Intelligent splint stress monitoring method, system and computer device

Abstract

The application relates to an intelligent splint stress monitoring method, device and computer equipment. The method comprises the following steps: acquiring stress detection data of a splint and a user recovery area, identifying each abnormal stress area of the splint based on the stress detection data, identifying the abnormal stress reason of each abnormal stress area based on the user recovery area corresponding to the splint and the abnormal stress area, inquiring the stress solution strategy of each abnormal stress area in a database, generating the stress solution of each abnormal stress area based on the stress detection data of the user and the stress solution strategy, reacquiring new stress detection data of the splint, replacing the stress detection data with the new stress detection data in the case that the new stress detection data does not satisfy normal stress data, and returning to execute the above steps until the new stress detection data satisfies the normal stress data. The method can improve the timeliness of user disease monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of artificial intelligence and splint stress detection, and particularly relates to an intelligent splint stress monitoring method and system and a computer device. BACKGROUND

[0002] With the intelligent development of medical technology, the function of medical equipment is not limited to assisting the treatment of a user's illness, but can also improve the treatment detection efficiency of the user by the doctor and reduce the pain of the user. Especially for people with fractures, through splint fixation and regular detection, the problems of difficult disassembly and difficult internal detection of traditional one-time fixing devices are often avoided. However, how to improve the detection of the recovery condition of the user through the splint is the current research focus.

[0003] The traditional technology is to detect the recovery condition of the splint area of the user by regularly going to the hospital, regularly disassembling and X-ray scanning, but the efficiency of this method is low, and disassembly and reinstallation may cause secondary injury to the sick area, and abnormalities in the sick position cannot be detected in time, and only abnormalities can be found during detection, thereby causing poor timeliness of the user's sick monitoring. SUMMARY

[0004] Therefore, it is necessary to provide an intelligent splint stress monitoring method, device, computer equipment, computer readable storage medium and computer program product in view of the above technical problems.

[0005] In a first aspect, the present application provides an intelligent splint stress monitoring method. The method comprises:

[0006] obtaining stress detection data of a splint and a user recovery area corresponding to the splint, and identifying each abnormal stress area of the splint based on the stress detection data;

[0007] identifying the abnormal stress reason of each abnormal stress area based on the user recovery area corresponding to the splint and the abnormal stress area of the splint, and querying the stress solving strategy of each abnormal stress area in the database based on the abnormal stress reason of each abnormal stress area;

[0008] generating a stress solving scheme for each abnormal stress area based on the stress detection data of the user and the stress solving strategy of each abnormal stress area, and reacquiring new stress detection data of the splint;

[0009] In a case where the new stress detection data does not satisfy normal stress data, the new stress detection data is replaced with the stress detection data, and a step of identifying each abnormal stress area of the clamping plate based on the stress detection data is executed until the new stress detection data satisfies normal stress data.

[0010] Optionally, the identifying each abnormal stress area of the clamping plate based on the stress detection data comprises:

[0011] generating stress distribution information of the clamping plate based on the stress detection data, and collecting normal stress distribution information of the clamping plate;

[0012] identifying position information of each abnormal stress point of the clamping plate based on the normal stress distribution information and the stress distribution information, and performing clustering processing on the position information of each abnormal stress point to obtain each abnormal stress area of the clamping plate.

[0013] Optionally, the identifying an abnormal stress reason of each abnormal stress area based on the user recovery area corresponding to the clamping plate and the abnormal stress area of the clamping plate comprises:

[0014] obtaining a target recovery position range of the user in the user recovery area, and screening a first abnormal stress area containing position information in the target recovery position range from each abnormal stress area;

[0015] performing three-dimensional distribution arrangement on each first abnormal stress area to obtain first abnormal three-dimensional distribution information, and performing three-dimensional distribution arrangement on each non-first abnormal stress area to obtain second abnormal three-dimensional distribution information;

[0016] extracting a first distribution feature of the first abnormal three-dimensional distribution information and a second distribution feature of the second abnormal three-dimensional distribution information through a three-dimensional feature extraction algorithm, and determining an abnormal stress reason of the first abnormal stress area and an abnormal stress reason of the second abnormal stress area based on the first distribution feature and the second distribution feature.

[0017] Optionally, the determining the abnormal stress reason of the first abnormal stress area and the abnormal stress reason of the second abnormal stress area based on the first distribution feature and the second distribution feature comprises:

[0018] identifying a stress feature of the first abnormal stress area in the target recovery position range based on the first distribution feature, and collecting a recovery processing mode of the user in the target recovery position range and a recovery processing angle of the user in the target recovery position range;

[0019] identify stress influence information corresponding to the stress feature based on the stress feature, the recovery processing mode, and the recovery processing angle, and query an abnormal stress cause corresponding to the stress influence information in a database to obtain an abnormal stress cause of the first abnormal stress region;

[0020] identify a range coincidence degree between a distribution range of the second distribution feature and a clamping plate support range of the clamping plate, and determine an abnormal stress cause of the second abnormal stress region corresponding to a coincidence degree range to which the range coincidence degree belongs.

