14results about How to "Improve real-time processing performance" patented technology

The invention discloses a decision-making system and method based on a Dores database, and the system comprises a data access layer which is used for accessing multi-source heterogeneous financial data in real time, a Dores storage calculation layer, and achieves the data storage, column-type bucket separation and materialized view pre-calculation based on the Dores database; the visual application layer is used for providing bidirectional interactive financial data visualization and decision support; wherein the data access layer acquires data in real time through a streaming computing engine and transmits the data to the Dores storage computing layer, and the visual application layer performs real-time data interaction with the Dores storage computing layer through a two-way communication protocol. According to the method, the synchronous delay of financial data is shortened from 6 hours to 1 second, second-level real-time integration is realized, the aggregation query response time of billion-level data is reduced to be within 1 second based on a Dores column type storage and materialized view technology, the user interaction response delay is lower than 500 milliseconds through WebSocket two-way communication, and real-time adjustment of parameters and dynamic refreshing of a billboard are supported.

The embodiment of the invention discloses a communication signal multi-parameter intelligent parallel extraction method. The method comprises the following steps: firstly, acquiring a to-be-measured signal and extracting an I / Q component; processing the I / Q components through a multi-scale feature extraction structure to obtain high-dimensional features; extracting shared features from the high-dimensional features through a plurality of residual block groups, inputting a plurality of parallel task branches, and enhancing the shared features in the branches by using a channel attention mechanism to obtain output features; the output features of all task branches are transformed through a learnable linear layer to form a key matrix and a value matrix, the current task features are transformed through a learnable linear layer to form a query matrix, and the input features are segmented into a plurality of heads by means of a multi-head attention mechanism; each head obtains output of a single head based on zoom dot product self-attention operation, and results of all the heads are integrated to obtain enhanced features of the current task; and determining a communication signal parameter prediction label according to the enhanced features, and realizing multi-parameter efficient parallel extraction.

This invention relates to an orbit recognition method based on EfficientNet, comprising the following steps: constructing a network structure based on the EfficientNetB0 structure; obtaining an orbit recognition model based on an orbit dataset through a network training method; acquiring real-time orbit images as input to the orbit recognition model to obtain the coordinates of the recognized orbit.

The application relates to the technical field of map construction, and provides a SLAM map optimization method and device based on local characteristic values. The method comprises the following steps: acquiring sensor data, and constructing a state matrix of a SLAM system based on the sensor data; performing eigenvalue decomposition on the state matrix, acquiring each eigenvalue, and calculating the change rate of each eigenvalue; determining an eigenvalue with a change rate exceeding a preset threshold as a to-be-optimized eigenvalue, selecting the first M eigenvalues with the largest change rates from the to-be-optimized eigenvalues, and forming a target eigenvalue set; updating a local block corresponding to the first M eigenvalues in the state matrix according to the target eigenvalue set, obtaining an updated state matrix; and optimizing and outputting pose data and map data based on the updated state matrix. By performing local recalculation on a small number of eigenvalues with significant changes in the state matrix, the calculation complexity is significantly reduced while ensuring the positioning and mapping accuracy, and the real-time requirement of high-frequency positioning scenes such as intelligent driving is met.

The invention discloses a broadband radar signal reconnaissance and parameter estimation system and method and a storage medium. The system comprises a frequency spectrum reconnaissance module used for performing Fourier transform on reconnaissance channel input data and detecting a first carrier frequency and a bandwidth of a signal in a frequency domain; the signal processing module is used for carrying out self-adaptive frequency conversion and filtering on the input data according to the parameters; the pulse detection module is used for performing pulse detection on the filtering signal and finely estimating a second or third set of carrier frequency and bandwidth parameters of the frequency modulation or phase modulation type signal; the direction-finding data FFT module is used for performing FFT conversion on the lateral channel data to extract phase information; and the packaging module is used for packaging the pulse detection result and the phase information into pulse description words and outputting the pulse description words. According to the invention, the integrated processing of the broadband radar signal from rapid reconnaissance, precise processing, modulation identification to direction finding information fusion is realized, and the real-time processing capability of the system, the throughput of the broadband signal and the parameter estimation precision are obviously improved.

