892 results about "Treatment plan" patented technology

The embodiment of the invention provides a water conservancy project intelligent processing system based on BIM and GIS. The water conservancy project intelligent processing system comprises the steps that material strength and construction timestamps of engineering components are extracted through a BIM parser to generate a topological relation graph; combining the remote sensing image and the geological data through a coordinate converter to determine a three-dimensional terrain field; establishing a spatial Hash grid correlation topological relation graph and a three-dimensional terrain field; constructing a digital twinborn body containing structural stress distribution and a hydrodynamic model on the basis of a correlation result; executing Monte Carlo simulation in the digital twin according to the real-time sensor data to generate a prediction scene set; when abnormal data is monitored, performing reverse tracing along the topological relation graph to generate a multi-factor association graph; and optimizing a disposal plan weight coefficient through a reinforcement learning algorithm and updating the knowledge base. Pixel-level matching of the rock-soil layer permeability coefficient and the concrete member strength is achieved, the system can recognize the coupling relation between a geological weak area and a structural high-risk point, meanwhile, a knowledge graph containing a construction time sequence is constructed, and a cause chain of quality defects can be traced.

The invention discloses an intelligent pulmonary tuberculosis recognition method and system based on image data, and relates to the technical field of image recognition, and the method comprises the steps: collecting and preprocessing conventional inspection data from different medical devices, and forming a standardized data set; extracting features from the standardized data set by using a deep learning model, and positioning a suspected focus area; based on the positioned suspected focus area, constructing a personalized risk assessment model, and generating a risk score; performing focus quantitative analysis based on the risk score, and generating a diagnosis report; the diagnosis report is applied to clinical practice, and a personalized diagnosis and treatment plan is made; according to the method, the personalized risk assessment model is constructed based on the positioned suspected focus area, the risk score is generated, and quantitative assessment of the health condition of the patient is realized.

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 a multi-modal image registration processing method and system based on deep learning, and relates to the technical field of medical image processing, and the method comprises the steps: calculating a mutual information loss value after registration through a mutual information loss method, optimizing a CNN model and UNet model parameters in combination with the total loss generated by the fusion of global and local deformation fields, and obtaining a registered sCT image. A CNN model and a UNet model are combined to generate global and local deformation fields, overall rigid transformation and local nonlinear deformation are effectively captured, the spatial alignment precision of sCT and reference CT is improved, model parameters are optimized through mutual information loss and total loss, the intensity distribution consistency is ensured, feature weights and treatment plan parameters are automatically adjusted through registration quality feedback, and the accuracy of the treatment plan is improved. The coverage precision and efficiency of the radiotherapy plan are remarkably improved, automatic and high-precision image registration and treatment plan optimization are realized, and the reliability and practicability of clinical image processing are enhanced.

The present invention relates to a cognitive computing-assisted clinical decision support system designed to enable personalized neurological rehabilitation. The system acquires structured user data across clinical, anatomical, radiological, etiological, pathological, and rehabilitation domains to create individualized profiles. These profiles are mapped against a repository of historical cases using analog matching and similarity scoring to generate stratified, evidence-based rehabilitation recommendations. Real-time monitoring of rehabilitation progress is performed using global recovery and function outcome indicators, allowing for dynamic adjustment of treatment plans. Clinician intervention modules ensure safety, interpretability, and context-aware customization. The system incorporates a continuous feedback mechanism to refine future predictions and recommendations, making it increasingly adaptive over time. The invention improves rehabilitation outcome prediction accuracy, reduces recovery variability, and optimizes functional outcomes by transforming static rehabilitation models into intelligent, responsive, and personalized care pathways.

The invention relates to the technical field of medical information processing, in particular to a type 2 diabetes precise typing and dynamic treatment system which comprises a clinical feature extraction module, a metabolism dynamic modeling module, a precise typing module, a treatment parameter generation module and a dynamic treatment output module. Wherein the clinical feature extraction module is used for collecting a multi-dimensional clinical data set of a patient for continuous 14 days; the metabolism dynamic modeling module is used for extracting blood glucose fluctuation characteristics and pancreas islet function compensation characteristics; the precise typing module is used for dividing metabolism-driven subtypes to which the patients belong through a clustering algorithm; the treatment parameter generation module is used for mapping a subtype-treatment rule base and outputting a matched personalized treatment parameter combination; and the dynamic treatment output module outputs a dynamic treatment scheme. According to the invention, through a dynamic treatment mechanism based on typing, linkage control of individual difference identification and real-time adjustment of the medication scheme of the type 2 diabetes patients is realized, and the accuracy and adaptability of treatment are improved.

