842 results about "Instruction data" patented technology

The invention relates to the technical field of process automation, in particular to an RPA service automation method capable of enabling a large language model MCP. The method comprises the following steps: acquiring user input data, and performing intention understanding through a multi-round intention recognition model according to the user input data to obtain semantic instruction data; performing standard semantic protocol conversion according to the semantic instruction data to obtain standard semantic protocol data; performing flow chart node matching according to the standard semantic protocol data to obtain flow chart node data; and generating a business process according to the flow chart node data to obtain business process data. According to the method, the multi-round intention recognition capability of the large language model and the standard semantic protocol conversion are fused, so that the accurate mapping from the natural language to the business process is realized, and the intelligent level and the user friendliness of the RPA system are improved. Through process node matching and process logic verification, the execution effectiveness of the automatic generation process is ensured, and the labor cost and configuration errors are reduced.

The invention discloses a task length prediction-based large language model reasoning calculation service energy consumption optimization scheduling method, which comprises the following steps of: firstly, reasoning by taking an Alpaca-52k instruction data set as an input source and a large language model (such as Llama3-8B), counting the number of output tokens of the large language model, and labeling each piece of input data; then, a Qwen2-1. 5B large language model is finely tuned by using an Alpaca-52k instruction data set and the response length of Llama3-8B reasoning, and computing resources of the prediction method are reduced on the premise that the prediction performance is guaranteed; then, the response length of the Llama3-8B reasoning task is predicted through the fine-tuned Qwen2-1. 5B model, task balanced sorting scheduling is carried out according to the response length so as to improve the large language model reasoning speed, and finally, a deep reinforcement learning power selection algorithm is used to reduce the calculation power as much as possible on the premise that the large language model reasoning task time delay is met so as to improve the large language model reasoning efficiency. Therefore, the energy consumption of large language model reasoning calculation is reduced.

The invention relates to the technical field of electric digital data processing, in particular to an artificial intelligence algorithm-driven digital human language understanding and corresponding reaction interaction method. The method comprises the following steps: collecting historical language data, text data, action instruction data and physical environment information of a user to obtain structured training data; vectorizing the text data to obtain a text semantic vector; environment feature vectors are extracted from the physical environment information; splicing the text semantic vector and the environment feature vector to obtain an environment enhanced text vector; and constructing a pre-training language model based on the environment enhanced text vector, and performing secondary pre-training to generate an optimized language model parameter. According to the invention, by fusing the user language and the physical environment information and combining with the multi-module intelligent component, context perception, efficient understanding and multi-modal natural interaction of the digital human in a complex scene are realized, and the intelligence, adaptability and user experience of the system are remarkably improved.

The invention relates to a memory enhanced vision-language-action submerged space dynamic fusion automatic driving method. Comprising the following steps: generating a bird's-eye view feature map; extracting scene, agent and map marks from the aerial view feature map, and fusing the mark at the current moment and the previous i historical marks to generate memory enhanced visual marks; the memory enhanced visual mark and the vehicle state information are converted into a submerged space, text input of a driver is marked and unified into the submerged space, the submerged space is represented and fused, and fusion representation is generated; an automatic driving instruction data set is introduced for adjustment, and a large language model adapting to an automatic driving task is obtained; according to the fusion representation, track planning is carried out in an autoregression mode by using a large language model, and path point coordinates are obtained; and designing a transverse controller and a longitudinal controller based on PID (Proportion Integration Differentiation) to track and control the coordinates of the path points. According to the invention, the visual representation capability of end-to-end driving is improved, and the visual-language-action fusion effect is improved.

