737 results about "Hidden layer" patented technology

The invention relates to the technical field of air conditioning, in particular to a laboratory heating and ventilation load prediction and self-adaptive regulation and control method. According to the method, the infrared frame and the power sampling time mark are synchronized, the sensing flow is aligned and packaged, and the thermal diffusion evolution rate is generated. Lagging characteristics are determined in combination with power jump and temperature rise moments, and a heterogeneous dynamic coupling model is established. Extracting a physical evolution parameter as a mechanism operator, injecting the mechanism operator into a hidden layer of the prediction model, reconstructing a phase space, and calculating an air load increment in a lag window. And reverse mapping is executed based on the heat exchange characteristics to generate a feedforward instruction, and when the rate exceeds a threshold value, the weight is issued and dynamically corrected, so that closed-loop correction is completed. According to the method, deep coupling of feedforward prediction compensation and feedback residual adjustment is executed, and accurate regulation and control of the air volume and cooling and heating loads of the laboratory are achieved.

The invention relates to a trajectory prediction model robustness enhancement method based on dynamic subspace projection decomposition. Comprising the following steps: firstly, extracting hidden layer semantic features containing historical tracks and map topology through a multi-modal feature encoder; secondly, constructing a dynamic routing mechanism based on scene self-adaption, and calculating projection weights of input features on a plurality of expert subspaces; then, executing truncation projection operation based on orthogonal decomposition, retaining core semantics located in a low-dimensional space, and filtering out adversarial disturbance located in an orthogonal complementary space; and finally, introducing a feature consistency constraint training mechanism, taking the reconstructed features of the clean sample as anchor points, and compulsively aligning the purified features of the confrontation sample with the anchor points. Compared with the prior art, the method has the advantages that the robustness of the model in white box gradient attack, black box query attack and physical semantic deception scenes is remarkably improved through feature purification of a physical level and structured consistency constraint, and the prediction reliability of the automatic driving system is ensured.

The invention relates to the technical field of metallurgical industry simulation and intelligent control, and discloses an intelligent metallurgical process virtual simulation method and system based on digital twinning. The method comprises the following steps: acquiring multi-modal high-dimensional data in a metallurgical process and reducing dimensions to obtain a feature vector set of a hidden layer space; performing disturbance injection simulation by using the feature vector set of the hidden layer space to obtain a multi-path state sequence with time dependence; predicting the abnormal path by using a recurrent neural network to obtain a predicted state evolution trajectory; utilizing a preset inverse mapping function to obtain multi-path state representation in the physical space; screening to obtain a risk path set; key evolution nodes are extracted from the set to be processed, and a virtual simulation scene is obtained; and performing optimization simulation on preset process adjustment parameters according to the virtual simulation scene, and determining optimized parameter configuration. The method can solve the problem that it is difficult to construct a comprehensive virtual simulation scene which truly restores the physical production rule.

The invention discloses a dynamic routing parameter efficient fine tuning method and system based on LoRA-MoE, and relates to the technical field of large model fine tuning. The method comprises the following steps: firstly, constructing a heterogeneous expert architecture-based LoRA module pool based on a multi-field data set; and secondly, coding the hidden layer features of the task through a dynamic gating network, realizing continuous differentiable expert activation, and improving the balance of expert allocation by adopting temperature attenuation and entropy regularization constraint. And then, dynamically selecting and carrying out weighted fusion on a plurality of LoRA parameter increments according to task semantics in a reasoning stage, so as to realize low-cost model adaptive updating. Finally, the module pool is continuously optimized through the confusion degree and manual evaluation feedback, low-efficiency modules are automatically eliminated, and a new LoRA module is generated to maintain task coverage. According to the method, the accuracy and generalization ability of the model in a complex scene can be remarkably improved on the premise of ensuring light weight, and rapid adaptation and dynamic optimization of a large model under a low-resource condition are realized.

The invention discloses a business risk management method and device, a computer program product and electronic equipment. The method relates to the field of artificial intelligence and big data, and comprises the following steps: obtaining business data, and encoding the business data to obtain a business vector; the business vector is input into a target model to obtain a risk prediction level, the target model comprises an input layer, a hidden layer, an attention mechanism layer and an output layer, the input layer is used for extracting a state vector from the business vector, the hidden layer is used for extracting hidden features from the state vector, the attention mechanism layer is used for calculating an attention weight, and the output layer is used for outputting the attention weight; the target feature is determined through the hidden feature and the attention weight, the attention weight is determined by the similarity between the business risk index and the hidden feature and the position code of the hidden feature, and the output layer outputs a risk prediction level based on the target feature; and determining a target risk management strategy through the risk prediction level. Through the method and the device, the problem of low business risk management efficiency in related technologies is solved.

