1112 results about "Instruction data" patented technology

An example assistant system can use a multimodal multitask medical machine-learned model to perform image processing to answer natural language queries. A device can process speech data or other natural language inputs to obtain a query. The query can be processed alongside image data that provides context for the query. The example system can receive a query associated with a particular task domain; generate, based on the query, a query input that comprises query instruction data from a first modality and query context data from a second modality; generate a combined input comprising the query input and an exemplar input, wherein the exemplar input comprises exemplar instruction data from the first modality and an exemplar context placeholder in lieu of exemplar context data from the second modality; process the combined input with a multimodal machine-learned model to generate output data; and output a query response based on the output data.

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 provides an enterprise product intelligent quotation collaborative decision-making platform. A platform architecture comprises a user interaction layer, a data processing layer and a decision-making layer, and platform functions are realized by relying on an intelligent agent. When a user releases a product quotation task, an authority management agent verifies the identity and access authority of the user, and a user interaction and demand analysis agent in a user interaction layer converts the natural language demand of the user into a structured instruction; the data processing layer receives an instruction, basic data and a special cost agent in the layer pull data from an external system through a cross-system calling agent, and a price calculation agent integrates the data through multi-objective optimization to generate candidate quoted prices; and finally, a man-machine cooperation and intelligent decision-making auxiliary agent in a decision-making layer displays all candidate schemes, and an expert performs fine adjustment to select an optimal quotation. According to the invention, rapid and accurate quotation is realized for enterprise products, the quotation model is continuously optimized through continuous feedback and self-learning, and finally a highly autonomous intelligent quotation system is formed.

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 provides an RAG-based table data processing method and system. The method comprises the steps of obtaining a table analysis path in a file; processing the content data in the table; receiving the processed content data, and establishing a mixed index suitable for different table structures of a sparse table and a multi-level table and a context joint index of the content data in the table and an adjacent file merge index; obtaining user query content, converting the user query content into a key value combination, and performing vector retrieval and keyword retrieval; according to results of key value combination, vector retrieval and keyword retrieval, dynamic template filling is carried out, an instruction data fine tuning model guides generation of an instruction, and a lightweight calculation result is generated at the same time; and adjusting the mixed index and the context joint index into a row, a column or a sub-table in combination with query delay and accuracy. The system comprises an analysis conversion module, a retrieval adaptation module and a generation integration module. The contradiction between query flexibility and result accuracy of a traditional table processing method is solved.

The invention relates to the technical field of carrying equipment. The multi-platform compatibility optimization method and system for the carrying equipment controller are provided, and the method comprises the following steps: carrying out dynamic monitoring and topology analysis processing on a network node connection relationship of a carrying equipment cluster, and generating a dynamic network topological graph and a link state data set; performing training processing on the mapping relation between the network state and the protocol parameter, and generating a protocol parameter dynamic adjustment strategy and a protocol decision table; performing real-time switching processing on the cross-platform communication protocol stack to generate a control instruction data stream compatible with multiple platforms; performing dynamic updating processing on a reward function of the reinforcement learning model, triggering protocol parameter optimization to be executed again, and generating an iterative optimization protocol parameter dynamic adjustment strategy; the problems that topology awareness and dynamic response depended on by static routing are missing, protocol switching and compression ratio self-adaptive adjustment of network parameter solidification are invalid, and cross-platform instruction conflicts and transmission efficiency degradation of heterogeneous protocol adaptation are caused are solved.

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 provides a self-adaptive adjustment control system and method applied to natural gas flow calibration, and relates to the technical field of automatic control and adjustment systems.The method comprises the steps that real-time pipeline operation data are collected, and real-time flow state recognition is conducted to obtain real-time flow state data of a preset type; the instruction generation module is used for determining a trigger coefficient, judging whether a trigger condition is met or not, and generating a calibration trigger instruction if the trigger condition is met; the data optimization module is used for receiving a calibration trigger instruction, acquiring a current compensation parameter, generating a preliminary correction coefficient, calling reference data and determining a final optimization correction parameter set in combination with the calibration data packet; the calibration updating module is used for updating the real-time compensation algorithm, determining the deviation degree, and re-triggering the calibration process if the deviation degree exceeds a preset allowable range; and the process optimization module is used for extracting current key calibration data, calling historical calibration data, determining an optimal calibration period and optimizing the calibration process based on the optimal calibration period after the calibration process is finished. And the calibration precision and efficiency of the flowmeter are improved.

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 method and device of an instruction generation agent based on a knowledge graph, a medium and equipment, and the method comprises the steps: obtaining a text, extracting a triple in the text, connecting the triple data to a graph database, and constructing a text knowledge graph; performing statistical analysis on the text knowledge graph from the aspects of entities, relationships and graph structures based on the text knowledge graph; based on the statistical analysis result, an instruction generation agent is constructed, an agent planning module, an agent tool module, an agent storage module and an agent execution module are constructed, and the agent execution module executes instruction generation operation based on the reasoning planning path and the cue word template of the corresponding category. And evaluating the quality of the instruction pair and executing an instruction rewriting operation on the data which is unqualified in evaluation. According to the method, the triple chain is constructed as the context knowledge point by utilizing the structural relationship of the knowledge graph, so that the illusion generated by the intelligent agent is reduced, and the quality of instruction data is ensured.

