50results about How to "Enter exactly" patented technology

The invention discloses a signal enhancement method and device in an industrial plant area, and relates to the field of wireless communication, and the method comprises the steps: firstly, obtaining complete channel state information, and guaranteeing that all decisions are based on real channel characteristics instead of theoretical assumptions; and secondly, if the complete channel state information does not meet the target QoS demand quantization parameter, maximizing a receiving signal to interference plus noise ratio and meeting the target QoS demand are taken as optimization targets, an optimization result is obtained based on channel field intensity distribution information of a preset digital twin model, and finally, the optimization result is controlled and executed to form closed-loop feedback. According to the method, optimization is carried out based on channel field intensity distribution information of a unit space in a digital twin model, and a complex problem needing to be solved by a high-dimensional iterative solution algorithm in related technologies is converted into table lookup and interpolation operation based on space coordinates, so that the calculation complexity is remarkably reduced, the time for obtaining an optimization result is greatly shortened, and the calculation efficiency is improved. Therefore, the time when the terminal is in a poor-quality signal state is greatly reduced.

The embodiment of the invention provides a sepsis prediction method, electronic equipment and a medium, and the method comprises the steps: inputting expression data corresponding to a plurality of different target genes of a patient into a sepsis prediction model, and obtaining a sepsis prediction result of the patient; wherein the sepsis prediction model is obtained through the following steps that a plurality of first training samples are obtained, and the first training samples comprise sample expression data of all target genes; for each first training sample, inputting the first training sample into a pre-trained analytical model to obtain a training prediction result of the first training sample and contribution relationship information between each piece of sample expression data in the first training sample and the training prediction result; and adjusting the initial sepsis prediction model according to the multiple pieces of contribution relationship information corresponding to the first training samples to obtain the sepsis prediction model. According to the embodiment of the invention, the accuracy and reliability of sepsis prediction can be improved.

The invention discloses a ship stern bearing interference press-fitting quality prediction method considering complex meteorological conditions. The method comprises the following steps: constructing a full-size multi-physical field simulation model; establishing a fluid calculation environment considering solar radiation; loading temperature field data for structural calculation; simulating a nonlinear contact interference press-fitting process; the press fitting curve, the press fitting contact area, the energy consumption in the press fitting process and the cylindricity error of the stern bearing after press fitting are used as evaluation indexes, and the press fitting failure risk is subjected to multi-dimensional quantitative evaluation. According to the method, the variable characteristics of the real environment temperature and the wind speed serve as the time-varying boundary conditions to be dynamically applied to the model, and quantitative evaluation of the dynamic change of the wind speed on the press-fitting quality is achieved; a solar radiation model and a DO radiation model are set in fluid calculation to realize accurate loading of non-uniform radiation heat flow, so that temperature field distribution of the stern shaft tube is accurately predicted; by constructing a multi-dimensional quantitative evaluation system, various failure risks in the press-fitting process are comprehensively identified.

The invention provides a distributed photovoltaic cluster cooperative scheduling method, system and device based on 5G, and relates to the technical field of photovoltaic clusters, and the method comprises the steps: for each distributed photovoltaic unit in a photovoltaic cluster, determining a mutation unit through distributed mutation point detection, and updating a meteorological-power space-time atlas; analyzing a spatio-temporal variation mode of the power data, tracking a disturbance front causing power fluctuation in real time, predicting a propagation path, a propagation speed and a power influence on a downstream region, and generating disturbance prediction information; performing fluctuation suppression analysis to generate a coordinated regulation instruction; and issuing to a downstream disturbance area through a 5G network, and executing an adjustment action when a disturbance front arrives. The technical problem that in the prior art, scheduling of a distributed photovoltaic cluster is usually locally optimized, a global view angle based on space-time coupling and cognition on space correlation of photovoltaic units are lacked, effective cooperative adjustment cannot be achieved, and then the stability of a power grid is poor is solved.

The application provides a multi-agent collaborative decision-making and disambiguation method and system. The method comprises the following steps: registering the dependency relationship between the task context and the event trigger condition concerned by the agent to the coordination center, updating the dynamic dependency view through the coordination center, analyzing the knowledge update time through the coordination center, and generating a task context identifier set of the agent; constructing a fact set based on the task context identifier set, inputting the fact set into a rule reasoning engine, executing the rules in the collaborative rule base to generate a strategy, and obtaining a collaborative strategy; and distributing the collaborative strategy to the corresponding agent and executing a cleaning process at the end of the task generation period. The application solves the efficiency bottleneck of event routing in a distributed intelligent system from the system architecture level, greatly improves the real-time response capability and scalability of the system in a high-concurrency scenario while ensuring decision consistency.

