Method and system for generating thoracolumbar correction equipment based on 3D printing technology

Abstract

The present invention provides a method and system for generating thoracolumbar correction equipment based on 3D printing technology, the method comprising obtaining the thoracolumbar problem information of the user, and the trunk scan data of the user, and generating the trunk structure model of the user based on the trunk scan data; based on the thoracolumbar problem information of the user, identifying the type of thoracolumbar abnormality of the user, and the degree of thoracolumbar abnormality of the user, and generating the thoracolumbar correction strategy of the user based on the type of thoracolumbar abnormality of the user, and the degree of thoracolumbar abnormality of the user; based on the trunk structure model, simulating the execution process of the thoracolumbar correction strategy to obtain the thoracolumbar correction equipment parameters of the user, and based on the thoracolumbar correction equipment parameters, generating the target thoracolumbar correction equipment of the user through an additive manufacturing strategy. The use of this solution can improve the accuracy of thoracolumbar correction for different users.

Description

Technical Field

[0001] The present invention relates to the field of 3D printing technology, and in particular to a method and system for generating a thoracolumbar correction device based on 3D printing technology. Background Art

[0002] The non-surgical treatment of thoracolumbar correction technology can achieve the purpose of thoracolumbar correction by patients wearing thoracolumbar correction equipment or thoracolumbar fixation equipment and other medical devices. Therefore, the device format, device structure parameters, and device adjustment method of thoracolumbar correction equipment are important factors in improving the thoracolumbar correction effect. How to adapt thoracolumbar correction equipment to different users is the current research focus.

[0003] Traditional thoracolumbar correction equipment is to correct different users by setting up uniform adjustable correction equipment for different age ranges and genders. However, due to the different thoracolumbar user degrees and thoracolumbar user conditions of different users, the thoracolumbar correction effects of the above method on different users are different, resulting in lower accuracy of thoracolumbar correction for different users. Summary of the invention

[0004] The main purpose of the present invention is to provide a method and system for generating thoracolumbar correction equipment based on 3D printing technology, aiming to solve the problem in the prior art that due to the different thoracolumbar user levels and thoracolumbar user conditions of different users, the above-mentioned method has different thoracolumbar correction effects on different users, resulting in low thoracolumbar correction accuracy for different users.

[0005] To achieve the above object, the present invention provides a method for generating a thoracolumbar correction device based on 3D printing technology, the method comprising:

[0006] Acquire the user's thoracic and lumbar spine problem information and the user's trunk scanning data, and generate the user's trunk structure model based on the trunk scanning data;

[0007] Based on the thoracolumbar problem information of the user, identifying the type of thoracolumbar abnormality of the user and the degree of thoracolumbar abnormality of the user, and generating a thoracolumbar correction strategy for the user based on the type of thoracolumbar abnormality of the user and the degree of thoracolumbar abnormality of the user;

[0008] Based on the trunk structure model, the execution process of the thoracolumbar correction strategy is simulated to obtain the thoracolumbar correction device parameters of the user, and based on the thoracolumbar correction device parameters, the target thoracolumbar correction device of the user is generated through an additive manufacturing strategy.

[0009] Optionally, generating the torso structure model of the user based on the torso scan data includes:

[0010] Splitting the trunk scan data into body shape scan data and thoracolumbar vertebrae scan data, and projecting the trunk scan data into the same three-dimensional space to obtain body shape three-dimensional structure data corresponding to the body shape scan data and thoracolumbar vertebrae three-dimensional structure data corresponding to the thoracolumbar vertebrae scan data;

[0011] Identify the relative spatial position information of the body shape three-dimensional structure data and the thoracolumbar vertebrae three-dimensional structure data, and construct a body shape structure model and a thoracolumbar vertebrae structure model through a three-dimensional modeling program based on the body shape three-dimensional structure data and the thoracolumbar vertebrae three-dimensional structure data;

[0012] Based on the relative spatial position information, the body structure model and the thoracic and lumbar structure model are spliced ​​to obtain the torso structure data of the user.

[0013] Optionally, the identifying the type of abnormality of the thoracolumbar spine of the user and the degree of abnormality of the thoracolumbar spine of the user based on the thoracolumbar spine problem information of the user includes:

[0014] Extracting thoracolumbar vertebrae problem features of the thoracolumbar vertebrae problem information, and calculating the similarity between historical problem features of different historical thoracolumbar vertebrae problem information corresponding to each thoracolumbar vertebrae abnormality type in the thoracolumbar vertebrae abnormality database;

[0015] The thoracolumbar abnormality type described in the target historical thoracolumbar problem information corresponding to the maximum similarity is filtered as the thoracolumbar abnormality type of the user, and based on the feature content corresponding to each of the thoracolumbar problem features, the degree of thoracolumbar abnormality of the user is analyzed through the degree analysis strategy corresponding to the thoracolumbar abnormality type of the user.

[0016] Optionally, generating a thoracolumbar spine correction strategy for the user based on the type of thoracolumbar spine abnormality of the user and the degree of thoracolumbar spine abnormality of the user includes:

[0017] Based on the thoracolumbar abnormality type, identifying the thoracolumbar compression range of the user and the thoracolumbar support range of the user in a thoracolumbar correction database;

[0018] Based on the abnormality of the user's thoracolumbar spine, the user's target pressurization depth is queried, and the target pressurization depth, the thoracolumbar spine pressurization range, and the thoracolumbar spine support range are used as the user's thoracolumbar spine correction strategy.

[0019] Optionally, simulating the execution process of the thoracolumbar correction strategy based on the torso structure model to obtain the thoracolumbar correction device parameters of the user includes:

[0020] Based on the user's target compression depth, a simulated depth progression sequence is generated, and in the trunk structure model, a first trunk range corresponding to the thoracolumbar compression range and a second trunk range corresponding to the thoracolumbar support range are marked;

[0021] Based on the simulated depth progressive sequence and the marked torso structure model, the torso correction process of the user is simulated through a simulation correction strategy to obtain a torso correction result of the user;

[0022] In the case that the trunk correction result is that the correction is incomplete, the new thoracic and lumbar abnormality type and the new thoracic and lumbar abnormality degree corresponding to the trunk correction result are identified, and the thoracic and lumbar abnormality type is replaced by the new thoracic and lumbar abnormality type, and the thoracic and lumbar abnormality degree is replaced by the new thoracic and lumbar abnormality degree, and the thoracolumbar abnormality degree is replaced by the new thoracic and lumbar abnormality degree, and the step of generating the thoracolumbar correction strategy for the user based on the thoracolumbar abnormality type and the thoracolumbar abnormality degree of the user is returned to be executed until the trunk correction result is that the correction is completed, and the target pressurization depth corresponding to each simulation process, the first trunk range of the thoracolumbar pressurization range corresponding to each simulation process, and the second trunk range of the thoracolumbar support range corresponding to each simulation process are obtained;

[0023] Based on the model parameters of the trunk structure model, the device structure parameters of the thoracolumbar correction device are determined, and based on the target pressurization depths, the first trunk ranges, and the second trunk ranges, the correction structure parameters corresponding to the thoracolumbar correction device in each simulation process are identified through a preset parameter data correspondence relationship;

[0024] Based on the device structure parameters and the correction structure parameters corresponding to each simulation process, determine the sub-thoracic and lumbar correction device parameters corresponding to each correction process, and based on the trunk correction result of each simulation process, identify the simulation duration corresponding to each simulation process and the simulation order corresponding to each simulation process;

[0025] The sub-thoracolumbar correction device parameters corresponding to each simulation process, the simulation order corresponding to each simulation process, and the simulation duration corresponding to each simulation process are used as the thoracolumbar correction device parameters of the user.

