173 results about "Optimal weight" patented technology

The invention belongs to the technical field of production quality management, and provides a production quality online prediction method for a cotton carding process. The method comprises the following steps: constructing and operating a fiber movement mechanism model based on a physical law and a data driving model based on machine learning; the first quality prediction result and the second quality prediction result are input into a dynamic weight fusion mechanism, and the dynamic weight fusion mechanism executes the following operations to generate a high-precision digital twinborn model: deploying the digital twinborn model integrated with optimal weight configuration on an industrial operation and maintenance platform, and driving the digital twinborn model by utilizing cotton carding process production data acquired in real time, so as to realize online dynamic prediction of the production quality. According to the method, the physical interpretability of the fiber motion mechanism model is reserved, the advantage of high precision of the data driving model is fully played, and the prediction precision and robustness of the digital twin model under different working conditions are remarkably improved through adaptive weight adjustment.

The invention provides an optical proximity correction method and system based on mask error model optimization, and a terminal, and the method comprises the steps: building a data sample set containing a plurality of process conditions through obtaining a large amount of mask manufacturing data under a specific photoetching process; and based on the sample set, constructing a physical optical sub-model and a machine learning sub-model to form a mask error model for outputting a final mask key size value. And then, according to a plurality of preset alternative weight coefficient combinations, reconstructing an objective function which introduces a mask error term, calculating corresponding objective function values, and correcting the photoetching pattern by using an OPC algorithm corresponding to each objective function value to obtain performance index data so as to determine an optimal weight coefficient combination. And finally, optimizing the OPC algorithm by using the target function of the optimal combination, thereby realizing the accurate correction of the photoetching pattern, and improving the precision and yield of semiconductor manufacturing. According to the method, the mask error model comprehensively considering physical optics and machine learning is established, and the OPC process is introduced, so that the influence of the mask error can be predicted and compensated more accurately, the critical dimension deviation and the like are reduced, and the device performance and the yield are improved. The method can adapt to different process conditions, and parameters and weight coefficients can be dynamically adjusted. And moreover, rework can be reduced, automatic adjustment is realized through closed-loop feedback control, the production stability and efficiency are improved, and the cost is reduced.

The invention relates to the technical field of process automation and artificial intelligence technologies, in particular to a multi-modal-based agent collaborative auditing method and system, and the method comprises the following steps: obtaining file information,..., carrying out the similarity analysis of a feature vector set and a decision vector set, obtaining a weight set corresponding to the historical file with the highest similarity from a database; setting the weight set as an initial weight set or a final weight set based on a similarity result; performing decision conflict analysis based on the decision vector set, and optimizing the initial weight set based on an analysis result to obtain a final weight set; and performing weighted fusion based on the final weight set and the decision vector set to obtain a fusion decision, and the method has the advantages of effectively solving decision conflicts in a multi-modal data fusion process, accurately positioning a problem root and improving man-machine cooperation efficiency.

The invention relates to the technical field of marketing data analysis, and discloses a marketing analysis control method and system. The method comprises the steps of collecting multi-dimensional interaction data of a target user group; performing time sequence alignment processing on the multi-dimensional interaction data, eliminating asynchronous interference caused by acquisition time offset, and generating a synchronization behavior matrix; potential mode mining is carried out based on the synchronization behavior matrix, behavior clusters with relevance are recognized, and each behavior cluster corresponds to one user decision mode; constructing a dynamic weight model according to the user decision-making mode, calculating the contribution degree weight of the data of each dimension in the current marketing scene, and updating the model by adopting an incremental learning mechanism in combination with real-time feedback data; establishing a marketing strategy mapping table, and selecting an adaptive strategy group according to a current optimal weight combination output by the model; when the strategy is executed, a responsivity index is monitored synchronously, if the responsivity index is lower than a dynamic threshold value, re-matching is triggered, incremental data is fed back to a potential mode mining link, and a closed-loop optimization mechanism is formed.

The invention belongs to the field of ocean internal temperature detection, and particularly relates to a method for optimizing a multi-source constraint weight in ocean thermohaline profile variational inversion, which comprises the following steps of: acquiring a historical actually measured thermohaline profile and matched satellite remote sensing sea surface data, constructing a training set and counting a climate state background field; constructing a weighted variational cost function containing multiple constraint terms, and introducing an adjustable weight parameter for a key constraint term; based on the training set, constructing an external optimization function taking inversion error minimization as a target; weight parameters are iteratively learned through a double-layer optimization framework, the weight is updated by adopting methods such as Bayesian optimization in outer-layer optimization, and the optimal profile is solved under the given weight in inner-layer optimization; and finally, applying the optimal weight obtained by learning to variation inversion of new observation data to realize high-precision reconstruction of the thermohaline profile. According to the method, objective and adaptive optimization of the weight is realized, the inversion precision and robustness are remarkably improved, and the method has good physical interpretability and system universality.

