Data processing method and system

Abstract

Embodiments of the present specification provide a data processing method and system, wherein the data processing method comprises: receiving a part determination instruction submitted by a movement disorder user in a user part map for a target part region in a movement management interface; analyzing the part determination instruction, obtaining movement disorder part data associated with the target part region, and determining user data of the movement disorder user; determining at least one movement project matched with the movement disorder user in a movement project library based on the user data and the movement disorder part data, and generating a movement plan based on the at least one movement project.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of data processing technology, and in particular to data processing methods and systems. Background Technology

[0002] In the field of sports and health management, existing exercise recommendation systems are mostly designed for the general population, typically generating standardized training plans based on common health indicators (such as age, weight, and heart rate), lacking the ability to personalize them for users with mobility impairments. For individuals with limited function in specific body parts due to injury, disability, or chronic disease, blindly applying conventional exercise programs not only fails to achieve the desired training effect but may also cause secondary injuries. Although some rehabilitation platforms offer customized guidance, they rely on manual assessment by professional physicians, which is cumbersome, has limited coverage, and users often struggle to independently and intuitively express the location and severity of their impairments. Therefore, they cannot meet the exercise needs of people with disabilities and injuries. Thus, a more effective data processing method is urgently needed to address these issues. Summary of the Invention

[0003] In view of the above, embodiments of this specification provide a data processing method. One or more embodiments of this specification also relate to a data processing system, a data processing apparatus, a computing device, a computer-readable storage medium, and a computer program product, to address the technical deficiencies existing in the prior art.

[0004] According to a first aspect of the embodiments of this specification, a data processing method is provided, comprising: Receive location determination instructions submitted by users with movement disorders in the sports management interface for the target location area in the user's location map; The command to determine the location is parsed to obtain the data of the movement disorder location associated with the target location area, and the user data of the movement disorder user is determined. Based on the user data and the data on the location of the movement disorder, at least one sport that matches the user with the movement disorder is determined from the sports program library, and a sports plan is generated based on the at least one sport.

[0005] Optionally, receiving the location determination instruction submitted by the user with a movement disorder in the sports management interface for a target location area in the user's location map includes: In response to the movement disorder user's command to select a target area in the user body part diagram in the sports management interface, the target body part editing page is displayed to the movement disorder user; Receive the movement disorder information determined by the user with the movement disorder in the target body part editing page for the editing area, and convert the movement disorder information into target movement disorder information; Receive the location determination instruction submitted by the user with the movement disorder regarding the target movement disorder information.

[0006] Optionally, parsing the location determination instruction to obtain motion obstacle location data associated with the target location region includes: The command for determining the location is parsed to obtain the movement disorder level, movement disorder cause, and movement disorder description data associated with the target location area. The movement disorder level, the cause of the movement disorder, and / or the movement disorder description data are used as the movement disorder location data.

[0007] Optionally, determining at least one sport matching the user with a movement disorder from the sports program database based on the user data and the movement disorder location data includes: The user data and the data on the movement impairment site are input into the data processing model to obtain the movement ability data of the user with the movement impairment. Based on the athletic ability data, at least one sport is determined from the sports program database that matches the user with the athletic disability.

[0008] Optionally, after generating the exercise plan based on the at least one sport, the method further includes: In response to the user with a mobility impairment confirming the target exercise in the exercise plan, a target exercise page is displayed to the user with a mobility impairment. The target exercise page includes a video playback area and an exercise prompt area. The system receives motion control commands submitted by the user with a mobility impairment for the video playback area, and plays the motion video corresponding to the target sport in the video playback area based on the motion control commands.

[0009] Optionally, after generating the exercise plan based on the at least one sport, the method further includes: Determine the exercise record data obtained by the user with the movement disorder when performing the exercise plan, as well as the behavioral parameters associated with the exercise plan; In the motion recording data, determine the motion data corresponding to the at least one sport, and determine the motion parameters corresponding to the motion data; The exercise health data corresponding to the exercise plan is generated based on the behavioral parameters and the exercise parameters corresponding to the at least one exercise.

[0010] Optionally, generating the exercise health data corresponding to the exercise plan based on the behavioral parameters and the exercise parameters corresponding to the at least one exercise item includes: Based on the sports and health data, sports and health analysis information is generated, and based on the sports and health analysis information, a sports and health view is generated; Based on the sports and health view, predict the sports and health trend information of the users with sports disabilities.

[0011] Optionally, receiving the location determination instruction submitted by the user with a movement disorder in the sports management interface for a target location area in the user's location map includes: The system identifies hand control commands, eye control commands, and / or voice control commands submitted by the user with the movement disorder in the movement management interface for the target area in the user body part diagram. The hand control commands, eye control commands, and / or voice control commands are used as the location determination commands.

[0012] According to a second aspect of the embodiments of this specification, a data processing system is provided, including a client and a server, comprising: The client is used to determine the part determination instruction submitted by the user with a movement disorder in the sports management interface for the target part area in the user part diagram, and send the part determination instruction to the server. The server is used to parse the location determination instruction, obtain the sports obstacle location data associated with the target location area, and determine the user data of the sports obstacle user; based on the user data and the sports obstacle location data, determine at least one sports program matching the sports obstacle user in the sports program library, generate a sports plan based on the at least one sports program, and feed the sports plan back to the client.

[0013] According to a third aspect of the embodiments of this specification, a data processing apparatus is provided, comprising: The receiving module is configured to receive location determination instructions submitted by users with mobility impairments in the sports management interface for target location areas in the user's location map; The parsing module is configured to parse the location determination command, obtain the motion obstacle location data associated with the target location area, and determine the user data of the motion obstacle user; The generation module is configured to determine at least one sport that matches the user with the movement disorder in the sports program library based on the user data and the movement disorder location data, and to generate a sports plan based on the at least one sport.

