3743 results about "Data prediction" patented technology

The invention discloses an AI-based automatic financial information processing method, and relates to the field of financial automation, and the method comprises the steps: achieving the automatic collection and storage of structured and unstructured data through the access of enterprise multi-source financial data; systematic preprocessing is carried out on the collected multi-source heterogeneous financial data, and a unified and high-quality financial data set is constructed; based on natural language processing and a knowledge graph technology, performing text semantic understanding, transaction automatic classification, field standardization and label generation on the cleaned and integrated financial data; comprehensively quantifying enterprise operation and financial performance based on the structured transaction data and the semantic annotation result; based on historical financial indexes, establishing a multi-model architecture to predict key financial variables; and based on the structured data, the prediction result and the historical rule, identifying potential financial abnormity and risk behaviors, and realizing intelligent early warning. According to the method, the intelligence, the real-time performance and the accuracy of financial information processing can be remarkably improved.

The invention provides a photovoltaic intelligent sensing fault diagnosis method and system for an energy internet of things, and relates to the field of photovoltaic technology, and the method comprises the steps: deploying a multi-dimensional sensor network based on a star topology structure; a deep learning model based on a graph attention network and a bidirectional gating loop unit is utilized to extract space-time correlation features, and matching learning is carried out on the space-time correlation features and historical fault samples to generate fault feature mapping; constructing a fault diagnosis classifier by adopting a comparative learning method and optimizing the fault diagnosis classifier through a knowledge distillation technology, and classifying the fault feature mapping to obtain a fault type probability distribution matrix and a fault early warning level; constructing a multi-dimensional fault evaluation model based on Bayesian reasoning and time sequence correlation analysis, and evaluating fault credibility; and finally, constructing a fault evolution prediction model based on deep reinforcement learning, predicting the fault of the photovoltaic module in combination with the fault credibility and historical data, and generating a health assessment report.

The invention discloses a thermal imaging temperature rise trend early warning system based on time-space sequence prediction, and particularly relates to the technical field of thermal imaging data prediction and early warning. The thermal imaging temperature rise trend early warning system comprises an image conversion module, a fluctuation feature extraction module, an edge prediction module, an anomaly characterization module and a prediction decision module; a temperature dynamic change rate and gradient intensity are calculated, edge model prediction is carried out based on a fluctuation index combination, when the fluctuation index combination does not exceed a stable interval, a lightweight deep network model deployed at a thermal imaging acquisition end is called, and when the fluctuation index combination exceeds the stable interval, a prediction decision module determines whether to switch to a high-order multi-modal model; the space-time information extraction capability is improved by constructing the temperature evolution data body, the prediction path is dynamically controlled based on the fluctuation index combination, and the prediction stability and efficiency are improved; and the abnormal activation index and the prediction offset index are combined to realize adaptive switching of model calling, so that the accuracy and adaptability of the early warning system are enhanced.

The invention discloses a data-mechanism dual-drive-based rock burst early warning method and system, and the method comprises the following steps: deploying a multi-modal sensor network to collect coal and rock stratum data, building a rock burst disaster precursor information sample database, providing a rock burst disaster multi-modal data precursor feature recognition algorithm, and carrying out the recognition of rock burst disaster multi-modal data precursor features. Mining the relevance between the multi-modal data and disaster-causing key risk indexes, and establishing a rock burst disaster multi-modal data prediction model; establishing a three-dimensional geological geometric model, fusing a multi-field coupling dynamics constitutive model and a catastrophe criterion, constructing a PINN physical information neural network prediction model of the rock burst disaster, and obtaining a time-space evolution rule of an energy field of a target area; providing a loss function coupling calculation method of a multi-modal data driving sample error and a physical driving control equation residual error, dynamic data and mechanism prediction result weight, comprehensively calculating a risk score, and accurately judging a top disaster danger level.

The invention discloses a cross-border e-commerce compliance intelligent auditing platform and a multi-language contract analysis method, and relates to the field of cross-border contract compliance auditing. In the multi-modal data access step, customs codes, laws and regulations and other multi-source data are collected, and 18 kinds of language contract texts are analyzed; in the cross-language semantic alignment step, a knowledge graph is constructed, and multi-language legal concept mapping is achieved; in the compliance risk reasoning step, a rule engine and an agent cooperatively check a contract, and the compliance conclusion confidence is calculated; the dynamic risk assessment step adopts an LSTM network to analyze historical data and predict a risk trend; in the multi-language report generation step, a multi-format bilingual or multilingual report is generated based on a template engine, encrypted and archived. According to the invention, cross-border contracts are audited efficiently and intelligently, dynamic adaptation laws and regulations are analyzed in multiple languages, compliance risks are identified accurately, and a multi-language report is generated quickly; therefore, the checking efficiency is improved, the manual workload is reduced, the compliance risk is reduced, and the enterprise cross-border business competitiveness and the risk response capability are enhanced.

