1037 results about "Adaptive method" patented technology

The invention provides a traffic control strategy self-adaption method and system based on simulation feedback, and belongs to the technical field of traffic prediction and control, and the method comprises the steps: firstly obtaining traffic control scene data containing traffic flow data and road condition information, then constructing a simulation evaluation environment, configuring scene parameters based on the traffic control scene data, and carrying out the simulation evaluation environment; the method comprises the following steps: simulating traffic operation states under different traffic control strategies, calling a pre-trained reinforcement learning model to perform simulation evaluation on each strategy in a traffic control strategy set, generating a strategy effect feedback set comprising a traffic operation efficiency index and a traffic order stability index, and according to the strategy effect feedback set, calculating the traffic order stability of the traffic control strategy. And performing parameter adjustment on the strategy in consideration of the index association relationship to obtain an adjusted strategy, and finally outputting the adjusted strategy to the traffic control system to realize strategy updating, thereby effectively improving traffic operation efficiency, ensuring traffic order stability, and realizing adaptive optimization of the traffic control strategy.

The invention discloses a self-adaptive NL2SQL method and system based on a large language model and retrieval enhancement. The method comprises four key steps of mode linking, example enhancement and SQL generation, multi-path thinking chain fusion reasoning and multi-round self-correction. The system comprises the following modules: a knowledge base management module, an input analysis and preprocessing module, a mode link module, an example enhancement module, an SQL (Structured Query Language) generation module, an SQL fusion and optimization module, an SQL execution and feedback module, a multi-round self-correction module, a Prompt construction and context management module, a system interface module and a content generation module. The method has the advantages that multiple large models are supported, a data source is quickly accessed, a user-defined prompt structure is configured, good engineering maintainability and scene adaptability are achieved, and SQL generation accuracy, performability and universality are improved.

The invention provides a high earth and rockfill dam digital twinborn model construction method, and belongs to the technical field of high earth and rockfill dam health monitoring, and the method comprises the steps: constructing a measuring point monitoring model based on sensor monitoring data; constructing a mapping model, and constructing a mapping model dynamic monitoring loss function for adversarial training by adopting a domain adaptive method; inputting predicted values of deformation monitoring effect quantity and osmotic pressure monitoring quantity of the measuring point monitoring model on the seepage process and the deformation process of the high earth and rockfill dam into the mapping model to obtain a digital twinborn model, and constructing an extreme working condition digital twinborn model based on the digital twinborn model; knowledge graph reasoning is combined with a dam slope stability coefficient to realize interaction of an extreme working condition digital twinning model and a solid model, and the high earth and rockfill dam deformation-seepage digital twinning framework is proposed for the first time on the basis of deep learning and finite element methods so as to realize a more economical, efficient and reliable monitoring scheme. And technical support is provided for health monitoring of the high earth and rockfill dam structure in the future.

The invention discloses a method and device for training a medical large language model, and the method comprises the steps: obtaining a training medical data set, the training medical data set comprises a plurality of training prompt words, and each training prompt word comprises a training medical text; extracting medical text features of each training medical text; performing fine tuning on the reference large language model by using a low-rank adaptive method LoRA, determining a rank for a low-rank matrix according to the medical text features of each training medical text so as to obtain a first low-rank matrix and a second low-rank matrix, and fusing the first low-rank matrix and the second low-rank matrix with the reference large language model to obtain a fine tuning large language model; and training the fine-tuning large language model by using the training prompt word to obtain a trained medical large language model which is used for classifying the medical text. According to the method, the calculation burden during fine tuning of the large language model is reduced, and the generalization ability and adaptability of the large language model are improved.

The invention discloses a hierarchical traffic prediction-based multi-terminal video display adaptive method, which comprises the following steps of: acquiring network traffic, user behaviors and equipment state information in real time by constructing a multi-dimensional feature acquisition module, and generating a dynamic feature matrix containing traffic, interaction and performance features; processing the feature matrix by using a deep learning model, and generating and dynamically adjusting a network traffic grading prediction result; formulating an optimized video content distribution strategy based on a prediction result and equipment performance parameters, and coordinating multi-terminal resource distribution; through user behavior analysis and network state monitoring, a self-adaptive video display control strategy is generated, dynamic adaptation of video resolution, frame rate and playing logic is realized, a full-link collaborative optimization module is constructed, terminal, network and application layer resource allocation is coordinated, a video transmission and display strategy is optimized, and network flow prediction and multi-terminal interaction requirements are combined to realize multi-terminal interaction. And generating a dynamic video layout and content switching strategy.

