2472 results about "Data dynamics" patented technology

The invention provides an urban intelligent drainage management system based on big data analysis, and relates to the technical field of urban drainage management, and the system comprises a multi-source data collection module which collects historical climate changes, rainfall curves, satellite meteorology, pipe network node water level flow and topological structure data in real time; the dynamic three-dimensional modeling module constructs a pipe network three-dimensional model according to a fluid mechanics principle, and binds real-time sensor data by a spatio-temporal data fusion algorithm; the drainage limit analysis module calculates the maximum drainage threshold value and the failure probability of the node under extreme rainfall; the intelligent early warning module fuses LSTM and ARIMA models, generates a drainage pressure thermodynamic diagram in the future 72 hours in combination with satellite meteorological data and triggers early warning; the health assessment module generates a node health index and a maintenance priority order; the resource scheduling engine generates a dynamic scheduling scheme. The urban drainage problem is effectively solved, the ability of a drainage system to deal with extreme weather and pipe network aging problems is improved, and accurate early warning and efficient resource allocation are achieved.

The invention relates to the technical field of data analysis, in particular to an operation decision intelligent analysis method and system based on cross-domain data fusion. The method comprises the following steps: firstly, based on an enterprise multi-domain ontology knowledge base, performing entity identification and relation mapping on heterogeneous data from different business systems through a semantic mapping-based multi-source heterogeneous data dynamic fusion algorithm, and establishing a unified data model; then, a causal reasoning and deep learning fused hybrid intelligent decision engine is adopted to analyze and process the model; then, a multi-level causal relationship network among business variables is constructed through a causal relationship discovery algorithm by utilizing an analysis result of the hybrid intelligent decision engine, and an adaptive business scene analysis model based on reinforcement learning is used to dynamically adjust an analysis strategy according to business environment changes; generating a Pareto optimal decision scheme set through a multi-objective optimization algorithm, and outputting operation decision suggestions; according to the invention, the comprehensiveness and accuracy of intelligent analysis of enterprise operation decisions are improved.

The invention discloses an underground oil and gas well fault prediction method based on a multi-modal space-time diagram neural network, and aims to solve the problems of insufficient multi-modal feature coupling, weak space-time correlation modeling, poor real-time performance and the like of a traditional method. The method is characterized in that multi-frequency sensor data features and maintenance log semantic knowledge are respectively extracted through a time expansion convolutional network (TCN) and a BERT-BiLSTM model, and a cross-modal gating attention mechanism is designed to realize heterogeneous data dynamic fusion; an equipment space topological graph is constructed based on Delaunay triangulation, dynamic causal association between nodes is quantized in combination with transfer entropy to generate a time graph, and a fault propagation path is jointly modeled through residual space-time graph convolution; a hierarchical prediction module is constructed by adopting a bidirectional LSTM and a graph attention network (GAT), short-term fault classification and long-term equipment residual life prediction are respectively realized, and the fault prediction precision and industrial landing feasibility under complex working conditions are effectively improved.

The invention discloses a full-life-cycle-based medical equipment monitoring analysis system and method, and relates to the technical field of medical equipment monitoring, and the method comprises the steps: collecting medical equipment data in real time, and dynamically constructing a full-life-cycle digital twin model of medical equipment; constructing a medical equipment knowledge graph based on the equipment type, the function association and the spatial distribution; when the medical equipment node detects abnormal data, an early warning signal is sent to a full-life-cycle digital twin model associated with the medical equipment in the medical equipment knowledge graph in combination with the medical equipment knowledge graph; preliminarily judging fault causes and fault location, and generating an analysis report; performing multi-dimensional verification on the diagnosis result in the digital twin environment, comprehensively evaluating the risk coefficient of the scheme, and outputting an optimal maintenance strategy; in the maintenance process, the maintenance process is recorded in real time, and maintenance data and equipment state updating are synchronously fed back to the equipment full-life-cycle digital twin model.