[0014] According to a fourth aspect of the embodiments of this specification, a computing device is provided, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the above-described data processing method.

[0015] According to a fifth aspect of the embodiments of this specification, a computer-readable storage medium is provided that stores computer-executable instructions, which, when executed by a processor, implement the steps of the data processing method described above.

[0016] According to a sixth aspect of the embodiments of this specification, a computer program product is provided, including a computer program or instructions that, when executed by a processor, implement the steps of the data processing method described above.

[0017] This specification provides a data processing method in one embodiment that receives a body part determination instruction submitted by a user with a disability in a body part diagram within a sports management interface, targeting a specific body part area. The instruction is parsed to obtain the disability body part data associated with the target body part area, and the user's data is determined. Based on the user data and the disability body part data, at least one sports activity matching the user with a disability is identified from a sports activity library, and an exercise plan is generated based on this at least one sports activity. Users with disabilities can submit body part determination instructions for a target body part area in a body part diagram within the sports management interface, allowing them to select the disability body part as needed. Exercise plans are created for users with disabilities based on these instructions, enabling them to engage in appropriate exercise. The methods can recommend more suitable and healthier exercise methods based on the user's degree of disability, thus meeting the exercise needs of people with disabilities and injuries. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a data processing method provided in one embodiment of this specification; Figure 2 This is a schematic diagram of a motion management interface for a data processing method provided in one embodiment of this specification; Figure 3 This is a schematic diagram of the target area editing page of a data processing method provided in one embodiment of this specification; Figure 4 This is a schematic diagram illustrating the determination of a location in a data processing method provided in one embodiment of this specification; Figure 5 This is a sports event demonstration diagram illustrating a data processing method provided in one embodiment of this specification; Figure 6 This is a flowchart illustrating the processing procedure of a data processing method provided in one embodiment of this specification. Figure 7This is a schematic diagram of the structure of a data processing system provided in one embodiment of this specification; Figure 8 This is a schematic diagram of the structure of a data processing apparatus provided in one embodiment of this specification; Figure 9 This is a structural block diagram of a computing device provided in one embodiment of this specification. Detailed Implementation

[0019] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0020] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of this specification. The singular forms “a,” “described,” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0021] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0022] Furthermore, 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 one or more embodiments of this specification are all information and data authorized by the user or fully authorized by all parties. Moreover, the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0023] First, the terms and concepts used in one or more embodiments of this specification will be explained.

[0024] Large Language Model (LLM) is a deep learning model in the field of artificial intelligence. It is mainly based on the Transformer architecture and is pre-trained on massive amounts of text data to enable it to understand, generate and process human language.

[0025] This specification provides a data processing method, and also relates to a data processing system, a data processing apparatus, a computing device, a computer-readable storage medium, and a computer program product, which will be described in detail in the following embodiments.

[0026] See Figure 1 , Figure 1 A flowchart of a data processing method according to an embodiment of this specification is shown, which specifically includes the following steps.

[0027] Step 102: Receive the location determination instruction submitted by the user with a movement disorder in the movement management interface for the target location area in the user's location map.

[0028] Specifically, users with movement disorders can be people with disabilities, or users with physical injuries or in a sub-healthy state. The movement management interface is a visual interface provided to users by the movement application. This interface provides a user body part diagram, which can be a diagram of multiple body parts, including but not limited to the head, shoulders, arms, chest, abdomen, and legs, with each body part corresponding to an area in the diagram. The target body part area can be a region determined by the user based on their restricted movement area within the user body part diagram. Users can select the target body part corresponding to the target body part area by touching it, thus designating it as the movement disorder area or restricted movement area. The body part determination command is a computer instruction sent by the user to the server to determine the target body part corresponding to the target body part area. The body part determination command carries information about the target body part area, including but not limited to the target body part name and the movement disorder level of the target body part.

[0029] Based on this, the system receives location confirmation commands submitted by users with disabilities in the user location map within the sports management interface, specifying the target location area. The sports management interface is a visual interface provided for users with disabilities, allowing them to intuitively view the user location map corresponding to their body parts. After identifying the affected area in the user location map, users can submit location confirmation commands for the corresponding target area.

[0030] In practical applications, the selection of a target area can be achieved through user touch operation, or by selecting the target area from a user body part map using hand, eye, or voice control methods, and then submitting a confirmation command. Hand control involves the user submitting a command via hand gestures. The terminal device with the fitness application installed activates its camera, identifies the user's hand position, and treats hand movement as cursor movement; a clenched fist triggers a click, thus achieving hand control. Eye control involves facial recognition and tracking of the user; eye movements trigger clicks, and changes in eye movement speed control page zooming. Voice control uses natural language recognition to determine the user's voice commands.

[0031] Furthermore, a sports application is provided for users with movement disorders. Users can identify and edit their movement disorders within the application's sports management interface. When a user has a need for exercise, they can input their disorder information and submit a command to identify the affected area. The specific implementation is as follows: In response to the user with a disability submitting a body part selection instruction for a target body part area in the user body part diagram in the sports management interface, the system displays a target body part editing page to the user with a disability; receives the disability information determined by the user with a disability for the editing area in the target body part editing page, and converts the disability information into target disability information; and receives the body part determination instruction submitted by the user with a disability for the target disability information.

[0032] Specifically, the exercise management interface can be a visual interface of an exercise application. The target body part editing page provides users with an opportunity to edit their exercise impairment status for the target body part. Users can edit their impairment information using text, voice data, and image input. The conversion of exercise impairment information can involve analyzing and summarizing the information, identifying the degree of impairment directly related to the severity of the impairment and exercise preferences, such as pain level and range of motion.

