A data processing system and method based on a podiometer calibration board

Abstract

This invention relates to the field of data processing technology, specifically a data processing system and method based on a foot scanner calibration board. The method includes: initial data calibration of the foot scanner using the calibration board; constructing a historical calibration data log and performing an initial status verification; obtaining multiple sets of raw foot data from the user through the foot scanner to form a raw foot data sequence; calculating the accuracy coefficient of each set of raw foot data in real time; filtering valid raw foot data from the raw foot data sequence; and dynamically adjusting the foot scanning strategy based on the valid raw foot data. This invention solves the problem in existing technologies where the accuracy of foot scanners decreases over long-term use, making it difficult to guarantee the accuracy of foot scanning and preventing the quantitative adjustment of the foot scanning strategy.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and is a data processing system and method based on a foot calibrator calibration plate. Background Technology

[0002] In the field of foot health assessment and diagnosis, foot calipers are a key device widely used for measuring foot geometric parameters, analyzing plantar pressure distribution, and evaluating foot morphology. However, existing foot calipers lack systematic calibration boards and precise initial data calibration methods during the initial data calibration process, relying heavily on manual calibration. This is not only time-consuming but also prone to introducing human error, making it difficult to guarantee the long-term accuracy and stability of measurement data. Furthermore, during foot scanning, user movements or equipment precision issues can lead to inconsistencies or errors in the scan data, affecting the accuracy of subsequent analysis and diagnosis. Existing technologies lack effective data verification strategies when processing multiple sets of raw individual foot data, failing to calculate the accuracy coefficient of each set in real time to filter out high-quality, valid data. This hinders subsequent data mining and utilization. Therefore, a new data processing system and method are urgently needed to improve the measurement accuracy and data processing capabilities of foot calipers, ensuring data consistency and reliability. Summary of the Invention

[0003] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0004] The technical problem to be solved by the present invention is that the accuracy of foot scanners decreases during long-term use, making it difficult to guarantee the accuracy of foot scanning and making it impossible to quantitatively adjust the foot scanning strategy. The present invention proposes a data processing system and method based on a foot scanner calibration plate.

[0005] To achieve the above objectives, the technical solution of the data processing method based on the calibration plate of a foot detector according to the present invention includes the following steps:

[0006] S1: Start the foot detector and perform initial data calibration on the foot detector using the calibration board;

[0007] S2: Based on the initial data calibration processing results, construct historical calibration data logs, calculate the mechanical wear factor based on the historical calibration data logs, and perform the first round of status verification of the foot detector's working status based on the mechanical wear factor;

[0008] S3: Set the foot scanner to foot scanning mode and scan the user's feet multiple times within a standard scanning period to obtain multiple sets of raw personal foot data, forming a raw personal foot data sequence.

[0009] S4: Extract the multiple sets of raw personal foot data and import them into the next round of data verification strategy, and calculate the data accuracy coefficient of each set of raw personal foot data in real time;

[0010] S5: Based on the data accuracy coefficient of each group of individual foot raw data, filter the valid foot raw data in the individual foot raw data sequence, and dynamically adjust the foot scanning strategy based on the valid foot raw data.

[0011] Specifically, in S1, the calibration plate includes: 8 cube blocks and an aluminum alloy middle plate, wherein 4 cube blocks are glued to the 4 vertices of the upper and lower surfaces of the aluminum alloy middle plate; the four sides of each cube block on the upper surface of the aluminum alloy middle plate are parallel to the four sides of the aluminum alloy middle plate; the four sides of each cube block on the lower surface of the aluminum alloy middle plate form a 45-degree angle with the four sides of the aluminum alloy middle plate.

[0012] The foot detector's workbench includes two rectangular glass panels of equal area and four cameras.

[0013] The rectangular glass panel is the foot measurement area, and a scanning device is installed below it.

[0014] Specifically, in S1, the initial data calibration process includes:

[0015] S11: Place the calibration plate on the worktable of the foot detector, and at the same time adjust the four top corners of the alloy middle plate of the calibration plate to be parallel to the scale of the glass panel on the left and right sides of the worktable of the foot detector, and start the initial data calibration process.

[0016] S12: Control the measurement reference lines of the two rectangular glass panels to move left and right, so that the adjusted two measurement reference lines coincide with the sharp corners of the four cube blocks on the lower surface of the aluminum alloy middle plate, wherein the measurement reference lines are parallel to the length of the rectangular glass panels and the length of the measurement reference lines is equal to the length of the rectangular glass panels.

