632 results about "Gradient boosting" patented technology

The invention relates to the crossing field of material design and artificial intelligence, in particular to a solid electrolyte intelligent inverse design method fusing a graph neural network and confidence analysis. According to the method, a prediction framework integrating multiple models is constructed, support vector regression, gradient boosting regression, a deep neural network and a graph neural network are included, component, process and structure parameter characteristics are fully fused, and the nonlinear mapping relation between input variables and performance parameters such as resistivity and conductivity is efficiently learned. In order to improve the credibility, a Bayesian neural network and a Monte Carlo method are further introduced, a confidence interval corresponding to each group of prediction results is output, and quantitative evaluation of the credibility of the prediction value is realized. In the inverse design module, high-dimensional submerged space parameters are generated based on a variational auto-encoder, and intelligent recommendation of parameter combination driven by target performance is realized in combination with strategies such as Bayesian optimization and a genetic algorithm. The design efficiency of the solid electrolyte and the success rate of material discovery can be effectively improved.

The invention belongs to the technical field of artificial intelligence, and discloses a PCB use fault early warning system based on artificial intelligence, which comprises a data acquisition module, a data processing module, an intelligent analysis module, a decision level fusion module, a self-adaptive modeling module, a multi-model cooperation module and a fault early warning module. The intelligent analysis module realizes double breakthrough of nonlinear feature capture and adaptive anomaly discrimination ability through a dynamic error threshold mechanism of an LSTM time sequence prediction engine and a depth autoencoder; an XGBoost-1DCNN hybrid classifier is constructed, a gradient boosting tree and multi-scale convolution features are fused in fault mode recognition, and the complex fault classification precision is remarkably improved; and the decision level fusion module constructs a multi-model decision conflict resolution mechanism based on an improved D-S evidence theory, and realizes great optimization of a false alarm rate through a confidence interval dynamic synthesis algorithm, thereby forming a closed-loop system with real-time response, multi-dimensional root cause analysis and intelligent hierarchical early warning.

The invention provides a customized energy-saving air conditioner control method and device for industrial process requirements, and is applied to the technical field of data processing. According to the method, electromagnetic interference and vibration noise are eliminated through Kalman filtering, and a standardized process-environment-energy consumption correlation sequence is generated; and converting the three-dimensional process load map into a three-dimensional process load map, and establishing an individualized air conditioner dynamic load prediction model in combination with heat and humidity characteristics of a scene through fluid dynamic simulation and self-adaptive grid division. Extracting process priority factors to construct an adjustment matrix, calculating an air conditioner parameter combination under target energy consumption, extracting energy consumption characteristics, dividing standard exceeding risk levels, and constructing a multi-dimensional characteristic matrix; and comparing real-time data with historical data to identify abnormity, and generating an energy consumption optimization correction factor. Users are grouped according to enterprise conditions, key factors are screened by using a gradient boosting tree, a personalized energy-saving control model is constructed by fusing multiple information, target parameters are output, and a real-time adjustment instruction and a time-phased energy-saving strategy are generated in combination with a time sequence.

The invention provides a weight metering automatic classification and calibration method and system based on image recognition, and relates to the technical field of weight metering, and the method comprises the steps: carrying out the imaging of the surface of a weight through dual-light-path high-speed camera shooting, carrying out the gamma correction, and extracting contour features and surface defect features; internal density distribution is obtained through X-ray imaging; performing multi-scale feature fusion on the surface features and the density distribution to construct a holographic feature model; analyzing the dynamic change trend of the characteristic parameters based on a deep variational Bayesian network, and performing evaluation and scoring in combination with a gradient boosting decision forest to obtain an initial classification; establishing a dynamic evaluation model by adopting a self-organizing competitive learning network, determining a weight grade and setting calibration parameters; selecting a corresponding reference weight to establish a grading calibration compensation model, and determining an adaptive weight coefficient for dynamic adjustment by combining defect distribution; and predicting a performance degradation trend by adopting a Shenchang differential equation network, and outputting calibration parameters and generating early warning information when the calibration precision meets a threshold value requirement.

