3D human shape physical examination method and system based on dynamic tree hierarchical structure, terminal and storage medium

Abstract

The invention discloses a 3D human shape physical examination method and system based on a dynamic tree-shaped hierarchical structure, a terminal and a storage medium, and the method comprises the steps: constructing a 3D human shape model, and deploying the dynamic tree-shaped hierarchical structure in the 3D human shape model; acquiring pathological parameters set on a target node in the dynamic tree hierarchical structure, and determining a pathological propagation path corresponding to the target node according to the pathological parameters; determining a target physical examination task, and obtaining physical examination operation of a student on the 3D humanoid model according to the target physical examination task; and verifying the physical examination operation according to the target node and the pathological propagation path to obtain a physical examination operation result of the student. According to the method, the 3D human shape model is constructed, the pathological parameters are set in the 3D human shape model, the physical examination operation of the student on the 3D human shape model can be accurately obtained, meanwhile, the physical examination operation of the student can be verified, and the accuracy and efficiency of the physical examination operation of the student are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of medical teaching technology, and in particular to a 3D human body examination method, system, terminal, and computer-readable storage medium based on a dynamic tree-like hierarchical structure. Background Technology

[0002] Young medical staff often lack sufficient knowledge and experience, making it difficult or inappropriate to handle many conditions. Therefore, it is necessary to improve their understanding of the dynamic and hierarchical relationships of human anatomy. To address this issue, current technologies typically employ three-dimensional medical teaching systems to assist medical staff in learning physical examination techniques through 3D instruction.

[0003] However, existing 3D medical teaching systems generally rely on manual verification of medical staff's physical examination results, resulting in low efficiency and accuracy in verifying these results.

[0004] Therefore, existing technologies still need to be improved and developed. Summary of the Invention

[0005] The main objective of this invention is to provide a 3D human body examination method, system, terminal, and computer-readable storage medium based on a dynamic tree-like hierarchical structure. This invention aims to solve the problem that in the prior art, the results of physical examinations conducted by medical personnel are generally judged through manual verification, which leads to low efficiency and accuracy in verifying the results.

[0006] To achieve the above objectives, the present invention provides a 3D human body examination method based on a dynamic tree-like hierarchical structure, the method comprising the following steps: Construct a 3D humanoid model and deploy a dynamic tree-like hierarchical structure within the 3D humanoid model; Obtain the pathological parameters set on the target node in the dynamic tree-like hierarchical structure, and determine the pathological propagation path corresponding to the target node based on the pathological parameters; Determine the target physical examination task and obtain the physical examination operations performed by the trainee on the 3D humanoid model according to the target physical examination task; The physical examination operation is verified based on the target node and the pathological transmission path to obtain the physical examination results of the trainee.

[0007] Optionally, the 3D human body examination method based on a dynamic tree-like hierarchical structure, wherein constructing a 3D human body model and deploying a dynamic tree-like hierarchical structure within the 3D human body model specifically includes: Construct a 3D humanoid model and deploy a dynamic tree-like hierarchical structure in the 3D humanoid model, the dynamic tree-like hierarchical structure including a trunk and branch nodes; The 3D humanoid model has a hierarchical structure, including a central mounting rod and multiple cubic frame units. Each cubic frame unit includes multiple first-level branches, multiple second-level branches, and multiple N-level branches. The multiple first-level branches are respectively connected to the central mounting rod. The central mounting rod corresponds to the trunk in the dynamic tree-like hierarchical structure, and the cube frame unit corresponds to the branch node in the dynamic tree-like hierarchical structure.

[0008] Optionally, the 3D human body examination method based on a dynamic tree-like hierarchical structure, wherein obtaining the pathological parameters set on the target node in the dynamic tree-like hierarchical structure and determining the pathological propagation path corresponding to the target node based on the pathological parameters specifically includes: Pathological parameters are set on target nodes in the dynamic tree hierarchy using the disease parameter editor, wherein the pathological parameters include the simulated disease and its corresponding severity. Based on anatomical and physiological principles, the dynamic influence of the pathological parameters on the dynamic tree-like hierarchical structure is simulated to obtain the pathological propagation path.

[0009] Optionally, the 3D human body examination method based on a dynamic tree-like hierarchical structure includes examination operations on the target node and related nodes in the pathological transmission path. The process of determining the target physical examination task and obtaining the student's physical examination operation on the 3D humanoid model according to the target physical examination task specifically includes: The target physical examination task is determined based on the target node and the pathological transmission path, and the target physical examination task is sent to the trainees who need to undergo physical examination. When the trainee comes into contact with the 3D humanoid model according to the target physical examination task, the first operation data is collected through the thin film pressure sensor deployed on the 3D humanoid model. The first operation data includes spatial position, movement trajectory and posture angle. The second operational data is collected by a three-dimensional posture sensor deployed on the 3D humanoid model, wherein the second operational data includes pressure magnitude, pressure distribution, and contact time.

[0010] Optionally, the 3D human body examination method based on a dynamic tree-like hierarchical structure, wherein verifying the examination operation based on the target node and the pathological propagation path to obtain the student's examination operation result specifically includes: Determine global 3D coordinates based on the first operation data, calculate the Euclidean distance between the global 3D coordinates and the target node, and obtain the first weight based on the Euclidean distance; The pressure time series waveform is determined based on the second operating data, the similarity between the pressure time series waveform and the standard operating waveform is calculated, and a second weight is obtained based on the similarity. Based on the pathological transmission path, obtain the number of physical examinations and the order of physical examinations performed by the trainee on all related nodes along the pathological transmission path; A third weight is obtained based on the number of physical examinations, and a fourth weight is obtained based on the order of the physical examinations; The physical examination results are obtained by comprehensively calculating based on the first weight, the second weight, the third weight, and the fourth weight. The physical examination results include a comprehensive score and a physical examination evaluation report.

[0011] Optionally, the 3D human body detection method based on a dynamic tree-like hierarchical structure, wherein determining the global 3D coordinates based on the first operation data, calculating the Euclidean distance between the global 3D coordinates and the target node, and obtaining the first weight based on the Euclidean distance, specifically includes: The sensor fusion algorithm is used to calculate the global 3D coordinates of the student's touch on the 3D humanoid model based on the first operation data; A preset standard anatomical atlas is determined, and the target node is searched within the preset standard anatomical atlas to obtain the preset target coordinate range corresponding to the target node; If the global 3D coordinates are within the preset target coordinate range, the Euclidean distance between the global 3D coordinates and the target node is calculated, and a first weight is obtained based on the Euclidean distance.

[0012] Optionally, the 3D human body examination method based on a dynamic tree-like hierarchical structure, wherein determining the pressure time series waveform based on the second operational data, calculating the similarity between the pressure time series waveform and the standard operational waveform, and obtaining a second weight based on the similarity, specifically includes: The pressure time series waveform is determined based on the second operation data, and the target operation type and target operation quality are obtained based on the pressure time series waveform. The target operation type is palpation, percussion or auscultation, and the target operation quality includes palpation force, percussion technique and stethoscope placement pressure. A preset matching rule is determined based on the target node, and the standard operation waveform corresponding to the preset matching rule is obtained; Calculate the similarity between the pressure time series waveform and the standard operating waveform. If the similarity is greater than a preset similarity threshold, then obtain a second weight based on the similarity.

