A method and system for designing an orthotic shoe insole based on foot pressure distribution

Abstract

This invention relates to the field of formal shoe insole design technology, and discloses a method and system for designing formal shoe insoles based on foot pressure distribution. The method includes: acquiring dynamic and static pressure data of the user's foot, fusing the data to generate a plantar pressure distribution map; dividing the sole into a heel area, arch area, and forefoot area based on characteristic points of the foot; traversing pressure data points and adjacent pressure data points within the heel, arch, and forefoot areas to determine peak pressure regions and pressure gradient change regions, and determining the pressure center coordinates and resultant pressure value of the peak pressure region, as well as the pressure change direction vector and pressure change rate of the pressure gradient change region; and determining the material hardness parameters, density parameters, and thickness distribution parameters corresponding to the heel, arch, and forefoot areas. This application integrates dynamic and static pressure data to characterize the changing trend of plantar force.

Description

Technical Field

[0001] This invention relates to the field of formal shoe insole design technology, and more specifically, to a formal shoe insole design method and system based on foot pressure distribution. Background Technology

[0002] With the development of personalized wearable products and the increasing emphasis on foot health and comfort, the structural design of insoles, as a crucial component of footwear that directly impacts the sole of the foot, is gradually shifting from traditional experience-based design to data-driven, refined design. This is especially true in the formal shoe sector, where high requirements exist for appearance, thickness, and structural stability; insoles typically need to balance support and cushioning within a limited space.

[0003] Existing patent CN121365563A discloses a parametric design method for orthotic insoles based on biomechanical data and a rule base. This method, based on 3D foot scanning, biomechanical analysis, and a parametric rule base, acquires foot point cloud data and reconstructs a 3D model. It then combines geometric and mechanical features such as arch morphology and heel posture to classify foot types and assess correction needs. Furthermore, it generates personalized insole structures through a parametric design scheme library and rule matching mechanism. However, in actual wear, the pressure distribution on the soles of different users varies significantly during standing, walking, and turning, and the force characteristics in different areas also differ. The aforementioned technology focuses on structural design based on foot geometry and overall biomechanical characteristics, paying insufficient attention to the dynamic pressure distribution characteristics of the sole during actual walking, making it difficult to accurately reflect the spatial distribution characteristics of sole force.

[0004] Therefore, it is necessary to design a method and system for designing formal shoe insoles based on foot pressure distribution to solve the problems existing in the current technology. Summary of the Invention

[0005] In view of this, the present invention proposes a design method and system for formal insoles based on foot pressure distribution, aiming to solve the problem that insufficient attention is paid to the dynamic pressure distribution characteristics of the sole during actual walking, making it difficult to accurately reflect the spatial distribution characteristics of the force on the sole.

[0006] In one aspect, the present invention proposes a design method for formal shoe insoles based on foot pressure distribution, comprising: Acquire dynamic and static pressure data of the user's feet, fuse the data, and generate a plantar pressure distribution map; Based on the feature points of the foot, the projected coordinates on the plantar pressure distribution map are used to determine the region segmentation line; according to the region segmentation line, the sole of the foot is divided into the heel area, the arch area, and the forefoot area; pressure data points in the heel area, the arch area, and the forefoot area are traversed, and pressure data points with pressure values ​​greater than a preset pressure threshold are selected to determine the peak pressure area, and the pressure center coordinates and the resultant pressure value of the peak pressure area are determined; adjacent pressure data points in the heel area, the arch area, and the forefoot area are traversed, and the pressure difference between adjacent pressure data points is determined; adjacent pressure data points with pressure differences greater than a preset gradient threshold are selected to determine the pressure gradient change area, and the pressure change direction vector and pressure change rate of the pressure gradient change area are determined; Based on the pressure center coordinates, the resultant pressure force value, the pressure change direction vector, and the pressure change rate, the material hardness parameters, density parameters, and thickness distribution parameters corresponding to the heel area, arch area, and forefoot area are determined, and multi-layer structure data of formal shoe insoles are constructed.

