819results about How to "Processing speed" patented technology

The invention discloses an intelligent security and protection monitoring method and system based on image recognition. The method comprises the following steps: S1, acquiring multi-modal data; s2, performing preprocessing to generate complete target information; s3, performing real-time detection on the generated complete target information, and performing primary processing on a conventional scene; s4, based on an improved visual SLAM technology, obtaining three-dimensional information of multiple cameras; s5, when the edge device detects a potential abnormal condition, performing abnormal behavior analysis on the data by using a Swin Transform model, and optimizing a detection result in combination with the three-dimensional information of the multiple cameras; s6, performing adaptive learning on historical data by using a depth deterministic strategy gradient algorithm, and dynamically adjusting a threshold value of abnormal behavior detection; and S7, after an abnormal condition is detected, processing data through differential privacy protection and an end-to-end encryption technology, automatically controlling security and protection equipment, and giving a real-time alarm. According to the invention, image recognition and deep learning technologies are fully utilized, and accurate real-time monitoring in a complex scene is realized.

The invention discloses a multi-source heterogeneous data fusion processing and intelligent analysis method, device and equipment, and relates to the field of data fusion processing.The method comprises the steps that firstly, data in data sources of corresponding types are collected through different collection strategies; performing data preprocessing on the collected data to obtain standard data in a unified format; performing data fusion on the standard data based on the structural features of the data to obtain a fused data set; and determining a target analysis model according to the data analysis target, and performing data analysis on the fused data set. According to the method, various types of data formats can be processed, multi-source heterogeneous data fusion is realized, and through data semantic understanding and a feature-based fusion algorithm, the accuracy and quality of data fusion are improved, and correct data association is established on the semantic level.

The invention relates to the technical field of data storage, in particular to a dynamic data distribution storage method and system for an RAID (redundant array of independent disks), and the method comprises the following steps: based on the access frequency of data blocks in a disk, recording the access frequency and timestamp of each data block, carrying out heat rating on the data blocks, and determining thresholds of hot data and cold data. According to the method, the dynamic distribution of data storage is effectively optimized by analyzing the access frequency of the data blocks, and the performance and the economic efficiency of a storage system are remarkably improved by migrating the frequently accessed data to the cache disk and migrating the less accessed data to the low-cost storage; according to the method, unnecessary occupation of high-performance storage resources is reduced, the operation cost is also reduced, heat rating is performed on the data blocks, and the storage positions of the data blocks are correspondingly adjusted, so that the data processing speed is higher, meanwhile, the data security is improved, when a hard disk fails, data recovery is faster, and the service continuity is ensured.

The invention relates to the technical field of mechanical fault diagnosis, in particular to a mechanical fault diagnosis method and system based on a pulse neural network. The method comprises the following steps: constructing a fault diagnosis network, wherein the fault diagnosis network comprises an improved gating attention coding module and a pulse residual network; the method comprises the following steps: replacing a common convolution layer of a gating attention coding module with a one-dimensional sparse convolution layer, and replacing a two-dimensional batch normalization layer with a one-dimensional batch normalization layer to obtain an improved gating attention coding module; setting a pulse neuron in the fault diagnosis network as a PSN neuron, and embedding a time step shrinkage layer in the pulse residual network; and inputting the mechanical vibration signal of the current sampling period into a fault diagnosis network, and outputting a predicted fault category label of the current sampling period. According to the invention, high-efficiency and high-precision mechanical fault intelligent diagnosis is realized.

The invention relates to the technical field of information processing, in particular to an electronic information dynamic scheduling and management system and method, and the system comprises a data flow dynamic monitoring module, a dynamic window adjustment module, a data distribution module and an instruction flow recombination module. According to the invention, the adaptability and flexibility of data processing are improved by monitoring the data flow in the electronic information in real time and carefully analyzing the size and type change, and the dynamic adjustment of the data processing window is realized by identifying the peak value and valley value of the flow and analyzing the flow fluctuation amplitude. By analyzing the dependency relationship and potential delay between execution instructions and re-arranging the instruction execution sequence, the system can more effectively distribute the load of a processor core, reduce the delay and enhance the processing speed, thereby not only optimizing the management of electronic information flow, but also enhancing the adaptive capacity of the system to a complex data environment, and improving the user experience. And a more efficient and safer information processing platform is provided.

