670 results about "Intelligent machine" patented technology

The invention discloses an artificial intelligence machine vision image acquisition system, and the system comprises a multi-mode perception layer which integrates a self-adaptive optical module, inhibits metal reflection, captures a visible light to short wave infrared image, and captures a motion edge; the dynamic adaptive layer adopts an illumination compensation and motion compensation module to dynamically adjust camera parameters and micro displacement compensation, feeds back an illumination trend, outputs a motion vector to the cognitive layer, generates a confrontation sample through a GAN, simulates virtual defects in combination with a physical engine, and expands training data; the cognitive reasoning layer is used for deploying a dynamic routing network, distributing computing resources according to image complexity and optimizing feature extraction efficiency; reducing data deviation through anti-fact analysis, and generating a thermodynamic diagram to explain a detection basis; and the collaborative decision-making layer is used for rapidly screening samples by edge nodes, training a global model by cloud aggregated data, automatically triggering manual rechecking when the confidence coefficient of the model is insufficient, synchronously optimizing a training set and a causal reasoning module by a rechecking result, and improving the labeling efficiency through AR assistance.

The invention discloses an operation and maintenance manipulator intelligent control method and system based on visual identification, and relates to the technical field of intelligent manipulator control, and the method comprises the steps: collecting RGB image data and depth image data of an operation and maintenance operation area, and obtaining a standardized image matrix and a mapping relation matrix; inputting the standardized image matrix into an improved ResNet residual network model, generating a comprehensive feature descriptor, and calculating a spatial position coordinate and an attitude angle of the target equipment based on the mapping relation matrix; based on the current joint angle state of the manipulator, an improved Jacobian matrix inverse kinematics algorithm is used for solving a target angle sequence of each joint, a preset operation mode library is matched based on the comprehensive feature descriptor, and a grabbing force parameter and a motion speed parameter are determined; and converting the target angle sequence into a control instruction, and sending the control instruction to each joint driver of the manipulator to drive the manipulator to complete action planning. According to the invention, full-process automation from environment perception to task execution is realized.

The invention relates to the technical field of intelligent mechanical arm control, and discloses a mechanical arm track optimization method and system based on deep learning and a fuzzy algorithm, and the method comprises the steps: building a kinematic model of a mechanical arm, determining the working space of the mechanical arm, carrying out the high-density random sampling, and generating a three-dimensional point cloud picture of a reachable region at the tail end of the mechanical arm; constructing a path planning model, designing a state space and an action space, and constructing a reward function; time-impact double-target optimization is carried out on the tail end path point sequence, a smooth joint trajectory is constructed, and balance between the shortest trajectory execution time and the minimum joint impact is achieved on the premise that speed and acceleration constraints are met; and tracking control is carried out on the trajectory, external disturbance and unmodeled dynamics are estimated and compensated in real time, a parameter adaptive law is designed, and the trajectory tracking precision of the system in a complex environment is improved. The autonomy, the accuracy and the anti-interference capability of the hot-line work mechanical arm in a complex environment are improved.

A symbolic neuroadaptive control system is disclosed for real-time arbitration, consent, and ethical modulation of artificial intelligence agents operating in wearable computing environments. The system integrates multimodal biometric telemetry—including high-resolution EEG signals—with a symbolic kernel that performs logic-driven arbitration over cognitive, emotional, and ethical states. Using Coq-verified invariants and zero-knowledge biometric consent tokens, the system constructs a deterministic symbolic execution graph, gating AI outputs based on internal user states such as trauma, stress, or intentionality. Unlike conventional black-box BCI models, the invention routes EEG-inferred affective-symbolic tokens through a formal ethics layer that enforces real-time interrupt control, utility bounding, and trust verification. The kernel enables AGI systems to defer or modify behavior based on user-state alignment, granting sovereign agency over all downstream actions. This neuro-symbolic architecture redefines the interface between human cognition and intelligent machines, enabling emotionally conscious, morally verifiable, and symbolically transparent AI governance in dynamic, high-stakes contexts.The present invention relates to artificial intelligence and neurotechnology, specifically to a real-time, neuro-symbolic operating system kernel that converts electroencephalography (EEG) signals into structured symbolic data for use in emotional cognition, ethical prioritization, autonomous agent dispatch, and real-time telecommunications routing. The invention bridges brain-computer interface (BCI) inputs with symbolic AI architectures to enable ethically aligned machine response during cognitively or emotionally intense events.