The invention relates to the technical field of minimally invasive ablation, in particular to a liver tumor microwave ablation postoperative contraction deformation prediction method, which comprises the following steps: performing unified standardization processing on different periods of liver MRI images; accurately marking a preoperative liver tumor area, a postoperative ablation area and a postoperative liver tumor ghost area of the liver MRI image to obtain masks of the corresponding areas; designing a distance perception function as an attention parameter of different areas of the liver MRI image; constructing a multi-sequence distance guide complementary network model; selecting a loss function; selecting an elastic registration method to perform pre-operation and post-operation registration; and calculating an ablation safety boundary through the ablation area mask and the adjusted tumor mask. The liver tumor microwave ablation postoperative curative effect evaluation accuracy can be effectively improved, and a foundation is laid for formulating a subsequent treatment plan of a patient.

The invention discloses an intensive care treatment scheme intelligent decision-making system based on data driving, and relates to the technical field of medical information, and the system comprises a data collection module which is used for obtaining multi-source clinical data of an intensive care unit in real time; and the decision generation module is connected with the data acquisition module and is used for generating a treatment scheme according to the clinical data. According to the intensive care treatment scheme intelligent decision-making system based on data driving, through four-dimensional snapshot construction of a dynamic decision-making chain and medical interpretable evolution relation labeling, continuous logic of an intensive care decision-making is guaranteed, data abnormal driving change and medical logic jump risks are effectively identified, medical errors caused by decision-making faults are reduced, and the decision-making efficiency is improved. The abstract decision-making process is converted into a concrete evidence chain capable of penetrating auditing, and the clinical responsibility traceability and the doctor-patient credibility are improved.

The invention relates to the technical field of medical image information processing, and discloses a medical image information management system based on wisdom medical treatment, which comprises a scene perception preprocessing module, a cross-modal attention fusion module, a weak supervision annotation module, a comparative analysis module, a collaborative report generation module, a knowledge graph reasoning decision module and a consultation collaborative platform module. The image quality is improved through scene adaptive preprocessing; a hierarchical cross-modal attention mechanism is adopted to realize multi-modal feature alignment and fusion; lesion labeling is carried out based on a cascade weak supervised learning strategy; obtaining a focus evolution trend through time sequence correlation analysis; generating a standardized diagnosis report by using a visual language collaborative network; providing treatment scheme recommendation based on individualized knowledge graph reasoning; and a multidisciplinary consultation cooperation platform is constructed. According to the method, the scene adaptability of image quality evaluation is improved, the multi-modal semantic alignment capability is enhanced, the dependence of focus labeling on a fine sample is reduced, and dynamic disease monitoring and individualized diagnosis and treatment decision support are realized.

Devices, systems, and methods for predicting palatal geometry are provided. In some embodiments, a system for predicting an anatomy of a patient's palate includes one or more processors and a memory operably coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the system to perform operations including accessing a first digital representation including an initial geometry of soft tissue corresponding to a patient's palate, determining an initial geometry of hard tissue of the patient's palate, predicting a change in the hard tissue during a treatment, using the predicted change in the hard tissue to predict a change in the soft tissue during the treatment, and outputting a second digital representation including a predicted geometry of the patient's palate at a future treatment stage of a treatment plan, based on the predicted change in the soft tissue.

The invention relates to the field of oral clinical orthodontics, and discloses a method for generating an oral correction scheme, comprising: acquiring a face composite model of an initial state of a patient, the face composite model comprising a dental jaw portion and a soft tissue portion; respectively obtaining P correction schemes of the patient, wherein each correction scheme corresponds to a target state of tooth jaw movement; correspondingly obtaining face prediction models of P target states according to the tooth jaw movement and deformation face composite models of the P correction schemes; and in the P correction schemes, selecting an optimal correction scheme as a target correction scheme according to evaluation of the face prediction model in the target state. The invention further discloses electronic equipment and a storage medium, the orthodontic scheme is determined by predicting the appearance, the predicted result is closer to the real effect after orthodontic treatment, more bases are provided for the patient to select the orthodontic scheme, the patient can conveniently determine the ideal orthodontic scheme from the face expression, and a more preferable orthodontic scheme is generated.