[0021] Optionally, based on the abnormal stress cause of each abnormal stress region, query a stress solution strategy of each abnormal stress region in a database, including:

[0022] identify an abnormal severity of the first abnormal stress region based on stress influence information of the first abnormal stress region and a range proportion of the first abnormal stress region in the target recovery position range, and query each initial stress solution strategy of the first abnormal stress region in a database based on the abnormal stress cause of the first abnormal stress region.

[0023] filter an initial stress solution strategy suitable for the abnormal severity from the initial stress solution strategies based on the abnormal severity of the first abnormal stress region, as the stress solution strategy of the first abnormal stress region.

[0024] query each initial stress solution strategy of the second abnormal stress region in a database based on the abnormal cause corresponding to the second abnormal stress region, and filter an initial stress solution strategy suitable for the range coincidence degree from the initial stress solution strategies based on the range coincidence degree corresponding to the second abnormal stress region, as the stress solution strategy of the second abnormal stress region.

[0025] Optionally, based on the stress detection data of the user and the stress solution strategy of each abnormal stress region, generate a stress solution of each abnormal stress region, including:

[0026] identify position information of each abnormal stress point of each abnormal stress region based on the stress detection data of the user, and construct an abnormal stress three-dimensional diagram of each abnormal stress region based on the position information of each abnormal stress point of each abnormal stress region.

[0027] generate a solution flowchart of the stress solution strategy, and construct a three-dimensional stress solution flowchart based on the solution flowchart of each stress solution strategy and the three-dimensional stress abnormality diagram of each abnormal stress region, and take each three-dimensional stress solution flowchart as a stress solution of each abnormal stress region.

[0028] In a second aspect, the present application further provides an intelligent splint stress monitoring device. The device comprises:

[0029] The acquisition module is configured to acquire stress detection data of a splint and a user recovery area corresponding to the splint, and identify each abnormal stress region of the splint based on the stress detection data;

[0030] The query module is configured to identify an abnormal stress cause of each abnormal stress region based on the user recovery area corresponding to the splint and the abnormal stress region of the splint, and query a stress solution strategy of each abnormal stress region in a database based on the abnormal stress cause of each abnormal stress region;

[0031] The acquisition module is configured to acquire stress detection data of a splint and a user recovery area corresponding to the splint, and identify each abnormal stress region of the splint based on the stress detection data;

[0032] The iteration module is configured to replace the stress detection data with new stress detection data of the splint in a case where the new stress detection data does not meet normal stress data, and return to execute the step of identifying each abnormal stress region of the splint based on the stress detection data until the new stress detection data meets normal stress data.

[0033] Optionally, the acquisition module is specifically configured to:

[0034] generate stress distribution information of the splint based on the stress detection data, and acquire normal stress distribution information of the splint;

[0035] identify position information of each abnormal stress point of the splint based on the normal stress distribution information and the stress distribution information, and perform clustering processing on the position information of each abnormal stress point to obtain each abnormal stress region of the splint.

[0036] Optionally, the query module is specifically configured to:

[0037] acquire a target recovery position range of the user in the user recovery area, and screen a first abnormal stress region containing position information in the target recovery position range from each abnormal stress region;

[0038] The first abnormal stress regions are arranged in three-dimensional distribution to obtain first abnormal three-dimensional distribution information, and the non-first abnormal stress regions are arranged in three-dimensional distribution to obtain second abnormal three-dimensional distribution information;

[0039] First distribution features of the first abnormal three-dimensional distribution information and second distribution features of the second abnormal three-dimensional distribution information are extracted by a three-dimensional feature extraction algorithm, and abnormal stress reasons of the first abnormal stress regions and abnormal stress reasons of the second abnormal stress regions are determined based on the first distribution features and the second distribution features.

[0040] Optionally, the query module is specifically used for:

[0041] Based on the first distribution features, stress features of the first abnormal stress regions in the target recovery position range are identified, and recovery processing manners of the user in the target recovery position range and recovery processing angles of the user in the target recovery position range are collected;

[0042] Based on the stress features, the recovery processing manners, and the recovery processing angles, stress influence information corresponding to the stress features is identified, and abnormal stress reasons corresponding to the stress influence information are queried in a database to obtain abnormal stress reasons of the first abnormal stress regions;

[0043] A range coincidence degree between a distribution range of the second distribution features and a splint support range of the splint is identified, and abnormal stress reasons corresponding to a coincidence degree range to which the range coincidence degree belongs are determined as abnormal stress reasons of the second abnormal stress regions.

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

[0045] Based on stress influence information of the first abnormal stress regions and a range proportion of the first abnormal stress regions in the target recovery position range, abnormal severity of the first abnormal stress regions is identified, and based on abnormal stress reasons of the first abnormal stress regions, each initial stress solution strategy of the first abnormal stress regions is queried in a database.

[0046] Based on the abnormal severity of the first abnormal stress regions, an initial stress solution strategy suitable for the abnormal severity is selected from the initial stress solution strategies as a stress solution strategy of the first abnormal stress regions.

