1744 results about "Analytic model" patented technology

The invention discloses an intelligent management system for energy consumption optimization and fault self-diagnosis of cleaning equipment, and relates to the field of intelligent maintenance of the cleaning equipment, and the system comprises the steps: obtaining three groups of core parameters, i.e., a historical vibration spectrum, a motor current harmonic component and a bearing temperature gradient, constructing a dynamic failure mode knowledge graph, performing time sequence correlation analysis on the historical fault data to obtain failure mode analysis data; establishing a multi-dimensional analysis platform, identifying a high-risk component, and updating a fault threshold value; introducing a service time attenuation factor and a working condition correction coefficient, establishing an aging degree quantitative model, and calculating an aging coefficient; and constructing and developing an energy consumption-reliability joint optimization module, and adjusting equipment operation parameters. The method has the advantages that the dynamic knowledge graph and the time sequence analysis model are constructed by integrating multi-source sensor data, precise diagnosis and self-adaptive threshold adjustment of the coupling fault of the cleaning equipment are achieved, aging evaluation and task scheduling optimization are combined, the energy consumption efficiency is improved, and the maintenance cost is reduced.

The invention discloses a network equipment integrated monitoring method and system. The method comprises the following steps: collecting multi-source heterogeneous data, constructing a protocol compatible layer, and supporting multi-protocol adaptation; data fusion and intelligent analysis: constructing a dynamic topology, analyzing an equipment configuration file, and generating a network topological graph; performing time sequence prediction according to a root cause analysis model, and predicting an abnormal trend; mining association rules, analyzing historical data, and extracting fault association rules; constructing an equipment fault knowledge base under the assistance of a knowledge graph, and accelerating root cause positioning; self-adapting an alarm threshold, analyzing historical data distribution, and dynamically adjusting the threshold; visual decision making and automatic processing are carried out, a 3D topological map is provided, and layered display is supported; and performing fault grading processing, comprehensively calculating a fault influence degree score, mapping to a fault grade and a work order type according to an influence degree score interval, and formulating a dynamic work order generation rule. A protocol compatible layer is constructed by deploying a lightweight agent program, multi-protocol adaptation is supported, and various network devices can be fully covered.

The invention discloses a multi-source heterogeneous data fusion system based on an RAG. According to the method, through deep knowledge fusion and a dynamic cognitive evolution mechanism, the decision-making intelligence level in a complex data environment is remarkably improved, and equipment operation parameters, environment indexes and a domain knowledge base are deeply associated to form a panoramic data view with space-time continuity. A generative enhancement mechanism endows original data with a self-evolution characteristic, industry empirical rules and real-time situation awareness are injected while the fidelity of the original characteristic is maintained, so that a decision model can capture micro data fluctuation and follow macroscopic business logic, accurate balance between risk early warning and resource scheduling is realized, and the risk early warning efficiency is improved. The dynamic adaptation characteristic enables the system to autonomously update a knowledge system and optimize a decision path in a complex and changeable industrial environment, and a post response mode of a traditional static analysis model is converted into an intelligent center with prospective pre-judgment and real-time regulation and control capabilities.

The invention relates to the technical field of intelligent water affairs, in particular to an intelligent water affairs dynamic monitoring system and method based on digital twinning, and aims to detect data drift in real time, calculate a standard deviation change rate to generate abnormal judgment, analyze model stability and drift rate synchronism to output deviation indexes, screen data abnormal items to form quality evaluation, and realize dynamic monitoring of the intelligent water affairs. And comparing node synchronization difference to trigger compensation correction, and generating a monitoring adjustment result through integrity verification. According to the invention, through comparison of the multi-cycle standard deviation change rate and the environmental parameters, sensor drift classification and confidence evaluation are realized, the reliability of the model is enhanced by constructing a composite attenuation coefficient, abnormal sampling is positioned by combining the data change rate and an outlier map, and data synchronization is guaranteed by adopting a difference matrix and state feedback. And version verification and repair records are introduced to realize closed-loop tracing.

