563 results about "Medical imaging data" patented technology

The invention discloses a multi-modal medical image data intelligent processing system, relates to the field of medical image analysis, and is applied to multi-modal medical image whole-process analysis of CT, MRI, PET, ultrasound and the like. According to the system, different modal image features are extracted and fused through a cross-modal manifold fusion network; a semantic guidance dynamic registration engine optimizes registration parameters to ensure that the registration error is less than or equal to 1.5 mm; the multi-task collaborative diagnosis network realizes multiple tasks such as disease classification; the clinical knowledge embedding and interpretable module generates a structured report and is in butt joint with an HIS system. Meanwhile, the model is optimized through a federated learning architecture, the adaptability of newly added data is improved by more than or equal to 20%, and intelligent processing and analysis of multi-modal medical images are realized.

The invention discloses a multi-granularity knowledge graph auxiliary diagnosis method based on DeepSeek and Agent. The multi-granularity knowledge graph auxiliary diagnosis method comprises the following steps: step 1, receiving electronic medical record text data and medical image data of a patient; 2, constructing a knowledge graph; updating the knowledge graph every day to reflect the latest medical research result; step 3, analyzing the text data of the electronic medical record through DeepSeek-R1; 4, extracting spatial structure feature nodes of the medical image data through a multilayer three-dimensional convolution kernel; the method comprises the following steps: segmenting medical image data into sequence blocks through a Vision Transform; 5, the output of the DeepSeek-R1, the output of the multi-layer three-dimensional convolution kernel and the output of the Vision Transform are input into a multi-modal fusion module; step 6, outputting a high-confidence diagnosis conclusion and probability distribution; and step 7, generating an intelligent report of the structured text. According to the method, by combining natural language processing, computer vision and the knowledge graph technology, accurate and efficient medical examination is achieved.

The invention relates to the technical field of medical image detection, and discloses a medical image quality detection method based on image processing. The method comprises the following steps: acquiring medical image data to be detected, wherein the medical image data comprises a multi-modal scanning image sequence and corresponding acquisition parameters; the medical image data are preprocessed, standardized image data are generated, and the standardized image data comprise unified parameters of spatial resolution, gray scale range and noise level; extracting structural features of the standardized image data, wherein the structural features comprise tissue boundary gradient distribution, texture consistency and local contrast information; constructing a quality evaluation model according to the structural features, wherein the quality evaluation model analyzes a mapping relationship between the structural features and preset quality indexes through a dynamic convolutional network; and outputting a quality defect detection result based on the quality evaluation model, wherein the quality defect detection result marks an image region with artifacts, fuzziness or distortion.

The invention discloses a heart failure treatment aid decision generation system based on multi-modal data fusion, and the system comprises a data collection module which is used for collecting the multi-modal data of a patient, and the multi-modal data comprises structured data, unstructured data and medical image data; the data processing module is used for carrying out standardization, quantization and vectorization processing on the multi-modal data; the knowledge graph construction module is used for constructing a knowledge graph of heart failure treatment, and the knowledge graph comprises a disease entity, a pathological feature, a treatment scheme and an association relationship thereof; the reasoning module is used for generating a personalized treatment decision based on the knowledge graph and the patient data; and the treatment scheme generation module is used for dynamically adjusting and outputting a personalized treatment scheme in combination with the real-time state data of the patient. The problems that multi-modal data are difficult to fuse and real-time disease change is difficult to dynamically adjust in heart failure diagnosis and treatment are solved, accurate diagnosis and personalized treatment are realized through knowledge graph reasoning and a dynamic correction mechanism, and the diagnosis and treatment efficiency and accuracy are remarkably improved.

The invention relates to the technical field of image enhancement, in particular to a medical image focus recognition method and system based on a neural network, and the method comprises the following steps: setting neighborhood windows of different sizes based on input medical image data, calculating the Shannon entropy value of each neighborhood window, calculating the local energy value, and forming a local energy diagram; and fusing the Shannon entropy value and the local energy map to obtain a fused information entropy energy map. According to the method, neighborhood windows of different sizes are set for medical image data, and the Shannon entropy value and the local energy value are calculated for each window, so that the information complexity and the local pixel active degree of the image in spatial distribution can be fully extracted, and the information entropy energy spectrum formed by fusing the information complexity and the local pixel active degree has higher region sensitivity; high-response areas are screened through the atlas, boundary coordinates of the high-response areas are recorded, and the positioning precision can be improved in high-intensity change areas.