The invention relates to the technical field of microbial fermentation, in particular to a biological fermentation monitoring method and system based on multi-source data fusion. The method comprises the following steps: acquiring multi-source monitoring data in a fermentation tank and constructing a multi-dimensional state vector; on the basis of data of the multi-dimensional state vector in a preset time window, constructing a metabolic entropy of the current fermentation process; a simulated annealing algorithm is adopted to optimize control parameters, the temperature of the simulated annealing algorithm is positively correlated with the metabolic entropy, and minimization of a second energy function is taken as a target; a catastrophe reignition mechanism is set in the optimization process, fermentation abnormity is diagnosed based on triggering of the catastrophe reignition mechanism, and abnormal state characteristics are matched with a preset abnormity database through iterative search of an optimal weight distribution scheme so as to determine the abnormity type with the highest matching degree. By introducing the metabolic entropy and the catastrophe reignition mechanism, the sensitivity, the accuracy and the robustness of monitoring the biological fermentation process are remarkably improved.

The invention discloses a power distribution network load access point dynamic weight preferential determination method considering new energy access, and belongs to the technical field of electric power. The method solves the problems that an existing load access point determination method depends on expert experience or static empowerment and is difficult to adapt to multi-source time-varying data of the power distribution network under new energy access, weight distribution lacks dynamic response, and consequently the deviation between a decision and actual operation is large. According to the technical scheme, the method comprises the steps of collecting multi-source data and preprocessing; constructing an evaluation index system, and performing dimension alignment based on the standardized data to form a feature tensor; generating an initial dynamic weight of a load access point by using a physical information space-time diagram network; optimizing the initial dynamic weight by adopting a constrained multi-objective Bayesian optimization method to obtain an optimal dynamic weight; and calculating a comprehensive score of each load access point based on the optimal dynamic weight and determining an optimal access point. According to the invention, the safety and operation efficiency of the power distribution network under new energy access are improved.

The invention discloses a multi-algorithm fusion cyclone similarity identification method and a storm surge forecasting method based on weight optimization, relates to the field of cyclone similarity identification, and establishes a similarity calculation function by constructing a multi-dimensional time sequence containing a plurality of key factors and combining a weighted dynamic time warping algorithm. In this way, multi-dimensional comprehensive characterization of the cyclonic space-time evolution process is achieved, and the identification deviation caused by the fact that a traditional method only depends on a single feature is overcome. On this basis, a genetic algorithm, a particle swarm optimization algorithm and a differential evolution algorithm are respectively adopted to carry out iterative optimization on the weight vectors, minimization of a deviation coefficient of a candidate similar cyclone set is taken as a target, and a global optimal weight vector corresponding to each algorithm is searched; and based on a preset fusion strategy, determining a target optimal weight vector from the plurality of globally optimal solutions. According to the closed-loop recognition process, multi-dimensional feature modeling and scientific weight optimization are effectively fused, and objectivity and accuracy of cyclone similarity recognition are remarkably improved.

Systems and methods for interpreting one or more borehole features are provided herein. The method can include deploying an azimuthal borehole measurement tool into a borehole, obtaining at least one azimuthal borehole image, generating a synthetic image by sparse convolution of a weight function and a plurality of feature kernels, determining an optimal weight function that minimizes a difference between the synthetic image and the at least one azimuthal borehole image, and determining one or more geological characteristics of the borehole based on the optimal weight function and the feature functional representation.

The invention discloses a bearing fault diagnosis method and system based on a simulated physical neural network. The method comprises the following steps: S1, preprocessing a collected vibration signal to obtain a to-be-diagnosed signal; s2, executing a hybrid optimization algorithm to obtain an optimal transmission band parameter and an optimal weight parameter; s3, configuring a feature extraction module according to the optimal transmission band parameter, and writing the optimal weight parameter into a classification module; s4, inputting the to-be-diagnosed signal into a feature extraction module to obtain a simulation feature vector; s5, inputting the simulation feature vector into a classification module, and outputting a classification voltage signal; and S6, determining a fault type, and outputting a result. According to the method, a hybrid optimization algorithm is adopted, the ratio of the inter-class dispersion degree to the intra-class polymerization degree of the feature vectors is calculated, optimal adaptation of feature extraction and classification tasks is achieved, and the diagnosis precision and generalization ability are improved; by constructing a full analog domain signal processing architecture, analog-to-digital conversion and digital calculation are not needed, and microwatt-level power consumption and microsecond-level real-time response are realized.

