816 results about "Man machine" patented technology

The invention discloses a cooperative steering control method considering man-machine mutual reliability, and aims to solve the problem of man-machine conflict in an existing man-machine co-driving system. The invention relates to the field of intelligent driving. The system specifically comprises an expected path module, a man-machine steering conflict evaluation module, a driver experience evaluation module, a weight distribution module, a non-cooperative game module and a man-machine co-driving vehicle. The expected path module generates an expected path between the driver and the controller; the man-machine steering conflict evaluation module outputs the trust degree of the driver to the controller based on the steering action of the driver, the vehicle state and the expected path of the driver and the controller; the driver experience evaluation module outputs the driver trust degree of the controller based on the vehicle state; the weight distribution module is used for outputting a driver control weight; and the non-cooperative game module outputs an optimal turning angle in combination with the expected path of the driver and the controller and the control weight of the driver, and controls the man-machine co-driving vehicle to complete the cooperative steering action.

The invention discloses a software requirement modeling method and device based on a natural language, equipment and a medium, and relates to the field of software requirement modeling. According to the software demand modeling method and device based on the natural language, the equipment and the medium, through automatic demand analysis and model generation, the manual workload is greatly reduced, and meanwhile, errors caused by manual understanding deviation are reduced; close connection among the steps is ensured by sharing intermediate representation and a unified data model, output of the previous step provides structured information for input of the next step, and information loss or distortion is avoided; a demand provider is allowed to participate in a model confirmation process through an iterative demand acquisition and modeling framework of man-machine cooperation, and the quality of a demand model is continuously improved; by means of the model consistency analysis technology based on the data flow dependency relationship, the problem of inconsistency between the requirement specification and the software model can be automatically detected, and the software product quality is improved.

The invention provides an exoskeleton man-machine motion intention cooperative control method and device and a storage medium, and relates to the technical field of robot motion control signal processing. According to the method, signal phase differences are acquired through energy buttons at human joints, and an original phase difference sequence is formed and preprocessed; calculating a cross correlation coefficient of the target and the associated joint based on the preprocessed sequence, and constructing a motion feature set in combination with the joint speed and acceleration; constructing a prediction model by using a long-short-term memory network, and outputting motion intention and joint position prediction information through multi-feature fusion training; and analyzing the prediction information by adopting a second-order linear impedance model, generating a driving torque instruction and sending the driving torque instruction to the servo motor for execution. According to the method, signal phase difference analysis, LSTM prediction and impedance control are fused, the problems of response lag and poor adaptability of a traditional exoskeleton are solved, the control real-time performance, accuracy and cooperative fluency are improved, and the use requirements of different users in different scenes are met.

The invention relates to the technical field of mechanical arms, in particular to a man-machine interaction system of an intelligent mechanical arm with a body. The system decomposes a long time sequence task into sub-tasks through a large language model, and introduces a task acquisition module, a task queue management module, a task re-planning module and a control execution module, uses visual detection to identify gestures and environment events to trigger temporary tasks, and maintains interruptible marks and safety anchor points based on priorities and interruption risks. And during interruption, the mechanical arm with the timestamp and the environment state are collected, during recovery, the states are compared, local re-planning is carried out, transition sub-tasks are automatically generated for failure sub-tasks, and the mechanical arm is controlled to execute after constraint verification. According to the system, on the premise that the structure consistency, the space consistency and the time consistency are guaranteed, safe interruption and efficient recovery in a long-time-sequence task can be achieved, and the autonomy, the real-time performance and the operation safety of the mechanical arm in a man-machine cooperation scene are remarkably improved.

