531 results about "Instruction data" patented technology

The invention relates to the technical field of data processing, and discloses a thinking instruction data generation method and system, electronic equipment and a storage medium, and the method comprises the steps: integrating a multi-source heterogeneous nuclear industry data corpus, and forming a standardized data source; thinking chain instruction data are constructed through an instruction generation framework driven by a large language model, multi-level security constraint processing is performed on the thinking chain instruction data, and contents which do not conform to nuclear industry security specifications are identified and eliminated; executing multi-dimensional quality evaluation on the thinking chain instruction data passing the security constraint, and removing the thinking chain instruction data which does not reach a quality threshold value; and performing real-time updating and dynamic optimization on the nuclear industry data corpus and the thinking chain instruction data based on newly collected nuclear industry field data. The method can improve the flexibility, accuracy and timeliness of instruction generation.

The invention discloses an intelligent accompanying robot system, and the system comprises the following modules: a multi-mode interaction module which is composed of a voice recognition unit, a voice synthesis unit, an emotion recognition unit, and a visual recognition unit, and is used for collecting the voice, facial expression, motion, and environment information of a user; the localized AI decision module is used for deploying a lightweight DeepSeekR1 large language model based on an ESP32-S3 edge computing chip, performing real-time processing on the multi-modal data and generating a social guidance strategy and an emotion intervention instruction; the data management module comprises a user behavior database and a privacy protection unit and is used for desensitizing the data and realizing sensitive data isolation through local storage; according to the method, the single-person intervention cost is reduced, and the time consumption of manual scene simulation is reduced.

The invention relates to a memory shader. The programmable atomic memory shader execution circuitry is a seamless component of the hierarchical memory system, and can receive and execute programmable atomic operation calls from any number of processors. Programmable atomic memory shader execution circuitry close to memory allows execution circuitry to access shader programs stored in memory, thereby eliminating latency that may occur when an upstream processor exchanges shader instructions, data, and memory lock / unlock commands with the execution circuitry. The locked / unlocked state of the programmable atomic memory shader execution circuitry (e.g., in the L2 or L3 cache memory) allows the system to quickly lock the memory resources, perform one or more operations in an atomic manner over one or more cycles, and then quickly unlock the memory resources.

The invention belongs to the field of man-machine interaction, and particularly discloses a man-machine interaction method for electronic screen control, which comprises the following steps: acquiring touch track data of a user according to a touch sensor, and performing feature extraction on the touch track data to obtain a touch feature vector; the method comprises the following steps: acquiring gesture action data of a user according to image acquisition equipment, and performing key point detection on the gesture action data to obtain a gesture feature vector; the method comprises the following steps: acquiring voice instruction data of a user according to an audio acquisition device, and performing acoustic feature extraction on the voice instruction data to obtain a voice feature vector; acquiring current environment parameters in real time through an environment detection module, wherein the environment parameters comprise illumination intensity, environment noise level and distance between a user and a screen; the objective of the invention is to solve the problem of insufficient reliability of a man-machine interaction mode in a complex environment in the prior art.

The invention discloses a low-resource language translation model training method based on a big language, which comprises the following steps of: continuously pre-training a basic big language model by utilizing a multilingual text corpus, and storing a plurality of middle check points as candidate models; selecting a model with the optimal downstream translation task performance from the candidate models based on the performance of the verification set, and performing instruction supervision fine tuning by using the parallel instruction data set to obtain an intermediate model; and finally, training the intermediate model by using a preference optimization algorithm to obtain a target translation model by using a preference data set consisting of preferred translation and rejected translation. According to the method, through three-step progressive training, the problems of data sparsity, insufficient model capability, single training method and the like in low-resource language translation are effectively solved, and the translation quality is remarkably improved.

The invention discloses an unmanned aerial vehicle formation flight control method and device, an unmanned aerial vehicle and a storage medium. The method comprises the steps that formation parameters, flight task instruction data and flight state data of other unmanned aerial vehicles in a formation are acquired or updated; judging a control mode according to the flight task instruction data, wherein the control mode comprises at least one of the following modes: a normal control enabling and conventional flight mode, a calculation mode, a suspended hovering mode or a disintegration mode; according to the judgment result of the control mode, based on an L1 guide algorithm of self-adaptive track tracking, fusing unmanned aerial vehicle spacing control logic and / or formation control logic of formation dynamic adjustment to obtain a control instruction of an expected track and / or attitude, and executing the control instruction; and circularly executing the steps to ensure the continuity and dynamic adaptability of the unmanned aerial vehicle and the formation flight thereof. According to the scheme, a completely decentralized distributed control architecture is adopted, and a fault-tolerant mechanism is matched, so that the reliability and survivability of the formation system are remarkably improved.

