High-air-flow seat cover processing method and system

Abstract

The present invention belongs to the field of production big data application technology, and in particular relates to a high-ventilation seat cover processing method and system, comprising: in the fabric piece design drawing, identifying the high-pressure area range according to the pressure distribution data and unified standards; determining the contour range that can cover the high-pressure area range, inputting the shape and pressure distribution data of the high-pressure area range within the contour range, and the material property data of the fabric piece into a neural network model, the neural network model outputs a silicone structure scheme, converts the silicone structure scheme into a three-dimensional form and adjusts it, forms the silicone structure according to the adjustment scheme, and hot presses the corresponding fabric piece on the back of the contour range. In the present invention, the neural network model is used to analyze and optimize the high-pressure area, output the silicone structure scheme, ensure that the high-pressure area is effectively supported, and at the same time maintain good ventilation performance through reasonably distributed air vents, thereby specifically solving the conflict between ventilation needs and structural strength needs.

Description

Technical Field

[0001] The present invention belongs to the technical field of production big data application, and in particular relates to a method and system for processing a high-ventilation seat cover. Background Art

[0002] The high-ventilation seat cover is a special structure applied to the seat surface, designed to provide better ventilation, especially when the seat is used for a long time, which can improve the user's comfort. By enhancing the circulation of airflow, the high-ventilation seat cover can effectively reduce the accumulation of heat and moisture, thereby alleviating sitting discomfort and reducing sweating. It is especially suitable for environments such as car seats and office chairs that are in contact with the human body for a long time.

[0003] At present, there is often a certain conflict between the ventilation requirements and the structural strength requirements in the design and manufacture of high-ventilation seat covers. When the ventilation is good, the structural strength is often reduced due to the increase in ventilation area. In addition, it is usually difficult to achieve a targeted balance between structural strength and ventilation performance for different positions of the seat cover. Summary of the Invention

[0004] The present invention provides a high-ventilation seat cover processing method and system, which can effectively solve the problems in the background technology.

[0005] To achieve the above object, the technical solution of the present invention is as follows:

[0006] A high ventilation seat cover processing method includes:

[0007] Obtaining pressure distribution data of the seat during use;

[0008] In the design drawings of each fabric piece of the seat cover, the high-pressure area is identified based on the pressure distribution data and the unified standard;

[0009] For each of the fabric pieces, determining an outline range that can cover the high-pressure area range, where the outline range is greater than or equal to the high-pressure area range, inputting shape and pressure distribution data of the high-pressure area range within the outline range and material property data of the fabric piece into a neural network model, and the neural network model outputting a silicone structure solution, where the silicone structure is used to support the outline range;

[0010] The silicone structure scheme is a data matrix, and each element in the data matrix corresponds to a different area within the contour range. The elements include position information of the area center on the silicone structure, average thickness information within the area, and judgment information on whether to set air holes;

[0011] The silicone structure scheme is converted into a three-dimensional form and adjusted according to processing requirements. The silicone structure is formed according to the adjustment scheme, and the corresponding fabric pieces are hot-pressed on the back of the contour range to complete the processing of the required fabric pieces.

[0012] Furthermore, the neural network model includes:

[0013] An input layer for inputting the shape and pressure distribution data and material property data;

[0014] A convolutional neural network, which captures the shape and force characteristics of the shape and pressure distribution data through convolution kernels, and gradually captures features of different scales in the force distribution through multiple layers of convolution;

[0015] A fully connected network is added with a plurality of hidden layers, each of which has a plurality of neurons, and the neurons are used to capture different combinations of material properties and the effects of the combinations on the silicone structure scheme;

[0016] A fusion layer combines the output features of the convolutional neural network and the fully connected network through a connection operation;

[0017] The output layer outputs the data matrix of the silica gel structure scheme according to the merging results.

[0018] Furthermore, through the connection operation, the fusion layer obtains a characteristic graph including position information of the center of the region on the silicone structure, average thickness information within the region, and judgment information on whether to set air holes;

[0019] The neural network model further includes an interpolation smoothing layer, which performs an interpolation operation on the feature map to smooth the thickness values ​​of adjacent areas;

[0020] The output layer outputs a data matrix of the silica gel structure scheme according to the smoothing processing result.

