2032 results about "Controllability" patented technology

The invention relates to a cross-domain intelligent reasoning method based on knowledge graph enhancement, and the method achieves the precise reasoning in a complex scene through the construction of a hierarchical knowledge expression framework and a dynamic optimization mechanism. A multi-source heterogeneous data fusion technology is adopted, subject fine-grained knowledge units are generated through multi-modal feature extraction, and a three-dimensional knowledge graph structure comprising a core common concept layer, a subject feature ontology layer and a dynamic semantic mapping layer is established; based on a path exploration algorithm driven by reinforcement learning, cross-domain implicit association is mined while subject independence is reserved, and controllability and interpretability of the reasoning process are achieved in combination with an attention fusion mechanism of a large language model. According to the method, the limitation of traditional unified ontology modeling is broken through, the problems of concept drift and path deviation existing in reasoning in the cross fields of medicine-finance, engineering-law and the like are effectively solved, and the accuracy and knowledge traceability of complex decision tasks are remarkably improved.

The invention discloses a security risk autonomous sensing system and method based on a digital twin industrial control network. The method comprises the following steps: performing four-dimensional multi-modal modeling on an industrial control network, and constructing a twin model integrating entities, rules, services and behaviors; constructing a high-fidelity simulation network by utilizing a containerization technology based on the model; deploying a gateway at an actual network boundary, collecting traffic characteristics in real time and identifying suspicious behaviors; performing protocol restoration and behavior reconstruction on the suspicious traffic to generate a behavior sequence; performing anomaly recognition and attack classification on the behavior sequence through a deep learning model; evaluating a risk level; and matching and verifying a defense strategy according to an evaluation result, and automatically synchronizing the defense strategy to a master control network to realize a detection-response-protection closed loop. According to the invention, autonomous identification of suspicious behaviors in the industrial control network and intelligent decision-making of security policies are realized, and the method has the advantages of high simulation, low invasion, closed-loop controllability and the like.

The invention relates to the technical field of intelligent building data processing, and provides a BIM-based bridge collision detection method, which is used for solving the problem of collision early warning lag caused by space-time dislocation of a static model and real-time monitoring data in bridge construction. A comprehensive point cloud data set with a unified space-time reference is generated through multi-source data dynamic fusion and a quantum optimization algorithm, a self-evolutionary dynamic BIM model is constructed, and digital mapping of millimeter-level precision of a construction scene is achieved. A collision risk prediction model is constructed based on causal reasoning and multi-physics field coupling, a multi-dimensional relationship among construction parameters, environment variables and structural response is associated, and a linkage effect caused by mechanical motion is predicted. According to the method, the time dimension limitation of a traditional static model is broken through, full-process closed-loop control of perception-analysis-decision-verification is formed, the real-time performance of collision early warning and the controllability of construction safety are remarkably improved, and the method is suitable for high-precision construction scenes such as complex bridge rotation and cantilever casting.

The invention relates to the technical field of digital twinning, in particular to a pile foundation construction whole process digital twinning system which comprises a twinning configuration module, a dynamic mapping module, a coupling analysis module and a twinning synchronization module. According to the method, a time axis driving model sequence is constructed based on space-time interpolation, burial depth coordinates, pouring time and segment numbers are dynamically bound, three-dimensional evolution mapping of a pile body model is achieved, axis coordinate deviation is combined with perpendicularity threshold extraction, virtual model coordinates are corrected in real time through point cloud registration, and a pump pressure value and settlement are fused to generate a pile sinking rate index. A bidirectional coupling relation between parameters and stratum response is established in combination with stratum counter-force and injection rate, the thickness of the model is adjusted according to a settlement rate difference value, the similarity of the injection rate and a counter-force curve is matched, a model parameter reverse updating mechanism is formed, and real-time intervention of construction abnormity, dynamic optimization of parameter coupling and model iteration correction are achieved. And the process controllability and the quality tracing precision are improved.