A dynamic optimization system for energy consumption of a refrigeration house based on digital twinning is characterized by comprising a data acquisition module used for acquiring basic structure data of the refrigeration house, technical parameters of a refrigeration system, real-time operation data and historical operation data, preprocessing the data and then outputting a standardized multi-dimensional real-time data stream; the model construction module is used for constructing a 3D geometric model, a thermodynamic transfer model and a refrigeration system mathematical model according to the multi-dimensional real-time data flow, performing machine learning calibration on model parameters through historical operation data, and performing fusion to construct a refrigeration house digital twin model; the prediction analysis module is used for predicting future energy consumption demand and load change according to the refrigeration house digital twin model and the real-time operation data, and outputting an energy consumption prediction result and a load analysis report; a strategy generation module; an execution feedback module; and a learning optimization module. Overall energy consumption of the refrigeration house is reduced, energy utilization efficiency is remarkably improved, and goods storage safety is guaranteed.

The embodiment of the invention provides a BIM-based hydraulic engineering digital management method and system. The method comprises the following steps: performing multi-dimensional monitoring system deployment on a to-be-monitored area to obtain multi-dimensional hydrological data; constructing a first BIM based on the topographic data of the to-be-monitored area, the real-time work area image, the hydraulic engineering construction information of the design stage and the equipment deployment information; fusing the multi-dimensional hydrological data, the water area change data, the construction progress data and the operation and maintenance monitoring data into the first BIM, and constructing a second BIM including a full life cycle; and through the equipment characteristic curve and the water conservancy project physical model, in combination with the multi-dimensional hydrological data and the water area change data, predicting a water conservancy project structure change trend and potential risk factors in the second BIM, fusing the water conservancy project structure change trend and the potential risk factors into the second BIM, and displaying an implementation effect and improvement suggestions in real time in the second BIM. A user is assisted to realize hydraulic engineering digital management, and the engineering management efficiency is improved.

A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

The invention discloses an energy-saving control method and system for a water chilling unit, and relates to the technical field of energy-saving control. During operation of the system, a data set is synchronously collected from a water chilling unit system through a multi-channel asynchronous sampling mechanism, time sequence compression and redundancy removal are carried out, an embedded perturbation calculation mechanism is used for calculating and generating a non-dominant control core coefficient, and the non-dominant control core coefficient is used for controlling the energy-saving control of the water chilling unit; a three-dimensional coefficient space is converted and output through an entropy state change structure and is used for constructing a state balance atlas, comprehensively calculating a control state index SEEI, evaluating the current energy state offset degree of a system, determining whether intervention is carried out or not, carrying out rule search and nonlinear modeling based on the control state index SEEI value, generating an adjustment matrix, and carrying out state balance analysis. And starting a data reconstruction micro-strategy of a short-time historical window, receiving an adjustment matrix, converting the adjustment matrix into a device-level instruction, executing an action through an edge controller, feeding back a response error epsilon (t) in real time, and predicting a potential performance degradation trend based on long-time system operation data.

The invention provides a high-speed traffic flow high-precision prediction method based on multi-source disturbance characteristics, and relates to the field of data prediction, and the specific steps are as follows: firstly, a multivariable entropy driving interaction field module maps the multi-source disturbance characteristics into a unified energy field, calculates joint information entropy density and constructs a joint interaction field; processing the original feature sequence; secondly, the collaborative disturbance reconstruction module adopts a learnable mapping matrix and a multi-scale mechanism to extract dynamic differences of features under different time scales, and generates enhanced disturbance response features through a decoupling network after global disturbance collaborative response is fused; then, a spatial manifold mapping and partitioning module realizes spatial expression and partitioning modeling of a traffic flow tension evolution trend; and then, the prediction module constructs an asymmetric prediction structure in combination with the disturbance amplitude factor and the weighted disturbance characteristics, adopts a mean square error, introduces a disturbance constraint term to train the model, and outputs a final traffic flow prediction result through the trained high-speed traffic flow prediction model.