[0026] Optionally, the generating the target thoracolumbar correction device of the user through an additive manufacturing strategy based on the thoracolumbar correction device parameters includes:

[0027] Obtaining a correspondence between printing parameters and device parameters of an additive manufacturing device, and identifying the printing parameters of the sub-thoracolumbar correction device corresponding to each simulation process based on the sub-thoracolumbar correction device parameters corresponding to each simulation process through the correspondence;

[0028] Based on the printing parameters of each sub-thoracolumbar correction device, an initial sub-target thoracolumbar correction device corresponding to each simulation process is generated through an additive manufacturing strategy, and in each initial sub-target thoracolumbar correction device, a simulation duration of the simulation process corresponding to each sub-target thoracolumbar correction device and a simulation order of the simulation process corresponding to each sub-target thoracolumbar correction device are added to obtain each sub-target thoracolumbar correction device;

[0029] All sub-target thoracolumbar correction devices are used as the target thoracolumbar correction devices of the user.

[0030] In addition, to achieve the above purpose, the present invention also provides a generation system of a thoracolumbar correction device based on 3D printing technology, comprising:

[0031] An acquisition module, used to acquire the user's thoracic and lumbar spine problem information and the user's trunk scanning data, and generate a trunk structure model of the user based on the trunk scanning data;

[0032] an identification module, for identifying the type of abnormality of the thoracolumbar spine of the user and the degree of abnormality of the thoracolumbar spine of the user based on the thoracolumbar spine problem information of the user, and generating a thoracolumbar spine correction strategy for the user based on the type of abnormality of the thoracolumbar spine of the user and the degree of abnormality of the thoracolumbar spine of the user;

[0033] A generation module is used to simulate the execution process of the thoracolumbar correction strategy based on the torso structure model, obtain the thoracolumbar correction device parameters of the user, and generate the target thoracolumbar correction device of the user through an additive manufacturing strategy based on the thoracolumbar correction device parameters.

[0034] Optionally, the acquisition module is specifically used to:

[0035] Splitting the trunk scan data into body shape scan data and thoracolumbar vertebrae scan data, and projecting the trunk scan data into the same three-dimensional space to obtain body shape three-dimensional structure data corresponding to the body shape scan data and thoracolumbar vertebrae three-dimensional structure data corresponding to the thoracolumbar vertebrae scan data;

[0036] Identify the relative spatial position information of the body shape three-dimensional structure data and the thoracolumbar vertebrae three-dimensional structure data, and construct a body shape structure model and a thoracolumbar vertebrae structure model through a three-dimensional modeling program based on the body shape three-dimensional structure data and the thoracolumbar vertebrae three-dimensional structure data;

[0037] Based on the relative spatial position information, the body structure model and the thoracic and lumbar structure model are spliced ​​to obtain the torso structure data of the user.

[0038] Optionally, the identification module is specifically used to:

[0039] Extracting thoracolumbar vertebrae problem features of the thoracolumbar vertebrae problem information, and calculating the similarity between historical problem features of different historical thoracolumbar vertebrae problem information corresponding to each thoracolumbar vertebrae abnormality type in the thoracolumbar vertebrae abnormality database;

[0040] The thoracolumbar abnormality type described in the target historical thoracolumbar problem information corresponding to the maximum similarity is filtered as the thoracolumbar abnormality type of the user, and based on the feature content corresponding to each of the thoracolumbar problem features, the degree of thoracolumbar abnormality of the user is analyzed through the degree analysis strategy corresponding to the thoracolumbar abnormality type of the user.

[0041] Optionally, the identification module is specifically used to:

[0042] Based on the thoracolumbar abnormality type, identifying the thoracolumbar compression range of the user and the thoracolumbar support range of the user in a thoracolumbar correction database;

[0043] Based on the abnormality of the user's thoracolumbar spine, the user's target pressurization depth is queried, and the target pressurization depth, the thoracolumbar spine pressurization range, and the thoracolumbar spine support range are used as the user's thoracolumbar spine correction strategy.

[0044] Optionally, the generating module is specifically used for:

[0045] Based on the user's target compression depth, a simulated depth progression sequence is generated, and in the trunk structure model, a first trunk range corresponding to the thoracolumbar compression range and a second trunk range corresponding to the thoracolumbar support range are marked;

[0046] Based on the simulated depth progressive sequence and the marked torso structure model, the torso correction process of the user is simulated through a simulation correction strategy to obtain a torso correction result of the user;

[0047] In the case that the trunk correction result is that the correction is incomplete, the new thoracic and lumbar abnormality type and the new thoracic and lumbar abnormality degree corresponding to the trunk correction result are identified, and the thoracic and lumbar abnormality type is replaced by the new thoracic and lumbar abnormality type, and the thoracic and lumbar abnormality degree is replaced by the new thoracic and lumbar abnormality degree, and the thoracolumbar abnormality degree is replaced by the new thoracic and lumbar abnormality degree, and the step of generating the thoracolumbar correction strategy for the user based on the thoracolumbar abnormality type and the thoracolumbar abnormality degree of the user is returned to be executed until the trunk correction result is that the correction is completed, and the target pressurization depth corresponding to each simulation process, the first trunk range of the thoracolumbar pressurization range corresponding to each simulation process, and the second trunk range of the thoracolumbar support range corresponding to each simulation process are obtained;

[0048] Based on the model parameters of the trunk structure model, the device structure parameters of the thoracolumbar correction device are determined, and based on the target pressurization depths, the first trunk ranges, and the second trunk ranges, the correction structure parameters corresponding to the thoracolumbar correction device in each simulation process are identified through a preset parameter data correspondence relationship;

[0049] Based on the device structure parameters and the correction structure parameters corresponding to each simulation process, determine the sub-thoracic and lumbar correction device parameters corresponding to each correction process, and based on the trunk correction result of each simulation process, identify the simulation duration corresponding to each simulation process and the simulation order corresponding to each simulation process;

[0050] The sub-thoracolumbar correction device parameters corresponding to each simulation process, the simulation order corresponding to each simulation process, and the simulation duration corresponding to each simulation process are used as the thoracolumbar correction device parameters of the user.

[0051] Optionally, the generating module is specifically used for:

[0052] Obtaining a correspondence between printing parameters and device parameters of an additive manufacturing device, and identifying the printing parameters of the sub-thoracolumbar correction device corresponding to each simulation process based on the sub-thoracolumbar correction device parameters corresponding to each simulation process through the correspondence;

[0053] Based on the printing parameters of each sub-thoracolumbar correction device, an initial sub-target thoracolumbar correction device corresponding to each simulation process is generated through an additive manufacturing strategy, and in each initial sub-target thoracolumbar correction device, a simulation duration of the simulation process corresponding to each sub-target thoracolumbar correction device and a simulation order of the simulation process corresponding to each sub-target thoracolumbar correction device are added to obtain each sub-target thoracolumbar correction device;

[0054] All sub-target thoracolumbar correction devices are used as the target thoracolumbar correction devices of the user.