[0007] Furthermore, determining the pressure center coordinates and resultant pressure value of the peak pressure region includes: Pressure data points with pressure values ​​greater than a preset pressure threshold are used as seed points; neighboring pressure data points that are adjacent to the seed points and have pressure values ​​greater than the preset pressure threshold are obtained; the seed points and the neighboring pressure data points are merged into a connected region, which is used as the peak pressure region. The coordinates of the pressure center are obtained by weighted averaging the coordinates and pressure values ​​of each pressure data point within the peak pressure region; the resultant pressure value is obtained by summing the pressure values ​​of each pressure data point within the peak pressure region.

[0008] Furthermore, determining the pressure change direction vector and pressure change rate in the pressure gradient change region includes: The plantar pressure distribution map is divided into a grid structure, with each grid corresponding to a pressure data point; the pressure data points within a preset neighborhood around each pressure data point are taken as the adjacent pressure data points. The direction of the line connecting the adjacent pressure data points with the largest pressure difference is taken as the pressure change direction vector; the pressure change rate is obtained by processing the ratio between the pressure difference and the distance between the adjacent pressure data points.

[0009] Furthermore, when constructing the multi-layered structural data for formal shoe insoles, the following is included: The material hardness parameter is determined based on the positive correlation between the resultant pressure value and the material hardness parameter; the density parameter is determined based on the positive correlation between the pressure change rate and the density parameter; the thickened area is determined based on the pressure center coordinates to obtain the thickness distribution parameter. Based on the material hardness parameter and the density parameter, bottom support structure data and surface buffer structure data are generated; between the bottom support structure data and the surface buffer structure data, functional transition layer data is generated; the bottom support structure data, the surface buffer structure data and the functional transition layer data are combined to obtain the multi-layer structure data.

[0010] Compared with existing technologies, the advantages of this invention are as follows: By integrating dynamic and static pressure data, a more complete plantar pressure distribution map is constructed, which can simultaneously reflect the dynamic force characteristics of the user during walking and the stable force situation in a static standing state, thereby avoiding information bias caused by a single data source and improving the accuracy of plantar force analysis. By dividing the sole into functional zones of the heel, arch, and forefoot, and extracting peak pressure areas, pressure centers, and resultant force characteristics in each zone, the insole design can be differentiated according to the force differences in different functional areas, thereby improving the matching degree of insole in terms of support and comfort. By analyzing the pressure difference between adjacent pressure data points, the pressure gradient change area is identified, and the direction and rate of pressure change are extracted, thus characterizing the trend of plantar force change. This helps to form a continuous transition mechanical response in the insole structure design, improving overall wearing comfort. By establishing a correspondence between the pressure center coordinates, resultant force values, and pressure change characteristics and material hardness, density, and thickness distribution parameters, the insole structure design is transformed from experience-driven to data-driven, realizing the automated determination of structural parameters.

[0011] On the other hand, this application also provides a formal shoe insole design system based on foot pressure distribution, for applying the above-mentioned formal shoe insole design method based on foot pressure distribution, including: The acquisition unit is configured to acquire dynamic and static pressure data of the user's feet, and perform data fusion to generate a plantar pressure distribution map; The processing unit is configured to determine region segmentation lines based on the projected coordinates of feature points of the foot on the plantar pressure distribution map; divide the sole of the foot into a heel area, an arch area, and a forefoot area according to the region segmentation lines; traverse the pressure data points in the heel area, arch area, and forefoot area, filter pressure data points with pressure values ​​greater than a preset pressure threshold, determine peak pressure areas, and determine the pressure center coordinates and resultant pressure value of the peak pressure areas; traverse adjacent pressure data points in the heel area, arch area, and forefoot area, determine the pressure difference between adjacent pressure data points; filter adjacent pressure data points with pressure differences greater than a preset gradient threshold, determine pressure gradient change areas, and determine the pressure change direction vector and pressure change rate of the pressure gradient change areas; The construction unit is configured to determine the material hardness parameters, density parameters, and thickness distribution parameters corresponding to the heel area, arch area, and forefoot area based on the pressure center coordinates, pressure resultant force value, pressure change direction vector, and pressure change rate, and to construct the multi-layer structure data of the formal shoe insole.