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 relates to the technical field of image processing, and discloses a steel structure installation and adjustment method and system based on column axis dynamic tracking. The method comprises the steps of collecting a steel structure column image to obtain contour data; segmenting the image and extracting column position features; matching actual and design positions of the stand column and calculating deviation; analyzing the deviation track to generate a displacement distribution diagram; calculating adjustment parameters to form instruction data; and constructing a virtual model to verify the adjustment effect and optimize parameters. Through comprehensive application of dynamic tracking, image processing and a multi-target optimization algorithm of the axis of the stand column, a complete closed-loop system from measurement, analysis, optimization to verification is established, and accurate control and intelligent adjustment of installation of the steel structure stand column are achieved.

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 invention provides a data storage instruction processing method, electronic equipment and a storage medium, and relates to the technical field of computers. The method comprises the steps that a block address of a target high-speed cache block corresponding to a target cache line is sent into a data storage instruction prefetching queue, all target data storage instructions cached by the target cache line are not fully written in the target cache line, and a data storage instruction address of the target data storage instruction comprises the block address of the target high-speed cache block; and executing data storage address hit check of the data cache on a target data storage instruction in the data storage instruction prefetching queue, and initiating a prefetching request and executing a block address access lower-layer storage system based on a target cache block under the condition that the data storage address is invalid, and the data of the target cache block is obtained and backfilled to the data cache, and the number storage instruction data included in the target number storage instruction which initiates the pre-fetching request and is successfully pre-fetched is written into the target cache block in the data cache, so that the pre-fetching accuracy and the pre-fetching timeliness of the number storage instruction can be improved.

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 the technical field of home appliance maintenance, in particular to a cross-brand home appliance maintenance knowledge sharing system and method based on federated learning, a multi-mode fault data collection and intelligent maintenance system integrates image, text, fault phenomena and multi-mode instruction data collection, and after pre-processing, the multi-mode fault data collection and intelligent maintenance system is established. A feature decoupling model is established through a maintenance knowledge analysis module, a cross-brand maintenance knowledge base stores general and specific fault information and assists knowledge migration, a federal learning training module adopts differential privacy homomorphic encryption, data privacy is protected, gradient aggregation cooperative training is achieved, a reality maintenance guidance terminal is augmented to visually guide maintenance, the efficiency is improved, and the maintenance efficiency is improved. The preventive maintenance module calculates the equipment maintenance cycle, generates a prevention scheme, and reduces the failure rate and cost. The system significantly improves the maintenance accuracy, rapidly fuses into a new brand, realizes knowledge sharing and utilization, and brings revolutionary revolutions to the household appliance maintenance industry.

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.

An audio translation labeling and analysis method based on a multi-agent cooperation mechanism belongs to the field of information technology and artificial intelligence, and is characterized by comprising the following steps: S1, translating an audio file into a text file; s2, outputting a dialogue set of text structure arrangement for the text file through a model in which domain knowledge and language style learning are introduced; s3, extracting and structurally expressing a time expression, a place name and an abstract paragraph by adding a large language model of a prompt project, and associating an event background with a session context to obtain an information set; s4, splicing the dialogue set and the information set to obtain a document set; s5, forming a text-label pair data set by a series of texts and the corresponding labels with inclusion relations, and constructing the text-label pair data set into an instruction data set conforming to instruction fine tuning format specifications; s6, performing fine adjustment on the large language model base according to the instruction data set to obtain a fine-adjusted model; and S7, inputting the document set into the fine-tuned model, and labeling all levels of labels to obtain the document set with the labels, which is used for full-process automatic processing from audio transcription to text optimization, information extraction, labeling and analysis report generation.

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 a hydraulic engineering data intelligent monitoring method based on digital twinning, and particularly relates to the technical field of hydraulic engineering intelligent monitoring, and the method comprises the following steps: obtaining control instruction data and entity hydraulic engineering real-time monitoring data, analyzing the time sequence characteristics and the change trend, and obtaining the real-time monitoring data of the control instruction data and the entity hydraulic engineering real-time monitoring data; detecting whether the control instruction data is returned and participates in system calculation, and identifying a data echo effect; after detection, calculating a short-term influence index and a long-term influence index based on the prediction error convergence degree, respectively evaluating the influence of the echo effect on the current operation state and the future scheduling precision, judging the level of the echo effect to be a low level, a middle level or a high level according to the influence degree, and processing by adopting a corresponding optimization strategy; according to the method, dynamic evaluation and grading judgment of the data echo effect are realized, and the early warning and adaptive capacity of the system is improved; fuzzy logic reasoning is adopted to optimize decisions, and regulation and control intelligence is enhanced; and through a differentiation strategy and a credible data source reconstruction model, the operation stability and the scheduling precision of the water conservancy project are guaranteed.

The present invention provides a multi-device collaborative control method and system for a digital workshop based on the Internet of Things, which relates to the technical field of data processing. The method includes: obtaining the status information of the first device during the execution of the processing task; generating a status change event when the process progress reaches a preset completion threshold or the running time exceeds a preset running time threshold; organizing various status change events into a queue in chronological order to generate a to-be-scheduled event stream; determining the target status change event associated with the second device; instructing the second device to execute the subsequent operation task, and calibrating the execution start time based on the trigger time to generate a second device action record; matching the second device action record with the target status change event, and comparing its recorded time with the trigger time. If the time deviation exceeds the tolerance interval, adjustment instruction data is generated; gradually correcting the control cycle of the second device. The present invention improves the autonomy and accuracy of multi-device collaborative control.

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.