The invention relates to the technical field of man-machine cooperation, and aims to solve the problems that the traditional man-machine cooperation process mainly depends on a preset flow and timing rhythm control, and is difficult to dynamically capture personnel misoperation, machine response delay or cooperation rhythm imbalance, so that response strategy lagging and low regulation and control efficiency are caused in the face of complex and variable man-machine interaction situations. According to the enterprise intelligent man-machine cooperation method and system, through comprehensive analysis with behavior response offset, cooperation rhythm consistency and a personnel fatigue state as core indexes, a set of intelligent man-machine cooperation process which is distinct in hierarchy, logic closed-loop and sustainable iterative optimization is constructed; the real-time response capability of man-machine cooperation and the production takt time stability are improved, the cooperation efficiency fluctuation can be dynamically monitored, the precise regulation and control strategy is output in time, quantitative evaluation and actual improvement of man-machine cooperation efficiency are achieved, and efficient production of a production line and the quality level of products are guaranteed.

The invention relates to the technical field of unmanned aerial vehicle cluster navigation, and discloses an unmanned aerial vehicle cluster navigation method based on man-machine interaction. The method comprises the steps that cluster task parameters and environment constraint conditions input by an operator are efficiently received through a multi-modal interaction interface, the cluster task parameters comprise a target point coordinate set and formation form identification, and the environment constraint conditions cover no-fly zone coordinates and meteorological interference levels. And the system calls a pre-stored topological relation template according to the formation form identifier to generate an initial track, completes space conflict detection in combination with the no-fly zone coordinates, and outputs a corrected track point sequence. And inputting the meteorological interference level into a dynamic anti-interference model to calculate a course correction amount, and superposing the course correction amount to a corrected track point sequence to generate an anti-interference navigation instruction set. And acquiring position data of the unmanned aerial vehicle in real time to calculate a trajectory deviation degree, triggering a manual intervention request if the trajectory deviation degree exceeds a dynamic threshold value, calling a correction instruction, fusing to generate a hybrid control strategy, and distributing the hybrid control strategy to each node of the cluster to realize reliable man-machine collaborative navigation.

The invention discloses an intelligent manufacturing workstation safety control system oriented to man-machine cooperation, and belongs to the technical field of intelligent manufacturing and man-machine cooperation. The system comprises a multi-modal sensor array, a personnel state sensing module, a robot state monitoring module, a dynamic safety area adaptive calculation module, an intention prediction and risk assessment module, a cooperative control decision module and an execution feedback module. The dynamic safety area self-adaptive calculation module constructs geometric representation of a man-machine interaction space based on a topological manifold theory, and calculates a dynamic safety boundary in real time; the intention prediction and risk assessment module is fused with multi-source information to predict a future trajectory of a person and assess a collision risk; the cooperative control decision module performs multi-objective optimization with safety and efficiency as double objectives to generate an optimal adjustment strategy, the system realizes prospective safety control based on intention prediction, the cooperative efficiency is improved by 25%-45% on the premise of guaranteeing safety, and the working safety accident rate is reduced to be close to zero.

The invention belongs to the technical field of man-machine cooperation. The invention provides a self-adaptive man-machine cooperation task scheduling method for an intelligent manufacturing scene. According to the embodiment of the invention, an extensible context state encoder is designed, scene features, task dependency relationships, resource states and the like are fused based on a graph neural network, and multi-dimensional constraint state representation is generated; an experience-driven task distributor is constructed, a cross-scene cooperation mode is mined through historical scheduling knowledge, and an initial distribution scheme is generated to reduce the search space dimension; an autonomous learning optimizer based on element reinforcement learning is developed, and field differences and scale elastic changes are quickly adapted through multi-scene element strategy pre-training and online lightweight fine tuning.

The invention discloses a robot Cartesian force damping control method and system based on a process energy mechanism, and the method comprises the steps: building a robot kinetic model, constructing an impedance control law and a force control law of a Cartesian space, and achieving the dynamic adjustment of control energy through combining a process energy container mechanism, global passivity and stability of the system in contact and non-contact states are maintained; the invention relates to the technical field of robot intelligent control and man-machine interaction. According to the robot Cartesian force damping control method and system based on the process energy mechanism, high-precision, safe and stable force control and damping adjustment can be achieved in the complex processes of polishing, assembling, wiping and the like, and a universal scheme with the energy self-management capacity is provided for safe man-machine cooperation.