[0013] Furthermore, to achieve the above objectives, the present invention also provides a 3D human body examination system based on a dynamic tree-like hierarchical structure, wherein the 3D human body examination system based on a dynamic tree-like hierarchical structure includes: A 3D humanoid model building module is used to build a 3D humanoid model and deploy a dynamic tree-like hierarchical structure in the 3D humanoid model; The pathological parameter setting module is used to obtain the pathological parameters set on the target node in the dynamic tree-like hierarchical structure, and determine the pathological propagation path corresponding to the target node based on the pathological parameters. The physical examination operation recording module is used to determine the target physical examination task and obtain the physical examination operation performed by the trainee on the 3D humanoid model according to the target physical examination task; The physical examination operation verification module is used to verify the physical examination operation based on the target node and the pathological transmission path, and obtain the physical examination operation results of the trainee.

[0014] Furthermore, to achieve the above objectives, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and a 3D human body examination program based on a dynamic tree-hierarchical structure stored in the memory and executable on the processor. When the 3D human body examination program based on a dynamic tree-hierarchical structure is executed by the processor, it implements the steps of the 3D human body examination method based on a dynamic tree-hierarchical structure as described above.

[0015] Furthermore, to achieve the above objectives, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a 3D human body examination program based on a dynamic tree-hierarchical structure, and when the 3D human body examination program based on the dynamic tree-hierarchical structure is executed by a processor, it implements the steps of the 3D human body examination method based on the dynamic tree-hierarchical structure as described above.

[0016] In this invention, a 3D humanoid model is constructed, and a dynamic tree-like hierarchical structure is deployed within the 3D humanoid model. Pathological parameters set on target nodes in the dynamic tree-like hierarchical structure are obtained, and the pathological propagation path corresponding to the target node is determined based on the pathological parameters. A target physical examination task is determined, and the physical examination operations performed by the trainee on the 3D humanoid model according to the target physical examination task are obtained. The physical examination operations are verified based on the target node and the pathological propagation path to obtain the trainee's physical examination results. This invention, by constructing a 3D humanoid model and setting pathological parameters within it, can accurately obtain the trainee's physical examination operations on the 3D humanoid model and verify these operations, effectively improving the accuracy and efficiency of the trainee's physical examination operations. Attached Figure Description

[0017] Figure 1This is a flowchart of a preferred embodiment of the 3D human body examination method based on a dynamic tree-like hierarchical structure of the present invention; Figure 2 This is a schematic diagram of the system architecture of a 3D human body model, representing a preferred embodiment of the 3D human body examination method based on a dynamic tree-like hierarchical structure of the present invention. Figure 3 This is a complete simulation diagram of a preferred embodiment of the 3D human body examination method based on a dynamic tree-like hierarchical structure of the present invention, from disease setting to multimodal feedback; Figure 4 This is a schematic diagram of the data flow and evaluation logic of two types of sensors working together in a preferred embodiment of the 3D human body examination method based on a dynamic tree-like hierarchical structure of the present invention. Figure 5 This is a schematic diagram of the student physical examination assessment process of a preferred embodiment of the 3D human body examination method based on a dynamic tree-like hierarchical structure of the present invention; Figure 6 This is a schematic diagram of the student operation prompts and error correction mechanism flow of a preferred embodiment of the 3D human body examination method based on a dynamic tree-like hierarchical structure of the present invention; Figure 7 This is a structural diagram of a preferred embodiment of the 3D human body examination system based on a dynamic tree-like hierarchical structure of the present invention; Figure 8 This is a structural diagram of a preferred embodiment of the terminal of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] Young medical staff often lack sufficient knowledge and experience, making it difficult or inappropriate to handle many conditions. Therefore, it is necessary to improve their understanding of the dynamic and hierarchical relationships of human anatomy. To address this issue, current technologies typically employ three-dimensional medical teaching systems to assist medical staff in learning physical examination techniques through 3D instruction.

[0020] However, existing 3D medical teaching systems generally rely on manual verification of medical staff's physical examination results, resulting in low efficiency and accuracy in verifying these results.

[0021] To address the aforementioned issues, this invention proposes a 3D human body examination teaching system based on a dynamic tree-like hierarchical structure.

[0022] The preferred embodiment of the 3D human body examination method based on a dynamic tree-like hierarchical structure described in this invention, such as... Figure 1 and Figure 2 As shown, the 3D human body examination method based on a dynamic tree-like hierarchical structure includes the following steps: Step S10: Construct a 3D humanoid model and deploy a dynamic tree-like hierarchical structure in the 3D humanoid model.

[0023] like Figure 2 As shown, this invention constructs a 3D humanoid system based on a tree-like hierarchical structure, capable of dynamically responding to physical examination operations and performing real-time assessments. Specifically, this is reflected in the dynamic tree-like anatomical structure: the system uses a central mounting rod as the trunk and cube-shaped frame units as branch nodes to construct the 3D humanoid figure, mapping a tree-like logical structure of "system-organ-tissue" onto this physical framework. This structure supports the expansion, collapse, and focusing operations of nodes, and can highlight the operated area and its physiological and pathological related nodes during physical examinations.

[0024] Specifically, a 3D humanoid model is constructed, and a dynamic tree-like hierarchical structure is deployed within the 3D humanoid model. The dynamic tree-like hierarchical structure includes a trunk and branch nodes. The 3D humanoid model is a hierarchical structure, including a central mounting rod and multiple cube frame units. Each cube frame unit includes multiple first-level branches, multiple second-level branches, and multiple N-level branches. The multiple first-level branches are respectively connected to the central mounting rod. The central mounting rod corresponds to the trunk in the dynamic tree-like hierarchical structure, and the cube frame units correspond to the branch nodes in the dynamic tree-like hierarchical structure.

[0025] It is understood that the 3D humanoid tree-structured system for physical examination teaching provided by this invention includes a 3D humanoid model constructed based on a central mounting rod and cubic frame units, a management module that maps human anatomical structures to a tree-like hierarchical logical structure, a disease configuration module for setting and simulating the spread of disease states within the tree structure, and a teaching evaluation module for real-time assessment of physical examination operations based on the tree structure. The tree-like hierarchical structure supports navigation from the system level to the cell level and supports node expansion, collapse, and focusing operations.

[0026] The "branch node" defined in this invention has a dual meaning: 1. These are branch nodes in the physical structure, including: Central mounting rod: This is the root node or trunk of the entire model, simulating the central skeleton of the human body (such as the spine). Cube frame units: Each frame unit is connected to the central rod or to other cubes; each is a branch node. Like "building blocks" or "tree branches," they are connected hierarchically and ultimately assembled into a complete, recognizable 3D humanoid figure.

[0027] The hierarchical relationship includes: First-level branches: Large nodes directly connected to the central pillar, such as cubes representing the head, chest cavity, abdominal cavity, and pelvis. Second-level branches: Nodes extending from the first-level branches, such as cubes representing the left lung, right lung, and heart extending from the chest cavity node. N-level branches: These can be further subdivided, such as extending from the left lung node to smaller cubes representing each lung lobe.

[0028] In simple terms, in physics, each "cube frame unit" is a "branch node," and together they form a tree-shaped, three-dimensional, and tangible physics teaching model.

