1254 results about "Instruction sequence" patented technology

The invention belongs to the technical field of circuit breaker management, and discloses a control system for managing a primary and secondary fusion circuit breaker, and the system collects the electrical parameters, mechanical states and environmental conditions of the circuit breaker through multiple channels, and constructs a standardized operation data set; establishing a circuit breaker health characteristic spectrum based on deep characteristic learning; establishing an environmental adaptability control parameter library through environmental factor correlation analysis and multi-scene simulation; performing fault mode identification and predictive diagnosis in combination with the health characteristic spectrum, and generating a fault risk early warning matrix; optimizing a multi-circuit-breaker cooperative control strategy based on the early warning matrix, and generating an optimal control instruction sequence; and reliable execution and effect feedback of the control instruction are realized through security encryption verification and a hierarchical execution mechanism. The problems that a traditional circuit breaker control system is difficult in data integration, insufficient in environment adaptability, weak in fault prediction capacity, incomplete in cooperative control and the like are solved, and the safety and reliability of power grid operation are remarkably improved.

The invention relates to the technical field of industrial Internet of Things, in particular to a predictive maintenance method for intelligent factory Internet of Things equipment, which comprises the following steps of: acquiring equipment operation parameters, environment monitoring data and historical maintenance records, constructing a multi-dimensional feature data set, extracting equipment degradation features by adopting a topological graph attention mechanism and a Bayesian network, and establishing a multi-dimensional feature data set; the method realizes equipment health state modeling and fault probability prediction, combines a dynamic adjacency matrix and a multi-objective optimization algorithm, comprehensively optimizes maintenance cost, equipment fault risk and associated equipment influence, dynamically generates an optimal maintenance plan, carries out constraint optimization based on a mixed integer programming method, automatically generates a maintenance instruction sequence, and achieves the optimal maintenance of the equipment. Tasks are issued through the computerized maintenance management system, the PLC control system and the industrial Internet of Things gateway, and the execution state is monitored and maintained in real time. The intelligent level of equipment maintenance is effectively improved, non-planned shutdown is reduced, and the equipment reliability and the production efficiency are improved.

The invention relates to the technical field of large language models, and discloses a voice data interaction feedback control processing method based on a large language model. The method comprises the following steps: acquiring an industrial voice instruction through an AMR main controller, and obtaining a standardized vector through industrial lexicon matching and intention classification; inputting an industrial large language model for reasoning processing, and generating an AMR execution scheme; carrying out distributed coordination and task allocation on the AMR cluster to form a control instruction sequence; and the motion controller executes monitoring, processes exceptions and outputs a feedback strategy. Accurate classification and semantic understanding of complex industrial instructions are achieved, the processing capacity of professional knowledge in the industrial field is improved, and meanwhile high-precision real-time state monitoring is achieved.

The invention relates to the technical field of computer communication, provides a distributed accounting asynchronous data processing method and system based on an MEMO state machine, and is used for improving the intelligent adaptability of a distributed accounting network to a dynamic service scene. The method comprises the following steps: acquiring a transaction request set in a distributed accounting network, extracting an asynchronous processing trigger event of the transaction request set, analyzing the asynchronous processing trigger event, generating a state transition instruction sequence containing M state transition paths associated with K transaction data blocks, a parallel prediction module based on an MEMO state machine performs asynchronous processing on the M state transition paths to generate N prediction state vectors and corresponding conflict detection marks; performing feature fusion on the N prediction state vectors according to the conflict detection mark to generate a global state updating matrix; and writing the global state updating matrix into a consensus node set of the distributed accounting network, and generating an asynchronous processing state log matched with the transaction request set.

The invention relates to the technical field of hydropower station operation and maintenance management, in particular to an operation and maintenance management method and system for a hydropower station, and aims to improve operation and maintenance efficiency and equipment reliability. Then dynamic feature extraction operation is executed, a state feature set containing equipment operation stability, environment coupling and abnormal fluctuation correlation features is generated, then abnormal state matching processing is conducted on the state feature set through a preset abnormal detection strategy network, the abnormal level and influence range parameters are determined, and the abnormal state is obtained. And finally, generating an equipment operation and maintenance optimization strategy set according to the parameters, including an equipment control instruction sequence and a maintenance resource scheduling scheme, and feeding back to a hydropower station control center to trigger operation and maintenance operation, thereby realizing intelligentization and precision of hydropower station operation and maintenance management, and ensuring safe and stable operation of the hydropower station.