The invention relates to a coal mine safety risk intelligent management and control method, device and equipment and a medium. The method comprises the following steps: constructing a multi-source heterogeneous coal mine safety data system according to received data of a target coal mine park; the multi-source heterogeneous coal mine safety data system comprises static structure data, dynamic environment data, personnel behavior data and management data; constructing a coal mine three-dimensional space model according to the static structure data and the dynamic environment data; risk indexes in the dynamic environment data are extracted based on multi-algorithm fusion for evaluation, and a risk level is obtained; and if the risk level reaches a preset threshold value, triggering a corresponding linkage response mechanism, and forming a visual result in the coal mine three-dimensional space model. By the adoption of the method, closed-loop logic from sensing, evaluation to linkage treatment can be achieved, and the real-time performance, predictability and controllability of coal mine safety management are effectively improved through algorithm support of each stage and fine design of implementation details.

The invention relates to the technical field of vehicle scheduling, in particular to a multi-terminal vehicle scheduling system based on reinforcement learning. The system comprises a heterogeneous data fusion module, a resource allocation module, a hierarchical reinforcement learning module, an optimization feedback module and a man-machine cooperative control module. Data of vehicle operation, operation tasks, environment monitoring and the like are collected and uniformly packaged into a structured data set, an upper-layer manager model generates a global scheduling instruction set based on a PPO algorithm, and a lower-layer worker model outputs a specific vehicle control instruction based on multi-agent reinforcement learning. The system also evaluates and optimizes a historical scheduling execution effect through an NSGA-II algorithm, selects a Pareto optimal solution set, and realizes continuous iteration of a scheduling strategy. The man-machine cooperative control module supports visual display and manual intervention operation, and improves the adaptability and controllability of the system in a complex operation scene.

The invention relates to the technical field of collaborative optimization, in particular to a virtual power plant power generation-utilization-price collaborative optimization system based on an AI large model, and the system comprises a load confidence matching module, a resource stability mapping module, a source-load capacity coupling module, an electricity price interval adjustment module and a comprehensive regulation and control linkage module. According to the method, the confidence interval prediction of the load demand is realized based on the hybrid neural network modeling of the load behavior data and the equipment temperature control characteristic sequence, and the scheduling matching confidence is measured according to the boundary overlapping condition of the prediction interval and the power generation response characteristic; a stability screening mechanism for adjusting resources is constructed in combination with the output fluctuation ratio and the equipment inertia characteristic, the controllability of load adjustment and the real-time performance of source side response are improved, the price adjustment rhythm is corrected through an electricity price response delay factor, dynamic closed-loop linkage between load adjustment and price guidance is achieved, and the load adjustment efficiency is improved. The execution priority is dynamically updated under the condition that multiple response conditions are matched, and the certainty of resource scheduling and the sensitivity of response are improved.

The invention provides a video generation control method and a computer readable storage medium, and is applied to the technical field of computers. According to the method, the structural defect of a split script is solved through a large split model, the consistency of split image features is ensured through image generation based on reference image feature constraint, audio and picture synchronization is realized in combination with emotional speech synthesis and dynamic mouth shape alignment, the video fidelity is improved through a split video cue word guiding multi-subject generation algorithm, and the user experience is improved. Therefore, the technical problems of missing emotion logic, poor consistency of split image features, difficulty in audio synchronization, insufficient controllability of video generation and the like in film and television creation in the existing AIGC technology are effectively solved, and the efficiency and the quality of video content production are improved.

The invention discloses a vehicle-mounted cross-protocol data time sequence cooperative processing method, which comprises the following steps: establishing a unified time reference, and providing an accurate space-time coordinate system for heterogeneous network nodes by electing a global master clock and constructing a clock relation model and a periodic correction mechanism; systematic cognition and management of communication resources are realized, and a verified protocol capability table is generated by automatically identifying protocol types and variants; constructing an automatic mapping system from demand to execution, performing formalized packaging on a target scene by introducing a unified intermediate model, generating a cross-protocol mapping rule based on the model and a protocol capability table, and then forming a global scheduling table in combination with a unified time reference; the cooperative communication of all nodes is finally realized by using the global scheduling table, and the end-to-end delay is constrained within the preset range, so that the problem that the end-to-end time sequence in the vehicle-mounted heterogeneous network is uncontrollable is solved, and the controllability and reliability of the time sequence of the vehicle-mounted network are improved.