[0029] 2. A branch node in the logical structure—this is a concept mapped in software systems, user interfaces, and instructional logic. Each physical "cube node" corresponds to a data node in the background logical system; this data node is the logical "branch node." This logical node contains the following information: Anatomical identity: Which anatomical structure does it represent? (For example: Is it the left upper lobe of the lung, the mitral valve, or the quadriceps femoris muscle?) Hierarchical relationship: Who is its "parent node"? (For example: the parent node of the left upper lobe of the lung is the left lung), and who are its "child nodes"? (For example: the child nodes of the left lung include the left upper lobe, the left lower lobe, etc.).

[0030] The unique feature of this invention lies in the real-time linkage between physical branch nodes and logical branch nodes, including: 1. Physical node operation: When a student touches or taps the cube representing the "liver area" on the model, the logic node will be triggered. 2. Triggering logic node: The system uses built-in sensors to detect that the physical branch node "liver area" has been operated.

[0031] 3. Logical Node Response: The system immediately locates the corresponding "Liver Area" logical branch node in the background and performs the following operations: Highlighting: The "Liver" node is highlighted in the tree structure diagram of the software interface. Tracing Path: Related anatomical paths (e.g., Liver -> Biliary System -> Gallbladder) or pathological paths (e.g., Hepatomegaly -> Portal Hypertension -> Ascites) are also highlighted. Disease Simulation: If the Liver node has preset "Hepatitis" pathological parameters, the system will trigger corresponding feedback (e.g., a prompt "Liver Area Percussion Tenderness"). Evaluation Basis: This operation is recorded and used as a basis for evaluating the accuracy and completeness of the student's operation.

[0032] In summary, in this invention, the "branch node" is a core concept with two sides. Externally, it is a cubic frame unit, a physical "building block" that constitutes the 3D humanoid model. Internally, it is a data unit, an intelligent "node" that carries anatomical, physiological, pathological information and logical relationships. It is this dual nature that makes this invention more than just a simple physical model or a pure software simulation; it is a deeply integrated, interactive, and intelligently teaching-capable innovative platform.

[0033] Step S20: Obtain the pathological parameters set on the target node in the dynamic tree-like hierarchical structure, and determine the pathological propagation path corresponding to the target node based on the pathological parameters.

[0034] The present invention also includes a dynamic disease module: through the disease parameter editor, medical teachers can set pathological parameters for specific nodes in the tree structure (i.e., target nodes in the present invention, such as a lung lobe). The system’s built-in disease transmission logic unit will automatically simulate and present the dynamic effects of the lesion in the tree structure (such as changes in breath sounds and pleural friction caused by lung lobe consolidation) based on anatomical and physiological principles.

[0035] Specifically, pathological parameters are set on target nodes in the dynamic tree-like hierarchical structure using a disease parameter editor. These pathological parameters include simulated diseases and their corresponding severity. Based on anatomical and physiological principles, the dynamic influence of these pathological parameters on the dynamic tree-like hierarchical structure is simulated to obtain the pathological propagation path.

[0036] The disease configuration module in this invention includes a disease parameter editor and a disease propagation logic unit, used to simulate the dynamic chain reaction of local lesions in a tree structure (i.e., the pathological propagation path in this invention). (Associations: For example, a cholecystitis node may be associated with a referred pain node in the right shoulder).

[0037] like Figure 3 As shown, the "presentation" of the disease transmission logic unit is not a single form, but a multimodal, multi-layered, and deeply integrated feedback system bound to a tree structure.

[0038] Figure 3 The complete simulation process, from disease setting to multimodal feedback, is clearly demonstrated, and the specific presentation method is as follows: 1. Visual presentation: Dynamically highlight and mark on the tree structure diagram; Color coding: Affected nodes will change color. For example, nodes with inflammation turn red, ischemic areas turn purple, and necrotic areas turn gray; Dynamic propagation path: When simulating disease progression, a visually flowing path (such as a flashing arrow or light stream) "spreads" from one node to the next related node, intuitively demonstrating the propagation process; Status icons: Small icons will appear on the nodes, such as "flame" to represent inflammation, "water drop" to represent fluid accumulation, and "exclamation mark" to represent abnormal function; Changes in 3D humanoid models include localized discoloration and shape alterations: the cube-shaped area corresponding to a diseased organ will change color. For example, an enlarged cube in the liver area may swell slightly and turn dark red.

[0039] Animation effects include: Breathing animation: If lung consolidation occurs, the animation of breathing movements on that side of the chest will weaken or disappear. Pulsation animation: If a heart arrhythmia occurs, the pulsation animation of the heart node will become rapid and irregular. Exudation animation: For example, when simulating ascites, an animation of fluid accumulation may appear at the abdominal cavity node.

[0040] 2. Auditory presentation: This is the most distinctive feature. The system will simulate pathological sound effects to replace normal physiological sounds.

[0041] Changes in auscultation sounds: normal vesicular breath sounds → moist rales, dry rales, wheezing (for pneumonia, bronchitis); normal heart sounds → heart murmurs, gallop rhythm (for valvular heart disease); normal bowel sounds → hyperactive or absent bowel sounds (for intestinal obstruction).

[0042] Percussion sound variation: The system simulates the differences between unvoiced, voiced, solid, and thumping sounds through a speaker or headphones.

[0043] Palpation feedback: When a tender point is touched, the system may emit a slight beep. When a tremor (such as a cat's panting) is felt, the controller may vibrate and produce a rumbling noise.

[0044] 3. Text and Data Presentation: Real-time pathology description: When a student focuses on or operates on a lesion node, a text box will pop up on the interface, briefly describing the current pathological state. For example: "Left lower lobe: Hepatic consolidation stage, alveoli filled with a large number of fibrin and neutrophils."

[0045] Vital signs data panel: Disease transmission will dynamically affect the virtual human's overall physiological data, such as increased body temperature, increased heart rate, and decreased blood oxygen saturation. This data will be displayed in real time on a monitoring panel.

[0046] Operation feedback prompts: When a student's operation triggers disease manifestations, the system will provide text prompts. For example: "Auscultation site: lower lobe of the left lung; findings: persistent moist rales."

[0047] Supplement to the simulation process (dynamic logic chain): Suppose that the teacher sets "lobar pneumonia" at the "left lower lobe" node.

[0048] Step 1: Initial setup. Teachers use the disease parameter editor to select the "Left Lower Lobe" node, choose "Lobar Pneumonia" as the disease type, and set the severity to "Consolidation Stage".

[0049] Step 2: The logic unit calculates the propagation path. Based on anatomical knowledge, the disease propagation logic unit automatically calculates possible impact paths, for example: 1. Direct spread: → Ipsilateral pleura (causing pleurisy); 2. Airway transmission: → ipsilateral bronchus (evoking bronchial breath sounds); 3. Systemic effects: → Immune system nodes (causing fever, elevated white blood cell count).

[0050] Step S30: Determine the target physical examination task and obtain the physical examination operations performed by the trainee on the 3D humanoid model according to the target physical examination task. The physical examination operations include physical examination operations on the target node and on related nodes in the pathological transmission path.

[0051] This invention incorporates a multi-dimensional physical examination operation assessment: the system captures the trainee's physical examination operations in real time through thin-film pressure sensors and three-dimensional posture sensors distributed on the model. The assessment system can not only determine the correctness of the trainee's operation results, but also perform multi-dimensional quantitative assessment of the operation process.