The invention provides a semantic understanding method based on natural language processing and a science and technology operation platform system.The method comprises the steps that firstly, a target interaction data set containing a text instruction sequence, collaboration document content and resource scheduling request records in a science and technology management scene is obtained, and then cross-modal semantic alignment processing is conducted on the target interaction data set; and generating a structured semantic analysis result containing an intention decision tree and the like. Then, based on a dynamic process arrangement rule, the result is mapped to a predefined process node topological space, an initial process execution path set containing a node execution sequence chain and the like is obtained, conflict resolution is conducted on the set according to real-time system resource state data, and an optimized executable process path containing resource allocation priority and the like is generated; and finally, the optimized executable flow path is input into a science and technology management central system, automatic deployment and execution state tracking of a cross-department cooperation flow are realized, and the semantic understanding and flow processing capability of a science and technology management scene is effectively improved.

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 relates to the technical field of gate control, and discloses a gate control method and system based on dynamic cooperative scheduling and a storage medium. The method comprises the following steps: carrying out fatigue quantification on gate torque change and adjustment times through a strain sensor to obtain a comprehensive health index; establishing an adjustment frequency and equipment degradation correlation model according to the health index to obtain degradation prediction data; coupling and fusing the water level flow data and the degradation prediction data to obtain a water regimen-equipment coupling prediction result; a coupling prediction result is optimized through an adaptive weight algorithm, and a differentiated scheduling strategy of the healthy gate and the aged gate is obtained; and performing corresponding control processing on the healthy gate and the aging gate to generate a cooperative control instruction sequence. The technical problem that in an existing gate control technology, water regimen changes and equipment state evolution are independently processed, and differentiated collaborative scheduling based on equipment health constraints cannot be achieved is solved.

The invention relates to the technical field of information management, in particular to an information scheduling scenarized service agent system. The system comprises an information scheduling acquisition and analysis module, a scene classification and service matching module, a service scheme instruction generation module and a service intelligent execution module, user behavior data, environment perception data and business system data corresponding to information scheduling can be obtained, and an original information set is generated; the original information set is preprocessed, an information scheduling scene classifier is constructed, the service matching degree is calculated in combination with the real-time service resource state, and a candidate service scheme set is generated; performing optimization sorting on the candidate service scheme set to generate an optimal service scheme, and disassembling the optimal service scheme into an executable service instruction sequence; receiving a service instruction sequence and generating a service evaluation report; and if the display does not reach the preset service quality threshold value, triggering a dynamic adjustment mechanism to realize self-iteration upgrading of the information scheduling scenarized service. According to the invention, intelligent scheduling and service of information in a complex scene can be realized.

The invention relates to the technical field of intelligent manufacturing, and discloses an extreme manufacturing process technological parameter optimization method and system fused with machine learning. The method comprises the following steps: acquiring multi-source data from a manufacturing equipment sensor, and fusing to generate a material state vector; inputting a pre-training model to obtain a material coefficient transition trend; judging whether the trend fluctuation amplitude exceeds a preset threshold value or not, and if yes, marking key nodes and extracting feature parameters; for the key nodes, according to the characteristic parameters and the real-time data of the key nodes, a control algorithm is adopted to calculate the parameter adjustment amount; optimizing the control parameters based on the parameter adjustment amount, generating a control instruction sequence and transmitting the control instruction sequence to an actuator; and obtaining adjusted feedback data, comparing the adjusted feedback data with the transition trend, and if the deviation exceeds an allowable range, updating the pre-training model. Through deep fusion of predictive monitoring and intelligent control, accurate optimization and adaptive control of process parameters are realized, the stability of the extreme manufacturing process and the product quality are improved, and the energy consumption and the defect rate are reduced.