[0055] In a third aspect, the present application provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any one of the methods in the first aspect are implemented.

[0056] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0057] In a fifth aspect, the present application provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0058] The present invention provides a method and system for generating a thoracolumbar correction device based on 3D printing technology. The method comprises: obtaining a user's thoracolumbar problem information and the user's trunk scan data, and generating a trunk structure model of the user based on the trunk scan data; identifying the user's thoracolumbar abnormality type and the user's thoracolumbar abnormality degree based on the user's thoracolumbar problem information, and generating a thoracolumbar correction strategy for the user based on the user's thoracolumbar abnormality type and the user's thoracolumbar abnormality degree; simulating the execution process of the thoracolumbar correction strategy in the trunk structure model to obtain the user's thoracolumbar correction device parameters, and generating the user's target thoracolumbar correction device through an additive manufacturing strategy based on the thoracolumbar correction device parameters. This solution generates a user's torso structure model through the user's torso scanning data, and constructs the user's thoracolumbar correction device based on this, so that the obtained thoracolumbar correction device can adapt to the torso condition of each user, thereby improving the thoracolumbar correction efficiency of each user, and then this solution generates a thoracolumbar correction strategy through the thoracolumbar abnormality type and degree of thoracolumbar abnormality corresponding to each user's thoracolumbar problem, so as to adapt the correction strategy exclusive to each user in a targeted manner, thereby improving the accuracy of thoracolumbar correction for each user, finally, this solution simulates the correction process of the user's thoracolumbar correction strategy, and the simulation process includes the process of correcting the torso structure model through the thoracolumbar correction strategy, and when the correction is not completed, the correction strategy can be adjusted and simulated again until the correction is completed, thereby generating thoracolumbar correction models for each user at different correction stages. The correction device parameters and the correction time of different correction stages are obtained by the method, thereby avoiding the inefficiency of re-scanning the user's torso, re-analyzing the user's current thoracic and lumbar spine conditions, and re-executing the generation of correction device parameters each time. This not only improves the correction accuracy for different users, but also generates correction device parameters for users in different correction stages through simulation strategies, thereby reducing the process of user review, re-correction inspection and generation of correction parameters, and improving the correction efficiency of thoracolumbar spine for different users. Moreover, this method can directly generate thoracolumbar spine correction device parameters for different users at different stages, so that users can directly wear thoracolumbar spine correction devices at different stages according to the stage, so that users can adapt to the thoracolumbar spine correction device with the best thoracolumbar spine correction effect in each thoracolumbar spine correction stage, thereby comprehensively improving the thoracolumbar spine correction accuracy for different users. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0060] Figure 1 is a flow chart of a method for generating a thoracolumbar correction device based on 3D printing technology provided by an embodiment of the present invention;

[0061] Figure 2 It is a structural schematic diagram of a generation system of a thoracolumbar correction device based on 3D printing technology provided by an embodiment of the present invention;

[0062] Figure 3 An internal structure diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The method for generating a thoracolumbar correction device based on 3D printing technology provided by an embodiment of the present invention is applied to a system for generating a thoracolumbar correction device based on 3D printing technology. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" in the specification and claims of the present application and the above-mentioned drawings and any variations thereof are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0064] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0065] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0066] The method for generating a thoracolumbar correction device based on 3D printing technology provided in the embodiment of the present application can be applied in an environment. The method can be applied to a terminal, a server, or a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, etc. Among them, the terminal generates the user's torso structure model through the user's torso scanning data, and constructs the user's thoracolumbar correction device based on this, so that the obtained thoracolumbar correction device can adapt to the torso condition of each user, thereby improving the thoracolumbar correction efficiency of each user, and then this solution generates a thoracolumbar correction strategy through the thoracolumbar abnormality type and the degree of thoracolumbar abnormality corresponding to each user's thoracolumbar problem, so as to adapt the correction strategy exclusive to each user in a targeted manner, thereby improving the accuracy of thoracolumbar correction for each user, and finally, this solution simulates the correction process of the user's thoracolumbar correction strategy, and the simulation process includes the process of correcting the torso structure model through the thoracolumbar correction strategy, and when the correction is not completed, the correction strategy can be adjusted and simulated again until the correction is completed, so as to generate the thoracolumbar correction model of each user at different correction stages. The correction device parameters and the correction time of different correction stages are avoided, thereby avoiding the inefficiency of re-scanning the user's torso, re-analyzing the user's current thoracic and lumbar spine conditions, and re-executing the generation of correction device parameters each time. This not only improves the correction accuracy for different users, but also generates correction device parameters for users in different correction stages through simulation strategies, thereby reducing the process of user review, re-correction inspection and generation of correction parameters, and improving the thoracolumbar correction efficiency for different users. Moreover, this method can directly generate thoracolumbar correction device parameters for different users at different stages, so that users can directly wear thoracolumbar correction devices at different stages according to the stage, so that users can adapt to the thoracolumbar correction device with the best thoracolumbar correction effect in each thoracolumbar correction stage, thereby comprehensively improving the thoracolumbar correction accuracy for different users.

[0067] In one embodiment, Figure 1 As shown, a method for generating a thoracolumbar correction device based on 3D printing technology is provided, and the method is applied to a terminal as an example for explanation, including the following steps:

[0068] Step S101, obtaining the user's thoracic and lumbar spine problem information and the user's trunk scanning data, and generating the user's trunk structure model based on the trunk scanning data.

[0069] In this embodiment, the terminal responds to the doctor's thoracolumbar problem upload operation and obtains the user's thoracolumbar problem information, which includes information such as the abnormal position of the thoracolumbar spine and the abnormal shape of the thoracolumbar spine. Then, the terminal obtains the user's trunk body shape scanning data by infrared three-dimensional scanning of the user's torso position, and then scans the user's thoracolumbar spine scanning data by X-ray radiation scanning technology, and uses the reminder scanning data and the thoracolumbar spine scanning data as the user's torso scanning data. Then, the terminal generates the user's torso structure model based on the torso scanning data. Among them, the torso structure model is a three-dimensional structure model obtained by model construction based on the three-dimensional structure model generation technology. The three-dimensional structure model generation technology is any generation technology that can generate a three-dimensional structure model through three-dimensional structure data, such as a 3D model generation program of MATLAB software. The specific generation process will be described in detail later. The torso structure model includes a thoracolumbar spine structure model and a body shape structure model.

[0070] Step S102, based on the user's thoracolumbar problem information, identify the user's thoracolumbar abnormality type and the user's thoracolumbar abnormality degree, and generate the user's thoracolumbar correction strategy based on the user's thoracolumbar abnormality type and the user's thoracolumbar abnormality degree.