[0017] S13: Control the viewpoint of the camera to move up and down so that the adjusted viewpoint coincides with the center of the upper surface of the four cube blocks on the upper surface of the aluminum alloy plate.

[0018] Specifically, S2 includes the following steps:

[0019] S21: Based on the initial data calibration processing results, construct a historical calibration data log, wherein the initial data calibration processing results include: the distance movement of the measurement baseline and the angle movement of the camera's viewpoint;

[0020] S22: Calculate the mechanical wear factor based on the initial data calibration processing results. The calculation strategy for the mechanical wear factor ms is as follows:

[0021]

[0022] Where α1 and α2 are the mechanical wear contribution ratio coefficients for distance movement and angular movement, respectively;

[0023] i is a subscript, indicating the i-th measurement baseline, l i Let be the distance movement of the i-th measurement baseline; The average distance traveled in the historical calibration data log; L min ,L max These are the minimum and maximum distance movement values ​​from the historical calibration data logs, respectively.

[0024] j is the subscript, representing the viewpoint of the j-th camera; θ j Let be the angular movement of the viewpoint of the j-th camera; The mean angular movement in the historical calibration data log; θ min ,θ max These are the minimum and maximum angle movement values ​​from the historical calibration data log, respectively.

[0025] S23: Perform the first round of status verification on the foot testing instrument based on the mechanical wear factor. The first round of status verification includes: setting the maximum tolerable mechanical wear factor of the foot testing instrument, comparing the mechanical wear factor of the initial data calibration result with the maximum tolerable mechanical wear factor of the foot testing instrument, and determining that the foot testing instrument meets the working conditions when the mechanical wear factor of the initial data calibration result is less than the maximum tolerable mechanical wear factor of the foot testing instrument. Otherwise, the foot testing instrument is manually calibrated by the staff.

[0026] Specifically, S3 includes the following steps:

[0027] S31: Adjust the foot scanner to foot scanning mode and scan the user's foot multiple times within a standard scanning period. The standard scanning period includes multiple data acquisition unit periods, and the duration of each data acquisition unit period is ten seconds.

[0028] S32: Obtain multiple sets of raw personal foot data from the user to form a raw personal foot data sequence. The raw personal foot data includes: foot geometric parameter data, foot surface image data, foot temperature data, and foot humidity data.

[0029] Specifically, in S4, the second-round data approval strategy includes: a pressure fluctuation frequency approval strategy and an image pixel distortion approval strategy;

[0030] The pressure fluctuation frequency approval strategy specifically includes:

[0031] S41: The measurement area of ​​the rectangular glass panel is evenly divided into pressure monitoring grids with an area of ​​1cm × 1cm, wherein the total number of pressure monitoring grids is U;

[0032] S42: Extract pressure data from each pressure detection grid within the measurement area of ​​the rectangular glass panel during a single data acquisition unit time period, and calculate the pressure fluctuation frequency value based on the pressure data, wherein the pressure fluctuation frequency value p r,yl The calculation strategy is as follows:

[0033]

[0034] Where u is a subscript, representing the u-th pressure monitoring grid;

[0035] r is a subscript, yl r (u),yl r-1 (u),yl r-2 (u) represents the pressure data of the u-th pressure monitoring grid within the r-th, r-1-th and r-2-th data acquisition unit time periods, respectively.

[0036] Specifically, the image pixel distortion probability approval strategy includes:

[0037] S43: Capture foot image data through various cameras, and obtain the foot edge curve through edge detection algorithm;

[0038] S44: Draw the tangent line and the perpendicular line of each pixel on the edge curve of the foot surface. The intersection point between the tangent line and the perpendicular line of the tangent line is the pixel itself.