The invention relates to the technical field of mechanical arms, in particular to a man-machine interaction system of an intelligent mechanical arm with a body. The system decomposes a long time sequence task into sub-tasks through a large language model, and introduces a task acquisition module, a task queue management module, a task re-planning module and a control execution module, uses visual detection to identify gestures and environment events to trigger temporary tasks, and maintains interruptible marks and safety anchor points based on priorities and interruption risks. And during interruption, the mechanical arm with the timestamp and the environment state are collected, during recovery, the states are compared, local re-planning is carried out, transition sub-tasks are automatically generated for failure sub-tasks, and the mechanical arm is controlled to execute after constraint verification. According to the system, on the premise that the structure consistency, the space consistency and the time consistency are guaranteed, safe interruption and efficient recovery in a long-time-sequence task can be achieved, and the autonomy, the real-time performance and the operation safety of the mechanical arm in a man-machine cooperation scene are remarkably improved.

The invention relates to the technical field of intelligent airport logistics, in particular to an airport luggage flow group intelligent AI-driven digital twinning low-carbon scheduling system, which comprises a physical sensing layer, a digital twinning engine layer, a group intelligent decision-making layer, a block chain evidence storage layer and a dynamic scheduling execution layer, and forms a'sensing-modeling-decision-evidence storage-execution 'closed loop; the physical sensing layer collects equipment and luggage state data; the digital twinborn engine layer constructs a total-factor twinborn body to realize synchronous mapping and carbon accounting of a physical system; the group intelligent decision-making layer adopts an improved carbon sensitive ant colony algorithm to generate a multi-objective optimization strategy of total energy consumption, residence time and load balance; the block chain evidence storage layer ensures credibility and traceability of the carbon data; and the dynamic scheduling execution layer converts the decision into a control signal and corrects simulation and actual deviation. According to the method, low-carbon, high-efficiency and reliable luggage scheduling is realized, and the method is suitable for green operation of a smart airport.

The invention discloses an airport flight quantity and throughput prediction method and system, and relates to the technical field of intelligent airport management. The method comprises the following steps: firstly, determining a future target time period to be predicted, and obtaining related multi-source data according to the future target time period; preprocessing the data and extracting time sequence and non-time sequence features; processing time sequence features by using the trained long-short-term memory neural network model, processing non-time sequence features by using the extreme gradient boosting tree model, and respectively obtaining prediction results; and integrating the outputs of the two types of models through a weighted fusion strategy to obtain an accurate prediction value of the flight sortie and the single-machine passenger capacity, and further calculating to obtain a passenger throughput prediction result. According to the method and the system provided by the invention, through the architecture of multi-source data fusion and hybrid intelligent model cooperation, the prediction precision and reliability are effectively improved, and scientific decision support is provided for airport operation management.

Embodiments provide a user device, UE, of a wireless communication network, the wireless communication network using one or more Artificial Intelligence / Machine Learning, AI / ML, models for one or more use cases, wherein the UE is configured or preconfigured with a plurality of AI / ML models for performing one or more certain operations, and wherein, dependent on one or more criteria, for performing the one or more certain operations, the UE is toswitch from a first AI / ML model to a second AI / ML model, ordeactivate one or more of the plurality of AI / ML models, orswitch from a current operation mode to a new operation mode.

The invention relates to the technical field of intelligent robots, and discloses an automobile cabin intelligent robot arm device which comprises a multi-axis robot arm, a controller, an in-automobile space sensing device and an intelligent interaction device, and the multi-axis robot arm, the controller, the in-automobile space sensing device and the intelligent interaction device are all installed in an automobile cabin. The robot arm comprises a base rotating motor, an arm motor, a palm motor, a capacitance sensor, a pressure sensor, a USB / Type-C interface, a wireless charging interface, a magnetic attraction interface and the like. The in-vehicle space sensing device comprises a camera, a millimeter wave radar and the like. According to the intelligent robot arm device of the automobile cabin, the intelligent robot arm is controlled through voice or keys and the like, and the robot arm replaces personnel to complete object taking operation at a long distance or a view blind area position; distraction caused by the fact that a driver takes objects towards the back row can be avoided, and driving safety is improved. Meanwhile, the intelligent robot arm is controlled through voice or keys and the like, and the robot arm replaces personnel to complete service operations such as article storage, article fixing, games and caring; and the intelligent experience of the cabin is improved.

