170 results about "Feedback analysis" patented technology

The invention discloses a large model prompt project optimization system and method fusing a domain knowledge graph. The system comprises an analysis module, a template generation engine module, a large model interaction interface module, a feedback analysis module and an optimization strategy module. And the analysis module forms a constraint coding signal containing an entity attribute incidence matrix. The template generation engine module forms an enhanced prompt text stream with a reservoir physical property parameter slot; the large model interaction interface module receives the enhanced prompt text stream and generates a question and answer response data stream containing geological terminologies; the feedback analysis module forms a feedback signal containing semantic deviation measurement through a semantic error vector calculation algorithm; and the optimization strategy module forms a parameter optimization instruction signal and transmits the parameter optimization instruction signal to the analysis module to complete iterative updating of the constraint condition. According to the large model prompt project optimization system fusing the domain knowledge graph, the problem of low answer accuracy of a large model in the oil-gas exploration field due to lack of professional constraints can be solved.

The invention provides a front-end automatic testing method of a large language model based on LLM, and belongs to the technical field of front-end automatic testing. The method comprises five core steps of test intention recognition and semantic analysis, page structure analysis and target element positioning, automatic generation of a test operation sequence and assertion logic, test script execution and dynamic regression verification, and test feedback analysis and continuous training. The strong semantic understanding and context reasoning capabilities of the LLM are utilized to perform semantic analysis and simulation on a front-end page structure, a user behavior process and business logic, so that automatic generation and maintenance of a test script are realized. According to the method, the test coverage rate and the generation efficiency are greatly improved, the maintenance cost is remarkably reduced, the readability and the intelligence of the test case are enhanced, and the method is suitable for the modern Web front-end application test of rapid iteration.

The invention discloses an online resource adaptive recommendation method based on multi-modal learning behavior analysis, and relates to the technical field of resource recommendation. The method comprises the following steps: firstly, dynamically collecting multi-modal data by using a heterogeneous sensor array, and carrying out noise reduction, probability distribution matching normalization and time-space alignment preprocessing; features are extracted through a hierarchical network, modeling learning behaviors such as a variational auto-encoder are combined, and the learning state is evaluated from multiple dimensions; recommendation decisions are generated based on reinforcement learning, recommendation is optimized in combination with personalized presentation and multi-source feedback analysis, meanwhile, the system has the functions of dynamic strategy adjustment, intelligent resource creation, cross-scene migration recommendation and the like, and accurate self-adaptive recommendation is achieved. According to the method, multi-modal data are comprehensively collected and deeply processed, learning behaviors and evaluation states are accurately analyzed, personalized resource recommendation is provided through intelligent recommendation and dynamic optimization strategies, recommendation accuracy and learning effects can be improved, user experience can be enhanced, and the utilization rate and competitiveness of platform resources can be improved.

The invention relates to the technical field of artificial intelligence, and discloses a professional knowledge question and answer intelligent customer service system based on a self-optimization mechanism. The system comprises a user intention analysis module, a knowledge processing module, a self-optimization learning module and an interactive presentation module. A semantic understanding unit of the user intention analysis module generates a user intention signal; after the knowledge processing module receives the signal, a knowledge retrieval unit outputs related knowledge fragments and confidence, an answer generation unit forms candidate answers, and a quality evaluation unit determines an optimal answer according to the confidence; in the self-optimization learning module, a feedback analysis unit adjusts answer generation parameters according to user interaction data, a strategy adjustment unit optimizes a retrieval strategy in combination with an optimal answer and a historical dialogue, and a knowledge updating unit updates a knowledge base depending on an external knowledge source; and a multi-round dialogue management unit of the interactive presentation module adjusts a dialogue process, and a visual presentation unit outputs a natural language text and collects user feedback to a feedback analysis unit.

The invention relates to the field of intelligent interactive design, and particularly discloses a visual intelligent interactive design system which comprises a three-dimensional structure design module, an intelligent color matching module, a visual rendering module and an interactive feedback analysis module. The three-dimensional structure design module can process imported or custom a three-dimensional model, detect and repair topology errors and construct a three-dimensional scene basic framework; the intelligent color matching module generates a color matching scheme based on the instruction, a rule base or user preference and displays a comparison view; the visual rendering module renders a three-dimensional scene in real time, collects rendering pipeline load parameters and quality indexes, and analyzes balance points to adjust rendering parameters; and the interaction feedback analysis module records interaction data, marks over-limit nodes by comparing a standard and analyzes a root cause. According to the system, efficient three-dimensional scene construction, intelligent color matching, rendering optimization and interaction experience improvement are realized through multi-module cooperation, and the system is suitable for multi-field scenes needing visual intelligent interaction design.