[0039] S45: Extract the pixel values ​​of each pixel on the foot edge curve, and calculate the pixel distortion probability value q based on the pixel values. r,xs The calculation strategy is as follows:

[0040]

[0041] Where h is a subscript, representing the h-th frame of the foot image, and H is the total number of foot images captured by the camera within the r-th data acquisition unit time period;

[0042] During the r-th data collection unit time period, xs r (h g ) represents the pixel value of the g-th pixel on the foot edge curve of the h-th frame of the foot image;

[0043] h g,up1 This represents the pixel whose pixel value is closest to that of the g-th pixel within the region above the perpendicular line to the tangent of the g-th pixel on the outer side of the foot's edge curve; xs r (h g,up1 ) represents the pixel value of that pixel;

[0044] h g,down1 This represents the pixel whose pixel value is closest to that of the g-th pixel within the region below the perpendicular line to the tangent of the g-th pixel on the outer side of the foot's edge curve; xs r (h g,down1 ) represents the pixel value of that pixel;

[0045] h g,up2 This represents the pixel whose pixel value is closest to that of the g-th pixel within the region above the perpendicular line to the tangent of the g-th pixel on the inner side of the foot's edge curve; xs r (h g,up2 ) represents the pixel value of that pixel;

[0046] h g,down2 This represents the pixel whose pixel value is closest to that of the g-th pixel within the region below the perpendicular line to the tangent of the g-th pixel on the inner side of the foot's edge curve; xs r (h g,down2 ) represents the pixel value of that pixel.

[0047] Specifically, in S4, the calculation strategy for the data accuracy coefficient K of each group of individual foot raw data includes:

[0048]

[0049] Where e is the natural logarithm;

[0050] p0 and q0 are the standard values ​​of pressure fluctuation frequency and pixel distortion probability, respectively.

[0051] Specifically, S5 includes the following steps:

[0052] S51: Extract the data accuracy coefficient of each group of individual foot raw data, and preset the data accuracy threshold;

[0053] S52: Filter the raw foot data of individuals whose data accuracy coefficient is greater than the data accuracy threshold in the raw foot data sequence to obtain valid raw foot data;

[0054] S53: Dynamically adjust the foot scanning strategy based on the valid raw foot data. Specifically, this includes: extracting the number of valid raw foot data sets in real time within a standard scanning period. When the number of valid raw foot data sets acquired within a standard scanning period is greater than or equal to 3, the foot scan is completed; otherwise, proceed to step S54.

[0055] S54: When the number of valid raw foot data sets acquired within a standard scanning period is less than 3, the foot scanning time is automatically extended by half of the standard scanning time, and the process returns to step S52 to re-execute steps S52-S53.

[0056] If the number of valid raw foot data sets obtained after dynamic adjustment of the foot scanning strategy is greater than or equal to 3, the foot scan is completed; otherwise, staff will be notified to manually guide the user to repeat the foot scan.

[0057] In addition, the data processing system based on the calibration plate of the foot detector of the present invention includes the following modules:

[0058] The system includes an initial data calibration module, a first-round status verification module, a scan data acquisition module, a second-round data verification module, and a scan strategy adjustment module.

[0059] The initial data calibration module is used to start the foot detector and perform initial data calibration on the foot detector through the calibration board.

[0060] The first-round status verification module constructs a historical calibration data log based on the initial data calibration processing results, calculates the mechanical wear factor based on the historical calibration data log, and performs the first-round status verification of the foot detector's working status based on the mechanical wear factor.

[0061] The scanning data acquisition module is used to adjust the foot scanner to the foot scanning mode, and to scan the user's foot multiple times within a standard scanning period to obtain multiple sets of the user's personal foot raw data, which constitute a personal foot raw data sequence.

[0062] The second-round data verification module is used to extract the multiple sets of personal foot raw data and import the multiple sets of personal foot raw data into the second-round data verification strategy, and calculate the data accuracy coefficient of each set of personal foot raw data in real time.

[0063] The scanning strategy adjustment module filters valid raw foot data from the individual's raw foot data sequence based on the data accuracy coefficient of each group of individual raw foot data, and dynamically adjusts the foot scanning strategy based on the valid raw foot data.

[0064] Compared with the prior art, the technical effects of the present invention are as follows:

[0065] 1. By introducing a dedicated systematic calibration board and combining it with a precise initial data calibration processing method, this invention significantly improves the accuracy and stability of data calibration, reduces the frequency and human error of traditional manual calibration, ensures the initial accuracy of the data, and can monitor and adjust the calibration data in real time after the equipment has been used for a long time, maintaining the consistency of measurement accuracy, thereby improving the long-term stability of the data.

[0066] 2. By introducing an effective data verification strategy, this invention can calculate the accuracy coefficient of each group of individual foot raw data in real time and screen out high-quality valid data. This greatly improves the accuracy and reliability of data processing and helps to improve the accuracy of subsequent analysis and diagnosis.

