1814 results about "Instruction data" patented technology

The invention discloses an intelligent driving multi-sensor fusion data processing system, which belongs to the technical field of fusion data processing and comprises a data preprocessing unit, a data fusion unit and a data post-processing unit. The data preprocessing unit normalizes the original data of the laser radar, the camera, the millimeter wave radar and the ultrasonic sensor through a standardization, cleaning, noise reduction and calibration synchronization module; the feature level fusion module deeply digs the characteristics of each sensor, and fuses geometric, visual, motion and close-range obstacle features by using a deep neural network; the decision-making level fusion module generates a driving instruction through weighted voting and fuzzy logic reasoning through classification and situation evaluation; and the data post-processing unit combines the vehicle and road condition optimization instruction, evaluates the risk, and stores the data feedback optimization system. The system can improve the sensing precision and decision reliability, enhances the expansion adaptability of the system, and provides guarantee for safe and efficient operation of intelligent driving.

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.

A system and method for user-level network trace and tracking which leverages the uniqueness of verified authentication objects as metadata tags on captured network packets to gain insight at the user-level of how a network and various applications interact. The tagged network packets may be tracked, and the resulting data formed into a trace and track dataset to create one or more user-level dependency graphs alongside captured temporal dynamics. The trace and track dataset may be enriched with application trace information and runtime instruction data to improve the executable graph and provide deeper insight into application and user security on a given network.

The invention discloses an aerospace knowledge large model construction method based on progressive knowledge injection and retrieval enhancement generation, which comprises the following steps: collecting and preprocessing multi-source knowledge data, and constructing an aerospace field knowledge base; the method comprises the following steps: based on a DeepSeek-R1-8B model, carrying out continuous pre-training by adopting a three-stage progressive mixed course learning framework; performing supervision fine tuning through instruction data and a knowledge guiding mechanism to complete basic construction of a large model; a retrieval enhancement generation module is constructed, a'retrieval-filtering-generation 'process is formed, and the knowledge coverage and fact consistency of the model are improved through deep fusion of real-time retrieval and generation reasoning; and through a multi-dimensional quantitative index and a dynamic test mechanism, the performance of the model is evaluated, and a complete evaluation ecology from data construction to feedback optimization is formed. According to the method provided by the invention, deep integration and efficient application of knowledge in the aerospace field are realized through multi-stage collaborative optimization.

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.

A system and method for attacker interdiction using user-level network trace and tracking which leverages the uniqueness of verified authentication objects as metadata tags on captured network packets to gain insight at the user-level of how a network and various applications interact. The tagged network packets may be tracked, and the resulting data formed into a trace and track dataset to create one or more user-level dependency graphs alongside captured temporal dynamics. The trace and track dataset may be enriched with application trace information and runtime instruction data to improve the dependency graphs and provide deeper insight into application and user security on a given network. Attacks may be detected by analyzing the dependency graphs, and attacker interdiction may be implemented by actively orchestrating network security and IT devices using SOAR workflows.

The invention relates to the technical field of unmanned aerial vehicles, in particular to a low-power-consumption unmanned aerial vehicle concealed control and encrypted communication method and system based on BLE 5.0. According to the method, link quality is dynamically evaluated based on a BLE node detection frame, a path decision result is generated, and a self-adaptive scattering network topological structure is constructed in combination with the real-time position of an unmanned aerial vehicle and electromagnetic environment changes; packaging the unmanned aerial vehicle control instruction into a GATT feature value update message, and generating a hidden control data stream; combining time, space and channel dimensions to generate a frequency hopping sequence parameter set; performing dynamic slicing and pseudo-random recombination on the hidden control data stream through a space-time dual encryption mechanism to generate an encrypted control instruction data stream; and transmitting the encrypted control instruction data flow to an unmanned aerial vehicle execution layer through a dynamic scattering network. On the premise of keeping the low power consumption characteristic, the unmanned aerial vehicle control system has high concealment, high reliability and high safety, and meanwhile, a tracking path of an enemy to a real flying hand is cut off through the dummy flying hand layer.