The invention relates to the technical field of data security, and discloses a risk prediction method based on mobile terminal equipment. The method comprises the following steps: acquiring a historical behavior data set of mobile terminal equipment; a dynamic input feature set and a static interference feature set are divided according to the data set, the dynamic input feature set is derived from a user operation behavior sequence updated in real time, and the static interference feature set is generated by fusing an equipment state change record and an environment parameter change track. And establishing a dynamic risk prediction model, taking the dynamic input feature set as a model input layer, taking the static interference feature set as a model hidden layer adjustment parameter, and outputting a risk probability value by means of a bidirectional feature coupling mechanism. Segmenting and intercepting the static interference feature set by adopting a sliding time window, extracting a fluctuation period and a frequency distribution rule of the interference feature, and generating an interference feature fluctuation map. And matching the current interference characteristic state based on the map, and generating a real-time risk early warning signal in combination with the risk probability value.

The invention provides a satellite communication channel prediction system and method based on a large language model, and the system comprises a preprocessing module which is used for obtaining input data, including channel information of a time-frequency domain and a time delay-Doppler domain of satellite communication in a historical time period, and carrying out the preprocessing of the input data, and obtaining the preprocessed input data; the embedding module is used for extracting channel characteristics of a time-frequency domain and a time delay-Doppler domain from the preprocessed input data, splicing the channel characteristics of the two domains, mapping the spliced channel characteristics into an embedding space of a large language model, and then carrying out position coding to obtain a channel characteristic sequence after position coding; the large language model is used for processing the channel feature sequence after position coding and taking a result output by a hidden layer of the model as a final channel feature sequence; and the output module is used for predicting the channel information of the time-frequency domain of the satellite communication in the future preset time period according to the final channel characteristic sequence.

The invention provides a gear peeling time-varying meshing stiffness prediction method and system based on a back propagation neural network, and the method comprises the steps: considering a tooth surface peeling fault based on a gear tooth bearing contact analysis method, and constructing a helical gear pair time-varying meshing stiffness calculation model; a real irregular tooth surface peeling area is fitted by adopting a least square ellipse fitting method to obtain an ellipse appearance representation, any peeling position is completely described through six key geometric parameters, the geometric parameters of the ellipse appearance are systematically traversed and fitted, and diversified peeling appearance samples are generated. Introducing a tooth profile deviation matrix corresponding to the peeling morphology sample into a tooth surface bearing contact analysis model, and constructing a training data set; and constructing a back propagation neural network model of multiple hidden layers, and performing end-to-end training by using the training data set, so that the back propagation neural network model learns a nonlinear mapping relationship from geometric parameters to a time-varying meshing stiffness curve, thereby predicting the time-varying meshing stiffness under any peeling morphology.

The invention discloses an electric meter load real-time monitoring method based on edge calculation. The method comprises the following steps: acquiring load data in real time at an electric meter end and constructing an initial hidden layer network structure; determining an initial reference threshold value of the load data change event; detecting load data change in real time by using an event triggering mechanism and generating a triggering signal; dynamically adjusting a hidden layer node structure based on real-time load data; updating the output weight matrix by adopting a sequential learning method and predicting load data in real time; identifying a key data event according to a prediction result and executing compression packaging; and uploading the packaged key data to a remote monitoring center. According to the invention, local real-time processing of load data is realized, data transmission delay is effectively reduced, and monitoring precision and real-time performance are improved.

The invention relates to the technical field of voice recognition intelligent home control, and discloses a non-specific person voice recognition intelligent switch control method and system based on deep learning. The non-specific person voice recognition intelligent switch control method is applied to intelligent switch control equipment, and specifically comprises the following steps of S101, receiving original audio signals continuously collected in a to-be-controlled environment, and preprocessing the collected original audio signals, and then a starting point and an ending point of an effective voice segment are positioned by adopting endpoint detection based on a double-threshold method and combining the characteristic parameters of the short-time energy and the short-time zero-crossing rate. A multi-layer hidden layer structure with Dropout regularization is adopted in a neural network model, the generalization ability of the model is enhanced, a context sensing mechanism is introduced into a semantic understanding module, a composite instruction containing azimuth information can be intelligently analyzed, crossing from recognition to understanding is achieved, and the method has the advantages of being high in practicability and easy to popularize. The system is ensured to maintain a high recognition rate for voice instructions of different users under different environment conditions.