The invention discloses a multi-target water supply scheduling method and system based on big data driving, and the method comprises the steps: collecting the multi-source data of a plurality of water supply targets, and enabling the scheme to obtain more reliable data support in the scheduling planning of the water supply targets; based on multi-source data serving as input features, the scheme combines the multi-source data with a graph space-time prediction model, a season statistical model and a gradient lifting regression model to obtain water consumption predicted by the three models respectively, prediction results of the three models are integrated through error adaptive weighting, more reliable predicted water consumption requirements are obtained, and the prediction efficiency is improved. And after the linear distribution model of the water source and the water supply targets is solved, initial water supply distribution schemes in one-to-one correspondence with the multiple water supply targets are obtained, the water supply accuracy of the water supply targets can be improved, multiple costs are used as targets to be minimized, and after multi-constraint combined solving, the water supply accuracy of the water supply targets can be improved. Therefore, the scheduling cost optimization is considered under the condition that the multi-target water supply scheduling meets flexibility and reliability.

The invention discloses an intelligent injection molding product quality prediction method, which comprises the steps of collecting product structure information, mold parameters and injection molding equipment operation parameters, and constructing a scientific mold testing process window based on simulation analysis; designing an orthogonal experiment scheme to generate a simulation data set, and forming a multi-source data training sample in combination with field test model data; training a quality prediction model by using a distributed gradient boosting tree model, inputting process parameters, and outputting quality indexes such as product gram weight, volume shrinkage and buckling deformation; the model interpretability is realized through a shop value analysis method, and the influence weight of the process parameters on the quality indexes is evaluated; the model is deployed in a production control system or a cloud platform, the product quality is predicted in real time, a dynamic feedback mechanism is established, and when the error between actual detection data and a predicted value exceeds a threshold value, model retraining and parameter correction are automatically triggered to form closed-loop control. The method effectively improves the accuracy, transparency and engineering adaptability of quality prediction of the injection molding product, and can be popularized.

The invention discloses a data security protection method and system combined with big data analysis, and relates to the field of data security protection, and the method comprises the steps: building a multi-dimensional data collection and fusion data set according to API access records, external threat intelligence and context metadata association data; based on an isolated forest algorithm, calculating a sample path length to evaluate a behavior anomaly probability score; risk indexes of external threats, data sensitivity and permission exceptions are quantified respectively; constructing a data security risk comprehensive assessment model based on a weighted summation algorithm, and updating and optimizing the weight in the model by using a gradient boosting tree algorithm; and according to an output result of the data security risk comprehensive assessment model, setting a security risk level, and according to the security risk level, dynamically responding to a protection measure. The method has the advantages that continuous and dynamic risk assessment and automatic response to third-party data access behaviors are realized by fusing multi-source data and an intelligent algorithm.

The invention provides an anesthesia virtual simulation training system fusing knowledge, skills and a thinking closed loop. The anesthesia virtual simulation training system comprises a medical knowledge base module, a clinical thinking module, a skill training module, an examination question brushing module, a knowledge graph module and an intelligent platform bottom layer framework. The intelligent platform underlying architecture comprises a data middle platform, an AI engine and a 3D engine, collects student behavior data of each module, constructs a dynamic student ability portrait through a gradient boosting tree algorithm and a collaborative filtering recommendation model, analyzes knowledge blind areas and skill shortages, plans a personalized learning path and pushes targeted training content, and provides a personalized learning result. A closed-loop process of evaluation, learning, practice and re-evaluation is formed; and deep fusion of theoretical knowledge, clinical thinking and skill operation is realized through a cross-module collaboration mechanism. The problems that traditional anesthesia teaching is high in practical operation risk, scattered in resource and insufficient in individuation are solved, and the clinical comprehensive ability and teaching quality of anesthetists are effectively improved.