The invention discloses a DDGS three-dimensional storage device based on PLC control and a management method of the DDGS three-dimensional storage device, relates to the technical field of intelligent storage management, and solves the problems that in traditional DDGS feed storage, the storage space utilization rate is low, the storage turning cost is high, risk control is not timely, and a quick response strategy is lacked. According to the method, the dynamic perception metadata warehouse is constructed, and DDGS material characteristic key data is monitored in real time to support anomaly detection; a storage control topology model is generated by using a graph theory modeling and dynamic attribute injection method to map an exception type; periodically collecting equipment data and fusing and updating a storage state; state diagram nodes are enhanced, risk response priorities are calculated, and grading operation is triggered; pLC control logic is dynamically adjusted based on historical data, and real-time monitoring and multi-device linkage control are achieved; the intelligent level of DDGS three-dimensional warehouse management is remarkably improved, the warehouse space utilization rate is increased, and the risk control capability is enhanced.

The invention discloses an integrated power distribution cabinet energy efficiency dynamic optimization method based on multi-modal data fusion, and relates to the technical field of intelligent power grids. The problems of failure of an energy efficiency optimization model and poor long-term operation stability caused by data time sequence misalignment and error accumulation of a multi-source sensor in the prior art are solved. According to the scheme, the time offset is dynamically corrected through the adaptive time sequence deviation prediction model; compensating missing data by adopting a non-uniform time step reconstruction algorithm and Kalman filtering; detecting an error drift trend through an exponentially weighted moving average model, updating a feature weight, and inhibiting long-term error accumulation; constructing a self-adaptive time sequence attention fusion network model, and fusing physical constraints and a data driving mechanism to generate an optimization decision; bayesian optimization is utilized to quantify parameter uncertainty, closed-loop feedback execution data is carried out, and model parameters are dynamically updated; according to the invention, the precision, long-term stability and equipment safety of energy efficiency optimization of the power distribution cabinet are remarkably improved, and efficient and reliable operation under multi-physics field coupling constraint is ensured.

The invention provides a modal layering enhanced multi-agent cooperation control method and a related device, and relates to the technical field of multi-agent dynamic confrontation and cooperation. Collecting data in real time through a multi-mode sensor, and generating high-order environment state representation; on the basis of high-order environment state representation, dynamic roles are allocated to all agents through a dynamic role migration network; constructing a multi-level strategy system, decomposing a decision into a high-level strategy layer, a middle-level cooperation layer and a bottom-level control layer, and respectively generating a tactical intention, a cooperation relationship and a physical control instruction; low-delay strategy synchronization under key events is realized based on an event triggering mechanism; introducing an antagonism element learning mechanism, and performing strategy prototype retrieval and online fine tuning; executing the bottom-layer control instruction and feeding back an execution state in real time to form closed-loop optimization; role distribution and strategy weight are dynamically adjusted according to feedback data, continuous evolution of the multi-agent cooperation system is achieved, and top-speed adaptation and continuous strategy self-evolution of novel opponents are achieved.

The invention relates to the technical field of data processing, and discloses a preoperative risk comprehensive assessment method and system for breast cancer and a storage medium. The method comprises the following steps: collecting clinical information and carrying out digital processing; segmenting the multi-modal image by using deep learning; analyzing a pathological section based on the image features; carrying out multi-level fusion modeling to obtain a risk heat map; performing digital twinborn simulation in combination with a knowledge graph; and dynamically processing the follow-up data to form a risk adjustment mechanism. According to the method, accurate quantification and individualized treatment decision making of the preoperative risk of the breast cancer patient are realized, and the prediction accuracy is improved.

The invention relates to the technical field of communication, in particular to a low-delay data transmission system and method for marine observation data, and the system comprises a data collection module which collects the multi-modal data of a marine environment, and carries out the priority classification of the data according to the real-time performance and importance; the route optimization module dynamically adjusts a multi-path fragmentation strategy and single-path task distribution in combination with a reinforcement learning algorithm and path optimization data; the communication module supports multi-mode communication modes such as laser communication and low-orbit satellite communication, and monitors the link state in real time; and the data fusion and analysis module performs dimension reduction, fusion and real-time analysis on the data by using manifold learning and a graph neural network, and meanwhile, predicts a long-term trend through time sequence analysis to generate observation report data. The method effectively reduces the transmission delay of high-priority data, improves the response efficiency of the system to abnormal events, and is suitable for the scenes of marine disaster early warning, environment monitoring and the like.