The invention provides a first-aid method and system based on 5G communication and an edge device. The method comprises the following steps: determining a communication protocol target adaptation protocol with a 5G base station based on the edge device; obtaining multi-modal data according to the target adaptation protocol based on the edge device, and performing priority classification on the multi-modal data to obtain a priority classification result; evaluating the 5G network resources of the edge device, determining a resource evaluation result, and determining a cloud transmission strategy based on the resource evaluation result and the priority classification result; performing association analysis on the vital sign data, the medical image data and the voice record data to obtain a calculation task and task complexity, processing the calculation task based on the task complexity and a cloud transmission strategy, and determining a data processing result; and data processing results are transmitted to the first-aid smart center and the mobile terminal in a grading manner. According to the method and the device, the compatibility problem caused by non-uniformity of device interfaces is solved, and the edge device processing capability is improved.

The invention relates to the technical field of medical image segmentation, in particular to a medical image segmentation method based on improved SwinUNet. According to the technical scheme, the method comprises the following steps that CT / MRI medical image data of an abdomen or heart area is collected and preprocessed, and preprocessing comprises image gray normalization, size resampling to 224 * 224 and enhancement processing; the preprocessed medical image is divided into image patch blocks, and initial feature representation is formed through linear embedding; through the improvement, the method is superior to a traditional convolutional neural network or a basic SwinUNet model in the aspects of segmentation precision, robustness, boundary processing, clinical applicability and the like, the problems of boundary discontinuity, poor small target recognition, insufficient cross-layer semantic alignment and the like existing in an existing method are effectively solved, a more reliable scheme is provided for automatic segmentation of medical images, and the method is suitable for large-scale popularization and application. The obvious clinical application value is realized.

The invention relates to the technical field of medical data integration, in particular to a block chain and AI-based medical whole industry data asset integration system and method. Comprising a data acquisition layer, a block chain right confirmation layer, a federal learning analysis layer, a data asset transaction platform and a supply chain traceability optimization module. The data acquisition layer comprises an edge computing node, a data cleaning engine and a desensitization algorithm are built in the edge computing node, and the edge computing node is used for carrying out localized cleaning and desensitization processing on hospital HIS system data, medicine RFID data, medicine research and development experiment data and medical image data; the block chain right confirmation layer constructs a medical data asset account book based on an alliance chain architecture, the asset account book comprises a data hash record, a contributor identity identifier and a use authorization log, and an integrated intelligent contract module is used for dynamic right management; according to the invention, medical data islands can be broken, and safe integration and efficient utilization of cross-domain data are realized.

The invention discloses a system and a method for predicting postoperative recurrence risk after triple negative breast cancer neoadjuvant therapy based on multi-modal time sequence medical image data, and belongs to the field of medical image analysis. The system comprises a data processing module used for constructing a multi-modal data set; the multi-modal feature extraction and screening module is used for extracting deep learning, radiomics and tumor habitat features from the region and carrying out feature screening; the model training module is used for constructing a time sequence model based on a Transform architecture and carrying out training through a multi-task learning strategy integrated with time consistency constraint and gene association auxiliary loss; and the recurrence risk prediction module is used for loading the trained model and outputting a recurrence probability and a risk level. According to the method, the multi-modal time sequence image and gene information are fused, so that the recurrence risk of the triple negative breast cancer patient is dynamically and accurately quantified, and support is provided for clinical individualized treatment decision.

The invention relates to the technical field of tumor interventional operations, and discloses a visual navigation method based on a tumor interventional operation robot. The method comprises the following steps: acquiring real-time medical image data of a tumor area containing multi-modal imaging information so as to comprehensively present anatomical details; and performing three-dimensional reconstruction on the image data to generate a tumor area three-dimensional anatomical structure model capable of visually displaying a space structure. Key anatomical feature points are extracted based on the model, space coordinates are calculated, a surgical robot intervention path is planned according to the coordinates, and an initial navigation track is generated; and continuously collecting real-time pose data of the robot in an operation, dynamically matching the real-time pose data with the initial navigation trajectory, adjusting motion parameters according to a matching result, and generating a corrected navigation instruction. The method can reflect the intraoperative anatomy condition in real time, dynamically optimize the path, solve the problems that traditional navigation depends on preoperative static images and lacks real-time adjustment, reduce operative complications and improve the treatment effect of patients.