The invention discloses a mold and formwork design system based on virtual simulation, and particularly relates to the field of mold design, the mold and formwork design system comprises a multi-mode perception fusion module, a digital twin modeling module, a multi-physics field coupling simulation module, a hybrid intelligent optimization module, a digital twin closed loop verification module and a data center module, and each module forms a design closed loop through a data center. The multi-modal sensing fusion module adaptively collects and fuses multi-source signals to generate high-credibility data; the digital twin modeling module constructs and iteratively corrects a model based on the data; the multi-physics field coupling simulation module realizes multi-solver co-simulation under a dynamic boundary; the hybrid intelligent optimization module accelerates to generate an optimal solution through secondary optimization and an agent model; the digital twin closed-loop verification module detects defects and generates a correction instruction; the data center module is responsible for data management, cross-module scheduling and precision control; the system improves the design precision and efficiency of the mold frame, reduces the physical mold testing cost, and is suitable for a high-precision mold development scene.

The invention relates to the technical field of motor control, and discloses a permanent magnet synchronous motor control system and method for an electric drive hydraulic source, and the system comprises a data collection module which is used for collecting the state parameters of the electric drive hydraulic source and the operation data of a permanent magnet synchronous motor in real time through multiple sensors; the sensor redundancy and fault-tolerant module is used for performing parallel sampling through hardware dual-redundancy configuration and dual sensors; the self-adaptive fuzzy logic control module is used for dynamically adjusting a rotating speed instruction and a torque instruction of the permanent magnet synchronous motor based on the collected state parameters; a data driving optimization module; and a digital twinning prediction maintenance module. A self-adaptive fuzzy logic control module is used for dynamically generating a motor rotating speed and torque instruction based on parameters such as real-time pressure, flow deviation and change rate, a fuzzy logic algorithm is used for processing a nonlinear time-varying load and quick response, a self-adaptive fuzzy logic control rule weight and a power conversion circuit parameter are optimized through a reinforcement learning algorithm, and the control precision is improved. And dynamic self-optimization of permanent magnet synchronous motor control is realized.

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 provides a construction progress dynamic regulation and control system and method for a building machine, relates to the technical field of special equipment for building construction, and solves the problem of limitation of an existing building machine regulation and control system in the aspects of dynamics, collaboration and the like. In the system, a sensing execution layer is used for collecting construction data in real time and correspondingly executing a regulation and control instruction, and transmitting the data to an edge calculation layer for processing; the edge computing layer is used for carrying out localized real-time preprocessing and decision generation on the data, and synchronizing key information to the cloud analysis layer to obtain a regulation and control strategy; the cloud analysis layer is used for constructing virtual mapping of a construction process, optimizing a regulation and control strategy and driving a dynamic regulation and control module to perform multi-dimensional decision making; and the dynamic regulation and control module is used for comprehensively predicting the construction progress, coordinating and optimizing the target, handling sudden anomalies, generating regulation and control instructions and feeding back the regulation and control instructions to the sensing execution layer to form a closed loop. According to the method, the construction resource utilization rate and the progress prediction precision of the building machine can be effectively improved.

The invention discloses a block chain-based scheduling instruction trusted storage system, which relates to the technical field of block chains, and comprises a block chain network layer, an instruction acquisition module, an instruction verification module, a cross-chain storage module, a trusted tracing interface, an instruction life cycle management smart contract and a quantum security module, the block chain network layer comprises a plurality of block chain service units deployed at power dispatching nodes, the instruction acquisition module is used for acquiring a dispatching instruction data set and an associated digital signature generated by a dispatching terminal, and the instruction verification module is configured with a compliance verification rule base and multi-level verification logic. And the cross-chain storage module realizes a sub-chain storage architecture of scheduling instruction metadata and content data. According to the method, a double-chain architecture in which a metadata chain and a content chain are separated is adopted, and an improved Merkletree cross-chain verification technology is combined, so that on the premise of ensuring data privacy, cross-regional instruction synchronization delay is reduced, and meanwhile, fine-grained access control of sensitive instruction content is realized.

