170 results about "Scene labeling" patented technology

The invention relates to a real-time process monitoring method based on data analysis, and the method comprises the following steps: S1, building a three-dimensional component priority evaluation model based on a component operation scene type, a real-time resource occupancy rate and a data dependence degree, and dynamically matching an acquisition strategy; s2, a cleaning rule is adapted according to a data source, a feature extraction dimension is adjusted in combination with process dynamic features, and an improved time sequence decomposition algorithm is used for separating data trends, fluctuations and abnormal residual errors; s3, constructing an exclusive baseline sub-model by using an online learning algorithm according to a scene label, and establishing a scene switching mechanism; s4, in combination with component interaction anomaly features, an anomaly level is judged through a mixed detection model; s5, on the basis of exception processing and user feedback, constructing an error correction model optimization parameter; and S6, generating a report containing an abnormal propagation path, and triggering hierarchical collaborative response of the associated component. The invention aims to solve the problems of single acquisition dimension, no scene adaptability in preprocessing, incomplete abnormal detection and the like in the existing monitoring technology.

The invention relates to the related technical field of intelligent scene labeling, in particular to an intelligent driving scene collaborative labeling method and system based on multi-modal fusion, and the method comprises the steps: constructing a dynamic feature fusion model, carrying out the cross-source correlation analysis of a multi-modal scene data set, setting organization structure elements, and setting a multi-dimensional scene labeling model. And configuring a potential danger interaction path and a scene risk index, and generating an annotation instruction set to carry out collaborative annotation operation. The technical problems that under interference of severe weather such as sensor noise, rain and fog and strong light and abnormal traffic behavior scenes, the labeling accuracy is insufficient, and complex scenes and multi-modal data requirements are difficult to adapt are solved, labeling noise data are introduced into an adversarial sample configuration assembly, a multi-dimensional scene labeling model is constructed in combination with organizational structure elements, and the multi-dimensional scene labeling accuracy is improved. The technical effects of improving the training data reliability, adapting to complex environment labeling requirements, configuring potential risk interaction paths and scene risk indexes, carrying out risk-oriented priority labeling and improving the risk scene labeling precision are achieved.

The invention relates to the crossing field of artificial intelligence technology and e-government, and discloses an e-government enterprise service intelligent response method based on AI cue word engineering, comprising: constructing and maintaining a government knowledge graph and a scenarized cue word template library; receiving and analyzing a service request input by an enterprise, and extracting an enterprise feature tag and a business intention; selecting a target cue word template based on the matching degree of the enterprise feature tag and the scene tag of the template in the scene cue word template library, and generating an adapted cue word template; retrieving associated knowledge data from the government affair knowledge graph according to the service request, and filling the adapted cue word template with the retrieved associated knowledge data to generate a structured cue word; inputting the structured cue word into an AI model to drive the AI model to generate a government affair service response; and collecting multi-dimensional evaluation feedback of government affair service response, and carrying out iterative optimization. The demand understanding precision is improved, and the risk of core information misjudgment is reduced.

The invention discloses a double-arm clothes folding robot control method and device, equipment and a storage medium. The method comprises the following steps: determining three-dimensional point cloud data of a target workbench according to a collected image through a visual perception assembly; determining a scene label graph and a desktop edge line set through a scene division assembly according to the three-dimensional point cloud data, determining a clothing mask of the to-be-processed clothing according to the scene label graph through a clothing classification assembly, and determining a clothing description vector according to the clothing mask; determining a low-risk action set and a protection area mask from the candidate clothes folding action set through a safety protection component according to the scene label graph, the desktop edge line set and the clothes mask; determining a target clothes folding action sequence through an action planning component according to the clothes description vector, the low-risk action set and the protection area mask; and folding the to-be-processed clothes through the robot control assembly according to the target clothes folding action sequence. According to the scheme, the clothes folding efficiency and reliability of the robot are improved.

The invention relates to a peripheral feedback effect control method and system, a storage medium and electronic equipment, and relates to the technical field of peripheral control, and the method comprises the following steps: determining whether a current target user has a target scene label in a target game according to actual game state data; if the target scene label exists, determining a first light parameter and a first vibration parameter of the actual peripheral according to the target scene label; optimizing the first light parameter and the first vibration parameter according to the actual electric quantity to obtain a final light parameter and a final vibration parameter, and controlling the feedback effect of the actual peripheral according to the final light parameter and the final vibration parameter; and if the target scene label does not exist, determining a second feedback parameter of the actual peripheral according to the user game state data, and controlling a feedback effect of the actual peripheral according to the second feedback parameter. The method and the device have the effect of improving the peripheral feedback effect and the user adaptability.

