716 results about "Neural net architecture" patented technology

The invention relates to the field of road traffic, in particular to a multi-feature fusion fatigue driving detection method and a fatigue driving detection system. The method comprises the following steps: extracting facial features from a face image of a driver; extracting vehicle features from the vehicle driving parameters of the vehicle driven by the driver; and fusing the facial features and the vehicle features to judge whether the driver is in fatigue driving. Extracting facial features by designing a CNN model; extracting basic convolution features; extracting local convolution features; extracting global convolution features; performing pooling operation; aggregating global features; and carrying out dimensionality reduction mapping. A self-encoder is designed to extract vehicle characteristics; a symmetric deep neural network structure is adopted, and high-dimensional time sequence data is compressed to a low-dimensional potential space through nonlinear mapping; through combination and matching of the CNN model and the auto-encoder, the technical defects of feature redundancy, noise interference, information loss and suboptimal decision existing in an existing multi-feature fusion fatigue driving detection system are thoroughly solved.

The invention relates to the technical field of exception prediction, in particular to a storage cabinet exception trend prediction system based on time series data analysis, which comprises a state monitoring module, an interval sensing module, a path reconstruction module, a symptom activation module and an evolution prediction module. According to the method, the state vectors including the temperature, the voltage, the current and the door lock state are constructed and combined with the timestamp information to form the time sequence data sequence, and the dynamic expression mode of state change is established; a jump characteristic is analyzed by using a ratio of a time interval to a state change amplitude, a short-time disturbance path and a trend evolution path are distinguished by combining a jump rate statistical index, and an evolution activation signal is identified based on trend maintenance and non-fallback characteristics. On the basis, a neural network structure with long-time dependent learning ability is introduced to capture an aperiodic thermal anomaly trend in a state sequence, and the accuracy and timeliness of anomaly recognition are improved through multi-dimensional parameter cooperative processing and path construction logic.

The invention discloses a power plant equipment fault prediction method based on a time sequence large model, and the method comprises the following steps: S1, collecting and preprocessing the time sequence data of a multi-source sensor of a power plant, and generating a standardized time sequence data set; s2, constructing a time sequence large model, inputting standardized data, and outputting a future operation state predicted value; s3, comparing the running state prediction value with an actual measurement value to generate a prediction residual sequence; s4, constructing a Bayesian neural network model, inputting a prediction residual sequence, and outputting error probability distribution; s5, optimizing a Bayesian neural network structure and hyper-parameters by adopting an ant colony optimization algorithm; s6, confidence interval estimation is executed, and whether the state is a high-risk state or not is judged; and S7, outputting a running state label, and dynamically acquiring a data closed-loop updating model. According to the invention, high-precision prediction and uncertainty evaluation of the operation state of the power plant equipment are realized, so that the accuracy and response time efficiency of fault early warning are improved.

The invention discloses a reinforcement learning method and system for source network load storage collaborative multi-scene optimization, and the method comprises the steps: constructing a power distribution network optimization model with the minimum cost, and converting a mixed integer nonlinear problem into a solvable mixed integer second-order cone optimization problem; converting a mixed integer second-order cone optimization problem into a reinforcement learning decision model, and performing multi-round assignment on boundary condition parameters by using different operation scene data to form a multi-scene training task set; designing a reinforcement learning decision model multi-scene training loss function, combining the reinforcement learning decision model to construct a strategy neural network, an evaluation network neural network and a scene representation embedded neural network, and completing reinforcement learning decision adaptive to multi-scene optimization; and training a power distribution network multi-scene optimization decision model based on the multi-scene training task set, the multi-scene training reinforcement learning loss function and the neural network structure, and deploying the power distribution network multi-scene optimization decision model to an actual system to complete reinforcement learning decision model application oriented to source network load storage collaborative multi-scene optimization.

The invention discloses a tight reservoir three-dimensional crustal stress field modeling method based on an improved neural network. The tight reservoir three-dimensional crustal stress field modeling method comprises the steps that S1, a unified-format multi-source geological physical data tensor set is constructed; s2, constructing a frequency domain hierarchical enhancement-SIREN implicit neural network structure based on the unified format multi-source geological physical data tensor set; s3, inputting the candidate hyper-parameter configuration into the frequency domain hierarchical enhancement-SIREN implicit neural network to complete one-time model training; s4, aiming at each candidate hyper-parameter configuration, initializing an inner-layer population of the black widow optimization algorithm, completing second model training, and obtaining an optimal model parameter of the frequency domain hierarchical enhancement-SIREN implicit neural network; s5, tight reservoir fracturing parameter optimization and real-time safety window adjustment are achieved. According to the method, the continuous stress field can be quickly generated at the resolution of 1 m, real-time well section updating and fracturing scheme optimization are supported, and the fracturing transformation effect, the fracturing safety margin and the reliability of economic productivity prediction are remarkably improved in practical application.