The invention relates to an automatic modeling agent system based on an MCP protocol and a large model, and the system comprises a multi-modal input interface module which is used for receiving, analyzing and standardizing modeling instructions or intention descriptions inputted by a user through different media to form structured feature representations; the semantic understanding central module is used for performing semantic analysis on the structured feature representation by using a pre-trained large language model and outputting a user modeling intention; the MCP instruction compiler module is used for converting a user modeling intention into an MCP instruction sequence following an MCP protocol; and the modeling execution engine module is used for analyzing and automatically executing the MCP instruction sequence to complete modeling of the target three-dimensional model. According to the method, three-dimensional modeling can be completed without knowing professional modeling terms and concepts by a user, and the modeling automation and intelligence level is greatly improved.

The invention discloses a control method and equipment for an intelligent robot with a body and a storage medium, and belongs to the technical field of robots. The method comprises the steps of receiving a task instruction and environment perception data, performing fusion processing on the task instruction and the environment perception data, generating task semantic information associated with the task instruction, analyzing the task semantic information through an implicit planner, generating an implicit action mark, and based on the implicit action mark, generating an implicit action. And decomposing a to-be-executed task corresponding to the task instruction into a plurality of task sub-targets layer by layer, generating an action instruction sequence based on the task sub-targets, and executing a control action corresponding to the action instruction sequence. According to the method, based on the constraint of the implicit action mark and the action instruction sequence, the robot with the body can flexibly respond to environment parameter changes such as object position deviation or new task requirements in the task execution process, the coupling degree of the action instruction of the robot with the body and the predefined scene is reduced, and high stability of task execution is ensured.

The invention relates to the technical field of operation and maintenance control, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an intelligent equipment configuration changing method and device, equipment and a medium. Generating a preliminary equipment configuration instruction sequence based on the change intention information; rechecking the configuration instruction through the intelligent checking component, and generating a rechecked configuration instruction sequence; sending the rechecked configuration instruction to the target equipment and executing the rechecked configuration instruction; and verifying whether the equipment state meets the expectation, and if not, executing a rollback operation. Configuration accuracy is ensured through knowledge representation, configuration errors are automatically detected and corrected through intelligent verification, configuration efficiency and safety are improved through automatic issuing and state verification, abnormity is quickly recovered through automatic rollback, and reliability and stability of configuration change are improved.

The invention discloses a spacecraft intelligent design method and system capable of autonomously learning, and belongs to the technical field of spacecraft design and simulation. The problems that in spacecraft design, natural language requirements are fuzzy and difficult to process, instruction generation is prone to ambiguity, linkage between simulation and design is not smooth, a design scheme lacks an effective iterative optimization mechanism, and traditional design is low in efficiency are solved. The method comprises the following steps: firstly, receiving a natural language demand, extracting key parameters to generate prompt words, inputting the prompt words into Design.AI, and generating a simulation instruction sequence; simulation is started through MCP Server conversion, if the result does not reach the standard, feedback correction is carried out, and iterative optimization is carried out. And repeating the steps until a design target is met, and generating a design report containing multiple items of content. Through natural language processing, real-time simulation, iterative optimization, knowledge accumulation and other technologies, the spacecraft design efficiency is greatly improved, the accuracy is enhanced, the threshold is lowered, and the flexibility is improved.

The invention relates to the technical field of industrial automation control, and discloses a change gear assembly processing technological parameter intelligent optimization system, which comprises a multi-dimensional data synchronous acquisition unit, an angular domain static map construction unit, a phase lead constraint control unit and an angle trigger execution unit, according to the method, a static angle-load topological graph with a main shaft angle as an index is established, a phase lead angle is calculated by combining inherent physical response lag time of a servo system, and a parameter adjustment instruction sequence is directly injected into a servo current loop at a preset angle window before physical load impact occurs; according to the feed-forward control mechanism based on angular domain reconstruction and phase lead constraint, the problem of physical lag of traditional time domain feedback control in response to high-frequency intermittent cutting is avoided, and zero time difference of periodic load impact and even pre-judgment torque compensation are achieved.