The invention relates to the technical field of data transmission and updating, in particular to a data transmission and updating system and method of a cloud mobile phone. According to the technical scheme, the system comprises an intelligent network monitoring module, a dynamic bandwidth allocation module, a server load balancing module, an intelligent data synchronization module and a cache management module, and the intelligent network monitoring module monitors network conditions, including network bandwidth, network congestion degree and network stability, between a user terminal and a cloud server in real time. Through the synergistic effect of the intelligent network monitoring module, the dynamic bandwidth allocation module, the server load balancing module, the intelligent data synchronization module, the cache management module and the like, the network condition is accurately monitored, the bandwidth is reasonably allocated, the server load is optimized, the data synchronization and completeness are ensured, and the cache efficiency is improved; stable and reliable network connection, preferential key data transmission and rapid data synchronization and loading are brought to users, and the use experience of the cloud mobile phone and the overall performance of the system are greatly improved.

The invention relates to a fractured coal and rock mass strength intelligent prediction method based on image recognition, and belongs to the technical field of rock mass mechanics prediction. The method comprises the following steps: (1) carrying out an indoor test according to different fracture parameters and lithology, and establishing an initial training data set; (2) introducing numerical simulation data, supplementing laboratory data through the simulation data, integrating the data into a data set, and constructing a sample database of coal and rock mass fractures and strength; (3) training a machine learning model by using the sample database, wherein the machine learning model is trained by adopting a gradient lifting decision tree model; (4) testing the precision of the machine learning model by using test set data; (5) acquiring a working face image; and (6) predicting the strength of the to-be-measured coal rock mass. According to the method, a traditional rock strength measurement method is improved, an accurate prediction model can be constructed by combining the nonlinear relationship between the geometrical characteristics and the strength of the coal-rock mass fractures with different lithology, and efficient prediction is realized.

The invention provides a textile defect type identification method, and relates to the technical field of textile defect identification, and the method specifically comprises the steps: collecting original images of a plurality of textile samples, and carrying out the detailed marking; adjusting the original image to 224 * 224 pixels, constructing a CNN model, extracting feature maps of different convolutional layers, and generating a fusion feature vector in combination with the weights of the feature layers; performing pooling processing on the fused feature map to generate a single-channel space attention map, and obtaining a weighted feature map in combination with a channel attention weight; constructing a segmentation model, outputting a mask after training, and identifying a normal region and a defect region; analyzing and identifying a defect area and extracting related features through a connected component to form a feature set; and a comprehensive scoring coefficient is constructed based on the features, so that the textile defect type can be accurately identified. According to the method, the recognition capability on fine defects can be effectively enhanced, and finer deviations can be captured, so that the recognition rate is remarkably improved.

The invention relates to artificial intelligence, and discloses a speech synthesis method and device with a conditional matching stream, equipment and a medium, and the method comprises the steps: receiving text information, coding the text information into a high-dimensional implicit vector, and obtaining a text information vector; label information is formed according to the alignment relation between the text information and the voice, and the relation between the acoustic features of the text information and the voice is predicted; performing matching in the voice data set according to the text information vector to obtain corresponding text-related voice, generating intermediate-state voice according to Gaussian noise, the text-related voice and the time step, performing noise prediction according to the intermediate-state voice and the time step by adopting a conditional matching flow model to obtain predicted noise, and outputting the predicted noise. Calculating the loss between the input Gaussian noise and the prediction noise, and combining the prediction noise with the intermediate state voice to extract and obtain the acoustic features of the text related voice; and generating a decoded speech waveform according to the obtained acoustic characteristics of the text-related speech by adopting an acoustic decoder. And the speech synthesis quality is improved.