[0033] Based on this, in response to the user's command to select a target area in the user's body part diagram within the sports management interface, a target body part editing page is displayed to the user. On this page, the user can edit their disability information through text input, voice input, and image upload. The system receives the disability information specified by the user for the edited area on the target body part editing page, analyzes and extracts key points from this information, and converts it into target disability information. Finally, the system receives the user's command to confirm the body part for the target disability information.

[0034] For example, such as Figure 2 The image shown is from the sports management interface, which includes a user body part diagram. Each circular and square area in the diagram represents a body part. For example, circles represent the head, hands, and joints (shoulder, knee, elbow, etc.), while squares represent the chest, abdomen, neck, upper arm, forearm, etc. If a user with a movement disorder identifies the target body part as the area corresponding to the right shoulder, then the target body part is the right shoulder. Triggering an action on the target body part area corresponding to the right shoulder will jump to... Figure 3 The target body part editing page is shown. On this page, users can edit the specific injury details of their right shoulder, such as selecting the impairment level, adding a brief description, adding notes, and uploading images (diagnostic images). Users can also describe their right shoulder injury using natural language input, providing information about the movement impairment. A large language model is then used to analyze and summarize the movement impairment information to obtain the user's current situation, i.e., the target movement impairment information. Figure 4 As shown, by triggering the "View Recommended Training" control, the command to determine the body part can be submitted.

[0035] In summary, the system converts movement disorder information into target movement disorder information and receives location identification instructions submitted by users regarding the target movement disorder. This conversion allows for the analysis and summarization of information provided by users to obtain the target movement disorder information, facilitating the subsequent development of precise exercise plans for them.

[0036] Furthermore, considering that different users of movement disorders have different types of movement disorders, in order to enable more users of movement disorders to use the sports application, accessibility controls can be provided for users of movement disorders, specifically implemented as follows: Identify the hand control commands, eye control commands, and / or voice control commands submitted by the user with the movement disorder in the movement management interface for the target area in the user body part diagram; and use the hand control commands, eye control commands, and / or voice control commands as the body part identification commands.

[0037] Based on this, hand control commands, eye control commands, and / or voice control commands submitted by users with movement disorders in the movement management interface for target body parts in the user body part diagram are identified. Hand control commands are used to control operations such as selecting target body parts, inputting movement disorder information, and page navigation through gesture recognition. Eye control commands are used to operate the movement management interface through eye movements, including but not limited to selecting target body parts, inputting movement disorder information, and page navigation. Voice control commands are used to operate the movement management interface through voice input, including but not limited to selecting target body parts, inputting movement disorder information, and page navigation. Using hand control commands, eye control commands, and / or voice control commands as body part identification commands provides accessible control for users with movement disorders.

[0038] Continuing with the previous example, for visually impaired users with movement disorders, voice control can be used to select target areas, input movement disorder information, and navigate between pages in the movement management interface. For users with limb disabilities, hand control (lower limb disabilities) or eye control (upper limb disabilities) can be used to select target areas, input movement disorder information, and navigate between pages in the movement management interface.

[0039] In summary, using hand control commands, eye control commands, and / or voice control commands as location-specific commands provides multiple control methods for users with movement disorders, is applicable to more types of movement disorders, and increases the coverage of users with movement disorders.

[0040] Step 104: Parse the location determination instruction to obtain the motion obstacle location data associated with the target location area, and determine the user data of the motion obstacle user.

[0041] Specifically, after receiving the location determination command submitted by the user with a disability in the sports management interface for the target location area in the user's location map, the command can be parsed to obtain the sports disability location data associated with the target location area, and the user's data can be determined. This sports disability location data can be the sports disability description data input by the user for the target location area, or it can be the sports disability summary data obtained after recognizing the user's input sports disability description data using a large language model, such as summarizing the user's sports disability level, activity range, and exercise intensity. The user's data refers to the user's attribute data, including but not limited to data representing the user's personal attributes such as gender, age, and sports preferences.

[0042] Based on this, after receiving the location determination command submitted by the user with a disability in the sports management interface for a target location area in the user's location map, the location determination command is parsed to obtain the sports disability location data associated with the target location area. This data is used to clarify the user's restricted movement areas and the degree of restriction in those areas. The user data of the user with a disability is then determined, and based on this data, information such as the user's personalized sports preferences can be identified.

[0043] Furthermore, the location determination command is used to trigger the analysis of movements where a user with a disability has an obstacle, and to develop a movement plan that matches the user's abilities. The location determination command carries data on the location of the movement obstacle. By parsing the location determination command, operational obstacle location data containing multiple types of data can be obtained. The specific implementation is as follows: The command for determining the location is parsed to obtain the movement disorder level, movement disorder cause, and movement disorder description data associated with the target location area; the movement disorder level, the movement disorder cause, and / or the movement disorder description data are used as the movement disorder location data.

[0044] Specifically, the movement disorder level indicates the degree of disability, functional impairment, injury, or pain in the target area of ​​a user with a movement disorder. Causes of movement disorders include, but are not limited to, chronic pain, external injury, and external disability. Descriptive data about movement disorders can include descriptions of limited movement, such as inability to raise the left arm or wrist pain.

[0045] Based on this, the command to determine the location is parsed to obtain the disability level, cause of disability, and description of disability associated with the target location area. The disability level can be determined with reference to disability standards. The disability level, cause of disability, and / or description of disability are used as disability location data to ensure that the disability location data fully describes the disability status of the user.

[0046] In summary, using data on movement disorder level, cause of movement disorder, and / or description of movement disorder as movement disorder location data, and obtaining movement disorder location data containing multiple types of data, is beneficial for subsequent accurate movement disorder analysis of users with movement disorders and for developing reasonable exercise plans.

[0047] Step 106: Based on the user data and the movement obstacle location data, determine at least one exercise that matches the user with the movement obstacle in the exercise library, and generate an exercise plan based on the at least one exercise.

