118 results about "State evolution" patented technology

The invention relates to the technical field of BIM, in particular to a BIM-based construction progress intelligent management and control method and system, and the method comprises the steps: constructing a BIM digital twin cloud model, fusing a panoramic image and LiDAR point cloud data, dynamically selecting a data source update model through an algorithm, extracting engineering topology, building a construction network diagram, and generating a state evolution trajectory in combination with environment and resource data. Predicting and visualizing milestone time, comparing field data with a BIM model to generate progress deviation information, performing stability analysis, triggering resource allocation when a threshold value is exceeded, synchronizing a material supplier and a field manager, dynamically adjusting personnel, equipment and materials, dynamically adjusting a milestone plan based on multiple data, and presenting and guiding allocation through the BIM model. The construction progress is ensured to be consistent with the plan, and the complex interaction relationship and dynamic characteristics in the construction process are captured through nonlinear dynamic system modeling in combination with LiDAR point cloud and other high-precision data.

The invention provides a software defect information fusion method and system based on multi-source data. The method comprises the following steps: acquiring static code characteristics, a runtime log sequence and user feedback from cross-platform software; constructing a code dependency graph based on static code features and a historical defect library, defining a log structure through code logic association, and performing space-time alignment with defect trigger nodes fed back by a user to generate a context matrix; synchronously acquiring physical parameters of hardware nodes, and dynamically coupling the physical parameters with the matrix to generate a state evolution graph; fusing the code dependency graph, the state evolution graph and user feedback, and extracting cross-modal defect features; and generating defect positioning probability distribution based on the features, and determining a repair scheme by combining physical parameter anomaly analysis. Through integration of codes, operation states and user feedback, accurate positioning and dynamic repair of defects are realized. According to the technical scheme provided by the invention, the precision and reliability of software defect information fusion can be improved.

The invention discloses a multi-mode printer state prediction method and system, and relates to the technical field of state prediction. According to the method, a state evolution trend analysis mechanism is introduced, state fragments with risks can be recognized in advance before obvious faults occur, early warning and trend guiding of key parameter changes in the printing task process are achieved, and the risk of printing quality reduction or equipment damage caused by sudden state change is effectively reduced. Meanwhile, a condition modeling and state clustering mode driven by multi-modal data is adopted, the adaptability and generalization ability of the prediction model to complex operation working conditions are improved, and the method is particularly suitable for actual industrial environments such as frequent switching of printing tasks and changeable equipment operation states.

The invention relates to the technical field of artificial intelligence and computer vision, discloses a subway key component fault detection method and system based on an AI visual large model, and aims to solve the problems of low detection precision, weak generalization ability, insufficient multi-mode understanding, poor real-time performance and lack of state evolution modeling in the prior art. The method comprises the following steps: acquiring images of key components through a multi-view industrial camera array, and performing distortion correction, illumination normalization and noise suppression; a pre-trained visual large model is utilized to extract deep space features, and modeling is carried out on a continuous frame feature sequence through bidirectional LSTM to capture a time sequence change trend. By introducing the large-scale visual large model and spatio-temporal joint modeling, the identification capability of tiny defects is improved, the discrimination stability is enhanced, high-precision and low-delay automatic detection is realized, the false alarm rate and the omission ratio are remarkably reduced, and the detection efficiency and the system maintainability are improved.

The invention relates to a fabricated building management method and system based on BIM and digital twinning. The method comprises the steps that a BIM project model is constructed; generating project plan information; building a project digital twinborn model based on the BIM project model and the project plan information; driving a project flow process to be executed; receiving feedback information of the Internet of Things to perform space-time constraint rule verification, and driving state evolution update of the digital twin model; when the risk information is recognized, the risk type is judged, and an adaptive regulation and control strategy is executed; according to the method, a bidirectional mapping mechanism of the BIM project model and the digital twinborn model is constructed, time-space constraint verification and state evolution updating are carried out on the construction process in combination with real-time data of the Internet of Things, an adaptive regulation and control strategy is triggered when the risk is identified, real-time monitoring and dynamic optimization of the whole process of the fabricated building are realized, and the construction efficiency is improved. The method has the advantages of improving the collaboration of the construction process, reducing the rework rate and guaranteeing the project progress and quality.