[0021] Furthermore, a global constraint optimization function is introduced into the neural network model to ensure that the interpolation algorithm automatically adjusts the thickness value during the smoothing process so that the average thickness of each region remains unchanged.

[0022] Furthermore, during the adjustment of the three-dimensional model, the total air permeability area of ​​the silicone structure and the air permeability area of ​​a single air permeability hole are determined, wherein the total air permeability area is proportional to the size of the high-pressure area, and the air permeability area of ​​a single air permeability hole is proportional to the sum of the average thicknesses of the regions in which the air permeability hole is provided;

[0023] Calculating the required number of ventilation holes based on the total ventilation area and the ventilation area of ​​each ventilation hole;

[0024] All of the ventilation holes are distributed at least in a portion of the contour range where ventilation holes need to be provided.

[0025] Furthermore, when the contour range is larger than the high-pressure area range, during the training of the neural network model, the element distribution density in the high-pressure area range is dynamically adjusted, and the element distribution density in other areas remains unchanged.

[0026] High ventilation capacity seat cover processing system, including:

[0027] A data acquisition module, which acquires pressure distribution data of the seat during use;

[0028] a design drawing processing module for identifying, in a design drawing of each fabric piece of the seat cover, a high-pressure region based on the pressure distribution data and the unified standard; and determining, for each fabric piece, a contour range that can cover the high-pressure region, wherein the contour range is greater than or equal to the high-pressure region;

[0029] a neural network module, integrating a neural network model, using the shape and pressure distribution data of the high-pressure area within the contour range and the material property data of the fabric piece as inputs of the neural network model, and using a silicone structure solution as output of the neural network model, the silicone structure being used to support the contour range;

[0030] The silicone structure scheme is a data matrix, and each element in the data matrix corresponds to a different area within the contour range. The elements include position information of the area center on the silicone structure, average thickness information within the area, and judgment information on whether to set air holes;

[0031] A three-dimensional model conversion module converts the silicone structure scheme into a three-dimensional form and adjusts it according to processing requirements;

[0032] The production and processing module forms the silicone structure according to the adjustment plan, and hot presses the corresponding fabric pieces on the back of the contour range to complete the processing of the required fabric pieces.

[0033] Furthermore, the neural network model includes:

[0034] An input layer for inputting the shape and pressure distribution data and material property data;

[0035] A convolutional neural network, which captures the shape and force characteristics of the shape and pressure distribution data through convolution kernels, and gradually captures features of different scales in the force distribution through multiple layers of convolution;

[0036] A fully connected network is added with a plurality of hidden layers, each of which has a plurality of neurons, and the neurons are used to capture different combinations of material properties and the effects of the combinations on the silicone structure scheme;

[0037] A fusion layer combines the output features of the convolutional neural network and the fully connected network through a connection operation;

[0038] The output layer outputs the data matrix of the silica gel structure scheme according to the merging results.

[0039] Furthermore, the fusion layer obtains a characteristic graph including position information of the center of the region on the silicone structure, average thickness information within the region, and judgment information on whether to set air holes through the connection operation;

[0040] The neural network model further includes an interpolation smoothing layer, which performs an interpolation operation on the feature map to smooth the thickness values ​​of adjacent areas;

[0041] The output layer outputs a data matrix of the silica gel structure scheme according to the smoothing processing result.

[0042] Furthermore, the three-dimensional model conversion module includes a vent design unit;

[0043] During the adjustment of the three-dimensional model, the vent design unit determines the total vent area of ​​the silicone structure and the vent area of ​​a single vent, and calculates the required number of vents based on the total vent area and the vent area of ​​the single vent, wherein the total vent area is proportional to the size of the high-pressure area, and the vent area of ​​a single vent is proportional to the sum of the average thicknesses of the regions where the vent is provided.

[0044] Furthermore, all of the ventilation holes are distributed at least in a portion of the contour range where ventilation holes need to be provided.