The invention provides a dual-antenna attitude and orientation and robust self-adaption method based on integrated navigation, which comprises the following steps: combining an INS (inertial navigation system) and a dual-antenna GNSS (global navigation satellite system), and enabling the system to output continuous and stable high-precision carrier attitude information when a GNSS signal is interfered by using dual-antenna integrated navigation. According to the method, most of satellite end clock correction, ionosphere and troposphere errors and receiver end clock correction are eliminated by using double-antenna GNSS pseudo-range and carrier phase double difference, robust statistics are calculated through heading prediction residual vectors to obtain robust factors, an observation noise covariance matrix is expanded to reduce the influence of system noise and observation noise, and the system performance is improved. A Mahalanobis distance based on a prediction residual vector is introduced to detect whether a system is abnormal or not, a state prediction covariance matrix is adjusted through a self-adaptive factor, weight reduction of an abnormal INS dynamic model is achieved, and the stability and reliability of system attitude information are improved through a noise covariance self-adaptive control mechanism.

The invention relates to the technical field of wind power generation. The invention provides a yaw static and dynamic error adaptive method based on big data backtesting, which comprises the following steps of: constructing a multi-dimensional feature vector through multi-source data fusion data, and establishing a dynamic feature data set; based on the dynamic characteristic data set, a space-time diagram convolutional network is adopted to establish a wind power plant dynamic error prediction model, and space-time evolution rules of wind shear, turbulent flow and wake flow effects are captured; constructing a variational self-coding reference model based on historical full wind speed section data, calculating a residual error between a current working condition and the variational self-coding reference model in real time, and taking the residual error as a static error prediction value; performing adaptive weight fusion on the static error prediction value and the dynamic error prediction value to obtain a fusion error; and the fusion error is converted into the yaw angle correction amount, and the wind facing action is executed. The problems that an existing yaw error recognition technology is high in data dependence, lack of dynamic analysis, poor in model adaptability and difficult in complex wind field processing are solved.

The invention discloses a big data read-write adaptive method based on a decision algorithm and a big data read-write system, and belongs to the technical field of intelligent decision of a data center, high-speed read-write operation of data is processed in real time in a service system, and a data read-write strategy and resource allocation are dynamically adjusted by analyzing a data access mode and a system load condition; data are preloaded through an intelligent prefetching strategy, and the storage and access paths of hotspot data are optimized, so that the waiting time of a user and the consumption of system resources are reduced. According to the method, data management can be simplified, data access efficiency can be improved, data consistency and security can be enhanced, the problem of multi-database management and query complexity faced by enterprises can be effectively solved, and more efficient and more consistent data access and management experience can be provided for the enterprises.

The invention relates to the technical field of large model algorithms, in particular to a knowledge distillation-based field large model lightweight adaptive method and system, and the method comprises the steps: obtaining knowledge distillation parameters and student model performance parameters, and building a nonlinear mapping relation between the knowledge distillation parameters and the student model performance parameters; the optimal parameter combination is optimized and solved based on the mapping relation, and target knowledge distillation parameters are generated; issuing the target parameters to a training engine, monitoring performance deviation in real time and triggering re-optimization; in the reasoning process, performance fluctuation is monitored, and model characteristics are managed and controlled; target domain data characteristics are collected, a mapping relation is corrected in combination with big data analysis, and the domain adaptation capacity is improved; a knowledge base and a case base of historical distillation data are constructed, a standardized adjustment scheme is formed, and self-adaptive matching is achieved. According to the scheme, through precise modeling, dynamic optimization, real-time monitoring and knowledge reuse, the knowledge distillation efficiency, model robustness and field adaptability are remarkably improved, and systematic technical support is provided for large model lightweight.

The invention relates to the technical field of multi-agent cooperative control, and discloses a multi-agent reinforcement learning formation adaptive method fusing a graph attention mechanism. According to the method, a dynamic graph state space is constructed, and a speed self-adaptive communication mechanism and a multi-target return function are combined, so that the problem of insufficient generalization ability during intelligent agent number change or topological structure dynamic adjustment is solved. The scheme comprises the following steps: establishing a virtual leader-following formation model and a motion dynamics model; designing an adaptive communication radius formula based on speed change; constructing a graph state space, and dynamically aggregating neighbor information by using a multi-head attention mechanism; designing a strategy network and a value evaluation network with residual connection; and a centralized training distributed execution framework is adopted to update model parameters. Adaptive extraction of key neighbor information is realized through a graph attention mechanism, and adaptability and stability of a formation system in a complex environment are improved by combining dynamic communication range adjustment. The method is suitable for unmanned aerial vehicle group control, robot cooperation and other scenes.