The invention discloses an accounting data intelligent processing method and system for enterprise financial auditing, and relates to the technical field of accounting data intelligent processing, and the method comprises the steps: obtaining multi-mode enterprise financial data, and carrying out the preprocessing; carrying out multi-modal semantic understanding analysis on the unstructured text and image data; constructing an enterprise financial space-time knowledge graph containing time attributes; inputting into an anomaly analysis model, extracting spatial structure characteristics of the financial entity in the topological network, and extracting dynamic characteristics of the financial relationship evolved along with the time sequence; identifying an abnormal source, evaluating a systematic risk value and generating an abnormal propagation path; and integrating to generate a final audit report. According to the method, structured and bill images are fused, identifiers and time calibers are unified, abnormal source and propagation are positioned based on the space-time knowledge graph, closed-loop counter-knock and cross-period anomalies are identified, the auditing accuracy and coverage rate are remarkably improved, the workload of false report, missing report and manual recheck is reduced, and a traceable structured report is quickly generated.

The invention relates to the technical field of data processing, and discloses a real-time data processing analysis method and system of an industrial PLC. The method comprises the following steps: transmitting temperature, pressure, current, vibration and acoustic parameters acquired by multiple sensors to an industrial PLC (Programmable Logic Controller) in real time to obtain multi-source heterogeneous original data; preprocessing the multi-source heterogeneous original data to obtain standardized fusion data; correlation calculation and anomaly recognition are carried out through the multivariate analysis model, and an abnormal state classification result is obtained; dynamically adjusting data interaction frequency and sampling rate between the edge nodes and the central PLC, and generating a real-time control decision instruction; and matching the real-time control decision instruction with the current motor load fluctuation state, and outputting the optimal frequency conversion control parameter. According to the invention, the response delay of the system is reduced, the control precision and reliability are improved, the dynamic balance between the safety and the energy efficiency is realized, and the system can intelligently adjust the control strategy according to the real-time safety situation.

The invention discloses an enterprise audit information extraction method based on artificial intelligence, and relates to the technical field of enterprise audit information processing, and the method comprises the steps: obtaining an audit original document from an enterprise, carrying out the multi-mode conversion processing, and generating a standard data set; performing deep semantic analysis on the standard data set, and constructing an audit knowledge graph; performing cross-modal alignment on the auditing knowledge graph, the image features, the table features and the time sequence features, and constructing a joint auditing analysis model; performing dynamic verification on an audit analysis result and a historical case library to form a dynamic evolution knowledge base; and generating a final audit document based on the updated knowledge base. Through cross-modal feature vector alignment and attention-driven clause applicability scoring, dynamic association analysis of financial anomaly and business pipeline fluctuation is realized, and the cross-modal clue discovery efficiency is improved.

The invention relates to the technical field of data processing, can be applied to business scenes such as financial science and technology and medical health, and discloses a self-adaptive data processing optimization method, device and equipment and a medium, and the method comprises the steps: collecting operation data, host performance data, network state data and historical task data of a target data source, and constructing an analysis model in combination with recovery parameters and strategy preference, predicting a task load state, resource consumption and execution duration, generating a task execution strategy, completing task scheduling and execution monitoring, and collecting execution feedback data for iterative optimization of the analysis model. According to the method, an analysis model is constructed by fusing multi-source system data and historical task information, a task execution strategy is generated in combination with a dynamic prediction result and a strategy weight, intelligent task scheduling and process monitoring are realized, and feedback data is used for model iterative optimization. The task execution efficiency is improved, the resource use rationalization is realized, and the model adaptive capability is enhanced.