[0045] The technical solution of the present invention can achieve the following technical effects:

[0046] The present invention collects pressure distribution data during seat use, accurately identifies the high-pressure area ranges in different regions, and uses a neural network model to analyze and optimize these areas, outputting a silicone structural solution. It can design a support structure with appropriate thickness and strength for different pressure areas to ensure that the high-pressure areas are effectively supported, while maintaining good ventilation performance through reasonably distributed air holes. This data-driven optimization design can accurately control the use of silicone materials, while reducing material waste and seat weight, and improving user comfort. Through the use of silicone structures, the face cover can be made of materials with better ventilation. While the overall ventilation performance is improved, the effective support of high-pressure positions ensures the overall service life of the product, and specifically resolves the conflict between ventilation needs and structural strength needs.

[0047] This method converts the silicone solution output by the neural network into a three-dimensional form, and through careful adjustment and hot pressing process, ensures the precise combination of the silicone structure and the fabric pieces, further enhancing the support and ventilation performance of the seat. Through precise processing and sewing technology, the final high-ventilation seat cover not only has good support effect, but also has the characteristics of uniform ventilation, comfort and durability, meeting the needs of long-term use. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 A flow chart of a method for processing a high ventilation seat cover;

[0050] Figure 2 Schematic diagram of the position of the silicone structure relative to the back of the fabric piece;

[0051] Figure 3 This is the framework diagram of the neural network model;

[0052] Figure 4 Flowchart for the design of vent holes;

[0053] Reference numerals: 1. fabric piece; 2. silicone structure. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0056] Example 1

[0057] High ventilation seat cover processing method, such as Figure 1 and 2 Shown, including:

[0058] S1: Obtaining pressure distribution data on the seat during use. This pressure distribution data can be collected during actual use or through targeted testing. Specifically, sensors can be used to record pressure changes in different parts of the seat during use, including pressure data applied to different parts of the seat by actions such as sitting, moving, and adjusting posture.

[0059] S2: In the design drawings of each fabric piece of the seat cover, high-pressure areas are identified based on pressure distribution data and a unified standard. The unified standard here means that the identification criteria for high-pressure areas are consistent across different fabric pieces, and high-pressure areas can be identified by setting thresholds.

[0060] S3: For each fabric piece, determine the contour range that can cover the high-pressure area range. The contour range is greater than or equal to the high-pressure area range. The shape and pressure distribution data of the high-pressure area range within the contour range and the material property data of the fabric piece are input into the neural network model. The neural network model outputs a silicone structure solution. The silicone structure is used to support the contour range. Whether the contour range is greater than the high-pressure area range in this step depends on whether the high-pressure area range is relatively regular, because the contour range determines the outer edge contour of the processed silicone structure. Therefore, when the high-pressure area range is irregular, covering it with a larger contour range can ensure the processability of the silicone structure. If the outer edge contour of the high-pressure area range itself is relatively regular, it can be directly used as the contour range for processing the silicone structure.

[0061] The silicone structure scheme is a data matrix, and each element in the data matrix corresponds to a different area within the contour range. The elements include the position information of the area center on the silicone structure, the average thickness information within the area, and the judgment information of whether to set air holes;

[0062] In this step, the shape and pressure distribution of the high-pressure area are analyzed through a neural network model, and a silicone structure that can adapt to complex force distribution can be designed, thereby ensuring that the support structure has different thicknesses and strengths in different areas, and can specifically support high-pressure areas, reducing the user's discomfort caused by long-term pressure. Through the automatic optimization of the neural network, the thickness and layout of the silicone structure are reasonably designed, and the thickness is only increased in areas that require additional support, while the use of materials is reduced in areas with lower pressure within the area, while also avoiding affecting the ventilation performance of the raw materials. This precise position and thickness control greatly reduces material waste and reduces the overall weight of the seat cover, making the seat lighter and suitable for cover materials that are more ventilated but require optimized structural strength; through the judgment information of the air vents, the location of the air vents can be reasonably planned;

[0063] S4: The silicone structure plan is converted into a three-dimensional form and adjusted according to the processing requirements. The silicone structure is molded according to the adjusted plan, and the corresponding fabric pieces are hot-pressed on the back of the contour range to complete the processing of the required fabric pieces. In this step, the three-dimensional form is carefully adjusted according to thickness, vent distribution, etc. to ensure that the final product meets ergonomic requirements. To meet the specific needs of the processing technology, the three-dimensional silicone structure is fine-tuned, such as adjusting the smooth transition of thickness, the size and distribution of vents within the vent setting area, etc., and finally, the corresponding mold and equipment can be used to mold a silicone structure that meets the design requirements.