The invention relates to the technical field of clinical auxiliary decision making, in particular to a vascular embolism clinical risk prognosis evaluation system based on multi-source data. The system comprises a patient multi-source data acquisition module, a clinical data feature processing module, a multi-source data fusion module and a clinical explanatory enhancement module. The method comprises the following steps: acquiring basic information, clinical examination results, vital signs and medical image data of a patient, and respectively performing structured and image feature extraction to form multi-modal feature representation; a key area is mined in combination with a self-attention mechanism, and the feature expression ability is improved; and through comparison of historical similar cases and contribution analysis of local features, interpretability enhancement of model output is realized, and an embolism risk prediction result with a clinical reference value is generated. The method effectively improves the prediction accuracy, stability and medical readability of the model, and is suitable for auxiliary diagnosis and treatment scenes of diseases such as vascular embolism.

The invention relates to a cloud-edge collaborative medical image intelligent diagnosis method and device, equipment and a medium. The method comprises the following steps: firstly, obtaining original image data, and carrying out calibration processing on the original image data through a preset multi-modal image standardization model to obtain a standard image data set; performing feature extraction and grading processing on the standard image data set at an edge end to obtain a grading feature set, transmitting the grading feature set to a cloud end through a bandwidth allocation strategy, and generating a cloud end receiving feature subset; constructing a feature expression matrix based on the cloud receiving feature subset, using a network topology structure to carry out graph analysis, and extracting a path to generate a diagnosis path set; and finally, feedback diagnosis information is formed based on the diagnosis path set, and a task allocation optimization result is obtained by dynamically adjusting a cloud side task allocation proportion. According to the method, efficient processing and diagnosis optimization of medical image data are realized, efficient collaboration of cloud edge resources is ensured, timeliness and accuracy of medical image diagnosis are effectively improved, and resource scheduling requirements in different scenes are met at the same time.

The invention belongs to the technical field of medical image segmentation, and particularly discloses a medical image segmentation method based on contextual information and multi-scale feature fusion, and the method comprises the steps: obtaining medical image data, and constructing a medical image segmentation model of a double-U-shaped encoder-decoder architecture; inputting the medical image data into a first U-shaped network for feature extraction to obtain multi-scale context features; multiplying the medical image data and the multi-scale context features element by element; inputting an element-by-element multiplication result into a second U-shaped network for global information modeling and local boundary feature enhancement, and outputting to obtain a final fusion feature; and splicing the multi-scale context features and the final fusion features to obtain a medical image segmentation result. According to the method, the problems that the segmentation precision is low, the calculation complexity is high, the small polyp segmentation effect is poor and efficient segmentation cannot be realized when an existing medical image segmentation method is used for carrying out polyp segmentation are solved.

The invention discloses an ultra-high dose rate radiotherapy plan optimization system based on compensator modulation, and relates to the technical field of radiotherapy. A data acquisition module is used for acquiring medical image data of a patient and receiving a clinical target; the clinical target comprises a target region prescription dose, an organ-endangering limit and a minimum dose rate threshold value required for triggering and maintaining a FLASH effect; and the FLASH biological effect evaluation module is used for calculating FLASH biological effective dose distribution corresponding to the physical dose distribution through an embedded biological effect model based on the input physical dose distribution and dose rate distribution. According to the method, the physical dose is converted into the accurate FLASH biological effective dose through the improved linear quadratic model embedded with the FLASH correction factor, the target tumor killing effect is guaranteed, normal tissue protection is enhanced with the help of the tissue differentiation correction factor, the industrial pain point that the physical dose reaches the standard but the biological effect does not reach the expectation is effectively solved, and the application prospect is wide. And the treatment plan better meets the clinical curative effect and safety core requirements.

The invention discloses an image result report intelligent generation method and system based on a large language model and feature driven prompt. The method comprises the steps that a user logs in a computer system and uploads medical image data; performing feature extraction and optimization on the medical image data to generate feature representation; performing classification and prompt conversion on the feature representation to generate an input prompt of the large language model; performing cross-modal feature fusion by combining the image features and the text features to obtain fusion features; dynamically adjusting a learning target and a loss function of the large language model by adopting a differentiated learning strategy; and generating a structured image processing result report by using a large language model according to the input prompt and the fusion feature. According to the method, image features can be automatically extracted, multi-modal data can be fused, a structured and interpretable report can be generated, and the efficiency and accuracy of image processing are improved.