The invention relates to the technical field of cloud computing and distributed storage, and discloses a distributed metadata management method and storage system based on cloud computing, and the method comprises the following steps: S1, collecting the access frequency of a plurality of metadata objects in a set time period; s2, performing weighting processing on the access frequency, and constructing access heat distribution under multiple time scales; s3, obtaining the available cache capacity of a plurality of cache nodes, and constructing cache resource weight distribution; s4, assessing the access cost and migration cost between each metadata object and each cache node; and S5, generating a scheduling strategy between the metadata object and the cache node based on a preset optimization target, wherein the optimization target is access cost minimization. According to the method, a heat, resource and cost joint modeling mechanism is constructed, and optimal scheduling and feedback adaptive control are combined, so that accurate decision, dynamic adjustment and migration cost controllability of copy deployment are realized, and the performance and stability of distributed metadata management are effectively improved.

The invention discloses a process parameter optimization control method and system for an automobile injection molding part, and belongs to the technical field of data processing, and the method comprises the steps: collecting sample data to train a feedforward neural network based on the historical processing record of a target injection molding part, and constructing a product quality prediction plug-in; performing parameter fluctuation influence analysis according to the historical processing record, and constructing a fluctuation influence analyzer; determining product demand characteristics of the target injection molded part in combination with the automobile use scene; and by taking the product demand characteristics as expectations, optimizing the process parameters by using the product quality prediction plug-in and the fluctuation influence analyzer, and outputting the optimal process parameters for processing control. The problems of large product quality fluctuation and low production efficiency caused by insufficient injection molding process parameter control precision in the prior art are solved. By combining historical records, product quality prediction plug-ins and fluctuation analysis, precise optimization of process parameters is realized, the product quality is stabilized, and the controllability and efficiency of the production process are improved.

Provided in the present disclosure are an information processing method, an electronic device, and a storage medium, which are applied to the technical field of artificial intelligence. The method comprises: determining information to be processed corresponding to an intelligent agent; and inputting the information to be processed and node sequence information into a large model, wherein the node sequence information is used for indicating a plurality of nodes included in a task flow and edges between the nodes, each node represents an action instruction for the intelligent agent, and each edge represents a transition between the nodes, such that the large model determines, on the basis of the information to be processed, a target node to be executed and controls the intelligent agent to execute the action instruction corresponding to the target node. The present disclosure enables the planning of a complex flow and controllability of an execution process, allowing the intelligent agent to complete tasks more efficiently and accurately. Furthermore, the present disclosure can support the customization of different task flows for different scenarios, thereby meeting usage requirements in the different scenarios and improving user experience.

The invention relates to the technical field of auxiliary decision making, and discloses a graph-enhanced intelligent game and operation planning optimization auxiliary decision making system and method, and the method comprises the steps: inputting multi-source heterogeneous data into a space-time semantic mapping frame, and generating a unified space-time semantic data structure; generating a cognitive-spatio-temporal manifold model through a cognitive spatio-temporal manifold generation algorithm; obtaining user interaction data, and generating an adaptive view expression through an interaction response manifold deformation algorithm; obtaining user interaction behavior data, and generating a cognitive balance view through a cognitive load perception projection algorithm; generating an interactive decision-making scene through an immersive space-time scene rendering technology; according to the method, a complex game and operation planning optimization problem is mapped into a visual space-time scene, so that a decision maker can foresee and control a decision result in a virtual space-time space, and the understandability and controllability of game and operation planning optimization are greatly enhanced.

The invention discloses an improved hybrid A star path planning method for an automatic driving vehicle, and relates to the technical field of automatic driving, laser radar sensing and unmanned vehicle navigation. According to the method, on the basis of inheriting the advantages of high global search efficiency, strong kinematics feasibility and the like of a hybrid A star algorithm, a multi-factor cost function is designed, a trajectory smooth constraint is introduced, and the feasibility and execution quality of a path are improved from two aspects of cost evaluation and path construction. In order to further enhance the smoothness and the dynamic controllability of the local path, the initial path is optimized and adjusted by fusing a time elastic band (TEB) algorithm in the method, so that the continuity and the stability of the path in a complex dynamic environment are improved. The method is suitable for a high-precision path planning task in an automatic driving system, and the comprehensive requirements of a path generation result in the aspects of reliability and environmental adaptability can be well met.