The invention discloses a plan issuing method based on emergency state dynamic change, and relates to the technical field of intelligent emergency, and the method comprises the steps: activating a pre-constructed knowledge graph frame according to an emergency type identifier obtained in real time, and forming an initialized dynamic knowledge graph; capturing a feedback data stream in real time, importing the feedback data stream into a state updating layer of the state evolution knowledge graph, and generating an updated state evolution knowledge graph; and based on the updated state evolution knowledge graph, generating an evacuation instruction, switching to a Mesh network through a damaged base station, broadcasting path change information, generating an execution effect, and re-triggering and activating the pre-constructed knowledge graph framework as a new emergency type identifier. According to the method, the emergency situation disturbance nodes are implanted in the state evolution knowledge graph and the diffusion path is deduced, so that the dynamic generation of the intelligent countering plan is realized.

The invention discloses a power grid equipment fault diagnosis method based on time sequence modeling and knowledge enhancement reasoning, and the method comprises the steps: extracting multi-modal features of multi-source data of equipment, and carrying out the fusion of the multi-modal features to obtain a fusion feature vector; on the basis of the fusion feature vector, combining static structured parameters of the equipment, and carrying out combined modeling to obtain a state evolution coding vector; based on the fusion feature vector and the state evolution coding vector, multi-path reasoning based on knowledge enhancement is adopted, and the fault confidence degree of each reasoning path is obtained; and carrying out interpretable fusion and adaptive optimization on each fault confidence coefficient to obtain an optimal fault diagnosis strategy. According to the method, the whole process of running state evolution is accurately depicted through fusion of multi-modal features, structuring and time sequence joint modeling; knowledge-enhanced multi-path reasoning is adopted, effective integration of data driving and expert experience knowledge is realized, interpretable fusion and adaptive optimization are carried out, and the accuracy and reliability of fault diagnosis are greatly improved.

The invention discloses a water conservancy project construction management informatization platform, and relates to the technical field of water conservancy project management, and the platform comprises an evaluation form generation module, a form filling verification module, an intelligent approval module, a classification filing module and a safety management module. According to the invention, a whole-user, full-service and full-life-cycle construction management standardization system is constructed; automatic filling and real-time verification are realized by means of a natural language processing technology and machine learning, and data entry experience and accuracy are improved; decision support is provided through big data analysis, a filling strategy and an approval path are recommended through a model, and process management is optimized; digitized upgrading of form full-life-cycle management is realized, and the method is suitable for a water conservancy project construction management scene with a complex approval process; the state evolution trend of hidden danger points or major hazard sources in a future period of time is simulated, and early warning and plan generation are carried out based on the state evolution trend.

The invention discloses a coal mine hidden danger event intelligent reasoning method based on a knowledge graph, and belongs to the technical field of coal mine safety, and the method specifically comprises the steps: obtaining a to-be-deduced entity with a real-time data flow recognition state parameter exceeding a threshold value range; calling a state transition rule set according to the entity type and constructing an independent deduction process; executing state transition calculation in each deduction process to generate a state evolution sequence; state combination matching is carried out based on the relation mode of the static knowledge graph, and entity state pairs meeting relation triggering conditions are recognized; establishing a cross-process data channel between the related deduction processes and performing parameter conversion; state deduction is executed again based on the input parameters, and deduction network topology is constructed; and finally, a complete hidden danger evolution path is obtained through reverse tracking parameter transfer relation integration. According to the method, dynamic deduction of the coal mine hidden danger forming process and accurate identification of the linkage risk conduction path are realized, and the accuracy of potential safety hazard early warning is effectively improved.