The invention provides a test risk digital twinning early warning method based on multi-domain cooperative monitoring, and belongs to the technical field of virtual-real fusion test and digital twinning, and the method comprises the steps: firstly, building a fine finite element simulation model which comprises a digital tool system, a digital sensor and a test piece and considers nonlinearity; secondly, performing nonlinear finite element simulation analysis, and constructing a multi-level mechanical response field inversion reduced-order model; thirdly, completing the construction of a complete sensor data set through a data filling algorithm, and carrying out the failure judgment of the first hierarchical structure based on the complete sensor data set; and finally, carrying out future loading level sensor data prediction and completing failure judgment of a second hierarchical structure. Carrying out full-field mechanical response inversion and online real-time correction; and performing response inversion of the region of interest to realize failure judgment of the third hierarchical structure. According to the invention, real-time dynamic monitoring and early warning of the structure test risk can be realized, the real-time performance, the robustness and the accuracy are high, and a powerful guarantee is provided for the safety and the reliability of the structure test.

The invention relates to the technical field of medical equipment and intelligent learning, in particular to a critical patient vital sign dynamic monitoring and intelligent early warning system which comprises a multi-modal data acquisition module, a medical equipment linkage control module and an intelligent early warning decision module. A dual-channel Kalman filtering state updating model, a biological impedance monitoring model and an anti-interference and signal optimization model are established in the multi-modal data acquisition module, and multi-dimensional physiological monitoring information of the critical patient is obtained through the models; a physiological information standardization model and a safety control mechanism are arranged in the medical equipment linkage control module, and then multi-dimensional physiological monitoring information is cooperatively controlled to output multi-modal vital sign data; and the intelligent early warning decision module receives and analyzes the multi-modal vital sign data to obtain power spectral density, and predicts the disease development condition of the critical patient in combination with the depth prediction model and the multi-modal vital sign data. All the modules work cooperatively to achieve dynamic monitoring and intelligent early warning of vital signs of critical patients.

The invention discloses an intelligent reconstruction and prediction method and system for an ocean three-dimensional flow field, and relates to the technical field of data processing, and the method comprises the steps: obtaining the multi-source observation data of an ocean through a large language model and an edge monitoring device; preprocessing and fusing the multi-source observation data to obtain fused observation data; taking the target observation parameters in the fused observation data as nodes, and taking the space-time correlation and physical quantity coupling relationship among the target observation parameters as edges to construct a space-time correlation map; reconstructing and determining reconstruction data corresponding to the multi-source observation data according to the space-time correlation atlas; the three-dimensional flow field of the ocean is obtained through prediction according to the reconstruction data, and a visual three-dimensional flow field is output. According to the method, the multi-source observation data are fused and reconstructed, and the accuracy of predicting the ocean three-dimensional flow field can be improved.

The invention discloses a substation evaluation method and system fused with an attention mechanism neural network model, and the method comprises the steps: organizing data related to the operation of a substation according to a time sequence, forming multi-dimensional time sequence data, building a data prediction model with a prediction set of the substation at a future moment as a target function, and carrying out the prediction of the data. The method comprises the following steps: training a data prediction model by taking multi-dimensional time sequence data as a sample data set, inputting real-time data of a transformer substation into the data prediction model for solving to obtain a prediction set of the transformer substation at a future moment, and constructing a multi-index scoring model by taking a transformer substation transformation time sequence score as a target function, and inputting the prediction set of the transformer substation at the future moment into the multi-index scoring model to obtain a transformer substation transformation time sequence score, and predicting the transformation demand degree of the equipment. The method not only can effectively capture the characteristics of the complex time sequence data and improve the accuracy of substation transformation evaluation, but also can improve the network expression ability through the convolutional neural network and reduce the overall calculation cost of the neural network.

The invention relates to the technical field of page rendering, and discloses a progressive page rendering method and system based on streaming data, and the method comprises the steps: carrying out the data segmentation through obtaining a data stream, user interaction data and equipment performance parameters, and obtaining data blocks; then, performing cache management in combination with the data, and constructing a multi-level cache pool; thirdly, performing priority grading and sorting on the data blocks to form a rendering task queue; and according to the equipment performance parameters, optimizing a task sequence and obtaining an optimized task sequence. Next, combining the optimized task sequence and user interaction data, predicting data about to enter a viewport, and generating a viewport pre-rendering task; and finally, according to the viewport pre-rendering task, the multi-level cache pool and the equipment performance parameters, performing rendering strategy optimization to obtain a dynamically adjusted rendering task flow. The method can realize dynamic resource scheduling.