[0071] In this embodiment, the terminal identifies the user's thoracolumbar abnormality type and the user's thoracolumbar abnormality degree based on the user's thoracolumbar problem information, and generates the user's thoracolumbar correction strategy based on the user's thoracolumbar abnormality type and the user's thoracolumbar abnormality degree. Among them, the thoracolumbar abnormality type includes but is not limited to thoracolumbar scoliosis type, thoracolumbar spondylolisthesis type, thoracolumbar anterior arch deformity type, thoracolumbar dislocation type and other types. Among them, the thoracolumbar abnormality degree includes but is not limited to the first stage degree, the second stage degree, the third stage degree, etc., wherein the thoracolumbar correction strategy is a correction strategy corresponding to the fixed correction method through the thoracolumbar correction device, and the correction strategy includes the target pressurization depth, the thoracolumbar pressurization range, and the thoracolumbar support range. The specific generation process will be described in detail later.

[0072] Step S103, based on the trunk structure model, simulate the execution process of the thoracolumbar correction strategy to obtain the user's thoracolumbar correction device parameters, and based on the thoracolumbar correction device parameters, generate the user's target thoracolumbar correction device through the additive manufacturing strategy.

[0073] In this embodiment, the terminal simulates the execution process of the thoracolumbar correction strategy based on the torso structure model, obtains the user's thoracolumbar correction device parameters, and generates the user's target thoracolumbar correction device through the additive manufacturing strategy based on the thoracolumbar correction device parameters. The additive manufacturing strategy is a 3D printing strategy for thoracolumbar correction equipment based on additive manufacturing technology (i.e., 3D printing technology). The specific simulation process will be described in detail later.

[0074] Based on the above scheme, a thoracolumbar correction strategy is generated through the thoracolumbar abnormality type and degree of thoracolumbar abnormality corresponding to each user's thoracolumbar problem, so as to adapt the correction strategy exclusive to each user in a targeted manner, thereby improving the accuracy of thoracolumbar correction for each user. Finally, this scheme simulates the correction process of the user's thoracolumbar correction strategy. The simulation process includes the process of correcting the torso structure model through the thoracolumbar correction strategy, and when the correction is not completed, the correction strategy can be adjusted and simulated again until the correction is completed, so as to generate the thoracolumbar correction equipment parameters for each user at different correction stages, as well as the correction time at different correction stages, thereby avoiding re-scanning the user's torso each time and correcting the user. The current thoracic and lumbar spine conditions are analyzed again, and the inefficiency of generating correction device parameters is solved again. This can not only improve the correction accuracy for different users, but also generate correction device parameters for users in different correction stages through simulation strategies, thereby reducing the process of user review, re-correction inspection and generation of correction parameters, and improving the correction efficiency of thoracolumbar spine for different users. Moreover, this method can directly generate thoracolumbar spine correction device parameters for different users at different stages, so that users can directly wear thoracolumbar spine correction devices at different stages according to the stage, so that users can adapt to the thoracolumbar spine correction device with the best thoracolumbar spine correction effect in each thoracolumbar spine correction stage, thereby comprehensively improving the thoracolumbar spine correction accuracy for different users.

[0075] Optionally, a torso structure model of the user is generated based on the torso scanning data, including: splitting the torso scanning data into body shape scanning data and thoracic and lumbar spine scanning data, and projecting the torso scanning data into the same three-dimensional space to obtain body shape three-dimensional structure data corresponding to the body shape scanning data and thoracolumbar spine three-dimensional structure data corresponding to the thoracolumbar spine scanning data; identifying the relative spatial position information of the body shape three-dimensional structure data and the thoracolumbar spine three-dimensional structure data, and based on the body shape three-dimensional structure data and the thoracolumbar spine three-dimensional structure data, constructing a body shape structure model and a thoracolumbar spine structure model through a three-dimensional modeling program; based on the relative spatial position information, performing model splicing processing on the body shape structure model and the thoracolumbar spine structure model to obtain the torso structure data of the user.

[0076] In this embodiment, the terminal splits the trunk scan data into body scan data and thoracolumbar scan data, and projects the trunk scan data into the same three-dimensional space to obtain body three-dimensional structure data corresponding to the body scan data and thoracolumbar three-dimensional structure data corresponding to the thoracolumbar scan data. Then, the terminal identifies the body three-dimensional structure data and the relative spatial position information of the thoracolumbar three-dimensional structure data. The relative spatial position information is the relative distance and relative angle between each position point in the thoracolumbar three-dimensional structure data and each position point in the body three-dimensional structure data.

[0077] The terminal constructs a body structure model and a thoracolumbar structure model based on the body shape three-dimensional structure data and the thoracolumbar vertebrae three-dimensional structure data through a three-dimensional modeling program. Then, the terminal splices the body structure model and the thoracolumbar vertebrae structure model based on the relative spatial position information to obtain the user's torso structure data.

[0078] Based on the above solution, the accuracy of the body model and the thoracic and lumbar spine model are improved by splitting the data and then modeling them separately. Then, the two models are connected through the actual relative spatial position information, which improves the actuality and accuracy of the trunk structure data in representing the user's current thoracic and lumbar spine condition and the current distance between the thoracic and lumbar spine and the body surface.

[0079] Optionally, based on the user's thoracic and lumbar problem information, the user's thoracic and lumbar abnormality type and the user's thoracic and lumbar abnormality degree are identified, including: extracting the thoracic and lumbar problem features of the thoracic and lumbar problem information, and calculating the similarity between the historical problem features of different historical thoracic and lumbar problem information corresponding to each thoracic and lumbar abnormality type in the thoracic and lumbar abnormality database; screening the thoracic and lumbar abnormality type of the target historical thoracic and lumbar problem information corresponding to the maximum similarity as the user's thoracic and lumbar abnormality type, and based on the feature content corresponding to each thoracic and lumbar problem feature, the user's thoracic and lumbar abnormality degree is analyzed through the degree analysis strategy corresponding to the user's thoracic and lumbar abnormality type.

[0080] In this embodiment, the terminal extracts the thoracic and lumbar problem features of the thoracic and lumbar problem information, and calculates the similarity between the historical problem features of different historical thoracic and lumbar problem information corresponding to each thoracic and lumbar abnormality type in the thoracic and lumbar abnormality database. Specifically, the terminal obtains each historical thoracic and lumbar problem information corresponding to each thoracic and lumbar abnormality type in the historical database, and then the terminal extracts the historical problem features of each historical thoracic and lumbar problem information through a neural network based on a self-attention mechanism, and extracts the thoracic and lumbar problem features of the thoracic and lumbar problem information. Afterwards, the terminal calculates each historical problem feature and the similarity between each thoracic and lumbar problem feature through a cosine similarity algorithm.