The invention discloses a fault detection and root cause analysis system for a multi-source energy system, and relates to the technical field of energy management and fault diagnosis. The system obtains various operation data and image data of the multi-source energy system in real time; preprocessing the operation data and the image data; inputting the preprocessed data into a hybrid algorithm combining wavelet transform, CNN and LSTM, and detecting an abnormal mode and a potential fault in the multi-source energy system; the digital twinborn model is dynamically updated according to data acquired in real time; and in combination with the feature extraction capability of a digital twin model and a hybrid algorithm, positioning key causes causing the fault, simulating a system operation state, analyzing potential causes of the fault, and outputting an analysis result. According to the invention, the comprehensive analysis capability of the system on various data sources is effectively improved, and a comprehensive and accurate solution is provided for intelligent operation and maintenance of the energy system.

The invention discloses an intelligent heat supply regulation and control system based on digital twinning and deep reinforcement learning. The system comprises a physical layer, a control layer and a control layer, wherein the physical layer is a physical heat supply system composed of heat source equipment, a transmission and distribution pipe network and a user terminal; according to the digital twinborn layer, a virtual heat supply system mapped with the physical layer in real time is constructed, the virtual heat supply system comprises a multi-physics field coupling model based on the thermodynamics and fluid mechanics principle, operation data of the physical layer are collected through a distributed sensor network, and the state vector of the virtual system is dynamically updated; and the intelligent decision-making layer is integrated with a DRL intelligent agent, the state space of the DRL intelligent agent is defined as a virtual system state vector output by the digital twin layer, the action space of the DRL intelligent agent is a regulation and control instruction combination of heat source power and pump valve opening, and a reward function fuses an energy consumption penalty term, a room temperature comfort reward term and a pipe network stability constraint term. According to the method, global optimization, high-precision continuous regulation and control and collaborative balance are realized through deep collaboration of digital twinning and deep reinforcement learning.

The invention discloses a control method and a control system for magnetic steel polishing, and relates to the technical field of polishing control, and the control method comprises the following steps: setting a magnetic steel positioning center coordinate and a polishing reference point, and configuring a polishing control path; a preset fixed position and a preset fixed posture are collected, and initial polishing process parameters are set in combination with the polishing control path; setting a magnetic steel polishing virtual environment, collecting pressure distribution simulation data of a polishing contact surface, dynamically correcting the feeding speed and the axial deflection angle of a polishing head, establishing a nonlinear mapping model, and adaptively adjusting the polishing power; and a laser contour scanning plug-in is arranged at the tail end of the polishing path, the simulated curved surface and the actually-measured point cloud data are compared for simulation compensation, and a polishing process parameter set is output to execute batch machining control. The technical problems that in the prior art, the grinding precision is not high, and technological parameters are not flexible to adjust are solved, and the technical effects of improving the grinding precision and achieving intelligent optimization of the technological parameters are achieved.

The invention relates to the technical field of data processing, provides a multi-node carbon emission intelligent contract verification method based on a block chain, and aims to solve the problems that data verification in an existing carbon asset transaction system is non-transparent, high in trust cost and difficult to adapt to policy changes. Distributed storage and dynamic verification of data are realized by constructing a hierarchical block chain architecture, the data are collected by using edge computing nodes, and third-party verification data are obtained through an oracle machine node cluster. The dynamic rule engine monitors data changes in real time, triggers a multi-stage verification process, and improves the data accuracy. And meanwhile, an improved Byzantine fault-tolerant mechanism is adopted to reach a consensus, and the system has the functions of strategy dynamic updating and security enhancement, so that the flexibility and security of the system are improved. According to the method, multi-party consensus and real-time verification of the carbon emission data are realized, trust cost of cooperation among enterprises is reduced, and healthy development of a carbon emission permit trading market is promoted.

