685 results about "Instruction sequence" patented technology

The invention provides a perception collaborative decision-making method and system based on multi-modal heterogeneous data fusion, and relates to the technical field of artificial intelligence, and the method comprises the steps: inputting a global environment situation perception graph into a pre-trained multi-target collaborative decision-making model; the multi-target collaborative decision-making model forms a multi-target decision-making feature set by analyzing the resource entities and the incidence relation in the graph; based on the multi-target decision feature set, decision optimization is carried out to obtain a comprehensive collaborative scheduling scheme; performing instruction analysis and packaging on the comprehensive collaborative scheduling scheme to obtain an executable instruction sequence; and issuing the executable instruction sequence to a corresponding decision node and a control terminal in parallel through a distributed communication architecture to complete real-time scheduling of resources and collaborative issuing of control instructions. According to the invention, by constructing a linkage mechanism of multi-modal data fusion, dynamic environment perception and collaborative decision execution, intelligent perception and quick response to a complex environment are realized.

The invention discloses a multi-agent dynamic arrangement method based on multi-modal analysis and adaptive retrieval, and relates to the technical field of artificial intelligence and information retrieval. Comprising the steps of S1, converting a text, an image, structured data and voice content input by a user into a unified multi-mode semantic representation, S2, converting the unified multi-mode semantic representation into a specific execution process, and S3, automatically scheduling a reasoning agent, a knowledge obtaining agent and an execution agent according to DAG nodes, task elements and available resources, and obtaining the task elements and the execution agent according to the reasoning agent, the knowledge obtaining agent and the execution agent. S4, after task process construction and agent arrangement are completed, dynamic retrieval, evidence convergence and strategy optimization are carried out on information requirements related to a user task, so that a reasoning agent obtains complete knowledge support with consistent context, and S5, knowledge evidence is combined with a task process, so that the task process is completed. The method comprises the following steps: step S6, implementing problem solving, strategy generation and task closed-loop execution through a reasoning agent, step S6, performing actual operation on a target task by an execution agent according to an executable instruction sequence output by the reasoning agent, and outputting a result, and step S7, performing result verification according to an output result returned by the execution agent, and the correctness, integrity and consistency of an output result are examined through rule verification, model evaluation and evidence alignment.

The invention belongs to the technical field of computers, and particularly relates to a software fault repairing method and system fused with intelligent analysis, which comprises the steps of collecting a multi-level running log and performing structured preprocessing, constructing a dynamic calling graph through a time sequence encoder and a graph neural network, inferring a fault root cause in combination with a Bayesian causal inference model, and repairing a fault fault according to the fault root cause. And matching the repair strategy to generate an atomization instruction sequence, and deploying the atomization instruction sequence to a production system after sandbox environment verification. The system comprises a log acquisition module, a feature coding module, a graph construction module, a causal reasoning module, a strategy matching module, an instruction generation module, a sandbox verification module, a deployment feedback module and the like. Through end-to-end intelligent analysis and a closed loop verification mechanism, the fault positioning precision and the repair safety are remarkably improved, system self-evolution is supported, and operation and maintenance are promoted to be transformed from passive response to active autonomy.

The invention relates to the fields of computer graphic processing, industrial software technology and the like, discloses a Cocos engine-oriented pipeline loop perception linear decomposition method, and aims to solve the problem that a complex topological graph which cannot be effectively processed and comprises a high-density nested loop in the prior art. The structure is completely and accurately decomposed into a linear drawing instruction sequence which can be accepted by a Cocos engine, and lossless conversion of original topological information is ensured; comprising the steps that after undirected topological graph data are input, a ring detection algorithm is called to recognize and record all independent ring structures in an undirected topological graph and nodes and edges contained in the independent ring structures; judging whether at least one ring is detected in the previous step, and if the at least one ring exists, assigning a determined traversal direction for each detected ring according to a preset ring direction rule; and if the ring does not exist, skipping, and directly performing depth-first search traversal on the undirected topological graph data.