The invention discloses a consumer feedback analysis method based on a multi-agent model, and the method comprises the steps: firstly sampling and generating consumer multi-dimensional feature data, and expanding the data into a semantic portrait through a large model; therefore, an intelligent agent containing a memory module is constructed, and part of the intelligent agent is set as an opinion leader. Commodity attributes, propaganda and competitive products are input into each agent, and initial evaluation is independently output; and then multiple rounds of viewpoint interaction are carried out in the same social evaluation area, the opinion leader has a higher sampled weight, and memory and comments are updated. And after simulation is finished, updating evaluation is generated again and compared with the initial evaluation, attitude changes are analyzed, all data are summarized, and a visual comprehensive report is output. According to the method, accurate simulation and feedback analysis of consumer behaviors are realized by combining a large language model and a multi-agent simulation test technology, and the defects of a traditional method in the aspects of consumer portrait dynamics, agent adaptability, interaction complexity, data dependence and the like are overcome.

The invention relates to the field of new-generation information technology and civil engineering, in particular to a frost heaving prevention technology of a roadbed in a seasonal frozen area, and particularly relates to a solar heating frost heaving prevention drainage roadbed in the seasonal frozen area and a construction method of the solar heating frost heaving prevention drainage roadbed. The method comprises the steps that environment sunlight data are obtained based on a solar heat collection device, heat energy is stored through an efficient heat storage material, and a heat energy output sequence is generated; acquiring soil temperature, humidity and frost heaving stress data from an environment sensor, predicting a frost heaving trend in combination with historical frost heaving characteristics, and generating regulation and control parameters; the heat energy output sequence and the frost heaving regulation and control parameters are input into a dynamic temperature control and drainage linkage module, heat distribution and optimization adjustment of a drainage path are carried out, and a linkage control strategy is generated; and performing feedback analysis on the operation condition of each module based on the data, and optimizing the control parameters of the dynamic temperature control and drainage linkage module. The temperature and humidity of the roadbed can be accurately adjusted, the frost heaving phenomenon is prevented, and roadbed drainage is optimized.

The invention discloses an intelligent verification system and method for full-process review based on multi-modal analysis, and belongs to the technical field of intelligent review. Comprising a processing analysis module, an experiment extraction module, a risk early warning module, a reproduction verification module, a chart comparison module, a dispute tracing module, a feedback analysis module, a load monitoring module, a report generation module and a preference analysis module. According to the invention, comprehensive and multi-angle intelligent review can be realized, more detailed risk early warning and positioning information can be generated, the experiment reproducibility can be quickly verified at low cost, the manual verification burden is greatly reduced, the innovation and risk of the thesis viewpoint are scientifically evaluated, the fatigue of a manuscript reviewer is relieved, the manuscript review efficiency and quality are improved, and the method is suitable for popularization and application. And dynamically adjusting the review content recommendation sequence.

The invention discloses an industrial control protocol intelligent fuzzy test method and system based on a multi-agent large model, and relates to an industrial information security test method and system.According to the industrial control protocol intelligent fuzzy test method and system based on the multi-agent large model, the large model is enhanced by integrating the field of the retrieval enhancement generation (RAG) and the low-rank quantization adaptation (QLoRA) technology, and a multi-agent cooperation mechanism is combined; and full-automatic, high-precision and end-to-end fuzzy testing of the industrial protocol is realized. The system generates seed data meeting protocol specifications through the seed generation agent, the test case generation agent executes field, structure and semantic variation based on the seed data, efficient test cases are dynamically generated, a test strategy is optimized in real time through the feedback analysis and strategy adjustment agent, and the vulnerability discovery capability is improved. By dynamically adjusting the variation strategy and optimizing the generation of the test case, the coverage, the accuracy and the efficiency of the test are improved, and the security and the vulnerability detection capability of the industrial control system are remarkably improved.