The invention relates to the technical field of underground drainage pipeline monitoring and hidden danger analysis, and provides an underground drainage pipeline hidden danger data analysis method based on big data, which comprises the following steps: acquiring data such as flow, water level, pressure and pipeline structure by using a multi-source sensor to form an original monitoring data set; preprocessing the data, extracting time domain, frequency domain and spatial topological features, and constructing a multi-dimensional feature vector; and inputting the feature vectors into a pre-trained hidden danger prediction model, and outputting hidden danger types and position coordinates. And constructing a spatio-temporal data analysis model based on the hidden danger position, generating a spatio-temporal correlation feature matrix, and performing dynamic risk assessment to obtain hidden danger development trend parameters. And the hidden danger information is mapped to a geographic information system, a three-dimensional visual analysis result is generated, and a graded early warning instruction is transmitted to a drainage pipe network operation and maintenance management system through a communication interface. The operation and maintenance management efficiency of the drainage pipeline can be improved, the hidden danger checking cost is reduced, and the safety and reliability of the drainage system are enhanced.

The invention relates to the technical field of computer vision, and discloses a real-time video analysis method based on deep learning. The method comprises the following steps: acquiring a real-time video stream through image acquisition equipment, and performing frame segmentation processing to generate a continuous video frame sequence; and extracting features of the video frame sequence by using a pre-trained convolutional neural network to obtain a multi-dimensional feature vector, inputting the multi-dimensional feature vector into the time sequence analysis model to calculate dynamic relevance, and outputting an inter-frame movement track and object behavior features. And constructing a scene understanding map containing a spatial position and a time evolution relationship according to the above-mentioned data, and carrying out abnormal event detection and generating event marking data based on the map. And performing semantic analysis on the event marking data, determining an abnormal event type and a confidence score, triggering a real-time alarm signal according to a result, and updating a historical event database. In the analysis process, the resource occupancy rate of the system is continuously monitored, the calculation precision is dynamically adjusted, a degradation processing mechanism is started when a preset threshold value is exceeded, and key area analysis is preferentially guaranteed.

The invention discloses an intelligent management system and method applied to a radio frequency energy output device, and belongs to the technical field of intelligent management of the radio frequency energy output device.An integrated sensor array is deployed on a radio frequency host and a hand tool electrode, and load voltage, output current, electrode temperature and tissue impedance are synchronously collected; a dynamic thermal impedance collaborative analysis model is constructed after time synchronization alignment, a three-dimensional thermal field simulation map is generated, and a thermal accumulation trend is predicted; a double-layer control framework is constructed, a first control layer generates a dynamic power adjustment rule base based on a preset treatment target and a safety threshold value and outputs an instruction, a second control layer receives the instruction and adjusts output parameters in real time, and a strategy is adjusted by combining a model prediction result in the execution process; meanwhile, the operation state is continuously monitored, a fault classification model is constructed for anomaly detection, a fault source is positioned, and a visual report is generated; and finally, constructing a mapping relation based on historical data, and generating an energy control scheme adapted to individualized requirements through iterative optimization.

The invention relates to the technical field of defect detection, in particular to a cross-process defect root cause tracing method and system. According to the method, the data feature matrix covering multiple dimensions is formed by integrating the process parameters, the equipment state and the quality detection information, so that the performance evaluation of each process is more comprehensive, the interaction and influence paths among the processes can be clearly described by constructing the process relation graph, and the performance evaluation efficiency is improved. Meanwhile, basic data support is provided for quantifying the relation between the procedures through introduction of procedure attenuation factors, the shortest propagation path and the propagation probability of the defects can be accurately recognized by analyzing a procedure relation graph, root cause tracing of the cross-procedure defects becomes systematized in combination with construction of a knowledge graph, and the defect tracing efficiency is improved. The knowledge graph not only can effectively integrate and display data, but also is convenient for quickly positioning problems, and by utilizing an adaptive correlation analysis technology, the system can intelligently adjust an analysis model and a path and continuously optimize a defect detection and tracing process when facing new data.

The invention relates to the technical field of path planning, and discloses a carton stacking path planning method and system based on artificial intelligence and a medium. The method comprises the steps that heterogeneous carton images are collected through a multi-angle camera and fused to generate a digital twinborn model; the analysis model predicts mechanical characteristics of the carton; optimizing an interlayer occlusion relation to form a stacking blueprint; planning an avoidance path to generate an execution instruction; monitoring deviation and establishing an abnormal scheme library; and evaluating an optimized path strategy in combination with cargo flow characteristics. According to the method, adaptive stacking strategies are provided for different cargo flow scenes, so that the space utilization rate, the structural stability and the operation efficiency of stacking are improved.