[0048] Specifically, after parsing the above-mentioned instructions for determining the body part, obtaining the data of the movement obstacle associated with the target body part region, and determining the user data of the user with a movement obstacle, at least one exercise matching the user with a movement obstacle can be determined from the exercise library based on the user data and the movement obstacle data. An exercise plan is then generated based on this at least one exercise. The exercise library refers to a library containing movements corresponding to multiple exercise movements. The movements corresponding to the exercise movements include, but are not limited to, full-body exercises and body part exercises. The at least one exercise matching the user with a movement obstacle can be an exercise determined based on the movement obstacle data and the user data, matching the user's obstacle body part. The exercise plan can be a schedule obtained by sorting the at least one exercise. The exercise plan includes, but is not limited to, daily, weekly, or monthly plans. Each exercise in the exercise plan corresponds to a specific start time.

[0049] Based on this, after parsing the above-mentioned instructions for determining the body part, obtaining the data of the movement obstacle parts associated with the target body part area, and determining the user data of the user with a movement obstacle, at least one sport matching the user with a movement obstacle is determined from the sport library based on the user data and the movement obstacle part data. The sport needs to match the user attributes corresponding to the user data and the restricted or obstacle parts corresponding to the movement obstacle part data to ensure that the user with a movement obstacle is not affected by the obstacle parts when performing the sport corresponding to the sport. An exercise plan is generated based on at least one sport. The exercise plan can be a daily plan, a weekly plan, or a monthly plan. Each day of the exercise plan can include at least one sport.

[0050] Furthermore, considering the large amount of data contained in user data and obstacle location data, directly matching sports in the sports database based on user data and obstacle location data may result in the omission of key information from these data. Therefore, before matching sports in the sports database based on user data and obstacle location data, the user data and obstacle location data can be input into a data processing model for data analysis and key information summarization. The specific implementation is as follows: The user data and the data on the movement impairment are input into the data processing model to obtain the movement ability data of the user with the movement impairment; based on the movement ability data, at least one sport that matches the user with the movement impairment is determined in the sports program library.

[0051] Specifically, the data processing model can be a large language model, capable of extracting and summarizing key information from user data and data on movement disorders. The movement ability data of users with movement disorders represents the movement abilities they possess and / or lack thereof, such as: males can enhance strength training; their arms can be raised but not bent.

[0052] Based on this, user data and data on movement impairment sites are input into a data processing model. The model's data analysis and reasoning capabilities are then used to extract key information from the user and impairment data, obtaining the user's motor ability data. Based on this motor ability data, at least one sport matching the user's motor ability is identified from a sports database.

[0053] Using the previous example, the user data is as follows: Name: Mr. Zhang, Age: 58, Gender: Male, Medical History: Recovery period of left brain injury (6 months after onset). Overall condition: Lower limb walking is basically normal, balance is fair; cardiopulmonary function is moderate. Data on motor impairment sites: Left upper limb: High muscle tone (spasticity), shoulder joint can be flexed and raised to 90 degrees, but elbow joint cannot be actively flexed, wrist and fingers cannot grasp (no fine motor skills). Right upper limb: Completely normal function. Trunk: Core stability is slightly weak, but can maintain an upright posture with assistance. Data processing model (large language model) analysis process: The above data is input into the large language model, the model extracts key information and generates motor ability data. Key information is good lower limb mobility, intact right arm function, and physiological basis for aerobic exercise. Weaknesses / limitations: Left arm cannot complete "pull", "push" (limited by the elbow), and "grasp" movements; unable to perform items requiring hand-eye coordination and balance (such as standard push-ups, dips). Movements that aggravate left upper limb spasticity should be avoided. Compensatory training should be conducted using the unaffected side (right side) and lower limbs, while also ensuring core stability. The generated motor ability data should include: the user possesses complete operational ability with one upper limb (right side) and independent support and movement with both lower limbs. The user should not possess: elbow flexion and extension ability of the left upper limb, hand gripping ability, or coordinated weight-bearing ability with both hands. Suitable exercises include lower limb-dominant movements, unilateral upper limb-assisted movements, or non-gripping core training. High-intensity activities requiring left-hand gripping of equipment, left-hand weight support, or rapid alternation of both hands are strictly prohibited. Based on the "motor ability data," the exercise library will be filtered and matched: select exercises that match the characteristics of "lower limb dominant" or "unilateral upper limb-assisted," such as stationary bike riding.

[0054] In summary, by identifying at least one sport that matches a user with a disability based on their athletic ability data, and by leveraging AI to analyze the user's athletic ability, we can better determine the sport that a user with a disability can participate in.

[0055] Furthermore, after generating an exercise plan based on at least one sport, the exercise plan can be executed to gradually complete the exercises within it. The specific implementation is as follows: In response to the user with a disability confirming a target exercise in the exercise plan, a target exercise page is displayed to the user with a disability. The target exercise page includes a video playback area and an exercise prompt area. The system receives exercise control commands submitted by the user with a disability for the video playback area and plays the exercise video corresponding to the target exercise in the video playback area based on the exercise control commands.

[0056] Specifically, the target sport can be any sport in the exercise plan, a sport selected by the user based on their needs, or the sport listed as the first item in the plan. The target sport page displays detailed information about the target sport to the user, including but not limited to a breakdown of the movement steps and a video or image demonstration. The video playback area can be used to play a demonstration video of the target sport or to display images showing the movement. The movement prompt area can display the corresponding body parts involved in the target sport and a textual breakdown of the movement steps. Movement control commands can be computer commands generated by triggering the playback controls in the video playback area, used to control the playback and pause of the movement video. The corresponding movement video can be a demonstration video of the target sport's movements.