[0047] Based on the abnormal reason corresponding to the second abnormal stress area, each initial stress solving strategy of the second abnormal stress area is queried in a database, and based on the range coincidence degree corresponding to the second abnormal stress area, an initial stress solving strategy applicable to the range coincidence degree is screened from each initial stress solving strategy as a stress solving strategy of the second abnormal stress area.

[0048] Optionally, the collection module is specifically configured to:

[0049] Based on the stress detection data of the user, position information of each abnormal stress point of each abnormal stress area is identified, and based on the position information of each abnormal stress point of each abnormal stress area, an abnormal stress three-dimensional graph of each abnormal stress area is constructed.

[0050] Based on the stress solving strategy of each abnormal stress area, a solving flowchart of the stress solving strategy is generated, and based on the solving flowchart of each stress solving strategy and the abnormal stress three-dimensional graph of each abnormal stress area, a three-dimensional stress solving flowchart is constructed, and each three-dimensional stress solving flowchart is taken as a stress solving scheme of each abnormal stress area.

[0051] In a third aspect, the present application provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the method of any one of the first aspect when executing the computer program.

[0052] In a fourth aspect, the present application provides a computer readable storage medium. A computer program is stored thereon, and the computer program implements the steps of the method of any one of the first aspect when executed by a processor.

[0053] In a fifth aspect, the present application provides a computer program product. The computer program product includes a computer program, and the computer program implements the steps of the method of any one of the first aspect when executed by a processor.

[0054] The aforementioned intelligent splint stress monitoring method, system, and computer equipment acquire stress detection data of the splint and the corresponding user recovery area of ​​the splint. Based on the stress detection data, they identify abnormal stress areas of the splint. Based on the user recovery area and the abnormal stress areas of the splint, they identify the cause of abnormal stress in each abnormal stress area and query a stress resolution strategy for each abnormal stress area in a database. Based on the user's stress detection data and the stress resolution strategy for each abnormal stress area, they generate a stress solution for each abnormal stress area and re-acquire new stress detection data of the splint. If the new stress detection data does not meet the normal stress data, the new stress detection data replaces the old stress detection data, and the process returns to the step of identifying abnormal stress areas of the splint based on the stress detection data, until the new stress detection data meets the normal stress data. This solution intelligently detects the stress conditions in each area of ​​the splint, thereby filtering out abnormal stress areas. Then, by identifying the regional correlation information between abnormal stress areas and user recovery areas, the causes of abnormal stress in each abnormal stress area are analyzed. This allows for the querying of stress resolution strategies for each abnormal stress area, generating stress solutions for each area and avoiding the need for periodic disassembly and reassembly checks. Furthermore, real-time detection and real-time generation of stress solutions improve the timeliness of monitoring and handling of abnormal stress areas, thereby enhancing the timeliness of patient monitoring. Attached Figure Description

[0055] Figure 1 This is a flowchart illustrating the intelligent clamping plate stress monitoring method in one embodiment;

[0056] Figure 2 This is a structural block diagram of an intelligent clamping plate stress monitoring device in one embodiment;

[0057] Figure 3 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0059] The intelligent splint stress monitoring method provided by the embodiments of the present application can be applied to the corresponding application environment of splint stress monitoring. The method can be applied to a terminal, a server, or a system including a terminal and a server, and is realized through the interaction of the terminal and the server. The terminal can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and the like. The terminal detects the stress conditions of each region of the splint, thereby screening the abnormal stress regions. Then, the region association information between the abnormal stress regions and the user recovery regions is identified, the abnormal stress causes of each abnormal stress region are analyzed, the stress solving strategies of each abnormal stress region are queried, the stress solving solutions of each abnormal stress region are generated, and the inspection process of regular disassembly and reinstallation is avoided. Then, the stress solving solutions are generated in real time through real-time detection, the timeliness of monitoring the abnormal stress regions and the timeliness of processing are improved, and the timeliness of monitoring the user's illness is improved.

[0060] In one embodiment, as shown in Figure 1 An intelligent splint stress monitoring method is provided. The method is described by taking the terminal as an example and includes the following steps.

[0061] In step S101, stress detection data of a splint and user recovery regions corresponding to the splint are obtained, and based on the stress detection data, abnormal stress regions of the splint are identified.

[0062] In the present fact instance, the terminal obtains stress data of each position point of the splint in the current compression stress through the stress sensors built in each position of the splint, and obtains the stress monitoring data of the splint. Then, the terminal obtains the user recovery regions of the user in response to the user's uploading operation. The recovery regions are the limb regions of the user, such as the forearm region, the upper arm region, the lower leg region, the thigh region, and the like. Then, the terminal identifies the stress regions with abnormal compression stress in the splint position range based on the stress detection data, and obtains the abnormal stress regions of the splint. The specific identification process will be described in detail later.

