error precision is too large, the precision of the object distance u at the four points and whether the step value corresponding to the clearest image is correct should be reconfirmed.

[0041] Another object of the present invention is to provide a physical step prediction focus curve system based on the auto-focus principle, comprising:

[0042] Create module for creating physical models;

[0043] A calculation module, used to calculate k1 and k0 in sequence according to the sampling points (u1, y1) and (u3, y3);

[0044] Verification module, used to verify the accuracy of the model;

[0045] Analysis module, used for error accuracy analysis.

[0046] Another object of the present invention is to provide a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the physical step prediction focus curve method based on the autofocus principle.

[0047] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to perform the steps of the method for physically stepping and predicting a focus curve based on the principle of auto-focusing.

[0048] Another object of the present invention is to provide an information data processing terminal, which is used to implement the physical step prediction focus curve system based on the auto-focus principle.

[0049] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0050] First, in view of the technical problems existing in the above-mentioned prior art and the difficulty of solving the problems, the technical solutions to be protected by the present invention and the results and data during the research and development process are closely combined to analyze in detail and deeply how the technical solutions of the present invention solve the technical problems, and some creative technical effects brought about after solving the problems. The specific description is as follows:

[0051] The present invention mainly solves the problems that for the same type of autofocus camera modules in the research and development stage, in order to obtain the original fitting curve within a certain range of equipment use distance, the image acquisition and data processing tasks of the identification device are heavy, time-consuming and easily affected by the quality of the module itself, which in turn affects the representativeness of the original fitting curve of the same type of modules and the efficiency of project promotion.

[0052] This invention mainly establishes a fast, accurate and easy-to-operate physical model for the method of obtaining the original focus curve, improves the accuracy of determining the original fitting curve for the same model module, facilitates the subsequent batch calibration of the model module, and ensures the consistency of mass-produced modules.

[0053] To ensure that the lens reaches the desired step value position accurately, the VCM system is usually equipped with position sensors (such as Hall effect sensors). These sensors can detect the actual position of the lens and feed the information back to the control system. If there is a deviation between the actual position and the target position, the control system will adjust the current applied to the coil until the correct focus step is achieved.

[0054] In order to obtain a clear target image, the existing method for predicting the focus curve is: within the designed object distance range, measure the mapping relationship between each working distance and the motor displacement, and obtain different object distances and the number of focus steps for the clearest image at the object distance. After obtaining the discrete data point information, use a polynomial function to fit the focus curve, and finally obtain the original fitting curve. Then sample and select at least two or more modules, execute the above steps respectively, and finally process the step value data of multiple modules to obtain the original fitting curve that is representative of this type of module. Based on this principle, the more sampling point data at different object distances, the more accurate the original fitting curve.

[0055] This patent considers the essential level of the module, introduces the mapping relationship between focal length and object-image distance and motor displacement, and obtains the original focus curve within the working range through the actual focal length value, maximum working distance and minimum working distance. The accuracy of the curve is analyzed and verified in combination with the error principle.

[0056] This model does not simply rely on the test values ​​of the actual module to fit the curve, but imports the focal length parameters of the module design, further provides a theoretical support framework, limits the change direction of the focus curve, avoids the impact of the quality deviation of the module itself, can accurately obtain the original fitting curve, and predict the step value of the clearest image corresponding to different object distances.

[0057] For different use distance ranges, this patented model only needs to take data from four points for fitting, and does not need to take a large amount of discrete data to verify the accuracy of the focus curve. It can quickly and operationally obtain a representative original fitting curve. Only for modules with no quality problems, the same data set is fitted using two models to obtain two curves with good consistency (such as Figure 3 ). For modules with quality problems, two models are used for the same data set to obtain problem curves that can clearly distinguish the problem modules and support the analysis of the quality problems of the modules.

[0058] Second, the expected benefits after the transformation of the technical solution of the present invention are mainly reflected in the R&D investment costs (including labor costs and time costs) and the waste costs caused by small-batch trial production.