[0057] Based on this, in response to the user's confirmation of the target exercise in the exercise plan, a target exercise page is displayed to the user. This page includes a video playback area and an exercise prompt area. The video playback area can be used to play a demonstration video of the target exercise or to display images showing the movement of the target exercise. The exercise prompt area can display the corresponding body parts involved in the target exercise, as well as the text parsing of the corresponding exercise steps. The system receives exercise control commands submitted by the user regarding the video playback area and plays the corresponding exercise video in the video playback area based on these commands. The exercise video includes a virtual human demonstrating the exercise steps for the target exercise to the user.

[0058] Following the previous example, if the target exercise is determined to be supine leg raises, after the user with a motor disability confirms their selection of the exercise, the following can be shown to them: Figure 5 The target sport page is shown. The target sport page includes a video playback area and a sport prompt area. The video playback area contains a demonstration video of the target sport. The sport prompt area displays the body parts targeted by the sport (the targeted muscle groups), and can also display a textual explanation of the sport's steps (key points), the targeted muscle groups, and the intensity level.

[0059] In summary, by playing the corresponding sports video of the target sports in the video playback area based on motion control commands, users with motor disabilities can follow the sports video to exercise, helping them to exercise with standard movements.

[0060] Furthermore, after the exercise plan is determined, users with mobility impairments can exercise according to the plan, generating and storing exercise data during the exercise process. This data can then be used for subsequent analysis. The specific implementation is as follows: The exercise record data obtained by the user with a movement disorder in performing the exercise plan, and the behavioral parameters associated with the exercise plan are determined; the exercise data corresponding to the at least one exercise item are determined from the exercise record data, and the exercise parameters corresponding to the exercise data are determined; the exercise health data corresponding to the exercise plan is generated based on the behavioral parameters and the exercise parameters corresponding to the at least one exercise item.

[0061] Specifically, within the context of an exercise plan corresponding to an exercise cycle, it's necessary to record the exercise data generated by the user with a disability during each exercise in the exercise plan within that cycle, thus obtaining the corresponding exercise record data. Each piece of exercise data records the exercise duration, intensity, calories burned, completion rate, and accuracy of movement. The behavioral parameters associated with the exercise plan can include additional exercises performed by the user with a disability during the execution of the exercise plan, as well as daily behaviors. Additional exercises refer to activities outside the exercise plan, such as walking and stretching. Daily behaviors include positive and negative behaviors. Positive behaviors include, but are not limited to, adequate hydration and sufficient sleep, while negative behaviors include, but are not limited to, sedentary behavior and excessive stress. The behavioral parameters associated with the exercise plan can be the behavioral scores for activities outside the exercise plan and daily behaviors. The exercise parameters corresponding to the exercise data refer to the individual exercise scores for each exercise in the exercise data.

[0062] Based on this, the study identifies exercise record data obtained by users with mobility impairments from training in at least one exercise program within an exercise plan, as well as behavioral parameters corresponding to exercises and daily behaviors outside the exercise plan associated with it. Within the exercise record data, exercise data corresponding to at least one exercise program is identified, along with the corresponding exercise parameters. Based on the behavioral parameters and the exercise parameters corresponding to at least one exercise program, exercise health data corresponding to the exercise plan is generated. This allows for the recording and quantification of the performance of both exercises within and outside the exercise plan, facilitating subsequent analysis of the exercise status of users with mobility impairments.

[0063] In summary, this method generates exercise health data corresponding to exercise plans based on behavioral parameters and exercise parameters corresponding to at least one sport, enabling the recording and quantification of the exercise status of users with mobility impairments, making it easier for users to understand their own exercise status and injury recovery.

[0064] Furthermore, considering that the sports and health data records the execution status of exercise plans for users with mobility impairments, and that these plans contain multiple exercise programs and are exercise plans with a certain duration and cycle, a sports and health view can be generated based on the sports and health data after the data is determined. The specific implementation is as follows: Based on the sports and health data, sports and health analysis information is generated, and a sports and health view is generated based on the sports and health analysis information; based on the sports and health view, sports and health trend information of the user with sports and health disorders is predicted.

[0065] Specifically, for users with movement disorders who are injured or ill, the sports health analysis information can include the degree of injury and rehabilitation progress for each exercise in the exercise plan. For users with movement disorders who are disabled, the sports health analysis information can include information on exercise intensity, calories burned, and exercise duration. The sports health view is used to visually display sports health analysis information over time; the sports health view can be various types of statistical charts such as line charts and bar charts. Sports health trend information represents a prediction of the physical health status of users with movement disorders, or a prediction of injury and rehabilitation progress.

[0066] Based on this, sports health analysis information is generated from sports health data to analyze the exercise status of each sport in the exercise plan. A sports health view is generated based on the sports health analysis information, visually displaying the exercise status of each sport, including but not limited to exercise duration, intensity, calories burned, and injury recovery progress. The sports health view is used to predict the sports health trends of users with mobility impairments. The sports health analysis information or sports health view can be input into a large language model for injury recovery stage analysis, health trend analysis, and alerts for abnormal exercise progress.

[0067] Continuing with the previous example, after completing each exercise in their exercise plan, users with movement disorders can analyze the corresponding health data. This analysis includes information such as exercise duration, intensity, calories burned, and injury recovery progress for each exercise, and generates a health view. The health view can include injury recovery trajectories, daily training reports, and daily records of pain, behavior, and environmental factors. Large language models can be used to analyze the health data and health view to identify recovery stages, predict recovery trends, and provide alerts for abnormal recovery progress.

[0068] In summary, a sports health view is generated based on sports health analysis information, and this view is used to predict sports health trends for users with sports disabilities, presenting their exercise status in a visual way. In cases where a user's sports disability is due to injury or illness, the sports health view can also be used to demonstrate their injury or illness recovery progress.