The invention relates to the technical field of video content analysis, and discloses a video scene content label determination method based on a knowledge graph. The method comprises the following steps: inputting a multi-modal feature sequence formed by visual, audio and text features extracted from a video stream into a knowledge graph inference engine comprising an entity relationship network and a semantic association rule base; performing node mapping through an engine to generate an initial scene entity set; performing hierarchical reasoning on the set based on a semantic association rule base to obtain a scene semantic topological structure; screening core entities according to entity weight distribution in the topological structure, and generating a candidate tag set; performing time sequence consistency verification on the candidate tag set, and correcting tag time sequence offset in combination with timestamp information; performing cross-modal disambiguation on the corrected label set by using an entity relationship network to eliminate semantic conflicts; and according to a disambiguation result, constructing a scene label knowledge sub-graph containing entity attributes and relation path constraints.

The invention relates to the technical field of health monitoring, in particular to a health state alarm response system based on multi-dimensional monitoring, which comprises a dynamic threshold module, a physiological monitoring module, an anomaly positioning module, an inducement output module, an anomaly intervention module and an early warning response module, according to the method, multi-dimensional original data of a user is collected, meanwhile, a timestamp alignment and scene label binding technology is adopted, an exclusive physiological index normal fluctuation interval library is constructed for different large classes of scenes, then the problem of misjudgment of a static threshold value is fundamentally solved, the cause of'abnormity 'is deeply and accurately positioned, and the accuracy of the abnormal condition is improved. The abnormity-scene-behavior-cause full-link association not only enables the user to clearly know the cause of the abnormity, but also provides data support for the medical personnel to formulate a targeted intervention scheme, and reasonably formulates the targeted intervention scheme based on the intervention list, thereby avoiding the ineffectiveness of general suggestions, enabling resources to be inclined to high-risk users, and improving the user experience. The complication occurrence rate is effectively reduced.

The invention belongs to the field of man-machine interaction, and particularly discloses an intelligent AI dialogue interaction method and system based on an RICH normal form, and the method comprises the steps: obtaining current dialogue context data and a user input signal, processing the data through an intention recognition model, and obtaining a user intention vector and a scene label; according to the user intention vector and the scene label, acquiring hierarchical state information in a preset multilayer interaction framework, and if the matching degree of the vector and the current hierarchical state is lower than a preset threshold value, activating an information sharing mechanism to obtain an updated shared knowledge graph; through the shared knowledge graph, a dynamic scene adaptation module is fused, a potential switching path and intention extension of a conversation are determined, and an optimized interaction path sequence is obtained; the objective of the invention is to solve the problems of inaccurate intention recognition, stiff interaction path switching and incoherent response logic of a traditional dialogue system in a complex scene in the prior art.

The invention relates to the technical field of automobile infotainment, and discloses an automobile infotainment theme production binding method, system and device and a storage medium, and the method comprises the steps: obtaining an original data flow of a vehicle multi-mode sensor; analyzing the original data stream to generate a standardized feature vector; outputting a scene label corresponding to the current vehicle by analyzing the feature vector; dividing the to-be-started application process into different grades according to a preset scene grading mapping table; allocating corresponding hardware resources to different levels of application processes, and dynamically adjusting an allocation scheme of the hardware resources of the vehicle-mounted system; according to the adjusted resource allocation scheme, the application processes of the corresponding levels are started in sequence; and when it is detected that the feature vector has a change exceeding a preset threshold, re-outputting the scene label, and synchronously updating the application process grading and resource allocation scheme according to the re-output scene label. Therefore, quick starting of the application process, reasonable distribution of system resources and optimization of user experience are realized.

The invention provides a full-scene adaptive intelligent recommendation method and system based on a graph neural network, and the method comprises the steps: constructing a parallel candidate recommendation model for generating a diversified candidate pool; a scene label of a user is determined through a CoT inference rule, the scene label is used as a query, a most matched strategy document is retrieved from a strategy knowledge base by utilizing an RAG module, and a currently recommended dynamic execution strategy is formulated; normalizing the original score of the parallel candidate recommendation model based on a dynamic execution strategy, calculating a preliminary fusion score according to a dynamic weight retrieved from a strategy knowledge base, and generating a preliminary sorting list; and performing rearrangement based on big language model reasoning on the preliminary sorting list in combination with scene and strategy guidance, and introducing evidence retrieved from a fact knowledge base based on an RAG module into a rearrangement result to obtain a final recommendation list with language interpretation. According to the method, the recommendation list which is highly personalized, self-adaptive in scene and clear in natural language interpretation can be obtained.