This invention provides a stable transmission method, system, and medium for high-concurrency industrial data streams, relating to the field of data transmission technology. The method includes: dynamically grouping industrial field devices to obtain multiple groups of edge autonomous devices, constructing multiple local self-organizing cell network topologies; forming a globally optimized topology based on inter-group logical coupling relationships; performing distributed collaborative verification to obtain multiple valid data streams; performing cross-cell network data association verification to obtain W verified compliant data packets; after time-series packet encoding, reassembling them into an asynchronous transmission channel sequence according to deterministic transmission delay requirements, transmitting them to a central computing platform, and then performing time-series packet decoding, restoration, and storage. This invention solves the technical problem in existing technologies where industrial data transmission typically employs a centralized architecture, which easily creates bottlenecks in the data transmission path, leading to increased data transmission latency or even data loss, thereby reducing the overall system's response speed and security.

The invention relates to the technical field of vehicle navigation, and discloses an airborne closed cabin vehicle path dynamic navigation planning method, which comprises the following steps: S1, navigation initialization, S2, real-time perception data acquisition and correction, and upward navigation reference based on a target established in the step S1, S3, map-free error correction and fusion, S4, dynamic path planning, and S5, accurate correction before landing. And S6, outputting a navigation control instruction based on the real-time navigation track in the step S4. Complete autonomous navigation of an airborne closed cabin vehicle in the whole process is achieved, dependence on external visual reference, a pre-built map or a ground guide signal is not needed, an absolute direction anchor point is provided through a target upward navigation benchmark, multi-source sensor decoupling correction and adaptive Kalman filtering fusion are combined, airflow disturbance compensation is synchronously brought in, and the navigation precision is improved. The inertial navigation accumulative error is effectively eliminated, the method can be adapted to an extreme airborne environment without reference, map and signal, and the independence and reliability of the navigation process are guaranteed.

The invention provides an explosion discharge disaster intelligent evaluation method based on a convolutional neural network, and relates to the technical field of combustible gas explosion protection. The method comprises the following steps: acquiring an explosion discharge disaster assessment multi-dimensional data set; according to the obtained multi-dimensional data set, determining an explosion discharge disaster evaluation model based on a convolutional neural network model; wherein the convolutional neural network model is used for learning and internalizing a high-dimensional mapping relationship between a multi-source working condition parameter and a disaster intensity field in an explosion discharge physical process; acquiring multi-source working condition parameters of a to-be-evaluated environment; and inputting the multi-source working condition parameters of the environment to be evaluated into the explosion discharge disaster evaluation model to obtain the on-way distribution of the explosion discharge peak overpressure, the peak temperature and the peak wind speed, thereby realizing the efficient and accurate prediction of the disaster peak at any position on the whole propagation path after the explosion discharge.

The utility model discloses a multi-strand twisting processing device for high-elasticity polyester yarns, and relates to the technical field of twisting processing. Comprising a mounting bracket, a first guide ring on the inner wall of a vertical rod and a second guide ring on the inner wall of an inclined rod are used for preliminarily guiding silk threads led out of a polyester silk winding drum, so that the silk threads move according to a preset path, shaking and winding of the silk threads are reduced, and the stability of the silk threads entering a subsequent processing link is ensured; the guide shell is of a cone-shaped structure, the lower end of the guide shell is open, the rubber ring is fixed to the edge of the guide shell, six strands of dispersed silk threads can be concentrated, the rubber ring plays a role in buffering and protecting the silk threads, and the silk threads are prevented from being damaged by friction with the edge of the guide shell; the guide pipe of the arc-shaped structure conducts secondary guiding on the concentrated silk threads, so that the silk threads enter the positioning ring more accurately, the position accuracy of the silk threads in the twisting process is further guaranteed, and the twisting stability and quality are improved.