[0081] The terminal screens the thoracolumbar abnormality type of the target historical thoracolumbar problem information corresponding to the maximum similarity as the user's thoracolumbar abnormality type, and based on the feature content corresponding to each thoracolumbar problem feature, analyzes the user's thoracolumbar abnormality degree through the degree analysis strategy corresponding to the user's thoracolumbar abnormality type. Each abnormal degree of each abnormal type corresponds to a range of related problem contents of an abnormal degree. Each degree analysis strategy is to query the range of specific data corresponding to the user's actual related content based on the range of related problem contents corresponding to each abnormal degree, so as to determine the strategy of abnormal degree. For example, each degree of thoracolumbar scoliosis type corresponds to a scoliosis angle range of thoracolumbar scoliosis and a scoliosis number range of thoracolumbar scoliosis. Then, based on the thoracolumbar problem feature of the thoracolumbar scoliosis type, the terminal identifies the thoracolumbar content corresponding to the thoracolumbar problem feature in the thoracolumbar problem information, and screens the thoracolumbar content corresponding to the thoracolumbar scoliosis angle and the thoracolumbar content corresponding to the thoracolumbar scoliosis number. Then, based on the above thoracic and lumbar content, the terminal identifies the scoliosis angle range to which the actual thoracolumbar scoliosis angle of the user belongs, and the scoliosis number range described by the actual number of thoracolumbar scoliosis of the user, and determines the abnormal degree of the thoracolumbar scoliosis type of the user.

[0082] Based on the above scheme, the thoracolumbar abnormality type to which the thoracolumbar problem information belongs is identified by similarity recognition, and the abnormality degree of each thoracolumbar abnormality type is identified based on range query.

[0083] Optionally, based on the user's thoracolumbar abnormality type and the user's thoracolumbar abnormality degree, a thoracolumbar correction strategy for the user is generated, including: based on the thoracolumbar abnormality type, identifying the user's thoracolumbar pressurization range and the user's thoracolumbar support range in the thoracolumbar correction database; based on the user's thoracolumbar abnormality degree, querying the user's target pressurization depth, and using the target pressurization depth, thoracolumbar pressurization range, and thoracolumbar support range as the user's thoracolumbar correction strategy.

[0084] In this embodiment, the terminal identifies the user's thoracolumbar spine pressurization range and the user's thoracolumbar spine support range in the thoracolumbar spine correction database based on the type of thoracolumbar spine abnormality; based on the user's thoracolumbar spine abnormality, the user's target pressurization depth is queried, and the target pressurization depth, thoracolumbar spine pressurization range, and thoracolumbar spine support range are used as the user's thoracolumbar spine correction strategy. Among them, the thoracolumbar spine pressurization range is the range on the user's body trunk surface that needs to be pressurized to correct the thoracolumbar spine shape here, and this range corresponds to the body trunk surface range. The thoracolumbar spine support range is the range of the user's waist bending angle fixed, so that the user can only move the waist within the range of the waist bending angle. The waist support range is the range of the user's body surface that limits the user's waist to move only within the range of the waist bending angle. The range corresponds to the user's body surface range. Among them, the thoracolumbar spine correction database includes the thoracolumbar spine pressurization range and thoracolumbar spine support range corresponding to each thoracolumbar spine abnormality type. Then, the terminal presets the corresponding relationship between the abnormality degree and the pressurization depth, thereby querying the target pressurization depth corresponding to the abnormality degree of the user, and obtaining the thoracolumbar correction strategy of the user.

[0085] Based on the above solution, the user's target compression depth, thoracolumbar compression range, and thoracolumbar support range are identified through corresponding relationship query, thereby improving the accuracy and recognition efficiency.

[0086] Optionally, based on the trunk structure model, the execution process of the thoracolumbar correction strategy is simulated to obtain the user's thoracolumbar correction device parameters, including: generating a simulated depth progression sequence based on the user's target pressurization depth, and marking the first trunk range corresponding to the thoracolumbar pressurization range and the second trunk range corresponding to the thoracolumbar support range in the trunk structure model; simulating the user's torso correction process through the simulation correction strategy based on the simulated depth progression sequence and the marked torso structure model to obtain the user's torso correction result; when the trunk correction result is that the correction is incomplete, identifying the new thoracolumbar abnormality type and the new thoracolumbar abnormality degree corresponding to the torso correction result, and replacing the thoracolumbar abnormality type with the new thoracolumbar abnormality type, and replacing the thoracolumbar abnormality degree with the new thoracolumbar abnormality degree, returning to execute the thoracolumbar correction strategy step for the user based on the thoracolumbar abnormality type and the thoracolumbar abnormality degree of the user, until the trunk correction result is that the correction is completed, obtaining each time. The target pressurization depth corresponding to the simulation process, the first trunk range of the thoracolumbar pressurization range corresponding to each simulation process, and the second trunk range of the thoracolumbar support range corresponding to each simulation process; based on the model parameters of the trunk structure model, the device structure parameters of the thoracolumbar correction device are determined, and based on the target pressurization depths, the first trunk ranges, and the second trunk ranges, the correction structure parameters of the thoracolumbar correction device corresponding to each simulation process are identified through the preset parameter data correspondence relationship; based on the device structure parameters and the correction structure parameters corresponding to each simulation process, the sub-thoracolumbar correction device parameters corresponding to each correction process are determined, and based on the trunk correction results of each simulation process, the simulation time corresponding to each simulation process and the simulation order corresponding to each simulation process are identified; the sub-thoracolumbar correction device parameters corresponding to each simulation process, the simulation order corresponding to each simulation process, and the simulation time corresponding to each simulation process are used as the user's thoracolumbar correction device parameters.

[0087] In this embodiment, the terminal generates a simulated depth progression sequence based on the user's target pressurization depth, and marks the first trunk range corresponding to the thoracolumbar pressurization range and the second trunk range corresponding to the thoracolumbar support range in the trunk structure model. Among them, the first trunk range and the second trunk range are both surface ranges of the body model surface in the trunk structure model. The simulated depth progression sequence is an increasing sequence that increases the simulated depth at depth intervals. For example, if the pressurization depth is 10cm, then the simulated depth progression sequence is a depth progression sequence in which the pressurization depth increases by 2cm every 10 minutes. The terminal stores a pressurization depth of 2cm from the first minute to the tenth minute, a pressurization depth of 4cm from the tenth minute to the twentieth minute, ..., a pressurization depth of 10cm from the fortieth minute to the fiftieth minute.

[0088] Then, based on the simulated depth progressive sequence and the marked trunk structure model, the terminal simulates the user's trunk correction process through a simulation correction strategy to obtain the user's trunk correction result. Among them, the simulation correction strategy is a progressive change strategy of the actual correction degree of the thoracic and lumbar spine in each time period obtained by the pressure depth of different correction positions and the support of different correction positions preset by the staff after a large number of clinical experiments and historical correction data.

[0089] When the trunk correction result is that the correction is not completed, the terminal identifies the new thoracic and lumbar abnormality type and the new thoracic and lumbar abnormality degree corresponding to the trunk correction result. Among them, the trunk correction result includes the new trunk structure model corresponding to the current trunk structure data, and the simulation duration. Then, the terminal obtains the new thoracic and lumbar abnormality type and the new thoracic and lumbar abnormality degree corresponding to the trunk correction result by executing step S102. Then, the terminal replaces the thoracic and lumbar abnormality type with the new thoracic and lumbar abnormality type, and replaces the thoracic and lumbar abnormality degree with the new thoracic and lumbar abnormality degree, and returns to execute the thoracic and lumbar correction strategy steps based on the user's thoracic and lumbar abnormality type and the user's thoracic and lumbar abnormality degree, until the trunk correction result is that the correction is completed, and the target pressurization depth corresponding to each simulation process, the first trunk range of the thoracic and lumbar pressurization range corresponding to each simulation process, and the second trunk range of the thoracic and lumbar support range corresponding to each simulation process are obtained.