The invention belongs to the technical field of intelligent and robot operation, and discloses a collaborative robot closed-loop operation method based on scene perception and thinking chain reasoning, which comprises the following steps of: firstly, generating structured semantic representation containing confidence by utilizing a visual language model of LoRA efficient fine tuning and fusing multi-view data, and providing semantic understanding for a robot; secondly, utilizing a three-dimensional diffusion model to generate explicit geometric priori under the condition of no real CAD, and correspondingly completing PnP initial pose estimation in combination with 2D-3D; then, carrying out pose optimization by adopting a tracking-refining strategy, and triggering reinitialization based on geometric prior when a residual error exceeds a threshold value; and finally, decomposing a global task by utilizing a thinking chain reasoning module, and realizing'perception-reasoning-execution 'closed-loop control based on visual servo in cooperation with an execution module. The method realizes effective combination of large model reasoning and physical execution, has strong robustness under dynamic disturbance, and is suitable for intelligent manufacturing and man-machine cooperation scenes.

The invention belongs to the technical field of path planning, and particularly relates to a multi-AGV textile workshop material distribution path dynamic planning method and system, and the method comprises the following steps: S1, obtaining the position and speed of each AGV, and the Lidar and visual data of the environment, and recognizing and classifying the barriers, such as textile equipment, raw material stacks, workers and the like, in the environment through a semantic segmentation network; and fusing the position information of the Lidar point cloud and the semantic category information of the obstacle, constructing an asymmetric probabilistic risk potential field for different categories of obstacles, and ensuring that the risk potential field gradient of a region corresponding to a worker is the highest. According to the method, the AGV can accurately distinguish different obstacles such as working personnel and equipment by utilizing the semantic segmentation network and constructing the asymmetric risk potential field, so that the operation safety in a complex man-machine co-fusion environment is improved, and particularly, the safety of the personnel is guaranteed.

The invention discloses an electric vehicle man-machine cooperative scheduling strategy for multiple scenes of an electric power traffic coupling network, and aims to solve the problem of electric vehicle charging optimization scheduling caused by deep coupling of an electric power system and a traffic system in different scenes. The method comprises the steps that firstly, topological information and behavior characteristics are fused through a graph generative adversarial network, a graph structured model is constructed, and an electric power traffic coupling operation scene is generated; secondly, establishing a multi-objective optimization mechanism by utilizing hierarchical reinforcement learning, constructing an electric vehicle charging optimization scheduling finite Markov decision model in a conventional scene and a fault scene, and designing an algorithm based on knowledge distillation to solve a scheduling strategy; and finally, realizing strategy migration of charging redistribution and path emergency adjustment in a fault scene by combining a man-machine cooperative regulation and control technology and fusing a user instruction. Experimental results show that the strategy can effectively improve the toughness of the power grid, relieve traffic congestion, reduce charging queuing time and increase user satisfaction.

The invention discloses an intelligent assembly guide and error correction system based on man-machine cooperation, and relates to the field of man-machine cooperation intelligent assembly, and the system comprises a task instantiation module which generates an assembly task instance aligned with a physical environment according to an assembly order and in combination with environment perception information; the cognitive intention state module is used for collecting and processing multi-mode physiological and behavior signals of an operator in real time and identifying a cognitive intention state related to a current assembly task; the man-machine cooperation strategy module is used for dynamically generating an adaptive man-machine cooperation strategy based on the recognized cognitive intention state and the process requirement of the current assembly task instance; and the operation mode pre-configuration module is used for generating operator-oriented guide information according to the man-machine cooperation strategy and carrying out corresponding operation mode pre-configuration on the robot. According to the method, upgrading from one-way programming to two-way self-adaptive collaboration is realized, and the intelligent level and collaboration efficiency of the assembly process are effectively improved.