The invention belongs to the field of combination of a large model and a knowledge graph, and particularly relates to a large model and knowledge graph combined reasoning method for false news detection, which breaks through the limitation of a traditional detection method in the aspects of knowledge instantaneity and reasoning controllability through heterogeneous architecture design. Based on multi-dimensional features such as timeliness, propagation mode and information density of news propagation, a double-engine-driven intelligent reasoning system is constructed, on one hand, important entity extraction and semantic understanding are carried out through a large language model, and on the other hand, structured fact verification is provided through a dynamic knowledge graph. And the inherent factual illusion problem of a large language model is effectively solved. Compared with a traditional knowledge iteration mode depending on single model parameter updating, the method supports an incremental updating strategy based on knowledge graph nodes.

The invention discloses a virtual power plant optimization system and method, and the system comprises a virtualization platform module which is used for integrating distributed energy, energy storage equipment, an electric vehicle, a flexible load and other resources, and achieves the virtualization and centralized management of the resources; the dynamic resource aggregation module is used for dynamically matching resource characteristics with power system requirements; the market transaction module is used for optimizing the earnings of the virtual power plant; and the core algorithm module is used for optimizing the load prediction precision. The market transaction module implements two-stage market bidding based on a multi-agent game model, analyzes the uncertainty of quotation of competitors through a multi-scene simulation technology, optimizes a dynamic transaction strategy, and achieves the maximization of spot market benefits and the controllability of risks. Through cooperative work of a virtualization platform module, a dynamic resource aggregation module, a market transaction module, a core algorithm module, a security management module and the like, efficient management of resources and maximization of market benefits are realized.

The invention discloses a cargo full-process tracking method for logistics transportation, and relates to the technical field of logistics transportation tracking, and the method comprises the following steps: collecting cargo state data and transportation route data in real time through a sensor and a camera which are installed on a transportation vehicle and a cargo; multi-type sensors and cameras are deployed on transport vehicles and goods, real-time acquisition of goods state and transport route data is realized, a multi-dimensional prediction model is constructed through a long-short-term memory network and a convolutional neural network, and anomaly identification is performed, so that the real-time prediction of the goods state and the transport route data is realized. According to the invention, accurate detection of goods theft, deviation of a vehicle from a predetermined route, abnormal temperature and humidity, abnormal pressure and the like is realized, automatic alarm and intelligent processing of abnormal conditions are realized through a preset hierarchical response mechanism, the problems of untimely monitoring, poor abnormal identification capability and the like in logistics transportation are effectively solved, and the logistics transportation safety is improved. And the safety, controllability and management efficiency of cargo transportation are obviously improved.

The present invention discloses a method and system for enhancing and generating defects of power equipment based on a stable diffusion model. By combining a stable diffusion model with a semantic guidance mechanism, the present invention overcomes the shortcomings of traditional defect data enhancement methods in terms of sample scarcity, category uncontrollability, and unstable generation quality. Through a standardized automation system and an innovative generation method, this application significantly improves the efficiency and quality of building defect data sets for power equipment, provides strong support for the optimization of defect detection models, and has good versatility and practical value.

The invention discloses a large-section long-footage mechanical blasting mixed excavation method suitable for a subway station, and relates to the technical field of underground rail transit engineering, and the method comprises the following steps: obtaining a tunnel section contour, a geologic structure and blasting parameters, building a three-dimensional blasting wave propagation inversion model, and combining geology and material heterogeneity to obtain a three-dimensional blasting wave propagation inversion model; and identifying a closed space region forming reflection interference, and predicting a space point location where blasting wave energy is abnormally accumulated. Based on the three-dimensional blasting wave propagation inversion model, the abnormal wave energy accumulation area is predicted in combination with geological heterogeneity, the high-risk blasting section is recognized, parameters are reconstructed, vibration real-time monitoring and model feedback correction are assisted, a closed-loop control system is constructed, local wave crest abnormity is effectively restrained, and the safety and controllability of large-section tunnel blasting construction are improved.

The invention relates to the technical field of integrated circuit manufacturing, in particular to a precise electroplating control system and method for an electroplated layer of a double-layer circuit board, and the method comprises the steps: building a multi-physical field coupling simulation model, and simulating the internal current density and ion concentration distribution of a via hole; constructing a mapping relation between the current density and the side wall electroplating thickness based on a simulation result, and predicting the electroplating thickness; through thickness prediction feedback, a time sequence adjustable pulse current control strategy is designed, the pulse amplitude, the duty ratio and the on-off period are dynamically adjusted, and current distribution optimization is guided; the state of the electroplating process is sensed in combination with real-time monitoring and analysis of multi-source parameters of electrode potential, conductivity and fluid velocity; and based on the prediction model and the real-time parameters, adjusting the pulse waveform and the time sequence through closed-loop compensation. According to the method, the uniformity and controllability of the thickness of the electroplated layer on the side wall of the via hole can be remarkably improved.