[0067] 3. This invention automates the entire process from data acquisition and processing to strategy adjustment. This highly automated mechanism not only improves equipment availability and user experience but also reduces manual operation and human intervention, lowering operational complexity and error rates, and achieving efficient, accurate, and reliable data processing.

[0068] 4. By introducing a real-time monitoring and adaptive adjustment mechanism, this invention enables the equipment to maintain optimal condition during long-term use, reducing downtime for maintenance and upgrades. Attached Figure Description

[0069] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0070] in:

[0071] Figure 1 This is a flowchart illustrating a data processing method based on a foot calibration plate according to the present invention.

[0072] Figure 2 This is a schematic diagram of the data processing system based on a foot calibrator calibration plate according to the present invention;

[0073] Figure 3 This is a top view schematic diagram of a calibration plate according to the present invention;

[0074] Figure 4 This is a side view schematic diagram of a calibration plate according to the present invention;

[0075] Figure 5 This is an example diagram of the workbench of a foot examination instrument according to the present invention. Detailed Implementation

[0076] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0077] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0078] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0079] Example 1:

[0080] like Figure 1 As shown, an embodiment of the present invention provides a data processing method based on a foot calibration plate, such as... Figure 1 As shown, the specific steps include the following:

[0081] S1: Start the foot detector and perform initial data calibration on the foot detector using the calibration board;

[0082] like Figure 3 and Figure 4 As shown in Figure S1, the calibration plate includes: 8 cube blocks and an aluminum alloy middle plate, wherein 4 cube blocks are glued to the 4 vertices of the upper and lower surfaces of the aluminum alloy middle plate; the four sides of each cube block on the upper surface of the aluminum alloy middle plate are parallel to the four sides of the aluminum alloy middle plate; the four sides of each cube block on the lower surface of the aluminum alloy middle plate form a 45-degree angle with the four sides of the aluminum alloy middle plate.

[0083] like Figure 5 As shown, the foot examination instrument's workbench includes two rectangular glass panels of equal area and four cameras.

[0084] The rectangular glass panel is the foot measurement area, and a scanning device is installed below it.

[0085] In S1, the initial data calibration process specifically includes:

[0086] S11: Place the calibration plate on the worktable of the foot detector, and at the same time adjust the four top corners of the alloy middle plate of the calibration plate to be parallel to the scale of the glass panel on the left and right sides of the worktable of the foot detector, and start the initial data calibration process.

[0087] S12: Control the measurement reference lines of the two rectangular glass panels to move left and right, so that the adjusted two measurement reference lines coincide with the sharp corners of the four cube blocks on the lower surface of the aluminum alloy middle plate, wherein the measurement reference lines are parallel to the length of the rectangular glass panels and the length of the measurement reference lines is equal to the length of the rectangular glass panels.

[0088] S13: Control the viewpoint of the camera to move up and down so that the adjusted viewpoint coincides with the center of the upper surface of the four cube blocks on the upper surface of the aluminum alloy plate.

[0089] S2: Based on the initial data calibration processing results, construct historical calibration data logs, calculate the mechanical wear factor based on the historical calibration data logs, and perform the first round of status verification of the foot detector's working status based on the mechanical wear factor;

[0090] S2 includes the following specific steps:

[0091] S21: Based on the initial data calibration processing results, construct a historical calibration data log, wherein the initial data calibration processing results include: the distance movement of the measurement baseline and the angle movement of the camera's viewpoint;

[0092] S22: Calculate the mechanical wear factor based on the initial data calibration processing results. The calculation strategy for the mechanical wear factor ms is as follows:

[0093]

[0094] Where α1 and α2 are the mechanical wear contribution ratio coefficients for distance movement and angular movement, respectively;

[0095] i is a subscript, indicating the i-th measurement baseline, l i Let be the distance movement of the i-th measurement baseline; The average distance traveled in the historical calibration data log; L min ,L max These are the minimum and maximum distance movement values ​​from the historical calibration data logs, respectively.

[0096] j is the subscript, representing the viewpoint of the j-th camera; θ j Let be the angular movement of the viewpoint of the j-th camera; The mean angular movement in the historical calibration data log; θ min ,θ max These are the minimum and maximum angle movement values ​​from the historical calibration data logs, respectively.