The invention discloses a man-machine collaborative additive repairing and remanufacturing method based on multi-modal perception, and belongs to the technical field of intelligent manufacturing and additive manufacturing, and the man-machine collaborative additive repairing and remanufacturing method comprises the following steps: S1, based on an additive repairing environment of a damaged part, establishing a three-dimensional grid map; s2, constructing a three-dimensional damage model based on the damage morphology data acquired by the multi-modal data acquisition module; s3, based on the three-dimensional damage model, a planned repair path and matched technological parameters thereof are obtained; and S4, based on the planned repair path, the repair equipment performs additive repair on the damaged part. According to the method, the additive repair environment three-dimensional grid map system is constructed, the repair environment and the damaged part are subjected to multi-angle scanning, and the visual sensor is synchronously adopted to collect the three-dimensional shape data of the damaged part, so that the sub-millimeter-level cooperative positioning of the repair equipment operation space and the damaged area is realized; and an accurate three-dimensional space reference is provided for subsequent repairing operation.

The invention discloses a multi-objective reinforcement learning man-machine cooperative assembly task allocation method based on neighborhood parameter migration, and the method comprises the steps: building a mathematical model which aims at minimizing the physiological fatigue accumulated value of a human operator and minimizing the maximum completion time for a multi-objective optimization problem of task allocation in a man-machine cooperative assembly system; a multi-target problem is decomposed into N standard sub-problems by adopting a weighting and decomposition strategy, and training is accelerated through a neighborhood parameter migration strategy; each sub-problem is solved based on a near-end strategy optimization algorithm of an Actor-Critic framework, Gaussian noise is added to an Actor network to simulate environment uncertainty, an action mask mechanism is introduced to process priority constraints of assembly tasks, and it is ensured that a generated task allocation scheme is always feasible. According to the method, the convergence speed and diversity of the Pareto solution set can be remarkably improved, and the assembly efficiency and operator fatigue are effectively balanced.

The invention relates to the technical field of software engineering and automatic testing, in particular to an E2E scene test optimization method and system based on target man-machine cooperation. According to the scheme, an optimization target is manually set, an initial test case and a business process are input, and a test resource library is constructed; the system automatically analyzes a service path, generates a use case dependency graph and a key path, and determines a core scene through manual verification; a candidate optimization strategy is generated by the system, and a target strategy is determined through manual screening according to resources and priorities; the system carries out simplification, merging and scheduling optimization on the use cases, and deploys and executes the use cases after manual sampling verification of coverage; the system collects execution data in real time and generates an evaluation report, and whether a use case is supplemented or a strategy is adjusted is determined after manual analysis; and finally updating the resource library and the strategy model, establishing a dynamic feedback mechanism, and triggering continuous optimization during system iteration. According to the method, the E2E test efficiency and the software delivery quality are remarkably improved through man-machine cooperation and closed-loop optimization.

The invention discloses an underwater explosive ordnance disposal control method and system based on an AI large model and a robot, and relates to the technical field of underwater robotics.Underwater optical images and sonar data are collected through a sensing module and input into a pre-trained AI large model for real-time analysis, potential threats are recognized, and a decision instruction containing a path planning and operation sequence is generated; when the analysis confidence coefficient is lower than a threshold value set based on historical data, interaction information containing multi-modal data abstracts, alternative schemes and risk assessment is automatically generated and sent to an operator, instructions are executed after being received, the system comprises a sensing module, an AI processing module, a communication module, a control execution module and a state monitoring module, and a robot body is integrated with a processor to solidify the method. The autonomous decision-making precision and efficiency of the robot in a complex environment are improved through an AI large model, the man-machine cooperation response speed is optimized by using an intelligent interaction mechanism, and the reliability and environmental adaptability of the system are significantly enhanced in combination with state monitoring and edge-cloud cooperative computing.