[0063] In step S102, the abnormal stress causes of each abnormal stress region are identified based on the user recovery regions corresponding to the splint and the abnormal stress regions of the splint, and the stress solving strategies of each abnormal stress region are queried in the database based on the abnormal stress causes of each abnormal stress region.

[0064] Based on the user recovery area corresponding to the splint and the abnormal stress area of the splint, the abnormal stress reason of each abnormal stress area is identified, and based on the abnormal stress reason of each abnormal stress area, the stress solving strategy of each abnormal stress area is queried in the database. The abnormal stress reason is divided into the abnormal stress reason of the target recovery area and the abnormal stress reason of the non-target recovery area. The target recovery area is the user's sick area, such as the fracture area, the bone fracture area, the trauma surgery recovery area, etc. The abnormal stress reason of the target recovery area includes the area swelling reason caused by secondary fracture, area cyst, splint misplacement, splint abnormal fixation, etc. The abnormal stress reason of the non-target recovery area includes splint misplacement, splint abnormal fixation, splint damage, splint abnormality, etc. The specific stress abnormality identification process will be described in detail. The stress solving strategy includes but is not limited to splint adjustment, splint re-fixing, target recovery area re-treatment, etc.

[0065] In step S103, based on the stress detection data of the user and the stress solving strategy of each abnormal stress area, the stress solving scheme of each abnormal stress area is generated, and new stress detection data of the splint is re-collected.

[0066] In this embodiment, the terminal generates the stress solving scheme of each abnormal stress area based on the stress detection data of the user and the stress solving strategy of each abnormal stress area, and re-collects new stress detection data of the splint. The stress solving strategy is a visual stress solving three-dimensional graph, which is used to assist medical personnel in treating the splint or the target recovery area of the user.

[0067] In step S104, in the case that the new stress detection data does not satisfy the normal stress data, the new stress detection data is replaced by the stress detection data, and the step of identifying the abnormal stress area of the splint based on the stress detection data is returned to execute until the new stress detection data satisfies the normal stress data.

[0068] In this embodiment, the terminal replaces the new stress detection data with the stress detection data in the case that the new stress detection data does not satisfy the normal stress data, and returns to execute the step of identifying the abnormal stress area of the splint based on the stress detection data until the new stress detection data satisfies the normal stress data.

[0069] Based on the above scheme, by identifying the area association information between the abnormal stress area and the user recovery area, the abnormal stress reason of each abnormal stress area is analyzed, so as to query the stress solving strategy of each abnormal stress area, and the stress solving scheme of each abnormal stress area is generated, thereby avoiding the inspection process of regular disassembly and reinstallation. Then, through real-time detection, the stress solving scheme is generated in real time, the monitoring timeliness of the abnormal stress area is improved, and the processing timeliness is improved, thereby improving the patient monitoring timeliness of the user.

[0070] Optionally, based on the stress detection data, the abnormal stress areas of the splint are identified, including: based on the stress detection data, stress distribution information of the splint is generated, and normal stress distribution information of the splint is collected; based on the normal stress distribution information and the stress distribution information, position information of each abnormal stress point of the splint is identified, and the position information of each abnormal stress point is clustered to obtain each abnormal stress area of the splint.

[0071] In this embodiment, the terminal generates stress distribution information of the splint based on stress detection data, and collects normal stress distribution information of the splint. The stress distribution information is a stress distribution three-dimensional graph corresponding to a splint three-dimensional structure graph constructed by three-dimensional position information of each stress sensor on the splint. The terminal identifies position information of each abnormal stress point of the splint based on the normal stress distribution information and the stress distribution information, and clusters the position information of each abnormal stress point to obtain each abnormal stress area of the splint. The abnormal stress point is a stress point corresponding to position information whose normal stress and detected stress have a stress difference greater than a preset stress difference domain value of the terminal.

[0072] Based on the above scheme, after stress distribution is performed on the stress monitoring data, the position information of each abnormal stress point of the splint is identified, thereby identifying each abnormal stress area of the splint, and the accuracy of identifying each abnormal stress area is improved.

[0073] Optionally, based on the user recovery area corresponding to the splint and the abnormal stress area of the splint, the abnormal stress reason of each abnormal stress area is identified, including: obtaining a target recovery position range of the user in the user recovery area, and screening a first abnormal stress area containing position information in the target recovery position range in each abnormal stress area; arranging each first abnormal stress area in three-dimensional distribution to obtain first abnormal three-dimensional distribution information, and arranging each non-first abnormal stress area in three-dimensional distribution to obtain second abnormal three-dimensional distribution information; extracting a first distribution feature of the first abnormal three-dimensional distribution information and a second distribution feature of the second abnormal three-dimensional distribution information through a three-dimensional feature extraction algorithm, and determining the abnormal stress reason of the first abnormal stress area and the abnormal stress reason of the second abnormal stress area based on the first distribution feature and the second distribution feature.