The embodiment of the invention provides an optimization method and system for a manipulator control system, and the method comprises the steps: firstly obtaining an operation data set containing a control instruction sequence and corresponding motion state data in a historical operation period of a manipulator, carrying out the feature extraction of the operation data set, and obtaining an associated motion feature set; calling a pre-trained control strategy optimization model to analyze and generate strategy adjustment parameters, adaptively updating the control instruction sequence of the current operation task according to the strategy adjustment parameters to obtain a target control instruction sequence, and finally inputting the target control instruction sequence into a control system to execute operation. And real-time motion state data is collected for subsequent iterative optimization, so that the performance of the manipulator control system can be continuously improved, and the operation precision and efficiency are improved.

The invention provides a hydraulic engineering information management method and system based on big data. The method comprises the following steps: firstly, uploading flood flow velocity dynamic data, terrain elevation data and spectral feature data to a cloud server, then carrying out multi-dimensional superposition on the flood flow velocity dynamic data, associating with a historical inundation path, and generating a superposition association result; constructing a three-dimensional geographic model based on terrain elevation data and spectral feature data, generating a spatial superposition map according to boundary features of a water body and a terrain abrupt change area and a flood routing path, performing position association with a material reserve position, and performing dynamic priority ranking according to a submerging probability gradient, and finally, according to the resource scheduling instruction sequence, correcting the flight path of the unmanned aerial vehicle cluster and the data acquisition area to form a closed-loop feedback mechanism. According to the technical scheme provided by the invention, the accuracy and timeliness of real-time data updating are ensured.

The invention provides a robot control instruction analysis method and system fusing continuous instructions, and relates to the technical field of electric digital data processing, and the method comprises the steps: carrying out the quantitative evaluation of the instruction execution quality and resource consumption according to prediction data, and generating a quality score and a consumption estimation; constructing a multi-objective optimization framework based on the quality score and the consumption estimation, and determining a candidate path set; switching cost and execution time are calculated according to the candidate path set, and optimized path schemes meeting constraint conditions are screened; the instruction execution priority and the resource allocation proportion are adjusted in combination with the real-time environment data, and a scheduling strategy is generated. According to the method, environment changes and task quality are monitored in real time, instruction priorities and resource allocation are dynamically adjusted, when execution efficiency is lower than a threshold value, online updating of a prediction model is triggered, model parameters are finely adjusted to adapt to new conditions, and through continuous optimization and monitoring, the execution efficiency and task quality of an instruction sequence can be improved, and the execution efficiency is improved. And intelligent instruction scheduling and resource allocation are realized.

The invention relates to the technical field of automatic control, in particular to an intelligent equipment management method and system, and the method comprises the following steps: obtaining a control cabinet log, extracting an equipment temperature load pressure sequence, comparing a set offset to generate a deviation label, collecting an operation time length and load change mapping early warning table, and generating an abnormal record; and extracting task configuration and equipment state re-marking alternating points to generate load configuration, resetting an instruction sequence to generate a scheduling sequence, extracting an instruction time-consuming marking interference section, and switching a standby link to generate an evasion list. According to the invention, by collecting the temperature, the load rate and the pressure sequence in the operation cycle of the equipment, the dynamic state identification capability is improved, the scheduling path is optimized, the resource contention is relieved, the delay path is marked in combination with the original response data, and the standby link is switched to avoid interference, so that the flexibility and stability of linkage scheduling are improved, and the task execution beat accuracy is enhanced; the instruction blocking and interruption risks are effectively reduced, and the operation toughness and response efficiency of a production line are improved.

The invention provides an AI-based assembly state intelligent monitoring method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting a multi-angle real-time assembly process image of a target product, constructing an assembly digital twin model, inputting the image into a hybrid neural network in combination with an assembly knowledge graph, analyzing the assembly node coordinate and torque data deviation, and obtaining an assembly state of the target product. And obtaining an optimal assembly path and torque control parameters, and finally generating an assembly quality evaluation matrix. Through fault feature extraction and mode recognition, potential faults are predicted, and a dynamic risk coefficient is calculated. And when the fault occurrence probability exceeds a preset threshold value, the position and torque compensation parameters are calculated, an execution instruction sequence is generated to control the assembly execution mechanism to perform adjustment, closed-loop optimization control in the assembly process is achieved, the assembly precision and efficiency are improved, and the fault rate is reduced.