The invention relates to the technical field of data processing, in particular to a modular task management system based on dynamic process configuration, which comprises a dynamic process configuration module, a task state circulation engine, a visual interaction interface module, a historical track tracing module and a modular expansion interface. The dynamic process configuration module dynamically binds enterprise configuration data with regional process original data through a process template ID to generate an enterprise exclusive process topological structure comprising a regional identifier, a node association relationship and a process classification label; and the task state circulation engine updates the task state data based on an event-driven and atomized transaction lock mechanism, and synchronizes the task state data to the change management general table. According to the method, flexible configuration of process logic, visual display of task states and cross-enterprise rapid adaptation are realized, and system expansibility and operation efficiency are improved.

The invention discloses a multi-source heterogeneous data dynamic protocol security exchange adaptation system, which comprises a protocol analysis engine, a data encryption module, a data format standardization engine and a routing strategy engine, and is characterized in that the protocol analysis engine is respectively connected with a data source, the data encryption module and the data format standardization engine; the data format standardization engine is connected with the routing strategy engine, and the routing strategy engine is connected with the nodes. The invention discloses a multi-source heterogeneous data dynamic protocol secure exchange adaptation method. Comprising the following steps of protocol analysis and data conversion, data encryption and sensitive data desensitization, integrity verification and anomaly detection, routing strategy and intelligent scheduling, data distribution and storage, and dynamic extension and protocol adaptation, and the dynamic conversion of different protocol data ensures that various types of data can be smoothly interacted. Meanwhile, an advanced data security enhancement technology is integrated, multiple protections such as encryption and authentication are carried out on transmitted and stored data, and the data security is effectively improved.

The invention relates to the technical field of press monitoring, in particular to a digital twinning-based three-dimensional monitoring system for a precision servo press, which comprises a physical layer sensing module for acquiring real-time operating parameters, environment variables and workpiece processing data of the press; the dynamic twin construction module constructs a total-factor digital twin, and simulates a force-heat-deformation coupling effect by using finite element analysis and a multi-body dynamics algorithm based on physical attributes and process parameters; the intelligent analysis center identifies a potential fault mode of the press machine and locates an abnormal source through multi-physics field simulation data in combination with an improved CNN-LSTM model; the three-dimensional visual interaction unit constructs an interactive immersive three-dimensional virtual scene, renders a running state and a processing process in real time, and generates a maintenance strategy; and the self-adaptive regulation and control unit predicts the residual life of the key component and dynamically adjusts parameters according to a maintenance strategy and real-time monitoring data. Therefore, the problems of single monitoring dimension, disjunction of maintenance strategies and the like in the prior art are solved.

The invention relates to the technical field of data information processing, in particular to an artificial intelligence-based index layer model construction analysis method and system, and the method comprises the following steps: 1, multi-source data synchronization and quality evaluation; 2, service context analysis and subject domain intelligent division are carried out, and service document and data table field semantics are analyzed; 3, index demand intelligent analysis and calculation logic generation: analyzing index demands input by a user through a natural language processing technology, matching service entities in the knowledge graph, generating initial calculation logic and optimizing execution efficiency; and 4, dynamically updating the index data and verifying the consistency, executing the optimized computational logic to generate the index data, tracking the blood relationship of the data, triggering dynamic updating, and setting a threshold value according to a historical fluctuation range to carry out abnormal alarm. Through automatic and intelligent processing, the probability of human intervention and human errors is reduced, and the efficiency and accuracy of data processing are improved.