The invention provides a life cycle evaluation automatic modeling method based on a large language model, and relates to the technical field of life cycle evaluation, and the method comprises the steps: extracting the multi-modal information of texts, tables, images and the like from the multi-source data of academic literatures, reports and the like, constructing an instruction data set in combination with LCA process knowledge, carrying out the instruction fine tuning of a base large model, and carrying out the automatic modeling of the life cycle evaluation. And obtaining the LCA field large language model. And generating a standardized prompt by utilizing a prompt word optimizer, driving a model to generate a product LCA model framework, filling list data, and perfecting process data in combination with semantic search and a correction mechanism. And intelligent connection recommendation among unit processes is realized by calculating the semantic similarity of input and output streams. And finally, the verified structured data is output to LCA modeling software, an automatic modeling process is completed, and the modeling efficiency and normalization are improved.

The invention provides a remote control method and device for intelligent multimedia interaction equipment, equipment and a medium, which are used for improving the real-time performance and the execution success rate of remote control and enabling complex multimedia interaction to be reliably operated on a heterogeneous network and heterogeneous equipment. The method comprises the following steps: carrying out hierarchical analysis processing on interaction data input by a user to generate perception command data, and carrying out execution planning and resource allocation on the perception command data according to equipment performance characteristics to generate an executable operation sequence, and performing transmission quality evaluation and path selection on the propagation path network according to the executable operation sequence to obtain a target transmission path, and performing protocol encapsulation on the executable operation sequence according to the target transmission path to obtain a remote control instruction data stream.

The invention relates to the technical field of electric digital data processing, in particular to a shock absorber data processing method and system based on digital twinning, and the method comprises the steps: obtaining a multi-channel data signal of a shock absorber, and constructing a panoramic vibration data tensor; extracting a discrete digital sequence on a panoramic vibration data tensor time dimension, executing a variational mode decomposition algorithm, and separating and extracting a transient impact feature vector flow; constructing a double-time-axis sliding mapping window, and mapping the transient impact feature vector flow to a future node of a prediction response time axis based on a front and rear wheel preview mechanism of a vehicle wheelbase; operating the time sequence recurrent neural network model, and predicting and generating a target state data matrix of future nodes; and in the numerical constraint space, a prediction type damping adjustment instruction data packet is solved and generated through a rolling optimization algorithm. Through prospective state prediction and optimization solution, the problem of data timeliness caused by calculation delay in digital twinning is solved, and real-time, predictive and safe adjustment of the shock absorber is realized.

The invention discloses a medical instruction data generation and use method based on a multi-modal knowledge graph. Firstly, medical images and text information in a public data set are collected, and high-quality image-text pair data are screened out from the medical images and the text information; secondly, performing entity recognition on the medical text and retrieving related triple knowledge from the multi-modal knowledge graph to form knowledge enhanced medical image-text data; secondly, inviting a medical expert to label real scene question and answer pairs based on the knowledge-enhanced medical image-text data, and taking the real scene question and answer pairs as a seed instruction data set to guide a large-scale visual language model to generate diversified instruction question and answer data; and finally, using the medical image understanding and question-answering ability of the supervised fine-tuning training model, and performing reinforcement learning supervised by a knowledge graph triple path. According to the method, the problem of scarcity of medical instruction data can be effectively relieved, so that the trained model has higher accuracy and interpretability in a medical question and answer task.

The invention relates to an FPGA-based satellite-borne file adaptive storage and scheduling method, which comprises the following steps: defining a data plane and a control plane, the data plane being responsible for high-frequency and high-speed data processing, and the control plane being responsible for low-frequency and low-speed data management and decision making; the data plane obtains a real-time data stream to be stored and stores the real-time data stream into a data cache; monitoring the running state of the data cache in real time, generating cache state data and reporting the cache state data to a control plane; the control plane makes a strategy decision based on the cache state data to generate a target strategy instruction; the data plane obtains the instruction and carries out hardware priority judgment in combination with cache state data so as to determine a final data writing instruction; and the data plane responds to the write-in instruction, executes error correction coding processing, data write-in and file verification processing, and reports a file closing state to the control plane. The method has the advantages that the system can dynamically respond to the real-time system load, and the stability and reliability of system scheduling are improved.