[0090] Then, the terminal determines the device structure parameters of the thoracolumbar correction device based on the model parameters of the torso structure model, and based on each target pressurization depth, each first torso range, and each second torso range, through the preset parameter data correspondence, identifies the correction structure parameters corresponding to the thoracolumbar correction device in each simulation process.

[0091] The terminal determines the sub-thoracic and lumbar correction device parameters corresponding to each correction process based on the device structure parameters and the correction structure parameters corresponding to each simulation process, and identifies the simulation duration corresponding to each simulation process and the simulation order corresponding to each simulation process based on the trunk correction result of each simulation process; the sub-thoracic and lumbar correction device parameters corresponding to each simulation process, the simulation order corresponding to each simulation process, and the simulation duration corresponding to each simulation process are used as the user's thoracic and lumbar correction device parameters.

[0092] Based on the above scheme, the simulation results obtained through simulation are iteratively simulated to identify the correction strategies of users at different stages, thereby generating parameters of thoracolumbar correction equipment at different stages. Through this scheme, the correction equipment parameters of users at different correction stages are generated, thereby reducing the process of user review, re-correction examination and generation of correction parameters, and improving the efficiency of thoracolumbar correction for different users. Moreover, this method can directly generate thoracolumbar correction equipment parameters for different users at different stages, so that users can directly wear thoracolumbar correction equipment at different stages according to the stage, so that users can adapt to the thoracolumbar correction equipment with the best thoracolumbar correction effect in each thoracolumbar correction stage, thereby comprehensively improving the accuracy of thoracolumbar correction for different users.

[0093] Optionally, based on the thoracic and lumbar correction device parameters, the user's target thoracic and lumbar correction device is generated through an additive manufacturing strategy, including: obtaining the correspondence between the printing parameters of the additive manufacturing device and the device parameters, and based on the sub-thoracic and lumbar correction device parameters corresponding to each simulation process and through the correspondence, identifying the sub-thoracic and lumbar correction device printing parameters corresponding to each simulation process; based on the printing parameters of each sub-thoracic and lumbar correction device, an initial sub-target thoracic and lumbar correction device corresponding to each simulation process is generated through an additive manufacturing strategy, and in each initial sub-target thoracic and lumbar correction device, the simulation duration of the simulation process corresponding to each sub-target thoracic and lumbar correction device and the simulation order of the simulation process corresponding to each sub-target thoracic and lumbar correction device are added to obtain each sub-target thoracic and lumbar correction device; all sub-target thoracic and lumbar correction devices are used as the user's target thoracic and lumbar correction device.

[0094] In this embodiment, the terminal obtains the correspondence between the printing parameters of the additive manufacturing equipment and the equipment parameters, and identifies the sub-thoracic and lumbar correction equipment printing parameters corresponding to each simulation process based on the sub-thoracic and lumbar correction equipment parameters corresponding to each simulation process through the correspondence. Then, based on the printing parameters of each sub-thoracic and lumbar correction equipment, the terminal generates the initial sub-target thoracic and lumbar correction equipment corresponding to each simulation process through the additive manufacturing strategy, and adds the simulation duration of the simulation process corresponding to each sub-target thoracic and lumbar correction equipment and the simulation order of the simulation process corresponding to each sub-target thoracic and lumbar correction equipment to each initial sub-target thoracic and lumbar correction equipment to obtain each sub-target thoracic and lumbar correction equipment. Finally, the terminal uses all sub-target thoracic and lumbar correction equipment as the user's target thoracic and lumbar correction equipment.

[0095] Based on the above scheme, the additive manufacturing technology corresponding to the additive manufacturing strategy is used to generate the sub-target thoracic and lumbar correction devices of each simulation process, thereby improving the accuracy of the generated correction devices.

[0096] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0097] Based on the same inventive concept, the embodiment of the present application also provides a system for generating a thoracolumbar correction device based on 3D printing technology for realizing the method for generating a thoracolumbar correction device based on 3D printing technology mentioned above. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of the generation system of one or more thoracolumbar correction devices based on 3D printing technology provided below can be referred to the limitations of the method for generating a thoracolumbar correction device based on 3D printing technology above, and will not be repeated here.

[0098] Further references Figure 2 , as a response to the above Figure 1 In order to realize the method shown in the figure, the present application provides an embodiment of a system 200 for generating a thoracolumbar correction device based on 3D printing technology, the system comprising an acquisition module 210, an identification module 220 and a generation module 230, wherein:

[0099] An acquisition module 210 is used to acquire the user's thoracic and lumbar spine problem information and the user's trunk scan data, and generate a trunk structure model of the user based on the trunk scan data;

[0100] The identification module 220 is used to identify the type of thoracolumbar abnormality of the user and the degree of thoracolumbar abnormality of the user based on the thoracolumbar problem information of the user, and generate a thoracolumbar correction strategy for the user based on the type of thoracolumbar abnormality of the user and the degree of thoracolumbar abnormality of the user;

[0101] The generation module 230 is used to simulate the execution process of the thoracolumbar correction strategy based on the torso structure model, obtain the thoracolumbar correction device parameters of the user, and generate the target thoracolumbar correction device of the user through an additive manufacturing strategy based on the thoracolumbar correction device parameters.

[0102] Optionally, the acquisition module 210 is specifically configured to:

[0103] Splitting the trunk scan data into body shape scan data and thoracolumbar vertebrae scan data, and projecting the trunk scan data into the same three-dimensional space to obtain body shape three-dimensional structure data corresponding to the body shape scan data and thoracolumbar vertebrae three-dimensional structure data corresponding to the thoracolumbar vertebrae scan data;

[0104] Identify the relative spatial position information of the body shape three-dimensional structure data and the thoracolumbar vertebrae three-dimensional structure data, and construct a body shape structure model and a thoracolumbar vertebrae structure model through a three-dimensional modeling program based on the body shape three-dimensional structure data and the thoracolumbar vertebrae three-dimensional structure data;

[0105] Based on the relative spatial position information, the body structure model and the thoracic and lumbar structure model are spliced ​​to obtain the torso structure data of the user.

[0106] Optionally, the identification module 220 is specifically configured to:

[0107] Extracting thoracolumbar vertebrae problem features of the thoracolumbar vertebrae problem information, and calculating the similarity between historical problem features of different historical thoracolumbar vertebrae problem information corresponding to each thoracolumbar vertebrae abnormality type in the thoracolumbar vertebrae abnormality database;

[0108] The thoracolumbar abnormality type described in the target historical thoracolumbar problem information corresponding to the maximum similarity is filtered as the thoracolumbar abnormality type of the user, and based on the feature content corresponding to each of the thoracolumbar problem features, the degree of thoracolumbar abnormality of the user is analyzed through the degree analysis strategy corresponding to the thoracolumbar abnormality type of the user.

[0109] Optionally, the identification module 220 is specifically configured to:

[0110] Based on the thoracolumbar abnormality type, identifying the thoracolumbar compression range of the user and the thoracolumbar support range of the user in a thoracolumbar correction database;

[0111] Based on the abnormality of the user's thoracolumbar spine, the user's target pressurization depth is queried, and the target pressurization depth, the thoracolumbar spine pressurization range, and the thoracolumbar spine support range are used as the user's thoracolumbar spine correction strategy.