The invention relates to the field of power networks, and provides an intelligent generation method for power equipment accident recovery. The method comprises the following steps: converting key semantic elements into a structured query instruction; acquiring operation state data of the power grid equipment from a power grid monitoring system in real time, and dynamically updating the operation state data to a pre-constructed power knowledge graph; on the basis of a structured query instruction, performing graph traversal and reasoning on the updated electric power knowledge graph, and searching all reachable electric paths from an available power supply point to a target recovery object; carrying out feasibility evaluation on the reachable electrical path in combination with an operation constraint condition, and generating a candidate recovery path; performing multi-objective optimization sorting on the candidate recovery paths to generate an operation instruction sequence; and converting the operation instruction sequence into at least one form of output scheme. According to the invention, automatic generation and optimization from the natural language instruction to the intelligent recovery scheme are realized.

The invention discloses a thermal spraying coating thickness online optimization control method based on digital twinning, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: synchronously collecting multi-modal online observation data and process control observation data in a thermal spraying process, carrying out the time-space alignment, and generating a multi-modal observation set; performing adaptive optimization of multi-modal uncertainty gating on the candidate control set according to the credible thickness field confidence distribution map to generate a control instruction sequence; and the control instruction sequence acts on the thermal spraying process, response feedback data are continuously collected in the execution period, a new multi-mode observation set is generated according to the response feedback data, and real-time closed-loop optimization control is kept. According to the method, the thickness field with high credibility and the credible thickness field confidence distribution diagram can be generated through forward generation and uncertainty quantification of physical guidance, and depth coupling of multi-modal data and physical rules and accurate mapping of uncertainty are achieved.

The invention provides an intelligent fault diagnosis method and system for electromagnetic valve test data and a computer program product, and relates to the technical field of electromagnetic valve fault diagnos.The intelligent fault diagnosis method comprises the steps that firstly, multi-dimensional test data of an electromagnetic valve under multiple working condition combinations are collected, and multiple types of data including action response and the like are contained; a data behavior collaboration model is constructed based on the behavior collaboration relation between the data, and laws such as synchronous response are embodied; an abnormal collaboration chain deviating from a normal collaboration mode is mined from the model, and abnormal performance of each link is determined; reversely deducing a fault core cause through the abnormal collaborative chain, and determining information such as cause types; and finally, generating an executable instruction sequence based on the fault core inducement and the abnormal collaborative chain conduction characteristic, and controlling the maintenance system to execute fault handling operation. Therefore, faults can be comprehensively and accurately diagnosed, and the operation reliability and the system stability of the electromagnetic valve are improved.

The invention relates to the technical field of multi-agent cooperative control, and discloses a multi-agent autonomous decision-making method based on deep reinforcement learning. The method comprises the steps of synchronously detecting an initial collaborative state of a cluster, performing joint situation assessment, and judging a collaborative operation mode according to a quantitative situation. And analyzing the capability of each agent and the real-time task load, and constructing a distributed task knowledge graph. And utilizing the atlas to drive a deep reinforcement learning network, coupling computing resource allocation and a task path, and generating a preliminary behavior strategy of each agent. And performing cluster-level conflict detection and iterative negotiation adjustment, and finally issuing an executable action instruction sequence. According to the method, integrated optimization of resource allocation and action paths is realized, the situation understanding and negotiation mechanism is enhanced through the knowledge graph to guarantee the collaborative consistency, and the collaborative decision-making efficiency and task execution robustness of a multi-agent system in a dynamic environment are improved.

The invention relates to the technical field of traffic event identification, and discloses an abnormal traffic event identification method and system based on a traffic large model, and the method comprises the steps: obtaining a traffic data flow, extracting an abnormal feature vector, and obtaining an abnormal signal candidate set; grouping the candidate sets and calculating a deviation degree, and if the deviation degree exceeds a threshold value, taking the deviation degree as a risk signal to form an input subset; environment variables are extracted from the subsets, a mapping relation is established, and anomaly recognition embedding representation is obtained; classifying the embedded representation, judging a congestion precursor and generating an early warning signal to obtain an early warning signal sequence; matching the sequence to obtain an abnormal event chain; if the integrity is higher than a threshold value, analyzing the type to obtain an abnormal event type; extracting a correlation feature vector from the type, pushing the correlation feature vector to a traffic management platform to obtain an instruction, and obtaining an emergency response trigger instruction sequence; and executing the instruction sequence to extract a feedback data stream, inputting the traffic large model to judge the accuracy rate, and if the judgment accuracy rate is met, determining an optimized anomaly recognition framework. The method can solve the problem of insufficient early warning capability.