The invention discloses a network traffic threat analysis method based on operating system instruction hierarchy, which relates to the technical field of network security, and comprises the following steps: monitoring operating system instruction data in real time, constructing a dynamic behavior matrix and generating an instruction-level traffic diagram; analyzing the instruction level flow diagram, calculating first digit distribution of instruction data, comparing the first digit distribution with Benford's Law expected distribution, calculating an instruction behavior deviation degree through a statistical method, generating an abnormal score, performing risk classification in combination with a decision tree classification algorithm, and determining an abnormal instruction; and carrying out abnormal instruction classification by using a graph neural network, and identifying known attack behaviors and unknown abnormal behaviors. According to the method, more accurate attack behavior identification capability can be provided, a complete attack chain can be identified, more intelligent threat analysis is realized, false alarms are reduced, and the detection precision is improved, so that the behavior track of an attacker is accurately traced, and stronger safety protection capability is provided.

The invention discloses an on-satellite full-autonomous task planning system for an agile remote sensing satellite. Comprising an imaging target database module, a high-precision orbit prediction and orbit calculation module, a target visible arc section calculation module, an imaging task preprocessing module, a subsystem constraint model module, an imaging task planning module, a data transmission task planning module, a solid memory autonomous erasing module, an instruction data block generation module and a satellite state real-time monitoring module. By carrying out constraint modeling on subsystems or single machines such as load, data transmission, control and the like, an on-satellite full-autonomous operation process from receiving of subsystem or single machine instruction blocks from user requirements is established, satellite control is effectively simplified, and remote control uploading efficiency and on-satellite autonomous operation capacity are improved. And through a satellite state real-time monitoring module, the execution condition of tasks on the satellite, satellite state changes and the like are monitored in real time, task execution stopping, whole satellite sun-facing or re-planning are further triggered, task requirements and environment changes are quickly responded, task plans are formulated and adjusted in real time, and the task execution efficiency is improved.

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 instruction prediction, and discloses a frequency modulation instruction prediction method and system for super capacitor coupling lithium battery energy storage. The method comprises the following steps: performing decomposition processing on a standardized frequency modulation sequence through a smoothing function to obtain an approximate integratable amount and an inintegratable amount; performing trigonometric function periodic conversion processing on the approximate integrable quantity to obtain a periodic characteristic sequence which is composed of a plurality of sine and cosine functions with different frequencies; carrying out linear merging transformation processing on the periodic feature sequence and the non-integrable quantity to obtain a combined feature sequence; performing training processing on the combined feature sequence through a deep neural network to obtain a frequency modulation instruction prediction model; and performing prediction processing on the real-time frequency modulation instruction data through the frequency modulation instruction prediction model to obtain a prediction result, and performing reverse normalization processing on the prediction result to obtain a frequency modulation instruction prediction value. According to the invention, the efficiency and accuracy of frequency modulation instruction prediction for super capacitor coupling lithium battery energy storage are improved.

The invention relates to the technical field of large models, in particular to a TNM (Tumor Necrosis Model) staging prediction method and system in a clinical text of a large language model. Comprising the steps of data acquisition and preprocessing, instruction data acquisition, large language model instruction fine tuning, clinical text preprocessing, medical event extraction, preliminary event processing and screening, cue word engineering optimization, RAG technical framework combination, TNM staging result generation and JSON formatting output. By combining the natural language processing capacity of the large language model and the external knowledge retrieval function of the RAG technical framework, the efficient, accurate and wide-adaptability TNM staging prediction method and system are provided, the automation degree of clinical tumor staging can be effectively improved, a reliable staging basis can be provided for doctors, and the clinical tumor staging prediction efficiency is improved. With the continuous development of medical big data and artificial intelligence technology, the method has a wide application prospect and potential market value.