[0074] In this embodiment, the terminal obtains the target recovery position range of the user in the user recovery area, and in each abnormal stress area, screens a first abnormal stress area containing position information in the target recovery position range. Then, the terminal arranges each first abnormal stress area in three-dimensional distribution to obtain first abnormal three-dimensional distribution information, and arranges each non-first abnormal stress area in three-dimensional distribution to obtain second abnormal three-dimensional distribution information. The first abnormal stress area containing position information in the target recovery position range can be an abnormal stress area contained in the target recovery position range, can be an abnormal stress area intersecting the target recovery position range, and can also be an abnormal stress area containing all target recovery position ranges.

[0075] Then, the terminal extracts a first distribution feature of the first abnormal three-dimensional distribution information and a second distribution feature of the second abnormal three-dimensional distribution information through a three-dimensional feature extraction algorithm, and determines the abnormal stress reasons of the first abnormal stress area and the abnormal stress reasons of the second abnormal stress area based on the first distribution feature and the second distribution feature. The three-dimensional feature extraction algorithm is a feature extraction algorithm of a three-dimensional model in a python program, which is ISS (Intrinsic Shape Signatures). The distribution feature is used to represent the distribution of each abnormal stress area in the splint, for example, the stress feature is distributed in the target recovery position range, distributed in the non-target recovery position range, distributed on both sides of the recovery position range, distributed in front of the recovery position range, and distributed in the back of the recovery position range. The specific determination process of the abnormal stress reason will be described in detail later.

[0076] Based on the above scheme, the accuracy of identifying the abnormal stress reason is improved through regional abnormal stress reason identification.

[0077] Optionally, based on the first distribution feature and the second distribution feature, determining the abnormal stress reasons of the first abnormal stress area and the abnormal stress reasons of the second abnormal stress area comprises: based on the first distribution feature, identifying the stress feature of the first abnormal stress area in the target recovery position range, and collecting the recovery processing mode of the user in the target recovery position range and the recovery processing angle of the user in the target recovery position range; based on the stress feature, the recovery processing mode, and the recovery processing angle, identifying the stress influence information corresponding to the stress feature, and querying the abnormal stress reason corresponding to the stress influence information in the database to obtain the abnormal stress reason of the first abnormal stress area; identifying the range coincidence degree between the distribution range of the second distribution feature and the splint support range of the splint, and determining the abnormal stress reason of the second abnormal stress area corresponding to the abnormal reason of the coincidence range to which the range coincidence degree belongs.

[0078] In this embodiment, the terminal identifies the stress feature of the first abnormal stress region in the target recovery position range based on the first distribution feature, and collects the recovery processing mode of the user in the target recovery position range and the recovery processing angle of the user in the target recovery position range. The recovery processing mode includes but is not limited to steel nail fixing, dislocation recovery, suture, etc. The recovery processing angle is the position angle of the user's limb fracture, dislocation, and suture position relative to the limb forward direction. The limb forward direction is the orientation of each limb of the body when the person stands upright.

[0079] Then, the terminal identifies the stress influence information corresponding to the stress feature based on the stress feature, the recovery processing mode, and the recovery processing angle, and queries the abnormal stress reason corresponding to the stress influence information in the database to obtain the abnormal stress reason of the first abnormal stress region. The stress influence information is used to represent the coincidence degree between the stress feature of the user and the range of the recovery processing mode and the recovery processing angle of the user. The higher the coincidence degree is, the higher the abnormality possibility of the target recovery region of the user is, and the lower the possibility of the splint abnormality is. The abnormality possibility of the target recovery region, for example, includes fracture recovery abnormality, secondary dislocation, wound swelling caused by wound line collapse, and wound internal abscess. The coincidence degree is lower, the abnormality possibility of the target recovery region of the user is lower, and the possibility of the splint abnormality is higher. The possibility of the splint abnormality, for example, includes splint dislocation, splint damage, splint structure abnormality, splint loosening, and splint over-tightening.

[0080] Then, the terminal identifies the range coincidence degree between the distribution range of the second distribution feature and the splint support range of the splint, and determines the abnormal stress reason of the second abnormal stress region corresponding to the abnormal reason of the range coincidence degree belonging to the coincidence degree range. Wherein,

[0081] Based on the above scheme, the abnormal reason is screened by identifying the range coincidence degree, thereby improving the accuracy of identifying the abnormal reason.

[0082] Optionally, based on the abnormal stress reason of each abnormal stress region, the terminal queries the stress solution strategy of each abnormal stress region in the database, including: based on the stress influence information of the first abnormal stress region and the range proportion of the first abnormal stress region in the target recovery position range, identifying the abnormal severity of the first abnormal stress region, and based on the abnormal stress reason of the first abnormal stress region, querying the initial stress solution strategy of the first abnormal stress region in the database; based on the abnormal severity of the first abnormal stress region, screening the initial stress solution strategy suitable for the abnormal severity from the initial stress solution strategies as the stress solution strategy of the first abnormal stress region; based on the abnormal reason corresponding to the second abnormal stress region, querying the initial stress solution strategy of the second abnormal stress region in the database, and based on the range coincidence degree corresponding to the second abnormal stress region, screening the initial stress solution strategy suitable for the range coincidence degree from the initial stress solution strategies as the stress solution strategy of the second abnormal stress region.