The invention relates to a road traffic condition controllable injection-based automatic driving image generation method, which comprises the following steps: denoising a noise image under the guidance of preset road traffic condition information by using a trained Unet model so as to automatically generate a continuous automatic driving image; when the Unet model is trained, noise is added to a real driving image, and conditions in the real driving image are used as guidance for denoising. According to the method disclosed by the invention, the synthetic data set with high quality and high controllability can be constructed to enrich the real automatic driving data set, the method is used for learning training of an automatic driving system, and the generalization ability of the automatic driving system is improved.

The invention relates to the technical field of computers, and discloses an integrated information system for clinical test archives and a data processing method thereof, and the method comprises the steps: constructing a standardized data model covering a whole process; multi-source data dynamic structured intake and integrity verification are carried out; performing multi-dimensional semantic association analysis based on a Bayesian network and a correlation coefficient; block chain type version tracing and difference comparison are carried out; performing role-sensitivity-operation-context four-dimensional access control; and task-driven collaborative sharing and auditing log records. The system comprises a unified modeling module, a data intake module, a semantic analysis module, a version tracing module, an access control module and a collaborative auditing module. The problems of data islands, semantic segmentation, poor dynamic adaptability, weak safety protection and the like in the prior art are solved, intelligent management, semantic interconnection, safety controllability and efficient collaboration of the whole life cycle of clinical test files are achieved, and data quality, audit compliance and multi-role collaboration efficiency are remarkably improved.

Techniques are discussed herein for generating and evaluating autonomous vehicle control systems. In examples, a driving scenario is determined and log data, e.g., generated from user-supported vehicles travelling in an environment, may be used to determine instances in which vehicles experience the driving scenario. Based on the log data, an exposure metric, a severity metric, and / or a controllability metric may be determined. A safety goal can be determined from the metric(s) and vehicle control parameters are determined to comport with the safety goal. The vehicle control parameters may be used to control an autonomous vehicle in the environment.

The invention discloses a BIM-based digital intelligent construction management method and system, and belongs to the technical field of construction management. A BIM collaborative management mechanism based on the combination of difference comparison, conflict detection, influence evaluation, approval control, version locking and recheck management is constructed; the problems that in the prior art, due to version control defects, multi-party model modification conflicts are not recognized, merging errors exist, construction errors exist and the like are solved, differences are automatically compared before the models are submitted, conflict areas are recognized, the influence of the conflict areas on the construction progress and space coordination is evaluated, merging can be achieved after approval of an approval system, and therefore the construction progress and space coordination accuracy is improved. According to the BIM collaborative construction method and system, a key area is constructed, a re-checking identifier is set for the key area, and meanwhile, related responsible person and operation information are recorded in the whole process to form a traceable data chain, so that the safety, accuracy and construction risk controllability of BIM collaboration are remarkably improved.

The invention provides a thermal behavior experiment system and method under a narrow rectangular channel reflooding cooling condition. The system comprises a heating water storage tank, a nitrogen tank, a steam tank, a shield pump, a preheating section, a first direct-current power supply, a second direct-current power supply, a narrow rectangular channel test section, a steam-water separator, a surge tank, a flow control valve, a silencer and an electromagnetic valve. The experimental working medium is deionized water, and re-submerging cooling experiments under different system pressures, working medium supercooling degrees, heating wall surface heat flux densities, working medium submerging speeds, heating wall surface initial temperatures and channel interior initial liquid levels can be carried out on an experimental section of a narrow rectangular channel structure. The device is suitable for narrow rectangular channel structures with different sizes, and can be used for carrying out re-submerging experiments on experiment sections with single-plate double-channel, double-plate three-channel and more channels. The system pressure and the inlet flow in the submerging process can be kept stable, and it can be ensured that the supercooling degree of a working medium, the heating wall surface temperature and the initial liquid level are controllable.