The invention discloses a lithium battery residual life prediction method and system and terminal equipment, and relates to the technical field of lithium battery health management. The method comprises the following steps: receiving a battery capacity attenuation sequence as an original input sequence, detecting and filtering abnormal data by adopting a 3 sigma criterion, and carrying out noise suppression processing on the battery capacity attenuation sequence through a Dropout mask; and carrying out normalization processing on the preprocessed battery capacity attenuation sequence, dividing the battery capacity attenuation sequence into a training set, a verification set and a test set through a sliding window algorithm, and generating a time sequence characteristic matrix and a corresponding residual service life label. According to the method, a neural network structure fusing trend prior perception and dynamic attention regulation is constructed, a multi-scale capacity modeling strategy is introduced to separate a degradation trend, fluctuation disturbance and high-frequency noise, and compared with a traditional time sequence neural network or a single attention model, pseudo fluctuation characteristics caused by capacity regeneration can be more effectively recognized, and the method is more efficient and more reliable. And the judgment accuracy of the model in a complex degradation scene is improved.

The invention discloses a millimeter wave SAR icing thickness inversion algorithm based on deep learning, and aims to solve the problems of insufficient icing monitoring precision and poor real-time performance under complex meteorological conditions. According to the algorithm, high-quality input is provided for deep learning modeling through data preprocessing including slope distance correction, polarization denoising, back scattering characteristic extraction and data enhancement. A multi-branch deep neural network structure is adopted for feature extraction, and in combination with a Swin Transformer V2 trunk, a Gated MLP module, cross-modal attention fusion and physical consistency constraint, the adaptability of the model to millimeter wave SAR data and multi-modal input is improved. Meteorological parameters and line operation data are integrated through multi-modal data fusion, and model robustness is enhanced. Finally, an icing thickness prediction value is output through real-time reasoning, intelligent early warning is triggered in combination with confidence analysis, end-to-end real-time response is achieved, the intelligent level of safe operation of a power grid is remarkably improved, and the method has wide application value.

The invention relates to the technical field of artificial intelligence and information propagation analysis, and discloses a deep learning-based network information trend prediction method and system.The deep learning-based network information trend prediction method comprises the steps of performing neural architecture search through combination of an evolutionary algorithm and reinforcement learning; an optimal neural network structure suitable for different types of network information is automatically found; dynamic reconstruction of a model structure is realized through an environment perception and event triggering mechanism; according to a deployment environment resource constraint, adopting an importance-perceived neuron self-adaptive pruning technology; turning point features in network information propagation are specially extracted and enhanced; knowledge migration from a large-scale high-precision model to a lightweight model is realized; an online learning and continuous optimization mechanism is adopted to prevent disastrous forgetting; according to the method, the key turning point of network information propagation can be accurately predicted, the prediction accuracy is improved, the early warning time is shortened, and the computing resource consumption is reduced.

The invention relates to the technical field of industrial robot control, and discloses an industrial robot trajectory optimization control method based on an intelligent algorithm. The method comprises the steps that joint position information, tool center point coordinates and a motion time sequence when a robot executes multiple tasks are collected and stored in a track database; after cleaning and screening data, extracting a feature set containing a path point sequence, speed distribution and an acceleration contour; constructing an intelligent optimization algorithm model of a neural network structure, and training by using the feature set to learn a trajectory optimization strategy; analyzing a target position coordinate and a motion constraint condition of the current task to obtain an initial track parameter; and inputting the initial parameters into the trained model, outputting an optimized track sequence containing a path point list and a speed curve, and generating a control instruction to drive the robot to move. The method can adapt to different tasks, improves track rationality and motion stability, and fits industrial production practice.