The invention relates to the technical field of geological disaster detection, in particular to a geological disaster detection method and monitoring system based on unmanned aerial vehicle scanning. Comprising the following steps: S1, configuring an unmanned aerial vehicle-mounted tilt camera, a multispectral laser radar and a high-precision positioning module, planning a route, carrying out multi-angle scanning on a target area, and obtaining earth surface three-dimensional point cloud data, a multispectral image and terrain elevation information; s2, preprocessing the collected original image data, including point cloud denoising, image distortion correction and multi-source data registration, and generating a high-resolution live-action model fused with three-dimensional geographic information data; s3, extracting various data based on the live-action three-dimensional model to construct a geological disaster hidden danger analysis model, calculating a risk index through multi-factor weighted fusion, training a transfer learning model in combination with historical disaster data, and outputting a hidden danger type and probability; according to the invention, the geological disaster type can be analyzed based on the geological condition and the targeted processing strategy can be specified.

The invention relates to the field of underground powerhouse construction risk identification, and provides an underground powerhouse construction risk identification and disposal method, system, device and medium, and the method comprises the steps: collecting multi-source heterogeneous data in real time, obtaining historical risk case data, and carrying out the preprocessing to obtain structured time-space correlation data; constructing a multi-dimensional analysis model based on a parallel computing algorithm, and performing multi-scale risk analysis on the structured time-space associated data to obtain multi-level risk feature data; performing risk feature recognition through a multi-modal machine learning model to obtain risk quantitative indexes, and performing recognition based on a fuzzy comprehensive evaluation algorithm to obtain construction risk levels; and matching emergency strategies of construction risk levels, carrying out parameter expansion through a combinatorial optimization algorithm, generating a plurality of candidate disposal schemes, carrying out weight calculation and sorting on the candidate disposal schemes by adopting a multi-criterion evaluation model, and outputting an optimal disposal scheme. According to the invention, efficient identification and accurate emergency decision-making of the construction risk of the underground powerhouse are realized.

The invention relates to the technical field of artificial intelligence data centers, and discloses a computing power resource scheduling method and system for an AI multi-service data center, and the method comprises the following steps: S1, obtaining multi-service task request data, processing the task request data based on a time sequence analysis model, and generating resource demand prediction data; s2, according to the resource demand prediction data, heterogeneous computing resource data is converted into virtual resource pool data, and priority queue data is generated based on task priority data; s3, processing the virtual resource pool data and the task priority data based on a reinforcement learning algorithm to generate resource allocation strategy data, and processing high-frequency access data through a prefetching strategy; and S4, generating edge-cloud hierarchical scheduling data according to the task delay demand data and the network topology data. According to the method, heterogeneous resources are dynamically allocated through time sequence analysis and a reinforcement learning algorithm, the resource utilization rate is increased to 85% from 60%, and the vacancy rate is reduced to 5%.

The invention discloses an air compressor frequency conversion and energy saving control method and system based on data analysis, and particularly relates to the technical field of air compressor control. Aiming at the problems of frequency jitter and motor instability of the permanent magnet variable-frequency screw air compressor in a high-frequency air consumption fluctuation scene, the method comprises the following steps of: acquiring exhaust pressure, air consumption flow, motor current, frequency converter output frequency and environment temperature data in real time, performing trend prediction by utilizing a data analysis model, and constructing a multi-dimensional working condition characteristic curve; a gas consumption fluctuation mode is accurately recognized, and predictive judgment is made before frequency fluctuation is about to exceed the limit; according to a prediction result, a frequency converter control strategy is dynamically adjusted, the frequency response rate is buffered in advance, an energy-saving mode is automatically triggered when the load change trend is abnormal, rotating speed control is optimized in real time, and according to the method, the operation stability and response precision of the air compressor are effectively improved, energy consumption is remarkably reduced, and the service life of equipment is prolonged; the method is suitable for the intelligent energy-saving operation requirement in a high-beat and high-precision manufacturing scene.