The invention discloses an intelligent dynamic triage system and method for emergency patients. The triage system comprises a data acquisition module used for acquiring basic information, physiological parameters and historical records of patients; the artificial intelligence analysis module generates a preliminary triage result; the Internet of Things connection module collects hospital resource data in real time; the central control processing unit is connected with each module to receive data; the dynamic grading decision-making module divides the patients into critical symptoms, severe symptoms, subcritical symptoms and common symptoms; the resource allocation module dynamically generates a resource allocation scheme according to the grading result; the diagnosis and treatment process management module tracks the whole process of the patient and updates state data; when the state of the patient changes, the system transmits data to the artificial intelligence analysis module again for evaluation, triggers the grading decision and resource allocation module, dynamically updates the triage priority and the resource allocation scheme, and achieves closed-loop feedback and continuous optimization.

An adaptive data system (ADS) for cognitive data processing is disclosed. The ADS includes an adaptive semantic preprocessor, a trigger detector, a temporal batching engine, a symbolic encoder, and a dynamic cognitive transformer engine. The adaptive semantic preprocessor is configured to receive input data from one or more databases and identify cognitive data attributes comprising one or more contextual, semantic, and temporal attributes from the received input data. The trigger detector is configured to identify semantic divergence of the identified cognitive data attributes and provide a standardized data. The temporal batching engine is configured to provide a high-dimensional cognitive data from the standardized data. The symbolic encoder compresses the high-dimensional cognitive data. The dynamic cognitive transformer engine is configured to determine decision making rules, analyze the compressed high-dimensional cognitive data based on the decision making rules and provide recommendations based on an outcome of the analysis to a user.

The invention discloses a meta-learning Bayesian optimization prediction method for multi-modal displacement of a tank body of a photo-thermal power station, and the method comprises the steps: collecting the data of displacement, temperature, vibration and the like through a multi-modal sensor, separating a displacement sequence into trend, season and residual components through STL decomposition, and carrying out the fusion with the data of the sensor, thereby constructing a 6-dimensional spatial-temporal characteristic matrix; a two-way LSTM-attention mechanism model is adopted, time sequence dependence is captured in a two-way mode, and key cross-modal features are dynamically weighted. And introducing meta-learning-guided working condition adaptive Bayesian optimization: pre-training a meta-model by using a historical working condition to establish a mapping relationship between working condition characteristics and hyper-parameters, dynamically dividing working conditions by real-time data, then activating a corresponding Gaussian sub-model, initializing a search space through meta-learning prior, and optimizing hyper-parameters in combination with an adaptive acquisition function. The test set evaluates the performance of the model through RMSE and MAPE, and finally three-way displacement real-time prediction and safety early warning are achieved. The prediction precision and the dynamic adaptability of the tank body of the photo-thermal power station under the complex working condition are remarkably improved.

The invention provides a three-dimensional space data analysis method and system based on deep learning, and relates to the field of computer vision, and the method comprises the steps: carrying out the modeling of a static scene fundamental model and a dynamic object motion track through a space-time separated dynamic nerve radiation field; a lightweight dynamic neural radiation field model is deployed at an edge computing node, multi-modal sensor data are processed in real time, local three-dimensional scene representation is generated, rendering and prediction computing of a neural radiation field are executed on the edge node, and the implicit feature difference quantity of scene change is uploaded to a cloud; and the cloud end aggregates feature difference data of multiple edge nodes through a federated learning framework, dynamically updates a global scene priori knowledge base and issues the global scene priori knowledge base to the edge nodes. The method can improve the processing efficiency, precision and applicability of the three-dimensional data, and is especially suitable for carrying out tasks such as object recognition, target detection and semantic segmentation in a complex environment.

The invention provides an information transmission device and implementation method based on multi-protocol dynamic conversion and data fusion, and belongs to the technical field of information transmission, and the device comprises a multi-protocol communication interface module, a data analysis unit, a dynamic protocol conversion engine, a data fusion unit, a data assembly reporting unit and a log storage module. According to the method, information transmission is realized by initializing an interface, analyzing multi-protocol original data, dynamically converting the multi-protocol original data into a uniform format, fusing de-duplicated data, packaging, reporting and recording logs. The device and the method solve information interaction obstacles of different protocol devices, support bidirectional conversion of multiple industrial protocols, realize cross-protocol device collaboration and centralized management, improve the system intelligence level, and are suitable for scenes requiring multi-device collaboration, such as electric power, intelligent transportation, intelligent buildings and the like.