The invention belongs to the field of communication transmission, relates to a data analysis technology, is used for solving the problems that time delay exists in channel state feedback and static weight cannot adapt to burst interference in the prior art, and particularly relates to an information communication method based on communication security protection, which comprises a link switching analysis sub-method and a feedback analysis sub-method. The link switching analysis sub-method comprises the following steps: acquiring channel parameters of a communication link and performing entropy conversion; carrying out weight distribution according to the entropy value of the channel parameter to obtain a weight coefficient of the sampling parameter in the acquisition period; analyzing the priority of the communication link; according to the method, the essential characteristics of the channel parameters can be dynamically captured, the timeliness and accuracy of the link switching decision are improved, the abnormal switching operation is effectively identified by a closed-loop feedback mechanism, the communication resource waste caused by invalid switching is reduced, and the method can maintain stable information transmission quality in a complex electromagnetic interference environment, so that the user experience is improved. And the communication interruption risk caused by channel abrupt change is avoided.

The invention provides a hybrid AI adaptive optimization system based on agent context engineering. The hybrid AI adaptive optimization system comprises a context knowledge base, a generation module, a feedback analysis module and a knowledge integration module. According to the method, the problems that in an existing context optimization technology based on a large language model (LLM), context Bias is simplified, Context Collapse is caused by overall rewriting, and the learning process is high in delay, high in cost, unstable and the like are solved.

The invention discloses a supply chain quality collaboration method and system based on a block chain, and relates to the technical field of block chain and supply chain quality management, and the method comprises the steps: collecting quality events according to a batch quality collaboration baseline table, generating a credible collection proof package and a to-be-verified quality event queue, executing dynamic endorsement verification, and writing the dynamic endorsement verification into a quality event account book, outputting a batch state table and an under-chain evidence pointer; performing rapid detection and judgment based on the batch state table and the under-chain evidence pointer, generating a pre-release report and establishing a pre-release mapping table; and triggering a difference correction contract according to the pre-release mapping table, updating the batch state table, generating a collaborative work order, pushing a quality collaborative strategy instruction, and outputting a quality collaborative closed-loop record. According to the method, the risk feedback analysis result table is generated by auditing and evaluating the quality collaborative closed-loop record, the risk layering strategy table is revised, and the batch quality collaborative baseline table is updated, so that the key field constraint is continuously calibrated, and the traceability and consistency of collaborative processing are improved.

The invention belongs to the technical field of numerical control machine tool manufacturing, and particularly relates to a five-axis machine tool spindle state dynamic monitoring method and system based on machining feedback. The method comprises the steps that key data in the machining process are collected in real time through multiple sensors installed on the five-axis machine tool; preprocessing the key data; performing processing feedback analysis according to the key data to obtain a processing feedback analysis result; performing spindle state evaluation according to the key data to obtain a spindle state evaluation result; and dynamically adjusting a monitoring strategy according to a processing feedback analysis result and a spindle state evaluation result. According to the method, key parameters in the machining process are monitored in real time, deep analysis and evaluation are carried out in combination with the machining characteristics of the five-axis machine tool, and therefore dynamic monitoring and accurate evaluation of the state of the main shaft are achieved, the monitoring accuracy and real-time performance are improved, various challenges possibly occurring in the complex machining environment of the five-axis machine tool are effectively coped with, and the machining efficiency of the five-axis machine tool is improved. And the safety and the stability of the machining process are ensured.

The invention belongs to the technical field of electronic teaching aids, and discloses a voice training system and a data training method after complete denture repair, and the system comprises a feedback analysis and decision device which is used for carrying out the error classification of a used object, and obtaining the type of a high-frequency problem through a clustering algorithm; the dynamic content generation engine is used for recording a historical error pattern of the use object, presetting pronunciation training logic and generating personalized training content; the intelligent difficulty adjusting device is used for adopting a dynamic difficulty algorithm: scoring according to a result of a use object, and automatically adjusting the level difficulty; and the adaptive content generator is used for analyzing the pronunciation content of the use object, identifying wrong phonemes or grammar problems, and generating training content according with difficulty in real time according to the current level and error type of the use object. The method can help a user to recover clear voice as soon as possible, improves the use enthusiasm, and can solve the problems that personalized training is not available, real-time monitoring and feedback cannot be performed, and pronunciation cannot be corrected in time.