The invention discloses a processing method for industrial internet identification analysis data, and the method specifically comprises the following steps: 1, carrying out the data processing near a device or a sensor, reducing the delay and bandwidth consumption, analyzing the data in real time, generating feedback, achieving the real-time decision through edge calculation, reducing the dependence on a cloud end, and improving the response speed; 2, dynamically adjusting a data stream processing strategy according to a real-time analysis result, preferentially processing important data, reducing unnecessary data processing burden, processing the data by adopting an event-driven mode, and triggering corresponding processing logic based on a specific event; the data transmission delay is reduced, the response time is shortened, the real-time processing capability is improved, unnecessary calculation is reduced, and the resource utilization rate is improved; the data security is enhanced by adopting a block chain technology, and the non-tampering and traceability of the data are ensured; the decision quality is improved through self-adaptive data stream processing, and the strategy is dynamically adjusted through real-time data.

The invention discloses a memory allocation method and system, a medium and a product, which are applied to the technical field of storage, and the method comprises the following steps: monitoring a memory access condition of a running task to obtain memory space release information of the running task; determining a memory space physical address to be addressed based on the memory space release information; addressing the memory space physical address to be addressed to obtain a continuous addressed address; and allocating the memory space for the memory space application instruction based on the continuous addressing address and the unaddressed memory space physical address. In this way, the memory utilization rate can be increased, and therefore the real-time task processing performance is improved.

The invention discloses a vector information sensing system and method, and the system comprises at least one group of sensing units, the output end of each group of sensing units is sequentially connected with a group of storage units and a group of calculation units, and each sensing unit comprises a scalar signal distribution input end, a horizontal vector output end and a vertical vector output end. The storage unit comprises a horizontal vector information storage subunit and a vertical vector information storage subunit which are respectively used for carrying out in-situ storage on horizontal vector signals and vertical vector signals to form two storage states, and the calculation unit comprises a horizontal vector information processing subunit and a vertical vector information processing subunit which are respectively used for carrying out in-situ storage on the horizontal vector signals and the vertical vector signals to form two storage states. The storage states of the horizontal vector signal and the vertical vector signal are respectively subjected to weighted calculation and summarized at an output end to obtain a calculation result, and the calculation result is used for classification or discrimination. According to the method, the problems of low calculation efficiency, high data redundancy and the like in the processing process of distribution of scalar signals such as images and the like are solved on the hardware level.

The invention provides a multi-platform cooperative processing system and method, belongs to the technical field of information processing, and aims to solve the problems of rigid scheduling strategy, low resource utilization rate and the like in the prior art. The system comprises a plurality of processing platforms, a service state management module and a task scheduling module. The method comprises the following steps: monitoring a service running state to generate a dynamic state parameter; responding to the service request, and obtaining a dynamic state parameter; based on the application requirements and the dynamic state parameters, an optimal processing platform is evaluated and searched through a multi-dimensional cost function; and sending service creation control information to the optimal platform. Through dynamic feedback and intelligent decision making, the task is accurately allocated to the most suitable platform, and the resource utilization rate, the real-time processing capacity and the system robustness are remarkably improved.

This invention discloses a memory allocation method, system, medium, and product, applied in the field of storage technology. The method includes: monitoring the memory access status of running tasks to obtain memory space release information of the running tasks; determining the physical address of the memory space to be addressed based on the memory space release information; addressing the physical address of the memory space to be addressed to obtain consecutive addressable addresses; and allocating memory space for a memory space request instruction based on the consecutive addressable addresses and the unaddressed physical addresses of the memory space. This improves memory utilization, thereby enhancing the real-time processing performance of tasks.