The invention discloses a high-computing-power data center operation and maintenance automatic execution system, and relates to the technical field of data center operation and maintenance control. The method comprises the following steps: acquiring human-machine-ring-task full-dimensional data, and integrating historical and static data; forming an ability state level Cs through multi-dimensional index weighted analysis; calculating a task risk level Rt from four dimensions; constructing a matrix and a rule base, and mapping a control strategy; the Cs and the Rt are dynamically updated, and the operation and maintenance scheme is adjusted in a linkage mode; and writing back abnormal data, triggering training and reevaluation, and optimizing the model and rules. According to the method, traditional operation and maintenance data islands are broken, pain points such as risk perception lag and regulation and control discretization are solved, accurate matching of personnel capabilities and task risks is realized through full-process data linkage, operation and maintenance safety, accuracy and efficiency are improved, continuous optimization of the personnel capabilities and a system model is promoted, and reliable support is provided for stable operation of a data center.

The invention relates to an electric power energy labeling method and system, and the method comprises the steps: carrying out the preprocessing of multi-mode original data in an electric power system, obtaining the cleaned preprocessing data, and enabling the preprocessing data to comprise text data, image data, time sequence signal data and audio data; the preprocessed data is dynamically bound based on a space-time-event three-dimensional rule, a structured multi-modal data card is generated, and the multi-modal data card takes a device ID-timestamp-event type as a unique identifier and comprises a text abstract, an image thumbnail, a time sequence curve fragment and an audio waveform; based on the multi-modal data card, intelligent labeling is carried out in a man-machine cooperation mode, and an initial label set is obtained; the initial label set is dynamically optimized, label errors are corrected, a high-quality labeling result is obtained, and the high-quality labeling result is stored in a structured mode and output to a downstream application system through an API interface.

The application discloses a cooperative steering control method considering human-machine trust degree, and aims at solving the human-machine conflict problem in the existing human-machine co-driving system. The application relates to the field of intelligent driving. Specifically, the application comprises an expected path module, a human-machine steering conflict evaluation module, a driver experience evaluation module, a weight distribution module, a non-cooperative game module and a human-machine co-driving vehicle. The expected path module generates the expected paths of the driver and the controller; the human-machine steering conflict evaluation module outputs the trust degree of the driver to the controller based on the steering action of the driver, the vehicle state and the expected paths of the driver and the controller; the driver experience evaluation module outputs the trust degree of the controller to the driver based on the vehicle state; the weight distribution module is used for outputting the driver control weight; and the non-cooperative game module combines the expected paths of the driver and the controller and the driver control weight to output the optimal steering angle, so as to control the human-machine co-driving vehicle to complete the cooperative steering action.

The application discloses a robot cognitive development method based on ontology semantics, and comprises the following steps: constructing a robot article identification professional knowledge base based on attribute function and ontology information representation of article definition; information determination based on attribute discrimination and semantic search; and robot cognitive development based on attribute information addition. The application simulates the process of human memory, learning and cognition of articles based on an ontology semantic knowledge base, and through machine learning and sensor attribute information, the robot can actively cognize, learn, expand and accumulate learned knowledge and experience according to information data; the robot can continuously develop its cognitive ability through learning, automatically construct a robot article identification professional knowledge base of unknown articles, and based on a semantic structure of triplets, share knowledge and exchange operation logic between man and machine, so that the cognitive level of the robot is improved and the operation experience of an operator is improved.

The invention provides a dynamic walking exoskeleton control method and system based on man-machine complementary optimization, and the method comprises the steps: defining the step height and normal contact force of a contact point, and constructing a nonlinear complementary problem constraint and model; defining quantitative targets of nine types of motion tasks including contact point centroid position tracking, centroid linear velocity tracking, foot yaw angle alignment, frame attitude tracking, base quaternion derivative regularization, contact force regularization, joint regularization, swing height control and contact control regularization, and establishing a multi-task target function; a static nonlinear complementary problem is coupled with exoskeleton continuous dynamics, and a coupling system is constructed; solving the optimization objective function by adopting a neurodynamics algorithm to obtain the optimal control input of the exoskeleton-human body system; and the control input mapping obtained through optimization is converted into a driving instruction of a robot actuator, and a motion control closed loop is completed. According to the method, the complex movement requirements under strong coupling constraint interaction of the humanoid and the robot can be more accurately met.