A computer-implemented method including receiving, at a computing device, a first treatment plan designed to treat a sexual performance health issue of a user. The first treatment plan comprises at least two exercise sessions that, based on the sexual performance health issue, enable the user to perform an exercise at different exertion levels. While the user uses a treatment apparatus to perform the first treatment plan for the user, the computing device receives cardiovascular data from one or more sensors configured to measure the cardiovascular data associated with the user, and transmits the cardiovascular data. A machine learning model is used to generate a second treatment plan that modifies at least one exertion level, and the modification is based on a standardized measure comprising perceived exertion, the cardiovascular data, and the cardiovascular health issue of the user. The method includes receiving the second treatment plan.

The invention relates to the field of radiotherapy plan design, and discloses a radiotherapy parameter intelligent recommendation system based on disease species, target area volume and dose characteristics, and the system comprises the steps: constructing a response surface model of machine physical parameters and plan quality indexes, generating a Pareto optimal solution set through a non-dominated sorting genetic algorithm, and building an optimization rule base; extracting anatomical and geometric features of historical cases, and training a CART decision tree to establish a mapping relation between the features and clinical scenes; analyzing to-be-planned case features, matching a target scene through the decision tree, and retrieving an optimal machine physical parameter combination in the rule base according to the user preference weight. According to the method, the width, the screw pitch and the modulation factor of the collimator can be automatically recommended, the problem that manual trial and error efficiency is low and depends on experience is solved, and balance between treatment plan quality and machine execution efficiency is achieved.

The invention discloses an electronic medical record intelligent writing and structured processing system based on AIGC, and relates to the technical field of medical information processing and artificial intelligence application, and the system comprises a medical record information extraction and analysis module which extracts diagnosis records, medication schemes and treatment plan data of a patient in different departments and time points from an electronic medical record system, and obtains a medical record information analysis module; analyzing semantic content and format differences of the data by adopting a natural language processing algorithm to obtain a standardized medical information set; the diagnosis and treatment map construction module is used for constructing a patient diagnosis and treatment map according to the standardized medical information set and determining potential heterogeneous data connection points in the map; according to the electronic medical record intelligent writing and structured processing system based on AIGC, medical record data consistency checking and conflict automatic coordination are achieved, and the cross-department diagnosis and treatment cooperation efficiency and the safety and accuracy of a patient treatment scheme are remarkably improved.

The invention relates to the technical field of oral medical treatment, in particular to a tooth orthodontic treatment scheme planning system, which comprises a database module, a processing module and a planning module, the data acquisition module is used for acquiring initial oral data of a patient before treatment and stage monitoring data in a treatment process; the scheme generation module is used for screening out a complete treatment scheme with the highest similarity in the database through feature similarity matching and generating an initial treatment scheme; the risk assessment module is used for extracting risk factors such as periodontal support force and calculating a risk level; the dynamic adjustment module is used for setting a tooth movement distance deviation threshold value according to the risk level, and comparing an actual tooth movement distance deviation value to generate an adjustment treatment scheme; and the doctor rechecking module is used for the doctor to adjust and recheck the initial treatment scheme and the adjusted treatment scheme according to the individual parameters of the patient. According to the scheme, intelligent planning, dynamic adjustment and scientific review of an orthodontic scheme can be realized, and the treatment accuracy and safety are improved.

The invention discloses a close-range radiotherapy plan optimization method and device based on a cumulative dose target. The method comprises the following steps: S1, importing current treatment data; s2, leading in a preorder treatment image and performing deformation registration to the current image; s3, mapping the preorder treatment dose to the current treatment image to serve as the background dose of the current treatment; s4, converting the dose data mapped to the current treatment image by the preorder treatment into a bioequivalent dose; s5, on the basis of the sketching data of the current treatment image and with reference to the clinical endangered organ dose limit value requirement, key area generation is carried out on each endangered organ; s6, based on the preorder treatment dosage, generating a corresponding organ-endangered high-risk dosage area and a target area insufficient area according to each organ-endangered dosage limit value and the target area prescription dosage; and S7, optimizing the treatment plan, and optimizing the treatment plan by taking the preorder treated dose and the sum of the planned dose to be optimized at the time to meet the clinical requirement as a target.