The invention relates to a liquid silica gel injection molding process optimization method and system in the field of intelligent manufacturing, and the method comprises the steps: obtaining mold structure data adaptive to a shrinkage rate, combining geometric characteristics of a complex structure product, adopting a topological optimization algorithm to redesign a parting surface and a demolding path, and determining a lossless demolding mold parting scheme; whether the product damage risk is lower than a preset threshold value or not is judged by simulating stress distribution of the demolding path, and if the product damage risk is lower than the threshold value, final mold manufacturing parameters are generated, and an optimized production process configuration file is obtained; extracting production rhythm data from the optimized production process configuration file, dynamically adjusting the operation cycle of the injection molding machine in combination with the demolding efficiency monitored in real time, and determining equipment parameter configuration for efficient mass production; and generating a control instruction sequence according to the equipment parameter configuration, and transmitting the control instruction sequence to the injection molding equipment and the mold heating system through the industrial Internet of Things to obtain operation state data of the automatic production line.

The invention discloses an aircraft attitude control method and device based on a semiconductor microcomputer system, and the method comprises the steps: collecting the multi-source sensor data of an aircraft, calculating an attitude error vector, and constructing a three-dimensional error space coordinate system; taking the three-dimensional error space coordinate system as a reference, adjusting the model in real time, and solving the optimal attitude adjustment amount in a prediction window period; the optimal attitude adjustment amount is input into an execution mechanism distribution module, and a thrust adjustment instruction sequence and a control surface deflection angle instruction queue of each vector propeller are generated through calculation in combination with a propeller response matrix and a control surface efficiency coefficient; dynamically correcting and predicting weight parameters of the controller through closed-loop verification; and updating the multi-objective optimization model based on the corrected parameters, adjusting the instruction distribution proportion through a dynamic reconfiguration interface, and writing the updated parameters into the attitude database to form a complete closed-loop period. According to the invention, high-precision real-time control of the attitude of the aircraft is realized, and the flight stability and the energy efficiency ratio in a complex environment are improved.

The invention discloses a high-rise building operation robot, and belongs to the technical field of engineering construction, the high-rise building operation robot comprises a base, the base is provided with a mechanical arm, a driving mechanism for driving the mechanical arm to move horizontally and a lifting mechanism for vertically moving along a wall surface, the end part of the mechanical arm is provided with a sand blasting gun, two sides of the sand blasting gun are provided with distance measuring sensors, and the base is provided with a wind direction and wind speed sensor; the modeling module is used for fusing sensor data to generate an environment model containing three-dimensional geometric and semantic information and a dynamic wind field distribution model; the cooperation module generates a cooperation control instruction sequence for coordinating each component based on prediction optimization control; the adjusting module corrects parameters of the sand blasting gun in real time through autonomous learning; the execution module generates a driving instruction in combination with feedforward and compliance control; and the decision module monitors and outputs an operation adjustment instruction or a re-planning instruction. The device can sense the condition of the outer wall in real time and dynamically adjust the operation strategy, the adaptability to the complex wall surface is improved, meanwhile, wind disturbance is effectively resisted, and the sand blasting quality is guaranteed.

The invention relates to a network topology dynamic optimization method and equipment for large-scale power supply and distribution equipment networking. The method comprises the following steps: step S101, real-time data acquisition and state sensing; step S102, carrying out network topology modeling and performance evaluation; step S103, dynamic risk assessment and optimization target generation; s104, carrying out topological optimization decision making based on a feasibility maintenance type genetic algorithm; step S105, carrying out optimal strategy verification and seamless switching; selecting an optimal network topology reconstruction scheme from the Pareto optimal solution set according to a preset decision strategy; and after the reconstruction scheme is verified on a control level, generating an equipment cascade and open circuit control instruction sequence, and guiding related nodes to complete undisturbed switching of the network topology in a preset time window through a distributed cooperative control mechanism. According to the method, generation of invalid solutions can be avoided, and the convergence efficiency of a large-scale network topology optimization algorithm is remarkably improved.