The invention provides a multi-modal fusion virtual simulation experiment teaching system, and relates to the technical field of virtual simulation, and the system comprises an information collection module which is used for obtaining the interaction information of a learner; the information fusion module is used for constructing a multi-modal input data set; the instruction analysis module is used for analyzing and generating an operation instruction sequence; the teaching intervention strategy generation module is used for generating a self-adaptive teaching intervention strategy; the teaching intervention strategy execution module is used for executing a self-adaptive teaching intervention strategy; and the learning effect evaluation module is used for quantifying the learning effect evaluation score. The technical problems that in traditional virtual simulation experiment teaching, multi-modal operation behaviors of learners are difficult to accurately capture in real time, personalized teaching intervention is lacked and learning effect evaluation is incomplete can be solved, and virtual experiment simulation and intelligent teaching intervention driven by multi-modal information fusion are achieved. And the experimental interaction experience and the learning effect evaluation precision are improved.

The invention discloses a natural language driven CAD (Computer Aided Design) modeling method based on multi-agent collaboration. According to the method, a collaborative mechanism of a structure planning agent, a parameter completion agent and an execution verification agent is introduced, and the method comprises the steps that firstly, a structure planning agent analyzes natural language description and generates an abstract syntax tree structure meeting a CAD modeling rule; then, on the premise that the structure of the abstract syntax tree is kept unchanged, a parameter completion agent predicts and completes all symbol and numerical parameters, and a complete CAD modeling instruction sequence is generated; and finally, the execution verification agent performs structure and parameter dual verification on the generation instruction by using the geometric kernel, and feeds back and corrects if necessary. According to the method, a strategy of combining field adaptive full-parameter pre-training and task specific fine tuning is adopted in a training stage, and a structure-parameter decoupling modeling and closed loop verification mechanism is introduced, so that the structure accuracy, the parameter matching property and the geometric performability of a generated result are effectively improved.

The invention relates to the technical field of sensor data fusion, in particular to a real-time dynamic attitude stability control method and system based on multi-sensor fusion, and the method comprises the following steps: collecting multi-source sensor data, and correcting to generate a synchronous sensor data set; calculating a stability coefficient, a signal quality coefficient and a consistency coefficient, weighting the signal quality coefficient and the consistency coefficient to generate a dynamic fusion weight coefficient, executing weighted fusion based on the weight, outputting a fusion attitude state estimation result through adaptive extended Kalman filtering, and generating an optimization control instruction sequence by combining with dynamic constraint model rolling optimization; according to the method, a dynamic weight mechanism based on noise and signal strength is introduced to achieve self-adaptive fusion, the synchronization precision and reliability are improved by combining consistency verification, abnormal signals are corrected through self-adaptive filtering, the attitude estimation stability is enhanced, and the performance of the system is improved. And a control instruction is generated through rolling optimization, so that rapid and accurate adjustment is realized, and attitude keeping and control consistency in a complex environment is improved.

The invention relates to the technical field of intelligent ship control, and particularly discloses an area control integration method and system based on an intelligent ship engine room, original operation data and multi-source communication data of equipment in each area of the engine room are collected, a unified real-time state vector is generated through protocol analysis and standardization processing, and a plurality of redundant processing nodes are processed in a unified mode. Synchronizing and verifying state data through a high-speed channel, constructing a global consistent state synchronization set, disassembling composite logic into atomic operation units with state constraints according to an upper-layer instruction and the synchronization set, packaging the atomic operation units into a private instruction sequence bound with the state, executing the instruction sequence by a main node, and executing the state synchronization set by a slave node; seamless switching to a standby node is realized through independent hardware monitoring when a fault occurs, and the standby node continuously executes based on the continuity of a state synchronization set, so that high-reliability uninterrupted operation of area control is realized; according to the method, the problems of instruction semantic distortion and single-point fault risk in heterogeneous equipment integration are solved, and the control accuracy and the intelligent level are improved.