The invention discloses a robot servo parameter self-adaption method and system based on gravity compensation. The method comprises the steps that before the mechanical arm is installed, instruction debugging is conducted on each joint under different loads, the rotating speed of the joints and the rotating speed of a motor are obtained, control parameters are adjusted and calibrated, and finally the calibration result is stored as joint feature information; modeling a mechanical structure, and calculating dynamic parameters of the mass, the mass center and the motor deflection angle of an arm lever; in the operation process, the load is monitored, and a gravity compensation value is calculated in combination with attitude information; uniformly distributing the compensation value to a control period through a feedforward data channel, and applying the compensation value to a motor and a load as a compensation amount of a torque ring instruction; and the servo system dynamically adjusts the control parameter according to the gravity compensation value and the setting parameter. By implementing the method provided by the embodiment of the invention, the defects in parameter setting and dynamic adaptability of the existing technical scheme can be overcome, and the robustness and performance of the servo system in a variable environment can be improved.

The invention relates to the technical field of computers, in particular to an adaptive method and device based on Warp scheduling and cache replacement and a medium. The method comprises the following steps: dynamically monitoring an access behavior of a Warp in an L1 data cache through a locality sensing unit, and identifying locality preference of a workload; according to the locality preference, adaptively selecting a Warp scheduling algorithm; periodically collecting cache access count and cache missing count through a cache state monitor, calculating the memory access missing rate of the current sampling period, and comparing the memory access missing rate with the missing rate of the previous sampling period; if the memory access missing rate of the current sampling period is higher than that of the previous sampling period, switching a cache replacement strategy; if not, the current strategy is kept. According to the method, the Warp scheduling algorithm and the cache replacement strategy are adaptively adjusted, so that the cache utilization rate and the performance of the GPGPU are optimized.

The invention discloses a fire point self-adaption method based on multi-source synchronous satellite data, which relates to the technical field of remote sensing and geographic information, and comprises the following steps of: aiming at satellite remote sensing images with different resolutions, respectively selecting fire point detection algorithms matched with the resolution characteristics of the satellite remote sensing images to independently finish preliminary detection of fire points, extracting fire point space distribution and feature data corresponding to respective resolution scale; preprocessing the extracted multi-source fire point spatial distribution and feature data, and establishing a multi-source fire point data set; according to the method, the scale consistency and fusion precision of multi-source satellite fire point detection are improved, and the problems of fire point boundary breakage, false splitting and the like caused by resolution difference are solved. Through scale difference quantification and machine learning evaluation, in combination with scale correction of high-resolution images, adaptive dynamic adjustment of fire point boundaries is realized, and the reliability of fire monitoring and emergency decision making is improved.

Systems, computer program products, and methods are described herein for AI-based adaptive security parameter calculation in license key generation with quantum-resistant protections. The present disclosure is configured to utilize advanced cryptographic techniques, including elliptic curve cryptography and SHA-3 hashing, to produce secure digital signatures for license keys. An AI-driven engine analyzes historical security data to adaptively adjust key generation parameters, ensuring enhanced protection against current and emerging threats. This adaptive approach allows for continuous learning and improvement of security measures, making the system robust against both classical and quantum cryptographic attacks. The disclosed methods integrate the generation, assessment, and validation of keys into a streamlined process that promotes high security while maintaining efficiency.

The invention relates to the field of inverter adaptive protocol matching, in particular to a distributed power supply access unit photovoltaic protocol adaptive method, which comprises the following steps: collecting inverter communication protocols of existing manufacturers and calculating proportions of supply quantities of different manufacturers to form a comparison library; setting a matching sequence of address fields and baud rates; matching the protocols to be compared with the existing protocols in the protocol library in sequence; if the matching is not successful, traversing the address field and the baud rate field in the comparison library in a polling incremental manner; and if the matching is completed, the roll call maintenance module performs periodic matching verification. The address matching module and the baud rate matching module are added, so that the protocol matching success rate is improved; the address field and the baud rate of the manufacturer with a large proportion are preferentially adopted, so that the matching success probability is improved, and the time cost in the matching process is reduced; in the protocol self-adaption process, feature data is adopted for comparison, and response data is required to be within an accepting range, so that the accuracy of protocol identification is ensured.