[0012] It is understandable that the above-mentioned design method and system for formal shoe insoles based on foot pressure distribution have the same beneficial effects, and will not be elaborated further here. Attached Figure Description

[0013] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating a method for designing formal shoe insoles based on foot pressure distribution, provided in an embodiment of the present invention; Figure 2 A functional block diagram of a formal shoe insole design system based on foot pressure distribution provided in an embodiment of the present invention. Detailed Implementation

[0014] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0015] In some embodiments of this application, see Figure 1As shown, a design method for formal shoe insoles based on foot pressure distribution is proposed, including: S100: Acquires dynamic and static pressure data of the user's feet, fuses the data, and generates a plantar pressure distribution map; S200: Based on the feature points of the foot and their projected coordinates on the plantar pressure distribution map, determine the region segmentation line; according to the region segmentation line, divide the sole of the foot into the heel area, arch area, and forefoot area; traverse the pressure data points in the heel area, arch area, and forefoot area, filter pressure data points with pressure values ​​greater than a preset pressure threshold, determine the peak pressure area, and determine the pressure center coordinates and resultant pressure value of the peak pressure area; traverse adjacent pressure data points in the heel area, arch area, and forefoot area, determine the pressure difference between adjacent pressure data points; filter adjacent pressure data points with pressure differences greater than a preset gradient threshold, determine the pressure gradient change area, and determine the pressure change direction vector and pressure change rate of the pressure gradient change area; S300: Based on the pressure center coordinates, the resultant pressure force value, the pressure change direction vector, and the pressure change rate, determine the material hardness parameters, density parameters, and thickness distribution parameters corresponding to the heel area, arch area, and forefoot area, and construct the multi-layer structure data of formal shoe insoles.

[0016] Specifically, in step S100, dynamic and static pressure data of the user's feet are first acquired using a plantar pressure acquisition device. Dynamic pressure data is collected during natural walking using pressure-sensing insoles or a pressure testing treadmill, recording the plantar pressure distribution at each moment within the complete gait cycle. Static pressure data is collected when the user is standing naturally with both feet, obtaining the plantar pressure distribution under stable conditions. The dynamic pressure data undergoes time-series alignment, dividing the data intervals based on the gait cycle, and extracting the pressure data from the support phase as valid dynamic data. Simultaneously, the static pressure data undergoes stability screening, removing abnormal data with large fluctuations and retaining stable pressure distributions. Further, the dynamic and static pressure data are fused. A weighted fusion method can be used, superimposing the two types of data according to preset weight coefficients. For example, the weight for dynamic data can be set to 0.6–0.8, and the weight for static data can be set to 0.2–0.4, thus taking into account both walking force characteristics and static support characteristics. The fused data is mapped to a unified coordinate system, forming a two-dimensional or three-dimensional plantar pressure distribution map.

[0017] Specifically, in step S200, after obtaining the plantar pressure distribution map, key structural feature points of the foot are identified, including the heel center point, the first metatarsal head, and the fifth metatarsal head. These feature points can be determined by combining the extreme values ​​of the pressure distribution with the geometric proportions of the foot. For example, the heel center point corresponds to the main stress concentration point in the posterior region of the pressure distribution map, and the first and fifth metatarsal heads correspond to the pressure concentration areas on the medial and lateral sides of the forefoot, respectively. The spatial coordinates of the feature points are mapped onto the plantar pressure distribution map, and region segmentation lines are constructed based on these feature points. For example, using the line connecting the heel center point and the midpoint of the forefoot as the longitudinal reference axis, and the line connecting the first and fifth metatarsal heads as the lateral boundary line, the sole of the foot is divided into the heel area, the arch area, and the forefoot area. Within each region, pressure data points are processed. First, a preset pressure threshold is set (e.g., determined based on 1.2 to 1.5 times the overall pressure average), and data points with pressure values ​​higher than this threshold are selected as candidate points. Through neighborhood connectivity analysis, adjacent data points that meet threshold conditions are clustered to form several peak pressure regions. For each peak pressure region, a weighted average is calculated based on the pressure values ​​and spatial coordinates of each data point within the region to obtain the coordinates of the pressure center; simultaneously, all pressure values ​​within the region are summed to obtain the resultant pressure value for that region. A pressure data grid structure is constructed within each partition, dividing the pressure distribution map into regular grids, with each grid corresponding to a pressure data point. For each data point, data points within a preset neighborhood (e.g., a 3×3 or 5×5 grid range) are selected as adjacent pressure data points, and the pressure difference between adjacent data points is calculated. By filtering data point pairs with pressure differences greater than a preset gradient threshold (e.g., a gradient threshold of 1.0 to 1.5 times the overall pressure standard deviation), pressure gradient change regions are determined. For each gradient change region, the direction of the line connecting the adjacent data points with the largest pressure difference is taken as the pressure change direction vector, and the ratio of the pressure difference to the distance between the corresponding data points is taken as the pressure change rate, thus characterizing the severity of pressure change.