The invention relates to the technical field of data processing, and discloses a multi-modal digital human generation method and system based on AIGC and a storage medium. The method comprises the steps of performing modeling and dynamic weight adjustment processing on multi-modal feature data to obtain dynamic personalized knowledge data; performing intelligent matching processing on the target scene information and the dynamic personalized knowledge data to obtain situational behavior instruction data; performing hierarchical action sequence generation, adaptive resolution optimization and speech synthesis processing on the initial digital human to obtain multi-modal digital human performance data; and behavior deviation identification is carried out on the multi-modal digital human performance data to obtain behavior deviation data and performance feedback data, and adaptive optimization processing is carried out on the multi-modal digital human performance data and the performance feedback data to obtain a target digital human. According to the invention, the efficiency and accuracy of multi-modal digital human generation based on AIGC are improved.

The invention relates to a multi-agent medical diagnosis and treatment system which comprises a plurality of agents. The plurality of agents comprise: a task planning agent analyzes input content and generates a corresponding task process, and the task planning agent sequentially issues task instructions to a data management agent, a tool use agent and a medical decision agent according to the task process; the task instructions comprise a data task instruction, a tool task instruction and a decision task instruction; the data management agent extracts patient data from the database and sends the patient data to the task planning agent according to a data task instruction issued by the task planning agent; the tool using agent performs feature extraction processing on the patient data according to the patient data and the tool task instruction issued by the task planning agent so as to obtain key features and send the key features to the task planning agent; and the medical decision-making agent carries out diagnosis and treatment reasoning according to the key features and the decision-making task instruction issued by the task planning agent, and a diagnosis report is obtained and displayed.

The invention discloses an LED driving system. The LED driving system comprises a controller and a plurality of LED driving chips, the controller and the plurality of LED driving chips form a serial communication link; each LED driving chip is provided with a first input port, a first output port, a second input port and a second output port; the first input port and the second output port of each LED driving chip are directly connected in the chip; and the LED driving chip is set to ignore the instruction data received by the second input port and only process the instruction data received by the first input port when receiving the instruction data from the second input port and then receiving the instruction data from the first input port. According to the invention, a breakpoint resuming function after a fault occurs in a serial communication link is realized by arranging redundant input and output ports. And meanwhile, by differentially processing the data received by different input ports, the driving chip can be ensured to correctly acquire the backlight brightness data even if a fault occurs.

The invention discloses an instruction execution acceleration method based on a GPGPU and a GPGPU architecture, and relates to the field of high-performance computation.The method comprises the steps that a thread execution task output by a host compiler is received, and the thread execution task is scheduled according to the current idle state of a computing unit; capturing instruction data output by the host compiler, and executing concurrent instruction decoding according to the corresponding instruction type to obtain a machine code corresponding to the instruction data; obtaining an operand corresponding to the instruction data, and generating a to-be-transmitted instruction based on the machine code corresponding to the instruction data and the operand after secondary verification; according to the calculation complexity, determining an optimal calculation unit, and sending the to-be-launched instruction to the optimal calculation unit; and receiving an execution result of the to-be-transmitted instruction, and returning the execution result to the terminal. The thread execution task is dynamically scheduled according to the idle state of the computing unit, so that computing resources can be fully utilized, and the operation efficiency is improved.

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 a PICO extraction method and device based on a large language model and a storage medium, and is applied to the technical field of medical information, and the method comprises the steps: obtaining and preprocessing titles and abstracts of a large number of medical literatures, then combining preset cues related to PICO with texts to construct an instruction data set, and storing the instruction data set in a database; a text in an instruction data set is subjected to word segmentation processing to be converted into an input sequence, a large language model framework is constructed, a false positive suppression loss function is set, the false positive rate of PICO element extraction by the model is remarkably reduced, the large language model framework is trained through the input sequence, and a trained PICO element extraction large language model is obtained. Preprocessing medical text data to be retrieved, inputting the preprocessed medical text data into the trained model, and outputting a PICO extraction text by the model; and finally, each PICO classification result is output through the PICO classifier. The data are data related to the crowd / problem, intervention, comparison and result in the to-be-retrieved data.