[0064] After the above steps are completed, the fabric pieces are sewn to obtain a high-ventilation seat cover. In the present invention, by collecting the pressure distribution data during the use of the seat, the high-pressure area range of different areas is accurately identified, and these areas are analyzed and optimized using a neural network model, and a silicone structure solution is output. A support structure with appropriate thickness and strength can be designed for different pressure areas to ensure that the high-pressure area is effectively supported, while maintaining good ventilation performance through reasonably distributed air holes. This data-driven optimization design can accurately control the use of silicone materials, while reducing material waste and seat weight, and improving user comfort. Through the use of silicone structures, the cover can be made of materials with better ventilation. While the overall ventilation performance is improved, the overall service life of the product is guaranteed through effective support of high-pressure positions, and the conflict between ventilation needs and structural strength needs is targeted.

[0065] This method converts the silicone solution output by the neural network into a three-dimensional form, and through careful adjustment and hot pressing process, ensures the precise combination of the silicone structure and the fabric pieces, further enhancing the support and ventilation performance of the seat. Through precise processing and sewing technology, the final high-ventilation seat cover not only has good support effect, but also has the characteristics of uniform ventilation, comfort and durability, meeting the needs of long-term use.

[0066] As a preferred embodiment of the above, Figure 3 As shown, the neural network model includes:

[0067] The input layer is used to input shape and pressure distribution data and material property data; the convolutional neural network captures the shape and force characteristics in the shape and pressure distribution data through the convolution kernel, and gradually captures the characteristics of different scales in the force distribution through multi-layer convolution; the fully connected network adds several hidden layers, each of which has several neurons, and uses neurons to capture different combinations of material properties and the impact of the combination on the silicone structure scheme; the fusion layer merges the output features of the convolutional neural network and the fully connected network through a connection operation; the output layer outputs the data matrix of the silicone structure scheme based on the merging result.

[0068] In this preferred solution, convolutional neural networks use convolution kernels to capture local features in shape and pressure distribution data, which can automatically identify important patterns and features in the data and is particularly suitable for processing pressure distribution data similar to images; through multi-layer convolution operations, the network can gradually capture features of different scales in the force distribution. Specifically, shallow convolution can capture local subtle features, such as small high-pressure points or pressure concentration areas of specific shapes, while deep convolution can capture broader, global pressure distribution patterns. This multi-scale feature extraction capability makes the model highly flexible and accurate when processing complex force shapes.

[0069] By adjusting the neuron connection weights in the fully connected network during training, the network can optimize the combination of material properties such as elasticity, breathability, strength, etc., thereby generating silicone support structure designs suitable for different materials. The flexibility of this design enables the model to effectively adapt to a variety of material properties.

[0070] Ultimately, the fusion operation helps the model better optimize the silicone structural solution, so that it can provide the best support effect and ventilation performance under the interaction of material properties and stress characteristics. The model not only focuses on the shape of the pressure concentration area, but also combines the physical properties of the material to generate a design that is more suitable for the actual use environment. The neural network model has powerful feature capture, fusion, and optimization capabilities. Combining the advantages of convolutional neural networks and fully connected networks, it can effectively process shape and pressure distribution data and material property data, and output high-precision silicone structural solutions.

[0071] In the above optimization scheme, the design of the silicone structure is a two-dimensional data matrix output by the neural network, including position information, thickness information, and information on whether to set air holes. However, in the actual manufacturing process, when converting the two-dimensional data into a three-dimensional silicone structure, it is necessary to adjust it according to the specific processing technology to ensure that the design scheme can be smoothly implemented in actual production. Different processing technologies, such as mold forming, hot pressing, injection molding, etc., have specific requirements for the shape, size and pore distribution of silicone. For example, the mold may have restrictions on the range of variation of silicone thickness, and the hot pressing process may have specific requirements for the layout of the holes. Therefore, the three-dimensional model must be adjusted according to the actual processing parameters, such as accuracy and tolerance, to ensure that the manufactured silicone structure meets the expected function and ensure that the molded structure has sufficient stability and support in terms of physical properties.