The invention discloses an enterprise operation global analog simulation method based on digital twinning. The method comprises the following steps: S1, collecting and preprocessing multi-source heterogeneous data; s2, constructing an enterprise operation digital twinning model, and defining an optimization objective function and constraint conditions; s3, optimizing the model by adopting a sea elephant optimization algorithm, and dynamically adjusting structural parameters, control variables and strategy configuration; s4, multi-scene simulation drilling is carried out based on the optimization model, and enterprise operation prediction results under different strategies are obtained; s5, evaluating and comparing key performance indexes corresponding to the strategies, and determining an optimal service configuration scheme; and S6, deploying an optimal service configuration scheme, collecting feedback data, and dynamically updating the model. According to the method, the enterprise operation digital twin model which can be optimized, simulated and fed back is constructed, so that operation simulation and optimal business scheme output under multi-strategy configuration are realized, and the enterprise decision-making intelligence level and the operation efficiency are improved.

The invention discloses an electromechanical installation engineering automatic debugging method and system based on intelligent control, and the method comprises the steps: enabling an intelligent control system to monitor the energy consumption of equipment in real time, optimizing an operation mode, adjusting the working state according to data, and enabling the equipment to work cooperatively through the intelligent control system; deploying temperature and humidity, pressure, current and voltage sensors for stable wiring, and configuring parameters by adopting an intelligent debugging tool; based on the preliminary data, optimizing the system by applying an adaptive control algorithm, adjusting a strategy, fusing multi-dimensional data, and identifying performance bottleneck and abnormality; the centralized monitoring platform monitors the equipment state in real time and sends out an early warning signal; after multiple rounds of debugging, the energy-saving effect and performance are evaluated, a control algorithm is optimized, and optimization is continuously carried out according to historical data and environmental changes; according to the electromechanical installation engineering automatic debugging method and system based on intelligent control, the adaptive control algorithm and the machine learning technology are introduced, and the strategy is dynamically optimized according to the equipment operation state, the environment change and the historical data.

The invention discloses a flywheel energy storage system control method based on data center photovoltaic power supply, and belongs to the technical field of power system and new energy grid-connected control, and the method comprises the steps: obtaining the output prediction data of photovoltaic power generation, the real-time load power, the voltage fluctuation parameter of a commercial power grid, and the real-time rotating speed state of flywheel energy storage; generating a power gap predicted value by taking the real-time rotating speed state of flywheel energy storage as a dynamic constraint variable; generating a flywheel commercial power cooperative control strategy according to the commercial power voltage fluctuation parameter; the temperature change rate of the flywheel motor is detected in real time, and a control instruction is generated; and synchronously sending the control instruction to the photovoltaic inverter and the flywheel motor, adjusting the parameters of the photovoltaic inverter and the flywheel rotating speed response data, and dynamically updating the control parameter set. According to the invention, a dual adjustment mechanism of flywheel rotating speed dynamic constraint and voltage fluctuation ratio weight distribution is adopted, and a temperature gradient harmonic collaborative suppression strategy is combined, so that millisecond-level power compensation response can be realized, the power grid dependence degree is reduced, and the service life of an energy storage system is prolonged.

The invention relates to the technical field of manufacturing process control, and discloses a multi-dimensional process data co-simulation control method and system and a storage medium. The method comprises the following steps: firstly, collecting a multi-dimensional process parameter set of a plurality of process equipment in a manufacturing production line, carrying out cross-dimensional feature extraction, and generating a comprehensive feature matrix containing a time sequence feature, a spatial distribution feature and an energy consumption feature; according to feature relevance of different dimensions in the comprehensive feature matrix, a process parameter dynamic coupling model is constructed, co-simulation is carried out on interaction between process equipment, and a process state evolution sequence is output; extracting abnormal fluctuation characteristics in the sequence, and generating a process parameter adjustment instruction set; and finally, according to the adjustment instruction set and real-time process feedback data, dynamically correcting model simulation parameters, generating an optimized process control strategy, and executing the optimized process control strategy. According to the method, collaborative analysis and dynamic control of multi-dimensional process parameters are realized, and the accuracy and adaptability of process control are improved.