The invention relates to the technical field of online medical treatment, in particular to an online inquiry intelligent diagnosis system based on a deep learning model, which comprises a patient interaction end, an intelligent diagnosis end and a hospital database end. The patient interaction terminal collects patient information and generates an inquiry request; the intelligent diagnosis terminal performs disease derivation and diagnosis and treatment scheme generation in combination with historical medical records and a medical knowledge base; and the hospital database end provides medical record storage and knowledge graph updating support. The system optimizes the diagnosis accuracy through a deep learning model processing platform, and has the functions of aging optimization and automatic registration. The problem of resource shortage of doctors can be relieved, the diagnosis efficiency and the medical record data utilization rate are improved, and efficient and accurate online inquiry service is provided for patients.

Agentic AI, Contextual AI, and Generative AI systems and methods are provided for quantitative or qualitative patient analytics, blood work analytics, medical image analytics (x-ray, ultrasound, CT Scan, MRI and the like), brain scan analytics, present or historical genetic analytics and the like, to be used to guide, assist or even replace some of these physician functions with improved patient outcomes or with an improved understanding of the source of ailments, the analysis of treatment efficacy or treatment side-effects for individuals or populations of individuals. Genetic information is used to create individualized or population-based analysis, diagnostics, or treatment plans with even better outcomes.

The invention discloses an interpretable clinical decision support system based on label generation and a knowledge graph. The method comprises the following steps: based on a breast cancer domain knowledge enhanced version Qwen-BrCaAdapt of a general large language model Qwen, analyzing an unstructured medical record text of a patient, and generating a structured result containing tags, values, evidences and explanations; calculating a reasoning label through a path matching engine by utilizing an editable structured path rule table, and matching a candidate treatment scheme according to the reasoning label; and taking the matched treatment scheme as a central node, calling a medical knowledge graph to bind entity information including clinical evidence, recommendation levels, medical insurance information, medication risks, usage and dosage, and generating a traceable JSON structure and a visual report. The method has the beneficial effects that the accuracy and efficiency of tag generation in the breast cancer field are improved, the rule maintenance cost is reduced, the interpretability and traceability of a clinical decision scheme are enhanced, and the acceptability of a doctor to a recommendation result is improved.

The invention belongs to the field of computational medicine, particularly relates to a method and a system for constructing full-parameter digital twinning of a patient and performing treatment simulation, and aims to solve the problems that an existing digital twinning model cannot fuse multi-scale data, lacks dynamic optimization capability and is disjointed with a clinical decision process. The method comprises the following steps: acquiring and integrating multi-source heterogeneous data of a patient; performing cross-scale fusion modeling based on the data, and constructing a personalized digital twinborn initial parameter set containing genetic background correction; driving a multi-physics field coupling engine to simulate an intervention effect, constructing a reverse optimization problem taking real-time monitoring data as a dynamic constraint, and solving an optimal intervention scheme parameter; and carrying out verification simulation on the optimization scheme and generating a treatment report containing quantitative evaluation and risk early warning. According to the invention, by constructing a full-parameter and mechanism model and introducing a dynamic closed-loop optimization verification process, the personalized precision, safety and clinical decision support value of treatment scheme simulation are significantly improved.

A computer-implemented system includes one or more processing devices configured to receive comorbidity information that includes a plurality of comorbidities or comorbidity-related conditions associated with a user, generate a selected set of the comorbidity information, determine, based on the selected set of the comorbidity information, respective probabilities of a plurality of different outcomes related to the comorbidity information, and generate, based on the respective probabilities and the selected set of the comorbidity information, a treatment plan comprising one or more exercises directed to changing the respective probabilities. A treatment apparatus is configured to implement the treatment plan while the treatment apparatus is being manipulated by the user.