This invention discloses a state monitoring method for distribution cable branch boxes based on state recognition, specifically relating to the field of power monitoring technology. It involves collecting multi-dimensional operational data from multiple branch boxes over a continuous time period to construct a state evolution sequence; calculating state deviation scores and adjacent equipment state consistency scores to determine whether an assessment process is triggered; after triggering, constructing a state influence probability map, identifying abnormal influence paths, extracting state disturbance diffusion indices and cooperative behavior deviation indices, inputting these into a pre-trained risk identification model, generating risk level intervals and causal probability distribution vectors, executing influence control measures, and updating the state recognition logic. This invention achieves dynamic perception of branch box states by constructing a state evolution sequence, combines state deviation scores and adjacent equipment state consistency scores to achieve joint judgment of individual and group behaviors, and drives control strategies and map updates using risk level intervals and causal probability distribution vectors, thereby improving monitoring accuracy and system adaptability.

The invention relates to the related technical field of intelligent operation and maintenance of power stations, in particular to an intelligent operation and maintenance system and method for a new energy power station, and the method comprises a data acquisition module which obtains the equipment operation data of the new energy power station; the correlation analysis module is used for extracting key features; the state characterization quantity configuration module is used for configuring state characterization quantity and determining an abnormal characteristic parameter group; and the operation and maintenance reminding module is used for generating a state evolution curve, determining a change rate and a mutual influence coefficient and evaluating a fault risk level to carry out operation and maintenance reminding. The technical problems of insufficient fault risk assessment accuracy and limited operation and maintenance reminding credibility caused by the fact that an operation and maintenance strategy is mostly triggered based on a fixed threshold value and interaction influence between electronic equipment is not considered are solved, dynamic simulation and trend deduction of a state evolution process under different working conditions are realized, a parameter change rate and a mutual influence coefficient are determined, and the reliability of operation and maintenance reminding is improved. The fault risk level is accurately evaluated, operation and maintenance reminding is carried out in time, and the technical effects of operation and maintenance timeliness, accuracy and pertinence are improved.

The invention discloses a beam guidance optimization method based on position prediction in unmanned aerial vehicle communication, and relates to the technical field of unmanned aerial vehicle communication optimization, and the method comprises the steps: obtaining the flight path data of an unmanned aerial vehicle, carrying out the adaptive segmentation according to the flight path data, and constructing a state evolution model for each flight path; performing short-term prediction and medium-term residual error calibration prediction according to the state evolution model, and constructing a prediction correction term to generate a prediction point set; mapping the prediction point set to a direction coordinate system of a ground communication array to construct an angle evolution tensor, and screening to obtain a candidate beam direction set; and screening a standby beam direction with the maximum path coverage redundancy from the candidate beam direction set. According to the method, a beam guiding optimization mechanism with state identification, adaptive prediction, space tolerance, historical correction and multi-path dynamic regulation and control capabilities can be realized, and the beam control precision, the failure recovery capability and the communication link stability in a complex airspace motion state during communication of the unmanned aerial vehicle are remarkably improved.

The invention discloses a predictive maintenance method for an implicit state of power equipment, and belongs to the technical field of intelligent operation and maintenance of the power equipment. According to the method, multi-mode information such as SCADA data, acoustic vibration data, infrared thermal image data and partial discharge data is collected, space-time alignment and attention mechanism fusion are carried out, and a hidden state code representing the internal health state of equipment is extracted; a dynamic state deduction model combining physical constraint and data driving is constructed, and prediction of the future state evolution trajectory of the equipment is achieved; and performing a virtual maintenance experiment in the digital twin based on a prediction result, and generating an optimal maintenance strategy through multi-objective optimization. According to the method, the problems that the hidden state of the equipment cannot be sensed and the prediction capability is lacked in the prior art are solved, the conversion from passive maintenance to predictive maintenance is realized, and the accuracy and foresight of operation and maintenance of the power equipment are improved.

The invention belongs to the technical field of intelligent patrol of substations, and particularly relates to a positive and negative sample collaborative patrol method and system based on time sequence state evolution analysis. The objective of the invention is to solve the problems of high state jump misjudgment rate, high slow change fault omission rate and poor environmental adaptability caused by independent operation of positive and negative sample algorithms and lack of time sequence state evolution analysis in an existing substation intelligent patrol system. According to the main scheme, the method comprises the steps that firstly, a system obtains and preprocesses equipment image flow and historical data, and a standardized time sequence set containing timestamps, state tags and change vectors is constructed; recent data is aggregated for each equipment state to form a time sequence state chain, and operation logic is embedded to identify illegal hopping; then, through a double-stage cooperation mechanism, a preliminary screening stage inhibits low-confidence-coefficient false alarm, and a trend stage fits a slowly-varying slope to trigger early warning; and finally, dynamically weighting and fusing positive and negative sample outputs according to environmental parameters, and generating a credible score and making a decision. The method is suitable for intelligent operation and maintenance scenes under complex working conditions.