The invention relates to the technical field of buildings, and particularly discloses a building space scheduling system based on multi-source perception and decision optimization. The system comprises a multi-source sensing module, a data processing module, a decision optimization module and an execution module. The multi-source sensing module collects environment data, personnel data and equipment operation data; the data processing module performs cleaning, feature extraction and fusion on the multi-source data to generate structured data; the decision optimization module comprises a global optimization layer and a local optimization layer, formulates a macroscopic scheduling strategy and a specific scheduling strategy based on structured data prediction, and dynamically optimizes the macroscopic scheduling strategy and the specific scheduling strategy by adopting a machine learning model; and the execution module regulates and controls equipment and resources in the building space according to the macroscopic scheduling strategy and the specific scheduling strategy. According to the method, decision optimization and multi-source data perception are combined, and resource scheduling, energy efficiency management and user comfort of the building space are innovatively optimized.

The invention provides a transformer area source load storage intelligent adjustment method based on multi-scene cooperation, and belongs to the technical field of intelligent power grid adjustment, and the method comprises the steps: a data collection module which collects the related data of a power supply, a load and a power grid, and generates corresponding feature parameters; the data analysis module is used for determining comprehensive adjustment parameters based on the characteristic parameters, obtaining a plurality of scenes and generating corresponding scene labels; the data prediction module is used for generating a prediction load curve and further determining a charging quantity parameter in a load trough period; the strategy execution module is used for executing a control strategy corresponding to the scene label based on the predicted load curve, the charging quantity parameter in the load trough period and the scene label; and the strategy updating module is used for comparing the real-time load curve and the predicted load curve after the strategy is executed, determining an adjustment deviation ratio, correcting the predicted load curve based on the adjustment deviation ratio, and then executing the control strategy again. And the energy utilization rate and the self-adaptive capability of the system are obviously improved.

The invention discloses a water quality prediction method based on a Transform-LSTM fusion model, and belongs to the technical field of water quality time series data prediction and artificial intelligence. Comprising the following steps: (1) acquiring water quality data from a water quality monitoring station; (2) carrying out pretreatment; (3) screening out water quality characteristic data; (4) dividing into a training set, a verification set and a test set; (5) inputting the data into a Transform-LSTM (Long Short Term Memory) fusion model; (6) embedding water quality data time sequence information by a Transformer encoder through position coding, extracting a global dependency relationship among features by utilizing a multi-head attention mechanism, and optimizing gradient propagation by combining residual connection and layer normalization; (7) the LSTM layer receives the high-order features after Transform coding, and captures a local time sequence dynamic mode; and (8) mapping the extracted water quality time sequence characteristics to a specific prediction result by a regression output layer by adopting a linear activation function, and calculating an evaluation index. According to the method, the water quality change trend of the surface water body can be effectively predicted, and powerful support is provided for water ecological protection and sustainable development.

Systems and methods for Data Loss Protection (DLP) utilizing distilled models include receiving a plurality of general data predictions from a teacher model; determining one or more strengths of the teacher model based on the received general data predictions; generating a synthetic dataset based on the one or more strengths of the teacher model; providing the synthetic dataset to the teacher model and receiving a plurality of synthetic data predictions from the teacher model based thereon; and performing knowledge distillation on a student model based on the synthetic data predictions received from the teacher model to produce a distilled model. The distilled model is then used in production for classifying inputs to a DLP system.

The invention discloses a power grid load prediction method and system based on a large model, and relates to the technical field of power grid load prediction. Comprising the following steps: S1, collecting multi-source load comprehensive data, and carrying out edge processing and data preprocessing; s2, monitoring a sudden change point in real time, constructing a behavior tag sequence, and identifying a high-frequency disturbance point; s3, constructing a prediction input vector of the multi-source load comprehensive data, performing load data prediction, and calculating a physical constraint regularization value; s4, performing dynamic credible interval evaluation, performing real-time judgment on a prediction result according to a credible boundary value interval, and feeding back a regulation and control instruction; and S5, performing error analysis and retraining triggering, comprehensively evaluating prediction accuracy and output stability, and performing model optimization, version management and feedback learning. The problem that the prediction model is easy to vibrate due to high load change speed in a high-density load scene is solved.