The invention discloses a machine tool running state collection and evaluation method and system, and relates to the field of intelligent machine tools, and the method comprises the steps: collecting machine tool multi-dimensional data through a multi-channel collection device; performing time alignment on different sampling frequency signals by adopting a dynamic synchronization algorithm of clock drift compensation; executing noise suppression, feature extraction and compressed encoding at the edge computing node to generate local state abstract data; sending the abstract data to a central server through a distributed message queue, and realizing multi-application concurrent access based on load dynamic scheduling; and the central server performs fusion analysis on the abstract data by using a multi-modal fusion deep network model, and outputs a health degree index and an overall operation score of the key parts of the machine tool, thereby realizing intelligent evaluation and health prediction of the operation state of the machine tool. The method realizes global perception and intelligent evaluation of the machine tool operation state, and can be widely applied to operation monitoring and health management of high-end numerical control machine tools, complex machining centers and unmanned production lines.

The invention relates to the technical field of robot perception and data generation technologies, in particular to a first-view-angle drilling method and device based on memory enhancement and a storage medium, and the method comprises the steps: extracting a plurality of target frames from a historical video stream stored in a space intelligent machine, obtaining a plurality of memory elements based on the plurality of target frames, performing three-dimensional reconstruction on each target frame, constructing a target world model, planning a first visual angle track of the target robot in the target world model, performing imaging simulation on the target robot, performing real-time rendering on a first visual angle video of the target robot, and performing real-time rendering on a second visual angle video of the target robot based on the same time axis and action script as the first visual angle video. A third-person video corresponding to the space intelligent machine is generated, and a drilling result is output; according to the method, the long-term memory data of the space intelligent machine is ingeniously used, high-quality drilling data can be quickly generated, the reliability of the first-view video is improved, and a reliable basis is provided for training and testing of a robot algorithm.

The invention provides a robot skill learning and sharing method and system, and relates to the field of artificial intelligence, and the method comprises the steps: uploading first person view data collected when a robot executes a whole initial task to a core control processor, third-person view data collected by the space intelligent machine when the robot executes the whole initial task are uploaded to the core control processor, complete task data of the initial task are obtained and stored in the core control processor, and the complete task data of the initial task are used for robot skill learning and sharing. Repeated operation is reduced, and task completing efficiency of the robot is improved.

A method for obtaining a domain-informed machine learning / artificial intelligence, ML / AI, model for drive analytics includes obtaining first data indicative of a set of data points, wherein each data point is associated with a behavior of a drive apparatus and / or drive system. The method further comprises obtaining second data indicative of domain knowledge comprising physics knowledge associated with a behavior of the drive apparatus and / or drive system and / or with an environment of the drive apparatus and / or drive system. The method further comprises training a machine learning / artificial intelligence, ML / AI, model by jointly utilizing the first data and the second data to obtain the domain-informed ML / AI model for drive analytics.

The invention provides an intelligent cabinet heat dissipation control method and system, and belongs to the technical field of cabinet heat dissipation control. According to the method, data items all have acquisition time and cabinet area identifiers; the method comprises the following steps: dividing a cabinet hot area by using fuzzy clustering based on a historical sample, dynamically modeling by combining an adaptive PID control algorithm, optimizing control parameters through a particle swarm optimization algorithm, adjusting a hot area control model weight coefficient according to real-time feedback, generating a hot area-equipment association map, and constructing an adaptive heat dissipation control network; predicting a temperature trend through a decision tree model by using a correlation map and real-time operation characteristics generated by the network, and generating a fan rotating speed adjusting strategy, an air conditioner power distribution scheme and a multi-stage heat dissipation control strategy according to the temperature trend; inputting a heat dissipation control strategy and an execution result as feedback into the self-adaptive heat dissipation control network, and dynamically optimizing a correlation map and control parameter modeling; and multi-cycle feedback closed-loop optimization control is realized through multi-time feedback and parameter adjustment.

A User Equipment (UE) for enabling Artificial Intelligence (AI) / Machine Learning (ML) functionalities is provided. The UE receives, from a Base Station (BS), a request for an AI / ML-based UE capability report; transmits, to the BS, the AI / ML-based capability report that indicates at least one AI / ML functionality supported by the UE; receives, from the BS, a list of configurations corresponding to the at least one AI / ML functionality supported by the UE; selects, based on the list of configurations, at least one available functionality from the at least one AI / ML functionality supported by the UE; transmits, to the BS, information of the at least one available functionality; receives, from the BS, an activation command for activating at least one designated functionality of the at least one available functionality; and activates the at least one designated functionality. In addition, a method and a Base Station (BS) are also provided.