[0083] In this embodiment, the terminal identifies the abnormal severity of the first abnormal stress region based on the stress influence information of the first abnormal stress region and the range proportion of the first abnormal stress region in the target recovery position range, and queries the initial stress solution strategy of the first abnormal stress region in the database based on the abnormal stress reason of the first abnormal stress region. Then, the terminal screens the initial stress solution strategy suitable for the abnormal severity from the initial stress solution strategies as the stress solution strategy of the first abnormal stress region based on the abnormal severity of the first abnormal stress region. Then, the terminal queries the initial stress solution strategy of the second abnormal stress region in the database based on the abnormal reason corresponding to the second abnormal stress region, and screens the initial stress solution strategy suitable for the range coincidence degree from the initial stress solution strategies as the stress solution strategy of the second abnormal stress region based on the range coincidence degree corresponding to the second abnormal stress region.

[0084] Based on the above scheme, the stress solution strategy is screened from two angles of abnormal stress reason and abnormal severity, which improves the screening accuracy of the stress solution strategy.

[0085] Optionally, based on the stress detection data of the user and the stress solution strategy of each abnormal stress area, a stress solution of each abnormal stress area is generated, including: based on the stress detection data of the user, identifying position information of each abnormal stress point of each abnormal stress area, and based on the position information of each abnormal stress point of each abnormal stress area, constructing an abnormal stress three-dimensional graph of each abnormal stress area; based on the stress solution strategy of each abnormal stress area, generating a solution flowchart of the stress solution strategy, and based on the solution flowchart of each stress solution strategy and the abnormal stress three-dimensional graph of each abnormal stress area, constructing a three-dimensional stress solution flowchart, and taking each three-dimensional stress solution flowchart as the stress solution of each abnormal stress area.

[0086] In this embodiment, the terminal identifies position information of each abnormal stress point of each abnormal stress area based on the stress detection data of the user, and constructs an abnormal stress three-dimensional graph of each abnormal stress area based on the position information of each abnormal stress point of each abnormal stress area. Then, the terminal generates a solution flowchart of the stress solution strategy based on the stress solution strategy of each abnormal stress area, and constructs a three-dimensional stress solution flowchart based on the solution flowchart of each stress solution strategy and the abnormal stress three-dimensional graph of each abnormal stress area, and takes each three-dimensional stress solution flowchart as the stress solution of each abnormal stress area.

[0087] Based on the above scheme, by generating a visual three-dimensional stress solution flowchart, the efficiency of treatment adjustment of medical staff, rescue of target recovery area, and adjustment of splint replacement is facilitated.

[0088] It should be understood that although each step in the flowchart involved in each embodiment as described above is displayed in sequence according to the direction of the arrow, these steps are not necessarily executed in sequence according to the direction of the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or steps or stages in other steps.

[0089] Based on the same inventive concept, the embodiments of the present application also provide an intelligent splint stress monitoring device for implementing the intelligent splint stress monitoring method described above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more intelligent splint stress monitoring device embodiments provided below can be referred to the limitations of the intelligent splint stress monitoring method in the foregoing, which will not be described here again.

[0090] In one embodiment, as shown in Figure 2 An intelligent splint stress monitoring device is provided, comprising: an acquisition module 210, a query module 220, a collection module 230 and an iteration module 240, wherein:

[0091] The acquisition module 210 is configured to acquire stress detection data of a splint and a user recovery area corresponding to the splint, and identify abnormal stress areas of the splint based on the stress detection data.

[0092] The query module 220 is configured to identify an abnormal stress reason of each abnormal stress area based on the user recovery area corresponding to the splint and the abnormal stress areas of the splint, and query a stress solution strategy of each abnormal stress area in a database based on the abnormal stress reason of each abnormal stress area.

[0093] The collection module 230 is configured to generate a stress solution of each abnormal stress area based on the stress detection data of the user and the stress solution strategy of each abnormal stress area, and re-collect new stress detection data of the splint.

[0094] The iteration module 240 is configured to replace the stress detection data with the new stress detection data in a case where the new stress detection data does not meet normal stress data, and return to perform the step of identifying the abnormal stress areas of the splint based on the stress detection data until the new stress detection data meets the normal stress data.

[0095] Optionally, the acquisition module 210 is specifically configured to:

[0096] Generate stress distribution information of the splint based on the stress detection data, and collect normal stress distribution information of the splint.

[0097] Identify position information of each abnormal stress point of the splint based on the normal stress distribution information and the stress distribution information, and perform clustering processing on the position information of each abnormal stress point to obtain each abnormal stress area of the splint.