The invention relates to the technical field of transformer voltage regulation cooperative control, in particular to a transformer voltage regulation cooperative control system based on Internet of Things data acquisition, and the system comprises a heterogeneous control strategy execution unit which is used for generating an expected control instruction signal; the power grid regulation toughness quantification unit is used for collecting real-time control instruction data and calculating to obtain an accumulated toughness attenuation index; the data attack identification unit is used for synchronously acquiring an original voltage measurement signal and an expected control instruction signal generated by the heterogeneous control strategy execution unit; obtaining an attack correlation index; the control right switching decision unit is used for calculating to obtain a switching trigger value; generating a final control output; the heterogeneous control strategy execution unit is also used for responding to the final control output and performing switching execution between a deep reinforcement learning strategy and a classic robust control strategy; according to the invention, accurate evaluation of physical consequences caused by attack behaviors is realized, and equipment failure caused by overfatigue is avoided.

The invention relates to a distributed integrated energy system instruction data set automatic generation method and system based on agent cooperation. The method comprises the following steps of: S1, purifying a seed data set in the field of the distributed integrated energy system by using an information metrology method; s2, on the basis of the purified seed data set, constructing an intelligent agent to generate an instruction data set in parallel; and S3, performing quality evaluation and closed-loop optimization on the instruction data set. According to the method, firstly, core questions and answers are purified from high-influence literatures and laws and regulations through information metrology and a knowledge graph to serve as seeds, and data accuracy is ensured; parallel expansion, screening and duplicate removal are carried out by a generator and a calibrator; and finally, a feedback closed loop is finely adjusted, cue words and verification rules are continuously optimized, and high-quality and wide-coverage question and answer pairs are quickly produced, so that the diversity and the accuracy are ensured, the labor cost is saved to the greatest extent, and the application effect of a large language model in the field of a distributed comprehensive energy system is improved.

Dynamic allocation of work and data on a spatial compute architecture in which a lead dispatch node dynamically assigns a task of an application to a selected compute tile, and routes a corresponding active message to a local data memory pool of the selected compute tile. The active message may include instructions, data, and / or configuration parameters to enable the selected compute tile to execute the task. If there is insufficient space in the local data memory pool, the lead dispatch node routes the active message to shared memory or to a local data memory pool of another compute tile, and subsequently routes the active message to the local data memory pool of the selected compute tile when space is available. Alternatively, the lead dispatch node may forward the task and the active message to another lead dispatch node.

The embodiment of the invention provides an intelligent operation method and system based on an energy production large model and a readable storage medium. The method comprises the steps of collecting energy production multi-source data for processing and cloud-side cooperative transmission, obtaining, training and optimizing an energy production large model, obtaining an application package, processing the energy production large model and the application package, obtaining control instruction data, executing production regulation and control and returning operation data, and processing according to the operation data. The method comprises the following steps: acquiring hierarchical early warning information and decision information, mapping, processing and updating an energy production large model according to operation data, dynamically adjusting computing power resources, optimizing data storage and transmission, and performing cloud-side transmission through global data acquisition and cloud-side transmission. According to the technical scheme of the invention, central model training and cloud development, edge intelligent reasoning and real-time control, intelligent decision early warning and business linkage and closed-loop measurement optimization and resource management are adopted, so that the monitoring, early warning and decision support of the whole energy production process can be realized, the production efficiency and safety can be improved, and the operation cost can be reduced.

The invention discloses an integrated galvanometer laser projection method and an integrated galvanometer laser control device. The integrated galvanometer laser projection method comprises the steps that 1, a control card stores various data in a classified mode; when an upper computer issues a system parameter instruction, a communication interface is automatically switched according to a communication mode of the upper computer, and instruction data is analyzed and stored in an EEPROM (Electrically Erasable Programmable Read-Only Memory); 2, after the upper computer issues an instruction, the control card cooperatively controls the on-off state of the laser light source and the deflection angle of the laser galvanometer, and the position and the focal length of the lens are adjusted; 3, positioning an instruction according to a target point of the upper computer; the control card cooperatively controls the laser and the galvanometer to coarsely position the center of the target; and performing fine positioning on the center of the target by taking the target point obtained by coarse positioning as an initial reference position. The integrated galvanometer laser control device has the advantages that the environmental adaptability, the control precision and the operation convenience of the integrated galvanometer laser control device are remarkably improved.