The invention discloses a hardware part numerical control machining real-time error control system which comprises the steps that historical machining information of a target numerical control machine tool is collected, error coupling relation analysis is conducted on the basis of the data, and therefore a coupling physical error model is constructed. Meanwhile, a processing error prediction model is established by using an LSTM network, and training is carried out by using historical data, so that the trained prediction model is associated with a physical model to form a composite error analysis model. Processing monitoring information is obtained through a machine tool sensor array, and after preprocessing, the processing monitoring information is input into the composite model for error prediction; and on the basis of a prediction result, an advanced compensation amount is generated by adopting an improved GA algorithm to control the machine tool, and whether self-correction needs to be performed on the composite model is judged according to a correction effect, so that efficient and accurate error control is realized. And processing errors and machine tool part maintenance are associated through historical maintenance information to form a maintenance knowledge graph, so that the state of the numerical control machine tool can be known, and maintenance early warning is realized.

The invention provides a property system virtual-real mapping inspection method, device and equipment based on digital twinning and a medium, and belongs to the technical field of property inspection. Real-time mapping of an equipment entity and a virtual model is achieved through layered design of a physical layer, a data layer, a twinning layer and an application layer; collecting static / dynamic data and reducing noise, and establishing a global coordinate system; constructing a 1: 1 parameterized model and binding equipment attributes; the state analysis model is used for evaluating the equipment health degree, and virtual-real identification and work order visualization are triggered when abnormity occurs; the inspection path is dynamically optimized, and the processing efficiency is improved in combination with AR assistance and dual-stage verification; and finally, updating the model weight and adjusting the inspection strategy through data-driven acceptance feedback. Real-time synchronization and intelligent decision making of the equipment state are realized, the inspection efficiency is improved, the fault omission ratio is reduced, the resource allocation is optimized, and the operation and maintenance transparency and reliability are enhanced.

The invention discloses a data production and application method based on index management, and the method comprises the steps: receiving an index query request containing a target index identifier, a dimension constraint condition and a time range parameter, carrying out the analysis and semantic verification of the target index identifier based on a global index asset library, and obtaining the index definition information; generating a standardized query statement according to the index definition information, and selecting an optimal data calculation engine to execute query; performing multi-level cache optimization and parallel computing acceleration on the query process to obtain an original data set; processing the original data set in real time according to a preset analysis model to generate structured index data containing trend analysis, anomaly detection or attribution inference results; and filtering and desensitizing the data based on a fine-grained permission control strategy, only returning contents in a permission range and recording an audit log. By means of the method, unified management, efficient query and intelligent analysis of the index data are achieved, the automation level and query performance of data production are improved, and meanwhile data safety and access controllability are guaranteed.

The invention provides an ecological circulation barrel-in-barrel culture data acquisition method and system, and relates to the technical field of intelligent control, and the method comprises the steps: carrying out the feature extraction of a historical data flow through a multivariable coupling analysis model, carrying out the mode matching through a preset environment threshold interval and a culture environment state matrix, and obtaining a culture environment state matrix; generating a water quality health degree evaluation index and an ecological imbalance early warning signal; and analyzing the water quality health degree evaluation index and the ecological imbalance early warning signal based on a fuzzy control algorithm, dynamically calculating a final feeding amount curve and a water change intensity function, and generating a regulation and control instruction set containing the feeding frequency, the bait particle size and the water pump rotating speed. According to the invention, real-time evaluation and accurate intervention of the water quality health state can be realized.