The invention provides a medical image report generation method and related equipment. The method comprises the following steps: acquiring medical image data; and inputting the medical image data into a pre-trained medical image report generation model, and outputting a medical image report corresponding to the medical image data by the medical image report generation model. The accuracy of the generated medical image report can be improved.

The invention proposes a thoracic surgery intelligent planning system based on multi-modal image fusion, and relates to the technical field of surgery planning, and the system comprises an image processing module which is used for receiving multi-modal medical image data, carrying out the preprocessing and cross-modal registration of the medical image data, and generating fused image data; the three-dimensional reconstruction module is used for segmenting the key anatomical structure in the thoracic cavity based on the fused image data and constructing a three-dimensional model; the operation planning module is used for generating a candidate operation path scheme according to a multi-objective optimization algorithm based on the three-dimensional model; the interaction adjustment module is used for determining a final operation path scheme and updating a risk assessment result in real time; and the report generation module is used for generating a surgical planning report. The method can solve the problems that the existing multi-modal image registration precision is insufficient, the sensitivity of three-dimensional reconstruction to a tiny anatomical structure is low, and the surgical path planning is lack of dynamic risk assessment and real-time interaction adjustment capability, and realizes the generation and optimization of a high-precision and high-safety thoracic surgery planning scheme.

The invention discloses a medical image semantic segmentation method based on attention mechanism optimization, and relates to the technical field of medical image processing, and the segmentation method comprises the specific steps: S100, data collection and label preprocessing: collecting medical image data from different medical institutions and a plurality of imaging devices, according to the method, the attention mechanism is introduced to carry out deep preprocessing on the medical image data, the precision and efficiency of semantic segmentation of the medical image are remarkably improved, the attention mechanism is utilized, key areas, such as diseased regions or tissue boundaries, in the image can be recognized and enhanced, meanwhile, noise and irrelevant information are effectively removed, and the accuracy of semantic segmentation of the medical image is improved. The refined preprocessing mode not only improves the quality of the image, but also provides a more accurate data basis for subsequent image detection and segmentation, and the method is also combined with a self-adaptive denoising algorithm, dynamic adjustment of contrast, brightness and color and a geometric transformation advanced preprocessing technology, so that the availability and diagnostic value of the image are enhanced.

The invention provides an analysis processing method and system for a multi-source medical image, is applied to the technical field of medical image processing, and provides a basis for subsequent processing by acquiring medical image data. All the images are resampled to a unified spatial resolution, the images of the same patient at different time points are registered and aligned to the same spatial coordinate system, and after spatial unification and registration processing, anatomical structures in the images are aligned at different time points. At the moment, feature vectors capable of describing textures and shapes are extracted from the registered images, the feature vectors are subjected to standardization processing and then fused, and finally a quantitative analysis result with cross-time-point comparability is obtained and used for disease analysis, curative effect evaluation and other procedures. Therefore, the method has the advantages of processing multi-source heterogeneous medical image data, processing long-term follow-up visit medical image data and generating a cross-time-point comparable quantitative analysis result.

The invention discloses a multi-modal medical image data analysis method and device, equipment and a medium, relates to the technical field of medical treatment, can be applied to a medical health business scene, and comprises the following steps: obtaining an image analysis task corresponding to multi-modal medical image data; the image analysis task is divided into a first computing power task and a second computing power task, the first computing power task is a preprocessing and real-time preliminary screening task, the second computing power task is a deep analysis task, and the output of the first computing power task serves as the input of the second computing power task; scheduling a first hybrid model deployed at the edge device side to execute a first computing power task, and scheduling a second hybrid model deployed at the cloud side to execute a second computing power task; and outputting a collaborative image analysis result of the first mixed model and the second mixed model for the multi-modal medical image data. According to the invention, the complex focus recognition sensitivity can be improved, and the misjudgment rate is reduced.

The invention provides a medical platform management system, and relates to the technical field of medical health. Comprising a multi-source heterogeneous data intelligent acquisition module, the multi-source heterogeneous data intelligent acquisition module is connected with an external system through a distributed data acquisition framework and collects and integrates multi-source heterogeneous data, and the multi-source heterogeneous data comprises but is not limited to medical text data, multi-modal medical image data and real-time medical sensor data; aiming at the problem, the multi-source heterogeneous data intelligent acquisition module designs an intelligent data interface adapter, supports multiple medical data transmission protocols such as RESTAPI, HL7FHIR, DICOM and the like, realizes data standardized mapping through an efficient data conversion engine, solves the problem of cross-mechanism and cross-system data intercommunication in a medical body, and improves the data transmission efficiency. And a foundation is laid for realizing real medical integration.