The invention discloses a tension optimization control system and method for a tensioning device of an underground coal mine belt conveyor, and relates to the technical field of tensioning device optimization control. The method comprises the steps that S1, multi-source tensioning data are collected in real time, and data preprocessing is conducted on the multi-source tensioning data; s2, predicting a coal flow load based on historical multi-source tensioning data, and taking a dynamic compensation measure for a tensioning device; s3, in combination with the multi-source tensioning data and the coal flow load prediction result, collaborative distribution is carried out on the hybrid execution mechanism, and automatic regulation and control are carried out based on the collaborative distribution result; s4, abnormity is detected in real time based on the multi-source tensioning data, and early warning is carried out; s5, performing safety risk assessment according to the real-time multi-source tensioning data, and taking adaptive regulation and control measures in combination with a safety risk assessment result and an anomaly detection result; the problem that faults are caused due to the fact that high-frequency pulsation and short-time impact of belt tension are difficult to adjust in time is solved.

The invention relates to a segmentation model based on a CNN and Transform parallel double-branch structure, and belongs to the technical field of medical data prediction. The method comprises the following steps: acquiring a CT image, and preprocessing the CT image by executing windowing processing and contrast limited adaptive histogram equalization; extracting features of lung lesions in the preprocessed CT image through a parallel double-branch structure; inputting the extracted features into a cross enhancement fusion module, and performing complementary fusion on the features through dynamic weight distribution to obtain fused features; the fusion features are input into a multi-scale context information extraction module, and lesion boundary sensitivity is enhanced through cavity convolution of different expansion rates; the encoder features and the decoder features are fused through jump connection, and a segmentation result is output after resolution is recovered based on up-sampling; and optimizing model training by adopting a weighted loss function. Accurate segmentation of the lung lesion in the pulmonary tuberculosis CT image is realized, and clearer and more accurate lesion area information can be provided.

The invention provides a data processing system for cooperative operation of an intelligent combined fleet, and relates to the technical field of data processing, and the system comprises an acquisition module which is used for acquiring basic state data of a ship; the prediction module is used for generating state trend data in combination with the historical motion trail and the near-time data sequence, and the state trend data comprises a speed change rate and a course angle change trend in a future short time period; the sensing fusion module is used for receiving the state trend data of other collaborative ships, aligning the state trend data with the state trend data of the ship, and performing synchronous calculation to generate fleet collaborative prediction data; the adjusting module is used for recognizing an included angle error between the current ship thrust applying direction and the future resultant force direction of the target structure and calculating the attitude adjusting amount; the control output module is used for mapping into a thrust adjusting instruction and sending an execution instruction to a power system of the ship; according to the invention, autonomy and accuracy of fleet collaborative operation are improved.

The invention relates to the technical field of artificial intelligence, and discloses an operation and maintenance automatic fault diagnosis and repair system based on artificial intelligence, and the system comprises the steps: obtaining equipment operation data, and carrying out the denoising and cleaning; calculating the deviation and trend change of the equipment operation data, and dividing the fault equipment into different abnormal levels according to the abnormal fluctuation degree; executing a shortest path algorithm strategy, and calculating path weights from the fault equipment to all possible fault sources; executing a deep learning algorithm strategy, and performing root cause analysis according to historical data and current fault information of the equipment; automatically generating a repair strategy according to the fault type, executing an AI self-learning repair strategy, automatically generating a corresponding repair scheme, and performing secondary repair on the fault equipment in combination with a distributed self-repair technology; operation data are analyzed in real time according to historical fault data of the equipment, and faults of the equipment are predicted and processed in time; the downtime of equipment can be effectively shortened, the operation and maintenance efficiency is improved, and the labor cost is reduced.

The invention discloses a bank loan business risk control system and method based on big data analysis, and relates to the technical field of financial risk control, and the method comprises the steps: collecting and preprocessing real-time behavior data, and obtaining a user behavior feature set; based on the user behavior feature set, calling a behavior map modeling engine to carry out structured mapping, matching with a risk anchor point rule base, identifying a potential risk mode and labeling an initial anchor point risk label; correcting the deviation between the initial risk anchor point tag and the actual default record by adopting a value function optimization method, and predicting the risk grade score of the current behavior of each user in combination with the historical behavior sample data and loan feedback data of the user; predicting probability distribution of migrating to a default state in the future through user risk grade scores and historical state evolution data; and in combination with the potential loss under each behavior path, evaluating the current loan business risk, and generating a risk control strategy through a risk level mapping rule and a strategy decision engine.