[0018] Specifically, in step S300, after obtaining the pressure center coordinates, resultant pressure force values, pressure change direction vectors, and pressure change rates of each zone, a mapping relationship between pressure characteristics and insole structural parameters is established. Material hardness parameters are determined based on a preset mapping relationship between the resultant pressure force value and material hardness. In this embodiment, a segmented mapping method can be used: when the resultant pressure force is large, a higher hardness material is used to enhance support; when the resultant pressure force is small, a lower hardness material is used to improve comfort. Material density parameters are determined based on the pressure change rate. A larger pressure change rate indicates a more drastic force change, requiring increased material density in the corresponding area to enhance structural stability; areas with slower pressure changes can use lower density materials to improve cushioning performance. Using the pressure center coordinates as a reference, local thickening structures are set in the corresponding areas. For example, a cushioning thickening area is formed at the pressure center position in the heel area, a propulsion support area is formed in the forefoot area, and a support structure is formed in the arch area, thereby achieving targeted support. After determining the material hardness, density, and thickness distribution parameters of each area, multi-layer insole structural data is constructed. Specifically, this includes: a bottom support structure using relatively high-hardness materials to form an overall support frame; a surface cushioning structure using low-hardness materials to improve foot comfort; and a functional transition layer placed between the bottom and surface layers, achieving smooth stress transfer through a gradual transition in materials or structure. Finally, the structural data of each layer are integrated to form a complete three-dimensional structural model of formal insoles, which can be used for subsequent processing and manufacturing.

[0019] Understandably, by integrating dynamic and static pressure data, the stress characteristics of the foot can be reflected, improving the accuracy of pressure analysis. Joint analysis of peak pressure regions and pressure gradient change regions enables refined identification of the stress distribution on the foot, enhancing the targeted nature of structural design. Establishing a mapping relationship between pressure characteristics and material hardness, density, and thickness distribution transforms insole structural design from experience-driven to data-driven, improving the scientific rigor and consistency of the design. Through multi-layered structural design and functional transition layers, the lightweight characteristics of dress shoes are maintained while achieving a harmonious balance between support and comfort.

[0020] Specifically, using the continuous timestamps output by the plantar pressure acquisition device as a benchmark, the original dynamic pressure data is resampled and normalized over time to align data from different sampling frequencies or acquisition periods on a unified time scale. Subsequently, by performing periodic analysis on the pressure curve (e.g., based on pressure change characteristics at the initial heel strike and toe lift-off), the complete gait cycle is identified and segmented, and pressure data for the support phase is extracted. The support phase is preferably the entire data interval encompassing the entire process of foot-to-ground contact. Simultaneously, the static pressure data undergoes stability screening. By calculating the variance or fluctuation range of the pressure data within a continuous time window, abnormal data segments caused by posture adjustments or slight swaying are eliminated, retaining the pressure values ​​under stable conditions as static benchmark data. Based on this, the pressure data of the supporting phase and the pressure values ​​of the steady state are weighted and fused. The fusion weight can be set according to the application requirements. For example, the weight of dynamic pressure data is set to 0.6 to 0.8, and the weight of static pressure data is set to 0.2 to 0.4. The fused pressure data is generated by point-by-point weighted superposition or matrix fusion. Finally, the fused pressure data is mapped to a unified plantar spatial coordinate system, and a continuous plantar pressure distribution map is generated by interpolation or grid reconstruction, thereby providing basic data support for subsequent regional division and structural parameter calculation.