The invention discloses a reciprocal calculation instruction sequence generation method oriented to a heterogeneous processor, and belongs to the field of compilation optimization and code generation. Aiming at the problems of instruction redundancy, weak precision control, poor hardware adaptation and high manual dependence of an existing method in a heterogeneous environment, characteristics of a reciprocal instruction and an operand are accurately identified by linearly scanning heterogeneous object codes (including vectorization, scalar and complex instruction sequences); in combination with hardware characteristics of RISC / SIMD / VLIW / DSP and the like, a multi-round iteration precision improvement and temporary register optimization allocation strategy is adopted, differential generation logic is formulated, and a high-precision low-redundancy instruction sequence is generated. The method comprises linear code scanning classification, reciprocal instruction and operand identification, cross-architecture generation logic rule formulation, instruction sequence generation and legality verification. Full-process automation is achieved, manual intervention is reduced, the execution efficiency and precision of reciprocal calculation of the heterogeneous processor are improved, and the method is suitable for embedded systems, high-performance calculation and other scenes.

The invention relates to the field of modern intelligent manufacturing and automation, and discloses a method for planning a path of an intelligent robot with a body based on visual inspection, which comprises the following steps: acquiring real-time image data, preprocessing and extracting position coordinates of an obstacle, and generating an obstacle distribution diagram; an A star algorithm is adopted to generate a preliminary optimal route sequence, and map information is updated; uncertain interference in the path is judged, and a path alternative set is generated through coordinate calibration; a time-varying parameter and a security constraint optimization path strategy are integrated through adaptive path optimization; key node coordinates are extracted and are fused with obstacle boundary identification to obtain a path execution sequence; real-time performance is checked, if calculation is overtime, a rollback mechanism is triggered, and an initial path is loaded and fused with real-time obstacle information to generate a final feasible route; a control instruction sequence is generated according to the final route and transmitted to the robot for execution. According to the method, efficient and adaptive path planning in a dynamic environment can be realized, and the real-time response capability and task execution reliability of the robot are improved.

The invention relates to the technical field of agent path optimization, in particular to a multi-agent dynamic high-dimensional manifold planning and conflict-free control system and method, and the method comprises the steps: obtaining the multi-dimensional dynamic state information and motion intention of an agent, carrying out the analysis processing, generating the state and intention data flow of the agent, and storing the state and intention data flow of the agent; constructing on a dynamic high-dimensional manifold in combination with a motion and dynamic model of the intelligent agent, and generating a high-dimensional space-time pipeline of the intelligent agent; detecting whether the high-dimensional space-time pipelines of any multiple agents have geometric overlap in the dynamic high-dimensional manifold or the probability of geometric overlap in a future preset time period is greater than a preset probability, generating manifold crossing information, performing collaborative planning, generating a high-dimensional space-time pipeline without geometric overlap, and converting to generate a control instruction sequence; and sending to the intelligent agent. According to the scheme, predictive and collaborative dynamic planning and optimization are carried out on behaviors of multiple agents, and the operation efficiency of the whole system and the operation efficiency of the individual agents are maximized on the premise of safety.

The invention discloses a self-adaptive adjustment method and system for the die-casting temperature of a die-casting die, and relates to the field of die-casting temperature adjustment, and the method comprises the steps: synchronously obtaining a real-time temperature data flow and a real-time control output flow of the die-casting die, and constructing a time sequence data model capable of representing the internal thermal dynamic characteristics of the die-casting die; on the basis, a depth time sequence prediction model is introduced, and the future temperature response track of the system is accurately deduced. Furthermore, process targets such as a temperature set value are fused, and a model predictive control framework is utilized to carry out prospective rolling optimization on future multi-step control output so as to solve an optimal control instruction sequence. And finally, taking the first instruction of the sequence as the actual control output of the current period. Therefore, the advanced and accurate output of the controlled quantity can be realized, and the thermal hysteresis effect is effectively overcome, so that the high-precision and high-stability self-adaptive adjustment of the die-casting temperature is realized.