The invention provides an intelligent control method and system for a body-equipped robot based on man-machine interaction data, and relates to the technical field of medical robots, and the method comprises the steps: building a 5G network two-way transmission channel between a control end and a surgical robot end; through a 5G network two-way transmission channel, synchronously transmitting two types of data streams between the control end and the surgical robot end, namely a control instruction data stream generated by the control end and a surgical image data stream collected by the surgical robot end; in the transmission process of the two types of data streams, real-time network time delay is continuously monitored, whether the current network time delay exceeds a preset time delay threshold value or not is judged, and the preset time delay threshold value can be dynamically corrected according to the network state; and when it is judged that the current network delay exceeds the preset delay threshold, dynamically deploying a plurality of network probes on the 5G network two-way transmission channel. According to the invention, the real-time performance, the accuracy and the safety of the remote operation can be ensured.

A data access method, apparatus and device, in the application, a physical space of a storage device is mapped into a logic space, the logic space comprises a plurality of logic blocks, and the physical space comprises a plurality of physical blocks. The storage device obtains a data reading instruction, wherein the data reading instruction is used for requesting to read the first data; the storage device determines a target index entry according to the logical address of the first data, the target index entry indicates a mapping relationship between a first logical block and a first physical block, and the first logical block is a logical block to which the logical address belongs. The storage device determines the physical address of the compressed first data according to the first physical block and a first target data volume, wherein the first target data volume is the data volume of the compressed first data; the storage device obtains the compressed first data according to the physical address; and decompressing the compressed first data, and obtaining and feeding back the first data. The construction granularity of the index entry is a physical block and a logic block, and the data volume of the compressed data does not need to be recorded, so that the complexity of data indexing is effectively reduced.

The embodiment of the invention provides a drive optimization method and system based on hardware state perception and a readable storage medium. The method comprises the steps that system data are collected, processed and stored, optimal adjustment instruction data are obtained according to standardized system data, verification processing is conducted through association mapping and execution adjustment, the program running state is monitored, and if the number of exceptions exceeds a preset threshold value, fault diagnosis is triggered, and a troubleshooting report is pushed. Performing interactive processing according to system standard parameters, updating bottom-layer codes and permission application logic and verification processes, updating mapping rules according to adjusted effect data, and optimizing a fault correlation analysis model; through hardware state perception and data acquisition, driving parameter dynamic matching and adjustment, fault intelligent diagnosis and report generation, cross-system version compatibility adaptation and data closed-loop optimization and rule iteration, balance between performance and resource consumption is realized, equipment operation stability and cruising ability are improved, and development and maintenance cost of a driving program is reduced.

An artificial intelligence-based process defect detection system may include: a photographing module that collects image data by capturing a process that progresses on an object; a machine learning model that generates work data that is a result of recognizing and reading the object based on the image data; and a detection module that receives instruction data recorded regarding a process for an object optimized for product production, detects a defect or a non-defect by comparing the work data with the instruction data, and generates defect information when the process is defective.

The invention relates to a vehicle-mounted passive optical network unit system and a communication method thereof. The vehicle-mounted passive optical network unit system comprises an optical line terminal; the plurality of optical network units receive downlink data; analyzing the downlink data to obtain an udp data packet; performing fcs verification on the instruction udp data packet; if the verification is successful, analyzing the instruction udp data packet to obtain instruction information; extracting the instruction information marked with the head and the tail, generating control information based on the instruction information marked with the head and the tail, issuing the control information to the vehicle-mounted terminal, and reading uplink data acquired by the vehicle-mounted terminal from the vehicle-mounted terminal; forming the uplink data into udp frames, and caching the processed uplink data; extracting the cached uplink data and transmitting the uplink data to the first interface; an optical splitter; a vehicle-mounted processor; and a vehicle-mounted terminal. The system has the advantages of low energy consumption, high broadband, low time delay, electromagnetic interference prevention, simple wiring, rapidness, stability, reliability and the like.