[0052] Specifically, a target physical examination task is determined based on the target node and the pathological transmission path, and the target physical examination task is sent to the trainees who need to undergo the physical examination test; when the trainee comes into contact with the 3D humanoid model according to the target physical examination task, first operation data is collected through the thin-film pressure sensor deployed on the 3D humanoid model, wherein the first operation data includes spatial position, movement trajectory and posture angle; second operation data is collected through the three-dimensional posture sensor deployed on the 3D humanoid model, wherein the second operation data includes pressure magnitude, pressure distribution and contact time.

[0053] The present invention incorporates a light feedback unit, through which the system highlights the examination site and related physiological and pathological nodes on a tree structure.

[0054] The term "operational part" is a complex concept with multiple layers of meaning, encompassing both physical and logical aspects. In this invention, "operational part" should be precisely defined as: the set of physical parts that the user contacts and one or more logical nodes that they map to in a dynamic tree-like hierarchical structure.

[0055] like Figure 4 As shown, the logical flow from the physical contact point to the definition of the complete operating part is clearly illustrated: 1. Physical level: "Operating part" refers to the sensor contact point, which is the most superficial definition, that is, the cube frame unit that the trainee's hand or examination tool (such as stethoscope or percussion hammer) actually contacts.

[0056] Example: The trainee places the stethoscope head on the cube representing the "lower lobe of the left lung".

[0057] The system detected that the thin-film pressure sensor and (potentially) contact sensor on the cube were triggered, informing the system that the physical node "left lower lobe" had been operated.

[0058] 2. Logical Level: "Operation Location" refers to the nodes mapped by the system. That is, the system will activate one or more logical nodes in the background tree-like hierarchical structure based on the physical contact point, including: A. Directly operated site: that is, the anatomical structural node that directly corresponds to the physical contact point.

[0059] Continuing from the previous example: The physical contact point "left lower lobe" cube corresponds to the directly manipulated part, which is the left lower lobe node in the logical structure.

[0060] B. Associated Operation Sites: Based on the established disease or normal physiological connections, the system will automatically activate other nodes that are pathophysiologically related to the directly operated site.

[0061] Continuing with the previous example: If pneumonia is set for the lower lobe of the left lung, the associated operation sites may include: pleural nodes (because pneumonia may cause pleural friction rub) and bronchial nodes (because pneumonia will cause bronchial breath sounds).

[0062] If the student is working on the liver area, the associated work site may include the right shoulder node (because cholecystitis may cause referred pain in the right shoulder).

[0063] In summary, the operating part in this invention refers to the physical framework unit directly triggered by the user's physical examination operation, and the set of direct logical nodes mapped in the dynamic tree-like hierarchical structure and associated logical nodes determined based on anatomical, physiological or pathological correlations.

[0064] The advantages of this definition are: 1. This invention not only protects the simple "contact-response" model, but also the advanced logic of intelligent association and traceability based on a tree structure. 2. The technical description is more accurate: it truly reflects the core technology of this invention, that is, mapping physical operations to a complex, dynamic logical network. 3. It highlights the difference from existing technologies: most existing teaching models can only identify "you touched here", while this invention can identify "you touched here, but other related parts here also have problems, so they need to be considered and fed back together".

[0065] Therefore, when "highlight the operation area" is mentioned, it likely refers to highlighting the entire associated path. During "operation evaluation," the system not only assesses whether the trainee operated on the correct location, but may also assess whether they missed any associated areas that needed to be checked.

[0066] In specific application scenarios, when students manipulate 3D humanoid models: Scenario 1: Trainee auscultating the "lower lobe of the left lung": Auditory feedback: Normal breath sounds are replaced by bronchial breath sounds (a type of tubular breath sound heard in the consolidation area).

[0067] Visual feedback: In the tree structure diagram of the software interface, the "left lower lobe" node is highlighted in red, and there is a flashing line connecting it to the "bronchus" node, which is also flashing.

[0068] Text feedback: The interface displays: "Auscultation: Clear bronchial breath sounds can be heard in the lower lobe of the left lung, indicating lung tissue consolidation."

[0069] Scenario 2: Trainee palpates the "left side of the chest": Auditory feedback: When the trainee's hand (via a pressure sensor) moves along the side of the chest, it triggers a simulated pleural friction sound (a creaking sound like leather rubbing) at a specific location.

[0070] Haptic feedback: The device may simulate the sensation of friction through a vibrating motor.

[0071] Visual feedback: In the tree structure diagram, the "pleura" node is activated and displayed in orange, connected to the "lower lobe of the left lung" node.

[0072] Scene 3: Trainees check vital signs: Data feedback: Vital signs panel shows: Body temperature: 39.2℃, Heart rate: 105 beats / min, WBC (white blood cell count): 15.0*10 9 / L.

[0073] In summary, the "presentation" of disease transmission is a comprehensive system integrating graphics, sound, vibration, text, and data. It goes beyond simply telling participants "this place is sick," but rather creates a multi-sensory, dynamic, and interactive pathological environment that allows participants to discover, reason, and understand the full picture of the disease within the entire anatomical tree structure, as if they were facing a real patient.

[0074] Furthermore, such as Figure 4 As shown, the data flow and evaluation logic of the two types of sensors working together are illustrated. The thin-film pressure sensor and the three-dimensional posture sensor set in this invention are like the system's "digital senses," capturing data from the two dimensions of force and contact, and space and motion, respectively, to jointly construct a comprehensive digital understanding of the trainee's physical examination operation.

[0075] For thin-film pressure sensors, these sensors primarily answer the question of "how well is it operating?" regarding force and contact quality. They are typically distributed in arrays on the surface or inside a cubic frame, providing a "tactile" dimension of operation, focusing on force, contact, and vibration.

[0076] Its specific applications in teaching evaluation are as follows: 1. Identify the operation type: Palpation: presents as a stable, continuous, moderate pressure sequence.

[0077] Percussion: manifested as a series of brief, sharp impact waveforms.

[0078] 2. Evaluate the quality of operation: Palpation pressure: Is it too light (unable to reach deep organs) or too heavy (causing patient discomfort)? Percussion techniques: Does the force and rhythm of the percussion meet the standards? Is the force generated by the wrist rather than the arm? Stethoscope placement: Is the pressure appropriate, ensuring good contact between the stethoscope head and the skin without being too tight? For 3D attitude sensors, these sensors primarily answer the questions "Where do I operate?" and "How do I move?". They are typically mounted inside each cube node to determine the node's state in 3D space, providing a "visual" dimension to the operation and focusing on position, trajectory, and attitude.

[0079] Step S40: Verify the physical examination operation based on the target node and the pathological transmission path to obtain the physical examination results of the trainee.

[0080] The invention also includes a teaching evaluation module, which is used to capture the operation actions of sensors (such as thin-film pressure sensors and three-dimensional posture sensors) in real time, and deploys a multi-dimensional evaluation algorithm based on operation integrity, accuracy and standardization.