[0097] S23: Perform the first round of status verification on the foot testing instrument based on the mechanical wear factor. The first round of status verification includes: setting the maximum tolerable mechanical wear factor of the foot testing instrument, comparing the mechanical wear factor of the initial data calibration result with the maximum tolerable mechanical wear factor of the foot testing instrument, and determining that the foot testing instrument meets the working conditions when the mechanical wear factor of the initial data calibration result is less than the maximum tolerable mechanical wear factor of the foot testing instrument. Otherwise, the foot testing instrument is manually calibrated by the staff.

[0098] S3: Set the foot scanner to foot scanning mode and scan the user's feet multiple times within a standard scanning period to obtain multiple sets of raw personal foot data, forming a raw personal foot data sequence.

[0099] S3 includes the following specific steps:

[0100] S31: Adjust the foot scanner to foot scanning mode and scan the user's foot multiple times within a standard scanning period. The standard scanning period includes multiple data acquisition unit periods, and the duration of each data acquisition unit period is ten seconds.

[0101] S32: Obtain multiple sets of raw personal foot data from the user to form a raw personal foot data sequence. The raw personal foot data includes: foot geometric parameter data, foot surface image data, foot temperature data, and foot humidity data.

[0102] For example, in this embodiment, the foot geometric parameter data includes: foot length, foot width, arch height, toe length, and toe width.

[0103] S4: Extract the multiple sets of raw personal foot data and import them into the next round of data verification strategy, and calculate the data accuracy coefficient of each set of raw personal foot data in real time;

[0104] In S4, the second-round data approval strategy includes: a pressure fluctuation frequency approval strategy and an image pixel distortion approval strategy;

[0105] The pressure fluctuation frequency approval strategy specifically includes:

[0106] S41: The measurement area of ​​the rectangular glass panel is evenly divided into pressure monitoring grids with an area of ​​1cm × 1cm, wherein the total number of pressure monitoring grids is U;

[0107] S42: Extract pressure data from each pressure detection grid within the measurement area of ​​the rectangular glass panel during a single data acquisition unit time period, and calculate the pressure fluctuation frequency value based on the pressure data, wherein the pressure fluctuation frequency value p r,yl The calculation strategy is as follows:

[0108]

[0109] Where u is a subscript, representing the u-th pressure monitoring grid;

[0110] r is a subscript, yl r (u),yl r-1 (u),yl r-2 (u) represents the pressure data of the u-th pressure monitoring grid within the r-th, r-1-th and r-2-th data acquisition unit time periods, respectively.

[0111] The image pixel distortion probability approval strategy includes:

[0112] S43: Capture foot image data through various cameras, and obtain the foot edge curve through edge detection algorithm;

[0113] S44: Draw the tangent line and the perpendicular line of each pixel on the edge curve of the foot surface. The intersection point between the tangent line and the perpendicular line of the tangent line is the pixel itself.

[0114] S45: Extract the pixel values ​​of each pixel on the foot edge curve, and calculate the pixel distortion probability value q based on the pixel values. r,xs The calculation strategy is as follows:

[0115]

[0116] Where h is a subscript, representing the h-th frame of the foot image, and H is the total number of foot images captured by the camera within the r-th data acquisition unit time period;

[0117] During the r-th data collection unit time period, xs r (h g ) represents the pixel value of the g-th pixel on the foot edge curve of the h-th frame of the foot image;

[0118] h g,up1 This represents the pixel whose pixel value is closest to that of the g-th pixel within the region above the perpendicular line to the tangent of the g-th pixel on the outer side of the foot's edge curve; xs r (h g,up1 ) represents the pixel value of that pixel;

[0119] h g,down1This represents the pixel whose pixel value is closest to that of the g-th pixel within the region below the perpendicular line to the tangent of the g-th pixel on the outer side of the foot's edge curve; xs r (h g,down1 ) represents the pixel value of that pixel;

[0120] h g,up2 This represents the pixel whose pixel value is closest to that of the g-th pixel within the region above the perpendicular line to the tangent of the g-th pixel on the inner side of the foot's edge curve; xs r (h g,up2 ) represents the pixel value of that pixel;

[0121] h g,down2 This represents the pixel whose pixel value is closest to that of the g-th pixel within the region below the perpendicular line to the tangent of the g-th pixel on the inner side of the foot's edge curve; xs r (h g,down2 ) represents the pixel value of that pixel.

[0122] In S4, the calculation strategy for the data accuracy coefficient K of each group of individual foot raw data includes:

[0123]

[0124] Where e is the natural logarithm;

[0125] p0 and q0 are the standard values ​​of pressure fluctuation frequency and pixel distortion probability, respectively.