The invention discloses a video recognition-based man-machine cooperative processing method for ditches and rivers in an urban area, and relates to the field of video recognition or understanding, and the method comprises the steps: constructing a data collection and analysis closed loop covering the whole periods before, during and after heavy rainfall: collecting images before and after treatment through an unmanned aerial vehicle and a camera before rainfall, generating a treatment operation influence map for quantitatively evaluating the treatment effect; in rainfall, the fixed camera is used for continuously monitoring the water level and the flow velocity; after rainfall, images are collected again, and the abnormal river reach is recognized. According to the method, camera data is taken as a boundary condition, a hydrological model is driven to perform inversion in combination with a governance influence map, missing heavy rainfall process data is deduced, and finally, accurate attribution of abnormal reasons is realized, and a differentiated governance scheme is generated. According to the method, the problem of monitoring data missing in severe weather is effectively solved, and quantitative evaluation of the treatment effect and scientific diagnosis of the abnormal cause of the river channel are realized.

The invention provides a collision detection and safety control method for a mobile mechanical arm in a man-machine co-fusion environment. The collision detection and safety control method comprises the steps that multi-source sensor data of the mobile mechanical arm is collected in real time; inputting the preprocessed multi-source sensor data into a pre-trained collision detection neural network model to obtain a collision risk prediction value; based on the collision risk prediction value, a detection threshold value dynamically adjusted based on the historical fluctuation trend of sensor data is combined, and collision event judgment is carried out; when it is judged that the collision event occurs, compliant safety recovery control is executed, specifically, cached historical motion tracks within a preset duration before collision occur are called, and the historical motion tracks comprise a historical pose sequence of a mechanical arm joint and a historical path of a movable chassis; based on the historical movement track, a mechanical arm joint rollback track and a movable chassis rollback path are generated through a minimum Jerk track planning method; and the mechanical arm joint and the movable chassis are controlled to conduct collaborative rollback motion according to the rollback tracks and the rollback paths generated by the mechanical arm joint and the movable chassis respectively.

The invention discloses a man-machine co-driving detection method and device based on steering wheel torque and a medium. The method comprises the steps that a steering wheel torque accumulated value of a target vehicle is obtained; performing nonlinear normalization processing on the steering wheel torque accumulated value to obtain a torque confidence coefficient, and if the torque confidence coefficient is greater than or equal to a driver take-over intervention threshold value, calculating to obtain a comprehensive confidence coefficient by combining the torque confidence coefficient and the correction confidence coefficient; and if the comprehensive confidence is greater than or equal to the driver take-over final judgment threshold, judging that the driver of the target vehicle intervenes in driving. According to the man-machine co-driving detection method and device based on the torque of the steering wheel and the medium, the extensive judgment mode of a single torque threshold value is abandoned through a progressive technical means in accordance with the real operation characteristics of a driver, layer-by-layer accurate screening and verification logic is formed from signal collection to comprehensive judgment, and the detection accuracy is improved. The problem that whether a driver actually intervenes in vehicle driving operation is difficult to accurately recognize and judge when auxiliary driving is started can be solved.

The invention discloses a man-machine collaborative generative engine optimization system and recommendation method, and relates to the technical field of generative engine optimizing.The method comprises the steps that dominant intentions and implicit intentions in user input content are analyzed through an intention management module, intention network nodes are constructed, and each node records the generation time sequence, intensity evolution and association dimension of the intentions; identifying intention activeness of the nodes through multi-dimensional dynamic scores, executing hotspot memory layer storage on high-activeness data, and executing temperature point memory layer degradation on low-activeness data; generating a cross-session index tag according to the association dimension between the intention network nodes; comparing the difference between the current decision and the historical optimal scheme, marking a key deviation point, and generating a backtracking report; and generating an optimization decision according to the key deviation point of the backtracking report, storing the optimization decision as a new node in a memory layer, and updating the multi-dimensional dynamic score according to a decision effect.