[0081] Specifically, a sensor fusion algorithm is used to calculate the global 3D coordinates of the student's touch on the 3D humanoid model based on the first operation data; a preset standard anatomical atlas is determined, and the target node is searched in the preset standard anatomical atlas to obtain the preset target coordinate range corresponding to the target node; if the global 3D coordinates are within the preset target coordinate range, the Euclidean distance between the global 3D coordinates and the target node is calculated, and a first weight is obtained based on the Euclidean distance.

[0082] Based on the second operational data, a pressure time series waveform is determined, and the target operational type and target operational quality are obtained from the pressure time series waveform. The target operational type is palpation, percussion, or auscultation, and the target operational quality includes palpation force, percussion technique, and stethoscope placement pressure. A preset matching rule is determined based on the target node, and the standard operational waveform corresponding to the preset matching rule is obtained. The similarity between the pressure time series waveform and the standard operational waveform is calculated. If the similarity is greater than a preset similarity threshold, a second weight is obtained based on the similarity. Based on the pathological propagation path, the number and order of physical examinations performed by the trainee on all associated nodes along the pathological propagation path are obtained. A third weight is obtained based on the number of physical examinations, and a fourth weight is obtained based on the order of physical examinations. A comprehensive calculation is performed based on the first weight, the second weight, the third weight, and the fourth weight to obtain the physical examination operation result, which includes a comprehensive score and a physical examination operation evaluation report.

[0083] Specific applications of thin-film pressure sensors and three-dimensional attitude sensors in teaching assessment: 1. Locate the operating area: accurately determine whether the trainee is being examined in the "liver area" or the "spleen area", even if they are physically adjacent.

[0084] 2. Assess the procedure sequence: Determine whether the trainee followed the correct examination route (e.g., whether the lungs were auscultated in the order of "apex -> base").

[0085] 3. Analysis of operational techniques: Liver palpation: Is the arm parallel to the costal margin? Does it rise slowly in rhythm with breathing? Percussion action: Is it a flexion and extension movement of the wrist, rather than the raising and lowering of the entire forearm? Identify specific movements: such as palpating the kidneys with both hands, or performing a percussion tenderness test on the spine.

[0086] By combining the thin-film pressure sensor and the three-dimensional attitude sensor, the system can not only know "where the trainee touched" (attitude sensor positioning), but also "how the touching happened" (pressure sensor identification of whether it was touching or knocking), and "whether the touching was correct and good" (the data from both are combined for standardization and accuracy assessment).

[0087] like Figure 5 As shown, the entire evaluation process is a closed-loop system, and the evaluation process is as follows: First, the correctness of the assessment (qualitative judgment) is the foundation for judging whether the operation is "correct".

[0088] 1. Assess the correctness of the operation site: Assess whether the trainee performed the procedure in the correct anatomical location; Data acquisition, including: 3D attitude sensor: Through sensor fusion algorithm, the global 3D coordinates of the manipulated cube node are accurately calculated.

[0089] System knowledge base: The system has pre-stored standard anatomical atlases and defines the standard target coordinate range for each physical examination operation (such as "liver area percussion" and "mitral valve auscultation").

[0090] Logic for judgment: IF student's node coordinates IN standard target coordinate range THEN correct ELSE incorrect.

[0091] 2. Matching of operation type and body part: Assess whether the correct examination methods were used in the correct locations; Data acquisition, including: Thin-film pressure sensor: Analyzes pressure time-series waveforms to identify operation type.

[0092] System knowledge base: Pre-stored "site-operation" matching rules (e.g., the "heart" node can perform "inspection, touch, percussion, and auscultation", while the "liver" node cannot perform "auscultation").

[0093] Judgment logic: The system first identifies what the trainee is doing (such as "palpation"), and then determines whether the operation is allowed to be performed on the current part.

[0094] II. Multi-dimensional quantitative assessment (process evaluation): This is a further evaluation of "whether it was done well" based on "correctness". The specific evaluation process is as follows: 1. Accuracy Position Precision Verification: Calculate the Euclidean distance (in mm) between the operation point and the standard target point. The smaller the distance, the higher the score (i.e., the first weight in this invention). The three-dimensional attitude sensor (providing the 3D coordinates of the operation point) and the system's pre-stored standard coordinates (i.e., the coordinates of the standard target point) are used for calculation. Operation Recognition Confidence: The matching degree (in %) between the operation recognized by the system (e.g., percussion) and the standard operation waveform. This can be achieved using a thin-film pressure sensor + machine learning model. The machine learning model, trained with a large amount of expert data, can output the similarity between the current operation waveform and the standard waveform (i.e., the second weight in this invention).

[0095] 2. Standardized Force Control Verification: This verifies whether the average and peak pressures are within the standard range (unit: kPa). For example, too light a pressure will prevent the palpation of organs, while too heavy a pressure will cause pain. Time-series data from the thin-film pressure sensor can be used to calculate statistical quantities such as mean and peak values. Manipulation Trajectory: This verifies whether the movement path is smooth and whether the acceleration is stable. For example, the arm should not tremble suddenly during liver palpation. Accelerometer and gyroscope data from the three-dimensional posture sensor are used to calculate the variance of acceleration and angular velocity in the movement trajectory; the smaller the variance, the more stable the movement. Rhythm and Frequency: Primarily used for percussion, this verifies whether the percussion rhythm is uniform and the frequency is appropriate (unit: times / second). The pressure time interval sequence from the thin-film pressure sensor can be used to calculate its standard deviation.

[0096] 3. Completeness and Site Coverage Verification: Determine whether all necessary related sites have been checked (unit: %). For example, when auscultating the lungs, have all key auscultation areas of the anterior chest, back, and armpits been checked? This can be achieved by comparing the motion trajectory data of the 3D posture sensor and all visited nodes recorded by the system with the node list required in the standard operating procedure (i.e., the third weight in this invention). Time Adequacy: Whether the dwell time at key sites is sufficient to complete the evaluation (unit: seconds). For example, at least 2-3 cardiac cycles should be spent in each cardiac auscultation area, which can be calculated based on the dwell time of the 3D posture sensor.

[0097] 4. Sequence and Operational Procedure Sequence Verification: Calculate the degree of conformity between the actual operational sequence and the standard physical examination procedure sequence (i.e., the fourth weight in this invention). For example, an abdominal physical examination should follow the sequence of "inspection, auscultation, percussion, and palpation." A "operational sequence" is generated using a three-dimensional attitude sensor and a thin-film pressure sensor, combined with a timestamp. The difference from the standard sequence is calculated using a sequence alignment algorithm (such as Dynamic Time Warping (DTW)).

[0098] 5. Coordination verification of timing and physiological rhythm: Determine whether the operation is synchronized with the physiological cycle of the simulated patient. For example, whether deep palpation is performed at the end of exhalation. The operation time point of the thin-film pressure sensor + the time stamp of the respiratory or heartbeat cycle simulated by the system physiological engine can calculate the phase of the operation in the physiological cycle.

[0099] Furthermore, for the calculation process of the evaluation score, it is as follows: 1. Data acquisition: The sensor continuously acquires the original voltage signal at a high frequency (such as 100 Hz).

[0100] 2. Signal processing, including: Filtering: Remove noise (such as high-frequency noise generated by hand tremors).

[0101] Feature extraction: Extract meaningful feature values (such as pressure mean, coordinates, acceleration variance, etc.) from the original signal.

[0102] 3. Data fusion: Align the feature values from different sensors at the time stamp to form a unified "operation event" data packet.