[0072] The output of a convolutional neural network is usually a multidimensional feature map, and the output of a fully connected network is a vector. Through the fusion operation, the convolutional network provides spatial information within the region, and the fully connected network provides optimization on material properties. The feature map obtained after merging contains all information related to shape, material properties and thickness design. As a preferred embodiment of the above,

[0073] Through the connection operation, the fusion layer obtains a feature map including the position information of the center of the region on the silicone structure, the average thickness information within the region, and the judgment information of whether to set air holes; the neural network model also includes an interpolation smoothing layer, which performs interpolation operations on the feature map to smooth the thickness values ​​of adjacent regions; the output layer outputs the data matrix of the silicone structure scheme based on the smoothing results.

[0074] In this preferred embodiment, the interpolation smoothing layer smoothes the thickness values ​​of adjacent areas to ensure a more natural and smooth transition between areas. The smoothed data matrix is ​​converted into a specific silicone structure design, which ultimately helps optimize the processing feasibility in production when it is finally formed in a three-dimensional form. The smooth transition of thickness values ​​makes the flow and molding of silicone materials smoother during the manufacturing process, especially when it is formed by molds or hot pressing processes, thereby reducing manufacturing defects and poor molding caused by sudden changes in thickness. In addition, in actual applications, overly abrupt thickness changes may affect the comfort of the seat. Especially during long-term use, the thickness transition in the body contact area should be as smooth as possible. The interpolation smoothing layer can ensure that the thickness changes in different pressure areas are more natural, thereby enhancing the support effect of the silicone structure on the user's body and making the pressure distribution of the seat cover in different areas more uniform.

[0075] Compared with the three-dimensional model of the silicone structure, the feature map has a lower dimension and a smaller amount of data. Therefore, performing interpolation and smoothing operations on the feature map can significantly reduce computational complexity and resource usage, which not only saves computational time but also reduces the demand for computing power. Smoothing on the feature map can enable faster model optimization and training, because the convolutional neural network has compressed complex three-dimensional shape information into a two-dimensional feature map when extracting features. By performing interpolation operations on the feature map, direct processing of complex three-dimensional data structures is avoided. Since the interpolation and smoothing operations are performed on the feature map of the neural network, the entire process can be optimized through gradient backpropagation, allowing the network to automatically learn the optimal smoothing parameters. However, smoothing operations in three-dimensional models are difficult to integrate into the network optimization process and require additional post-processing steps.

[0076] As a preferred embodiment of the above, a global constraint optimization function is introduced into the neural network model to ensure that the interpolation algorithm automatically adjusts the thickness value during the smoothing process so that the average thickness of each area remains unchanged.

[0077] This optimization solution can maintain the overall mechanical properties and design requirements of each area during thickness smoothing. Keeping the average thickness unchanged can ensure that while the transition is smooth, the support performance and material properties will not be changed by local adjustments, thereby avoiding an imbalance in the thickness distribution of the region. This not only improves the structural stability and service life of the product, but also ensures that the functional requirements of the initial design are met. Maintaining the key performance indicators in the design unchanged can improve the product's adaptability to different body shapes and postures and reduce the discomfort caused by excessive local thickness changes.

[0078] By introducing a global constrained optimization function during implementation, the interpolation algorithm can not only smooth the thickness transition between adjacent regions, but also strictly adhere to the thickness limit of each region when performing the smoothing operation. This gives the interpolation algorithm greater flexibility and control, allowing the smooth transition to be performed under established constraints rather than blindly performing numerical smoothing. This control can ensure that the thickness change is not only smooth but also remains within a reasonable range, enhancing the stability of the interpolation algorithm.

[0079] In the above embodiment, information on whether ventilation holes are provided in a set area of ​​the silicone structure can be obtained, that is, the requirement for providing ventilation holes can be clarified. In the actual application of the silicone structure, the reasons why ventilation holes are not provided in certain areas may include:

[0080] In high-pressure areas or areas where the body's weight is concentrated, the silicone structure needs to provide stronger support. Setting air holes in these areas may weaken the overall strength of the structure, resulting in reduced support effect, or even deformation or failure.

[0081] Structural strength and stability are crucial at the edges or joints of silicone structures. These areas are often subject to large tension or shear forces. If vents are opened, the strength of these areas may be weakened, creating the risk of tearing or breaking.