[0069] This specification provides a data processing method in one embodiment that receives a body part determination instruction submitted by a user with a disability in a body part diagram within a sports management interface, targeting a specific body part area. The instruction is parsed to obtain the disability body part data associated with the target body part area, and the user's data is determined. Based on the user data and the disability body part data, at least one sports activity matching the user with a disability is identified from a sports activity library, and an exercise plan is generated based on this at least one sports activity. Users with disabilities can submit body part determination instructions for a target body part area in a body part diagram within the sports management interface, allowing them to select the disability body part as needed. Exercise plans are created for users with disabilities based on these instructions, enabling them to engage in appropriate exercise. The methods can recommend more suitable and healthier exercise methods based on the user's degree of disability, thus meeting the exercise needs of people with disabilities and injuries.

[0070] The following is in conjunction with the appendix Figure 6 Taking the data processing method provided in this specification as an example of its application in generating exercise plans for users with movement disorders, the data processing method will be further explained. Figure 6 A flowchart illustrating the processing procedure of a data processing method according to an embodiment of this specification is shown, specifically including the following steps.

[0071] Step 602: In response to the movement disorder user's command to select a target area in the user body part map in the movement management interface, display the target body part editing page to the movement disorder user.

[0072] In practical applications, users with movement disorders are the users of the sports application, including those with disabilities or injuries. The sports management interface is the user interface displayed to users within the sports application. The user body part diagram is a view including body parts, divided into joint parts and limb parts. Joint parts include, but are not limited to, the shoulder, knee, and elbow joints, while limb parts include, but are not limited to, the upper limbs, lower limbs, chest, and abdomen. The target body part area can be the obstacle area determined by the user based on their own movement disorder, such as the right shoulder. The target body part editing page is used to edit the obstacle information for the target body part, such as injury status, obstacle level, and pain level.

[0073] Step 604: Receive the target movement disorder information determined by the user of the movement disorder for the editing area on the target body part editing page, and receive the body part determination instruction submitted by the user of the movement disorder for the target movement disorder information.

[0074] The editing area on the target body part editing page can include a text editing area, an obstacle level selection area, and an image and video upload area. Target movement obstacle information refers to the user's own obstacle condition, such as: slight functional limitation of the right shoulder, or a sprain.

[0075] Use hand control commands, eye control commands, and / or voice control commands as location identification commands.

[0076] Step 606: Parse the location determination command to obtain the motion obstacle location data associated with the target location area, and determine the user data of the motion obstacle user.

[0077] In practical applications, data on movement impairment sites can be descriptive data of the movement impairment input by the user for the target area, or it can be summary data of the movement impairment obtained after recognizing the descriptive data input by the user using a large language model. For example, it can summarize the movement impairment level, range of activity, and exercise intensity of the user. User data for users with movement impairments refers to the attribute data of the users with movement impairments, including but not limited to data representing the user's personal attributes such as gender, age, and exercise preferences.

[0078] Step 608: Based on user data and movement disorder data, identify at least one sport that matches the user with movement disorder in the sports program library, and generate a sports plan based on at least one sport.

[0079] Step 610: In response to the user with a disability confirming the target exercise in the exercise plan, display the target exercise page, which includes a video playback area and an exercise prompt area, to the user with a disability.

[0080] In practice, the target sport page includes a video playback area and a sport prompt area. The video playback area can be used to play a demonstration video of the target sport or to display images showing the movement of the target sport. The sport prompt area can be used to display the corresponding human body parts involved in the target sport, as well as textual explanations of the corresponding movement steps.

[0081] Step 612: Receive motion control commands submitted by users with mobility impairments for the video playback area, and play the motion video corresponding to the target sport in the video playback area based on the motion control commands.

[0082] Motion control commands can be motion start commands.

[0083] Step 614: Determine the exercise record data obtained by users with movement disorders from performing exercise plans, and generate an exercise health view based on the exercise record data.

[0084] Step 616: Predict the sports and health trend information of users with movement disorders based on the sports and health view.

[0085] The sports and health view is used to visually represent sports and health analysis information over time. The sports and health view can be various types of statistical charts, such as line charts and bar charts. Sports and health trend information represents predictions of the physical health status of users with sports disabilities, or predictions of injury and illness recovery.

[0086] In summary, the data processing method provided in this embodiment receives a body part determination instruction submitted by a user with a disability in the sports management interface, targeting a specific body part area in a user body part diagram. The instruction is parsed to obtain the disability body part data associated with the target body part area, and the user's data is determined. Based on the user data and the disability body part data, at least one sports activity matching the user with a disability is identified from the sports activity library, and an exercise plan is generated based on this at least one sports activity. Users with disabilities can submit a body part determination instruction for a specific body part area in the user body part diagram in the sports management interface, allowing them to select the disability body part as needed. Exercise plans are developed for users with disabilities based on the body part determination instruction, enabling them to engage in reasonable exercise. The method recommends more suitable and healthier exercise methods based on the degree of disability, meeting the exercise needs of people with disabilities and injuries.

[0087] Figure 7 A schematic diagram of a data processing system according to an embodiment of this specification is shown. The data processing system 700 includes a client 710 and a server 720. The client 710 is used to determine a body part determination instruction submitted by a user with a disability in a sports management interface for a target body part area in a user body part diagram, and sends the body part determination instruction to the server 720. The server 720 is used to parse the body part determination instruction, obtain sports disability body part data associated with the target body part area, and determine the user data of the user with a disability. Based on the user data and the sports disability body part data, it determines at least one sports activity matching the user with a disability in a sports activity library, generates a sports plan based on the at least one sports activity, and feeds back the sports plan to the client 710.