The invention provides a multi-scene-oriented intelligent light adaptive control method and system, and the method comprises the steps: recognizing the current behavior state of a user based on visual sensor data, predicting the behavior state of the user, and outputting a light control behavior tag; combining the behavior label with a scene type and ambient light intensity to generate a target illumination intention vector; according to the target illumination intention vector, in combination with an illumination control conflict situation, real-time lamp parameters and user space state data, calculating an illumination weight of each lamp to a user, distributing brightness and color temperature parameters, and in combination with the target dimming process duration, generating a lamp control parameter set; and converting the lamp control parameter set into a dimming instruction sequence, performing time synchronization and delay compensation, and issuing the dimming instruction sequence to each lamp for execution. According to the method, accurate mapping from behavior intention to physical illumination is realized, and the method has extremely high universality, deployability and engineering controllability.

The invention discloses a method for simulating a mold temperature field and curing deformation through finite elements and a mold optimization design method based on the method. The finite element analogue simulation method comprises the following steps: firstly, modeling and simulating a mold grid; secondly, calculating equivalent thermal and mechanical parameters of the composite material by adopting a multi-scale homogenization method; then, a heat conduction model of the mold and the composite material blade is constructed and coupled with the curing dynamic model through temperature, and mold temperature distribution is simulated; and finally, establishing a mold thermal expansion and curing stress coupling model, and predicting thermal deformation and stress change of the mold. By identifying the weak area of the mold in the simulation process, the mold is optimally designed. By improving a mold temperature field distribution simulation method, optimizing a grid modeling process and introducing an accurate calculation method of equivalent material parameters, the precision of mold design and the controllability of a curing process are effectively improved, so that the problem of inaccurate mold temperature field distribution and curing deformation prediction is solved.

The invention relates to the technical field of network analysis, and discloses an industrial network topology structure safety robustness simulation analysis method and system, and the method comprises the steps: carrying out the modeling of each equipment node in an industrial network as a node, carrying out the modeling of a communication link between equipment as an edge, and constructing an industrial network topology model; setting a disturbance scene model; performing disturbance simulation on the industrial network topology model in a simulation environment, and recording a topological structure change process and connectivity evolution information thereof after disturbance; performing feature learning and embedding on the network topological graph before and after disturbance by adopting a graph neural network model, and outputting a robustness score R-score in a corresponding disturbance scene; calculating a robustness evaluation index based on the simulation data; and outputting a weakness identification result and an optimization suggestion of the topological structure. According to the invention, the structure robustness, safety controllability and adaptive adjustment capability of an industrial network in the face of a complex operation environment and potential threats are improved.

The invention relates to the technical field of soil remediation management, in particular to a method and system for soil remediation and digital management, and the method comprises the steps: data construction: fusing multi-source monitoring soil environment parameters with space-time background data such as a GIS layer and a meteorological time sequence to form a data set; state quantification: outputting a comprehensive state index for quantifying restoration urgency and complexity through the soil health assessment model; scheme generation: matching the restoration target library and the resource library, and generating a digital restoration scheme containing an expected state trajectory through an optimization algorithm; closed-loop control is carried out, feedback data comparison tracks are continuously collected, if the deviation exceeds a threshold value, the index is recalculated, the scheme is dynamically adjusted, and the system comprises a data construction module, a state quantification module, a scheme generation module and a closed-loop control module and executes the method. According to the method, the data reliability and evaluation scientificity are improved, the scheme adaptability is guaranteed, dynamic and controllable remediation is realized, and digital and efficient management of soil remediation is promoted.

The invention relates to the field of intelligent automatic driving, in particular to a multi-vehicle cooperative controllable confrontation test method based on a diffusion model. Comprising the following steps: S1, a diffusion model training stage: training a diffusion model based on real driving data, and learning vehicle behavior distribution through forward noise addition and reverse denoising to obtain fixed model parameters; s2, scene and multi-vehicle space-time modeling: fusing the road map, the lane topology and the vehicle state information, and constructing a space-time scene representation of multi-vehicle interaction as input of diffusion generation; s3, a diffusion generation mechanism based on adversarial guidance; s4, performing partial diffusion control and diversified confrontation generation; and S5, evaluating the authenticity and controllability of the multi-agent confrontation scene. Compared with the prior art, the method has the advantage that the authenticity, controllability and closed-loop consistency of multi-vehicle cooperative confrontation scene generation are obviously improved.