[0082] In some high-friction or high-stress concentration areas, such as areas with prolonged contact with the skin or other surfaces and frequent friction, the setting of vents may accelerate the wear or fatigue of these areas, leading to premature failure of the silicone structure.

[0083] On the basis of the above situation, since the formation of the silicone structure is also affected by other factors, the specific setting scheme of the vent holes needs to be further optimized and selected, rather than directly output through the neural network model. As a preferred embodiment of the above embodiment, in the process of adjusting the three-dimensional model, Figure 4 As shown, the total air permeability area of ​​the silicone structure and the air permeability area of ​​a single air pore are determined, wherein the total air permeability area is proportional to the size of the high-pressure area, and the air permeability area of ​​a single air pore is proportional to the sum of the average thicknesses of the areas where the air pores are set; the required number of air pores is calculated based on the total air permeability area and the air permeability area of ​​a single air pore; and all the air pores are distributed at least in the portion of the area within the contour where the air pores are required.

[0084] In the above embodiment, the design of the vent hole specifically adopts three steps:

[0085] The first step is to determine the setting position on the silicone structure, which is completed by the neural network model;

[0086] Step 2: Determine the ventilation area of ​​the vents based on the average thickness information within the area in the obtained data matrix;

[0087] Step 3: Determine the number of ventilation holes, which is achieved under the premise of determining the total ventilation area.

[0088] The above logic effectively combines intelligent tools with precise control of thickness, area, and quantity, ensuring that the silicone structure maintains its support and durability while providing efficient breathability, greatly improving design accuracy, manufacturing feasibility, and user comfort.

[0089] The specific distribution plan here can be determined based on the requirements of aesthetics and processability. During implementation, when the high-pressure area is larger, the overall pressure distribution area is larger, so more ventilation is required to dissipate heat and perspiration. By making the total ventilation area of ​​all air holes proportional to the size of the high-pressure area, sufficient ventilation capacity is ensured in areas with higher pressure, thereby effectively regulating the airflow and improving user comfort. The design in which the area of ​​a single air hole is proportional to the sum of the average thickness of the areas where the air hole is set helps to appropriately increase the size of a single air hole in thicker silicone structures, thereby increasing breathability, and appropriately reducing the size of a single air hole in thinner silicone structures to avoid insufficient support due to excessively large air holes, thereby achieving a balance between structural support and breathability.

[0090] At the same time, the use of a ventilation hole design with a uniform ventilation area simplifies the manufacturing process, improves production efficiency and design flexibility, and enables the silicone structure to meet ventilation needs while also having good structural stability and durability.

[0091] When the contour range is larger than the high-pressure region, the element density in the high-pressure region is dynamically adjusted during neural network model training, while the element density in other regions remains unchanged. Specifically, during training, the model gradually increases the element density in the high-pressure region through error feedback, reducing errors. This refined dynamic adjustment optimizes design accuracy in the high-pressure region, ensuring support performance, comfort, and durability. Dynamically adjusting the element density in the high-pressure region avoids the computational burden of increasing the number of elements globally, optimizing computational efficiency. The model invests more computing resources in the high-pressure region while maintaining a simplified computational process in the low-pressure region. This effectively allocates computing resources, speeds up training, reduces overall training time, and achieves faster convergence. By constraining the density in the low-pressure region to remain constant, the model focuses on the optimization task in the high-pressure region, reducing error fluctuations in the low-pressure region and improving the overall model's generalization. Specifically, a maximum element density limit can be set in the high-pressure region to ensure that the model does not become difficult to train or incur unnecessary computational burden due to excessive elements.