The invention relates to the technical field of marine ecological environment monitoring and data analysis, and particularly discloses a sea area phytoplankton biodiversity index prediction method and system based on multi-model integration and feature engineering. After preprocessing, constructing four groups of nonlinear interaction characteristics of a temperature-salt relationship, oxygen-salt balance, chlorophyll chemical oxygen demand coupling and a nitrogen-phosphorus ratio based on environmental factors, combining station characteristics with basic environment and interaction characteristics, carrying out variance threshold screening, inputting a characteristic set into a multi-model integration framework containing models such as linear regression and gradient lifting, and carrying out multi-model integration; training and tuning according to a time sequence segmentation strategy, selecting model output according to a decision coefficient, using a result if the decision coefficient of the support vector regression model is within a preset range, and otherwise, taking a gradient lifting and extreme gradient lifting tree model to predict a mean value. And the prediction accuracy and the model generalization, stability and reliability are improved.

The invention discloses a machine learning assisted polyethylene reaction performance prediction model training method, prediction method and device. The method comprises the following steps: acquiring a training set; screening feature items used for model training; obtaining a gradient boosting regression model for catalytic activity, a gradient boosting regression model for molecular weight and a gradient boosting regression model for molecular weight distribution; extracting feature items for model training from the data of the training set so as to obtain feature vectors; and respectively inputting the feature vectors into each model so as to train each model, thereby respectively obtaining hyper-parameters of the trained gradient-boosted regression model for catalytic activity, hyper-parameters of the trained gradient-boosted regression model for molecular weight and hyper-parameters of the trained gradient-boosted regression model for molecular weight distribution. According to the method, a model relationship between input characteristics and polymerization results (including catalytic activity, molecular weight, molecular weight distribution and the like) is established through training set learning.

The invention discloses an emission reduction system for chloride ions in waste incineration fly ash based on machine learning, relates to the technical field of solid waste resourceful treatment, is used for data-driven fly ash multi-stage dosing collaborative removal, and is divided into fly ash pretreatment and data acquisition, online ion monitoring and dosing decision, multi-stage reaction and dynamic feedback, OTA upgrading and cross-plant migration. And removing the multi-dimensional pollutants synergistically. The dosing ratio and the reaction stage are optimized in real time through a gradient boosting tree-reinforcement learning model, and the concentrations of chloride ions, heavy metals and dioxin precursors are monitored on line. According to the system, monitoring and decision-making paths of all stages are stored in a cloud knowledge graph and can be upgraded through OTA, the chloride ion removal rate is increased by more than three percent, the agent dosage is reduced by 25%, the scheme can adapt to the difference of fly ash components in different factories, economy and environmental protection are both considered, and the system has remarkable popularization value in large-scale waste incineration and hazardous waste disposal.

The invention discloses a water supply system scheduling optimization method and device, electronic equipment and a storage medium, and relates to the field of intelligent scheduling of water supply systems. The method comprises the steps of collecting and preprocessing multi-dimensional data to obtain a data set; the pressure data is analyzed from the space-time dimension, and the unfavorable points and the pressure requirements thereof are accurately identified; the method comprises the following steps: constructing a water volume prediction model by adopting a time sequence model, and constructing a total water head difference prediction model by adopting a Light GBM gradient boosting tree in combination with a MultiOutputRegressor multi-output regression framework; constructing a minimum total water production cost objective function based on a prediction result, and outputting an optimal scheduling scheme by combining water volume and pressure constraint iterative optimization; and establishing a model updating mechanism to ensure dynamic adaptation of the strategy. According to the method, the problems of insufficient pressure guarantee, extensive cost control and weak model practicability and generalization ability are solved, the inherent contradiction that a traditional mechanism model is high in complexity and a pure data driving pressure prediction model is poor in generalization and lacks physical significance is overcome, and safe, stable and efficient intelligent technical support is provided for a water supply system.