The application discloses a kind of multi-feature enhanced deep learning clothes classification method based on auxiliary information, including determining clothes and user information needing classification;Mu-Cloth Net model is constructed based on convolutional neural network;Mu-Cloth Net model is trained, optimal weight is saved, and Mu-Cloth Net model is determined;Actual clothes image and user information are input into Mu-Cloth Net model, and clothes are classified.This scheme greatly improves the classification efficiency and improves the accuracy of classification by integrating auxiliary information, reduces the harmful effects of clothes deformation on the recognition result.And this scheme is respectively convolved to two information, and different scale features are extracted, so that the network can more effectively utilize auxiliary information to establish effective constraints.

This invention provides a sound source localization method, apparatus, and product based on incremental learning and imbalance correction. The method includes: acquiring a dataset for a new task, a parameter-frozen feature extractor, and regularization parameters; enhancing the tail category of the new task dataset; initializing an autocorrelation matrix, a cross-correlation matrix, and a category count based on the number of categories in the new task dataset; iterating through each sample in the new task dataset, updating the autocorrelation matrix, cross-correlation matrix, and category count; calculating the category weight for each category and calculating the Gini coefficient describing the category distribution; adaptively adjusting the regularization parameters based on the Gini coefficient; and solving for and outputting the optimal weight matrix based on the adjusted regularization parameters and aggregating the global autocorrelation matrix and global cross-correlation matrix of different categories. The optimal weight matrix is ​​used to update the sound source localization model. This invention addresses the problem of intra-task and inter-task imbalance in sound source localization, improving the sound source localization effect.

The invention belongs to the technical field of virtual power plant operation evaluation, and particularly relates to a virtual power plant operation comprehensive evaluation method and system.The method comprises the steps that a three-level multi-dimensional operation risk evaluation index system is constructed, and three types of weight vectors are obtained through an analytic hierarchy process, an entropy weight method and an inter-index correlation and conflict objective weighting method; carrying out collaborative correction through a non-cooperative game model to obtain an optimal comprehensive weight vector; a standard cloud model of each risk level is constructed based on an extension cloud model, an actual evaluation cloud model is constructed in combination with the score of a professional scoring system and an optimal weight, the score weight of the professional scoring system is determined through a grey correlation degree, an evaluation value is corrected, the actual cloud model is updated, and finally the digital characteristics of the actual and standard cloud models are compared. And quantitatively judging the operation risk level of the virtual power plant. According to the method, the comprehensiveness, reliability and scientificity of evaluation are improved, and support is provided for risk prevention and control of the virtual power plant.

The application provides a broadband array electronic detection anti-interference method and device and a storage medium, and relates to the technical field of array signal processing.The method comprises the following steps: defining a signal receiving carrier core parameter and constructing an interference signal model, arranging the interference signal in a space-time two-dimensional manner to form a data matrix, and decomposing to obtain an interference characteristic vector set; constructing a target signal model and fusing the interference model to form a mixed signal matrix, and estimating a covariance matrix thereof; constructing a target direction vector based on a search direction, establishing an optimization constraint in combination with the interference characteristic and the covariance matrix, and solving to obtain an optimal weight vector; and performing weighted processing on the mixed signal through the optimal weight vector, and outputting a target signal after interference suppression.The application constructs a data adaptive anti-interference logic, the core of which is to separate the interference and the target signal characteristics, and the fixed filtering parameter is not needed, so that the complex electromagnetic interference can be adaptively adapted, the interference can be suppressed, and the target signal can be completely reserved.

The embodiment of the invention provides a power prediction method and device, computer equipment and a medium, and relates to the technical field of power prediction. The method comprises the following steps: acquiring target multi-source data; respectively inputting the target multi-source data into the plurality of power prediction models to obtain a plurality of initial powers in one-to-one correspondence with the plurality of power prediction models; performing weighted average processing on the plurality of initial powers based on an optimal weight combination in the plurality of weight combinations to obtain a target power; wherein each weight combination comprises weight coefficients of a plurality of power prediction models. Compared with other weight combinations except the optimal weight combination in the plurality of weight combinations, the optimal weight combination enables the error between the target power and the real power of the target multi-source data to be smaller than the preset error, that is, the obtained target power is close to the real power of the target multi-source data, so that the real power of the target multi-source data is obtained. And the accuracy of the obtained power is improved.