The invention belongs to the technical field of fluid velocity pressure field deduction, and discloses a fluid velocity pressure field deduction method and system based on a fusion network. The method comprises the following steps: obtaining a flow field information data set required by training by utilizing a computational fluid dynamics method; a KAN network is constructed; constructing a fusion network based on the KAN and the PINN; and the constructed training set is used for training of the constructed KAN and PINN-based fusion network, verification is carried out by using the verification set, and finally a flow field deduction result is obtained. According to the method, the neural network data set is established, the KAN type neural network structure is constructed, and the neural network is constrained by using the fluid-related physical equation, so that the values of the speed and the pressure in the flow field are predicted in real time, the surrounding flow field condition can be predicted in real time in the ship traveling process, and speed and pressure field deduction can be performed for a specific fluid domain.

The invention relates to the technical field of data analysis and processing, in particular to an identity data analysis method based on a spatial frequency sensing fusion network, and the method specifically comprises the following steps: collecting identity data to be detected, and marking real identity information; preprocessing the collected identity data, and dividing the collected identity data into a training set and a test set in proportion; constructing a spatial frequency sensing multi-scale network for real-time deep identity analysis, and training the network; inputting the identity data in the test set into the trained network for forgery detection and prediction, and outputting a probability score that each identity is true or false so as to obtain an identity analysis result. By constructing a lightweight deep neural network structure which combines space and frequency feature perception and has a multi-scale fusion capability, the method can be used for efficiently detecting identity information in actual scenes such as video conferences and social media.

The invention discloses a microseismic source positioning method, device and system, and a storage medium. The method comprises the following steps: dividing a microseismic data sample data set into a training set and a test set; according to the training set, constructing a microseism inversion subnet containing a Swin Transform encoder, and according to the training set, constructing a microseism inversion subnet containing a Swin Transform encoder; the micro-seismic forward modeling subnet realizes seismic wave field continuation by inputting a micro-seismic source position and a speed model and utilizing a recurrent neural network structure and a convolution operator, and establishes a forward modeling subnet based on a wave equation; constructing an inversion-forward modeling closed-loop neural network according to the micro-seismic inversion subnet and the forward modeling subnet based on the wave equation; and inputting the test set into the inversion-forward closed-loop neural network to carry out micro-seismic source positioning. By adopting the technical scheme of the invention, the limitations on physical constraint, feature modeling and anti-noise capability in the prior art are overcome.

A medical code allocation method based on hierarchical association and diversity enhancement comprises the following steps: S1, data preprocessing and graph construction: based on a tree hierarchical classification architecture of ICD codes, adopting a hierarchical graph model to perform visual representation on the tree hierarchical classification architecture, and constructing a co-occurrence matrix of the clinical record text and the ICD codes for the clinical record text; s2, applying a Graph-BERT graph neural network structure to perform graph structure conversion on a tree structure of an ICD code system, and learning based on an encoder structure of a graph transformer to obtain embedded representation of ICD codes; s3, performing word segmentation processing on a clinical text, inputting the processed clinical text into a bioBERT model to obtain a feature vector of the clinical text, and unifying semantic features of clinical records, hierarchical features of ICD codes and related association information between the semantic features and the hierarchical features through a multi-modal fusion mode; s4, after the final feature vector of the clinical record text is obtained, correlation enhancement prediction is performed by using a CorNet network, so that a related probability matrix is generated, a binary cross entropy loss function is expanded, the binary cross entropy loss function, hierarchical diversity loss and semantic diversity loss jointly form a diversified loss function, and the diversified loss function and a classic model are subjected to comparative analysis, so that the final feature vector of the clinical record text is obtained. Therefore, the accuracy and effectiveness of the method are verified.

The invention discloses an automatic driving system extreme environment decision-making method based on a neural evolutionary game. The method comprises the following steps: S1, collecting and constructing a real-time environment data set around a vehicle through an automatic driving system; s2, generating an environment feature vector; s3, based on the environment feature vector, analyzing the environment feature data through an extreme environment identification module; s4, activating a corresponding multi-target dynamic reward mechanism based on the extreme environment label, and setting a dynamic reward weight for the neural evolution process; s5, gradually evolving to generate an optimal neural network structure and parameter configuration suitable for different extreme environments; s6, generating a multi-target game strategy in combination with the environment feature vector; s7, generating an optimal driving path, vehicle speed adjustment, obstacle avoidance mode and direction control decision; and S8, inputting real-time feedback data into the neural evolution model, and further adjusting the structure and parameter configuration of the neural network. The automatic driving system can make a safer, more accurate and more efficient driving decision in an extreme environment.