The invention discloses a raw material uniformity mixing control method based on wet flue gas desulfurization gypsum slurry, which comprises the following steps: constructing a time sequence working condition data set by collecting process data such as feeding rate, slurry concentration, particle size distribution and stirring parameters, and extracting main dynamic characteristics of shearing and agglomeration based on an SINDy algorithm; the method comprises the following steps: establishing a state evolution model, inputting a current working condition into the model to generate a state evolution path, collecting shear strength and aggregation degree observation data at key nodes of the path, and identifying an implicit state and an estimation error of a system by adopting a horizon estimation algorithm and combining a historical process trajectory and a control constraint condition. When the prediction deviation exceeds the limit, model structure adjustment and parameter updating are triggered, regulation and control output quantity is generated based on the model deviation, a control instruction is output, dynamic optimization control over the slurry mixing uniformity is achieved, working condition changes can be dynamically adapted, and the slurry mixing uniformity and the process stability are improved.

The invention relates to an RGB-T target tracking method and system based on spatio-temporal state evolution, and the method comprises the steps: constructing a double-branch RGB-T target tracking model, enabling RGB and TIR branches to share the weight of a ViT encoder, employing an iterative processing frame, and transmitting spatio-temporal context information between frames through an updatable context memory Tokens. In each iteration, after RGB and TIR modal features are extracted respectively, cross-modal time sequence context modeling is carried out through a modal perception time sequence Mamba module, and the module realizes long-time target perception representation learning through a cross-modal coupling state transition mechanism and a prompt guide strategy; and carrying out intra-modal and inter-modal feature fusion in a spatial dimension through a cross-modal Mama aggregation module, and finally outputting a target position through a prediction head. According to the method, lasting cross-modal state evolution can be realized with linear complexity, the spatial-temporal characteristics of visible light and infrared light are effectively fused, and the tracking robustness and accuracy are improved in complex scenes such as rapid target movement, shielding or modal degradation.

The invention relates to the technical field of natural resource monitoring and evaluation, and discloses a natural resource dynamic monitoring and evaluation system based on multi-source spatio-temporal data fusion, comprising a semantic mapping module for analyzing a business document to generate a constraint rule and constructing a basic spatio-temporal tensor; the perception strategy module monitors state node evolution and generates an acquisition strategy instruction to drive an external sensor to obtain enhanced perception data; the state evolution module deduces an expected legal state sequence and reconstructs an actual physical state sequence in combination with enhanced perception data; the energy quality fingerprint module establishes nonlinear mapping by using the time sequence energy quality data, and calculates a fingerprint consistency index to verify the authenticity of a physical state; and the deviation risk module calculates a space-time semantic distance and a propagation risk value based on the topological network in combination with the consistency index, and generates a resource audit report. According to the invention, through event-driven active perception and cross-modal energy quality implicit verification, the problems of insufficient monitoring data space-time precision and difficult camouflage compliance identification are effectively solved.

The invention discloses a circuit breaker adaptive protection method based on reinforcement learning, and the method comprises the following steps: collecting the original operation data of a circuit breaker, and carrying out the preprocessing; a DreamerV2 model is constructed; carrying out modeling on a state evolution process by utilizing a world model sub-module in the DreamerV2 model; generating observation reconstruction loss and reward estimation loss according to the potential state sequence and the hidden state sequence; making a decision on the current potential state through a strategy optimization module, and outputting a circuit breaker control action; the control action is applied to the circuit breaker, and a reward value composed of an actual execution result and a feedback state is obtained; and constructing a strategy optimization objective function and a joint loss function to update the strategy network and the world model sub-module respectively. According to the method, the DreamV2 reinforcement learning model is introduced, self-adaptive optimization of the circuit breaker control strategy is achieved, and the method has the advantages of being rapid in response and high in strategy precision.