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 present disclosure provides a model training method and device, and a data processing method and device. The method of the present disclosure uses a reference model to screen an existing instruction dataset for an instruction whose model fitting difficulty meets a preset condition, and the method can screen the instruction dataset for a challenging instruction having high model fitting difficulty as a seed instruction. A plurality of similar instruction samples are generated by means of expansion on the basis of the seed instruction, thereby obtaining more challenging instruction samples, a training set comprising the instruction samples and reference responses for the instruction samples is constructed, and knowledge distillation can be achieved by using the reference model on the basis of the existing instruction dataset, thereby obtaining a training set containing higher-quality instruction data. Furthermore, using the training set to train a deep learning model enhances the capability of the deep learning model to handle more complex and challenging tasks, and improves the performance of a trained target model.

The invention relates to the technical field of CSMS general collector data processing, in particular to a data collector operation control method and system. The method comprises the following steps: collecting an analog input signal and a switching value input signal of a CSMS universal collector to obtain original input data; performing filtering and drift correction on the original input data to obtain effective acquisition data; performing multi-protocol format identification and conversion processing on the effective acquisition data to obtain standardized acquisition data; state identification, fault early warning and equipment life prediction are carried out on the standardized acquisition data to obtain operation evaluation data; and reasoning execution logic based on the operation evaluation data and a preset control strategy rule, and generating control instruction data. Through high-precision data acquisition and filtering, multi-protocol adaptation, edge intelligent state evaluation, closed-loop control execution and reliable communication management and log recording, stable, intelligent, efficient and robust intelligent operation and maintenance data processing is realized.

The invention discloses a material synthesis data extraction method and system based on a knowledge enhancement large model, and relates to the related field of artificial intelligence, and the method comprises the steps: retrieving literature data, and constructing a material synthesis knowledge text by executing data denoising and OCR text conversion; performing LoRA fine tuning on the basic large model, introducing a field instruction data set to perform fine tuning learning, and determining an extraction large model; performing semantic partitioning and vectorization on the material synthesis knowledge text, constructing a multi-level retrieval framework, performing retrieval enhancement in combination with a material science knowledge base, and determining an enhanced knowledge text; and constructing a data extraction prompt, combining the enhanced knowledge text with a material to synthesize a knowledge text, inputting the knowledge text into an extraction large model, and executing knowledge extraction processing. The problem that the accuracy of data extraction is insufficient in the prior art is solved, and the effect of improving the accuracy of data extraction is achieved. Meanwhile, manual work can be replaced to complete literature analysis extraction and domain knowledge association, and support is provided for material synthesis process recommendation.

The invention relates to an intelligent control method, system and device for an automobile seat and a medium, and belongs to the field of automobile seat adjustment control. The intelligent control method comprises the steps that a control instruction data packet from a mobile terminal is received and converted into a vehicle CAN bus protocol data frame; analyzing an operation mode identifier in a vehicle CAN bus protocol data frame, and matching a corresponding target attitude parameter set; based on the real-time temperature data in the vehicle, dynamic compensation calculation is executed on the target attitude parameter set, and an after-compensation execution instruction set is generated; a motor control signal is sent to a seat executing mechanism; calculating a deviation value between the feedback signal data and the compensated execution instruction set, and if any deviation value exceeds a preset deviation threshold value, generating a mechanism calibration instruction; and acquiring final execution state data of the seat execution mechanism, and feeding back the final execution state data to the mobile terminal. Intelligent seat adjustment based on real-time feedback can be achieved, and it is ensured that a driver obtains the best comfortable experience in different scenes.

The invention discloses a method for automatically capturing semantic information of a binary code block and a related device, and relates to the technical field of computers.The method comprises the following steps that code disassembling and instruction level analysis are conducted on the binary code block, and a binary stream original instruction sequence is generated; performing semantic intermediate representation conversion and vocabulary overflow standardization on the binary stream original instruction sequence to obtain a binary code standardized instruction sequence; obtaining an instruction data dependency relationship and an instruction control dependency relationship corresponding to the binary code block, and constructing a corresponding binary code program dependency graph; semantic code node embedding is carried out on the binary code program dependency graph to generate a binary code representation vector corresponding to context semantics; and performing automatic semantic capture reasoning on the binary code representation vector corresponding to the context semantics through a pre-trained semantic understanding model to obtain semantic information of the binary code block. According to the method, the semantic information of the binary code block can be efficiently and accurately captured.