The application relates to a recognition method for matching scene behaviors by using multi-modal features, and belongs to the technical field of scene behavior matching. The method acquires multi-modal scene behavior data, and carries out noise self-adaptive purification processing on the multi-modal scene behavior data, so as to obtain preprocessed scene image data, scene audio data and scene label text. Then, feature collaborative extraction is carried out on the preprocessed data, so as to obtain visual features, audio features and text features. The collaboratively extracted features are input into a scene behavior matching network, scene feature fusion and behavior feature fusion are respectively carried out, scene feature vectors and behavior feature vectors are obtained, a bipartite graph is constructed according to the scene feature vectors and the behavior feature vectors, the semantic similarity between nodes of the bipartite graph is calculated, the edge weight of the bipartite graph is dynamically updated according to the semantic similarity between the nodes, the updated bipartite graph is normalized, and a scene behavior matching result is obtained. The application can accurately quantify the correlation degree of scenes and behaviors, and greatly improves the accuracy of the matching result.

The invention discloses a digital annotation adaptive optimization method and system based on deep learning, and relates to the field related to deep learning, and the method comprises the steps: uploading an annotation target to an annotation platform, carrying out the decoupling reconstruction of the annotation target based on the annotation confidence according to a meta-cognitive threshold, and determining a first annotation target and a second annotation target; according to the first labeling component, performing automatic labeling processing on the first labeling target and the second labeling target to generate a first labeling result and a second labeling result; according to an annotation judgment component, anti-fact judgment based on physical attributes and annotation ambiguity is conducted on the second annotation result, a second annotation component is triggered to execute directional projection annotation, a second optimized annotation result is generated, and the first optimized annotation result and the second optimized annotation result are coupled to serve as a target annotation result; and labeling a platform display port for displaying. The technical problem that existing digital labeling is poor in complex scene labeling effect is solved, and the technical effect of improving the adaptability to complex scenes and the overall output quality is achieved.

The application discloses a body intelligent agent interaction system and a control method, and relates to the technical field of artificial intelligence. The method comprises the following steps: a terminal sensor module is configured to collect physical signals of environment or user interaction, and generate standardized event data; a reflection processing module comprises a lightweight decision unit, which is used for receiving scene labels and user feedback information to self-optimize protected core behavior logic, and deep evolution is performed after a growth authorization signal is received; a narrative processing module is used for processing complex interaction cognition, generating scene labels and sending the scene labels to the reflection processing module, sending an inhibition instruction and a growth authorization signal to the reflection processing module, and performing context interpretation; wherein, the reflection processing module and the narrative processing module communicate through a preset interface, and the narrative processing module has no right to directly modify the core behavior logic; the system guarantees the explanation right and adjustment right of the narrative layer to the reflection layer, and prevents tampering of the core instinct at runtime through strict permission isolation.

This invention relates to a multi-mode intelligent switching method for human-machine collaborative outbound calling scenarios, belonging to the field of artificial intelligence technology. The method includes: acquiring voice semantic information during the outbound calling interaction process, determining the human-machine interaction adaptability, maintaining an intelligent outbound calling mode and triggering a switching command for a manual outbound calling mode; classifying outbound calling tasks by scenario tags, matching corresponding interaction processing strategies, dynamically monitoring interaction fluency and problem complexity, adjusting human-machine processing weights, and switching outbound calling modes; acquiring historical interaction data and real-time interaction information, establishing a human-machine collaborative switching judgment model, generating human-machine mode judgment results, and autonomously switching between intelligent and manual outbound calling modes; dividing outbound calling interaction stages, setting switching conditions, verifying the interaction response effect in real time, and switching outbound calling modes based on the verification results. This invention can achieve stable, intelligent, and adaptive switching of outbound calling modes, improving the efficiency and interaction effect of human-machine collaborative outbound calling.