The invention discloses a model illusion detection method and device based on internal state fusion and a medium, and relates to the technical field of natural language processing. The method comprises the following steps: extracting multi-modal features in a forward propagation process of a target large language model, wherein the multi-modal features comprise a hidden layer embedding feature, an attention feature, a feedforward network activation feature and a text feature; aligning the multi-modal features to the lexical element length of the generated text through an interpolation method, and calculating the weight of the position of the lexical element corresponding to the multi-modal features; performing weighted fusion on the multi-modal features according to the weights to generate a fusion feature sequence; and constructing the fusion feature sequence into a graph structure, reasoning the graph structure by using a multi-layer attention network, and outputting a lexical-level illusion probability through a classification head. According to the method, the target model parameters do not need to be modified, and high-precision detection can be completed only through single-time forward propagation.

The invention relates to the technical field of printed circuit board signal management, and discloses an intelligent management method and system for a printed circuit board signal control process, and the method comprises the steps: obtaining the noise data of a target channel of a target management object and all adjacent channels in real time, analyzing the noise data of all channels, generating a frequency domain collaborative management domain, and carrying out the frequency domain collaborative management domain; the target management object is a server printed circuit board; the method comprises the following steps: S1, analyzing noise data to generate a frequency domain collaborative management domain, S2, mapping transient current into an N * N matrix and establishing a two-layer graph convolutional network, and S4, acquiring load data in a 100ms window, processing by using a two-way long-short-term memory network of two hidden layers, and adapting to signal transmission of a server printed circuit board, so as to avoid misjudgment caused by crosstalk. Coupling of a strong electromagnetic field is suppressed, and the signal management effect and the transmission stability are improved.

The invention relates to the technical field of space structure design and parameter optimization, in particular to a neural network model-based reticulated shell structure node parameter automatic optimization method, which comprises the following steps of: acquiring an input-output data pair of a single-layer cylindrical reticulated shell constructed by aluminum alloy plate type nodes, the input parameters comprise design parameters and initial node parameters of the single-layer cylindrical reticulated shell. According to the method, node optimization data under different parameters are obtained through interaction of a genetic algorithm, finite element software and a programming tool, an example is supplemented to construct a training data set covering a common design parameter range of a project, and then a three-layer full-connection neural network model containing two hidden layers is trained; and the model precision is ensured by matching an early stop strategy and a learning rate attenuation strategy. During application, target single-layer cylindrical reticulated shell design parameters are input, the model can automatically output optimized node parameters, repeated iteration and manual intervention of a traditional method are not needed, optimization time consumption is greatly shortened, and the multi-working-condition batch optimization requirement is efficiently met.

A QR Code Verification Engine provides a multi-layered security framework for generating, validating, and authenticating QR codes while preventing tampering, fraud, and unauthorized access. The system embeds a hidden security layer within the QR code using steganographic encoding or invisible watermarking techniques, ensuring detection of any modifications. The hidden layer is encrypted using asymmetric cryptography, allowing only an authorized verification system to extract and validate it. An AI-powered tamper detection module analyzes QR codes for anomalies, while cryptographic hash verification ensures integrity. The system employs biometric authentication, push notification approvals, and contextual security measures to enhance user verification. Dynamic QR codes with expiration rules prevent replay attacks. Secure offline verification allows authentication without network connectivity. The system integrates with financial platforms, web security tools, and real-time fraud detection mechanisms, ensuring a highly secure and scalable QR code validation framework for transactions, identity verification, and access control applications.

The invention provides a loan risk assessment method and device, a storage medium and electronic equipment, and is applied to the technical field of artificial intelligence. According to the method, firstly, standardization processing is performed on loan application data, a pre-trained deep learning risk assessment model is input, and a loan risk score and hidden layer representation are obtained; and through the optimized orthogonal rotation matrix, mapping the hidden layer representation to a predefined loan risk key causal variable space, and outputting a corresponding causal variable. The variables are used for performing causal intervention verification, after an intervention result is obtained, the intervention result and a loan risk score are input into a visual interface together, a layered causal graph is generated, and a decision path of the risk score is visually displayed, so that the interpretability and transparency of loan risk assessment are improved.