The invention discloses a seismic source positioning method considering the influence of an abnormal value and a dynamic wave velocity, and the method comprises the steps: arranging sensors to detect sound waves emitted by a seismic source, and observing the time when the sound waves reach each sensor; subtracting the control equation established based on the reference sensor from the control equation established based on the measurement sensor, and carrying out linearization and matrix representation on a nonlinear equation line obtained by subtraction; the matrix is solved, an analytic solution enabling the sum of squares of the residual errors to be minimum is found, the obtained analytic solution is substituted into an overdetermined matrix equation to obtain a fitting model, and weight estimation is conducted according to the relation between the residual errors and weights; iteration is carried out between weight estimation and overdetermined matrix solving, after optimal weight estimation is obtained, overdetermined matrix solving is completed according to the optimal weight, and a final seismic source positioning result is obtained based on an analytical solution. According to the method, the technical problem that the influence of abnormal values and wave velocity measurement errors on the seismic source positioning precision in a complex engineering environment is large is solved, and the fault tolerance and the real-time performance of seismic source positioning are improved.

The invention relates to an ultra-short-term photovoltaic power prediction method and system based on physical constraint and multi-source data space-time fusion, and the method comprises the steps: carrying out the feature extraction through ViT, and obtaining a cloud picture feature; performing feature extraction by using TCN to obtain time sequence features; fusing the cloud picture features and the time sequence features based on a cross attention mechanism to obtain predicted photovoltaic power, and calculating basic prediction loss; extracting an instantaneous motion vector field of the cloud picture data to calculate the acceleration, divergence and optical flow edge intensity of the cloud picture, and matching a grade coefficient corresponding to each physical quantity; on the premise that only the loss corresponding to the single physical quantity is activated, training is carried out, and the optimal weight coefficient of each physical loss is obtained; taking the optimal weight coefficient of each physical loss as an initial point of a search space, and obtaining an optimal combined weight coefficient through Bayesian search; and physical constraint loss is calculated, and training of the ultra-short-term photovoltaic power prediction model is realized in combination with the basic prediction loss.

The invention discloses an auditable asset allocation system and method based on time-varying risk preference estimation. The system comprises a data acquisition and preprocessing module; the time-varying risk preference estimation module is used for carrying out online Bayesian estimation on the risk preference time-varying hidden state by adopting a sequential Monte Carlo method based on the state space model; the dynamic asset allocation optimization module is used for taking the estimated value as an endogenous variable to be embedded into an optimization model to solve the optimal weight; and the compliance and auditing module is used for recording the full-link log in a tamper-proof manner and generating an interpretable report. According to the method, closed-loop optimization from dynamic risk preference estimation to asset allocation decision is realized, and auditing performance of the whole process is ensured.

The application discloses a fish coated product whole life cycle quality tracing method based on Internet of Things, and belongs to the technical field of food production, and specifically comprises the following steps: obtaining individual morphological parameter set and actual weight gain parameter set of each fish product to establish an individual coating powder characteristic database; extracting morphological feature vectors and matching optimal weight gain labels to construct a feedforward prediction model; extracting actual coating powder control parameters and slurry rheological parameters to form a deviation compensation parameter set; reversely propagating to calculate closed-loop feedback correction parameters and splicing the deviation compensation parameter set into a joint parameter correction set; training to generate an adaptive closed-loop control strategy model; obtaining coating process optimization instructions based on the morphological feature vectors of the current product; and generating equipment warning instructions when the numerical symbols of the coating process optimization instructions are the same and the difference between adjacent numerical values presents a continuous one-way change. The application solves the problems of uneven coating and quality fluctuation caused by constant process by time series correlation storage of individual level data.

The invention provides a PSO-FPA-BP-based friction and wear performance prediction method and system for a resin-based brake material, and the method and system carry out the preprocessing of a friction and wear performance data set of the brake material, and obtain a preprocessed sample data set; the initial weight and the initial threshold of the BP neural network are used as initial population positions of a PSO-FPA hybrid optimization algorithm for optimization, and the optimal weight and the optimal threshold of the BP neural network are obtained; using the preprocessed sample data set to train the brake material friction and wear performance prediction model formed by the BP neural network with the optimal initial threshold value and the optimal initial weight value, and using samples which do not participate in training to test the friction and wear performance prediction model which is trained to be qualified. According to the method, the friction wear performance of the brake material can be accurately predicted.