[0126] S5: Based on the data accuracy coefficient of each group of individual foot raw data, filter the valid foot raw data in the individual foot raw data sequence, and dynamically adjust the foot scanning strategy based on the valid foot raw data.

[0127] S5 includes the following specific steps:

[0128] S51: Extract the data accuracy coefficient of each group of individual foot raw data, and preset the data accuracy threshold;

[0129] S52: Filter the raw foot data of individuals whose data accuracy coefficient is greater than the data accuracy threshold in the raw foot data sequence to obtain valid raw foot data;

[0130] S53: Dynamically adjust the foot scanning strategy based on the valid raw foot data. Specifically, this includes: extracting the number of valid raw foot data sets in real time within a standard scanning period. When the number of valid raw foot data sets acquired within a standard scanning period is greater than or equal to 3, the foot scan is completed; otherwise, proceed to step S54.

[0131] S54: When the number of valid raw foot data sets acquired within a standard scanning period is less than 3, the foot scanning time is automatically extended by half of the standard scanning time, and the process returns to step S52 to re-execute steps S52-S53.

[0132] If the number of valid raw foot data sets obtained after dynamic adjustment of the foot scanning strategy is greater than or equal to 3, the foot scan is completed; otherwise, staff will be notified to manually guide the user to repeat the foot scan.

[0133] Example 2:

[0134] like Figure 2 As shown, an embodiment of the present invention provides a data processing system based on a foot calibration plate, such as... Figure 2 As shown, it includes the following modules:

[0135] The system includes an initial data calibration module, a first-round status verification module, a scan data acquisition module, a second-round data verification module, and a scan strategy adjustment module.

[0136] The initial data calibration module is used to start the foot detector and perform initial data calibration on the foot detector through the calibration board.

[0137] The first-round status verification module constructs a historical calibration data log based on the initial data calibration processing results, calculates the mechanical wear factor based on the historical calibration data log, and performs the first-round status verification of the foot detector's working status based on the mechanical wear factor.

[0138] The scanning data acquisition module is used to adjust the foot scanner to the foot scanning mode, and to scan the user's foot multiple times within a standard scanning period to obtain multiple sets of the user's personal foot raw data, which constitute a personal foot raw data sequence.

[0139] The second-round data verification module is used to extract the multiple sets of personal foot raw data and import the multiple sets of personal foot raw data into the second-round data verification strategy, and calculate the data accuracy coefficient of each set of personal foot raw data in real time.

[0140] The scanning strategy adjustment module filters valid raw foot data from the individual's raw foot data sequence based on the data accuracy coefficient of each group of individual raw foot data, and dynamically adjusts the foot scanning strategy based on the valid raw foot data.

[0141] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0142] In summary, compared with the prior art, the technical effects of the present invention are as follows:

[0143] 1. By introducing a dedicated systematic calibration board and combining it with a precise initial data calibration processing method, this invention significantly improves the accuracy and stability of data calibration, reduces the frequency and human error of traditional manual calibration, ensures the initial accuracy of the data, and can monitor and adjust the calibration data in real time after the equipment has been used for a long time, maintaining the consistency of measurement accuracy, thereby improving the long-term stability of the data.

[0144] 2. By introducing an effective data verification strategy, this invention can calculate the accuracy coefficient of each group of individual foot raw data in real time and screen out high-quality valid data. This greatly improves the accuracy and reliability of data processing and helps to improve the accuracy of subsequent analysis and diagnosis.

[0145] 3. This invention automates the entire process from data acquisition and processing to strategy adjustment. This highly automated mechanism not only improves equipment availability and user experience but also reduces manual operation and human intervention, lowering operational complexity and error rates, and achieving efficient, accurate, and reliable data processing.

[0146] 4. By introducing a real-time monitoring and adaptive adjustment mechanism, this invention enables the equipment to maintain optimal condition during long-term use, reducing downtime for maintenance and upgrades.