The invention relates to the technical field of data processing, in particular to an FPGA (Field Programmable Gate Array) and CPU (Central Processing Unit) cooperative network data processing system, which comprises a network interface module for receiving and forwarding an external data flow, and a protocol analysis module for distinguishing a service message and a protocol message based on a message header characteristic and a load mode; the service processing module executes protocol format conversion, security encryption and data extraction in the FPGA; the protocol stack interaction module transmits a protocol message to the CPU through the MAC layer communication interface; the protocol stack module activates necessary protocol services in the CPU and generates a resource allocation instruction; and the data integration module receives the service data flow and the protocol processing result, and dynamically adjusts the encryption rule. According to the invention, FPGA business data closed-loop processing and CPU protocol service physical isolation are realized, a double-processing-unit architecture supports financial signature and industrial filtering cascade operation, the MAC layer interface is additionally provided with cyclic redundancy check to guarantee transmission reliability, and equipment processing efficiency and anti-attack capability under a fusion scene are improved.

The invention discloses an industrial cutting machine wireless communication method based on multi-channel redundancy switching, which relates to the technical field of industrial cutting machine wireless communication, and comprises the following steps of: before a control instruction is written into a transceiving buffer area, executing label comparison operation on a target buffer area address according to a composite label in the control instruction; judging whether the address has a source conflict risk or not, and if so, stopping the current writing; and when writing is stopped, executing a buffer area address redistribution operation, and according to a preset interference distance rule, screening an address section with the lowest conflict risk with the composite tag from the current buffer area as a new writing target of the control instruction. According to the method, the problems of overlapping and conflicting of control instruction data caused by the fact that multiple channels share the buffer area are solved, a safe write-in mechanism based on label identification and dynamic mapping is achieved, and data isolation and system stability in the wireless communication process are improved.

The invention relates to the technical field of automobile electronic control, discloses a control system of a three-dimensional interactive automobile atmosphere lamp, and aims at solving the problems that an existing atmosphere lamp is single in function, insufficient in space sense, poor in adaptability and poor in user experience. The system comprises a multi-mode sensing unit which collects environment, operation, passenger behavior and interaction instruction data in real time; the data fusion and situation analysis unit is used for preprocessing the fused data, identifying a prediction situation and generating a situation representation vector; the interaction intention recognition unit is used for analyzing gesture, voice and sight line information of the passenger and recognizing an interaction intention; the three-dimensional light field generation engine is used for dynamically generating a three-dimensional light effect scheme based on the situation representation and the interaction intention; and the distributed light-operated execution network drives the light units to realize a lighting effect scheme. According to the scheme, intelligent dynamic and humanized control over the light environment is achieved, and system intelligence, interaction experience and driving and riding comfort and safety are remarkably improved.

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 multi-protocol fusion VPN (Virtual Private Network) communication method and system based on dynamic tunnel switching, and relates to the technical field of computer network communication. According to the method, collection points are respectively arranged at an inlet and an outlet of a VPN, and a time synchronization timer, a data packet capture probe and a protocol analysis tool are utilized; the method comprises the following steps: accurately acquiring control instruction time delay Delay trl, control instruction packet loss rate Lossctrl, burst packet loss rate Burst and common flow delay variance VarDelay data, introducing a priority label into a VPN protocol encapsulation layer, marking a control instruction data flow as a first priority and binding the control instruction data flow to a rapid switching tunnel, marking a common monitoring data flow as a second priority and binding the common monitoring data flow to a conventional tunnel, and performing data transmission on the control instruction data flow and the common monitoring data flow; and differential transmission guarantee for different types of traffic is realized. After the method is optimized, it can be guaranteed that the key control instruction is not interfered by common flow in the network fluctuation or tunnel switching process, and the real-time performance and transmission reliability of the control instruction are remarkably improved.