The invention discloses a gas reservoir three-dimensional geological modeling method and system and a medium. The method comprises the following steps: acquiring multi-source data of a target work area; in combination with vertical seismic section data and virtual well data, depth correction is performed on the horizon after time-depth conversion, and a three-dimensional tectonic framework model is established based on the corrected horizon and fault data; fusing and generating a sand body space development probability trend body based on the seismic attribute body, the seismic inversion body and the determined optimal weight coefficients of the seismic attribute body and the seismic inversion body; a three-dimensional tectonic framework model is used as a space framework, a sand body space development probability trend body is used as a transverse soft constraint, a plurality of isoprobable lithofacies model initial implementation is established, and a three-dimensional lithofacies model is obtained; a three-dimensional distribution model of reservoir physical property parameters is generated; and importing the three-dimensional lithofacies model and the three-dimensional distribution model into a numerical simulator, performing historical fitting by using gas reservoir dynamic production data, analyzing fitting differences, and performing iterative modeling to obtain a high-precision and low-uncertainty three-dimensional gas reservoir geologic model.

The application belongs to the technical field of electric digital data processing, and particularly relates to an adaptive ensemble learning model optimization method for electroencephalogram signal classification, which comprises the following steps: acquiring a multi-channel electroencephalogram sample segment, extracting features containing time-frequency energy, phase locking and spatial mode, the phase locking containing phase locking values among the multi-channels, and constructing three base learners; constructing a weighted brain network through the phase locking values and constructing a brain state feature vector; taking the fusion weight of each base learner as an optimization variable, combining the weight into a particle, grouping according to feature preferences, constructing fitness, iteratively updating the particle based on the fitness, and iteratively optimizing to obtain an optimal weight; weighting and fusing the prediction probabilities of each base learner by using the optimal weight, selecting the category corresponding to the maximum prediction probability as the classification result, and triggering re-optimization based on the change of the adjacent brain state feature vector. The method realizes online adaptive optimization of the weight, and improves the accuracy and long-time stability of motor imagery electroencephalogram signal classification.

A power system planning evaluation method based on random multi-criterion decision comprises the following steps: 1) constructing a multi-dimensional index system, and obtaining a plurality of power system planning schemes; 2) performing initial weight assignment on three-level indexes in the multi-dimensional index system by adopting an AHP method; 3) determining positive and negative ideal solutions of each three-level index in the multi-dimensional index system based on the randomness of the power system planning scheme to the three-level index parameters; 4) constructing a nonlinear programming optimization problem; and 5) solving the nonlinear planning optimization problem to obtain the optimal weight of each three-level index, and selecting an optimal scheme from the plurality of power system planning schemes according to the optimal weight of each three-level index. According to the method, the volatility and randomness of new energy output can be fully considered, and the subjectivity of expert judgment and the objectivity of a mathematical algorithm can be combined, so that the applicable value of a planning evaluation result is improved, and more reliable decision support is provided for planning of a high-proportion renewable energy power system.

The invention discloses a fuzzy rule-based weight coefficient design method, which comprises the following steps of: after a system is initialized, acquiring a plurality of network parameters and a plurality of auxiliary parameters in a measurement window, and standardizing the plurality of acquired auxiliary parameters; performing adaptive reward aggregation on the plurality of standardized auxiliary parameters to obtain a final reward; an objective function is established according to the final reward, and candidate weight coefficients are generated; performing KL constraint on the candidate weight coefficient to obtain KL divergence, and when the KL divergence is smaller than or equal to a KL threshold value, performing the next step; when the KL divergence is greater than a KL threshold value, carrying out step reduction to recalculate a candidate weight coefficient or carrying out rollback to calculate a safety weight coefficient; and smoothing the candidate weight coefficient or the safety weight coefficient to obtain a final weight coefficient. According to the design method provided by the invention, a better weight coefficient can be automatically approached in different service scenes, a sawtooth effect and periodic degradation caused by multi-stream synchronization are reduced, and frequent manual parameter adjustment is not needed.