[0147] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A data processing method based on a foot calibration plate, characterized in that, The data processing method includes the following specific steps: S1: Start the foot detector and perform initial data calibration on the foot detector through the calibration board; S2: Based on the initial data calibration processing results, construct historical calibration data logs, calculate the mechanical wear factor based on the historical calibration data logs, and perform the first round of status verification of the foot detector's working status based on the mechanical wear factor; S3: Set the foot scanner to foot scanning mode and scan the user's feet multiple times within a standard scanning period to obtain multiple sets of raw personal foot data, forming a raw personal foot data sequence. S4: Extract the multiple sets of raw personal foot data and import them into the next round of data verification strategy, and calculate the data accuracy coefficient of each set of raw personal foot data in real time; S5: Based on the data accuracy coefficient of each group of individual foot raw data, filter the valid foot raw data in the individual foot raw data sequence, and dynamically adjust the foot scanning strategy based on the valid foot raw data; In S1, the calibration plate includes: an aluminum alloy middle plate and 8 cubes, wherein 4 cubes are attached to each of the 4 vertices of the upper surface and the 4 vertices of the lower surface of the aluminum alloy middle plate; the four sides of each cube on the upper surface of the aluminum alloy middle plate are parallel to the four sides of the aluminum alloy middle plate; the four sides of each cube on the lower surface of the aluminum alloy middle plate form a 45-degree angle with the four sides of the aluminum alloy middle plate. The foot examination device's workbench includes: two rectangular glass panels of equal area and four cameras; In S1, the initial data calibration process specifically includes: S11: Place the calibration plate on the worktable of the foot detector, and at the same time adjust the four top corners of the aluminum alloy middle plate of the calibration plate to be parallel to the scale of the rectangular glass panel on the left and right sides of the worktable of the foot detector, and start the initial data calibration process. S12: Control the measurement reference lines of the two rectangular glass panels to move left and right, so that the adjusted two measurement reference lines coincide with the sharp corners of the four cube blocks on the lower surface of the aluminum alloy middle plate, wherein the measurement reference lines are parallel to the long side of the rectangular glass panels and the length of the measurement reference lines is equal to the length of the long side of the rectangular glass panels. S13: Control the viewpoints of the four cameras to move up and down so that the adjusted viewpoints coincide with the center of the upper surface of the four cube blocks on the upper surface of the aluminum alloy plate. S2 includes the following specific steps: S21: Based on the initial data calibration processing results, construct a historical calibration data log, wherein the initial data calibration processing results include: the distance movement of the measurement baseline and the angle movement of the camera's viewpoint; S22: Calculate the mechanical wear factor based on the initial data calibration processing results. The calculation strategy for the mechanical wear factor ms is as follows: ; in, These are the mechanical wear contribution ratios for distance movement and angular movement, respectively. i is a subscript indicating the i-th measurement baseline. Let be the distance movement of the i-th measurement baseline. The average distance movement in the historical calibration data log; These are the minimum and maximum distance movement values ​​from the historical calibration data logs, respectively. j is a subscript indicating the viewpoint of the j-th camera; Let be the angular movement of the viewpoint of the j-th camera; The average angular movement in the historical calibration data log; These are the minimum and maximum angle movement values ​​from the historical calibration data log, respectively. S23: Perform the first round of status verification on the foot tester's working status based on the mechanical wear factor. The first round of status verification includes: setting the maximum tolerable mechanical wear factor of the foot tester, comparing the mechanical wear factor with the maximum tolerable mechanical wear factor of the foot tester, and determining that the foot tester meets the working conditions when the mechanical wear factor is less than the maximum tolerable mechanical wear factor of the foot tester, and adjusting the foot tester to the working mode. Otherwise, the foot tester is manually calibrated by the staff.

2. The data processing method based on a foot calibration plate according to claim 1, characterized in that, S3 includes the following specific steps: S31: Adjust the foot scanner to foot scanning mode and scan the user's foot multiple times within a standard scanning period. The standard scanning period includes multiple data acquisition unit periods, and the duration of each data acquisition unit period is ten seconds. S32: Obtain multiple sets of raw personal foot data from the user to form a raw personal foot data sequence. Each set of raw personal foot data includes: foot geometric parameter data, foot surface image data, foot temperature data, and foot humidity data.

3. The data processing method based on a foot calibration plate according to claim 2, characterized in that, In S4, the second-round data approval strategy includes: a pressure fluctuation frequency approval strategy and an image pixel distortion approval strategy; The pressure fluctuation frequency approval strategy specifically includes: S41: Divide the measurement area of ​​the rectangular glass panel evenly into sections with an area of... A pressure monitoring grid, wherein the total number of the pressure monitoring grids is U; S42: Extract pressure data from each pressure monitoring grid within the measurement area of ​​the rectangular glass panel during a single data acquisition unit time period, and calculate the pressure fluctuation frequency value based on the pressure data. The calculation strategy is as follows: ; Where u is a subscript, representing the u-th pressure monitoring grid; r is a subscript These represent the pressure data of the u-th pressure monitoring grid within the r-th, r-1-th, and r-2-th data acquisition unit time periods, respectively.