The invention discloses a data interaction self-adaptive method for a photovoltaic protocol converter, and particularly relates to the technical field of power grid dispatching cooperative control. The method comprises the following steps: monitoring and calculating a voltage instantaneous change rate of a grid-connected point of a power grid in real time, and comparing the voltage instantaneous change rate with a threshold value; when a threshold value is exceeded, synchronously collecting a current phase and generating a voltage and current phase difference change direction; extracting a voltage high-frequency component, analyzing frequency band energy distribution through wavelet packet decomposition, and calculating a Shannon entropy representing an energy concentration ratio; when the phase difference change direction accords with a preset power grid short-circuit fault feature and the Shannon entropy value is lower than a preset entropy threshold value, determining that a power grid transient event occurs; at the moment, a data caching mechanism of the protocol converter is stopped immediately, and a real-time stream transmission channel is activated; an emergency control instruction issued by a power grid dispatching system is directly transmitted to the photovoltaic inverter through the channel; and when the voltage instantaneous change rate is continuously lower than the preset threshold value for a preset duration, recovering the data caching mechanism of the protocol converter.

An adaptive transformation matrix generation is used simultaneous compression and encryption of data. The system continuously monitors input data streams to detect changes in distribution patterns, dynamically updating transformation matrices in response to these changes. Using performance evaluation criteria, the system selects and deploys optimal matrices that maintain both compression efficiency and cryptographic security as data characteristics evolve. The system implements configurable adaptation policies that govern when and how matrices are updated, ensuring system stability while maximizing performance. By employing sliding window analysis and distribution variance metrics, the system can quantify data drift and trigger appropriate adaptations. The system maintains compatibility with various operational modes including lossless, lossy, and modified lossless configurations, applying mode-specific optimization criteria to each scenario. This adaptive approach offers a resilient solution for data transmission and storage scenarios where both data reduction and security must be maintained across diverse and evolving data streams.

The invention discloses an offshore wind turbine foundation scouring full-period intelligent monitoring method and system based on multi-source data fusion and a dynamic coupling model, and relates to the technical field of intelligent monitoring, and the method comprises the steps: deploying a multi-source monitoring module, and constructing a finite element model; carrying out load calculation and parameter inversion; and training full-cycle dynamic updating of the washout failure function model. According to the method, a self-adaptive Kriging-Bayesian method is adopted, a Bayesian inversion framework and a self-adaptive agent model are fused to solve optimal soil body parameters, full-period model dynamic updating based on dynamic monitoring data is achieved, a multi-fidelity deep kernel learning model is adopted, three types of data are fused into a training set, full-period intelligent monitoring of offshore wind turbine foundation scouring is achieved, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. The dynamic identification of soil parameters is realized by combining a self-adaptive inversion framework with a displacement error closed-loop optimization mechanism, and the technical problem that a traditional static model cannot adapt to the spatial-temporal variability of seabed geology is solved.

The invention relates to a heavy-duty commercial vehicle train hybrid energy management equivalent factor adaptive method, which comprises the steps of classifying operation conditions based on cloud historical data of the operation conditions, and calculating an optimal equivalent factor under each operation condition in an off-line manner by utilizing simulation software after classification is completed; selecting a working condition updating period taking mileage as a reference, identifying the working condition according to the characteristic parameters in the period, and obtaining a corresponding optimal equivalent factor as a reference equivalent factor of the next control period; the equivalent factors are adjusted in real time based on the whole vehicle state parameters; adjusting equivalent factors considering terrain prediction according to the difference between the allowable maximum SOC before downhill and the current SOC in combination with front terrain prediction; corresponding control instructions are sent to the motor and the engine; periodically updating the reference equivalent factor to realize continuous loop iteration of the optimal equivalent factor; and the fuel economy of the whole vehicle is improved.

A system and methods for adaptive bandwidth-efficient data encoding comprising: a sequence analyzer configured to analyze a received sequence dataset, maintain a count of unique characters, and identify positions where the unique character count increases by a power of two; an adaptive sourceblock optimizer that determines and dynamically adjusts optimal sourceblock sizes based on dataset characteristics; and a data deconstruction engine that deconstructs the dataset into sourceblocks and creates codewords for storage or transmission. The system analyzes sequence complexity, alphabet size, and character frequency distribution to optimize sourceblock sizes, and uses machine learning to improve decision-making over time. This adaptive approach enhances compression efficiency across varied genomic data types, including genome graphs, while maintaining data integrity and security. The system efficiently encodes, stores, and transmits complex genomic and bioinformatic datasets, addressing the growing challenges in data storage and bandwidth limitations.