The invention relates to the technical field of information processing, and discloses a standard file query method and device, electronic equipment and a storage medium, and the method comprises the steps: receiving a standard query request input by a user, and carrying out semantic analysis to obtain a query keyword and a query scene tag; constructing a domain knowledge graph of each domain; screening a candidate file set based on historical query preferences and query scene tags; calculating the correlation degree between the candidate standard file and the query keyword based on the domain knowledge graph to which the candidate standard file belongs and the historical query preference; and calculating a comprehensive score based on the historical query preference and the correlation degree corresponding to the candidate standard files, and generating a query result based on the comprehensive scores of all the candidate standard files. The objective correlation degree between the standard file and the user query and the subjective preference of the user are fused, it is ensured that the query result is matched with the query requirement and meets the user requirement, the query result is enriched, and the query accuracy and the user query experience are improved.

The invention provides a label generation method based on a large model, electronic equipment and a storage medium. The method comprises the following steps: acquiring to-be-labeled single-person voice data; performing automatic labeling on the single-person voice data to obtain a plurality of basic labels, generating a first cue word according to the plurality of basic labels and the scene feature information, and inputting the plurality of basic labels and the first cue word into a labeling model to obtain at least one scene label, the scene feature information comprises association degrees between at least one scene and a plurality of basic labels; and according to each basic tag and each scene tag, generating a target tag of the single-person voice data. According to the method, the multi-dimensional basic label of the voice data is generated, the scene label is generated according to the multi-dimensional basic label, and the target label is further obtained, so that the labeling efficiency is improved, and meanwhile, the multi-dimensional and high-depth label requirements of a large model are met.

The invention discloses a network space data security compliance management system based on an artificial intelligence technology, and the system comprises a real-time compliance response module which is used for analyzing a supervision file and outputting structured clause information; the deep data cognition module is used for processing the service data and generating a scene label; the dynamic strategy management module is connected with the real-time compliance response module and the depth data cognition module, and is used for receiving the clause information and the scene label, compiling the clause information and the scene label into an executable strategy code, and carrying out strategy conflict resolution based on a conflict arbitration algorithm; the risk assessment model is connected with the dynamic strategy management module and the depth data cognition module; a unified management module; all the modules are in interactive connection with a block chain network through a system bus to form a closed-loop governance system from data analysis, strategy generation, risk assessment to audit execution. According to the method, the data security and the compliance check automation level are comprehensively improved, and meanwhile, the compliance cost is remarkably reduced, and the overall improvement of the whole-process management efficiency is realized.

The application relates to the field of video processing, and discloses a video label classification method, system and product based on metadata extraction and intelligent scene division, which comprises the following steps: S1, acquiring an input video and converting the input video into an intermediate format code; S2, separating a video image track, an audio track and a subtitle track; S3, extracting metadata of the video, specifically comprising the following steps: S301, extracting image metadata, comparing the similarity of video intervals based on the image metadata, thereby dividing a video scene and generating a scene visual label; S302, extracting scene audio metadata according to the divided scene, thereby generating a scene auditory label; S303, extracting text metadata according to the divided scene, thereby generating a scene text label; and S4, based on different scene labels, combining the weight components and time points of each scene in the video, and comprehensively obtaining a video label. The application can comprehensively extract and summarize metadata of a video, widen the dimension of a video label, and improve the accuracy of video recognition and matching.

This invention provides a method for automatic cross-system invocation of AI marketing based on the MCP protocol, comprising the following steps: receiving marketing task instructions generated by the AI ​​marketing system; parsing the marketing task instructions, extracting task elements and labeling them with scene tags; matching available tools from the MCP registered tool library based on the scene tags and task type; performing permission verification and security verification; invoking the available tools according to the standardized format of the MCP protocol by calling the execution engine; collecting the execution results of each tool and generating an execution report in a unified format; and feeding the execution report back to the AI ​​marketing system. This invention provides a method for automatic cross-system invocation of AI marketing based on the MCP protocol, which has a high degree of standardization and reduces integration costs: it extends marketing-specific standards on the basis of the standard MCP protocol, unifies the task description format, communication protocol, and data format, and solves the API fragmentation problem.