The invention discloses a marine unmanned ship formation obstacle avoidance and cooperation method based on an improved artificial potential field method, and the method comprises the steps: obtaining a control resultant force according to the attraction force, repulsive force, cooperation force and a speed correction term of a ship body; updating the velocity vector of the boat body by using the control resultant force so as to optimize the path; the space position of the boat body is divided into two areas, the minimum value of the minimum distance of the two areas serves as the minimum distance, and the distance between the boat bodies is monitored and adjusted; and dynamically adjusting the path by using the expected path direction of the hull, the path updating step factor and the direction disturbance suppression correction term. According to the embodiment of the invention, multiple intelligent mechanisms such as nonlinear potential field adjustment, inter-boat collaborative modeling and division and conquering real-time calculation are fused, and the safety, stability and adaptability of unmanned boat formation are effectively improved in a template-free formation scene. The method has high adaptive capacity in multi-task, multi-obstacle and autonomous coordination tasks, and path conflict and collision risks can be remarkably reduced.

The invention relates to a multifunctional on-site material evidence investigation and evidence obtaining system based on an intelligent robot dog, and belongs to the technical field of criminal investigation. According to the system, a robot dog is driven by a communication device and a positioner to reach a trace detection site, and collected trace detection image data are transmitted to a main control device; attitude precision adjustment is carried out through a joint encoder arranged on the angle driving framework in combination with the collected trace detection image data; calculating the actual distance between the microscope and the target trace through a trace detection distance model; based on the actual distance, the focusing driving framework is controlled to move in combination with the corresponding initial focusing parameters; and performing real-time definition evaluation on the acquired trace detection image through a definition evaluation framework, recording an optimal focusing position and related trace detection image data after the evaluation is passed, and updating a database. The multi-scene application of trace detection is realized, the problem of blurring of the trace detection image caused by environmental interference or sample movement is solved, and the focusing quality and the shooting success rate of trace detection shooting are effectively improved.

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. There is disclosed a first method for reporting user equipment (UE) artificial intelligence (AI) / machine learning (ML) capability to a network. The first method comprises: transmitting, to the network, an indication of the UE AI / ML capability. There is also disclosed a second method for reporting network AI / ML capability to a UE. The second method comprises: transmitting, to the UE, an indication of the network AI / ML capability.

The invention belongs to the technical field of building terraces, and particularly relates to a paint terrace shrinkage joint repairing device and a repairing method thereof.The repairing method includes the steps that intelligent recognition of crack features is achieved through a multi-modal sensing network, feature fusion is conducted on multi-source data through a convolutional neural network algorithm, structural cracks and surface cracks are automatically distinguished, and a repairing result is obtained. Obtaining a detection map; based on the detection map, comprehensively considering crack parameters, environmental conditions and construction period requirements, and automatically generating an optimal repairing scheme; the self-adaptive mixing system is driven to complete dynamic modulation of the repairing material; high-precision filling operation is executed through the intelligent mechanical arm; a full-periodicity performance evaluation model is constructed through an Internet of Things monitoring terminal, a repairing result is monitored, an intelligent mechanical arm is adopted, a path is re-planned in real time through an RRT algorithm, and Z-shaped layered filling is executed, so that the filling fullness is improved.

The invention provides a multi-UUV security cooperative control method in a complex environment based on memory event triggering, and designs an intelligent mechanism for triggering communication only when a specific event occurs, namely, a controller for uncertain multi-UUV memory events is designed for UUVs working in a communication scene with data tampering and time delay. Therefore, cooperative control is realized. Compared with a traditional time triggering mode, the method has the advantages that the communication burden can be effectively reduced, and the system can still realize consistency and has better universality and flexibility under the conditions of communication delay and communication errors; meanwhile, the problems of parameter uncertainty, data tampering and the like are better solved through uncertainty constraints and energy constraints of attack signals causing communication errors.