The invention belongs to the technical field of medical health care equipment, and particularly provides a treatment control method of an intelligent self-adaptive wave conduction health care instrument, which comprises the following steps of: acquiring a physiological state signal of a user in real time, and generating an abnormal trigger instruction when the physiological state signal is detected to deviate from a preset health threshold range; inputting physiological index data corresponding to the abnormal trigger instruction into a dynamic adjustment model, wherein the dynamic adjustment model generates a treatment scheme based on a mapping relation between physiological indexes and wave conduction parameters; generating a driving control signal according to the treatment scheme, wherein the driving control signal is used for adjusting a particle resonance wave output device in the wave conduction health care instrument; and outputting a target particle resonance wave through a particle resonance wave output device according to the resonance frequency and the amplitude corresponding to the driving control signal, and continuously driving the action duration corresponding to the control signal. By collecting the physiological state signal of the user in real time and dynamically comparing the physiological state signal with the preset health threshold value, accurate anomaly detection and immediate intervention are achieved, and the limitation of fixed parameter output of traditional equipment is broken through.

The invention relates to the field of remote quality control of kinesio, and provides a remote kinesio treatment guidance and quality control system. The image acquisition module is used for acquiring high-definition image data of a to-be-treated area through intelligent terminal equipment and marking anatomical feature points; wherein the high-definition image data comprises a static image and a dynamic muscle movement video stream; the AI analysis engine is used for carrying out feature extraction on the high-definition image data based on a muscle dynamics model of a to-be-treated region constructed by a convolutional neural network, identifying a muscle tension abnormal region and grading a nerve injury degree, and generating an initial sticking path recommendation scheme; the remote auditing interface transmits the recommended scheme generated by the AI to the doctor end interaction interface, obtains a target treatment scheme after the doctor end performs scheme correction based on the medical history data of the patient, and confirms a final treatment instruction through a digital signature; and the block chain quality control module is used for encrypting key operation data in the treatment process and then writing the key operation data into a distributed account book to generate a treatment process traceability chain which cannot be tampered.

The invention discloses a household collaborative nursing management system for diabetic sleep disorders and an implementation method, relates to the technical field of nursing management, and aims to automatically establish and adjust a layered nursing structure according to health data of a patient, realize correlation analysis among multiple targets, ensure that the most urgent health problem is effectively intervened firstly, and improve the nursing efficiency. The information collaboration degree is utilized to fuse related data of a plurality of nursing targets, accuracy and high efficiency of the data are ensured, so that treatment and nursing strategies are optimized, mutual support and coordination of various nursing targets are ensured, conflicts among targets in a traditional treatment scheme are avoided, dynamic adjustment is carried out through a self-adaptive optimization algorithm, and the treatment efficiency is improved. According to the health state change of the patient, the nursing scheme is adjusted in real time, it is guaranteed that the patient can obtain personalized and continuously-optimized nursing management, the treatment effect of diabetes and sleep disorder dual management is remarkably improved, and the life quality and treatment compliance of the patient are improved.

The invention provides an auxiliary doctor seeing decision method and system based on multiple agents and a storage medium, and the method comprises the steps: carrying out the structural information collection of a target patient according to a pre-diagnosis agent, and obtaining the structural data of the patient; inputting the structured data of the patient into a diagnostic reasoning agent for diagnostic reasoning to obtain a diagnostic predicted disease; inputting the diagnosed and predicted disease and the current medical resource load information of the doctor-seeing hospital into a resource scheduling agent for diagnosis and treatment analysis to obtain a diagnosis and treatment recommendation scheme; inputting the diagnosis and treatment recommendation scheme into a medical insurance strategy agent for medical insurance analysis to obtain a medical insurance recommendation scheme; and combining the patient structured data, the diagnosis and prediction disease, the diagnosis and treatment recommendation scheme and the medical insurance recommendation scheme to obtain auxiliary treatment information. According to the embodiment of the invention, based on the auxiliary doctor-seeing information, the auxiliary doctor-seeing decision corresponding to the disease diagnosis, the diagnosis and treatment scheme and the medical insurance scheme can be effectively output, and the diversity of the auxiliary doctor-seeing decision function is guaranteed.