The invention discloses a heavy crane multi-task intelligent collaborative scheduling and path optimization control system, which is characterized in that by collecting operation state data, environment perception data and task queue data of a plurality of heterogeneous cranes, a time-space unified modeling module constructs and updates a collaborative operation digital map in real time; the collaborative operation digital map and the task queue are input into a multi-task dynamic collaborative scheduling module, a task-equipment allocation mapping relation and a plan time window are output, and then the collaborative operation digital map is combined to be input into a layered collaborative path planning module to generate a real-time motion control instruction sequence; and finally, issuing to a crane bottom layer control system through a protocol adapter to drive collaborative operation. According to the method, the problems of insufficient global optimization and high conflict risk when a plurality of heterogeneous cranes cooperatively work in a dynamic complex environment are solved, intelligent scheduling and safe planning are realized, and the working efficiency and safety are improved.

The invention discloses an intelligent regulation and control method and system for temperature and humidity data of a logistics warehouse, and relates to the technical field of data identification, and the method comprises the steps: collecting the spatial and temporal distribution data of a temperature and humidity field in the warehouse through a multi-source sensor network; processing the data by adopting a multi-source time sequence data alignment algorithm and a feature extraction method; constructing and updating a spatio-temporal evolution prediction model; generating a coordinated regulation and control instruction sequence; a regulation and control instruction is issued, and closed-loop feedback is formed. The system comprises an environment sensing module, a data fusion and feature extraction module, a dynamic prediction modeling module, a multi-target optimization decision module and an instruction execution and feedback control module. According to the method, the spatial-temporal heterogeneity of the temperature and humidity field can be captured at high resolution, the temperature and humidity evolution trend in a future time window can be accurately predicted, the environmental safety and the energy consumption efficiency are comprehensively considered, the stability of the storage environment is remarkably improved, and energy consumption optimization is realized on the premise that the cargo storage safety is guaranteed.

The invention relates to the technical field of call center control, and discloses an unmanned vehicle formation control and obstacle avoidance reconstruction system based on complex road conditions. The system call data acquisition module is used for acquiring multi-source interaction data in real time; the dynamic feature extraction module is used for performing semantic intention hierarchical analysis on the voice stream text conversion data, performing event fragment segmentation on the user behavior time sequence data, and generating an operation track feature vector; the scene clustering engine module is used for performing space-time alignment on the semantic intention hierarchical analysis result and the event fragment segmentation result to generate a multi-dimensional scene clustering tag; the strategy matching core module is used for retrieving an interaction strategy set matched with the current scene from a distributed strategy library according to the multi-dimensional scene clustering label and generating a dynamic control instruction sequence; and the instruction distribution execution module is used for distributing the dynamic control instruction sequences to corresponding seat terminals and voice routing equipment after sorting the dynamic control instruction sequences according to priorities.

The invention discloses an air source heat pump unattended control method and system, and relates to the field of equipment control, and the method comprises the steps: predicting a load demand according to an environment parameter and a building load parameter, and calculating a target water supply temperature based on the load demand; sorting the heat pump units according to the equipment operation duration parameters, distributing a temperature deviation value for each heat pump unit, and calculating the target water supply temperature and the temperature deviation value corresponding to the heat pump unit to obtain the personalized set temperature of each heat pump unit; a starting instruction sequence is generated according to a preset starting condition and the personalized set temperature, and after the water return pressure parameter reaches a preset parameter threshold value after the circulating water pump is started, all the heat pump units are sequentially started according to the personalized set temperature sequence; and a make-up pump and a pressure release valve are controlled according to the return water pressure parameter and the liquid level parameter, and the temperature deviation value of each heat pump unit is adjusted according to the change of the environment parameter and the building load parameter. By implementing the method, the balance of equipment loads in the multi-unit system can be improved.