The invention relates to the field of artificial intelligence, machine learning and data mining, and discloses a user behavior information intelligent analysis method based on a knowledge graph, and the method comprises the following steps: (1) carrying out the preprocessing of multi-source user behavior data, and generating a materialized behavior sequence; (2) constructing a domain basic knowledge graph; (3) performing incremental expansion on the knowledge graph according to the real-time behavior data to generate a user personalized sub-graph; (4) carrying out dynamic updating and pruning on the personalized subgraph; (5) fusing multi-user sub-graph knowledge through a federal learning framework; and (6) providing an intelligent analysis interface based on the updated knowledge graph. According to the method, real-time semantic modeling of user behaviors is realized through incremental expansion and causal verification, and the analysis precision and timeliness are improved; and in combination with federal fusion and map path reasoning, traceable decision support is provided under privacy protection, and the service credibility is enhanced.

The invention discloses an unmanned aerial vehicle cluster autonomous navigation control system based on multi-mode state perception and collaborative navigation decision, which comprises a multi-mode combination state perception information module, a collaborative decision navigation module and an algorithm evaluation module, and is characterized in that data passes through a graph attention network perception module through the multi-mode combination state perception module; grouping and aggregating the information of the neighbor agent group, the external obstacle group and the target group in the observation state; stacking a plurality of moments to obtain a multi-modal combination state description; and the collaborative decision-making module generates a motion instruction of each unmanned aerial vehicle in an end-to-end manner by extracting space and time sequence characteristics described by a multi-modal combination state, so that collision-free navigation of the unmanned aerial vehicles is ensured. According to the method, neighbor intelligent machine bodies, obstacles and target information are grouped and aggregated by using the image annotation sensing module, and reconstruction, splicing, gray scale and dimension reduction processing are performed on original visual data, so that the accuracy and robustness of environment sensing are remarkably improved.

The invention provides a drilling and blasting method tunnel section contour and blast hole position automatic design method, and relates to the technical field of tunnel engineering, and the method comprises the following steps: S1, determining the type of a tunnel section contour; s2, performing parameterized representation and automatically drawing a contour line based on the tunnel section contour type; s3, based on the drawn tunnel section contour, conducting parameterized arrangement of cut holes, peripheral holes, bottom plate holes and auxiliary holes, and generating coordinates of various blast holes; automatically designing blast hole positions through different types of tunnel section contours; and the arrangement of the blast holes is converted into the generation of coordinate points, so that the requirements of intelligent mechanical equipment for tunnel excavation in the drilling and blasting method on the coordinates of the blast holes are met, and finally, the intelligent and refined blast hole arrangement of a tunnel excavation site in the drilling and blasting method is realized. The problems that in the contour drawing and blast hole arrangement link, manual lofting is often low in efficiency and large in error are solved, and it is avoided that the excavation section deviates from the design, the over-excavation or under-excavation phenomenon occurs, and the construction quality is affected.

Methods and systems are described for employing AI / ML (artificial intelligence / machine learning) techniques to facilitate and further improve the process of slice SLA fulfillment to ensure more optimal outcomes in radio access networks. Certain embodiments can decentralize 5 the highly complex process of network wide decision making for actions to be taken to achieve network-wide slice SLA fulfillment. By pushing the decision-making process to the RAN nodes, the proposed solution enables each RAN node to take simpler actions for node-level or cluster-level slice SLA fulfillment based on the local context information. The outcome of these unitary actions taken by each node can sum into the result of network-wide slice SLA 0 fulfillment.

The invention relates to the technical field of machine room management, in particular to an intelligent machine room management system based on three-dimensional visualization, and the system comprises a data collection module which is used for collecting the state data of all devices and environments in a machine room in real time; the three-dimensional scene construction module is used for constructing a corresponding three-dimensional visual scene model according to the physical layout and equipment information of the machine room; the data analysis module is used for determining a data change type and a data change rate, and determining a risk level and a response strategy of the machine room according to the data anomaly index; the data storage module is used for determining a pre-stored grade and strategy according to the data evaluation value; and the control module is respectively connected with the data analysis module and the data storage module, and is used for determining a storage level corresponding to the change data according to the data change rate of the updated model and the initial model, and determining a change mode of the storage level based on the data activeness index within a preset duration. The operation and maintenance efficiency of machine room management is improved.