The invention discloses a method for performing efficient lossless text compression based on a large language model, which relates to the technical field of text compression and comprises the following steps of: preprocessing to-be-compressed text data to obtain an initial token sequence; selecting and loading a pre-trained big language model, analyzing the current context information by using the big language model, obtaining a probability value of each token, forming a probability distribution list, and recording a ranking sequence number of the current token; the context window is moved backwards by one token, and context information is updated until all the tokens are processed; and recording the ranking sequence numbers of all the tokens in sequence to form a sequence number list so as to obtain a final text compression result. According to the method, the compression rate is remarkably increased, the requirements for storage space and transmission bandwidth are reduced, the high processing speed is kept, and the method is suitable for real-time compression and decompression of large-scale text data.

The invention discloses an unmanned warehouse shelf-oriented automatic cargo checking method and system, and the method is characterized in that the method comprises the following steps: employing a target detection algorithm to extract a cargo boundary frame from captured continuous cargo images; for each cargo bounding box, calculating shape features, color features, texture features, size features and local features of the cargo in the cargo bounding box, and splicing feature vectors of the cargo according to the features; pre-defining cargo templates of different types of cargos, matching the cargo templates with the feature vectors of the cargos so as to obtain a category label of each cargo bounding box, and iteratively optimizing the target detection algorithm through a loss function; and tracking the goods extracted by the target detection algorithm by using a Kalman filtering target tracking algorithm, and obtaining the position and quantity information of the goods from the continuous goods images.

The embodiment of the invention discloses a log management method and device, equipment and a storage medium. The method comprises the steps that multiple pieces of to-be-stored log information generated by the electronic rearview mirror are acquired, and scenes corresponding to the multiple pieces of to-be-stored log information are determined; determining storage priorities corresponding to the multiple pieces of to-be-stored log information based on the scenes corresponding to the multiple pieces of to-be-stored log information and preset scene priorities; if the storage priorities of the multiple pieces of to-be-stored log information are the same, obtaining data sizes and receiving time corresponding to the multiple pieces of to-be-stored log information; and determining a storage sequence of the to-be-stored log information based on the data size and the receiving time, and managing and recording the to-be-stored log information based on the storage sequence. According to the embodiment of the invention, the log information in an important scene can be ensured to be stored preferentially, the importance of the data is considered, the timeliness of the data is also considered, the overall processing speed of the log can be improved, and delay or resource waste caused by an improper processing sequence is reduced.

The invention provides a biological signal analysis method and system, a storage medium and electronic equipment. The method comprises the following steps: acquiring a biological signal of a testee; extracting a spectrum feature of the biological signal in a first specified frequency band; optimizing the spectrum features to obtain a second specified frequency band; and obtaining trigger time of a target phase in the second specified frequency band based on a prediction model so as to stimulate the biological signal based on the trigger time. According to the biological signal analysis method and system, the storage medium and the electronic equipment, the prediction precision of the specific phase point of the biological signal is effectively improved.

The invention proposes a semiconductor device test optimization method, and the method comprises the steps: initializing a test environment, obtaining the test data of a semiconductor, and automatically configuring the test environment; constructing a signal adjustment model to dynamically generate a preliminary test signal; designing a sensor network to monitor a preliminary test signal and response of the semiconductor device in real time, dynamically adjusting sensor data, and designing a data optimization acquisition strategy; dividing the parameters into a high priority and a low priority, dynamically compressing the parameters, implementing fast Fourier transform, extracting spectrum components as features, and analyzing the features; decompressing and preprocessing the compressed data, and performing deep fault analysis and positioning; and optimizing test data of the conductor, carrying out secondary test, and verifying a parameter adjustment result. According to the invention, the test quality and efficiency of the semiconductor device can be obviously improved, the product failure rate caused by insufficient test can be reduced, and the method plays an important role in semiconductor manufacturing industry, especially high-end chip production.