[0092] Example 2

[0093] High ventilation capacity seat cover processing system, including:

[0094] A data acquisition module, which acquires pressure distribution data of the seat during use;

[0095] A design drawing processing module identifies the high-pressure area in the design drawings of each fabric piece of the seat cover based on the pressure distribution data and the unified standard; and, for each fabric piece, determines the contour range that can cover the high-pressure area, where the contour range is greater than or equal to the high-pressure area;

[0096] The neural network module integrates a neural network model, which uses the shape and pressure distribution data of the high-pressure area within the contour range and the material property data of the fabric piece as the input of the neural network model, and uses the silicone structure solution as the output of the neural network model. The silicone structure is used to support the contour range;

[0097] The silicone structure scheme is a data matrix, and each element in the data matrix corresponds to a different area within the contour range. The elements include the position information of the area center on the silicone structure, the average thickness information within the area, and the judgment information of whether to set air holes;

[0098] 3D model conversion module, which converts the silicone structure scheme into 3D form and adjusts it according to processing requirements;

[0099] The production and processing module forms the silicone structure according to the adjustment plan, and hot presses the corresponding fabric pieces on the back of the contour range to complete the processing of the required fabric pieces.

[0100] As a preferred embodiment of this invention, the neural network model includes:

[0101] The input layer is used to input shape and pressure distribution data and material property data; the convolutional neural network captures the shape and force characteristics in the shape and pressure distribution data through the convolution kernel, and gradually captures the characteristics of different scales in the force distribution through multi-layer convolution; the fully connected network adds several hidden layers, each of which has several neurons, and uses neurons to capture different combinations of material properties and the impact of the combination on the silicone structure scheme; the fusion layer merges the output features of the convolutional neural network and the fully connected network through a connection operation; the output layer outputs the data matrix of the silicone structure scheme based on the merging result.

[0102] As a preferred embodiment of this embodiment, the fusion layer obtains a feature map including the position information of the center of the region on the silicone structure, the average thickness information in the region and the judgment information of whether to set air holes through a connection operation; the neural network model also includes an interpolation smoothing layer, which performs an interpolation operation on the feature map to smooth the thickness values ​​of adjacent regions; the output layer outputs the data matrix of the silicone structure scheme according to the smoothing results.

[0103] Among them, the three-dimensional model conversion module includes a vent design unit; in the process of adjusting the three-dimensional model, the vent design unit determines the total vent area of ​​the silicone structure and the vent area of ​​a single vent, and calculates the required number of vents based on the total vent area and the vent area of ​​a single vent, wherein the total vent area is proportional to the size of the high-pressure area, and the vent area of ​​a single vent is proportional to the sum of the average thicknesses of the areas where the vents are set; and all the vents are distributed at least in the part of the area within the contour where the vents need to be set.

[0104] The technical effects achieved by this embodiment are the same as those of the above-mentioned embodiment 1 and will not be repeated here.

[0105] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for processing a high ventilation seat cover, characterized in that: include: Obtaining pressure distribution data of the seat during use; In the design drawings of each fabric piece of the seat cover, the high-pressure area is identified based on the pressure distribution data and the unified standard; For each of the fabric pieces, determining an outline range that can cover the high-pressure area range, where the outline range is greater than or equal to the high-pressure area range, inputting shape and pressure distribution data of the high-pressure area range within the outline range and material property data of the fabric piece into a neural network model, and the neural network model outputting a silicone structure solution, where the silicone structure is used to support the outline range; The silicone structure scheme is a data matrix, and each element in the data matrix corresponds to a different area within the contour range. The elements include position information of the area center on the silicone structure, average thickness information within the area, and judgment information on whether to set air holes; The silicone structure scheme is converted into a three-dimensional form and adjusted according to processing requirements. The silicone structure is formed according to the adjustment scheme, and the corresponding fabric pieces are hot-pressed on the back of the contour range to complete the processing of the required fabric pieces.

2. The high air flow rate seat cover processing method according to claim 1, characterized in that: The neural network model includes: An input layer for inputting the shape and pressure distribution data and material property data; A convolutional neural network, which captures the shape and force characteristics of the shape and pressure distribution data through convolution kernels, and gradually captures features of different scales in the force distribution through multiple layers of convolution; A fully connected network is added with a plurality of hidden layers, each of which has a plurality of neurons, and the neurons are used to capture different combinations of material properties and the effects of the combinations on the silicone structure scheme; A fusion layer combines the output features of the convolutional neural network and the fully connected network through a connection operation; The output layer outputs the data matrix of the silica gel structure scheme according to the merging results.