The invention discloses a public building cold load short-time prediction method fusing physical information, and the method comprises the steps: collecting and preprocessing the historical cooling capacity, indoor environment, outdoor weather and equipment operation state data of a public building at a fixed time interval, and obtaining multi-dimensional input features; respectively establishing a workday sub-model and a holiday sub-model according to workday and holiday scene division; the workday sub-model and the holiday sub-model jointly form a cold load prediction model, the workday sub-model adopts a long short-term memory (LSTM) network, and the holiday sub-model adopts a light gradient elevator (Light GBM); a physical constraint loss function based on building energy balance and heat conduction residual error is introduced in the training process, and the physical constraint loss is fused into a total loss function according to a weighting coefficient so as to constrain that the output of each sub-model accords with the law of energy conservation and thermal inertia; monitoring the prediction error MAPE in real time and performing online calibration; and outputting a short-time cold load prediction result.

The invention belongs to the technical field of power distribution networks, and discloses a load prediction and early warning method and system for a transformer area containing distributed resources, and the method comprises the steps: combining a two-dimensional time sequence data sample set of a high-risk transformer area with a transformer area feature operation data set according to the transformer area and a timestamp, and generating a prediction model training sample data set; building a lightweight gradient boosting tree as a main prediction model, inputting a training sample data set for training, and optimizing hyper-parameters of the main prediction model by adopting a Bayesian optimization algorithm; and establishing a residual error correction model based on local weighted Gaussian process regression, superposing a load prediction result of a prediction day of the main prediction model of the to-be-predicted transformer area with a residual error correction value of a prediction day of the residual error correction model to obtain a final load prediction result, and outputting transformer area weight / overload early warning information. According to the method, the LGBM is adopted as the main prediction model for load prediction, the residual error correction model is adopted for residual error correction, and the robustness and adaptability of the model are improved.

The invention discloses a water supply pipe network leakage locating method based on a lightweight gradient elevator algorithm, and belongs to the technical field of intelligent water affair and pipe network monitoring. The method comprises the following steps: integrating hydraulic modeling basic information, and constructing an initial hydraulic model by utilizing hydraulic software; actual measurement values of key node pressure and main pipe flow under different working conditions are collected and compared with simulation values to be calibrated to reach the standard, and a high-precision hydraulic model is obtained; simulating each leakage working condition in different time periods on a selected potential leakage node / pipe section by using the calibration model, collecting pressure change data of a pressure monitoring node, and performing preprocessing and data division; the data set is used for training a LightGBM model, the LightGBM model learns the relevance of node pressure in spatial topology and the dynamic change characteristics of a time sequence at the same time, a spatial position differentiation weight mechanism is introduced, and accurate positioning of leakage points and quantitative estimation of leakage coefficients are achieved. According to the method, the pipe network leakage positioning precision and efficiency are remarkably improved, and support is provided for rapid and accurate leakage detection and leakage amount evaluation.

The invention discloses a machine learning-based low-voltage transformer area electric leakage risk intelligent identification method and system. The identification method comprises the following steps of 1, multi-dimensional electric leakage feature construction and transformer area information aggregation analysis; 2, dynamically evaluating the electric leakage risk and adaptively adjusting a threshold value; and step 3, an electric leakage type intelligent identification and confidence degree determination mechanism. The electric leakage risk identification and classification method has the beneficial effects that by introducing a graph nerve enhanced gradient boosting tree model (G-GTBoost) and an improved residual error convolution-time sequence neural network (Res-CNN-LSTM) model and cooperating with a multi-modal feature fusion and dynamic threshold adjustment mechanism, the performance is remarkably improved in electric leakage risk identification and classification.

The invention relates to the technical field of water quality prediction, in particular to a river water quality prediction method and system based on a machine learning coupling hydrological model, and the method comprises the following steps: obtaining a data set, and dividing the data set into a training set and a test set; constructing a limit gradient lifting model, and performing hyper-parameter optimization on the model; performing correlation analysis on the water quality parameter set and the water quality index WQI value based on the trained limit gradient lifting model, and screening to obtain key water quality parameters influencing the water quality index WQI value; constructing an LSTM model and a soil and water evaluation tool model; simulating a future hydrological water quality process based on the soil and water evaluation tool model, and calculating and outputting a future water quality parameter result; and inputting a future water quality parameter result into the trained LSTM-WQI model to predict a future river WQI value so as to obtain a prediction result. According to the invention, the machine learning algorithm is coupled with the hydrological model to construct the water quality evaluation model which is convenient to use and adapts to local conditions, and efficient and accurate prediction of future water quality is realized.