The present application relates to the technical field of power supply detection, and particularly relates to a power supply load abnormal value detection method and device, comprising obtaining power supply load data, fitting the power supply load data into lower layer Gaussian distribution, then training a multi-level Gaussian distribution mixed optimization model, obtaining optimal weight, performing aggregation operation on the lower layer Gaussian distribution with the optimal weight to obtain upper layer Gaussian distribution, generating a reliability rating of the current processing power supply load time point data, determining whether the power supply load time point data is abnormal based on the reliability rating, providing decision support for power grid managers, and then judging the abnormal value; the present application can effectively identify the abnormal value existing in the power supply load data, so as to avoid possible economic or property loss.

This invention relates to a hysteresis modeling and loop-forming structured control method for piezoelectric actuators, comprising: constructing a piezoelectric actuator model using a Hammerstein structure; describing the uncertainty of the piezoelectric actuator model based on the v-gap metric, and setting open-loop amplitude constraints and the structure and parameter vector search domain of the controller based on the uncertainty description; setting initial weight functions, accuracy coefficients, and an initial controller, and constructing inequality constraints for the loop-forming system under the open-loop amplitude constraints; iteratively solving a first optimization problem and a second optimization problem under the inequality constraints until a preset iteration condition is met, obtaining the optimal weight function and the desired controller; if no solution to the optimization problem is obtained until the preset iteration condition is met, the open-loop amplitude constraints and the parameter vector search domain of the controller are reset, and the next round of iterative solution is performed until a solution is obtained. This invention achieves significant improvements in dynamic response, control accuracy, and robustness.

The application provides an MPC-LSTM air quality prediction method combined with adaptive weights, comprising the following steps: constructing a data set of air quality samples and preprocessing the data set; constructing an MPC-LSTM air quality prediction model; training the MPC-LSTM air quality prediction model based on the preprocessed data set; and obtaining predicted air quality data based on the trained MPC-LSTM air quality prediction model. The application combines a multi-scale parallel convolution fusion network, a long short-term memory neural network and an attention mechanism, realizes further mining of potential feature relationships of data, selects optimal weights, constructs an optimal air quality prediction model, and thus improves air quality prediction accuracy.

The invention discloses an infrastructure whole process data management method and system based on digital twin and block chains, and relates to the technical field of infrastructure project management, and the method comprises the steps: collecting original data of an infrastructure project, carrying out the preprocessing, obtaining normalized data, carrying out the encryption and hashing, and forming a binding data package for storage; extracting the stored binding data packet for decryption and verification to obtain normalized data, setting a mapping relation table to map the normalized data, constructing a digital twin model to simulate the mapped normalized data, and outputting simulation results for weighted summation to obtain a predicted risk score; based on the predicted risk score, constructing an objective function, using a hybrid optimization algorithm to iteratively update the objective function, generating an optimal weight set, and feeding back the optimal weight set to weighted summation to obtain a final risk score; the data management efficiency and the safety operation and maintenance level of the capital construction project are remarkably improved.

The invention provides a buried pipe network optical fiber sensing signal classification method and system by using an optimized and improved extreme learning machine. The method comprises the following steps: firstly, converting a vibration signal sample into a normalized one-dimensional matrix and generating a corresponding one-hot coding label; then network parameters are initialized, an optimization model with classification accuracy and recall ratio as objective functions is constructed, optimal parameters are searched through collaborative iteration of an exploration group, a mining group and an alternative group, the exploration group is responsible for expanding a search range around the whole space, the mining group is responsible for searching for a better solution between member ranges, and the alternative group is responsible for providing an optional solution; and finally, training a classifier by using the optimal parameter, and verifying the model performance on a test set. According to the method, the network weight is iteratively updated according to the rule, the purpose of quickly searching the optimal weight is achieved, and the recognition rate of the algorithm can be remarkably improved.

The application provides a space-time model direct positioning method and system based on medium and low orbit satellite fusion, comprising: position rough measurement; constructing a direct positioning scene and a satellite receiving data model; constructing a space-time model of satellite receiving data; deriving a maximum likelihood estimator target function based on a least square estimation criterion; introducing an optimal weight vector, solving the weight vector according to a minimum variance distortionless response criterion, and deriving a target function based on the minimum variance distortionless criterion; drawing a space spectrum diagram according to the target function value, performing spectrum peak search, and determining the position of a ground radiation source. The application reduces the search range and the calculation amount by using the rough estimation result of single satellite direction finding positioning to obtain the direct positioning area to be searched; the space-time direct positioning model based on the angle of arrival and the Doppler frequency shift is established by using Doppler information, high-precision positioning can be realized under low signal-to-noise ratio, and high resolution can still be realized when the ground radiation sources are very close.