There is provided a volume preserving trainable artificial neural network and a system and a method for building a volume preserving trainable artificial neural network. In an aspect, an artificial neural network including: an input layer to receive input data; one or more sequentially connected hidden layers, the first hidden layer connected to the input layer, to perform operations on the input data, each hidden layer including: one or more volume-preserving rotation sublayers; one or more volume-preserving permutation sublayers; one or more volume-preserving diagonal sublayers; and an activation sublayer; and a downsizing output layer connected to the activation sublayer of the last hidden layer. In some cases, the activation sublayer includes a grouped activation function acting on a grouping of input variables to the activation sublayer.

The invention discloses a skin stretch forming residual stress rapid calculation method which comprises the following steps: randomly generating a combination of a pre-stretching amount, a coating elongation rate and a friction coefficient, submitting the combination to finite element analysis software to execute batch simulation, and generating a result file named by process parameters; traversing all the result files, extracting node numbers and residual stress values, and storing the node numbers and the residual stress values as a text format data set corresponding to the process parameters; a full-connection neural network model is constructed, an input layer receives the three process parameters of the pre-stretching amount, the coating elongation and the friction coefficient, a hidden layer comprises multiple layers of neurons and adopts a ReLU activation function, and an output layer generates residual stress values of all nodes; training the neural network model by using the data set, and adjusting the network weight through an optimizer; and inputting target process parameters to the trained neural network model, and outputting residual stress calculation results of all nodes of the skin. The technical purposes of rapidness, high efficiency and low cost are achieved.

The invention discloses a photovoltaic power prediction method based on improved empirical mode decomposition and an optimized long short-term memory network, and the method comprises the steps: firstly carrying out the preprocessing of abnormal value elimination, missing value filling, normalization and the like of photovoltaic power and related meteorological data, and improving the data quality; then, an improved empirical mode decomposition (EE-ANEMD) algorithm is adopted to decompose the preprocessed power sequence into a multi-scale intrinsic mode function component and a residual term, and high-frequency noise, intermediate-frequency fluctuation and a low-frequency trend are effectively separated; global optimization is carried out on the hidden layer unit number, the initial learning rate and the maximum number of training times of the LSTM network through an improved sparrow search algorithm (ISSA), finally, the optimized LSTM is utilized to carry out training prediction on each component, and results are fused and subjected to reverse normalization to obtain a final value. Experiments show that the test set RMSE of the method is reduced compared with that of a single LSTM, the mid-term prediction precision is remarkably improved, and reliable technical support is provided for power system dispatching, new energy consumption planning and photovoltaic power station operation and maintenance.

The invention discloses a fault tree Boolean function equivalent mapping method based on an untrained neural network, and relates to the field of fault tree analysis. In order to solve the problems that in the prior art, a Boolean function mapping structure is not beneficial to parallel expansion, the calculation efficiency is limited, and the Boolean function mapping structure is difficult to efficiently realize on high-parallel platforms such as a GPU, the invention provides a method for generating topological structure data by analyzing a fault tree model; the basic events, the intermediate events and the top events are mapped into neurons of an input layer, a hidden layer and an output layer respectively, a feedforward network with fixed weight and bias is constructed, and a logic activation function is defined in nodes to realize Boolean logic propagation. The input layer receives a basic event state vector, outputs a top event result through forward propagation, and can realize large-scale Boolean function mapping on a parallel platform through batch input matrixes. The method is suitable for reliability analysis, minimum cut set simplification, top event probability calculation, parallelization fault tree solving and the like of a large-scale complex system.

The invention provides a Tibetan multi-dialect speech recognition system, and the system comprises a Tibetan self-supervision model which is used for extracting hidden layer speech representation of an original speech signal to obtain a first input feature vector; the original voice signal is Tibetan dialect voice; the dialect feature extraction module is used for extracting dialect features of multiple pieces of dialect information in a one-hot coding form to obtain a second feature vector; the multiple pieces of dialect information at least comprise Anduo, Kangba and defense and Tibetan dialects; the encoder-decoder model is used for routing the corresponding dialect expert module according to the first input feature vector and the second input feature vector and outputting a Tibetan text; and the dialect expert module is used for routing each input feature to the most relevant feedforward network for joint learning. In a multi-dialect environment, unique characteristics of different dialects can be distinguished and understood more accurately, corresponding dialect experts can be effectively and dynamically called according to characteristics of input signals by introducing the hybrid expert module, and processing efficiency and accuracy are remarkably improved.