3. The high air flow rate seat cover processing method according to claim 2, characterized in that: Through the connection operation, the fusion layer obtains a characteristic map including position information of the center of the region on the silicone structure, average thickness information within the region, and judgment information on whether to set air holes; The neural network model further includes an interpolation smoothing layer, which performs an interpolation operation on the feature map to smooth the thickness values ​​of adjacent areas; The output layer outputs a data matrix of the silica gel structure scheme according to the smoothing processing result.

4. The high air flow rate seat cover processing method according to claim 3, characterized in that: A global constraint optimization function is introduced into the neural network model to ensure that the interpolation algorithm automatically adjusts the thickness value during the smoothing process so that the average thickness of each region remains unchanged.

5. The high air flow rate seat cover processing method according to claim 2, characterized in that: During the adjustment of the three-dimensional model, determining the total air permeability area of ​​the silicone structure and the air permeability area of ​​a single air permeability hole, wherein the total air permeability area is proportional to the size of the high-pressure area, and the air permeability area of ​​a single air permeability hole is proportional to the sum of the average thicknesses of the regions in which the air permeability hole is provided; Calculating the required number of ventilation holes based on the total ventilation area and the ventilation area of ​​each ventilation hole; All of the ventilation holes are distributed at least in a portion of the contour range where ventilation holes need to be provided.

6. The high air flow rate seat cover processing method according to claim 1, characterized in that: When the contour range is larger than the high-pressure area range, during the training of the neural network model, the element distribution density in the high-pressure area range is dynamically adjusted, and the element distribution density in other areas remains unchanged.

7. High ventilation capacity seat cover processing system, characterized by: include: A data acquisition module, which acquires pressure distribution data of the seat during use; a design drawing processing module for identifying, in a design drawing of each fabric piece of the seat cover, a high-pressure region based on the pressure distribution data and the unified standard; and determining, for each fabric piece, a contour range that can cover the high-pressure region, wherein the contour range is greater than or equal to the high-pressure region; a neural network module, integrating a neural network model, using the shape and pressure distribution data of the high-pressure area within the contour range and the material property data of the fabric piece as inputs of the neural network model, and using a silicone structure solution as output of the neural network model, the silicone structure being used to support the contour range; The silicone structure scheme is a data matrix, and each element in the data matrix corresponds to a different area within the contour range. The elements include position information of the area center on the silicone structure, average thickness information within the area, and judgment information on whether to set air holes; A three-dimensional model conversion module converts the silicone structure scheme into a three-dimensional form and adjusts it according to processing requirements; The production and processing module forms the silicone structure according to the adjustment plan, and hot presses the corresponding fabric pieces on the back of the contour range to complete the processing of the required fabric pieces.

8. The high air flow rate seat cover processing system according to claim 7, characterized in that: The neural network model includes: An input layer for inputting the shape and pressure distribution data and material property data; A convolutional neural network, which captures the shape and force characteristics of the shape and pressure distribution data through convolution kernels, and gradually captures features of different scales in the force distribution through multiple layers of convolution; A fully connected network is added with a plurality of hidden layers, each of which has a plurality of neurons, and the neurons are used to capture different combinations of material properties and the effects of the combinations on the silicone structure scheme; A fusion layer combines the output features of the convolutional neural network and the fully connected network through a connection operation; The output layer outputs the data matrix of the silica gel structure scheme according to the merging results.

9. The high air flow rate seat cover processing system according to claim 8, characterized in that: The fusion layer obtains a characteristic graph through the connection operation, including position information of the center of the region on the silicone structure, average thickness information in the region, and judgment information on whether to set air holes; The neural network model further includes an interpolation smoothing layer, which performs an interpolation operation on the feature map to smooth the thickness values ​​of adjacent areas; The output layer outputs a data matrix of the silica gel structure scheme according to the smoothing processing result.

10. The high air flow rate seat cover processing system according to claim 7, characterized in that: The three-dimensional model conversion module includes a vent design unit; During the adjustment of the three-dimensional model, the vent design unit determines the total vent area of ​​the silicone structure and the vent area of ​​a single vent, and calculates the required number of vents based on the total vent area and the vent area of ​​the single vent, wherein the total vent area is proportional to the size of the high-pressure area, and the vent area of ​​a single vent is proportional to the sum of the average thicknesses of the regions where the vent is provided. Furthermore, all of the ventilation holes are distributed at least in a portion of the contour range where ventilation holes need to be provided.

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