The present application relates to the technical field of temperature control, in particular to a temperature control method and device for seedling greenhouse cultivation environment and a storage medium. The method comprises the following steps: establishing a greenhouse temperature influence model, the greenhouse temperature influence model can predict the greenhouse air temperature according to the greenhouse soil temperature, and the influence of the outside air temperature on the greenhouse air temperature is considered for optimization; obtaining the target soil temperature range and the target air temperature range of the current seedling growth stage, when it is monitored that the real-time greenhouse air temperature is not in the target greenhouse air temperature range, the air temperature adjustment strategy is started, and whether the greenhouse soil temperature is in the target greenhouse soil temperature range is judged, if the greenhouse soil temperature is not in the target soil temperature range, the soil temperature is adjusted. The method greatly improves the response speed and adaptability to environmental changes, and provides more stable and suitable growth conditions for seedlings.

The invention relates to the technical field of oil quality analysis, in particular to an aviation oil property analysis method and system based on spectral oblique projection. According to the method, when the oblique projection operator is constructed, the limitation of a traditional oblique projection operator is broken through through dynamic multi-component cooperation and self-adaptive residual error separation, key information which is in complex association with properties in the aviation oil spectrum is extracted more accurately, the pertinence and robustness of feature extraction are improved, and the accuracy of feature extraction is improved. Higher-quality input variables are provided for an aviation oil property prediction model, and the method is a core technical support for realizing high-precision aviation oil property analysis.

This invention discloses an intelligent projection positioning and drilling accuracy control system for blast holes at tunnel faces, belonging to the field of tunnel drilling and blasting construction technology. It aims to solve the technical problems of inaccurate blast hole positioning and large drilling direction deviations in existing tunnel face systems, leading to low construction efficiency. The key technical points are: the control system includes a structured light projector mounted on a tunnel drilling and blasting trolley, at least two industrial cameras, a fiber optic shape demodulation module, and a controller. A fiber optic shape sensing unit is installed inside the drill rod at the end of the drill arm of the drilling and blasting trolley; the structured light projector projects structured light, the industrial cameras acquire binocular images, the fiber optic shape demodulation module calculates the curvature and torsion angle sequence of the sensing unit, and the controller controls the drill arm drive mechanism to correct the drill bit position and direction through 3D reconstruction, coordinate comparison, and real-time deviation adjustment. This invention achieves intelligent positioning and drilling accuracy control of blast holes at tunnel faces, improving the accuracy and efficiency of tunnel drilling and blasting construction.

This invention relates to the field of medical technology, specifically to a method for monitoring anesthesia depth based on a neural network model. It includes the following steps: collecting physiological indicator data of patients under different degrees of anesthesia; preprocessing the physiological indicator data to obtain interference-free physiological indicator data; and transmitting the processed physiological indicator data to a data management system; constructing a neural network model; inputting the physiological indicator data into the neural network model through the data management system; and training the neural network model. This invention, by collecting physiological indicator data of patients under different degrees of anesthesia to establish a training dataset, and training the neural network model, enables the neural network to accurately predict the patient's anesthesia depth. This method of collecting and processing the training dataset provides sufficient data samples, allowing the neural network model to be trained on diverse data, thus improving the model's generalization ability.

This invention discloses an intelligent control method and system for BFB biomass boilers based on DCS, relating to the interdisciplinary fields of industrial process automatic control and artificial intelligence. It includes: a multi-source heterogeneous data synchronous acquisition module for acquiring data; an intelligent fusion and construction module for operating condition features that dynamically aligns, adaptively normalizes, and constructs combustion state features based on process delay mechanisms to form a unified operating condition feature vector; a digital twin-driven multi-objective collaborative optimization decision-making module that uses a dynamic digital twin model constructed from a physical information neural network as a predictor and employs a multi-agent optimization framework based on a two-layer interactive collaborative evolution algorithm to solve in real time a collaborative control instruction set that simultaneously optimizes combustion efficiency, initial pollutant concentration, and operational economy; and an instruction secure execution and adaptive fault-tolerant control module that securely issues and executes the instructions.