[0103] 4. Comparison and scoring, including: Rule base: For indicators with a clear range (such as strength, position), the system has an "expert rule base" that defines the ideal range and threshold for each operation.

[0104] Scoring example (strength): IF the measured pressure P is within [P_min, P_max] THEN score = 100; IF P < P_min THEN score = (P / P_min) * 100; IF P > P_max THEN score = (P_max / P) * 100.

[0105] Machine learning model: For more complex patterns (such as whether the manipulation is standardized), the system uses a pre-trained classification or regression model to directly output a "standardization score".

[0106] 5. Weight aggregation: Each evaluation dimension is assigned different weights (such as 30% for accuracy, 30% for standardization, 20% for integrity, 20% for sequentiality). The system calculates a comprehensive score based on the weights and generates a detailed evaluation report, clearly pointing out the advantages and areas for improvement.

[0107] In summary, through multi-sensor data fusion and intelligent algorithms based on rules and models, the present invention converts the physical operations of trainees into a set of quantifiable and multi-dimensional performance indicators. This not only gives a conclusion of "right or wrong", but also provides a process guidance of "how to improve", greatly enhancing the scientificity and effectiveness of teaching.

[0108] The real-time transmission process is as follows: Real-time transmission is fundamental, referring to a low-latency closed loop from data acquisition to feedback.

[0109] 1. Sensor data stream: Data Acquisition: Thin-film pressure sensors and three-dimensional attitude sensors continuously acquire raw signals at high frequencies (e.g., 100-500Hz).

[0110] Transmission: After initial filtering by the built-in microprocessor, the data is transmitted in real time to the central processing unit via a high-speed data bus (such as CAN bus or Ethernet).

[0111] Synchronization: All sensor data is tagged with high-precision timestamps to ensure that the system can accurately reconstruct "the state of the model and the operations performed at a certain moment".

[0112] 2. Processing and Evaluation Engine: Real-time processing: The central processing unit runs lightweight signal processing algorithms and evaluation models to complete feature extraction and comparison within milliseconds.

[0113] Status Update: The evaluation results are updated in real time to update the status of the "virtual patient" in the system and feedback information is prepared.

[0114] 3. Issuance of feedback instructions: The processing results are sent to the execution unit in real time via the control bus; Lighting control unit: controls which LED lights are lit and what color they are.

[0115] Audio output unit: generates and plays prompts or voice messages.

[0116] Vibration motor drive unit: controls the intensity and mode of vibration.

[0117] The delay of the entire "operation-sensing-calculation-feedback" loop is controlled within 100 milliseconds to ensure that the feedback feels immediate and natural to the learner.

[0118] Furthermore, such as Figure 6 As shown, the system provides real-time guidance and error correction prompts for the physical examination process. This is the system's "intelligent coach" logic, which goes beyond judging right and wrong, and guides trainees to self-correct. The error correction process is as follows: Step 1: Error diagnosis and classification. The system first determines the type and severity of the error.

[0119] Level 1 error: Critical error (immediate interruption correction): Example: Auscultation is performed in the liver area (operation type and location are completely mismatched). System diagnosis: "Operation type-location" mismatch, severity: high.

[0120] Level 2 error: Technical error (guideline correction): Example: Percussion pressure was too light, failing to elicit effective signs. System diagnosis: "Operational Standardization - Pressure" error, severity level: moderate.

[0121] Level 3 error: Optimization error (suggestive warning): Example: The order of palpation does not affect the result, but it does not conform to the standard procedure. System diagnosis: "Operation sequence" error, severity level: low.

[0122] Step 2: Hierarchical and multimodal correction prompts. The system selects the most appropriate combination of feedback based on the error level and type.

[0123] 1. Correction of Level 1 errors (critical errors): Visually: The cube at the manipulation site emits a rapid red flashing light. In the tree diagram on the screen, the node turns red and a prohibition icon pops up. A dynamic demonstration animation may pop up on the side of the interface, visually showing the correct operation to be performed at this site (such as demonstrating palpation instead of auscultation).

[0124] Auditory: Play a prompting sound (such as a "buzzing" warning sound), followed by a clear voice prompt: "Error: This area is not suitable for auscultation. Please try palpation or percussion."

[0125] Haptic (if hardware supports): The operating handle or the cube in that area generates a short, strong vibration to simulate the tactile sensation of "operation denied".

[0126] 2. Correction of Level 2 errors (technical errors): Visually: The treated area emits a soft, slow yellow flash. Numerical prompts are displayed on the screen: for example, a force bar is displayed, the current pointer is in the "too light" zone, and the target area is highlighted in green. A text prompt is displayed: "Insufficient percussion force, please increase wrist drop speed."

[0127] Auditory: Play a gentle prompt. The voice prompt may focus more on guidance: "Please feel and imitate a standard percussion force." The system can then play a correct percussion sound for the trainee to compare.

[0128] 3. Correction of Level 3 errors (optimization errors): Visual: The system may display a blue question mark or directional arrow on the node that should have been performed but was not, after the trainee completes the current operation and before the next operation begins. Text prompts are displayed in the suggestion area: "Tip: The standard abdominal examination sequence is 'inspection, auscultation, percussion, palpation'."

[0129] Auditory: Brief voice prompts may only be given in the initial learning mode, while in the assessment mode, points will be deducted silently.

[0130] Step 3: Gradual prompts and a "help" mechanism. To balance "independent exploration" and "effective teaching," the system can be designed with intelligent prompting strategies: First mistake: Give gentle, targeted hints (such as "the percussion pressure seems too light").

[0131] Repeated mistakes: The prompts become more and more specific and direct (e.g., “Use your wrist, not your whole arm, to produce a crisp tapping sound”).

[0132] Help Function: Students can say "help" or press the help button at any time, and the system will immediately: freeze the current scene; play a complete demonstration video of the standard operation; and highlight the location and technique for the next operation.

[0133] In summary, the core value of this system's error correction mechanism lies in: 1. Contextual awareness: Not only can it judge right from wrong, but it can also understand the nature and context of the error.

[0134] 2. Personalized guidance: Provide tiered and targeted feedback based on the type of error and the learner's level.

[0135] 3. Multi-sensory reinforcement: Through the synergistic effect of vision, hearing, and touch, deepen trainees' understanding and memory of correct operation.

[0136] 4. Cultivate clinical thinking: By prompting the "sequence" and "related sites" of the operation, guide trainees to establish a complete diagnostic logic chain, rather than just mechanically performing actions.

[0137] Furthermore, this invention can record and replay the complete physical examination sequence and generate a visualized assessment report. This assessment report is the final output of the entire teaching system; it is far more than just a simple score—it is a multi-dimensional, visualized, and traceable personalized learning diagnostic report. This report aims to transform the entire process of the student's virtual physical examination into structured, quantifiable data, providing clear directions for improvement for both students and teachers.

[0138] The following are the core contents that a complete evaluation report should include: 1. Overall Overview: Student Information: Name, ID, Learning Stage.

[0139] Simulated Case: The name of the disease set in this exercise (e.g., "Lobar pneumonia (lower lobe of the left lung)").

[0140] Type of physical examination: The type of physical examination performed this time (e.g., "chest examination", "full physical examination").

[0141] Overall score: A prominent overall score (e.g., 85 / 100) and grade (e.g., Good).