[0059] Whether the technical solution of the present invention solves the technical problem: The field has always been eager to solve the problem of being able to quickly obtain the original focus curve of a representative module. This patent solves the problems of the existing method, such as long investment time and cumbersome data processing, and can timely discover and analyze abnormal module solutions, providing a reliable model method for rapid development and verification of products. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a flow chart of a method for predicting a focus curve by physical stepping based on the auto-focus principle provided by an embodiment of the present invention.

[0061] Figure 2 It is a structural block diagram of a physical step prediction focus curve system based on the auto-focus principle provided by an embodiment of the present invention.

[0062] Figure 3 It is an optical definition diagram provided by an embodiment of the present invention.

[0063] Figure 4 It is a physical step prediction focus curve model diagram provided by an embodiment of the present invention.

[0064] Figure 5 It is a matlab fitting curve 1 diagram under two models provided in an embodiment of the present invention.

[0065] Figure 6 2 are comparison diagrams of MATLAB fitting curves under the patent model provided in the embodiment of the present invention.

[0066] Figure 7 It is a position relationship diagram of the device and the iris acquisition unit provided in the embodiment of the present invention.

[0067] Figure 8 It is the MATLAB original focus curve fitting simulation provided by the embodiment of the present invention.

[0068] Fig. 9 1 and 2 are four module diagrams provided in an embodiment of the present invention: (a) module No. 1; (b) module No. 2; (c) module No. 3; and (d) module No. 4. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0070] like Figure 1 As shown, a method for predicting a focus curve by physical stepping based on the auto-focus principle provided by an embodiment of the present invention comprises the following steps:

[0071] S101, creating a physical model;

[0072] S102, according to the sampling points (u1, y1), (u3, y3), calculate k1 and k0 in sequence;

[0073] S103, verify model accuracy;

[0074] S104, error accuracy analysis.

[0075] The present invention first constructs a physical model based on the actual physical characteristics of the autofocus system, and the model is centered on the optical parameters of the lens system, the displacement information of the sensor, and the clarity function of the imaging plane. The model provides a theoretical basis for subsequent calculations by defining the nonlinear relationship between focal length adjustment and image clarity. For example, a functional relationship between focal length and clarity is established, and this functional relationship is obtained by quantitative analysis and experimental fitting of the lens optical system.

[0076] Based on the created physical model, the sampling points are used. The calculated prediction curve needs to be verified by experimental data for accuracy. A series of actual focal length and clarity data points obtained in the experiment are substituted into the physical model to calculate the deviation between the model prediction value and the actual data. For example, the predicted clarity value is compared with the actual measured clarity to calculate the residual. The distribution of the residual value is statistically analyzed to determine the prediction accuracy of the model and ensure that it has sufficient reliability and applicability in practical applications.

[0077] In the error analysis step, the error distribution of the model is studied in depth to find out the main reasons that affect the prediction accuracy (such as sensor noise, nonlinear response, etc.). The physical model can be further optimized through quantitative analysis of different error sources. For example, correction factors or higher-order fitting parameters are introduced in high nonlinear areas to reduce system errors. The results of error analysis and optimization are ultimately reflected in the adjustment and accuracy improvement of the focus curve, so that the predicted focus curve has higher accuracy in a wider focal length range.

[0078] In summary, the present invention effectively implements the predicted focus curve generation method based on autofocus through the four steps of physical model creation, parameter calculation, model verification and error analysis, and provides high-precision and high-reliability technical support for the autofocus system.

[0079] The physical model created in the embodiment of the present invention is as follows:

[0080] Any optical imaging system can be simplified as Figure 3 Form, according to Gaussian formula in optics Among them, f is the focal length, u is the distance from the object to the lens (object distance), and v is the distance from the image plane to the lens (image distance);

[0081] A. Establish physical model:

[0082] u*f / (uf)-u0*f / (u0-f)=k1*(y-y0)+k0

[0083] u is the object distance, with an error of 3-4%

[0084] f is the focal length of the lens, with a nominal error within 5%;

[0085] y is the step position value of the driver IC corresponding to u

[0086] y0 is the driver IC step position value corresponding to the motor starting current under u0;

[0087] u0 is the farthest object distance corresponding to y0;

[0088] k1 is the linear step-displacement ratio corresponding to the motor-driver IC, um / step, which is the linear motor stroke displacement represented by each step of the driver IC;

[0089] k0 is the linear compensation of displacement;

[0090] B. Determine the physical model parameters k1 and k0.