This invention provides a robotic zero-value insulation detection system based on intelligent sensing, belonging to the field of electrical variable measurement technology. By constructing and solving a state evolution model driven by both intrinsic health state and future external stress, and by generating future health state trajectories, it improves the accuracy and foresight of predicting critical failure events, reducing the risk of unplanned outages caused by sudden insulator failures. Secondly, by conducting in-depth attribution analysis on the dominant stresses that lead to the deterioration of the state trajectory, it provides decision support for power grid operation scheduling and maintenance strategies, enabling resources to be more effectively allocated to the highest-risk links. It enhances the robustness and intelligence of the entire detection system in complex and ever-changing operating environments, promoting the transformation of transmission line operation and maintenance models towards higher-order predictive maintenance and proactive risk management.

This invention provides an intelligent operation and maintenance system for prefabricated substations based on big data, belonging to the field of prefabricated substation operation and maintenance technology. The data access unit unifies the time alignment of electrical operation, environmental monitoring, and historical operation and maintenance data, constructing a constrained time-series data structure based on equipment topology; the state evolution modeling unit extracts multi-scale features, generating three sets of evolution parameters: impedance, capacity, and load; the control variable reconstruction unit generates enhanced control input vectors through component mapping and iterative feedback; the real-time control execution unit outputs reactive power regulation and voltage control commands accordingly; and the human-machine feedback unit embeds manually corrected information and updates control parameters. This invention solves the problems of lack of topological constraints in multi-source data and the difficulty of embedding human experience into automated control, improving the intelligence level, control accuracy, and adaptability of prefabricated substation operation and maintenance.

The invention discloses a distribution box operation energy efficiency analysis system based on artificial intelligence, and the system comprises a data processing module which is used for collecting operation data of a distribution box and carrying out the preprocessing of the operation data; the state evolution module is used for constructing a continuous state evolution trajectory and generating a state change path; the parameter modeling module is used for constructing a state embedding stream and performing parametric modeling; the residual sharing module is used for inputting the state evolution tensor into the multi-channel A3C architecture; the strategy aggregation module is used for executing independent strategy updating and constructing a cross-channel attention map; the record control module is used for controlling the on-off logic of the load of the distribution box and generating an energy efficiency behavior record with a time sequence label; and the asynchronous updating module is used for inputting the asynchronous gradient into the shared parameter pool for synchronous updating. According to the invention, the continuous modeling of the operation state of the distribution box and the closed-loop optimization of the intelligent control decision are realized, and the accuracy and real-time performance of energy efficiency management are remarkably improved.

The application discloses a kind of communication power supply operating state intelligent monitoring methods based on machine learning, comprising the following steps: S1, obtains multiple-source monitoring data and pre-processes;S2, sequence segmentation is carried out using sliding time window and statistical feature vector is extracted, forms state variable set;S3, each state variable is encoded to construct state node and establish weighted directed connection, and construct operating state evolution diagram;S4, state transition matrix is constructed, main transition feature is extracted and low rank is approximately reconstructed, sequence learning is carried out to construct state update function;S5, to state node, multiple-step recursion is carried out, and the Euclidean distance between recursive state and risk boundary is calculated, to determine state monitoring result;S6, error is calculated and state update function parameter is iteratively updated.The application can realize the multiple-step recursion prediction and risk trend identification of communication power supply operating state, improve the accuracy and stability of communication power supply operating state monitoring.

The present application relates to the field of remote calibration technology, and in particular to a remote calibration method and system for a smart water station. The present application proposes the following scheme: by acquiring the status information of each liquid path, constructing a path state matrix, and calculating the pollution risk response value based on the calibration task, a pollution adaptability matrix is ​​generated, from which the most compatible path is screened to perform the calibration task; the modeling of the path state introduces a state evolution graph and a graph neural network deduction mechanism, and uses interrupt edges to control the propagation path, dynamically reflecting the impact of the cleaning operation on the state inheritance; after the task is completed, the operation information is written into the log and backfilled into the state graph to realize the continuous evolution and update of the path state.