The invention provides a power distribution network single-high-frequency resonance rapid tracking and adaptive suppression method, a multi-mode dynamic cooperative adaptive suppression system is established based on a PCCVF adaptive method, and the damping characteristics of a power distribution network are remodeled by injecting compensation current into a power distribution network system so as to realize broadband resonance suppression of the power distribution network. The compensation current injection method comprises the following steps: step 1, detecting resonant frequency deviation of a power distribution network through real-time FFT (Fast Fourier Transform); 2, performing dynamic phase angle correction based on a phase prediction residual error of real-time frequency deviation; step 3, constructing a layered impedance remodeling module, and ensuring stable power transmission; 4, establishing a frequency-variable impedance model of the power distribution network based on resonance energy spectral density analysis, dynamically optimizing parameters through fuzzy logic of a bell-shaped membership function, and feeding harmonic compensation current into the power distribution network by using a space vector pulse width modulation technology; according to the invention, single high-frequency resonance can be effectively suppressed, the response and tracking performance of the system is improved, and the spectrum analysis efficiency and bandwidth occupation are optimized.

The invention discloses an asymptotic tracking control method for a mobile double-flexible mechanical arm network. The method comprises the following steps: constructing the mobile double-flexible mechanical arm network; the uncertainty of a leader is considered, and a self-adaptive distributed switching observer is constructed; a servo system is introduced, transverse displacement is generated, and a tracking error model is constructed; an adaptive neural network controller is constructed by considering unknown gain faults and parameter uncertainty; on the basis of a distributed switching observer and a neural network controller, asymptotic consistency tracking control over a mobile double-flexible-mechanical-arm network is achieved. According to the method, asymptotic fault-tolerant consistency tracking control of the mobile double-flexible mechanical arm network can be effectively realized under heterogeneous linear leader and denial of service attacks, and the problems of unknown gain faults and unknown parameters are solved by utilizing a self-adaptive method and a neural network technology; the moving position of the moving double-flexible mechanical arm and the angle position of the two flexible mechanical arms reach the specified transient performance, and asymptotic consistency tracking is achieved.

A method and processor for real-time frame processing of a sequence of frames, focusing on motion vector determination, decision metric calculation, and selective triggering of modes for frame processing. It involves calculating motion vectors and their metrics based on pixel displacement and gradient magnitude between consecutive frames to decide on the most appropriate processing mode-copying, generating via neural networks, or rendering using GPUs. The method adapts over time by adjusting thresholds for motion vector metrics, employing decay functions to refine the decision-making process for subsequent frames. This adaptive approach aims to optimize frame generation and rendering quality in dynamic sequences, providing a sophisticated method for managing and enhancing visual media in real-time applications. The processor is configured to execute these steps, adjusting its operations as it processes more frames, ensuring efficient and high-quality visual outputs.

The invention discloses a cross-modal remote sensing image unsupervised adaptive method based on unreliable pseudo tag guidance. The method comprises the following steps: step 1, acquiring data of a source domain and a target domain and preprocessing the data; 2, constructing a teacher-student self-training framework, generating a pseudo tag for a target domain by a teacher network, and filtering noise through confidence evaluation; 3, dividing the image into reliable pixels and unreliable pixels according to the confidence coefficient, and generating a mask; 4, constructing positive samples, negative samples and anchor point features; 5, designing an unreliable sample guide pixel contrast loss function; 6, optimizing the loss function training model until the optimal performance is achieved; and 7, predicting test set data by adopting the trained model to obtain a semantic segmentation result. According to the method, the potential of pseudo labels of which modals are difficult to label is fully mined, the problem that unreliable pixels are insufficient in utilization during training is solved, and finally more effective cross-modal domain alignment and better cross-modal unsupervised domain adaptive semantic segmentation precision are realized.

The invention discloses a large-model multi-field self-adaption method and system based on an adapter, and the method comprises the steps: collecting and preprocessing data, and obtaining training data; according to the training data, selecting a base universal large model and a parameter fine tuning method, and performing multiple adapter-based parameter fine tuning on the large model to obtain a plurality of fine-tuned adapters; using a deep neural network as an adaptive module, and combining the deep neural network with the base general large model and a plurality of fine-tuned adapters to form a large model multi-field adaptive network based on the adapters; training an adaptive module in the large-model multi-field adaptive network by using the training data to obtain a trained adaptive module; and according to the input of a user, reasoning the input by using the large-model multi-domain adaptive network to obtain the output of the corresponding domain. According to the method, under the condition that only a small number of parameters are added to the large model, the large model can be self-adapted in multiple fields, the large model does not need to be switched, the cross-field adaptive capacity of the large model is improved, and the performance of the large model is enhanced.