The invention relates to a multi-modal medical image fusion navigation management system. The system comprises an image processing module which is used for acquiring a medical image data set and performing independent optimization processing on the medical image data set to generate a first image set; and the image fusion module is used for extracting multi-scale key points based on the first image set, generating a second feature set, performing cross-modal registration on the second feature set, calculating to obtain a third mapping set, and performing reconstruction to obtain an accurate image fusion image when the registration error of the third mapping set exceeds a preset threshold value. And the visual diagnosis and treatment module is used for extracting dynamic organization boundary features and a multi-modal fusion result based on the image fusion image, performing joint optimization by using a generative adversarial network and a reinforcement learning strategy, and finally generating a visual diagnosis and treatment path plan. The system provides comprehensive and clear comprehensive image information of diseased regions and surrounding tissues, assists in accurate diagnosis of illness states, and greatly improves scientificity and operability of diagnosis and treatment schemes.

The invention belongs to the technical field of medical images, and particularly relates to an anterior mediastinal surgery three-dimensional imaging method and system based on big data. According to the method, parallel preprocessing is carried out on the multi-modal medical image data through a distributed computing framework, the data processing speed and efficiency are effectively improved, through comparative analysis of different reference image data, an overlapping area and an independent area can be determined, corresponding partition processing is carried out on the overlapping area, and in the three-dimensional reconstruction stage, the three-dimensional reconstruction efficiency is improved. Three-dimensional reconstruction is respectively carried out on the independent region and the overlapped sub-regions, a three-dimensional image model of the independent region and a fused overlapped sub-region three-dimensional image model are generated, the spatial topological structure and the surface geometric characteristics of image data are fully considered, the accuracy of the reconstructed image is ensured, and in the image reconstruction stage, the image reconstruction efficiency is improved. And the three-dimensional image model of the independent region and the three-dimensional image model of the overlapped sub-regions are combined to generate a complete anterior mediastinal surgery three-dimensional image, so that a powerful guarantee is provided for accurate implementation of the surgery.

The invention relates to the technical field of medical image processing, and discloses an adversarial generative network model for three-dimensional reconstruction of a brain cell-level vascular network. The model comprises a three-dimensional blood vessel feature extraction module, a dynamic attention generator, a multi-scale discriminator and the like. Characteristics are obtained through multi-modal medical image data (magnetic resonance angiography and confocal microscope scanning data), and high-precision vascular network three-dimensional reconstruction is realized by utilizing cooperative work of all the modules. The dynamic attention generator calculates the topological connection probability of the vascular branches, and the multi-scale discriminator globally and locally evaluates the reconstruction result. Meanwhile, model training is optimized through an adversarial training controller, and the blood vessel network is perfected through a capillary network completion module. According to the method, the advantages of multi-modal data are effectively fused, the reconstruction precision is improved, and powerful support is provided for research and diagnosis of brain vascular diseases.

The invention discloses a three-dimensional surface shape digital measurement-based cosmetic plastic auxiliary analysis method and system, and the method comprises the steps: obtaining a medical image data set, carrying out the noise processing through employing a Gaussian filtering algorithm, judging a noise point if the difference between a pixel gray value and a neighborhood mean value exceeds a preset threshold value, and carrying out the smoothing processing, and obtaining a first image data set; constructing an energy function according to the first image data set, and filling and repairing a defect region in the image through superposition calculation of a single-point energy item and an adjacent-point interaction energy item to obtain a second image data set; extracting point cloud data from the second image data set and constructing a gradient field, and if the point cloud density is lower than a preset threshold value, executing local encryption processing to generate a three-dimensional face model; grid vertex coordinates and normal vector information of the three-dimensional face model are obtained, and the environment light component, the diffuse reflection component and the mirror reflection component are fused through an illumination intensity calculation formula to generate a visual image, so that the precision and efficiency of medical image processing and three-dimensional visualization are improved.