The invention discloses an energy flow distribution method and device for a fuel cell combined cooling heating and power system. The method comprises the following steps: acquiring power side data, cold and hot side data and global data of the fuel cell combined cooling heating and power system; respectively predicting a power supply and demand difference corresponding to the power side data, a minute-level load change corresponding to the cold and hot side data and a next-day load curve corresponding to the global data through a multi-time scale distributed prediction algorithm; adjusting the battery operation power of the fuel battery combined cooling heating and power system in real time according to the power supply-demand difference; adjusting the running state of a cooling and heating device of the fuel cell cooling, heating and power combined supply system according to the minute-level load change and the cell running power; a multi-target optimization model is constructed according to the next-day load curve, and the running state of a hydrogen production device of the fuel cell combined cooling heating and power system is adjusted in combination with the multi-target optimization model; the overall energy utilization rate of the fuel cell combined cooling heating and power supply system is improved through overall cooperation, and the real-time performance of power adjustment is guaranteed.

The invention discloses a substation constant value remote proofreading method based on a hybrid enhanced intelligent and elastic process. The method comprises the following steps: S1, three-stage screening and scene adaptation interrogation; s2, intelligently generating and optimizing a constant value template; s3, carrying out online closed-loop circulation and intelligent decision making; s4, performing semantic normalization on the heterogeneous parameters; s5, working condition sensing difference comparison and pseudo difference filtering are carried out; s6, performing elastic downloading execution and real-time monitoring; s7, performing feedback optimization; according to the method, a target is accurately locked through a three-stage screening structure, classification is performed through a CNN-LSTM model, an interrogation strategy is dynamically adjusted, a to-be-loaded setting value list is generated based on online closed-loop circulation and a hybrid enhanced intelligent framework, and space limitation is broken; heterogeneous parameter semantic normalization is realized through a BERT model, and comparison ambiguity is eliminated in combination with working condition sensing dynamic threshold and pseudo difference filtering; abnormality is handled based on a BPMN2.0 elastic engine, verification and feedback optimization are carried out after downloading, a complete closed loop is formed, and iteration upgrading of the system is promoted.

This application relates to a vehicle suspension control method, apparatus, computer device, and readable storage medium. The method includes: combining acquired vehicle dynamics time-series data, vehicle handling time-series data, and environmental perception time-series data during vehicle operation into multi-channel time-series data; determining the adaptive mode number for the time-series data in each channel; performing mode decomposition on the time-series data based on the adaptive mode number to extract frequency domain features; integrating the frequency domain features of the time-series data from multiple channels and inputting them into a trained suspension control parameter prediction model to obtain target time-series features for predicting suspension control parameters; predicting vehicle suspension control parameters based on the target time-series features; and controlling the vehicle's suspension system based on the vehicle suspension control parameters. This method can significantly reduce the risk of vehicle rollover.

The invention relates to the technical field of human body radiation detection, and discloses an integrated whole body radioactivity counting, measuring and analyzing system which comprises a detection head, a high-voltage module, a multi-channel analyzer, a scale preparation module, an analyzer control module, a data acquisition and storage module and a parameter analysis module. Through collaborative design of data-scale integrated storage management and hardware-software closed-loop control, pulse data and scale configuration are subjected to strong association storage, and uniqueness and accuracy of analysis and traceability are ensured; centralized and programmable control over front-end hardware is achieved through the analyzer control module, so that the system can adjust the working state in a self-adaptive mode and conduct real-time quality verification. In the system integration level, the integrated architecture design realizes the whole process from physical signals to quantitative analysis results, the operation steps are simplified, and the detection efficiency is improved.

The present application belongs to the field of medical image processing and biomedical engineering, and particularly relates to a dynamic contrast-enhanced magnetic resonance imaging reconstruction method based on pharmacokinetic model optimization. The steps are as follows: obtaining original image data, performing multi-scale data correction preprocessing on the original data to generate preprocessed image data; extracting a large blood vessel region based on the preprocessed image data as a candidate region of a contrast agent input function; constructing a heterogeneous nonlinear pharmacokinetic model; establishing a cooperative reconstruction mechanism of the heterogeneous nonlinear pharmacokinetic model and the preprocessed image data; integrating the kinetic constraint condition of the heterogeneous nonlinear pharmacokinetic model into the objective function of the preprocessed image data reconstruction as a loss term, and further iteratively updating the intermediate parameters and the initial reconstructed image. The method of the present application improves the accuracy of parameter estimation in the tumor lesion region through the adaptive heterogeneous nonlinear pharmacokinetic model.