The invention relates to a basin flood simulation optimization method based on a physical information neural network, and aims to construct a basin flood acceleration solution mode based on PINNs, and ensure the continuity and the physical characteristics of mass conservation during model operation, and the method comprises the following steps: simulating basin flood by using an urban flood space-time simulation model; obtaining a data set required for constructing the physical neural network; physical rules are integrated and simplified into limiting conditions, so that integration of a neural network structure is facilitated; establishing a partial differential equation (PDE) used for describing river flood routing, and taking the partial differential equation as a loss function of the physical information neural network model; constructing and training a PINNs drainage basin flood model constructed on the basis of a CNN (Convolutional Neural Network); and establishing a model evaluation index and performing evaluation. The method has the beneficial effect that the speed and precision of basin flood simulation are improved.

Aspects of the present disclosure provide methods and apparatuses for learning an artificial intelligence or machine learning (AI / ML) model over a self-organized topology to enable heterogeneous neural network structures in distributed AI / ML training processes. The method comprises: a first node receives a first AI / ML model from a second node and transmits the first AI / ML mode and a model collection indicator to one or more nodes associated with the first node. The first node receives reports related to respective associated AI / ML models of its associated node(s) and generates, based on the reports, a second AI / ML model having a neural network (NN) structure equivalent to that of the first AI / ML model. However, the AI / ML models of the first node's associated node(s) may have NN structures that differ from the NN structure of the first and second AI / ML models.

The invention relates to an underwater target detection method based on a pulse neural network, which can realize underwater target detection with high precision and low power consumption. By fusing the cross-stage partial network and the YOLO architecture, the problem of pulse degradation is effectively solved, and the feature extraction capability of the model is enhanced; in order to solve the problem of underwater noise interference, the pulse-based underwater image denoising method is designed, only integer addition is used in the method, a pulse neural network structure can be seamlessly embedded, and the quality of a feature map is enhanced; in order to solve the problem that a traditional normalization method is low in precision in the spiking neural network, separate batch normalization is provided, by independently normalizing a feature map in multiple time steps and optimizing a residual structure, the time dynamic state of the SNN can be effectively captured, and the detection precision is improved. The network shows excellent performance in underwater target detection, and compared with an artificial neural network of the same scale, the network has higher performance and lower energy consumption.

The invention discloses a low-sample neural network structure reliability evaluation system and evaluation method, and relates to the technical field of engineering structure safety monitoring, and the evaluation system comprises a cloud server which is used for constructing a recurrent neural network model containing a static variable embedding mechanism, completing model training and converting a model format; the edge calculation terminal is used for receiving and preprocessing real-time data of the sensor, executing multi-step prediction to output a future time period response sequence, and calculating a future failure probability through virtual Monte Carlo simulation; the sensor assembly is used for collecting structure state time sequence data; and the communication module is used for realizing data interaction and alarm signal transmission operation. According to the method, collaborative modeling of time-varying and static uncertainty is realized by adopting a static variable embedded recurrent neural network model, failure probability distribution is generated at an edge computing terminal in combination with a virtual Monte Carlo technology, failure risk prediction in a future time period is supported, and real-time and accurate reliability early warning can be realized in a resource limited scene.

The invention provides a method for predicting tunnel excavation water inflow through horizontal drilling, and relates to the technical field of tunnel construction water inflow prediction.The method comprises the steps that water inflow time sequence data and geological parameter data are obtained; calculating the permeability coefficient of the position where each monitoring point is located according to the water inflow time sequence data by using a Goodman formula based on rock integrity correction; decomposing the water inflow time sequence data to obtain a plurality of intrinsic mode functions and a residual term; forming a training data set; constructing a prediction model comprising a GRU neural network structure and a physical constraint mechanism, training the prediction model by using the training data set until the loss function converges, and obtaining a trained prediction model; and obtaining current monitoring data, inputting the current monitoring data into the trained prediction model, and predicting the water inflow and the water inflow risk level of the to-be-excavated section. According to the method, refined and high-precision prediction of the water inflow in the tunnel excavation process can be achieved, and potential safety hazards caused by coarse-grained prediction of a traditional method are avoided.

The invention discloses a livestock and poultry health state intelligent evaluation method based on multi-sensor fusion and an AI prediction model, and belongs to the technical field of livestock breeding, and the method comprises the following steps: setting multiple types of sensors in a livestock and poultry breeding area, collecting multi-source data such as temperature, humidity, carbon dioxide concentration, ammonia gas concentration, illumination intensity, weight and current, and carrying out the analysis of the data; wherein the weight data is obtained through the weighing platform, and the current data is used for sensing the operation state of drinking, ingestion, ventilation or lighting equipment; various types of sensors such as temperature and humidity sensors, carbon dioxide sensors, ammonia gas sensors, illumination sensors, weight sensors and current sensors are deployed in livestock and poultry breeding areas, all-directional data acquisition of environments and behavior states is realized, and dynamic modeling and feature extraction of key behavior nodes are realized by introducing a deep neural network structure of a gating circulation unit and an attention mechanism.