[0091] The embodiment of the present invention provides the following method for determining the parameters k1 and k0 of the physical model:

[0092] Take 3 different u points and corresponding y points, which are 1 / 4, 1 / 2, and 3 / 4 of the effective distance range respectively.

[0093] For example, if it is 40-80cm, then choose 50, 60, 70cm.

[0094] Three groups of sampling points (u1, y1), (u2, y2), and (u3, y3) are obtained, among which (u2, y2) is used to verify the accuracy of the model.

[0095] The embodiment of the present invention provides a method for calculating k1 and k0 in sequence based on the sampling points (u1, y1) and (u3, y3):

[0096] k1=[u3*f / (u3-f)-u1*f / (u1-f)-k0] / (y3-y1);

[0097] k0=u1*f / (u1-f)-u0*f / (u0-f)-k1*(y1-y0);

[0098] or

[0099] k0=u3*f / (u3-f)-u0*f / (u0-f)-k1*(y3-y0).

[0100] The accuracy of the verification model provided by the embodiment of the present invention is:

[0101] According to the calculated k1, k0, u2 is substituted into the model to obtain y2', and the error accuracy d between verification and actual (u2, y2) is verified;

[0102] d=|y2'-y2|.

[0103] Error accuracy analysis provided by the embodiment of the present invention:

[0104] (1) Perform error processing on the step value range corresponding to the clearest image at the object distance of the sampling point to obtain a mean value;

[0105] (2) If the error precision is too large, the precision of the object distance u at the four points and whether the step value corresponding to the clearest image is correct should be reconfirmed.

[0106] like Figure 2 As shown, a physical step prediction focus curve system based on the auto-focus principle provided by an embodiment of the present invention includes:

[0107] Create module for creating physical models;

[0108] A calculation module, used to calculate k1 and k0 in sequence according to the sampling points (u1, y1) and (u3, y3);

[0109] Verification module, used to verify the accuracy of the model;

[0110] Analysis module, used for error accuracy analysis.

[0111] Another object of the present invention is to provide a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the physical step prediction focus curve method based on the autofocus principle.

[0112] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to perform the steps of the method for physically stepping and predicting a focus curve based on the principle of auto-focusing.

[0113] Another object of the present invention is to provide an information data processing terminal, which is used to implement the physical step prediction focus curve system based on the auto-focus principle.

[0114] Physical step prediction focus curve model, such as Figure 4 .

[0115] This model does not simply rely on the test values ​​of the actual module to fit the curve, but imports the focal length parameters of the module design, further provides a theoretical support framework, limits the change direction of the focus curve, avoids the impact of the quality deviation of the module itself, can accurately obtain the original fitting curve, and predict the step value of the clearest image corresponding to different object distances.

[0116] For different use distance ranges, this patented model only needs to take data from four points for fitting, and does not need to take a large amount of discrete data to verify the accuracy of the focus curve. It can quickly and operationally obtain a representative original fitting curve. Only for modules with no quality problems, the same data set is fitted using two models to obtain two curves with good consistency (such as Figure 5 ). For modules with quality problems, two models are used for the same data set to obtain problem curves that can clearly distinguish the problem modules and support the analysis of the quality problems of the modules.

[0117] Figure 6 Comparison of matlab fitting curves under this patent model 2.

[0118] 1. Specific application fields or related products of the present invention.

[0119] like Figure 7As shown, the present invention is mainly applied to the auto-focus camera module in the field of biometric identification technology. The original focus curve of the products equipped with this type of camera module can be obtained by applying the present invention, and the problem modules can be screened and analyzed to improve the qualified rate of product output.

[0120] 2. Relevant evidence of the technical effects obtained by the embodiments of the present invention.

[0121] Example 1:

[0122] Optical test data

[0123] The optical data of the four modules are collected according to the existing method, and the step value corresponding to the clearest image at different object distances is analyzed. The data is as follows

[0124]

[0125] Matlab simulation, such as Figure 8 S110 is the patented method, and S111 is the existing method. The comparison analysis chart analyzes four modules to verify the accuracy of obtaining the most representative original focus curve.