[0142] Core Capabilities Radar Chart: An intuitive radar chart that shows performance in several core dimensions such as accuracy, standardization, completeness, sequence, and timing, allowing you to see your strengths and weaknesses at a glance.

[0143] 2. Detailed analysis of each operation item: This is the core part of the report, analyzing each item item by operation sequence or body part as a guide. The specific content is as follows: Operation sequence | Target area | Operation type | Accuracy score | Standardization score | Remarks / Error description; :--- | :--- | :--- | :--- | :--- | :--- ; | 1 | Left upper lobe | Auscultation | 95 / 100 | 88 / 100 | Accurate location, correct breath sounds |; | 2 | Left lower lobe | Auscultation | 90 / 100 | 65 / 100 | Pathological bronchial breath sounds could not be identified |; | 3 | Left lateral chest wall | Palpation | 85 / 100 | 92 / 100 | Successfully triggered and identified pleural friction sensation |; | 4 | Liver area | Percussion | 70 / 100 | 80 / 100 | Positional deviation >2cm, but technique is standard |; | ... | ... | ... | ... | ... .

[0144] Each row can be clicked to expand and view more detailed data, including: Operation Replay: A small window plays the 3D model animation and sensor data curves during this operation.

[0145] Error Analysis: Specific explanations for the points deducted. For example: "During auscultation of the 'left lower lobe', the system provided pathological bronchial breath sounds, but you recorded them as 'normal breath sounds'"; "When you percussed the liver, the starting point was too far off the midline; the correct position should be on the midclavicular line."

[0146] 3. Source tracing analysis and clinical reasoning evaluation, including: The path diagram has been checked: it is displayed in a tree diagram format, highlighting all the nodes that the trainees actually operated on; Green: Nodes that have been operated correctly and are in place; Yellow: Nodes with operational flaws; Red: Key nodes where operations were incorrect or not performed; Recommended examination path diagram: The system generates a standard and ideal physical examination and tracing path based on the set disease. By comparing with the "examined path", trainees can clearly see which examinations that are crucial to diagnosis they have missed (for example, pneumonia was found, but the examination for pleural friction rub was missed). Clinical reasoning assessment: The system generates a summary text to evaluate the trainee's clinical reasoning. For example: "The trainee is able to locate the main lesion site, but lacks awareness of disease complications (such as pleurisy) and has insufficient correlational examinations."

[0147] 4. Summary of key errors and outstanding performances: List of major errors: List the 2-3 most serious errors in this exercise and provide suggestions for improvement.

[0148] Example 1: Omission of important signs: Failure to correctly identify pathological breath sounds in the lower lobe of the left lung.

[0149] Example 2: Improper procedure sequence: During an abdominal examination, if palpation is performed before percussion, it may interfere with the auscultation of bowel sounds.

[0150] Record of outstanding performance: Record the good points as well and give positive encouragement.

[0151] Example: The palpation technique is standardized, the pressure control is stable, and it can well simulate deep palpation.

[0152] 5. Data visualization charts, including: 1. Pressure-Time Curve: Shows how the applied pressure changes over time during key procedures (such as percussion and palpation), and compares it with the shaded area of ​​the "Standard Pressure Range".

[0153] 2. Operation trajectory heat map: On a human body outline map, different colors are used to show the position and frequency of the trainee's hands or stethoscope, intuitively showing the concentration and coverage of the examination.

[0154] 3. Timeline: Displays the timeline of the entire physical examination process, marking the start and end times of each operation, and analyzing the operation rhythm and efficiency.

[0155] 6. Personalized learning suggestions and resource links: Based on the above analysis, the system automatically generates an action guide, including: Recommended practice modules: "Highly recommended: Auscultation training module for pneumonia-related pathological sounds", "Needs to be strengthened: Abdominal organ surface projection and localization practice"; Recommended learning resources: "Please review Chapter 3 of the textbook, 'Standard Procedure for Chest Examination'", "Click to watch the expert's standard operation video for 'Liver Palpation'"; In summary, this assessment report achieved the following: 1. From emotion to reason: Transform subjective "whether it is done well or not" into objective data and scores.

[0156] 2. From results to process: It is not only about whether the final diagnosis is correct, but also about whether the entire physical examination process is scientific and standardized.

[0157] 3. From Operation to Thinking: Through source path analysis, directly assess and guide trainees' clinical diagnostic logic.

[0158] 4. From general to personalized: Providing tailored feedback and learning paths for each learner.

[0159] Furthermore, such as Figure 7 As shown, based on the above-mentioned 3D human body examination method based on a dynamic tree-hierarchical structure, the present invention also provides a 3D human body examination system based on a dynamic tree-hierarchical structure, wherein the 3D human body examination system based on a dynamic tree-hierarchical structure includes: The 3D humanoid model construction module 51 is used to construct a 3D humanoid model and deploy a dynamic tree-like hierarchical structure in the 3D humanoid model; The pathological parameter setting module 52 is used to obtain the pathological parameters set on the target node in the dynamic tree-like hierarchical structure, and determine the pathological propagation path corresponding to the target node based on the pathological parameters. The physical examination operation recording module 53 is used to determine the target physical examination task and obtain the physical examination operation performed by the trainee on the 3D humanoid model according to the target physical examination task. The physical examination operation verification module 54 is used to verify the physical examination operation based on the target node and the pathological transmission path, and obtain the physical examination operation result of the trainee.

[0160] Furthermore, such as Figure 8 As shown, based on the above-mentioned 3D human body examination method and system based on dynamic tree hierarchical structure, the present invention also provides a terminal, which includes a processor 10, a memory 20 and a display 30. Figure 8 Only some of the terminal components are shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0161] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory. In other embodiments, the memory 20 may be an external storage device of the terminal, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc. Further, the memory 20 may include both internal and external storage devices. The memory 20 is used to store application software and various types of data installed on the terminal, such as the program code installed on the terminal. The memory 20 can also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 20 stores a 3D human body examination program 40 based on a dynamic tree-hierarchical structure, which can be executed by the processor 10 to implement the 3D human body examination method based on a dynamic tree-hierarchical structure in this application.

[0162] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in the memory 20 or process data, such as executing the 3D human body examination method based on a dynamic tree-like hierarchical structure.

[0163] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display 30 is used to display information on the terminal and to display a visual user interface.

[0164] In one embodiment, when the processor 10 executes the 3D human body examination program 40 based on a dynamic tree hierarchy in the memory 20, it implements the steps of the 3D human body examination method based on a dynamic tree hierarchy as described above.

[0165] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a 3D human body examination program based on a dynamic tree-hierarchical structure, and the 3D human body examination program based on a dynamic tree-hierarchical structure, when executed by a processor, implements the steps of the 3D human body examination method based on a dynamic tree-hierarchical structure as described above.

[0166] In summary, this invention provides a 3D human body examination method, system, terminal, and storage medium based on a dynamic tree-like hierarchical structure. The method includes: constructing a 3D human body model and deploying a dynamic tree-like hierarchical structure within the 3D human body model; acquiring pathological parameters set on target nodes in the dynamic tree-like hierarchical structure and determining the pathological propagation path corresponding to the target node based on the pathological parameters; determining a target examination task and acquiring the examination operations performed by the trainee on the 3D human body model according to the target examination task; and verifying the examination operations based on the target node and the pathological propagation path to obtain the examination operation results of the trainee. This invention, by constructing a 3D human body model and setting pathological parameters within it, can accurately acquire the trainee's examination operations on the 3D human body model and verify these operations, effectively improving the accuracy and efficiency of the trainee's examination operations.