The invention relates to the technical field of digital twinning, and discloses a converter valve power module digital twinning construction method, system, equipment and medium, and the method comprises the steps: obtaining a training sample set needed by a model through the physical field simulation data of a converter valve power module, and carrying out the training through a Transform-based neural network model, the simulation digital twinning prediction model obtained through training of a Tranformer neural network structure can output physical field data of the converter valve power module according to input operation condition parameters of the converter valve power module, so that the purpose of digital twinning is achieved, online monitoring of the converter valve power module in engineering equipment is achieved, and the safety of the engineering equipment is improved. And the model calculation efficiency is improved, and the method can adapt to different working condition scenes.

The application discloses a memory reinforcement deep unfolding multi-modal image fusion method with downstream task enhancement, which comprises the following steps: collecting infrared images and visible light images, and dividing training set and test set; establishing an optimization target and solving, to obtain an iterative formula; using a neural network to replace a proximal operator in the iterative formula, to obtain a neural network structure; sending the training set into the neural network to obtain a fusion picture, and calculating a total loss according to the fusion picture, the infrared image and the visible light image; updating parameters of the neural network according to the total loss to obtain an updated neural network; inputting the infrared image and the visible light image of the test set into the updated neural network to obtain a fusion image. The application makes the fused image have characteristics easy to be distinguished by a downstream task network, and can realize the best performance on a data set, while performance and interpretability are taken into account, and the application has rationality and applicability.

The invention discloses a tea drying equipment dynamic control method based on deep reinforcement learning, comprising the following steps: S1, collecting parameters of each control unit of tea drying equipment, and generating a state observation data set; s2, constructing an equipment graph structure, and outputting equipment graph structure information; s3, constructing an action value calculation network containing a dried graph neural network structure based on an improved MADDPG algorithm, and generating a global joint feature embedding set; s4, constructing a strategy network set, and generating a control action set; s5, inputting the global joint feature embedded set and the control action set into an action value prediction module, and outputting an action value prediction result; s6, the improved MADDPG algorithm is used for executing action value loss function calculation and parameter reverse updating; and S7, deploying the trained strategy network set to a tea drying equipment control system. According to the method, the MADDPG and drying graph neural network is improved, and dynamic closed-loop regulation and control of the tea leaf drying equipment are realized.

The invention discloses a self-adaptive multi-scale coverage path planning method and system based on deep reinforcement learning, and relates to reinforcement learning and unmanned aerial vehicle technologies, and the method comprises the steps: constructing a multi-scale map; the multi-scale map is converted into self-center representation with an intelligent agent as the center, and a state space and an action space are configured; setting a reward function according to the configured state space and action space; a deep neural network model is constructed, a scale grouping convolutional neural network structure is adopted, the deep neural network model comprises a map feature extractor, a sensor feature extractor and a feature fusion module, the deep neural network model is trained by using a soft actor-commentator algorithm, and after training, the deep neural network model is subjected to feature fusion. And performing coverage path planning by using the trained deep neural network model. The method and the device are used for solving the problem of coverage path planning in an unknown environment in the prior art.

The invention relates to the technical field of deep learning and gas recognition, in particular to a gas recognition method based on a grouped convolution-TCN neural network, and the method comprises the following steps: 1, collecting gas data through an MEMS gas sensor array, and obtaining MEMS gas sensor array gas data sets under different flows; step 2, preprocessing the obtained test data; step 3, model establishment: constructing a model combining a grouped convolutional neural network and a TCN neural network for extracting characteristics of a gas sensor irrelevant to flow and realizing gas recognition; 4, completing model training and performance evaluation; and step 5, quantifying and deploying the grouping convolution-TCN neural network model. According to the method, flow compensation is carried out on the data of the gas sensor by using grouping convolution, so that the detection precision of the target gas under different flows is improved; meanwhile, the TCN neural network structure is improved, and classic convolution is replaced with depth separable convolution, so that the model size and the reasoning time are remarkably reduced.