The invention relates to the technical field of computing power resource scheduling, and particularly discloses a green data center computing power demand prediction and energy consumption control method, system and device. Firstly, computing power information and environmental parameters of a data center are collected in real time based on edge equipment; afterwards, a time sequence trend is predicted by using an LSTM neural network, a CNN network is fused to extract computing power features, an LSTM-CNN hybrid model is constructed, repeated training is performed through an Adam optimization algorithm, and the model can accurately pre-judge a future computing power demand; and in combination with analysis of historical energy consumption and real-time monitoring data, an energy consumption analysis model is established to evaluate power consumption conditions under different computing power requirements. Based on the prediction result and the energy consumption analysis, the working state of the server cluster is dynamically adjusted, the load is scheduled, the energy use is optimized, the energy efficiency ratio of the data center is continuously improved by verifying and adjusting the energy consumption strategy, the overall operation cost and the energy consumption are remarkably reduced, and the development requirements of green energy conservation are met.

The invention relates to the field of coating material production, and discloses a coating formula calculation method and system based on self-learning feedback parameter correction, and the method comprises the steps: obtaining raw material basic parameters, target performance requirements and process feedback data of historical production batches, combining a material screening mechanism and an environment working condition correction strategy, and calculating a coating formula; constructing a coating formula initial input matrix; performing multi-dimensional variable normalization processing on the initial input matrix of the coating formula, introducing a feature sensitivity analysis model, and extracting a key variable group influencing the coating performance in the feature sensitivity analysis model; based on the performance response relation mapping model, analyzing error distribution between model prediction output and actual detection data by using a prediction deviation recognition mechanism, and extracting learning error features; a self-learning feedback updating mechanism is introduced, and dynamic weight optimization is conducted on the parameter correction factor set; and performing sample test and performance verification on the corrected candidate formula set. The method has the advantage that the coating formula precision is improved.

The invention is suitable for the technical field of data asset management, and provides a data asset management system based on a block chain and big data analysis, and the system comprises a first terminal which carries out the storage of the ownership information of data assets through a block chain network, and generates a storage data package; generating an evaluation result based on a preset technical index analysis model; writing the hash value of the evaluation result into transaction data of the main chain of the block chain, and generating an evidence storage voucher matched with the block chain transaction; an interaction data sequence is packaged for identity signature, encryption and compression to generate a target code stream, and the target code stream is sent to the second terminal; the second terminal decrypts the encrypted data in the target code stream by using a private key, decompresses the data by using a compression algorithm matched with the first terminal, and restores the data into an interactive data sequence; verifying the access authority and the transaction condition of the evidence storage data packet; checking the anchoring state of the evidence storage voucher on the main chain of the block chain; and the data transaction is completed, the data asset state report is generated, and the data element marketization configuration efficiency is improved.

The invention belongs to the technical field of electric power environment monitoring, and particularly relates to an intelligent terminal data acquisition control system and method based on an electric power environment. The intelligent terminal data acquisition control method based on the electric power environment comprises the following steps that S10, an intelligent terminal acquires environment data and operation data in a target object, associates the environment data with the operation data according to acquisition time, generates an associated data set with a timestamp and stores the associated data set to the local; and S20, the intelligent terminal calls the local historical fault data and the associated data set, and an analysis model is constructed based on a multi-factor coupling analysis model. According to the scheme, the problem that the comprehensive assessment accuracy of the overall operation risk of the target object is low due to the fact that the intelligent terminal cannot capture the hidden risk formed by multi-factor coupling is solved through multi-factor coupling analysis, and meanwhile, the fault probability is cooperatively reduced, the environmental adaptability is enhanced, decision-making intelligence is achieved by combining hierarchical response and dynamic self-adaptive adjustment, and the risk assessment efficiency is improved. And full-life-cycle intelligent management of the power equipment is achieved.

The invention relates to the technical field of public transportation systems, and particularly discloses a bus departure scheduling method, which comprises the following steps of S1, integrating multi-dimensional data; s2, a dynamic prediction model containing machine learning parameters is adopted to calculate the passenger demand in the future period; s3, calculating the number of required vehicles according to the predicted demand, the vehicle capacity and the dynamic load coefficient; s4, constructing a multi-objective function including energy consumption optimization, and solving the optimal departure interval and route; and S5, according to the real-time data, correcting a scheduling scheme, collecting real-time feedback data through a passenger mobile application, analyzing the emotion and demand of the passenger by using a natural language processing technology, based on feedback intention recognition of an emotion analysis model, constructing a passenger demand knowledge base in combination with historical complaint data, and optimizing a dynamic prediction model and a scheduling strategy. Through technology integration and system innovation, the static and single bottleneck of traditional scheduling is broken through, and an intelligent solution considering efficiency, low carbon and user experience is provided for urban buses.