The invention discloses an intelligent precise part machining quotation method and system based on a large language model, and belongs to the technical field of intelligent manufacturing and artificial intelligence. Semantic analysis is performed on unstructured manufacturing demand information through the large language model, and key manufacturing process parameters are extracted; if the demand is not complete, a structured process instruction list is output through multiple rounds of clarification inquiry; after the three-dimensional part model is analyzed, machining features are recognized, and when to-be-confirmed features exist, a machining feature list is generated through a man-machine collaborative confirmation mechanism; calculating technical cost according to the structured process instruction list and the processing feature list; non-technical factor information is obtained through a large language model, verification and adjustment are carried out until compliance is achieved, and non-technical cost is calculated; integrating the two types of costs to obtain a total quotation, visually displaying the total quotation and the cost composition, and outputting a quotation sheet after confirmation; the problems that an existing quotation system is difficult in natural language demand analysis, unreliable in feature recognition and lack of an interaction confirmation mechanism are solved.

The invention relates to the technical field of food processing automation, and discloses a vegetable production and packaging system which comprises a machine body, and a cleaning module, a draining module, a cutting module, a vegetable packaging module, a nominal weight pasting module, a material bag packaging and weighing module and a total packaging module are sequentially arranged in the machine body. The draining module further comprises a draining plate, a winding plate, a fixing plate, an outer rod and an inner rod, draining holes are formed in the surface of the draining plate, a second spring is arranged in the inner rod, the two ends of the second spring are connected with the inner rod and the outer rod respectively, a fluctuating seat is fixedly mounted on the bottom surface of the fixing plate, and a wavy notch is formed in the surface of the fluctuating seat. The whole production line adopts full-automatic design, manual intervention is reduced to the maximum extent from cutting, conveying to weighing and packaging, the production efficiency is greatly improved, the labor cost is reduced, man-machine isolation is achieved, secondary pollution in the production process is effectively avoided, the product quality is stable, and the sanitation level is high.

The invention provides a multimedia interaction control method and system based on a man-machine feedback closed loop, and relates to the technical field of financial science and technology, and the method comprises the steps: obtaining the multi-mode original feedback data of a user in an interaction environment in real time; preprocessing the multi-modal original feedback data to obtain preprocessed multi-modal feedback data; performing space-time coupling processing on the preprocessed multi-modal feedback data to generate multi-modal fusion features; dynamically determining a plurality of feature reference points based on feedback data of different modalities in a feature space corresponding to the multi-modal fusion features; the feature reference points at least comprise a user pupil center coordinate point in the visual feedback data, a fundamental frequency energy peak point in the voice feedback data and a pressure distribution gravity center point in the tactile feedback data. According to the method, space-time cooperative processing, dynamic feature quantification and closed-loop adaptive compensation of multi-modal feedback in a financial scene can be realized, and the instruction precision and information response timeliness of financial interaction are improved.

The invention provides a large model content generation auditing method and system based on man-machine collaboration, and relates to the technical field of large models, and the method comprises the steps: deconstructing the generation process data of to-be-audited content, constructing a content risk propagation map, mapping a multi-level auditing strategy library, and recognizing an uncertainty region needing manual intervention. And obtaining manual auditing judgment and correction operation, forming auditing knowledge precipitation and perfecting an auditing strategy library. According to the method, the content risk source can be accurately positioned, the auditing efficiency is improved, a closed-loop optimized man-machine collaborative auditing mechanism is formed, the misjudgment rate is effectively reduced, and the large model content security is improved.