[0021] Understandably, while preserving the true force change characteristics during dynamic gait, the introduction of static support state as a reference benchmark avoids the bias problem caused by relying on only a single data source, thereby improving the accuracy and stability of plantar pressure distribution characterization; through time series alignment and support phase extraction processing, data from different users or under different test conditions are made comparable.

[0022] Specifically, by performing threshold segmentation and region connectivity analysis on the pressure distribution map, the spatial distribution characteristics of pressure concentration areas can be extracted. Combined with the anatomical proportions of the foot, the centroid of the relatively concentrated pressure distribution area in the heel region can be calculated to determine the heel center point. In the forefoot region, by identifying the two main pressure peak areas on the medial and lateral sides, the first and fifth metatarsal heads are determined, respectively. The first metatarsal head corresponds to the geometric center of the medial pressure concentration area in the forefoot, and the fifth metatarsal head corresponds to the geometric center of the lateral pressure concentration area in the forefoot. After obtaining these feature points, their two-dimensional or three-dimensional coordinate information in the original pressure data coordinate system or image coordinate system is extracted. Then, through coordinate system transformation relationships (including scale normalization, translation alignment, and rotation correction), they are uniformly mapped to the standard coordinate system used in the plantar pressure distribution map, thereby obtaining the projected coordinates of each feature point in the plantar pressure distribution map.

[0023] Understandably, automatic identification and localization of key anatomical features of the foot based on objective pressure distribution data improves the consistency and accuracy of feature point extraction. Through unified coordinate system mapping, data from different sources can be analyzed under the same spatial reference, which is conducive to the standardized processing of region division and structural parameter calculation. The identified feature points can accurately reflect the key force positions of the heel and forefoot, providing a reliable basis for the design of foot zoning and insole structure.

[0024] Specifically, when determining the peak pressure region, a preset pressure threshold is first set based on the numerical distribution of each pressure data point in the plantar pressure distribution map. The pressure threshold can be determined according to the proportion of the global pressure mean, standard deviation, or maximum pressure value; for example, 1.2 to 1.5 times the overall average pressure or 60% to 80% of the maximum pressure value can be used as the judgment threshold. Then, all pressure data points in the plantar pressure distribution map are traversed point by point, and data points with pressure values ​​greater than the preset pressure threshold are marked as candidate seed points. For each seed point, neighboring pressure data points are searched within a preset neighborhood of its grid (e.g., a 3×3 or 5×5 neighborhood centered on that point), and neighboring data points that also meet the condition of having pressure values ​​greater than the preset pressure threshold are selected. Further, starting from the seed point, neighboring data points that meet the threshold condition are expanded and merged layer by layer using region growing or connected component analysis methods until no further expansion is possible, thus forming several spatially continuous connected regions. Each connected region is then determined as a peak pressure region. After forming a connected region, it can be subjected to area filtering or shape constraint processing, such as removing noise regions that are too small or isolated, to improve the stability and accuracy of peak region identification.

[0025] Understandably, the automatic identification and spatial clustering of high-pressure areas in the plantar pressure distribution map can reflect the actual range of the concentrated pressure area on the plantar surface; by merging connected components, the interference of local noise data on the results can be suppressed, thus improving the robustness of peak pressure area identification.

[0026] Specifically, the spatial coordinates and corresponding pressure values ​​of all pressure data points within the peak pressure region are first obtained. Each pressure data point's two-dimensional (or three-dimensional) coordinates are determined by its grid position on the plantar pressure distribution map, and matched one-to-one with its corresponding pressure value. Based on this, a weighted average is performed on the spatial coordinates using the pressure values ​​of each data point as weights. Specifically, the abscissa and ordinate of each data point are weighted and summed, then divided by the sum of the pressure values ​​of all pressure data points within the peak pressure region. This yields the pressure center coordinates, reflecting the location of the center of gravity of the force distribution in that region. Furthermore, the pressure values ​​of all pressure data points within the peak pressure region are accumulated point by point to obtain the resultant pressure value for that region, which characterizes the overall stress intensity of the area.

[0027] Understandably, transforming discrete pressure data into lumped parameters with clear physical meaning can not only accurately locate the center of force in the high-pressure area of ​​the foot, but also quantify the overall stress level in that area, reflecting the actual stress state.