The invention provides a water quality index fusion data anomaly detection method, system and device and a medium, and relates to the technical field of water quality detection.Physical, chemical and biological parameters are obtained from a water source station in real time, then on the basis that original data are reserved, cross-parameter correlation features are constructed through multi-modal feature reconstruction, and the water quality index fusion data anomaly detection accuracy is improved. An adaptive sensitivity factor is synchronously calculated, the factor fuses the degree of deviation of parameters from a dynamic baseline and the mutation degree of a multi-parameter coupling relation, then original parameters and associated features are input into a multi-task time sequence prediction model, and the adaptive sensitivity factor guides an attention mechanism to focus abnormal signals preferentially; and synchronously predicting the future evolution trend of the indexes and the relationship change trend among the parameters, and finally realizing anomaly diagnosis through triple criteria: detecting whether original parameters exceed a safety threshold, analyzing model prediction deviation, evaluating the fracture degree of coupling characteristics, dividing pollution levels according to the results, and positioning core anomaly parameters based on attention weights.

The invention relates to the technical field of wind power generation. The invention provides a yaw static and dynamic error adaptive method based on big data backtesting, which comprises the following steps of: constructing a multi-dimensional feature vector through multi-source data fusion data, and establishing a dynamic feature data set; based on the dynamic characteristic data set, a space-time diagram convolutional network is adopted to establish a wind power plant dynamic error prediction model, and space-time evolution rules of wind shear, turbulent flow and wake flow effects are captured; constructing a variational self-coding reference model based on historical full wind speed section data, calculating a residual error between a current working condition and the variational self-coding reference model in real time, and taking the residual error as a static error prediction value; performing adaptive weight fusion on the static error prediction value and the dynamic error prediction value to obtain a fusion error; and the fusion error is converted into the yaw angle correction amount, and the wind facing action is executed. The problems that an existing yaw error recognition technology is high in data dependence, lack of dynamic analysis, poor in model adaptability and difficult in complex wind field processing are solved.

The invention discloses a multi-dimensional real-time operation monitoring and alarm linkage system for an urban direct drinking water plant station, and relates to the technical field of intelligent water affairs. Water quality, water quantity and equipment operation state data are collected in real time and fused to form a multi-dimensional data set, and a threshold adjustment accuracy evaluation model is established based on an instantaneous flow fluctuation rate and a daily variation deviation value; a machine learning method is utilized to judge and correct an inaccurate threshold adjustment action, and a self-adaptive genetic algorithm is adopted to continuously optimize a multi-dimensional analysis model and an adjustment strategy, so that the recognition capability of the system on serious abnormities such as pipe burst and water leakage is effectively improved, the continuity and safety of direct drinking water supply are guaranteed, and the system is suitable for popularization and application. And the stability and the intelligent level of the whole operation safety guarantee system are obviously improved.

The invention relates to the technical field of digital engineering, and particularly provides a BIM-based engineering construction dynamic three-dimensional visual management method and system, and the method comprises the steps: collecting construction multi-dimensional data, carrying out the correlation mapping with an initial BIM model component after preprocessing, and forming a structured construction element data set; dynamically reconstructing an initial BIM model based on the data set, generating a three-dimensional dynamic twinborn body, and dynamically displaying information such as superposition progress, quality, resources and environment; a prediction analysis model is called to generate progress prediction, quality early warning and resource optimization suggestions, and a final scheme is determined after visual simulation verification; and updating the data set and the twin according to an execution result, and iteratively optimizing the prediction model. According to the scheme, dynamic integration and visual management of construction total elements are achieved, the timeliness, accuracy and intelligent level of construction management are effectively improved through intelligent prediction and closed-loop optimization, the construction efficiency is improved, and the management cost is reduced.