The invention relates to a machine tool spindle thermal deformation prediction method based on a GCN-Transform model. The method comprises the following steps: constructing a sensor network for a machine tool; a space-time diagram data set is generated in the three-dimensional model of the machine tool, and the spatial characteristics of the sensor network are represented through an adjacent matrix; constructing a GCN-Transform model, wherein the model comprises an input layer, a one-dimensional convolution component, a multi-head self-attention mechanism, a feedforward neural network layer, an addition and normalization layer and an output layer; performing prediction, extracting spatial features through GCN, extracting time features through Transform, an adjacent matrix and sensor data as input, and performing thermal error; according to the method, the time characteristics are captured by using the Transform model, and then the spatial characteristics in the topological space of the sensor are captured by using the GCN, so that the time characteristics of the thermal error can be accurately captured, and the spatial information of the sensor can be fully utilized, so that the prediction result is more accurate and robust.

The invention discloses an internal combustion engine fault diagnosis and defect detection intelligent agent system and a construction method, a large language model can process complex fault modes and variable data input, and the model can more accurately identify fault types and causes of an internal combustion engine through training of a large amount of historical data and expert knowledge. Compared with a traditional method, the method is not limited by a fixed mathematical model and a rule base and can deal with a new fault mode, so that the diagnosis accuracy is improved. Besides, the model generally has a relatively high processing speed, can realize real-time or near-real-time fault diagnosis, is helpful for finding and solving problems in time, and reduces downtime and maintenance cost.

The invention discloses a calculation method of an automatic processing tool based on abnormal link identification. The method comprises the steps of S1, receiving network flow data, S2, analyzing the data to determine a reference mode, S3, detecting an abnormal link in a network based on the reference mode, and S4, generating and executing a corresponding repair instruction to process the abnormal link. According to the calculation method of the automatic processing tool based on abnormal link identification, new threats can be found and dealt with in time, the defect of response lag in the face of unknown anomalies in the prior art is overcome, the safety of the system is improved, and the method is suitable for popularization and application. The problems that in the prior art, threshold adjustment is not timely, data preprocessing lags behind, multi-dimensional feature selection is inaccurate, automatic coping strategies are insufficient, and the unknown anomaly recognition capacity is limited are solved.

The invention relates to the related technical field of digital data processing, in particular to an intelligent ancient book information processing method and device based on artificial intelligence, and the method comprises the steps: scanning an ancient book, constructing a knowledge graph, dividing a domain database, and training a vertical processing model; introducing a multi-form historical carrier to form a multi-mode sensing set, establishing a multi-information network, and embedding a processing model; the method comprises the following steps: establishing a dialog box, realizing multi-round interaction and combining threshold optimization output, solving the technical problems of digitization and fragmentation limitation in ancient book information processing and incapability of effectively integrating and utilizing ancient book information due to diversity and complexity of ancient books and literatures, and realizing construction of a knowledge graph and division of a domain database. A knowledge system with a hierarchical structure and logic association is formed, deep learning and optimization are performed according to specific requirements in the field of ancient books by training a vertical processing model, a dialog box is established, multi-round interactive feedback with the user is performed, and the user is assisted to quickly and deeply explore the ancient books.

The invention discloses a judicial field semantic abstracting method, system and device based on a large model and a storage medium. The method comprises the steps that voice recognition is conducted on a court trial video, voice is converted into a court trial text, the court trial text is labeled, and a labeled text set is obtained to serve as a training set; training the large model by using the labeled text set, and finely adjusting the large model after each round of training; performing voice recognition on the actual court trial video, converting the voice into an actual court trial text, and extracting a timestamp; utilizing the trained large model to identify the court hearing process of the actual court hearing text, and reasoning a court hearing process text; matching the reasoned court hearing process text to timestamps by using a matching algorithm, and generating video anchor points when the timestamps at the starting positions of all the processes in the court hearing process text correspond to the anchor points of the processes of the court hearing video; abstracting the reasoned court hearing process text; and combining the abstract text with the video anchor points to generate structured court hearing information. According to the invention, the court trial video processing efficiency is improved.