[0028] Specifically, the plantar pressure distribution map is first processed into a grid according to a preset spatial resolution, discretizing the continuous pressure distribution area into a regularly arranged grid structure. Each grid cell corresponds to a pressure data point and contains the pressure value and its spatial coordinates at that location. The grid division can use an equally spaced rectangular grid or a polar coordinate grid. The grid size can be set according to the resolution of the pressure acquisition device or the accuracy requirements of subsequent calculations, for example, within the millimeter range. Based on this, for any target pressure data point, a preset neighborhood range is set with its own grid as the center. For example, one or more rings of grid cells are selected around it (such as a 3×3 neighborhood, a 5×5 neighborhood, or a neighborhood range based on a Euclidean distance threshold), and all pressure data points within this neighborhood other than itself are determined as its neighboring pressure data points.

[0029] It is understandable that transforming the plantar pressure distribution into a grid data structure with a clear topological relationship provides a unified data foundation for pressure difference calculation and pressure gradient analysis. By flexibly setting the neighborhood range, a balance can be struck between calculation accuracy and calculation efficiency, ensuring the effective capture of local pressure change characteristics while avoiding data redundancy caused by an excessively large neighborhood.

[0030] Specifically, firstly, based on the established relationships between adjacent pressure data points, the pressure difference between each target pressure data point and its neighboring pressure data points is calculated, forming a corresponding pressure difference set. Then, the pair of adjacent pressure data points with the largest pressure difference is selected from this set, and the direction of the line connecting the spatial coordinates of the target data point and its corresponding adjacent data point is used as the pressure change direction vector. This direction vector can be uniformly defined as pointing from a low-pressure point to a high-pressure point to ensure consistency. Based on this, the spatial distance between the target data point and its adjacent data points is obtained. This distance can be determined by the grid spacing or the actual physical distance. The ratio of the maximum pressure difference to the corresponding distance is then calculated to obtain the pressure change rate at that location, which characterizes the drastic degree of pressure change per unit distance.

[0031] Understandably, accurately depicting the changing trend and intensity of foot pressure in a spatial dimension can reflect the changing characteristics of the force distribution on the foot compared to analyzing only the magnitude of pressure. The pressure change direction vector can be used to indicate the force transmission path, while the pressure change rate can quantify the sensitivity of force changes, thus providing a reliable basis for the density distribution and structural gradient design of insole materials.

[0032] Specifically, a material hardness mapping relationship is established based on the calculated resultant pressure values ​​within each zone. Specifically, the resultant pressure values ​​are graded according to preset intervals, for example, divided into high, medium, and low levels based on the proportion of the maximum resultant pressure. Hardness ranges corresponding to each level (such as Shore hardness ranges) are selected from a pre-established material parameter library to determine the material hardness parameters for each zone. Areas with larger resultant pressure values ​​correspond to higher material hardness to enhance load-bearing and support capabilities. Further, material density parameters are determined based on the pressure change rate. The pressure change rate can be normalized and mapped to a preset density interval. The corresponding material density value is determined through table lookup or function mapping. Areas with larger pressure change rates use higher-density materials to improve structural stability and suppress local deformation. Simultaneously, based on the location of the pressure center coordinates within each zone, locally thickened areas are determined, and a thickness distribution function is set with the pressure center as the core. For example, a radially decreasing or elliptical distribution method is used to gradually transition the thickness from the center outwards, thus forming thickness distribution parameters that conform to the stress characteristics.

[0033] Understandably, establishing a clear correspondence between the stress characteristics of the foot and the structural parameters of the insole enables pressure data-driven structural design, improving the scientific rigor and consistency of parameter configuration. Controlling material hardness through combined pressure enhances support performance in high-load areas, adjusting material density through the rate of pressure change improves adaptability to stress-affected areas, and designing thickness distribution based on the pressure center enables precise cushioning and support for critical stress areas. Thus, while maintaining the lightweight characteristics of dress shoes, overall comfort and functionality are enhanced.