The invention relates to the technical field of knowledge fusion, and discloses an interactive health science popularization question and answer guidance system for constructing a medical knowledge graph, comprising: a guidance instruction generation module extracting attribute variation dimensions between to-be-aligned data and candidate nodes and generating a guidance instruction; the feedback analysis module converts the feedback data into a feedback correction vector; the atlas fusion actuator determines an initial fusion weight according to the coordinate distance, determines a weight correction coefficient based on the topological centrality of the target node, constructs a weighted feature update function by using a feedback correction vector and a local topological relation to calculate a feature increment, and completes feature convergence fusion; according to the method, a priori constraint force balance feedback feature pointing value provided by a map structure is utilized, it is ensured that heterogeneous knowledge fusion conforms to medical ontology logic, and the global stability of science popularization map evolution is guaranteed.

The invention discloses a thoracic surgery patient postoperative remote monitoring method and system based on the Internet of Things, and relates to the technical field of medical rehabilitation monitoring. Feature index extraction is carried out by taking body position pressure and respiration collaborative response as a two-factor dynamic evaluation basis, so that high-sensitivity judgment on a thoracic cavity load state is formed; after the abnormal state is recognized, body position adjustment is carried out, acoustic feedback analysis is linked, secondary evaluation after body position adjustment is achieved, intervention effectiveness of body position adjustment is ensured, personalized adjustment suggestions are automatically generated, the whole process of'perception-recognition-execution 'of remote monitoring is opened, the intelligent level and response real-time performance of postoperative recovery management are improved, and the postoperative recovery management efficiency is improved. According to the overall scheme, health risks caused by position misjudgment, intervention lag and monitoring blind areas in a traditional system are effectively avoided.

The invention discloses a charger charging current control system and method based on feedback analysis, and relates to the technical field of chargers, and the method comprises the steps: carrying out the real-time collection of key charging behavior parameters, such as connection time, voltage waveform, steady-state building time, and the like through a plurality of built-in modules, such as a USB / PD handshake monitor, an ADC sampling module, and a current sampler; data cleaning and standardization are carried out in the central processing unit, and a behavior stability scoring function Wdev for representing the stability of the plugging behavior of the user is constructed. And comparing the function with a behavior stability threshold value Wth to judge whether the current charging state is an unstable charging state or not. Compared with the traditional technology only based on single current and voltage signal current limiting, the scheme has higher behavior identification capability and preposed decision-making capability, and can perform stability modeling and risk pre-judgment on the charging behavior of the user on the premise of not changing the hardware structure.

The invention provides a personalized exercise plan generation and management method and system based on user data, and the method comprises the steps: obtaining multi-source user data, and carrying out the data cleaning and feature extraction; on the basis of the extracted feature data, confirming the exercise preference and the exercise target of the user; based on the exercise preference and the exercise target, a machine learning algorithm is adopted to generate a personalized exercise plan, and recommendation is carried out; and tracking user feedback for the personalized exercise plan, performing dynamic analysis on information fed back by the user, correcting exercise preferences and exercise targets, and regenerating the personalized exercise plan. Multi-source data features are extracted and analyzed to obtain a personalized exercise target of a user, a machine learning algorithm is utilized to generate a personalized exercise plan for the personalized exercise target, and finally, based on customer feedback, the goal reaching progress and the sign change of the user are analyzed, the exercise effect is evaluated, and the user experience is improved. And adjusting the motion target and the motion plan in real time, and managing the motion plan.

The invention discloses a model robustness automatic evaluation method based on large model driving and related equipment. The method comprises the following steps: inputting a task template of a tested model into a dynamic Prompt engine module to obtain a sample generation instruction; generating an adversarial sample according to the sample generation instruction through a sample generation and verification module, and performing model evaluation on the adversarial sample; if the confrontation sample passes the model verification, extracting a high-dimensional multi-modal feedback vector of the tested model through a multi-modal feedback analysis module; a reusable semantic instruction is generated through a strategy control module according to the high-dimensional multi-modal feedback vector so as to optimize a generation strategy; and if the confrontation sample does not pass the model verification, performing effect evaluation on the confrontation sample through an evaluation and interpretation generation module and generating an interpretability evaluation report. According to the method, progressive generation of the adversarial sample and exponential-level improvement of the model robustness evaluation efficiency can be realized, a clear model improvement direction is provided for developers, and the method can be widely applied to the technical field of computers.