4. The data processing method based on the calibration plate of a foot detector according to claim 3, characterized in that, The image pixel distortion approval strategy includes: S43: Capture foot image data through various cameras, and obtain the foot edge curve through edge detection algorithm; S44: Draw the tangent line and the perpendicular line of each pixel on the edge curve of the foot surface. The intersection point between the tangent line and the perpendicular line of the tangent line is the pixel itself. S45: Extract the pixel values ​​of each pixel on the foot edge curve, and calculate the pixel distortion probability value based on the pixel values. The calculation strategy is as follows: ; Where h is a subscript, representing the h-th frame of the foot image, and H is the total number of foot images captured by the camera within the r-th data acquisition unit time period; During the r-th data collection unit time period, This represents the pixel value of the g-th pixel on the foot edge curve of the h-th frame of the foot image; This represents the pixel whose pixel value is closest to that of the g-th pixel within the region above the perpendicular line to the tangent of the g-th pixel on the outer side of the foot edge curve. This represents the pixel value of that pixel. This represents the pixel whose pixel value is closest to that of the g-th pixel within the region below the perpendicular line to the tangent of the g-th pixel on the outer side of the foot edge curve. This represents the pixel value of that pixel. This represents the pixel whose pixel value is closest to that of the g-th pixel within the region above the perpendicular line to the tangent of the g-th pixel on the inner side of the foot surface edge curve. This represents the pixel value of that pixel. This represents the pixel whose pixel value is closest to that of the g-th pixel within the region below the perpendicular line to the tangent of the g-th pixel on the inner side of the foot edge curve. This represents the pixel value of that pixel.

5. The data processing method based on a foot calibration plate according to claim 4, characterized in that, In S4, the calculation strategy for the data accuracy coefficient K of each group of individual foot raw data includes: ; Where e is the natural logarithm; These are the standard values ​​for pressure fluctuation frequency and pixel distortion probability, respectively.

6. The data processing method based on a foot calibration plate according to claim 5, characterized in that, S5 includes the following specific steps: S51: Extract the data accuracy coefficient of each group of individual foot raw data, and preset the data accuracy threshold; S52: Filter the raw foot data of individuals whose data accuracy coefficient is greater than the data accuracy threshold in the raw foot data sequence to obtain valid raw foot data; S53: Dynamically adjust the foot scanning strategy based on the valid raw foot data. Specifically, this includes: extracting the number of valid raw foot data sets in real time within a standard scanning period. When the number of valid raw foot data sets acquired within a standard scanning period is greater than or equal to 3, the foot scan is completed; otherwise, proceed to step S54. S54: When the number of valid raw foot data sets acquired within a standard scanning period is less than 3, the scanning time of this foot scan is automatically extended by half the scanning time of a standard scanning period, and the process returns to step S52 to re-execute steps S52-S53.

7. A data processing system based on a foot calibrator calibration plate, used to implement the data processing method based on a foot calibrator calibration plate as described in any one of claims 1-6, characterized in that, The data processing system includes the following modules: The system includes an initial data calibration module, a first-round status verification module, a scan data acquisition module, a second-round data verification module, and a scan strategy adjustment module. The initial data calibration module is used to start the foot detector and perform initial data calibration on the foot detector through the calibration board. The first-round status verification module constructs a historical calibration data log based on the initial data calibration processing results, calculates the mechanical wear factor based on the historical calibration data log, and performs the first-round status verification of the foot detector's working status based on the mechanical wear factor. The scanning data acquisition module is used to adjust the foot scanner to the foot scanning mode, and to scan the user's foot multiple times within a standard scanning period to obtain multiple sets of the user's personal foot raw data, which constitute a personal foot raw data sequence. The second-round data verification module is used to extract the multiple sets of personal foot raw data and import the multiple sets of personal foot raw data into the second-round data verification strategy, and calculate the data accuracy coefficient of each set of personal foot raw data in real time. The scanning strategy adjustment module filters valid raw foot data from the individual's raw foot data sequence based on the data accuracy coefficient of each group of individual raw foot data, and dynamically adjusts the foot scanning strategy based on the valid raw foot data.

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