[0126] like Fig. 9 As shown. By comparison, it was found that the curve fitted by the existing method for module No. 1 had a large deviation from the curve fitted by this patent. After analysis and testing, it was found that the VCM motor selected for the lens module was not compatible with the lens, and the lens weight exceeded the motor load, which helped to quickly locate the source of the problem in the project solution verification during the R&D stage and avoided the waste of small batch production.

[0127] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0128] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A physical step prediction focus curve method based on the auto-focus principle, characterized in that: The following steps are involved: Step 1, create a physical model; Step 2, calculate k1 and k0 in sequence according to the sampling points (u1, y1) and (u3, y3); Step 3: Verify model accuracy; Step 4: Error accuracy analysis.

2. The method for predicting a focus curve by physical stepping based on the principle of automatic focusing as claimed in claim 1, characterized in that: The physical model is created as follows: Any optical imaging system, according to the Gaussian formula in optics A. Establish physical model: u*f / (uf)-u0*f / (u0-f)=k1*(y-y0)+k0 u is the object distance, with an error of 3-4% f is the focal length of the lens, with a nominal error within 5%; y is the step position value of the driver IC corresponding to u y0 is the driver IC step position value corresponding to the motor starting current under u0; u0 is the farthest object distance corresponding to y0; k1 is the linear step-displacement ratio corresponding to the motor-driver IC, um / step, which is the linear motor stroke displacement represented by each step of the driver IC; k0 is the linear compensation of displacement; B. Determine the physical model parameters k1 and k0.

3. The method for predicting a focus curve by physical stepping based on the principle of automatic focusing as claimed in claim 2, characterized in that: Determine the physical model k1, k0 parameters: Take 3 different u points and corresponding y points, which are 1 / 4, 1 / 2, and 3 / 4 of the effective distance range respectively. For example, if it is 40-80cm, then choose 50, 60, 70cm. Three groups of sampling points (u1, y1), (u2, y2), and (u3, y3) are obtained, among which (u2, y2) is used to verify the accuracy of the model.

4. The method for predicting a focus curve by physical stepping based on the principle of automatic focusing as claimed in claim 1, characterized in that: According to the sampling points (u1, y1), (u3, y3), k1 and k0 are calculated in sequence: k1=[u3*f / (u3-f)-u1*f / (u1-f)-k0] / (y3-y1); k0=u1*f / (u1-f)-u0*f / (u0-f)-k1*(y1-y0); or k0=u3*f / (u3-f)-u0*f / (u0-f)-k1*(y3-y0).

5. The method for predicting a focus curve by physical stepping based on the principle of automatic focusing as claimed in claim 1, characterized in that: The validation model accuracy: According to the calculated k1, k0, u2 is substituted into the model to obtain y2', and the error accuracy d between verification and actual (u2, y2) is verified; d=|y2'-y2|.

6. The method for predicting a focus curve by physical stepping based on the principle of automatic focusing as claimed in claim 1, characterized in that: The error precision analysis: (1) Perform error processing on the step value range corresponding to the clearest image at the object distance of the sampling point to obtain a mean value; (2) If the error precision is too large, the precision of the object distance u at the four points and whether the step value corresponding to the clearest image is correct should be reconfirmed.

7. A physical step prediction focus curve system based on the auto-focus principle, which implements the physical step prediction focus curve method based on the auto-focus principle as claimed in any one of claims 1 to 6, characterized in that: The physical step prediction focus curve system based on the auto-focus principle includes: Create module for creating physical models; A calculation module, used to calculate k1 and k0 in sequence according to the sampling points (u1, y1) and (u3, y3); Verification module, used to verify the accuracy of the model; Analysis module, used for error accuracy analysis.

8. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the physical step prediction focus curve method based on the autofocus principle as described in any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the method for predicting a focus curve by physical stepping based on the principle of autofocus as claimed in any one of claims 1 to 6.

10. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the physical step prediction focus curve system based on the auto-focus principle as described in claim 7.

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