The invention discloses an industrial intelligent control system and method, relates to the technical field of industrial automatic control, and mainly aims to solve the problems of low data cross-layer interaction efficiency, system global optimization, poor safety, poor expansion capability, difficulty in function upgrading and difficulty in fusion of AI control and traditional control in the existing industrial intelligent control scheme. The system comprises a basic control layer, an intelligent I / O layer and an intelligent control layer. The basic control layer comprises an extended I / O cabinet, a common controller and a main control network A / B; the intelligent I / O layer comprises an intelligent I / O cabinet and an intelligent I / O switch; the intelligent control layer comprises an intelligent controller, a credibility regulator, a hyper-fusion module and an intelligent middle table, an end-edge-cloud intelligent collaborative architecture is constructed, the computing power of a DCS is enhanced, the development collaborative safety capability of an intelligent algorithm of the system is improved, and safety fusion and smooth transition of AI intelligent control and traditional control are promoted.

The invention relates to a shaped SPCS cavity wall assembly type combined system and a construction method thereof. The shaped SPCS cavity wall assembly type combined system comprises an SPCS cavity wall, assembly type ring ribs, a height-adjustable base, a laminated plate, a supporting angle adjusting piece and an adjustable supporting rod. An SPCS cavity wall and a ground anchor are arranged on the laminated slab; fabricated ring ribs are arranged in the SPCS cavity wall, and supporting angle adjusting pieces are arranged on the upper side face and the lower side face of the SPCS cavity wall correspondingly. The group of adjustable supporting rods are respectively connected with the ground anchor piece and the supporting angle adjusting piece; the SPCS cavity wall and the laminated slab are connected through a height-adjustable base, and the side face is sealed through a sealing plate. And a group of lifting lugs are arranged at the upper end of the SPCS cavity wall. According to the method, BIM modeling is adopted to extract data, intelligent machine tool machining is adopted, an SPCS cavity wall assembly type combined system is formed, the construction procedures are greatly reduced, size control is accurate, the reinforcing mode is reliable, and the forming effect is good; the method not only has a good construction error-tolerant rate, but also guarantees the safety of construction workers, and improves the efficiency of building construction.

The invention relates to the technical field of numerical control machine tool intelligent manufacturing and industrial data collection, in particular to a machining data collection method and system applied to a numerical control machine tool, and the method comprises the following steps: S1, the classification and access scheme design of the numerical control machine tool: dividing the numerical control machine tool into a class I intelligent new machine tool, a class II semi-intelligent machine tool and a class III old dumb equipment according to the digitization capability, a classification access scheme of combination of a general protocol adapter, a serial port-Ethernet converter and a non-intrusive sensor is respectively adopted to realize data access of heterogeneous and old equipment; uniform access of 10 + brand numerical control machine tools is supported, the data intercommunication rate of heterogeneous equipment reaches 100%, the problem of'information isolated island 'is solved, six types of equipment states are recognized, and the OEE calculation precision is improved to + / -2% and is improved by 5 times compared with a traditional method.

The invention provides a modular data center machine room system, and belongs to the technical field of intelligent machine rooms, the modular data center machine room system comprises a plurality of stackable functional unit modules, and each functional unit module comprises a composite load-bearing frame, an integrated standardized mechanical interface and an electrical fast-assembly interface; the computing unit supports plug and play of heterogeneous computing equipment; the storage unit supports RAID hot plug of the mixed medium; the network unit provides a programmable switching matrix; the power supply unit is internally provided with a multi-mode adapter and is compatible with AC / DC input and lithium battery / super capacitor hybrid energy storage; and the central intelligent management platform monitors the real-time load rate and the energy efficiency ratio of each module, coordinates the resources of each module, detects the interface attribute of a new module and configures a network address when judging that the new module needs to be accessed, executes a topology reconstruction instruction, carries out topology reconstruction and outputs a prompt. Compared with the prior art, the crossing from a fixed framework to elastic ecology is realized.

Systems and methods are disclosed for automated generation, validation, and deployment of infrastructure-as-code (IaC) using a multi-agentic artificial intelligence / machine learning (AI / ML) architecture. An extraction agent parses multimodal artifacts (e.g., diagrams and / or configuration data) to derive or generate infrastructure set-up parameters. A coding agent generates IaC units based on the extracted parameters. A validation agent determines the syntactic and semantic compliance of the generated IaC, classifying the IaC as executable, non-executable, or identifying corrective actions. A deployment agent transmits executable IaC to target computing environments and manages automated provisioning of the IaC in the target environments. In cases of validation failure, error indicators are provided to a user (e.g., via an agentic chat bot) for clarification or correction, enabling iterative refinement.