This invention belongs to the field of printing service technology and discloses a method for accurate printing service recommendation based on multi-dimensional user behavior data. The method includes: constructing user profile tags and parsing file attribute data; obtaining the user's historical printing order information and quantifying preference features; generating final scene tags based on multi-dimensional scene data; filtering feasible basic printing parameters according to the file attribute data, final scene tags, and a preset business rule base; expanding and generating N candidate solutions based on feasible basic printing parameters and the preference features; determining the utility value of each candidate solution through utility function calculation; determining the recommended solution based on the utility value; generating presentation content based on the recommended solution and the decomposition result of the utility function and pushing it to the user; and generating a revised recommended solution based on the feedback information. This invention achieves highly personalized and accurate recommendations, improving user experience.

The invention belongs to the technical field of data governance, and relates to a data governance method and device, computer equipment and a storage medium, and the method comprises the steps: generating a scene label based on state data of multiple governance nodes, intelligently generating a governance action through a reinforcement learning model, selecting a target node for execution, then collecting feedback, and synchronizing the feedback to nodes of a whole network, and dynamic optimization and system collaboration of the governance strategy are realized. According to the invention, real-time intelligent decision-making of the governance strategy is realized through the scene label generated by the multi-source data and the reinforcement learning model. And meanwhile, the execution feedback of a single node is synchronized to the whole network, so that the system obtains the continuous optimization capability, the problem that a traditional static rule treatment method shows insufficient adaptive capacity when facing a complex and changeable distributed environment is solved, and the treatment accuracy and the overall efficiency of the system are remarkably improved.

The invention discloses a tool screening method and device, electronic equipment, a storage medium and a program product. The tool screening method comprises the steps that historical calling data corresponding to a user, a preset tool library and scene tags of all tools to be called in the preset tool library are obtained; determining a tool calling scene and a preset calling priority strategy of each to-be-called tool in a tool calling request initiated by a user; determining a candidate calling tool set in the to-be-called tools based on the historical calling data, the scene labels, the tool calling scenes and a preset calling priority strategy; and calling a preset large language model to determine a target calling tool in the candidate calling tool set. The tools are screened by integrating the historical calling data of the user, the scene label of each to-be-called tool, the preset priority strategy and other multi-dimensional information, it can be ensured that the screened candidate tools not only conform to the past use habits of the user, but also adapt to the calling scene of the current calling tool of the user, and the user experience is improved. And the suitability and the accuracy of tool screening are improved.

The invention discloses an intelligent power grid state monitoring method and system based on digital twinning, and the method comprises the steps: firstly generating a scene label based on a semi-supervised learning model according to collected power grid data, and generating a model template matched with the scene label according to the scene label and a semi-supervised learning model template library; constructing a power grid digital twinborn model based on the model template, and generating an abnormal path set based on the power grid digital twinborn model; calculating an independent risk value of a single power grid device based on the abnormal path set; and finally, generating a comprehensive risk value of the intelligent power grid according to the independent risk value of each power grid device. According to the method, seamless conversion from equipment-level risks to system-level risks is ensured, and visual and reliable decision support is provided for state monitoring of the intelligent power grid.

This application discloses a display parameter adjustment method, an electronic device, and a computer-readable storage medium, comprising: acquiring display scene-related metadata through an application and framework layer and reporting it to a system service layer; the system service layer aggregating the metadata to generate standard scene tags; the display driver layer performing scene prediction based on the standard scene tags, and querying a preset mapping table according to the prediction results to determine the target drive voltage parameters and target refresh rate, thereby generating corresponding collaborative control instructions, and sending the collaborative control instructions to the power management chip and the display processing unit respectively, thereby realizing the collaborative adjustment of drive voltage and refresh rate, while reducing the relevant power consumption of the display module.

The invention discloses a commodity recommendation method and system, a storage medium and electronic equipment, and the method comprises the steps: obtaining the multi-modal data of a to-be-recommended commodity, and carrying out the normalization and feature extraction processing of the multi-modal data, so as to obtain the features of the multi-modal data; determining a data change feature and a user preference evolution feature according to the multi-modal data feature, and fusing the multi-modal data feature, the data change feature and the user preference evolution feature through a preset fusion mode to obtain a fused feature vector; determining a profit rate prediction value of the to-be-recommended commodity in the target scene according to the fusion feature vector and the multiple scene labels; and determining a target commodity combination according to the profit rate prediction value and a preset constraint condition. According to the method, by obtaining the multi-modal data and integrating and fusing the multi-modal data, the problem that product selection prediction and actual requirements are disjointed due to a single data dimension is solved, and thus the product selection accuracy is improved.