The invention provides a public-network-free communication area multi-nest cooperative unmanned aerial vehicle autonomous flight patrol method and system, and relates to the technical field of intelligent control, and the method comprises the steps: building a communication coverage model based on a patrol patrol partitioning scheme and nest resource states; generating a cooperative scheduling plan according to the communication coverage model; according to the collaborative scheduling plan, performing three-dimensional flight path optimization calculation on each aircraft nest to obtain flight path data; processing the flight path data to obtain a three-dimensional environment situation data set; based on the three-dimensional environment situation data set, a cooperative control instruction sequence is generated, and the cooperative control instruction sequence comprises a trajectory optimization strategy and a task redistribution strategy; and executing distributed control according to the cooperative control instruction sequence. According to the method, the communication coverage capability of each nest can be dynamically evaluated, and the optimal cooperative scheduling plan is generated.

The invention discloses an industrial operation and maintenance high-risk instruction intelligent identification method and system based on instruction sequence analysis, and the method comprises the steps: intercepting an operation and maintenance instruction inputted by a remote user in real time through an operation and maintenance gateway, and constructing an instruction sequence according to a time window; performing vectorization representation on the instruction sequence, and generating an overall sequence representation vector by adopting a sequence coding model; performing similarity calculation on the overall sequence representation vector and the high-risk sequence center vector, and outputting risk confidence; calculating a comprehensive risk score in combination with the permission coefficient, the time sensitivity coefficient and the context factor; and making a decision according to a comparison result of the comprehensive risk score and a risk threshold, and identifying an industrial operation and maintenance high-risk instruction. The method can accurately identify the potential high-risk instruction behavior, reduces the system security risk, and has the advantages of high identification precision, strong real-time performance and good expandability.

The invention discloses a land leveler automatic control and guidance method and system based on Beidou high-precision positioning. The method comprises the following steps: acquiring comprehensive state information of a land leveler including a three-dimensional position, a course, an attitude and a real-time elevation of a scraper knife; outputting future state parameters of a scraper knife elevation adjusting mechanism and a machine body steering mechanism of the land leveler in the rolling time domain through a preset land leveler kinematics model on the basis of the comprehensive state information and the current target working plane design information; based on the future state parameters, determining an optimal pre-control instruction sequence for a scraper knife elevation adjusting mechanism and a fuselage steering mechanism through a multi-objective optimization mode; and the optimal pre-control instruction sequence is sent to an electro-hydraulic control system to drive an execution mechanism to act. Complex factors such as topographic changes and fuselage dynamics coupling can be effectively coped with by predicting future states by adopting a kinematic model and prospectively generating a coordination control instruction, and the accuracy and smoothness of scraper knife control in the dynamic operation process are ensured.

The invention relates to the technical field of building block toy programming, and discloses an extensible modular building block toy intelligent programming control system and method. A building block topology identification module of the system collects the physical connection state of modular building blocks through a multi-modal sensor, and extracts module identity features, a connection relation matrix and combined configuration parameters; the programming logic conversion engine establishes a mapping rule of the module identity characteristics and the connection relation matrix, and analyzes a correction rule of the combination configuration parameters on the two; the instruction sequence generator constructs an instruction conversion model based on the mapping rule and the correction rule, and generates a target control instruction queue; and the actuating mechanism driver analyzes the instruction queue and outputs a driving control signal matched with the current building block combination. The system can accurately identify the building block combination state, flexibly convert the programming logic, dynamically generate the adaptive instruction and accurately execute the driving action, the expansibility and adaptability of the system are improved, and the use experience of a user is improved.