The invention discloses a dam seepage stability analysis method based on flood forecasting, and relates to the technical field of hydraulic engineering safety monitoring, comprising the following steps: S1, integrating a flood forecasting system and a seepage monitoring system into a data acquisition layer, and acquiring flood forecasting data and seepage monitoring data by a sensor of the data acquisition layer; s2, performing space-time alignment on the flood forecast data and the seepage monitoring data through a space-time coupling interface of the dynamic fusion layer, and generating a dynamic driving variable of a seepage field boundary condition based on a flood routing model and a geographic information system spatial interpolation technology; through the space-time coupling interface, space-time alignment and dynamic driving variable generation of flood forecast data and seepage monitoring data are achieved, the problem that the real-time flood routing process cannot be reflected due to the fact that the boundary condition is static or lagged in traditional analysis is solved, the seepage field calculation boundary condition can be dynamically updated along with flood forecast, and the real-time flood routing process cannot be reflected. And the timeliness and accuracy of model calculation are improved.

The invention relates to the technical field of energy management, in particular to a building energy-saving analysis system based on big data. According to the method, through time sequence segmentation of the real-time energy consumption data and the historical load records and in combination with Z-score standardization processing, accurate description of the dynamic fluctuation range of the building energy consumption data is achieved, and interference of seasonal changes and equipment operation fluctuation on an analysis result is effectively avoided. According to the outlier detection algorithm constructed based on multi-dimensional features, the fine granularity level of anomaly recognition is improved through composite analysis of curve slope and power fluctuation. A dynamic threshold self-adaptive mechanism is combined with an operation mode and an equipment state, so that the sensitivity and response adaptation capability of energy consumption data monitoring are enhanced. A multi-dimensional parameter fusion method constructed by an association index and a credibility factor is adopted, and the credibility modeling and risk grade division of the energy consumption data are effectively realized by quantifying the association among the fluctuation amplitude, the duration and the data deviation degree.

The invention discloses an error correction system and method for marine environment observation data. The error correction system comprises a data acquisition module, a compensation modeling module, an error analysis module, a data compensation module, a data fusion module and a feedback optimization module. The data acquisition module acquires ADCP flow measurement, GPS, IMU, temperature, pressure and water quality monitoring data. And the error analysis module detects a drift error by using Kalman filtering and a hidden Markov model, and analyzes an environmental error based on a long short-term memory network. The data compensation module optimizes a compensation model and improves data precision. And the data fusion module fuses multi-sensor data by adopting a particle filter and a Bayesian optimization algorithm. The feedback optimization module dynamically adjusts the weight of the compensation model according to the error correction data, and improves the adaptability of the system. According to the invention, the accuracy of ocean current monitoring data is improved, and more reliable data support is provided for underwater environment research.

The invention relates to a building material mining life cycle process carbon emission improvement potential quantification method comprising the following steps: constructing a full life cycle carbon footprint digital model covering mining, transportation, processing and restoration, and integrating a digital twinning and BIM technology dynamic correlation carbon emission factor library; key carbon emission influence factors are identified through Monte Carlo simulation and regression analysis; establishing a multi-objective optimization model, generating an optimal process parameter combination by adopting an intelligent algorithm, and quantifying the emission reduction potential; model parameters are dynamically calibrated in combination with real-time data, and a closed-loop optimization mechanism is formed; space-time distribution visualization and block chain evidence storage technologies are introduced, and accurate monitoring, intelligent decision making and credible tracing of carbon emission are achieved. According to the method, the problems of data lag and single optimization in a traditional method are solved, the accuracy of carbon emission and the feasibility of an emission reduction scheme are remarkably improved, meanwhile, carbon sink offset evaluation is supported, and a whole-process technical support is provided for mine green transformation.