[0098] Optionally, the query module 220 is specifically configured to:

[0099] obtain a target recovery position range of the user in a user recovery area, and screen a first abnormal stress area containing position information in the target recovery position range from each of the abnormal stress areas;

[0100] arrange each of the first abnormal stress areas in a three-dimensional distribution to obtain first abnormal three-dimensional distribution information, and arrange each of the non-first abnormal stress areas in a three-dimensional distribution to obtain second abnormal three-dimensional distribution information;

[0101] extract a first distribution feature of the first abnormal three-dimensional distribution information and a second distribution feature of the second abnormal three-dimensional distribution information respectively by a three-dimensional feature extraction algorithm, and determine an abnormal stress reason of the first abnormal stress area and an abnormal stress reason of the second abnormal stress area based on the first distribution feature and the second distribution feature.

[0102] Optionally, the query module 220 is specifically used for:

[0103] identify a stress feature of the first abnormal stress area in the target recovery position range based on the first distribution feature, and collect a recovery processing manner of the user in the target recovery position range and a recovery processing angle of the user in the target recovery position range;

[0104] identify stress influence information corresponding to the stress feature based on the stress feature, the recovery processing manner, and the recovery processing angle, and query an abnormal stress reason corresponding to the stress influence information in a database to obtain the abnormal stress reason of the first abnormal stress area;

[0105] identify a range coincidence degree between a distribution range of the second distribution feature and a splint support range of the splint, and determine an abnormal stress reason of the second abnormal stress area corresponding to an abnormal reason of a range coincidence degree range to which the range coincidence degree belongs.

[0106] Optionally, the query module 220 is specifically used for:

[0107] identify an abnormal severity of the first abnormal stress area based on stress influence information of the first abnormal stress area and a range proportion of the first abnormal stress area in the target recovery position range, and query each initial stress solution strategy of the first abnormal stress area in a database based on the abnormal stress reason of the first abnormal stress area;

[0108] screen an initial stress solution strategy suitable for the abnormal severity from each of the initial stress solution strategies based on the abnormal severity of the first abnormal stress area, as a stress solution strategy of the first abnormal stress area;

[0109] Based on the abnormal reason corresponding to the second abnormal stress area, in the database, the initial stress solving strategies of the second abnormal stress area are queried, and based on the range coincidence degree corresponding to the second abnormal stress area, in the initial stress solving strategies, the initial stress solving strategies applicable to the range coincidence degree are screened as the stress solving strategies of the second abnormal stress area.

[0110] Optionally, the collection module 230 is specifically configured to:

[0111] Based on the stress detection data of the user, the position information of each abnormal stress point of each abnormal stress area is identified, and based on the position information of each abnormal stress point of each abnormal stress area, an abnormal stress three-dimensional graph of each abnormal stress area is constructed.

[0112] Based on the stress solving strategies of each abnormal stress area, a solving flowchart of the stress solving strategies is generated, and based on the solving flowchart of each stress solving strategy and the abnormal stress three-dimensional graph of each abnormal stress area, a three-dimensional stress solving flowchart is constructed, and each three-dimensional stress solving flowchart is taken as a stress solving scheme of each abnormal stress area.

[0113] The above-mentioned various modules in the intelligent splint stress monitoring device can be all or part realized by software, hardware and combinations thereof. The above-mentioned various modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to call and execute the operations corresponding to the above-mentioned various modules by the processor.

[0114] In one embodiment, a computer device is provided, which can be a terminal, and its internal structure diagram can be as shown in Figure 3 The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. 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 operating system and the computer program in the non-volatile storage medium to run. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be realized through WIFI, mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by the processor to realize an intelligent splint stress monitoring method. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or can be a key, trackball or touchpad arranged on the shell of the computer device, or can be an external keyboard, touchpad or mouse, etc.

[0115] Those skilled in the art can understand that, Figure 3 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0116] In one embodiment, a computer device is provided, comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps of the method of any one of the first aspect when executing the computer program.

[0117] In one embodiment, a computer readable storage medium is provided, storing a computer program, and the computer program implementing the steps of the method of any one of the first aspect when executed by a processor.

[0118] In one embodiment, a computer program product is provided, comprising a computer program, and the computer program implementing the steps of the method of any one of the first aspect when executed by a processor.

[0119] 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 for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties.

[0120] 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. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to 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 (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric memory (Ferroelectric Random Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0121] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0122] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for monitoring stress in intelligent clamping plates, characterized in that, The method includes: Acquire stress detection data of the clamp and the user recovery area corresponding to the clamp, and identify each abnormal stress area of ​​the clamp based on the stress detection data; Based on the user recovery area corresponding to the clamp and the abnormal stress area of ​​the clamp, identify the abnormal stress cause of each abnormal stress area, and based on the abnormal stress cause of each abnormal stress area, query the database for stress resolution strategies for each abnormal stress area. Based on the user's stress detection data and the stress solution strategy for each abnormal stress area, a stress solution for each abnormal stress area is generated, and new stress detection data for the clamping plate is collected again. If the new stress detection data does not meet the normal stress data, the new stress detection data replaces the existing stress detection data, and the process returns to the step of identifying the abnormal stress areas of the clamping plate based on the stress detection data, until the new stress detection data meets the normal stress data.