The present application belongs to laser stripe image data processing, and particularly relates to a multi-scale gradient barycenter laser center line extraction method and system, which comprises the following steps: S1, feature extraction and enhancement are performed on the laser stripe image, details branches are strengthened through a spatial channel weight map, and a boundary box positioning result is obtained based on a depth separable convolution and a residual connection; S2, a prediction method is used to obtain a predicted center for each column, a gray peak value is searched in a normal self-adaptive window to realize coarse positioning; the robustness under the condition of fracture and noise interference is improved through confidence evaluation, and a coarse center line is formed after smoothing; S3, a local coordinate system and an elliptical window are established along the coarse center line, a multi-scale Gaussian pyramid is constructed for background suppression and image enhancement; S4, a weighted graph is generated through cross-scale fusion, and a weighted gray barycenter is calculated in each window, and a high-precision laser stripe center line is output after smoothing. The accuracy and robustness of the extracted laser center line are improved.

The invention discloses an adaptive variational mode noise reduction method and system based on bit plane decomposition, and belongs to the field of signal processing. According to the technology, binary conversion is carried out on a time domain signal through an innovative bit plane decomposition method, the signal is decomposed into time domain components under different bit resolutions, and the optimal decomposition parameter of VMD is adaptively determined based on component characteristics. The method comprises the following steps: firstly, carrying out bit plane decomposition on a noisy signal to obtain time domain components of different bits; calculating an autocorrelation test value of each component, and distinguishing a signal component and a noise component; dynamically calculating the modal number and penalty factor of the VMD according to the number of signal components and the energy entropy; and finally, performing VMD decomposition and reconstructing a noise reduction signal. Transient features are reserved through bit plane decomposition, and the feature retention rate is remarkably improved; vMD parameters are directly calculated, iterative optimization is avoided, and the processing speed is greatly increased; hardware resource occupation is greatly reduced, and power consumption is greatly reduced; and in a strong noise environment, the accuracy is greatly improved.

The invention is suitable for the technical field of underwater target detection, and provides a broadband signal DOA estimation method based on fast sparse Bayesian learning. The method comprises the following steps: acquiring frequency domain array receiving signal data and establishing a sparse signal model; constructing a Bayesian probability model based on the sparse signal model; deducing a fixed point updating formula of the hyper-parameter, and performing parameter iterative calculation by using the formula to obtain estimation of a sound source signal space energy distribution vector; according to the method, by using the fixed point updating formula, the convergence rate of parameter iteration of sparse Bayesian learning is remarkably increased, and the real-time processing capacity of the technical scheme is effectively improved; by using parameterized signal variance in sub-band signal prior distribution modeling, effective description of spatial sparsity of different sub-band signals is realized, and the frequency band adaptability is improved; the angle refinement processing based on the marginal likelihood maximum criterion is designed for optimizing the DOA coarse estimation, the DOA estimation with higher precision is obtained, and the DOA estimation precision is effectively improved.

The invention relates to the field of semantic translation methods, in particular to an overseas multilingual translation method capable of achieving semantic optimization, which comprises the following steps: step 1, a text preprocessing unit performs standardization processing and meta-information extraction on an input text to obtain a source text; 2, performing semantic analysis on the source text by a multi-level semantic analysis unit, and generating a comprehensive multi-dimensional semantic representation through a self-adaptive weighted fusion mechanism; 3, a culture conversion unit recognizes culture sensitive vocabularies and expressions in the source text, culture adaptability conversion is conducted through a cross-culture knowledge base, and it is ensured that a translation result has a proper expression mode in target culture; 4, the consistency constraint unit establishes a global consistency constraint mechanism in terms, pronouns and semantics, and ensures the complete consistency of long document translation through term weight scoring, a global term comparison table, pronouns-antecedents mapping and document-level semantic constraint; and 5, performing multi-dimensional quality evaluation on a translation result.