[0034] Specifically, firstly, based on the aforementioned determined material hardness and density parameters, the functional areas of the insole are modeled in layers. Specifically, during the construction of the bottom support structure, material properties with corresponding hardness levels are selected according to the material hardness parameters of the heel, arch, and forefoot areas. Continuous or partitioned bottom structure data is generated in the 3D modeling environment. This bottom structure preferably has high stiffness and load-bearing capacity to provide the overall support framework. Subsequently, during the construction of the surface cushioning structure, corresponding low-density or medium-density material properties are selected according to the density parameters of each area, and combined with the aforementioned thickness distribution parameters to generate the surface geometry, making it conform to the foot's contour and possess good flexibility and cushioning performance. Based on this, functional transition layer data is introduced between the bottom support structure and the surface cushioning structure. The functional transition layer is achieved by setting gradual changes in material properties or structure, for example, by gradually adjusting the material hardness or density parameters along the thickness direction, or by using a multi-unit hierarchical structure to achieve performance transition, thereby avoiding stress concentration caused by abrupt performance changes between different layers. Finally, the underlying support structure data, functional transition layer data, and surface buffer structure data are combined and assembled according to the preset spatial relationships to form a complete multi-layer structure model data.

[0035] Understandably, the coordinated design of support, cushioning, and transition layers within the same insole structure allows for a rational allocation of different functional needs in space, thereby improving the overall mechanical performance and comfort of the structure. The bottom support structure provides stable support, the surface cushioning structure improves the feel of the foot, and the functional transition layer achieves a smooth transition in mechanical performance, thus reducing the risk of local stress concentration.

[0036] Based on another preferred embodiment described above, see [link to preferred embodiment]. Figure 2 As shown, this embodiment provides a formal shoe insole design system based on foot pressure distribution, used to apply the above-mentioned formal shoe insole design method based on foot pressure distribution, including: The acquisition unit is configured to acquire dynamic and static pressure data of the user's feet, and perform data fusion to generate a plantar pressure distribution map; The processing unit is configured to determine region segmentation lines based on the projected coordinates of feature points of the foot on a plantar pressure distribution map; divide the sole of the foot into heel, arch, and forefoot regions according to the region segmentation lines; traverse the pressure data points in the heel, arch, and forefoot regions, filter pressure data points with pressure values ​​greater than a preset pressure threshold, determine peak pressure regions, and determine the pressure center coordinates and resultant pressure value of the peak pressure regions; traverse adjacent pressure data points in the heel, arch, and forefoot regions, determine the pressure difference between adjacent pressure data points; filter adjacent pressure data points with pressure differences greater than a preset gradient threshold, determine pressure gradient change regions, and determine the pressure change direction vector and pressure change rate of the pressure gradient change regions; The building unit is configured to determine the material hardness parameters, density parameters, and thickness distribution parameters corresponding to the heel area, arch area, and forefoot area based on the pressure center coordinates, pressure resultant force value, pressure change direction vector, and pressure change rate, and to construct the multi-layer structure data of the formal shoe insole.

[0037] In summary, by integrating dynamic and static pressure data, a more complete plantar pressure distribution map is constructed, simultaneously reflecting the dynamic force characteristics of a user during walking and the stable force under static standing conditions. This avoids information bias caused by a single data source and improves the accuracy of plantar force analysis. By functionally dividing the foot into heel, arch, and forefoot areas, and extracting peak pressure regions, pressure centers, and resultant force characteristics within each area, insole design can be differentiated to address the force differences in different functional areas, thereby improving the match between support and comfort. Analyzing the pressure difference between adjacent pressure data points identifies pressure gradient change areas and extracts the direction and rate of pressure change, thus characterizing the trend of plantar force changes. This helps to create a continuous transition in mechanical response in insole structural design, improving overall wearing comfort. By establishing a correspondence between pressure center coordinates, resultant force values, and pressure change characteristics with material hardness, density, and thickness distribution parameters, insole structural design shifts from experience-driven to data-driven, achieving automated determination of structural parameters.