The invention relates to a freight train future speed prediction method and device based on brain-like acceleration and a medium, the method inputs obtained freight train related data into a speed prediction model, a freight train future speed prediction result is output, the speed prediction model adopts a three-level network structure of an MLP encoder-Transformer encoder-MLP decoder, and the speed prediction model is used for predicting the freight train future speed. After the MLP encoder encodes the two paths of input respectively, the two paths of input are input into the Transform encoder for spatio-temporal feature extraction, decoding is carried out through the MLP decoder to obtain a prediction result, the forward propagation process of the Transform is converted into a brain-like instruction sequence, and the brain-like chip is controlled based on the brain-like instruction sequence to carry out operation so as to realize accelerated prediction. Compared with the prior art, the method has the advantages that the static characteristics can be dynamically processed in a time sequence mode, and the prediction efficiency can be improved by fully utilizing the brain-like chip.

The invention discloses a power electronic dynamic system simulation model generation method, and belongs to the field of power electronics. Performing fine adjustment on the basic model to establish a simulation general large model; analyzing a simulation demand input by a user by utilizing the trained simulation large model, and generating a structured design intention map; inputting the structured design intention graph into the simulation large model, generating a simulation model basic instruction sequence or an execution code, and utilizing simulation model creation software to execute the instruction sequence or the execution code to create a simulation basic model; and carrying out automatic verification and iterative optimization on the simulation basic model by adjusting parameters, so that the simulation basic model outputs the capability of the simulation model with the optimal performance, and completing the generation of the power electronic dynamic system simulation model. Natural language driven modeling is achieved through the large simulation model, the technical threshold is lowered, the development period is shortened, the generated model can be directly integrated into a power electronic dynamic system simulation software environment, and the method is suitable for education, scientific research and industrial application.

The invention provides a smart building environment adaptive regulation and control method based on digital twinning and AI, and relates to the technical field of artificial intelligence, and the method comprises the following steps: extracting dynamic features based on multi-source system data after time alignment, so as to construct a dynamic feature set; inputting multi-source system data into the digital twin, performing iterative simulation on key environment parameters of a plurality of time steps in the future by taking a current building equipment state as a boundary condition, and outputting a multi-step prediction sequence; inputting into a model prediction controller, and solving to obtain an equipment regulation and control instruction sequence in a future first preset duration by taking the lowest total energy consumption in a prediction period as an optimization target and taking the condition that the key environment parameters do not exceed a comfort interval as a constraint condition; and the first equipment regulation and control instruction in the equipment regulation and control instruction sequence is issued to the field execution mechanism, so that self-adaptive regulation and control of the building environment are realized, the defect that passive response is performed after environmental parameters exceed the standard is overcome, and the quick response capability of the system to sudden environmental changes is remarkably improved.

The invention provides a carrier rocket ground test system and method supporting visual process configuration. The system mainly comprises: (1) a visual process configuration module, which configures a test process file through a graphical interface; (2) a process analysis and scheduling module for analyzing a process file and converting the process file into a task sequence; the test execution engine module (3) is used for driving bottom hardware and executing a test instruction sequence; (4) the test resource library module is used for storing and managing various information of test elements and various information of test equipment; the execution monitoring and data management module (5) is used for displaying a test execution state in real time and recording whole-process data; according to the method, the test logic and the execution program are separated, and a user can quickly construct, modify and reuse an automatic test process in a graphical mode, so that the test flexibility is greatly improved, and the test adaptability and efficiency are remarkably improved.

The invention discloses an oil well intelligent monitoring and remote control system, which belongs to the technical field of oil well monitoring and control, and is characterized by comprising the following steps: acquiring oil well production data, and obtaining a real-time indicator diagram and indicator diagram characteristics according to a preset electrical parameter inverse demonstration indicator diagram model; sending a preset security perturbation instruction sequence and obtaining response data corresponding to the security perturbation instruction sequence; obtaining core mechanism parameters of the oil pumping unit system based on the response data and a preset safety perturbation instruction sequence; obtaining a digital shadow model according to the core mechanism parameters and the indicator diagram characteristics; according to the method, core mechanism parameters are identified through anti-demonstration indicator diagram characteristics of an electric parameter anti-demonstration indicator diagram model and a safety perturbation instruction, and closed-loop intelligent control from oil well state monitoring to liquid production flow regulation and control is formed through the digital shadow model. And the accuracy of oil well flow control, the safety of system operation and the energy efficiency management level are improved.