The invention discloses a power network anomaly detection method and system, and relates to the technical field of intelligent power grid monitoring, and the method comprises the steps: constructing a neuron-like power network graph model, carrying out the node processing of equipment, introducing a multi-factor anomaly score function, and dynamically activating an abnormal node through combining with an improved Winner-Take-All mechanism; the connection weight is dynamically optimized through a topology-aware composite gradient descent method, and a multi-layer neural network structure is constructed to realize cross-layer forward propagation and feedback adjustment; and establishing an abnormal path model in combination with the propagation phase difference and the information entropy, identifying an abnormal path by using a propagation score function and a weighted shortest path algorithm, and carrying out fault tracing. According to the power network anomaly detection method provided by the invention, the neuron-like graph model is constructed, and a multi-factor anomaly activation mechanism is introduced, so that comprehensive perception of a power grid node state under a multi-dimensional time characteristic can be realized, and the anomaly recognition sensitivity and the multi-point simultaneous activation capability are effectively improved.

The invention discloses a topological optimization method based on a physical information neural network (PINN). The topological optimization method comprises the following steps of data preparation, neural network design, loss function definition, dynamic sampling strategy and optimization process implementation. Specifically, the method comprises the following steps: firstly, acquiring the center coordinates of each unit, then inputting the unit coordinates into a sine representation network, outputting a unit density value, calculating displacement through finite element analysis, then calculating a flexibility value according to node displacement so as to obtain loss, and finally, carrying out back propagation on the loss and updating the network weight. The method has the advantages that the deep learning technology is combined with the physical information neural network, so that the structural performance is improved, the dependence on experimental data is reduced, and the optimization efficiency and precision are improved. Particularly, the structure of the neural network is different from that of a conventional neural network, and deep embedding of physical information is considered in the design of the neural network, so that the prediction accuracy and the generalization ability of the network are improved.

The invention discloses a low-voltage power distribution network line loss prediction method, a model training method and a related device, and the model training method comprises the steps: (1) employing a neural network structure combining a time sequence feature extraction module and a multi-scale feature extraction module, and capturing the long-term trend of photovoltaic output and load fluctuation, line loss change modes under different time scales are effectively modeled; (2) a deep feature extraction module introduces a deep feature extraction mechanism with mutually different architectures, and the expression ability and generalization performance of the model to a complex nonlinear relationship are improved; (3) a crown porcupine optimization algorithm is adopted to perform joint optimization on model parameters and structures, efficient search can be realized in a high-dimensional non-convex space, local optimum is avoided, and prediction precision and stability are improved; based on the line loss prediction model, the line loss prediction method provided by the invention can be directly used for power distribution network operation situation evaluation and energy efficiency analysis, and has good engineering application value under the background of wide distributed photovoltaic access.

The invention relates to the field of numerical reservoir simulation, and provides a proxy modeling method for embedding a coarse grid simulator, so as to solve the problems of poor space-time extrapolation performance and unstable long-term prediction. According to the method, a coarse mesh numerical simulator is used as a physical prior module to be embedded into a multi-resolution fusion neural network structure (MNN), and a complex nonlinear relation in the physical prior module is captured by means of a Fourier neural operator (FNO), so that efficient, long-time-sequence and stable prediction of multiple physical fields such as pressure, oil saturation and gas saturation is realized. According to the method, a coarse mesh numerical solution is used as an intermediate drive, multi-scale physical information is fused through a modular MNN network, and the physical consistency and the space-time generalization ability of the proxy model are remarkably improved. In the model, the FNO is utilized to extract global frequency domain characteristics of a coarse grid simulation result field, and through verification of multiple cases, the model shows good space-time extrapolation performance under various coarsening scales and well control conditions. The method has the robustness of numerical simulation and the high efficiency of deep learning.

The invention discloses a pressure sensor temperature compensation method combining polynomial fitting and a neural network algorithm, and belongs to the technical field of sensors, and the method comprises the following steps: collecting output data of a pressure sensor in a wide temperature and pressure range, carrying out the cleaning and normalization processing of the collected data, and removing abnormal values; according to the collected data, polynomial fitting is carried out by using a least square method and the like, initial compensation is carried out on the original data, and a pressure value after initial compensation is calculated; constructing a neural network structure, training the neural network by using the preliminarily compensated data, and adjusting the weight and bias of the neural network to minimize the error between the output of the neural network and the actual pressure value; a temperature value measured in real time and a sensor output value are input into a trained system, primary processing is carried out through a polynomial fitting model, then a result is input into a neural network, and the neural network outputs a final pressure value after temperature compensation.