2. The method according to claim 1, characterized in that, The step of identifying abnormal stress areas of the clamping plate based on the stress detection data includes: Based on the stress detection data, stress distribution information of the clamping plate is generated, and normal stress distribution information of the clamping plate is collected. Based on the normal stress distribution information and the stress distribution information, the location information of each abnormal stress point of the clamping plate is identified, and the location information of each abnormal stress point is clustered to obtain each abnormal stress region of the clamping plate.

3. The method according to claim 2, characterized in that, The step of identifying the cause of abnormal stress in each abnormal stress area based on the user recovery area corresponding to the clamp and the abnormal stress area of ​​the clamp includes: Obtain the target recovery location range of the user in the user recovery area, and in each of the abnormal force areas, filter out the first abnormal force area that contains the location information in the target recovery location range; The first abnormal stress regions are arranged in a three-dimensional distribution to obtain the first abnormal three-dimensional distribution information, and the non-first abnormal stress regions are arranged in a three-dimensional distribution to obtain the second abnormal three-dimensional distribution information. Using a three-dimensional feature extraction algorithm, the first distribution feature of the first abnormal three-dimensional distribution information and the second distribution feature of the second abnormal three-dimensional distribution information are extracted respectively. Based on the first distribution feature and the second distribution feature, the abnormal force cause of the first abnormal force area and the abnormal force cause of the second abnormal force area are determined.

4. The method according to claim 3, characterized in that, The step of determining the cause of abnormal stress in the first abnormal stress region and the cause of abnormal stress in the second abnormal stress region based on the first distribution characteristics and the second distribution characteristics includes: Based on the first distribution characteristics, the force characteristics of the first abnormal force area in the target recovery position range are identified, and the recovery processing method of the user in the target recovery position range and the recovery processing angle of the user in the target recovery position range are collected. Based on the force characteristics, the recovery processing method, and the recovery processing angle, the force influence information corresponding to the force characteristics is identified, and the abnormal force cause corresponding to the force influence information is queried in the database to obtain the abnormal force cause of the first abnormal force area. Identify the distribution range of the second distribution feature and the degree of overlap between the distribution range and the clamping support range of the clamping plate, and determine the abnormal force cause of the second abnormal force area by identifying the abnormal cause corresponding to the overlap range to which the overlap range belongs.

5. The method according to claim 4, characterized in that, Based on the cause of abnormal stress in each abnormal stress region, the system queries the database for stress resolution strategies for each abnormal stress region, including: Based on the force impact information of the first abnormal force area and the proportion of the first abnormal force area in the target recovery location range, the severity of the abnormality of the first abnormal force area is identified, and based on the cause of the abnormal force of the first abnormal force area, each initial force resolution strategy of the first abnormal force area is queried in the database. Based on the severity of the first abnormal stress region, among the initial stress resolution strategies, the initial stress resolution strategy that is applicable to the severity of the abnormality is selected as the stress resolution strategy for the first abnormal stress region. Based on the cause of the abnormality corresponding to the second abnormal stress area, the database is queried for each initial stress resolution strategy for the second abnormal stress area. Based on the range overlap degree corresponding to the second abnormal stress area, the initial stress resolution strategy that is applicable to the range overlap degree is selected from each initial stress resolution strategy and used as the stress resolution strategy for the second abnormal stress area.

6. The method according to claim 2, characterized in that, The process of generating a stress solution for each abnormal stress area based on the user's stress detection data and the stress resolution strategy for each abnormal stress area includes: Based on the user's stress detection data, the location information of each abnormal stress point in each abnormal stress region is identified, and based on the location information of each abnormal stress point in each abnormal stress region, a three-dimensional map of the abnormal stress in each abnormal stress region is constructed. Based on the stress resolution strategies for each of the abnormal stress regions, a solution flowchart for each stress resolution strategy is generated. Based on the solution flowcharts for each stress resolution strategy and the abnormal stress three-dimensional diagrams for each of the abnormal stress regions, a three-dimensional stress resolution flowchart is constructed. Each of the three-dimensional stress resolution flowcharts is used as the stress solution for each of the abnormal stress regions.

7. An intelligent clamping plate stress monitoring device, characterized in that, The device includes: The acquisition module is used to acquire stress detection data of the clamping plate and the user recovery area corresponding to the clamping plate, and to identify each abnormal stress area of ​​the clamping plate based on the stress detection data. The query module is used to identify the cause of abnormal stress in each abnormal stress area based on the user recovery area corresponding to the clamp and the abnormal stress area of ​​the clamp, and to query the database for stress resolution strategies for each abnormal stress area based on the cause of abnormal stress in each abnormal stress area. The acquisition module is used to generate a stress solution for each abnormal stress area based on the user's stress detection data and the stress solution strategy for each abnormal stress area, and to re-acquire new stress detection data for the clamping plate. An iterative module is used to replace the stress detection data with the new stress detection data when the new stress detection data does not meet the normal stress data, and return to the step of identifying each abnormal stress area of ​​the clamping plate based on the stress detection data, until the new stress detection data meets the normal stress data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

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

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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