The invention discloses a laser radar-assisted wind power generation wind shear modeling method, and relates to the technical field of wind shear modeling, and the method comprises the steps: collecting the wind speed measurement data of a laser radar at a plurality of heights in a target fan region, and constructing a measurement volume integral operator matrix; constructing a unit induction field correction model based on large eddy simulation in combination with a gradient boosting tree; coupling the measurement volume integral operator with the unit induction field correction model, and obtaining a high-precision wind speed vertical section and a corresponding wind shear parameter through inversion; on the basis of the high-precision wind speed vertical section and the corresponding wind shear parameters, physical constraints are fused, a wind shear model is generated, and uncertainty evaluation is carried out; according to the wind shear modeling method, the problems that the measurement error of the laser radar is difficult to correct, the wind shear modeling lacks the physical constraint and the result credibility cannot be quantified are solved by constructing the volume measurement integral operator and the unit induction field correction model for coupling inversion and fusing the physical constraint and the Bayesian uncertainty evaluation method.

The invention discloses a total primary productivity estimation method and system based on multi-model coupling deep learning, and the method comprises the steps: obtaining the multi-source data of meteorological data, remote sensing images, latent heat flux, sensible heat flux and solar radiation, carrying out the quality control, missing value processing and nearest neighbor interpolation of different data sources, and carrying out the prediction of the total primary productivity. Unifying to a target spatial resolution and a time resolution; in a light energy utilization rate (LUE) model family, a solar radiation phase factor is introduced into a photosynthetically active radiation absorption ratio (FPAR) to obtain a phase modulation type FPAR, drought duration is introduced into a water stress function f (W) to obtain an exponential decay type f (W), and a GPP time sequence of a plurality of improved mechanism models is calculated according to the exponential decay type f (W); extracting spatial texture features from a remote sensing image stack by using a convolutional neural network (CNN), and performing cross-modal fusion on the spatial features and the GPP estimated by the plurality of improved mechanism models in a gating mode to form fusion representation; carrying out learning and collaborative optimization on the fusion representation of the GPP and CNN spatial features estimated by the plurality of improved mechanism models by adopting a gradient lifting tree model; and high-precision estimation of the GPP is realized through multi-model collaborative optimization. The method aims at solving the problem that a traditional light energy utilization rate model is insufficient in response under the extreme environment conditions of drought and intense radiation, the adaptability limitation of a traditional single model under the complex environment is broken through, and therefore high-precision GPP estimation under the complex environment is achieved.

The invention relates to the technical field of fiber production process simulation modeling, in particular to a Lyocell fiber spinning pulp porridge process simulation and quality prediction method and system.The method comprises the following steps that a simulation system is used for simulating the swelling process of cellulose in an NMMO solvent, and multiple sets of input and output data pairs are generated to construct a data set; a physical model of a simulation system is introduced into the gradient boosting tree algorithm to serve as regularization constraint, and an improved gradient boosting tree algorithm is obtained; and training an improved gradient boosting tree algorithm by using the data set to predict the quality of the Lyocell fiber spinning pulp porridge. According to the method, a slurry porridge quality prediction scheme based on simulation data and the improved gradient boosting tree algorithm is put forward for the first time, the slurry porridge research and development time and cost are greatly reduced, the improved gradient boosting tree algorithm integrates a physical model of a simulation system into a model training process by adding a physical constraint regularization item into a loss function, and the development efficiency of the slurry porridge is improved. The scientificity of a prediction result is enhanced, and the reliability of industrial application of the model is improved.

Particular embodiments relate to personalized document field prediction based on user behavior and feature generation. Specifically, various embodiments have the technical effect of improved accuracy with respect to field / entity value prediction (e.g., predicting that the amount due is X via a Gradient Boosting Model) relative to document processing technologies by learning through user behavior data or feedback (e.g., through continuous reinforcement learning from human feedback (RLHF)). This is at least partially because of the technical solution of accessing or generating unique features from one or more documents previously used by a user.