The invention discloses an interactive intention understanding and rapid learning system based on multi-mode information fusion, which aims at semantic understanding of an operation instruction of a robot for picking and placing an article, constructs a key information extraction framework based on a large language model in a task semantic understanding module, and comprises multiple key position and action text information extraction, and multi-mode interactive fusion is carried out on the extracted information, vision and a touch screen, so that correct understanding of the instruction intention is realized. The key information extraction architecture based on the big language model is based on a BERT big language model, and a language processing model is trained through the BERT big language model; the language processing model comprises a long-sequence multi-task instruction and a human instruction data set of mood, scene, positive-sequence and inverse-sequence request instructions, and realizes input of long-sequence multi-task instruction statements and output of a key action sequence; according to the system, efficient and accurate human intention understanding can be realized through a voice, touch screen and vision multi-mode human-computer interaction technology, so that the robot can quickly learn new articles.

The invention discloses an AI intelligent control pressure sore prevention self-adaptive mattress system which comprises a mattress body provided with a polymer support column structure covering in all directions and designed into a plurality of areas, and the areas are accurately divided to support a human body and disperse pressure; the sensor array module is used for recording monitoring data of contact between each part of a human body and a plurality of areas of the mattress body in real time; the AI chip control module integrates an advanced algorithm and a processor and is used for receiving and analyzing the monitoring data from the sensor and sending out a control instruction based on an analysis result; and the database management module is used for storing monitoring data of patients with different weights, heights, ages and body positions and is used for predicting and early warning pressure sore risks. According to the invention, pressure sore risk areas can be accurately identified in a zoning manner, pressure, temperature and humidity information can be monitored in real time, and meanwhile, a proper pressure state is automatically adjusted, so that the occurrence of pressure sores is effectively prevented.

The invention discloses a data analysis method, device and equipment based on an intelligent gateway and a storage medium, and relates to the technical field of artificial intelligence and edge computing, and the method comprises the steps: obtaining sensor data, video monitoring data, control instruction data and other data from various types of intelligent equipment; performing time alignment and format conversion on the data to obtain standardized gateway data; performing sampling diffusion and gain updating on the standardized gateway data to obtain fused gateway data; performing feature extraction and weighted adjustment on the fusion gateway data to obtain fusion gateway data features; performing linear dimension reduction on the fused gateway data features to obtain a low-dimension gateway data feature matrix; abnormal features are detected from the low-dimensional gateway data feature matrix by fitting the normal data range, and corresponding abnormal gateway data are determined according to the abnormal features. According to the invention, mass data from different devices can be effectively integrated, processed and analyzed, and abnormal data and behaviors of the devices can be identified.

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.

Techniques for computer science, data science, data analytics, computer software, algorithmic analysis, and networked technologies for sourcing, procurement, manufacturing, and supply chain management in small-to-medium manufacturing (“SMM”) industries that involve processes whereby implementing user input and extracted image and text from engineering drawings may be used to map manufacturing requirements to a subset of manufacturers via natural language agent programs. More specifically, natural language agent programs may be implemented to apply input and extracted image and text from engineering drawings to a large language model (“LLM”). An example method may include receiving a user input including data representing requirements to manufacture a physical structure, concatenating an engineering drawing summary data, a shoptype description, and estimated part size instruction data to combine the at least two of the engineering drawing summary data, the shoptype description, and the estimated part size instruction data to generate a subset (e.g., a list) of qualified manufacturers (e.g., SMMs).

The invention discloses a vertical field large model-oriented high-quality data set construction method and system, and relates to the technical field of text processing, and the method comprises the steps: selecting seed text data, carrying out long text processing, segmenting the seed text data into a plurality of text segments, processing the text segments by using a large model tool to generate seed question and answer pairs, and constructing a seed question and answer pair; auditing and optimizing are carried out according to a preset auditing standard; making an instruction generation data set for the cue word, the text fragment and the seed question and answer pair according to a preset organization rule, performing fine adjustment on a pre-selected base model by using the instruction generation data set, and performing iterative optimization on the large model by testing the performance of the large model after fine adjustment and adjusting and optimizing parameters, thereby obtaining an intelligent extraction large model IELM; and based on the intelligent extraction big model, processing the text data by utilizing a preset script code, automatically generating knowledge question and answer pair data corresponding to the text data, and performing format conversion processing to obtain an instruction data set which is used for a training data set of the vertical domain knowledge big model.

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.