The present application relates to the field of image processing, and more particularly to a kind of image defogging method and system.The present application carries out blocking to the image to be defogged, for each block region, the RGB vector of all target pixel points in each block region is fitted, and the initial fitting matrix of each block region is obtained;Based on the inner product of the RGB vector of each pixel point in each block region and the initial fitting matrix of the block region, the effective pixel point of each block region is screened;The effective pixel point of each block region is fitted, and the optimization direction matrix of each block region is obtained;The luminance average value, optimization direction matrix of each block region are combined to obtain the fusion feature matrix of each block region;Based on the fusion feature matrix of each block region, the atmospheric light value of each block region is obtained;Based on the atmospheric light value and the transmissivity of each block region, the image after defogging is generated.The present application effectively improves the precision of defogging image.

The application discloses a quantitative prediction method for atmospheric pollutant corrosion rate of metal under multi-gas coupling effect, relates to the technical field of atmospheric corrosion science and industrial protection, and comprises the following steps: step one, constructing a multi-gas corrosion rate calculation basic framework; taking the rate threshold value data of metal in a graded corrosion environment, the framework provides a unified operation platform for subsequent integration of other gases and adaptation of different metals. The quantitative prediction method for atmospheric pollutant corrosion rate of metal under multi-gas coupling effect realizes full-coverage quantitative analysis of the corrosion effects of three core atmospheric pollutants, namely Cl ‑ , H2S and NH3, effectively solves the technical difficulty that Cl ‑ cannot be independently calculated in the prior art, and realizes precise coupling and linkage calculation of Cl ‑ , H2S and NH3 by converting Cl into a dedicated corrosion contribution item.

This invention relates to the interdisciplinary field of artificial intelligence and intelligent education, and provides a method for adaptively constructing virtual simulation training scenarios based on generative AI. It aims to address the problems of insufficient personalization and poor dynamic adaptability in existing training technologies due to their reliance on static pre-built scenario libraries. The method dynamically constructs and updates the cognitive state graph of trainees to achieve accurate state perception, and generates optimal scenario strategies through multi-agent collaborative inference and multi-objective game evaluation based on this. The optimal scenario strategies are then integrated with a static domain knowledge graph to generate structured instructions that drive a multimodal generative AI model to synthesize specific training scenario content. Finally, the virtual simulation training engine is driven to update the training scenario and collect feedback data, thereby constructing a closed loop of "perception-decision-generation-execution." This allows for the dynamic generation of personalized training content based on the trainees' real-time state, significantly improving the timeliness, adaptability, relevance, and personalization of the training.

The invention relates to the technical field of crop disaster early warning, and provides a crop pest early warning method and system based on multi-modal perception. The method comprises the following steps: synchronously acquiring image and audio signals and preprocessing to obtain a standardized data packet; extracting visual feature vectors and acoustic feature vectors from the standardized data packet, and combining the visual feature vectors and the acoustic feature vectors into a node feature set; performing cross-modal fusion judgment on the basis of the node feature set, and generating a node state list containing pest existence and activity intensity indexes; performing spatial interpolation and time sequence prediction on the node state list, and constructing a pest dynamic risk field containing prediction risk information; and based on the pest dynamic risk field, applying a preset threshold to carry out risk partitioning, and generating a structured early warning instruction set. According to the method, a regionalized and prospective quantitative prevention and control basis is provided for farmland insect pests.

The invention relates to an audio quality detection method and device, a medium and a product. The method comprises the following steps: acquiring an original audio, and extracting a time domain feature and a frequency domain feature of the original audio; performing anomaly detection of different anomaly types on the original audio according to the time domain feature and the frequency domain feature to obtain multiple anomaly detection results of the original audio; inputting the original audio into a pre-trained deep learning model for reconstruction to obtain a plurality of reconstructed audios; performing reconstruction performance detection on each reconstructed audio to obtain a reconstruction performance detection result of each reconstructed audio; wherein different reconstruction performance detection results are used for representing different global qualities of the original audio; obtaining one or more groups of associated detection results from each anomaly detection result and each reconstruction performance detection result; and outputting a quality detection result of the original audio according to the correlation detection result. By adopting the method, high-efficiency and high-accuracy audio quality detection can be realized.