The invention discloses an intelligent bidding method and device based on an Apple advertisement marketing interface, and relates to the technical field of advertisement putting, and the method comprises the steps: constructing a double-branch machine learning model comprising a long and short-term memory time sequence branch and a gradient boosting tree feature branch; training the model by using historical delivery data to obtain a target prediction model; collecting real-time delivery data through an Apple advertisement marketing interface, and generating a plurality of candidate bidding strategies according to a preset business rule; inputting the real-time data and the candidate strategy into a target prediction model to obtain a conversion probability prediction value and a cost distribution prediction value of the candidate bidding strategy; based on the predicted value, calculating a strategy score value through a score function, and screening out a high-score strategy combination; and integrating keywords, audiences, time periods and region adjustment parameters of the strategy combination, and generating an optimal bidding strategy package. According to the application, the Apple Ads advertisement bidding strategy can be efficiently and automatically optimized so as to improve the putting efficiency and the cost control.

The invention provides an RAG retrieval enhanced SQL generation method based on a multi-dimensional knowledge base, and the method comprises the steps: respectively constructing a technical metadata knowledge base, a business knowledge base and an experience knowledge base based on a target database; respectively slicing knowledge contents of the technical metadata knowledge base, the business knowledge base and the experience knowledge base; receiving a query request of a user, and simultaneously performing parallel retrieval from the technical metadata knowledge base, the business knowledge base and the experience knowledge base to obtain a retrieval result; a large language model is called based on the retrieval result, SQL is output in a staged SQL generation mode, and the large language model adopts a multi-model collaborative architecture and adopts a gradient lifting algorithm for iterative optimization. Through the organic combination of the technical metadata knowledge base, the business knowledge knowledge base and the experience knowledge knowledge base, a comprehensive database query knowledge system is constructed, and the problem that knowledge coverage is not comprehensive in a traditional method is effectively solved.

The invention discloses a temperature field estimation system for a metal part additive manufacturing process, and relates to the technical field of temperature field estimation. The temperature field estimation system for the metal part additive manufacturing process comprises a data acquisition module, a temperature field preliminary estimation module and a temperature field correction compensation module, controllable and uncontrollable parameters are integrated through the data acquisition module, and the temperature field preliminary estimation module and the temperature field correction compensation module are utilized to estimate the temperature field in the additive manufacturing process. Multi-level pre-estimation of the temperature field is realized by integrating a random forest and a gradient boosting tree model, correction is performed by combining uncontrollable parameters, temperature field parameters can be acquired more accurately, powerful support is provided for optimization of additive manufacturing process parameters, control of part quality and defect analysis, precision and reliability of metal part additive manufacturing are improved, and the application prospect is wide. The problem that in the prior art, it is difficult to accurately estimate the temperature field in the additive manufacturing process is solved.

The invention provides an intelligent scheduling method and device for a multi-hole gate and a server, and relates to the technical field of hydraulic engineering automation and artificial intelligence optimization scheduling, and the method comprises the steps: determining target input characteristics through the historical operation data and simulation data of the multi-hole gate, and improving the target input characteristics through a lightweight gradient lifting tree model; performing nonlinear regression training processing on the target input features, and determining a target traffic prediction model at the current moment; performing hierarchical expansion processing on the gate opening degree combination through a multi-constraint search model to obtain a gate expansion scheduling scheme, and performing batch prediction processing and scoring processing on the gate expansion scheduling scheme by using a target flow prediction model to obtain a gate candidate scheduling scheme set; and performing upper limit detection screening processing and error tolerance screening processing on the gate candidate scheduling scheme set, and determining a target scheduling scheme of the porous gate. The method can significantly improve the prediction precision of the outlet water of the gate and the scheduling efficiency of the multi-hole gate.