[0088] This specification provides a data processing system according to one embodiment, including a client and a server. The client determines a body part determination instruction submitted by a user with a disability in the exercise management interface, targeting a specific body part area in the user body part diagram, and sends the instruction to the server. The server receives the body part determination instruction submitted by the user with a disability in the exercise management interface, retrieves the disability body part data associated with the target body part area, and determines the user's data. Based on the user data and the disability body part data, at least one exercise matching the user with a disability is determined from the exercise library, and an exercise plan is generated based on at least one exercise. Users with disabilities can submit body part determination instructions for a specific body part area in the user body part diagram in the exercise management interface, allowing them to select the affected body part as needed. Exercise plans are created for users with disabilities based on the body part determination instructions, enabling them to engage in reasonable exercise. The system recommends more suitable and healthier exercise methods based on the user's degree of disability, meeting the exercise needs of people with disabilities and injuries.

[0089] Corresponding to the above method embodiments, this specification also provides data processing apparatus embodiments. Figure 8 A schematic diagram of the structure of a data processing apparatus according to one embodiment of this specification is shown. Figure 8 As shown, the device includes: The receiving module 802 is configured to receive a location determination instruction submitted by a user with a disability in the sports management interface for a target location area in the user's location map. The parsing module 804 is configured to parse the part determination instruction, obtain the movement obstacle part data associated with the target part area, and determine the user data of the movement obstacle user; The generation module 806 is configured to determine at least one sport that matches the user with the movement disorder in the sports program library based on the user data and the movement disorder location data, and to generate a sports plan based on the at least one sport.

[0090] In an optional embodiment, receiving a location determination instruction submitted by a user with a movement disorder in the movement management interface for a target location area in a user location map includes: In response to the movement disorder user's command to select a target area in the user body part diagram in the sports management interface, the target body part editing page is displayed to the movement disorder user; Receive the movement disorder information determined by the user with the movement disorder in the target body part editing page for the editing area, and convert the movement disorder information into target movement disorder information; Receive the location determination instruction submitted by the user with the movement disorder regarding the target movement disorder information.

[0091] In an optional embodiment, parsing the location determination instruction to obtain motion obstacle location data associated with the target location region includes: The command for determining the location is parsed to obtain the movement disorder level, movement disorder cause, and movement disorder description data associated with the target location area. The movement disorder level, the cause of the movement disorder, and / or the movement disorder description data are used as the movement disorder location data.

[0092] In an optional embodiment, determining at least one sport matching the user with a motor disability from a sports database based on the user data and the disability location data includes: The user data and the data on the movement impairment site are input into the data processing model to obtain the movement ability data of the user with the movement impairment. Based on the athletic ability data, at least one sport is determined from the sports program database that matches the user with the athletic disability.

[0093] In an optional embodiment, after generating the exercise plan based on the at least one sport, the method further includes: In response to the user with a mobility impairment confirming the target exercise in the exercise plan, a target exercise page is displayed to the user with a mobility impairment. The target exercise page includes a video playback area and an exercise prompt area. The system receives motion control commands submitted by the user with a mobility impairment for the video playback area, and plays the motion video corresponding to the target sport in the video playback area based on the motion control commands.

[0094] In an optional embodiment, after generating the exercise plan based on the at least one sport, the method further includes: Determine the exercise record data obtained by the user with the movement disorder when performing the exercise plan, as well as the behavioral parameters associated with the exercise plan; In the motion recording data, determine the motion data corresponding to the at least one sport, and determine the motion parameters corresponding to the motion data; The exercise health data corresponding to the exercise plan is generated based on the behavioral parameters and the exercise parameters corresponding to the at least one exercise.

[0095] In an optional embodiment, generating the exercise health data corresponding to the exercise plan based on the behavioral parameters and the exercise parameters corresponding to the at least one exercise item includes: Based on the sports and health data, sports and health analysis information is generated, and based on the sports and health analysis information, a sports and health view is generated; Based on the sports and health view, predict the sports and health trend information of the users with sports disabilities.

[0096] In an optional embodiment, receiving a location determination instruction submitted by a user with a movement disorder in the movement management interface for a target location area in a user location map includes: The system identifies hand control commands, eye control commands, and / or voice control commands submitted by the user with the movement disorder in the movement management interface for the target area in the user body part diagram. The hand control commands, eye control commands, and / or voice control commands are used as the location determination commands.

[0097] This specification provides a data processing apparatus in one embodiment that receives a body part determination instruction submitted by a user with a disability in a body part diagram within a sports management interface, targeting a specific body part area. The apparatus parses the instruction to obtain the disability body part data associated with the target body part area and identifies the user's data. Based on the user data and the disability body part data, it determines at least one sports activity matching the user from a sports activity library and generates an exercise plan based on that activity. Users with disabilities can submit body part determination instructions for a specific body part area in a body part diagram within the sports management interface, allowing them to select the affected body part as needed. The apparatus generates exercise plans based on these instructions, enabling users with disabilities to engage in appropriate exercise. It recommends more suitable and healthier exercise methods based on the user's degree of disability, thus meeting the exercise needs of people with disabilities and injuries.

[0098] The above is an illustrative scheme of a data processing apparatus according to this embodiment. It should be noted that the technical solution of this data processing apparatus and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the data processing apparatus, please refer to the description of the technical solution of the data processing method described above.

[0099] Figure 9 A structural block diagram of a computing device 900 according to one embodiment of this specification is shown. The components of the computing device 900 include, but are not limited to, a memory 910 and a processor 920. The processor 920 is connected to the memory 910 via a bus 930, and a database 950 is used to store data.

[0100] The computing device 900 also includes an access device 940, which enables the computing device 900 to communicate via one or more networks 960. Examples of these networks include Public Switched Telephone Network (PSTN), Local Area Network (LAN), Wide Area Network (WAN), Personal Area Network (PAN), or combinations of communication networks such as the Internet. The access device 940 may include one or more of any type of wired or wireless network interface (e.g., a network interface card (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) wireless interface, a Wi-MAX (Worldwide Interoperability for Microwave Access) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, or a Near Field Communication (NFC) interface.