The present application relates to power distribution system technical field, especially to distributed intelligent power distribution network self-healing control method and system.The method comprises the following steps: step S1, real-time collection of distributed intelligent power distribution network operation data; step S2, fault detection; step S3, parallel protection control; step S4, building fault section positioning model and island division model, and positioning fault section according to fault section positioning model, and dividing island according to island division model; step S5, building double-target optimization model; step S6, solving double-target optimization model according to improved ant colony algorithm, and obtaining optimal switch action table; step S7, safety check of optimal switch action table, and self-healing control of distributed intelligent power distribution network according to safety check result; step S8, feedback of self-healing control situation of distributed intelligent power distribution network.The present application improves fault recovery speed, accuracy and reliability of distributed intelligent power distribution network.

The invention provides a watershed fish habitat matching method and system based on model simulation, and the method comprises the steps: obtaining environment monitoring data of different monitoring points in a watershed in a preset time period, and constructing a watershed environment scene data set in combination with the landform basic data of the watershed; performing multi-dimensional feature analysis processing on the data set to generate a fish habitat demand feature set; performing cross-scale feature matching based on the environment feature evolution sequence and the fish habitat demand feature set, and constructing a habitat suitability incidence matrix; performing space-time planning processing on the habitat suitability incidence matrix to generate a habitat suitability index distribution map; and determining an optimal habitat space range of the target fishes, performing ecological protection priority ranking on the optimal habitat space range, and generating a watershed fish habitat planning result. According to the invention, the practicability and pertinence of the planning result of the watershed fish habitat are improved.

This invention discloses a multi-factor driven logical sequence generation method and apparatus, comprising: a feature input module, a multi-factor fusion module, a dynamic constraint module, a logical generation module, a reordering and evaluation module, and a feedback learning module. The method involves receiving text information from multiple sources, performing structured parsing and normalization to obtain a standardized set of text feature vectors; extracting multi-factors and constructing multi-factor constraints; generating a multi-factor driven logical sequence based on the set of multi-factor constraints; performing dynamic information updates and output constraint adjustments; generating candidate logical sequences; and designing multiple-dimensional indicators for re-evaluation. This invention significantly improves the accuracy, adaptability, and optimality of logical text sequences by addressing the problems of insufficient input information processing, lack of dynamic adaptability, and insufficient post-optimization mechanisms in the logical text sequence generation process.

This application discloses a machine vision-based defect detection system for seamless steel pipe production, relating to the field of image analysis technology. The system includes: an acquisition device for acquiring a first inspection image of the seamless steel pipe; a processing device for acquiring multiple segmented regions based on the first inspection image; acquiring a first segmented region from each segmented region; acquiring a texture consistency value based on the first segmented region; acquiring a first defect probability based on the texture consistency value; and if the first defect probability is greater than or equal to a first preset value, inputting the first segmented region into a defect detection model to obtain defect information in the first segmented region. This application's segmentation of the first inspection image into multiple segmented regions eliminates interference from irrelevant background and noise in the image, allowing defect identification to focus more specifically on a single region, thus solving the problem of traditional overall analysis being easily interfered with by redundant information.

The utility model relates to a kind of damping platform, including including magnetic suspension damper, first connecting plate and second connecting plate, magnetic suspension damper is configured as multiple, second connecting plate is parallelly arranged with first connecting plate, and accommodating space is formed between second connecting plate and first connecting plate, and all magnetic suspension dampers are arrayed in accommodating space.The utility model can be commonly supported by multiple magnetic suspension dampers The precision equipment on top, so that the precision equipment load can be evenly distributed to each magnetic suspension damper, to improve the carrying capacity of damping platform.Meanwhile multiple magnetic suspension dampers can work cooperatively, jointly offset vibration in different directions, to realize the efficient suppression of multi-degree-of-freedom vibration.

The invention discloses a digital human interaction strategy optimization method based on big data recognition, and relates to the technical field of intelligent interaction and digital human, and the method comprises the steps: collecting and storing multi-modal interaction data, such as texts, voices, expressions and time lengths, generated in a digital human interaction process of a user; performing emotional state labeling on the multi-modal interaction data based on a pre-trained emotion recognition model, and extracting an emotional feature vector containing emotional polarity, emotional intensity and emotional stability; constructing a user emotion change track map to recognize an emotion state conversion mode; evaluating and sorting candidate interaction strategies according to the conversion mode in combination with historical interaction data, and selecting an optimized interaction strategy; and the strategy evaluation index is updated according to user feedback, so that the adaptive optimization of the digital human interaction strategy is realized.