The invention discloses a migraine attack advanced prediction method, system and device and a medium. The method comprises the following steps: acquiring patient health investigation and auxiliary examination data related to migraine; preprocessing the health investigation and auxiliary examination data of the patient; based on the preprocessed data, key features related to migraine are determined through feature screening; a migraine attack advanced prediction model is established based on a Gradient Boosting algorithm, the input quantity of the prediction model is the key features, and the output quantity of the prediction model is a migraine attack probability prediction result; and carrying out interpretability analysis on an output result of the prediction model based on an SHAP method. The method provided by the invention has the advantages of high accuracy, strong practicability, quick response, clear prediction time efficiency, window prediction time and strong interpretability, and is particularly suitable for scene prevention application scenes of medical institutions.

The invention discloses a medical equipment state monitoring and early warning method and system based on a full life cycle, and relates to the technical field of medical equipment state monitoring and early warning. Comprising a full-life-cycle data acquisition module, a multi-dimensional data preprocessing module, a full-life-cycle state evaluation module, a hierarchical early warning decision module, a full-life-cycle management module and an edge-cloud collaborative storage module. The full-life-cycle data acquisition module is used for acquiring multi-dimensional data of the medical equipment in the whole stage from purchase acceptance, operation and use, operation and maintenance to scrap evaluation, and outputting original data in the whole stage. Differentiation quantitative indexes are set for different life cycle stages, an evaluation model is constructed by adopting an analytic hierarchy process and dynamic weight adjustment, and an early warning threshold value and a four-level hierarchical response mechanism are dynamically adjusted in combination with a gradient lifting tree algorithm. The problems that a traditional evaluation mode is poor in adaptability, early warning misinformation lags behind, response pertinence is insufficient, and clinical safety guarantee is weak are solved.

The invention provides a cold chain supply chain meat inventory optimization regulation and control method and system, and the method comprises the steps: collecting multi-dimensional data to construct an impact factor data set, fusing a long and short-term memory network model, a gradient boosting tree algorithm model and an attention mechanism, and precisely predicting meat demands. Comparing the inventory, the threshold value and the predicted value, and generating an instruction according to a preset rule; and continuously optimizing the model through online learning, error evaluation and rolling prediction. According to the invention, the problem of overstock or stockout caused by inaccurate demand prediction and lack of a scientific regulation and control mechanism in the existing cold chain supply chain meat inventory management is solved.

The invention provides a database fault root cause positioning method and device based on causal discovery, and belongs to the technical field of database operation and maintenance and fault diagnosis. Comprising the following steps: generating a two-dimensional data table and a statistical information set by using a multi-source operation log of a database system; constructing a causal discovery algorithm set A, and training the gradient boosting tree model by using the training set and the path combination to obtain an agent model Fb; establishing a Monte Carlo tree, selecting child nodes of root nodes according to performance expectation and exploration rewards, and expanding the child nodes to leaf nodes layer by layer; running the Monte Carlo tree, and obtaining a causal graph G * and a performance score by using a voting mode according to all causal relationships in the causal graph obtained by each node; calculating an exploration reward and a performance expectation of each node in a complete algorithm path, and returning the exploration reward and the performance expectation upwards to a root node from a leaf node along the path; updating Fb based on the searched path; and processing the new task by using a Monte Carlo tree, and identifying an affected processing variable according to a path pointing to an abnormal result variable in the causal graph.

The invention relates to the technical field of real estate assessment, and provides a land real estate assessment method and system based on big data, and the method comprises the steps: building a data collection layer, obtaining multi-source land real estate data through a big data platform, setting a geographic boundary, and verifying the data integrity; combining a value influence factor system to set a feature acquisition dimension, and optimizing a weight to obtain a multi-dimensional feature scheme; a model construction layer is established, dynamic modeling is performed based on market fluctuation parameters, key feature values are extracted, and an evaluation reference value is calculated by adopting a gradient lifting algorithm; establishing an evaluation analysis layer, collecting and cleaning real-time transaction data, and comparing an evaluation reference value to generate a value deviation report; and a decision optimization layer is constructed, fluctuation causes are analyzed, model parameters are optimized, and finally an evaluation report is generated. The accuracy and timeliness of land real estate value evaluation can be improved, and a scientific basis is provided for market decision making.