[0101] In one embodiment of this specification, the above-described components of the computing device 900 and Figure 9 Other components, not shown, can also be connected to each other, for example, via a bus. It should be understood that... Figure 9 The block diagram of the computing device shown is for illustrative purposes only and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0102] The computing device 900 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smartphones), wearable computing devices (e.g., smartwatches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or personal computers (PCs). The computing device 900 can also be a mobile or stationary server.

[0103] The processor 920 is used to execute the following computer-executable instructions, which, when executed by the processor, implement the steps of the above-described data processing method.

[0104] The above is an illustrative scheme of a computing device according to this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the computing device, please refer to the description of the technical solution of the data processing method described above.

[0105] An embodiment of this specification also provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the above-described data processing method.

[0106] The above is an illustrative scheme of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the storage medium, please refer to the description of the technical solution of the data processing method described above.

[0107] An embodiment of this specification also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described data processing method.

[0108] The above is an illustrative scheme of a computer program product according to this embodiment. It should be noted that the technical solution of this computer program product and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the computer program product, please refer to the description of the technical solution of the data processing method described above.

[0109] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0110] The computer instructions include computer program code, which may be in the form of source code, object code, executable file, or certain intermediate forms. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added or removed according to the requirements of patent practice. For example, in some regions, according to patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0111] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments in this specification are not limited to the described order of actions, because according to the embodiments in this specification, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments in this specification.

[0112] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0113] The preferred embodiments disclosed above are merely illustrative of this specification. Optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments described in this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize this specification.

Claims

1. A data processing method, characterized in that, include: Receive location determination instructions submitted by users with movement disorders in the sports management interface for the target location area in the user's location map; The command to determine the location is parsed to obtain the data of the movement disorder location associated with the target location area, and the user data of the movement disorder user is determined. Based on the user data and the data on the location of the movement disorder, at least one sport that matches the user with the movement disorder is determined from the sports program library, and a sports plan is generated based on the at least one sport.

2. The data processing method according to claim 1, characterized in that, The process of receiving a location determination instruction submitted by a user with a movement disorder in the sports management interface for a target location area in the user's body part diagram includes: In response to the movement disorder user's command to select a target area in the user body part diagram in the sports management interface, the target body part editing page is displayed to the movement disorder user; Receive the movement disorder information determined by the user with the movement disorder in the target body part editing page for the editing area, and convert the movement disorder information into target movement disorder information; Receive the location determination instruction submitted by the user with the movement disorder regarding the target movement disorder information.

3. The data processing method according to claim 1, characterized in that, The step of parsing the command to determine the location and obtaining motion obstacle location data associated with the target location region includes: The command for determining the location is parsed to obtain the movement disorder level, movement disorder cause, and movement disorder description data associated with the target location area. The movement disorder level, the cause of the movement disorder, and / or the movement disorder description data are used as the movement disorder location data.

4. The data processing method according to claim 1, characterized in that, The step of determining at least one sport that matches the user with a motor disability from the sports program database based on the user data and the disability location data includes: The user data and the data on the movement impairment site are input into the data processing model to obtain the movement ability data of the user with the movement impairment. Based on the athletic ability data, at least one sport is determined from the sports program database that matches the user with the athletic disability.

5. The data processing method according to claim 1, characterized in that, After generating the exercise plan based on the at least one sport, the method further includes: In response to the user with a mobility impairment confirming the target exercise in the exercise plan, a target exercise page is displayed to the user with a mobility impairment. The target exercise page includes a video playback area and an exercise prompt area. The system receives motion control commands submitted by the user with a mobility impairment for the video playback area, and plays the motion video corresponding to the target sport in the video playback area based on the motion control commands.

6. The data processing method according to claim 1, characterized in that, After generating the exercise plan based on the at least one sport, the method further includes: Determine the exercise record data obtained by the user with the movement disorder when performing the exercise plan, as well as the behavioral parameters associated with the exercise plan; In the motion recording data, determine the motion data corresponding to the at least one sport, and determine the motion parameters corresponding to the motion data; The exercise health data corresponding to the exercise plan is generated based on the behavioral parameters and the exercise parameters corresponding to the at least one exercise.

7. The data processing method according to claim 6, characterized in that, After generating the exercise health data corresponding to the exercise plan based on the behavioral parameters and the exercise parameters corresponding to the at least one exercise, the method further includes: Based on the sports and health data, sports and health analysis information is generated, and based on the sports and health analysis information, a sports and health view is generated; Based on the sports and health view, predict the sports and health trend information of the users with sports disabilities.

8. The data processing method according to claim 1, characterized in that, The process of receiving a location determination instruction submitted by a user with a movement disorder in the sports management interface for a target location area in the user's body part diagram includes: The system identifies hand control commands, eye control commands, and / or voice control commands submitted by the user with the movement disorder in the movement management interface for the target area in the user body part diagram. The hand control commands, eye control commands, and / or voice control commands are used as the location determination commands.

9. A data processing system, characterized in that, Including both client and server sides, including: The client is used to determine the part determination instruction submitted by the user with a movement disorder in the sports management interface for the target part area in the user part diagram, and send the part determination instruction to the server. The server is used to parse the location determination instruction, obtain the sports obstacle location data associated with the target location area, and determine the user data of the sports obstacle user; based on the user data and the sports obstacle location data, determine at least one sports program matching the sports obstacle user in the sports program library, generate a sports plan based on the at least one sports program, and feed the sports plan back to the client.

10. A computing device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the data processing method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, It stores computer-executable instructions that, when executed by a processor, implement the steps of the data processing method according to any one of claims 1 to 8.

12. A computer program product, characterized in that, It includes a computer program or instructions that, when executed by a processor, implement the steps of the data processing method according to any one of claims 1 to 8.

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