The invention relates to a data replication method and device based on DMA, equipment and a storage medium. The method comprises the following steps: receiving a data copying instruction, wherein the data copying instruction comprises an initial source address, an initial destination address and a copying data length; determining an alignment difference between the initial source address and the initial destination address; an association ID table is determined according to the data copying instruction and the alignment difference, and in the association ID table, the association ID of one write request corresponds to the association ID of one or two read requests; and for one write request, obtaining a corresponding read request according to the associated ID table and the alignment difference, and writing a target result corresponding to the read request into a target storage medium according to the address of the write request. By adopting the method, the problem that the initial address is not aligned during data copying can be effectively solved.

The invention discloses a multi-modal large model training method based on a position reflection thinking chain, and the method comprises the following steps: S1, constructing position reflection type thinking chain data, and associating the position reflection type thinking chain data with the coordinates of a chart visual region through distinguishing the data extraction and logical reasoning steps in the thinking chain; automatically generating position annotation data through drawing code editing, re-rendering verification and image analysis technologies; and S2, training a structured reasoning model, constructing a multi-type instruction data set containing visual positioning and logical reasoning, jointly optimizing generation of answer prediction, position positioning and reasoning steps by adopting a multi-task loss function, and enhancing the perception ability of the model to chart elements through a bounding box reflection mechanism. According to the method, the problems of numerical illusion and lack of visual interaction of the thinking chain due to the fact that an existing model depends on OCR are effectively solved, the chart understanding accuracy and the thinking chain interpretation are improved, and the performance is remarkably superior to that of an existing method on the mainstream basis.

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 relates to the technical field of information, and discloses an instruction data screening method and device, a medium and a product. The method comprises the following steps: determining a first confusion degree and a second confusion degree according to a pre-trained lightweight general language model and a target sample set; the first confusion degree is used for representing the prediction confusion degree under the instruction condition; the second confusion degree is used for representing the prediction confusion degree under the instruction-free condition; determining an information gain score according to the first confusion degree and the second confusion degree; the information gain score is used for quantifying the contribution degree of the instruction content to reduction of the response content generation difficulty; determining a training data set according to the information gain score and the target sample set; the training data set is used for improving the generalization ability and instruction following precision of the large language model in the instruction fine tuning stage. The technical problems of high deployment threshold, high cost, supervision dependence, low training efficiency and poor model generalization ability can be at least solved.

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 embodiment of the invention provides a robot action generation method and system, a medium, equipment and a program product. The method comprises the following steps: acquiring visual input data of a robot and corresponding language instruction data; obtaining an intermediate action representation by using a pre-constructed cross-modal shared semantic space, a corresponding relationship between the shared semantic representation and the intermediate action representation, the visual input data and the language instruction data; wherein the shared semantic representation is obtained on the basis of visual input data and / or language instruction data by using a cross-modal shared semantic space; and action parameters are generated according to the intermediate action representation so as to drive the robot to execute corresponding actions. Due to the fact that the cross-modal shared semantic space can enable semantic information from different modalities to be measured under the unified scale, and the corresponding relation can serve as middle bridging representation from semantics to middle action representation, the ability of a robot to understand and execute common language instructions in diversified real scenes is improved.

The invention provides an instruction cache and a prefetching method thereof. The instruction cache comprises a hit detection module, an instruction prefetching module and an operation module. The hit detection module checks whether a current instruction fetching request of a current thread bundle is hit. The instruction prefetching module dynamically determines an instruction prefetching range corresponding to the current instruction fetching request according to the execution progress of the current thread bundle. The operation module accesses the external storage to prefetch the instruction data corresponding to the instruction prefetch range. And in response to the fact that the hit detection module judges that the current instruction fetching request is hit, the operation module feeds back instruction data corresponding to the current instruction fetching request to the current thread bundle. And in response to the fact that the hit detection module judges that the current instruction fetching request is missed, the operation module accesses external storage so as to feed back instruction data corresponding to the current instruction fetching request to the current thread bundle.

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.