The invention discloses a bolt tightening rotation angle reachability prediction system for a tripping type torque wrench, and relates to the technical field of industrial intelligence. The method is used for solving the problem that the rotation angle reachability is difficult to predict under nonlinear friction interference. The method comprises the steps that firstly, a system obtains a dynamic tightening sequence and a static working condition context through a wrench communication interface, a composite heterogeneous input tensor is constructed through an entity embedding and sliding window algorithm, and multi-source data fusion is achieved; then, local spatial features and time-dependent features are extracted in parallel through a convolutional neural network and a long-short-term memory network, a high-dimensional spatial-temporal feature vector sequence is generated through cascade fusion, and a hidden layer friction state is captured; then, based on a self-attention mechanism, calculating an attention weight matrix and generating a context vector, calculating an estimated endpoint moment and a confidence probability value, and quantitatively evaluating rotation angle accessibility; and finally, mapping the confidence coefficient into discrete state codes by using a probability decoding function, packaging and outputting the discrete state codes, and constructing a closed-loop system from data perception to state pre-judgment.

The invention relates to the technical field of camera detection, and discloses a camera detection method and system based on a twin neural network, and the method comprises the steps: collecting the conductive detection data of a to-be-detected camera, generating a conductive feature vector of a fixed dimension, and processing the conductive feature vector based on the twin neural network, the twin neural network comprises a first neural network branch and a second neural network branch, each neural network branch input layer is used for receiving a conductive feature vector, fixing dimensions and serving as an input signal of a subsequent neural network, the hidden layer is composed of a plurality of full-connection layers, each full-connection layer is connected with the activation function layer, and the activation function layer is connected with the second neural network branch. And the embedding output layer is used for mapping the extracted high-dimensional features into a low-dimensional embedding vector, and calculating a similarity value of the two embedding vectors to judge whether the to-be-detected camera is qualified or not. Whether the to-be-detected camera meets the qualified characteristic standard or not can be automatically judged, and fuzziness of human experience judgment is avoided.

The invention discloses a GRU-based vehicle-mounted CAN intrusion detection method and system, a storage medium and a computer system. According to the method, an automatic encoder (AE) is introduced to deepen the understanding of a model on input sequence characteristics, a sliding window is used for selecting batch CAN data to be preprocessed to obtain 13-dimensional time sequence data, and a scalar value within the range of [0, 1] is obtained through processing of the encoder, a GRU, a decoder, a full connection layer and a sigmoid activation function and used for classification of abnormal data. The Conv1D is used as a hidden layer, and compared with two-dimensional convolution, the one-dimensional convolution parameter quantity is smaller, and the calculation is simpler and more convenient. An attack message and a normal message can be completely distinguished, the precision and the accuracy rate reach 100%, and the precision in Fuzz detection is 0.9983; compared with the prior art, the method has high accuracy and reliability in the aspect of intrusion behavior detection, can effectively identify most intrusion events, and can keep a relatively low overall error rate, so that good balance between safety and availability is realized.

The invention provides an industrial product quality prediction method based on geometry preserving cross-scale difference, and relates to the technical field of industrial product quality prediction.The method comprises the steps that collected time sequence data of industrial process variables are preprocessed, a sample sequence is constructed through a sliding window, and the sample sequence is divided into a training set, a verification set and a test set according to the time sequence; the method comprises the following steps: constructing a double-branch coding architecture to independently process trend features and differential features; a geometric perception attention mechanism is introduced into each branch encoder, it is ensured that hidden layer representation and output target space keep geometric consistency, and the stability and interpretability of the model are enhanced; and deep interaction and adaptive fusion of double-branch information are further realized by adopting cross-scale cross attention, so that the comprehensive modeling capability of long-term trend and short-term dynamic in the industrial process is remarkably improved.

The invention discloses an intraocular light field simulation method and system based on physiological constraint and pathology traceability, the system takes a Physics Informed Kolmogorov-Arnold Net (PI-KAN) network as a core, namely physical information KAN, the method comprises the following steps: obtaining personalized parameters of eyeballs; 5-dimensional light field parameters including space, wavelength and time are input into a pre-trained PI-KAN model for light field solving, a three-layer network architecture including an input layer, a hidden layer and an output layer is established, a physical information edge function is constructed, and training is performed through fusion of a Helmholtz equation and a loss function of boundary conditions; generating an OCT image based on the light field solved by simulation; by analyzing side function mapping, visualization and pathological traceability of a light field propagation physical mechanism are realized. According to the method, the sparsity and interpretability of PI-KAN are utilized, the problems that a traditional method is low in calculation efficiency, difficult in high-dimensional modeling, weak in physical constraint and poor in interpretability are solved, millisecond-level, high-precision and interpretable simulation of the eye light field is achieved, and the method is suitable for ophthalmic clinical auxiliary diagnosis, surgical planning and equipment optimization.