[0167] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal that includes that element.

[0168] Of course, those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.). The program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The computer-readable storage medium can be a memory, magnetic disk, optical disk, etc.

[0169] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A 3D human body examination method based on a dynamic tree-like hierarchical structure, characterized in that, The 3D human body examination method based on a dynamic tree-like hierarchical structure includes: Construct a 3D humanoid model and deploy a dynamic tree-like hierarchical structure within the 3D humanoid model; Obtain the pathological parameters set on the target node in the dynamic tree-like hierarchical structure, and determine the pathological propagation path corresponding to the target node based on the pathological parameters; Determine the target physical examination task and obtain the physical examination operations performed by the trainee on the 3D humanoid model according to the target physical examination task; The physical examination operation is verified based on the target node and the pathological transmission path to obtain the physical examination results of the trainee.

2. The 3D human body examination method based on a dynamic tree-like hierarchical structure according to claim 1, characterized in that, The construction of the 3D humanoid model and the deployment of a dynamic tree-like hierarchical structure within the 3D humanoid model specifically includes: Construct a 3D humanoid model and deploy a dynamic tree-like hierarchical structure in the 3D humanoid model, the dynamic tree-like hierarchical structure including a trunk and branch nodes; The 3D humanoid model has a hierarchical structure, including a central mounting rod and multiple cubic frame units. Each cubic frame unit includes multiple first-level branches, multiple second-level branches, and multiple N-level branches. The multiple first-level branches are respectively connected to the central mounting rod. The central mounting rod corresponds to the trunk in the dynamic tree-like hierarchical structure, and the cube frame unit corresponds to the branch node in the dynamic tree-like hierarchical structure.

3. The 3D human body examination method based on a dynamic tree-like hierarchical structure according to claim 1, characterized in that, The step of obtaining the pathological parameters set on the target node in the dynamic tree hierarchy and determining the pathological propagation path corresponding to the target node based on the pathological parameters specifically includes: Pathological parameters are set on target nodes in the dynamic tree hierarchy using the disease parameter editor, wherein the pathological parameters include the simulated disease and its corresponding severity. Based on anatomical and physiological principles, the dynamic influence of the pathological parameters on the dynamic tree-like hierarchical structure is simulated to obtain the pathological propagation path.

4. The 3D human body examination method based on a dynamic tree-like hierarchical structure according to claim 1, characterized in that, The physical examination operation includes physical examination of the target node and the associated nodes in the pathological transmission path; The process of determining the target physical examination task and obtaining the student's physical examination operation on the 3D humanoid model according to the target physical examination task specifically includes: The target physical examination task is determined based on the target node and the pathological transmission path, and the target physical examination task is sent to the trainees who need to undergo physical examination. When the trainee comes into contact with the 3D humanoid model according to the target physical examination task, the first operation data is collected through the thin film pressure sensor deployed on the 3D humanoid model. The first operation data includes spatial position, movement trajectory and posture angle. The second operational data is collected by a three-dimensional posture sensor deployed on the 3D humanoid model, wherein the second operational data includes pressure magnitude, pressure distribution, and contact time.

5. The 3D human body examination method based on a dynamic tree-like hierarchical structure according to claim 4, characterized in that, The step of verifying the physical examination operation based on the target node and the pathological transmission path to obtain the physical examination result of the trainee specifically includes: Determine global 3D coordinates based on the first operation data, calculate the Euclidean distance between the global 3D coordinates and the target node, and obtain the first weight based on the Euclidean distance; The pressure time series waveform is determined based on the second operating data, the similarity between the pressure time series waveform and the standard operating waveform is calculated, and a second weight is obtained based on the similarity. Based on the pathological transmission path, obtain the number of physical examinations and the order of physical examinations performed by the trainee on all related nodes along the pathological transmission path; The third weight is obtained based on the number of physical examinations, and the fourth weight is obtained based on the order of the physical examinations; The physical examination results are obtained by comprehensively calculating based on the first weight, the second weight, the third weight, and the fourth weight. The physical examination results include a comprehensive score and a physical examination evaluation report.

6. The 3D human body examination method based on a dynamic tree-like hierarchical structure according to claim 5, characterized in that, The step of determining global 3D coordinates based on the first operation data, calculating the Euclidean distance between the global 3D coordinates and the target node, and obtaining a first weight based on the Euclidean distance specifically includes: The sensor fusion algorithm is used to calculate the global 3D coordinates of the student's touch on the 3D humanoid model based on the first operation data; A preset standard anatomical atlas is determined, and the target node is searched within the preset standard anatomical atlas to obtain the preset target coordinate range corresponding to the target node; If the global 3D coordinates are within the preset target coordinate range, the Euclidean distance between the global 3D coordinates and the target node is calculated, and a first weight is obtained based on the Euclidean distance.

7. The 3D human body examination method based on a dynamic tree-like hierarchical structure according to claim 5, characterized in that, The step of determining the pressure time series waveform based on the second operating data, calculating the similarity between the pressure time series waveform and the standard operating waveform, and obtaining a second weight based on the similarity specifically includes: The pressure time series waveform is determined based on the second operation data, and the target operation type and target operation quality are obtained based on the pressure time series waveform. The target operation type is palpation, percussion or auscultation, and the target operation quality includes palpation force, percussion technique and stethoscope placement pressure. A preset matching rule is determined based on the target node, and the standard operation waveform corresponding to the preset matching rule is obtained; Calculate the similarity between the pressure time series waveform and the standard operating waveform. If the similarity is greater than a preset similarity threshold, then obtain a second weight based on the similarity.

8. A 3D human body examination system based on a dynamic tree-like hierarchical structure, characterized in that, The 3D human body examination system based on a dynamic tree-like hierarchical structure includes: A 3D humanoid model building module is used to build a 3D humanoid model and deploy a dynamic tree-like hierarchical structure in the 3D humanoid model; The pathological parameter setting module is used to obtain the pathological parameters set on the target node in the dynamic tree-like hierarchical structure, and determine the pathological propagation path corresponding to the target node based on the pathological parameters. The physical examination operation recording module is used to determine the target physical examination task and obtain the physical examination operation performed by the trainee on the 3D humanoid model according to the target physical examination task; The physical examination operation verification module is used to verify the physical examination operation based on the target node and the pathological transmission path, and obtain the physical examination operation results of the trainee.

9. A terminal, characterized in that, The terminal includes: a memory, a processor, and a 3D human body examination program based on a dynamic tree-hierarchical structure stored in the memory and executable on the processor. When the 3D human body examination program based on a dynamic tree-hierarchical structure is executed by the processor, it implements the steps of the 3D human body examination method based on a dynamic tree-hierarchical structure as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a 3D human body examination program based on a dynamic tree-hierarchical structure. When the 3D human body examination program based on the dynamic tree-hierarchical structure is executed by a processor, it implements the steps of the 3D human body examination method based on a dynamic tree-hierarchical structure as described in any one of claims 1-7.

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