The invention relates to the technical field of carton forming control, in particular to a packaging carton forming control system and method, and the system comprises a structure analysis module, a sequence judgment module, a state matching module, an action constraint module and an instruction generation module. According to the method, the paperboard size and the fold line position are subjected to structured analysis, the section sequence relation is established, the forming sequence can be adaptively adjusted along with the change of the carton structure, the folding process is mapped into the state evolution relation, and action constraint judgment is conducted in combination with the real-time position of an execution mechanism; the broken line actions form a mutual exclusion coordination relation in space and time, so that mechanical interference and sequence conflicts among different forming stages are avoided, forming instructions have continuity and consistency, stable operation is kept under the conditions of specification switching and complex structures, manual intervention requirements are reduced, and the overall forming reliability is improved.

The application discloses a multi-modal printer state prediction method and system, and relates to the technical field of state prediction.The application can identify a state segment at risk in advance before an obvious fault occurs by introducing a state evolution trend analysis mechanism, early warning and trend guidance of key parameter changes in a printing task process are realized, and the risk of printing quality decline or equipment damage caused by state mutation is effectively reduced.Meanwhile, the application adopts a multi-modal data-driven conditional modeling and state clustering mode, adaptability and generalization ability of the prediction model to complex operating conditions are improved, and the application is especially suitable for practical industrial environments such as frequent switching of printing tasks and variable equipment operating states.

This invention relates to the field of data processing and discloses a method and system for project risk identification, early warning, and control. The method includes: acquiring state parameters of logical state nodes in a multi-dimensional data logical network, constructing state evolution trajectories, and calculating decay acceleration within adjacent time windows; identifying parallel monitoring node pairs sharing a common underlying resource pool and calculating the correlation of decay acceleration; when the correlation is negative and the difference exceeds a threshold, generating directed edges pointing to asymmetric resource offset occupancy in the topology graph and outputting early warning and control instructions. This invention identifies the asymmetry in the decay rate of evolution between parallel nodes, constructs asynchronous decay scissor difference judgment rules, achieves reverse mapping of resource scheduling imbalance, eliminates the blind spots of cross-path resource dependency perception in traditional monitoring systems, locks down the risk source before local fluctuations trigger global cascading failures, and improves the timeliness of early warning.

The invention discloses an intelligent water affair management system based on deep learning, and the system comprises a data collection and preprocessing module which is used for collecting the data of a water supply network and constructing a standardized water affair input tensor; the topology modeling module is used for generating a water supply pipe network diagram structure and diagram structure representation thereof; the state initialization module is used for fusing the input tensor and the graph structure to generate an initial state vector; the state evolution calculation module is used for establishing an augmented state evolution equation and carrying out adaptive integration to generate a prediction state sequence and source sink estimation; the multi-objective decision module is used for constructing a comprehensive cost function and generating a pump station and valve control instruction; the physical constraint projection module is used for executing water pressure, flow velocity and water age constraint projection and outputting an executable scheduling instruction; and the dynamic simulation feedback module is used for performing digital twin simulation and updating the model and sparse estimation weight. According to the invention, fine control and dynamic feedback optimization of the water affair system are realized, and the operation efficiency and the scheduling intelligence level are improved.

The invention provides a big data mining method and device applied to LPDDR performance detection, and the method comprises the steps: receiving an original performance record stream, carrying out the data reconstruction, and generating a time-aligned data reconstruction set; and performing association rule mining on the data reconstruction set, extracting a frequent co-occurrence relationship between performance states in a time dimension, and screening to generate a performance influence association mode set. And mapping the set to a state transition space, constructing a directed weighted performance state evolution graph, and calculating and identifying a key state transition path through graph density. And generating a detection adjustment instruction based on the path, and transmitting the detection adjustment instruction back to a control unit of the detection system so as to reconfigure the sampling frequency and the detection item execution sequence. According to the method, accurate self-adaptive adjustment of the LPDDR detection process is realized by mining the deep association mode of the performance data.