The invention relates to the technical field of intelligent lighting control, and discloses an intelligent lighting control method and system based on scene perception, and the method comprises the steps: determining a scene label according to a personnel existence signal of a PIR sensor, an environment illumination original value of an illumination sensor, and a time period code of a current moment; calculating a cold white channel current proportion and a warm white channel current proportion according to the scene label, and respectively writing the cold white channel current proportion and the warm white channel current proportion into the reference control ends of the cold white constant current driving channel and the warm white constant current driving channel; according to the method, the problem of color point jump caused by the fact that color temperature switching depends on mechanical gears in the prior art is solved, the problem of current drift caused by power supply fluctuation is solved, it is ensured that the illumination of the working face is stably converged to a target value under all scenes, and the working efficiency is improved. Therefore, the cooperative accurate control of the color temperature and the illumination is realized.

The invention relates to a visualization method and device for cloud native monitoring data and computer equipment. Relates to the technical field of data processing. The method comprises the following steps: generating a scene label of a cloud native environment based on pre-collected cloud native monitoring data of the cloud native environment; distributing dynamic weights of the system index data and the business index data based on a preset data processing algorithm, and generating a corresponding conventional baseline; dynamically adjusting the conventional base line based on the scene label to obtain a temporary base line; determining a deviation detection result and a traceability link of the cloud native monitoring data based on a pre-constructed multi-scale knowledge graph associated with the cloud native monitoring data, and generating a dynamic detection threshold in combination with a preset service evaluation standard; and based on the temporary baseline, the deviation detection result, the deviation traceability link and the dynamic detection threshold, visualizing a target dynamic baseline, a target deviation detection result and a target traceability link of the cloud native environment. By adopting the method, the visualization effect of the cloud native monitoring data can be improved.

The invention relates to the technical field of computers, and discloses a vehicle-mounted system process control method, device and equipment based on large model decision, and the method comprises the steps: obtaining an original data flow of a vehicle multi-mode sensor; analyzing the original data stream to generate a standardized feature vector; outputting a scene label corresponding to the current vehicle by analyzing the feature vector; dividing the to-be-started application process into different grades according to a preset scene grading mapping table; allocating corresponding hardware resources to different levels of application processes, and dynamically adjusting an allocation scheme of the hardware resources of the vehicle-mounted system; according to the adjusted resource allocation scheme, the application processes of the corresponding levels are started in sequence; and when it is detected that the feature vector has a change exceeding a preset threshold, re-outputting the scene label, and synchronously updating the application process grading and resource allocation scheme according to the re-output scene label. Therefore, quick starting of the application process, reasonable distribution of system resources and optimization of user experience are realized.

The application discloses a heterogeneous computing power cooperative adaptation method based on distributed pooled computing power, and relates to the technical field of heterogeneous computing power resource scheduling; basic data sets are collected from a computing power heterogeneous platform, time sequence characteristic sets and scene characteristic sets are obtained through standardization processing; relying on a computing power prediction model composed of a basic time sequence prediction layer, a scene characteristic enhancement layer and an output fusion layer, the computing power demand trend of each heterogeneous cluster is accurately predicted; the demand priority is set in combination with the computing power growth rate threshold and the scene label; then, a scheduling decision is generated through a coordination scheduling model, and the resource allocation is optimized with the aid of a dynamic adaptive adjustment mechanism; the application realizes fine representation of heterogeneous computing power and accurate matching of tasks, breaks the rigid limitations of the adaptation strategy of traditional schemes, effectively improves the accuracy of heterogeneous computing power adaptation and the comprehensive utilization rate of resources, significantly reduces the performance fluctuation of core businesses, and achieves the dynamic balance of efficient resource utilization and stable business operation.

The application relates to a peripheral feedback effect control method and system, a storage medium and an electronic device, and relates to the technical field of peripheral control. The method comprises the following steps: determining whether a target scene tag exists in a target game of a current target user according to actual game state data; if the target scene tag exists, determining first light parameters and first vibration parameters of an actual peripheral according to the target scene tag; optimizing the first light parameters and the first vibration parameters according to actual power to obtain final light parameters and final vibration parameters, and controlling the feedback effect of the actual peripheral according to the final light parameters and the final vibration parameters; and if the target scene tag does not exist, determining second feedback parameters of the actual peripheral according to user game state data, and controlling the feedback effect of the actual peripheral according to the second feedback parameters. The application has the effect of improving the adaptation of the peripheral feedback effect to users.