[0038] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A design method for formal shoe insoles based on foot pressure distribution, characterized in that, include: Acquire dynamic and static pressure data of the user's feet, fuse the data, and generate a plantar pressure distribution map; Based on the feature points of the foot, the projected coordinates on the plantar pressure distribution map are used to determine the region segmentation line; according to the region segmentation line, the sole of the foot is divided into the heel area, the arch area, and the forefoot area; pressure data points in the heel area, the arch area, and the forefoot area are traversed, and pressure data points with pressure values ​​greater than a preset pressure threshold are selected to determine the peak pressure area, and the pressure center coordinates and the resultant pressure value of the peak pressure area are determined; adjacent pressure data points in the heel area, the arch area, and the forefoot area are traversed, and the pressure difference between adjacent pressure data points is determined; adjacent pressure data points with pressure differences greater than a preset gradient threshold are selected to determine the pressure gradient change area, and the pressure change direction vector and pressure change rate of the pressure gradient change area are determined; Based on the pressure center coordinates, the resultant pressure force value, the pressure change direction vector, and the pressure change rate, the material hardness parameters, density parameters, and thickness distribution parameters corresponding to the heel area, arch area, and forefoot area are determined, and multi-layer structure data of formal shoe insoles are constructed.

2. The design method for formal shoe insoles based on foot pressure distribution according to claim 1, characterized in that, Determining the pressure center coordinates and resultant pressure value of the peak pressure region includes: Pressure data points with pressure values ​​greater than a preset pressure threshold are used as seed points; neighboring pressure data points that are adjacent to the seed points and have pressure values ​​greater than the preset pressure threshold are obtained; the seed points and the neighboring pressure data points are merged into a connected region, which is used as the peak pressure region. The coordinates of the pressure center are obtained by weighted averaging the coordinates and pressure values ​​of each pressure data point within the peak pressure region; the resultant pressure value is obtained by summing the pressure values ​​of each pressure data point within the peak pressure region.

3. The design method for formal shoe insoles based on foot pressure distribution according to claim 2, characterized in that, Determining the pressure change direction vector and pressure change rate in the pressure gradient change region includes: The plantar pressure distribution map is divided into a grid structure, with each grid corresponding to a pressure data point; the pressure data points within a preset neighborhood around each pressure data point are taken as the adjacent pressure data points. The direction of the line connecting the adjacent pressure data points with the largest pressure difference is taken as the pressure change direction vector; the pressure change rate is obtained by processing the ratio between the pressure difference and the distance between the adjacent pressure data points.

4. The design method for formal shoe insoles based on foot pressure distribution according to claim 3, characterized in that, When constructing the multi-layered structural data for formal shoe insoles, the following is included: The material hardness parameter is determined based on the positive correlation between the resultant pressure value and the material hardness parameter; the density parameter is determined based on the positive correlation between the pressure change rate and the density parameter; the thickened area is determined based on the pressure center coordinates to obtain the thickness distribution parameter. Based on the material hardness parameter and the density parameter, bottom support structure data and surface buffer structure data are generated; between the bottom support structure data and the surface buffer structure data, functional transition layer data is generated; the bottom support structure data, the surface buffer structure data and the functional transition layer data are combined to obtain the multi-layer structure data.

5. A formal shoe insole design system based on foot pressure distribution, used to apply the formal shoe insole design method based on foot pressure distribution as described in any one of claims 1-4, characterized in that, include: The acquisition unit is configured to acquire dynamic and static pressure data of the user's feet, and perform data fusion to generate a plantar pressure distribution map; The processing unit is configured to determine region segmentation lines based on the projected coordinates of feature points of the foot on the plantar pressure distribution map; divide the sole of the foot into a heel area, an arch area, and a forefoot area according to the region segmentation lines; traverse the pressure data points in the heel area, arch area, and forefoot area, filter pressure data points with pressure values ​​greater than a preset pressure threshold, determine peak pressure areas, and determine the pressure center coordinates and resultant pressure value of the peak pressure areas; traverse adjacent pressure data points in the heel area, arch area, and forefoot area, determine the pressure difference between adjacent pressure data points; filter adjacent pressure data points with pressure differences greater than a preset gradient threshold, determine pressure gradient change areas, and determine the pressure change direction vector and pressure change rate of the pressure gradient change areas; The construction unit is configured to determine the material hardness parameters, density parameters, and thickness distribution parameters corresponding to the heel area, arch area, and forefoot area based on the pressure center coordinates, pressure resultant force value, pressure change direction vector, and pressure change rate, and to construct the multi-layer structure data of the formal shoe insole.

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