The invention relates to the technical field of ink-jet printing, and discloses a multi-nozzle scheduling method, system and device and a storage medium, and the method comprises the steps: collecting an initial state data set; generating configuration parameters and printing parameters based on the initial state data and a preset printing task requirement; activating an integrated channel based on the configuration parameters; analyzing the printing parameters to obtain an instruction data packet and a spray hole data packet, and distributing the instruction data packet and the spray hole data packet to each integrated channel; performing compression coding processing on the instruction data packet or the spray hole data packet to form a driving data packet; the nozzle driving board is controlled to convert the driving data packet into a driving voltage waveform matched with the nozzle, and the driving voltage waveform is transmitted to the nozzle to complete the jet printing action; according to the invention, by constructing an OLED ink jet equipment transmission link and utilizing integrated channel directional distribution processing, multi-nozzle efficient scheduling is realized, and the requirements of large-size OLED printing equipment on stable scheduling and efficient jet printing of a large number of nozzles are met.

The invention provides a complex construction site construction equipment control system and method, and the system constructs a basic state semantic model of each type of equipment, and covers all perception and control instruction data; the comprehensive management and control platform dynamically extracts and issues one or more data packet formats from the basic model according to construction scenes, areas and equipment types, and fine adjustment of sensing information and instruction formats is achieved; the edge computing device is deployed at an equipment end or a cluster end and is responsible for dynamic mapping analysis, redundancy removal, priority screening and unified protocol packaging of a multi-heterogeneous protocol; the equipment end reports a null instruction sensing data packet, the comprehensive management and control platform generates an optimal control instruction, compresses recent sensing data and then issues the compressed recent sensing data in the same data packet format; the equipment end decides whether to execute the instruction or not, if not, problem expression is fed back, and a safe closed loop is formed; in a cluster scene, gathering fusion and instruction distribution of multi-level edge computing devices are supported, and efficient collaboration of heterogeneous clusters is achieved.

The invention relates to the technical field of water conservancy project automation control, and discloses a self-adaptive coordinated regulation gate control method and system and a storage medium. The method comprises the following steps: acquiring gate node real-time instruction data and performing time synchronization calibration to generate an initial instruction sequence; instruction features are extracted and classified based on the sequence, and an instruction sorting list is obtained; when the emergency instruction proportion exceeds a threshold value, the emergency instruction is separated, an exclusive channel is distributed, an accelerated propagation path is generated, and an optimized transmission path is determined through path delay evaluation; selecting a path with the lowest delay from the optimized paths to perform emergency instruction distribution, and acquiring a feedback signal; verifying the feedback signal and recording a response speed index, and determining the adjusted transmission configuration based on the index; the configuration is utilized to synchronize and integrate the conventional instructions to the main propagation sequence to generate a complete regulation and control instruction set. According to the method, the multi-node instruction time sequence consistency and the emergency response capability are improved, the transmission conflict is reduced, and the cooperative regulation and control efficiency is improved.

The invention relates to a cluster unmanned aerial vehicle communication method based on a multi-dimensional key encryption and decryption algorithm. The method comprises the following steps: acquiring unique identifiers of an unmanned aerial vehicle array and marshalling; extracting a single-user key of the unmanned aerial vehicle in the target group, and obtaining a key element by using a multi-dimensional key algorithm; generating an action instruction according to a preset coding rule, encrypting the action instruction, and combining the action configuration classification area to obtain a remote control instruction data packet; transmitting the remote control instruction data packet through a broadcasting mechanism, and receiving the remote control instruction data packet by the unmanned aerial vehicle through a monitoring mechanism; filtering and analyzing the received data packet to obtain a configuration data unit matched with the unmanned aerial vehicle marshalling; extracting a key element based on the configuration data unit, and calculating to obtain an original key; decrypting the action element encryption value to restore the action instruction, and executing the instruction after verification; the execution process of the action instruction is monitored in real time, and abnormal conditions are processed; the cluster unmanned aerial vehicle communication method using multi-dimensional key encryption and decryption is realized.

The invention discloses an agent and tool chain collaborative data processing method and device and a medium, and relates to the technical field of agent application. The method comprises the steps of receiving a data processing task target and related configuration parameters input by a user, and initializing task context data based on the task target and the configuration parameters; executing a circular processing process by an agent based on a ReAct framework, and dynamically adjusting a task strategy by iteratively executing a thinking stage, an action stage and an observation stage until a task completion condition is met; in the circular processing process, based on standardized tool calling instruction data generated by the intelligent agent, cooperatively calling the MCP tool chain and a plurality of tools in the external tool adaptation layer to execute a data processing task; performing aggregation processing and data verification on the multi-source result data returned by the tool execution to generate integrated analysis result data; and displaying task execution process data and a final analysis result in real time through a front-end interaction interface, and dynamically adjusting a task execution process based on user intervention instruction data.