The invention discloses a space-time decoupling efficient electroencephalogram basic model construction method and device, and relates to the technical field of brain signal processing. The method comprises the following steps: constructing an electroencephalogram basic model based on linear attention and spatio-temporal data decoupling; segmenting the acquired electroencephalogram signal data, inputting the segmented electroencephalogram signal data into an electroencephalogram embedding layer, and outputting an embedding vector; inputting the embedded vector into a space-time mixing module for separation and decoupling to obtain a spatial feature and a time feature; performing coding calculation on the spatial features and the time features based on an enhanced bidirectional weighted key value algorithm of a linear attention mechanism to obtain new spatial hidden layer features and time hidden layer features; mixing the two hidden layer features to obtain spatial-temporal features; inputting the spatial-temporal characteristics into a forward propagation network, and pre-training the model to obtain a trained model; and inputting a downstream task to be executed into the trained model for processing, and outputting a task result. According to the invention, the brain signal processing efficiency can be improved.

The invention relates to an MPPT (Maximum Power Point Tracking) method, system and equipment based on distributed photovoltaic and a medium, and relates to the technical field of distributed photovoltaic power generation, and the method comprises the following steps: preprocessing photovoltaic array data, and obtaining irradiance, temperature and MPP voltage data sets; determining the optimal number of hidden layer nodes of the ElmanNN by adopting K-fold cross validation and RMSE evaluation, and dividing a data set into a training set, a validation set and a test set; inputting the training set to firefly algorithm global search and ant colony optimization algorithm local optimization, and defining a stable solution as an FA-EAS optimal parameter after convergence; updating an ElmanNN weight threshold based on the optimal parameter, and deploying a training model to carry out MPP voltage online prediction; and the difference value of the predicted voltage and the reference voltage is input into a PID controller, the control quantity is calculated through incremental PID, a PWM signal is generated to control the duty ratio of the Boost converter, and maximum power point tracking is achieved. According to the invention, the tracking speed, stability and success rate of MPPT can be improved.

A controller controls an end-effector of a milking robot to move to a desired position (pset) according to a desired velocity profile via: a feedforward module producing predicted control signal(s) (cpred) based on a set vector (vset) specifying the desired velocity profile, a closed-loop controller, based on a modified position (Δp), producing primary control signal(s) (cprim) for controlling the end-effector to the desired position (pset), and first and second summation modules deriving modified control signal(s) (cinput) to be fed to the milking robot and deriving the modified position (Δp) respectively. The feedforward module contains a trained artificial neural network with an input layer configured to obtain the set vector (vset), an output layer configured to provide the at least one predicted control signal (cpred), and a number of hidden layers interconnecting the input layer and the output layer. The respective nodes in said layers have weights that were assigned through a training process in which output signals (pout) from the robot were used as training data and registered control signals for controlling the end-effector of the milking robot were used as reference data.

A method initializes and accelerates training of neural network based large language model, including by: (i) accessing a corpora for training a neural-network based large language model having word embeddings and word projections in respective word embedding and word projection layers and at least one hidden layer; (ii) counting raw token frequencies associated with content within the corpora; (iii) smoothing the raw token frequencies into a series of vector norms based on log or scaled log functions parameterized by maximum norm information; and (iv) injecting vector norm information into word embeddings and / or word projections based on norm-angle reparameterization to prepare the large language model for training.

The invention discloses an oil-based drilling fluid performance parameter prediction method, and relates to the technical field of oil-gas field development. According to the method, firstly, multiple training sets are obtained, each training set comprises a drilling fluid formula and temperature which serve as input and drilling fluid performance parameters which serve as output, then the training sets are used for training an Adaboost-BP prediction model, and the trained model can be used for prediction; the Adaboost-BP prediction model takes a three-layer BP neural network as a sub-model, and meanwhile, a feature enhancement-fusion module is arranged on a hidden layer of the sub-model. According to the method, multiple key indexes of the oil-based drilling fluid can be predicted at a time, and compared with a traditional method, the prediction efficiency is improved, and the application range is widened.