The invention discloses a modularized intelligent identification and configuration system for a portable Internet of Things electric energy metering box, and relates to the technical field of Internet of Things. Electric energy data are collected in real time through an integrated sensor network, and are cleaned, compressed and preliminarily filtered by a data processing module; a deep learning algorithm is adopted to perform feature extraction and abnormal power consumption behavior detection on the data, and rapid response is ensured; a graphical operation interface is provided through the user interface module, and a user is allowed to update configuration immediately according to requirements; the link state is monitored through the LACP, the optimal link is automatically selected, and stable data transmission is ensured; temporarily storing data through a local cache and backup module when the network fluctuates, and automatically synchronizing the data to the cloud after the network is recovered; the system load is monitored through the self-adaptive optimization module, resource allocation is dynamically adjusted, and efficient and stable operation of the system is guaranteed; the data processing speed of the metering box equipment of the electric power internet of things is greatly improved, and the flexibility and the stability are enhanced.

The invention discloses a dialogue generation method and device, equipment, a storage medium and a computer program product, and relates to the technical field of artificial intelligence, and the method comprises the steps: determining multiple groups of official document question and answer pairs based on a corpus, and adding a quality score to an answer text in each group of official document question and answer pairs to obtain multiple groups of training question and answer pairs, the quality score is used for representing the quality of the content of the answer text; inputting the question text in each group of training question and answer pairs into a pre-trained big language model to obtain a model output text carrying a quality score, the pre-trained big language model being a big language model that has learned a natural language structure; the quality scores of the model output texts are input into a preset loss function to obtain loss values, model parameters of the large language model are updated based on the loss values to obtain an official document question and answer model, and the loss function is constructed based on a preference optimization model. According to the method, official document questions and answers can conform to values, intentions and preferences of human beings.

The invention relates to the technical field of data assimilation, and discloses a data assimilation method, device, equipment and medium, the method comprises the following steps: firstly obtaining observation data and meshing, the observation data comprising at least one variable; or, a background field of at least one time and corresponding observation data are obtained, the observation data are gridded, and the background field and the observation data both comprise at least one variable; next, the constructed data assimilation model is trained by using the grids corresponding to all the variables to obtain a trained data assimilation model; and inputting the to-be-processed gridding observation data or the to-be-processed background field and the gridding observation data into the trained data assimilation model, and outputting a corresponding analysis field. The data assimilation method, device, equipment and medium provided by the invention have the characteristics of high reasoning speed, high assimilation precision and the like, and are suitable for data assimilation in the fields of weather climate, ocean, land surface and the like.

The invention provides a GPU-based data processing method and device, electronic equipment and a medium, and relates to the technical field of chips, in particular to the technical field of GPU memories. According to the implementation scheme, a first matrix to be processed and a second matrix to be processed are obtained from a global memory of a GPU; storing the first matrix into a first sub-memory of a shared memory corresponding to a target thread block in a GPC (Graphics Processing Cluster); storing the second matrix into a second sub-memory of the shared memory corresponding to the target thread block; performing multiplication operation on the first matrix and the second matrix in the shared memory; the third matrix obtained through multiplication operation is stored in a shared GPC memory of a plurality of thread blocks in a GPC of the GPU, the plurality of thread blocks comprise target thread blocks, and the shared GPC memory is addressed uniformly for the plurality of thread blocks; and performing reduction operation on the third matrix in the shared GPC memory.

The invention relates to a signal compensation method and device based on a power carrier OFDM (Orthogonal Frequency Division Multiplexing) system. The method comprises the following steps: acquiring an accumulated sampling phase deviation value of each subcarrier of a symbol to be demodulated in a frame signal according to a sampling phase deviation value of each subcarrier and an initial position of the frame signal; and then judging whether the absolute value of the accumulated sampling phase deviation value of each subcarrier of the symbol to be demodulated is greater than a preset phase deviation threshold, and performing phase compensation on the subcarrier signal of the symbol to be demodulated according to the judgment result. According to the embodiment of the invention, the selective phase compensation is carried out through the judgment of the accumulated sampling phase deviation value of each subcarrier, so that the calculation resources can be greatly saved, the signal processing speed can be increased, and the signal processing efficiency can be improved on the premise of guaranteeing the signal quality no matter for a frame signal with a short frame length or a frame signal with a long frame length. And the overall signal processing efficiency is improved.