[0112] Optionally, the generating module 230 is specifically configured to:

[0113] Based on the user's target compression depth, a simulated depth progression sequence is generated, and in the trunk structure model, a first trunk range corresponding to the thoracolumbar compression range and a second trunk range corresponding to the thoracolumbar support range are marked;

[0114] Based on the simulated depth progressive sequence and the marked torso structure model, the torso correction process of the user is simulated through a simulation correction strategy to obtain a torso correction result of the user;

[0115] In the case that the trunk correction result is that the correction is incomplete, the new thoracic and lumbar abnormality type and the new thoracic and lumbar abnormality degree corresponding to the trunk correction result are identified, and the thoracic and lumbar abnormality type is replaced by the new thoracic and lumbar abnormality type, and the thoracic and lumbar abnormality degree is replaced by the new thoracic and lumbar abnormality degree, and the thoracolumbar abnormality degree is replaced by the new thoracic and lumbar abnormality degree, and the step of generating the thoracolumbar correction strategy for the user based on the thoracolumbar abnormality type and the thoracolumbar abnormality degree of the user is returned to be executed until the trunk correction result is that the correction is completed, and the target pressurization depth corresponding to each simulation process, the first trunk range of the thoracolumbar pressurization range corresponding to each simulation process, and the second trunk range of the thoracolumbar support range corresponding to each simulation process are obtained;

[0116] Based on the model parameters of the trunk structure model, the device structure parameters of the thoracolumbar correction device are determined, and based on the target pressurization depths, the first trunk ranges, and the second trunk ranges, the correction structure parameters corresponding to the thoracolumbar correction device in each simulation process are identified through a preset parameter data correspondence relationship;

[0117] Based on the device structure parameters and the correction structure parameters corresponding to each simulation process, determine the sub-thoracic and lumbar correction device parameters corresponding to each correction process, and based on the trunk correction result of each simulation process, identify the simulation duration corresponding to each simulation process and the simulation order corresponding to each simulation process;

[0118] The sub-thoracolumbar correction device parameters corresponding to each simulation process, the simulation order corresponding to each simulation process, and the simulation duration corresponding to each simulation process are used as the thoracolumbar correction device parameters of the user.

[0119] Optionally, the generating module 230 is specifically configured to:

[0120] Obtaining a correspondence between printing parameters and device parameters of an additive manufacturing device, and identifying the printing parameters of the sub-thoracolumbar correction device corresponding to each simulation process based on the sub-thoracolumbar correction device parameters corresponding to each simulation process through the correspondence;

[0121] Based on the printing parameters of each sub-thoracolumbar correction device, an initial sub-target thoracolumbar correction device corresponding to each simulation process is generated through an additive manufacturing strategy, and in each initial sub-target thoracolumbar correction device, a simulation duration of the simulation process corresponding to each sub-target thoracolumbar correction device and a simulation order of the simulation process corresponding to each sub-target thoracolumbar correction device are added to obtain each sub-target thoracolumbar correction device;

[0122] All sub-target thoracolumbar correction devices are used as the target thoracolumbar correction devices of the user.

[0123] Each module in the generation system of the thoracolumbar correction device based on 3D printing technology can be implemented in whole or in part by software, hardware and their combination. Each module can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each module above.

[0124] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a communication interface, a display unit and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for generating a thoracolumbar correction device based on 3D printing technology is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input system of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse, etc.

[0125] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0126] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of any one of the methods in the first aspect are implemented.

[0127] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0128] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps of any one of the methods in the first aspect.

[0129] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0130] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0131] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0132] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for generating a thoracolumbar correction device based on 3D printing technology, characterized in that: The method comprises: Acquire the user's thoracic and lumbar spine problem information and the user's trunk scanning data, and generate the user's trunk structure model based on the trunk scanning data; Based on the thoracolumbar problem information of the user, identifying the type of thoracolumbar abnormality of the user and the degree of thoracolumbar abnormality of the user, and generating a thoracolumbar correction strategy for the user based on the type of thoracolumbar abnormality of the user and the degree of thoracolumbar abnormality of the user; Based on the trunk structure model, the execution process of the thoracolumbar correction strategy is simulated to obtain the thoracolumbar correction device parameters of the user, and based on the thoracolumbar correction device parameters, the target thoracolumbar correction device of the user is generated through an additive manufacturing strategy; Generating a thoracolumbar spine correction strategy for the user based on the type of thoracolumbar spine abnormality of the user and the degree of thoracolumbar spine abnormality of the user comprises: Based on the thoracolumbar abnormality type, identifying the thoracolumbar compression range of the user and the thoracolumbar support range of the user in a thoracolumbar correction database; Based on the abnormality of the user's thoracolumbar spine, query the user's target pressurization depth, and use the target pressurization depth, the thoracolumbar spine pressurization range, and the thoracolumbar spine support range as the user's thoracolumbar spine correction strategy; The method of simulating the execution process of the thoracolumbar correction strategy based on the trunk structure model to obtain the thoracolumbar correction device parameters of the user includes: Based on the user's target compression depth, a simulated depth progression sequence is generated, and in the trunk structure model, a first trunk range corresponding to the thoracolumbar compression range and a second trunk range corresponding to the thoracolumbar support range are marked; Based on the simulated depth progressive sequence and the marked torso structure model, the torso correction process of the user is simulated through a simulation correction strategy to obtain a torso correction result of the user; In the case that the trunk correction result is that the correction is incomplete, the new thoracic and lumbar abnormality type and the new thoracic and lumbar abnormality degree corresponding to the trunk correction result are identified, and the thoracic and lumbar abnormality type is replaced by the new thoracic and lumbar abnormality type, and the thoracic and lumbar abnormality degree is replaced by the new thoracic and lumbar abnormality degree, and the thoracolumbar abnormality degree is replaced by the new thoracic and lumbar abnormality degree, and the step of generating the thoracolumbar correction strategy for the user based on the thoracolumbar abnormality type and the thoracolumbar abnormality degree of the user is returned to be executed until the trunk correction result is that the correction is completed, and the target pressurization depth corresponding to each simulation process, the first trunk range of the thoracolumbar pressurization range corresponding to each simulation process, and the second trunk range of the thoracolumbar support range corresponding to each simulation process are obtained; Based on the model parameters of the trunk structure model, the device structure parameters of the thoracolumbar correction device are determined, and based on the target pressurization depths, the first trunk ranges, and the second trunk ranges, the correction structure parameters corresponding to the thoracolumbar correction device in each simulation process are identified through a preset parameter data correspondence relationship; Based on the device structure parameters and the correction structure parameters corresponding to each simulation process, determine the sub-thoracic and lumbar correction device parameters corresponding to each correction process, and based on the trunk correction result of each simulation process, identify the simulation duration corresponding to each simulation process and the simulation order corresponding to each simulation process; The thoracolumbar correction device parameters corresponding to each simulation process, the simulation order corresponding to each simulation process, and the simulation duration corresponding to each simulation process are used as the thoracolumbar correction device parameters of the user; The method of generating the target thoracolumbar vertebra correction device of the user through an additive manufacturing strategy based on the thoracolumbar vertebra correction device parameters includes: Obtaining a correspondence between printing parameters and device parameters of an additive manufacturing device, and identifying the printing parameters of the sub-thoracolumbar correction device corresponding to each simulation process based on the sub-thoracolumbar correction device parameters corresponding to each simulation process through the correspondence; Based on the printing parameters of each sub-thoracolumbar correction device, an initial sub-target thoracolumbar correction device corresponding to each simulation process is generated through an additive manufacturing strategy, and in each initial sub-target thoracolumbar correction device, a simulation duration of the simulation process corresponding to each sub-target thoracolumbar correction device and a simulation order of the simulation process corresponding to each sub-target thoracolumbar correction device are added to obtain each sub-target thoracolumbar correction device; All sub-target thoracolumbar correction devices are used as the target thoracolumbar correction devices of the user.