The invention discloses a battery abnormal data prediction and state evaluation method, and relates to the technical field of battery state prediction, and the method mainly comprises the steps: carrying out the preprocessing of an experiment data set, and obtaining multi-dimensional time series data; a combined feature encoder, a pre-response encoder and a memory analysis module are constructed to realize a battery abnormal data fault prediction model; training the model by using the multi-dimensional time sequence data to obtain a trained model, and predicting the to-be-predicted data to obtain a prediction result; and calculating a reconstruction error between a prediction result and original data, constructing an AUROC evaluation model, and evaluating the battery abnormal data fault prediction model. By implementing the battery abnormal data prediction and state evaluation method provided by the invention, the feature extraction efficiency, the abnormal recognition precision, the detection stability and the generalization ability can be improved.

The mass concrete curing method based on the intelligent temperature control system comprises the following steps that concrete is divided into a top layer, a bottom layer and a middle layer, each layer is provided with an independent temperature control pipeline, and temperature monitoring points are designed; constructing a three-dimensional thermal field finite element model containing a hydration heat release function, temperature control pipeline heat sink and boundary conditions, and generating data by using a calculation result of the finite element model to train a neural network prediction model; a temperature control pipeline and a temperature monitoring point are installed before mass concrete is poured, and heat preservation is covered after pouring; data is collected in real time and input into the prediction model, the predicted temperature of each temperature monitoring point is output, if the temperature difference control threshold value or the temperature reduction control threshold value is exceeded, temperature control pipeline parameters and heat preservation measures of the corresponding layer are adjusted, and a temperature control strategy is executed after calculation is repeated till the requirement is met. The concrete temperature field is accurately controlled, temperature difference over-limit is effectively avoided, and the mass concrete curing quality is improved.

The invention discloses an LSTM-COF-based power equipment data anomaly detection method and system, and relates to the technical field of power equipment state monitoring, and the method comprises the following steps: collecting historical data, and constructing a three-dimensional data matrix; predicting equipment parameters at the extreme temperature through an LSTM model; compressing the features, quantifying the covariance deviation degree between the features in combination with a correlation abnormal factor algorithm, and detecting abnormal points; the system integrates a data collection module, a data preprocessing module, a data prediction module, a detection model generation module and a visualization module. According to the method, the multi-dimensional historical operation data of the power equipment is collected, the equipment parameters in the extreme temperature environment are predicted by using the LSTM model, the principal component analysis dimensionality reduction and correlation abnormal factor algorithms are combined, insulation degradation type and electrical connection type faults can be dynamically identified, the data distribution change is adapted through the incremental learning mechanism, and the fault diagnosis accuracy is improved. The problems of low high-dimensional data processing efficiency and poor anomaly detection adaptability due to manual experience dependence in a traditional method are solved.

The invention relates to a spare part service life prediction method, device and equipment and a computer readable medium. The method comprises the steps of collecting multi-mode state data of a target spare part; verifying the historical consistency of the multi-modal state data; and under the condition that the historical consistency verification of the multi-modal state data is passed, inputting the multi-modal state data into a target residual life prediction model so as to predict a degradation track of the target spare part based on the multi-modal state data by using the target residual life prediction model, the target residual life prediction model is a neural network model obtained by training by taking a physics degradation mechanism of the spare part as priori knowledge; and determining the predicted remaining life of the target spare part based on the degradation trajectory. According to the method, the evaluation one-sidedness caused by insufficient single data dimension is avoided, the prediction credibility is improved through a data verification and physical mechanism constraint model, and the technical problem of low life prediction accuracy caused by spare part life counterfeiting is effectively solved.

The invention proposes a flood disaster dynamic prediction method based on multi-source remote sensing data and a knowledge graph, and relates to the field of data prediction, and the method comprises the specific steps: firstly, building a space-time disaster dynamic model through a physical drive text generation module, and describing the evolution process of a flood disaster; generating personalized flood disaster description in combination with the static characteristics and the dynamic remote sensing data; then, a multi-scale residual diffusion enhancement module improves sensitivity to dynamic change of disasters through multi-scale trend extraction and residual calculation, noise is removed, and useful information is reserved; then, the multi-modal fusion module enhances interdependence and information sharing among different modals by using adaptive modal mapping, a cross attention mechanism and a weighted fusion strategy; and finally, training through a regression model, dynamically adjusting the feature weight by using an adaptive feature weighting mechanism and an incremental learning mechanism, and finally obtaining a flood disaster prediction value through the prediction set.