The invention discloses a knowledge base construction and learning feedback analysis method and device for a data structure course, equipment and a storage medium, relates to the technical field of course analysis, and discloses a knowledge base construction and learning feedback analysis method for a data structure course, which comprises the following steps: acquiring multi-source data associated with the data structure course, and identifying the multi-source data to obtain a learning feedback analysis result; obtaining structured data; identifying the structured data, generating an entity set, associating entities with course targets, and generating knowledge point course target mapping; the entities are fused according to the character string similarity and the semantic similarity, and a dynamic knowledge base is generated; searching a dynamic knowledge base according to the question content, generating a question answer according to the associated triple, and determining answer feedback according to the question answer; and according to the answer feedback, calculating a course goal achievement degree, according to the course goal achievement degree and knowledge point-course goal mapping, generating a teaching adjustment suggestion, and completing data structure course knowledge base construction and learning feedback analysis.

The invention relates to the technical field of semantic analysis, can be applied to business scenes such as financial science and technology and medical health, and discloses a bidirectional cue word collaborative optimization method, device and equipment and a medium. Generating a dialogue response set with a dialogue content input language model, and obtaining a feedback analysis result; determining an optimization operation type according to the uncertainty index, and updating the system cue word and the user cue word to obtain an updated cue word combination; and storing the updated cue word combination into a cue word memory library, and retrieving candidate cue word combinations from the cue word memory library for use in new task processing. Through an uncertainty-driven bidirectional cue word collaborative optimization mechanism, semantic consistency, dynamic adjustability and reusability among cue words are realized, so that the model has higher adaptability and generalization ability, and the task processing quality and efficiency are improved.

Systems, devices, and methods for delivering laser energy directed toward target tissue using a spectroscopic feedback. An exemplary laser feedback control system comprises a feedback analyzer to receive a signal from a target tissue using a spectroscopic sensor, and a laser controller to determine whether the received signal generally equals a first preset. If the received signal meets the first preset, the laser controller can send a control signal to a laser system to change from a first state to a second state of the first laser system. The laser system can deliver laser energy via an optical fiber towards the target tissue.

The present application relates to a multi-agent collaborative product closed-loop optimization system and method based on user feedback data, which is used to solve the technical problem that the existing technology cannot efficiently utilize user feedback data to obtain optimization improvement requirements, resulting in poor optimization effect. It includes: a data collection module that captures user feedback data in real time from public and private data sources; a data analysis module that calls a multi-dimensional analysis agent to perform AI multi-dimensional analysis and mining on the user feedback data after data normalization, generating feedback analysis results; an image construction module that constructs and calls expert role prompt information templates, fills in the context by inputting feedback analysis results into the templates, obtains expert role prompt information, and calls an intelligent portrait generation agent to construct a structured user intelligent portrait; a task generation module that calls a business design expert agent to automatically generate product optimization requirements and task collaboration based on feedback analysis results and structured user intelligent portraits.

The invention relates to the technical field of building cost prediction, and discloses a building engineering cost intelligent prediction method based on BIM and machine learning, which comprises the steps of extracting a component parameter table from a plurality of BIM model files of heterogeneous versions based on a preset unique identifier, and further, performing multi-scale discrete barrel separation processing on component attribute data to obtain a component parameter table; generating a bucket coding matrix by combining disturbance feedback analysis, performing joint coding mapping processing on the bucket coding matrix to generate an attribute structure vector with a stable structure and unified semantics, constructing an edge index matrix by combining component connection data, and performing node attribute reconstruction processing on the attribute structure vector to obtain an edge index matrix; therefore, a component diagram structure with a topological structure and attribute information fusion capability is constructed, automatic prediction of the building cost can be realized on the premise that a manual mapping rule is not needed, and the adaptation stability of a cost estimation model in a multi-version BIM environment and the consistency of prediction results are remarkably improved.

The invention provides an eye movement tracking system for aphasia rehabilitation training, a vocabulary difficulty adaptive adjustment method, equipment and a medium. The method comprises the following steps: determining semantic diffusion characteristics of a current vocabulary for representing a target user; determining a semantic recognition gradient of a target user on the initial rehabilitation training voice; performing response feedback analysis on speech recognition of the target user in the initial rehabilitation training process based on the semantic diffusion features and the semantic recognition gradient to obtain a semantic response deviation of the target user in the current stage; determining a gaze association relationship of the target user under different semantic structure features; and adaptively adjusting the vocabulary difficulty of the rehabilitation training voice in the next stage according to the cognitive performance index of the target user in the current stage determined by the semantic response deviation and all the gaze association relationships. By adopting the scheme of the invention, the eye movement fixation characteristics of the patient can be deeply associated with the word order structure of the training voice content, and then the training voice in the rehabilitation training process can be adaptively adjusted.