A method for diagnosing and recommending treatment to a patient that may or may not present with occlusal disease includes acquiring 3D scans of the patient's maxillary and mandibular arches, and acquiring occlusal assessment data of the patient's teeth and dental arches. The method also includes automatically generating a treatment plan recommendation, and a recommended treatment plan projection and a non-treatment projection based, at least in part, on the acquired 3D scans of the patient's maxillary and mandibular arches, and the acquired occlusal assessment data of the patient's teeth and dental arches. The method also includes automatically displaying a rendering of the patient's simulated dental arch N years in the future based on the automatically generated recommended treatment plan projection, and a rendering of the patient's simulated dental arch N years in the future based on the automatically generated non-treatment projection, where N is greater than 1.

The invention provides a special disease first-aid full-process auxiliary decision-making method based on multi-modal perception and AI, and relates to the technical field of medical first aid, and the method comprises the steps: obtaining first-aid field multi-modal data, constructing a feature dependence graph, and recognizing the type and symptom features of a special disease; indexing special disease diagnosis and treatment data based on the symptom feature vector, and determining a reference case and a treatment path; the action space is constructed in combination with clinical specifications, the expected income is predicted by applying Monte Carlo search, and the optimal treatment scheme is generated, so that the first-aid special disease recognition accuracy can be improved, the treatment decision time can be shortened, and the first-aid intervention effect can be improved.

The invention discloses an online gynecological disease consultation and inquiry interaction system, and relates to the technical field of artificial intelligence, and the system comprises a user information and symptom collection module which collects the basic information of a user; the intelligent diagnosis module is used for analyzing data input by the user based on the trained gynecological disease diagnosis model; the disease analysis module is used for extracting disease factors based on the user information and the symptom data of the user, extracting gynecological disease characteristics of the user and establishing a gynecological disease similarity calculation model; the expert inquiry module is connected with online gynecological experts and performs real-time online inquiry interaction with the user; and the interaction feedback module supports real-time interaction between the user and the intelligent diagnosis system and between the user and the expert, and feeds the diagnosis result and the treatment suggestion back to the user. Through the intelligent diagnosis module, the disease analysis module, the expert inquiry module and the interactive feedback module, preliminary diagnosis and personalized treatment schemes are rapidly provided, the regional limitation is broken through, the medical experience is improved, and the medical resource utilization rate is increased.

The invention discloses a medical video risk identification and diagnosis and treatment scheme generation method for a surgical operation and a layered multi-agent framework, and relates to the technical field of artificial intelligence medical auxiliary diagnosis. The method comprises the following steps: constructing an association database according to collected multi-type operation videos, clinical data of patients and a surgical field knowledge database; receiving a surgical scene query, generating an initial retrieval instruction according to the query, and dividing a query task into a visual semantic task and a cognitive reasoning task according to the initial retrieval instruction; respectively executing instrument identification and action identification according to the initial retrieval instruction and the associated database, and executing a risk early warning task and a diagnosis and treatment scheme generation task according to the results of instrument identification and action identification and the associated database to form an intraoperative risk assessment report and a postoperative diagnosis and treatment scheme of the patient. According to the method, the whole process of'intra-operative risk investigation and postoperative diagnosis and treatment scheme generation 'is covered, high-risk positioning precision and interpretability are achieved, and the reliability of clinical decision making is guaranteed.

The invention relates to the technical field of medical equipment, and provides a focused ultrasound system with multiple treatment modes. A power driving unit; the treatment head comprises a first ultrasonic transducer which is connected with the power driving unit and is used for responding to the excitation signal to form focused ultrasound and outputting the focused ultrasound to a focus; the control unit is suitable for determining a treatment plan according to the tissue characteristic information, and the treatment plan comprises the following steps: performing focused ultrasound irradiation on the target tissue by adopting one or more cooperative treatment modes adaptive to the tissue characteristic information; and controlling the power supply unit and the power driving unit to perform corresponding signal output based on the treatment plan, wherein the plurality of treatment modes comprise a heat effect mode and a histotripsy mode. The focused ultrasound system integrating multiple treatment modes is achieved, one or more matched modes can be selected for cooperative treatment based on tissue characteristics, the treatment efficiency and effect are effectively improved, and operation is convenient.