The invention provides a conversational software operating system and method based on a capability description model. Comprising a software capability modeling module used for generating capability description files corresponding to target functions; the dialogue analysis module is used for identifying a target capability corresponding to the dialogue request according to the capability description file and generating a parameter set related to the target capability; the dialogue management module is used for generating a task execution request matched with the capability description file when it is determined that the necessary parameters corresponding to the target capability are complete; the task execution module is used for converting the task execution request into an execution instruction submitted to target software, and the execution instruction comprises an interface calling instruction and / or an interface automatic operation instruction; and the data management module is used for providing data access service for the dialogue analysis module, the dialogue management module and the task execution module. According to the method, the cross-system operation efficiency can be improved, the interface development and maintenance cost is reduced, and the stability and accuracy of complex business process processing are improved.

The embodiment of the invention provides an access method, system and equipment of Internet of Things equipment and a medium, and belongs to the field of Internet of Things application development and system integration. The method comprises the following steps: receiving an access request of Internet of Things equipment, identifying a communication protocol used by the access request, and converting an original data format in the request into a unified intermediate data format; based on the standardized equipment capability description model, forming an equipment capability object irrelevant to a protocol, and providing an equipment operation interface for an upper-layer application; and according to the operation request and the corresponding standardized equipment capability description model and the communication protocol, converting and packaging the operation type and the parameter into an instruction data packet conforming to the equipment communication protocol. Through the Internet of Things protocol conversion capability, the complexity of underlying physical connection and protocol interaction is shielded, and through introduction of a standardized equipment capability description model, the strong dependency relationship between an upper-layer application and an underlying equipment model and protocol is decoupled, the development process of the upper-layer application is greatly simplified, and the development efficiency is improved.

The invention discloses a remote sensing image indication segmentation method based on multi-modal large language model enhancement fine tuning, and aims to realize high-precision and high-generalization pixel-level remote sensing target segmentation of any natural language instruction. According to the method, a two-stage enhanced fine tuning framework of reasoning and segmentation decoupling is constructed, and a remote sensing task-oriented semantic positioning alignment hybrid verifiable reward function is combined, so that the deep reasoning and space understanding capabilities of a multi-modal large language model are effectively excited. The method comprises the following specific steps: 1, reading a remote sensing image and corresponding natural language instruction data; 2, performing supervised fine tuning (SFT) based on a pre-trained multi-modal large language model to obtain a preliminary alignment capability; 3, designing an inference model and segmentation model collaborative architecture, generating a structured thinking chain and a position prompt by the inference model and segmentation model collaborative architecture, and outputting a high-precision mask based on an SAM architecture by the segmentation model collaborative architecture; 4, a group relative strategy optimization (GRPO) algorithm is introduced, and enhanced fine tuning is carried out in combination with triple rewards of format, positioning accuracy and remote sensing semantic consistency; and 5, executing end-to-end indication segmentation, and outputting a pixel-level binary mask. The method breaks through a traditional closed set segmentation normal form, has the advantages of open vocabulary understanding, few sample generalization, interpretable reasoning and the like, and remarkably improves the automation level and adaptive capacity of remote sensing intelligent interpretation.

The invention relates to an intelligent control method and system based on multi-protocol task scheduling and a medium. The intelligent control method comprises the steps that original instruction data input by a user is received and subjected to semantic analysis, instruction keywords are extracted, and a structured instruction data packet is generated; converting the structured instruction data packet into a multi-protocol connection service calling parameter, and sending the multi-protocol connection service calling parameter to a multi-protocol connection service processing core through an internal communication interface; the multi-protocol connection service processing core schedules a corresponding execution unit to execute the operation instruction set according to the target system identifier, and generates operation result data corresponding to the task type identifier; performing state analysis on the operation result data to generate decision instruction data containing a decision type identifier; and executing task refinement processing, abnormal alternative scheme generation or task completion feedback processing based on the decision type identifier, and converting a processing result into natural language feedback output. According to the invention, unified coordination and autonomous operation of the heterogeneous intelligent system in the home environment are realized.