The present invention relates to the technical field of carbon pollution prediction, and particularly to a carbon pollution prediction system, method and storage medium based on emission reduction and carbon sequestration, including a carbon pollution prediction platform, a carbon information database, a regional environmental analysis unit, a sub-region assessment unit, a regional pollution prediction unit, a prediction verification unit and a carbon pollution management unit; the present invention initially analyzes from the perspective of the environmental information of the target region to understand whether the emission reduction and carbon sequestration effect in the current period reaches the expected effect, and conducts an analysis on the supervision and division of carbon emissions in sub-regions through information feedback to further understand the emission reduction and carbon sequestration effect of each sub-region in the target region. At the same time, a regional carbon pollution risk prediction analysis is carried out to reasonably plan the carbon pollution management decision of the target region, and a prediction model construction verification feedback analysis is carried out on historical environmental information in a progressive manner to judge whether the carbon pollution prediction result of the target region is standard, so as to improve the accuracy of carbon pollution prediction in the target region.

The invention relates to the field of cell analysis, in particular to a cell chamber real-time feedback analysis system based on multi-parameter fusion weighting. Comprising a multi-parameter acquisition module for acquiring cell growth related parameter information in a cell chamber; an artificial intelligence algorithm model is built in the weight calculation module, and the weights of the multiple pieces of cell growth related parameter information are dynamically adjusted through machine learning; the parameter fusion module is used for fusing the multiple pieces of cell growth related parameter information according to the weight calculated by the weight calculation module; the real-time feedback module is used for generating feedback information in real time according to the fusion information; the visual display module is used for carrying out visual display on the feedback information; and the adaptive learning module is used for collecting experimental result information in the cell culture process, so that the weight calculation module optimizes the artificial intelligence algorithm model. According to the method, related parameter information of growth of each cell is dynamically adjusted according to the actual condition of cell culture, and fusion is carried out according to the weight, so that the requirements of modern cell research and application are met.

The present invention discloses a method and system for optimizing the operation of a generator set based on feedback analysis, which relates to the technical field of generator sets. The method includes: obtaining a first operating circuit of the generator set, performing real-time fault signal feedback on the first operating circuit to determine a faulty circuit component; filtering and marking the faulty circuit component in the initial operating circuit to obtain the remaining operating circuit after filtering, and performing connected loop reconstruction optimization on the remaining operating circuit after filtering through the Kruskal algorithm to obtain a second operating circuit; performing transient operation on the generator set according to the second operating circuit until the faulty circuit component is repaired, and then restoring to operate the generator set with the first operating circuit. It solves the technical problem in the prior art that the entire circuit needs to be disconnected for maintenance when a fault occurs in the generator set, resulting in a long downtime, and achieves the technical effect of realizing partial continuous operation, reducing the downtime, and improving the operation efficiency through real-time fault feedback and circuit reconstruction optimization.

The embodiment of the application discloses a method and system for detecting the state of a bone conduction hearing device, the bone conduction hearing device at least comprising a microphone, a speaker, a feedback analysis unit and a signal processing unit, the method comprising: generating a third sound by the speaker based on a first signal; wherein the first signal is generated by the signal processing unit; receiving the third sound by the microphone and generating a feedback signal; determining, by the feedback analysis unit, the feedback path transfer function from the speaker to the microphone of the bone conduction hearing device based on the feedback signal of the microphone and the first signal; obtaining at least one preset feedback path transfer function; comparing the feedback path transfer function with the at least one preset feedback path transfer function; and determining, by the signal processing unit, the state of the bone conduction hearing device according to the comparison result.

The application discloses a kind of simulation exercise devices for preventing dysphagia of cerebral apoplexy patient, it is related to cerebral apoplexy patient swallowing simulation technical field, including: placement bottom plate, adjustable dysphagia exercise mechanism and feedback analysis mechanism, adjustable dysphagia exercise mechanism is located in the right side of feedback analysis mechanism, adjustable dysphagia exercise mechanism: adjustable dysphagia exercise mechanism includes adjustable exercise component for swallowing mastication exercise intensity adjustment, by adjustable dysphagia exercise mechanism adjustable exercise component And the setting of flexible sensitivity promoting component, it is different from the function of rehabilitation training equipment in the prior art is relatively single, and the training device is replaced back and forth during rehabilitation training, and the linkage of training between devices cannot be guaranteed, effectively achieve when mastication training, patient lip and tongue are trained simultaneously with sensory force, close to life, effective simulation, better effect.