The invention belongs to the technical field of medical image processing, and particularly relates to a linear Transform general focus recognition method based on multiple perception and context guidance, and the method comprises the steps: extracting the multi-scale features of a medical CT image through a backbone network, obtaining the edge gradient features in parallel, making up the missing of focus boundary information through an edge perception feature enhancement module, and carrying out the recognition of the focus. And then global feature modeling under linear complexity is realized through a polarity perception feature interaction module, a high-discrimination-force multi-scale feature map is generated by using a context-guided feature pyramid network, and finally the model is optimized by combining a Hungary algorithm and a joint loss function based on an end-to-end detection architecture of set prediction. The problems that the focus boundary is fuzzy, feature interaction and calculation efficiency are balanced, and the focus and background separation degree is weak are effectively solved, double improvement of calculation efficiency and detection precision on massive medical image data is achieved, and reliable support is provided for clinical precise auxiliary diagnosis.

The invention discloses a medical image segmentation method and system for an aortic dissection true cavity, an aortic dissection false cavity and an aortic dissection false cavity thrombus. The method comprises the following steps: firstly, acquiring three-dimensional medical image data of the aortic dissection, performing preprocessing operation on the image data to obtain a two-dimensional slice image, and performing local region division on the two-dimensional slice image; secondly, performing feature extraction on the divided image through a hierarchical feature extraction module; then, a dynamic region self-adaption module is used for carrying out region adjustment on the extracted feature map, a region response map is generated through a guide path, and explicit adjustment weights are given to different space regions in the feature map; and finally, uniformly aggregating the multi-scale feature maps after region adjustment, outputting a segmentation mask through a prediction module, and generating a segmentation result with a clear structural boundary and complete semantics. According to the method, while the recognition precision of the complex anatomical structure is improved, the detection capability and boundary sensing performance of a small-size focus are enhanced, and the method has good universality and stability.

The invention belongs to the technical field of medical auxiliary diagnosis, and particularly relates to an AI-based LoRA fine-tuning medical decision Agent system and an implementation method. The multi-modal data acquisition module is used for acquiring vital sign data, medical image data, laboratory inspection data and electronic medical record text data of a patient in real time; the dynamic weight adjustment LoRA fine adjustment module is used for carrying out low-rank adaptive fine adjustment on a pre-trained medical decision model by introducing a time attenuation factor, a data importance weight and a modal emergency degree coefficient based on the multi-modal data so as to generate a personalized decision model for the current patient; the multi-modal risk assessment module is used for fusing feature vectors output by the fine-tuned personalized decision-making model, constructing a comprehensive assessment function in combination with an attention mechanism and a feature interaction coefficient, and realizing dynamic quantitative assessment of the illness state risk of the patient; according to the method, the timeliness, the importance and the scene emergency degree of the multi-modal data can be dynamically balanced.

The invention discloses a rectal cancer tumor T stage differentiation labeling method based on an enhanced CT image, and relates to the field of medical image data processing, and the method comprises the following steps: a standardized manual labeling module which is used for constructing three-dimensional pixel-level labeling data of a rectal cancer tumor T stage according to a medical image and a pathological stage standard; the data set construction module is used for uniformly storing and organizing the original CT image, the annotation mask file and the matched label description document to form a structured data set which can be used for modeling; according to the rectal cancer tumor T-stage differentiation labeling method based on the enhanced CT image, a high-quality and standardized three-dimensional T-stage labeling data set is constructed, a three-dimensional pixel-level stage labeling process for the rectal cancer enhanced CT image is provided based on the AJCC eighth version tumor stage standard, the labeling content covers tumor focuses and normal intestinal wall structures around the tumor focuses, and the three-dimensional T-stage labeling data set is established. A plurality of high-annuity imaging department doctors perform independent blind marking, expert re-checking, quality rating and the like.

The invention discloses a hip joint rehabilitation method and system based on multi-parameter real-time monitoring and digital twinning, and the method and system are used for postoperative monitoring of hip joints of patients, and the method comprises the steps: S1, collecting medical image data of the patients, and constructing a hip joint biomechanical model of the patients; acquiring a myoelectricity activity signal, a hip joint activity angle and joint bearing pressure of the hip of the patient after the operation; s2, constructing a hip joint digital twinning model of the patient, and dynamically updating the state of the hip joint digital twinning model; s3, guiding the patient to perform rehabilitation training through the intelligent display terminal; s4, collecting and recording rehabilitation myoelectricity activity signals, rehabilitation hip joint activity angles and rehabilitation joint bearing pressure of the hip of the patient in the rehabilitation process in real time, judging the rehabilitation stage of the patient, and dynamically adjusting a rehabilitation training scheme. In the rehabilitation process, the rehabilitation training scheme is dynamically adjusted according to the rehabilitation condition of the patient, and the effectiveness and safety of the rehabilitation process of the patient are guaranteed.