A system and method for random-access manipulation of compacted data files with adaptive method selection, utilizing a reference codebook, a random-access engine, a data deconstruction engine, and a data deconstruction engine. The system may receive a data query pertaining to a data read or data write request, wherein the data file to be read from or written to is a compacted data file. A random-access engine may facilitate data manipulation processes, transforming the codebook into a hierarchical representation and traversing the representation scanning for specific codewords associated with a data query request. In an embodiment, an estimator module may be configured to utilize cardinality estimation to determine a starting codeword to begin searching the compacted data file for the data associated with the data query. The random-access engine may encode the data to be written, insert the encoded data into a compacted data file, and update the codebook as needed.

The invention discloses a reinforcement learning-based quadrotor unmanned aerial vehicle attitude preset performance control method, which comprises the following steps of: establishing an unmanned aerial vehicle attitude kinetic equation by considering a time-varying inertial parameter and external unknown interference; a sub-channel preset performance controller is constructed, and a robust control quantity is generated in combination with preset performance and a non-singular fast terminal sliding mode control technology; designing a reinforcement learning parameter generator, and dynamically optimizing 12 time-varying parameter estimated values through a time sequence feature extraction network and a residual network; and establishing an online reinforcement learning training mechanism, constructing a multi-target reward function, and optimizing the output of the parameter generator through the multi-target reward function. According to the method, a reinforcement learning method is adopted to replace a traditional self-adaptive method to estimate time-varying parameters, multi-degree-of-freedom decoupling optimization is achieved through a sub-channel control architecture, tracking error preset performance constraint is achieved under the working condition of time-varying inertial parameters, and the dynamic adjustment capacity and anti-interference robustness of an attitude system are remarkably improved.

The invention discloses a distributed sensing test adaptive method, system and terminal, and the method comprises the steps: inputting batch data into a deep learning model, determining a first data feature, statistical information and data type of the batch data in a first batch normalization layer, adjusting batch normalization parameters by using a preset weight, and determining a second data feature, statistical information and data type of the batch data in a second batch normalization layer; carrying out batch normalization processing on the batch data by utilizing the adjusted batch normalization parameters; and if the batch data is homogeneous data, updating the batch normalization layer. According to the method, the adaptive capacity of the model during testing is realized through a plurality of batch normalization technologies, so that the model can effectively deal with dynamic data streams under the condition that source data and real labels are not needed, and the model is ensured to keep good performance and robustness in various testing environments.

The invention relates to the technical field of satellite navigation and positioning, in particular to a zenith troposphere wet delay calculation method based on deep learning, and the basic steps of segmented ZWD multi-source data fusion based on an improved Transform model are as follows: establishing a ZTD model, establishing a TCA-Transform model, and fusing data. The task of establishing the ZTD model is to calculate the atmospheric refractive index according to meteorological data so as to obtain ZTDPP; the TCA-Transform model establishment comprises the following steps of: improving a Transform model by using a domain self-adaptive method; according to the data fusion, the ZWDPP and the ZWDMODIS are fused by using a TCA-Transform model. And finally, a ZWD product with high temporal-spatial resolution and high precision can be obtained. The generalization ability of an original Transform model is improved, so that the troposphere delay correction ability in a specific area is improved, and a zenith troposphere delay (ZWD) product with high resolution and high precision is obtained.

The invention discloses a sensor coverage optimization method based on deep reinforcement learning and domain adaptive multi-task optimization. The method comprises the following steps: constructing a target function by taking the maximum sensor coverage rate, the minimum sensor radius and the minimum sensor number as targets; and solving the target function by adopting an improved multi-task algorithm to obtain the optimal coverage rate, radius and number of the sensors. According to the method, an appropriate task is selected as a source task to perform information exchange with a target task through deep reinforcement learning, so that the possibility of negative migration in the process is reduced; when knowledge migration occurs, individuals in a population are adjusted through a domain self-adaption mechanism, so that a source task is converted into a target task, and the domain self-adaption method effectively reduces the distribution difference between tasks, reduces the negative migration risk and improves the optimization efficiency. Therefore, efficient optimization of sensor network coverage is realized through dynamic task selection, feature distribution alignment and shape similarity adjustment, and the coverage efficiency of the sensor network is remarkably improved.