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 provides a resource unified management method and system based on heterogeneous computing power, and relates to the technical field of resource allocation of heterogeneous computing power. According to the method, operation instructions of different computing units are converted into bidirectional irreversible instruction sequences by constructing a heterogeneous presentation layer, and physical resources are abstracted to generate a virtual computing power unit. A multi-dimensional efficiency baseline is constructed based on parameters such as real-time load and network jitter tolerance, and a time-varying efficiency curve is dynamically generated in combination with a hardware aging rate. A user task is decomposed into sub-task chains, intelligent resource binding is realized by matching hardware sensitivity characteristics with an efficiency curve, and conflicts are solved by adopting reinforcement learning. And monitoring efficiency offset in real time during execution, performing reverse backtracking during abnormality, and generating a recombination strategy based on the alternative resource set. According to the system, efficient management and dynamic optimization of heterogeneous computing power resources are realized, and the computing efficiency and reliability are remarkably improved.

The invention discloses an intelligent substation secondary equipment maintenance control method and system, and relates to the technical field of secondary equipment maintenance control, and the method comprises the following steps: obtaining first data of a substation to construct a static matrix, and generating a dynamic topological graph; determining the security level of the controlled equipment according to the dynamic topological graph, and establishing a priority table of mapping of the equipment type and the security level; detecting concurrent instruction conflicts through a priority table, identifying an N-order virtual loop based on a dynamic topological graph, generating a minimum isolation set, and outputting a virtual terminal isolation instruction sequence with timing constraint according to a signal level; and when the state of the maintenance pressing plate or the GOOSE link is changed, the affected equipment is positioned based on the static matrix, and incremental updating of the dynamic topological graph is controlled according to the instruction sequence. According to the method, the path optimization strategy based on the cross-interval coefficient is matched with the time window constraint, so that the maintenance efficiency is guaranteed, potential safety hazards caused by disordered operation time sequences are avoided, and the maintenance process is standardized and intelligent.

The invention relates to the technical field of data processing, and provides an automatic control system and method for a hollow plate extrusion production line, and the method comprises the steps: collecting a real-time temperature data sequence, a speed change data sequence and a pressure response data sequence in a hollow plate extrusion process, and carrying out the multi-dimensional feature extraction and response collaborative analysis; obtaining a traction speed correction instruction sequence and a screw rotation speed adjustment instruction sequence; and performing linkage control on the hollow plate extrusion production line according to the traction speed correction instruction sequence and the screw rotation speed adjustment instruction sequence. Through multi-dimensional feature extraction and response collaborative analysis, accurate monitoring and adjustment of multiple key parameters such as temperature, speed and pressure are achieved, the production efficiency and the stability of product quality are improved, a hollow plate extrusion production line can operate efficiently and stably in a complex and changeable production environment, and the production efficiency is improved. The problems that an existing control method is low in reaction speed and insufficient in adjusting precision in the face of complex production working conditions are solved.

The invention discloses a Web business process intelligent automatic execution system based on deep learning, and relates to the technical field of Web automation, and the system comprises a data acquisition and semantic analysis module which is used for acquiring business process description and Web page multi-modal data and generating structured business process representation; the deep learning feature extraction module is used for performing deep learning feature extraction on the multi-modal data to obtain corresponding structural feature vectors, visual feature vectors and text feature vectors; the cross-modal association fusion module is used for generating unified page feature representation; the page element intelligent identification module is used for identifying and positioning the target element based on the unified feature representation; the instruction generation module is used for generating an operation instruction sequence; and the execution control module is used for executing the instruction and dynamically adjusting the strategy. According to the method, a complete automatic link from business description to technology execution is established, conditional branch processing and complex dependency relationship management are supported, and the workload of manual programming and maintenance is reduced.