2. The method according to claim 1, characterized in that The step of generating a torso structure model of the user based on the torso scan data comprises: Splitting the trunk scan data into body shape scan data and thoracolumbar vertebrae scan data, and projecting the trunk scan data into the same three-dimensional space to obtain body shape three-dimensional structure data corresponding to the body shape scan data and thoracolumbar vertebrae three-dimensional structure data corresponding to the thoracolumbar vertebrae scan data; Identify the relative spatial position information of the body shape three-dimensional structure data and the thoracolumbar vertebrae three-dimensional structure data, and construct a body shape structure model and a thoracolumbar vertebrae structure model through a three-dimensional modeling program based on the body shape three-dimensional structure data and the thoracolumbar vertebrae three-dimensional structure data; Based on the relative spatial position information, the body structure model and the thoracic and lumbar structure model are spliced ​​to obtain the torso structure data of the user.

3. The method according to claim 1, characterized in that The step of identifying the abnormality type of the user's thoracolumbar spine and the abnormality degree of the user's thoracolumbar spine based on the user's thoracolumbar spine problem information includes: Extracting thoracolumbar vertebrae problem features of the thoracolumbar vertebrae problem information, and calculating the similarity between historical problem features of different historical thoracolumbar vertebrae problem information corresponding to each thoracolumbar vertebrae abnormality type in the thoracolumbar vertebrae abnormality database; The thoracolumbar abnormality type described in the target historical thoracolumbar problem information corresponding to the maximum similarity is filtered as the thoracolumbar abnormality type of the user, and based on the feature content corresponding to each of the thoracolumbar problem features, the degree of thoracolumbar abnormality of the user is analyzed through the degree analysis strategy corresponding to the thoracolumbar abnormality type of the user.

4. A system for generating thoracolumbar correction equipment based on 3D printing technology, characterized in that: The system comprises: An acquisition module, used to acquire the user's thoracic and lumbar spine problem information and the user's trunk scanning data, and generate a trunk structure model of the user based on the trunk scanning data; an identification module, for identifying the type of abnormality of the thoracolumbar spine of the user and the degree of abnormality of the thoracolumbar spine of the user based on the thoracolumbar spine problem information of the user, and generating a thoracolumbar spine correction strategy for the user based on the type of abnormality of the thoracolumbar spine of the user and the degree of abnormality of the thoracolumbar spine of the user; A generation module, for simulating the execution process of the thoracolumbar correction strategy based on the torso structure model, obtaining the thoracolumbar correction device parameters of the user, and generating the target thoracolumbar correction device of the user through an additive manufacturing strategy based on the thoracolumbar correction device parameters; The specific implementation of the recognition module generating the thoracolumbar spine correction strategy for the user based on the type of thoracolumbar spine abnormality of the user and the degree of thoracolumbar spine abnormality of the user is as follows: Based on the thoracolumbar abnormality type, identifying the thoracolumbar compression range of the user and the thoracolumbar support range of the user in a thoracolumbar correction database; Based on the abnormality of the user's thoracolumbar spine, query the user's target pressurization depth, and use the target pressurization depth, the thoracolumbar spine pressurization range, and the thoracolumbar spine support range as the user's thoracolumbar spine correction strategy; The generation module simulates the execution process of the thoracolumbar correction strategy based on the trunk structure model to obtain the thoracolumbar correction device parameters of the user as follows: Based on the user's target compression depth, a simulated depth progression sequence is generated, and in the trunk structure model, a first trunk range corresponding to the thoracolumbar compression range and a second trunk range corresponding to the thoracolumbar support range are marked; Based on the simulated depth progressive sequence and the marked torso structure model, the torso correction process of the user is simulated through a simulation correction strategy to obtain a torso correction result of the user; In the case that the trunk correction result is that the correction is incomplete, the new thoracic and lumbar abnormality type and the new thoracic and lumbar abnormality degree corresponding to the trunk correction result are identified, and the thoracic and lumbar abnormality type is replaced by the new thoracic and lumbar abnormality type, and the thoracic and lumbar abnormality degree is replaced by the new thoracic and lumbar abnormality degree, and the thoracolumbar abnormality degree is replaced by the new thoracic and lumbar abnormality degree, and the step of generating the thoracolumbar correction strategy for the user based on the thoracolumbar abnormality type and the thoracolumbar abnormality degree of the user is returned to be executed until the trunk correction result is that the correction is completed, and the target pressurization depth corresponding to each simulation process, the first trunk range of the thoracolumbar pressurization range corresponding to each simulation process, and the second trunk range of the thoracolumbar support range corresponding to each simulation process are obtained; Based on the model parameters of the trunk structure model, the device structure parameters of the thoracolumbar correction device are determined, and based on the target pressurization depths, the first trunk ranges, and the second trunk ranges, the correction structure parameters corresponding to the thoracolumbar correction device in each simulation process are identified through a preset parameter data correspondence relationship; Based on the device structure parameters and the correction structure parameters corresponding to each simulation process, determine the sub-thoracic and lumbar correction device parameters corresponding to each correction process, and based on the trunk correction result of each simulation process, identify the simulation duration corresponding to each simulation process and the simulation order corresponding to each simulation process; The thoracolumbar correction device parameters corresponding to each simulation process, the simulation order corresponding to each simulation process, and the simulation duration corresponding to each simulation process are used as the thoracolumbar correction device parameters of the user; The specific implementation of the generation module generating the target thoracolumbar correction device of the user through the additive manufacturing strategy based on the thoracolumbar correction device parameters is as follows: Obtaining a correspondence between printing parameters and device parameters of an additive manufacturing device, and identifying the printing parameters of the sub-thoracolumbar correction device corresponding to each simulation process based on the sub-thoracolumbar correction device parameters corresponding to each simulation process through the correspondence; Based on the printing parameters of each sub-thoracolumbar correction device, an initial sub-target thoracolumbar correction device corresponding to each simulation process is generated through an additive manufacturing strategy, and in each initial sub-target thoracolumbar correction device, a simulation duration of the simulation process corresponding to each sub-target thoracolumbar correction device and a simulation order of the simulation process corresponding to each sub-target thoracolumbar correction device are added to obtain each sub-target thoracolumbar correction device; All sub-target thoracolumbar correction devices are used as the target thoracolumbar correction devices of the user.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

7. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

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