The invention relates to the technical field of audio processing, and discloses a multi-mode audio processing method and system based on an SOC. The method comprises the following steps: inputting multiple paths of audio input signals into an ARM processor of an SOC chip for processing, and generating a preprocessed audio data stream; inputting the preprocessed audio data stream into a basic audio processing stream executed by a digital signal processor (DSP) and a multi-modal feature extraction stream executed by an artificial intelligence (AI) accelerator in parallel; performing adaptive noise reduction processing and multi-channel audio signal sound mixing processing in the basic audio processing stream to generate a basic processing feature stream; performing multi-dimensional feature extraction and scene type identification in the multi-modal feature extraction stream to generate a high-level feature stream; according to the method, the processing capability of the SOC chip on different types of audio signals is improved, the continuity and smoothness of the reconstructed audio signals are ensured, and signal distortion is effectively avoided.

The invention discloses a high-concurrency inventory cancel-after-verification method, and the method comprises the following steps: S1, a distributed lock stage: locking an inventory item to be modified; s2, at an optimistic lock stage, a version number is added to an inventory record, when the inventory is updated each time, the inventory number needs to be updated, and whether the version number is consistent with the version number during reading needs to be checked; and S3, a pre-deducted inventory stage: when the user submits an order or an exchange request, immediately subtracting a corresponding quantity from the pre-deducted inventory, keeping the actual inventory, and efficiently and accurately processing the inventory management problem, improving the processing speed, avoiding overselling and improving the system stability through methods of integrating a distributed lock, an optimistic lock mechanism, the pre-deducted inventory, asynchronous processing and the like. The high-concurrency inventory cancel-after-verification method provided by the invention has the effects of improving the processing capability of the system under large flow impact, ensuring the accuracy and consistency of inventory data and optimizing the user experience.

The invention relates to the technical field of pilot state monitoring, in particular to a pilot state monitoring and flight data real-time synchronous fusion system, which comprises an information acquisition module used for acquiring physiological state information of a pilot and flight data information of an airplane in real time; the intelligent control module is used for controlling the operation of the camera, the electroencephalogram acquisition device and the electrocardiograph; the information processing module is used for carrying out preprocessing operation on the collected pilot physiological state information and the collected aircraft flight data; and the data fusion module is used for fusing the physiological state information of the pilot and the flight data parameters of the aircraft. The early warning assistance module is used for immediately sending out an early warning signal when an abnormality is found; and positioning information and rescue guidance are provided for a pilot under the emergency condition that the aircraft encounters danger. According to the current attention state and the task requirement of the pilot, the system can dynamically adjust the collection frequency and the collection position of the electroencephalogram collection device so as to improve the efficiency and the pertinence of data collection.

The invention discloses an image restoration method and system based on gated convolution and a fused gated attention mechanism, and belongs to the field of image super-resolution restoration frameworks, and the method comprises the steps: taking a preprocessed image as the input of an image restoration model, carrying out the feature extraction and dimension raising, and obtaining a feature map; the image features are extracted by fusing the attention and self-attention mechanism of the convolutional neural network type, and iterative optimization is carried out by using the loss function, so that the local and global features of the image can be processed more effectively, the image reconstruction quality is improved, and the authenticity and detail richness of the high-resolution image are ensured at the same time.

The invention relates to the technical field of resource allocation, in particular to an edge computing resource allocation method and system, and the method comprises the following steps: collecting network node information, recognizing key nodes through embedded learning, and analyzing a communication mode. A graph convolutional network and a self-organizing mapping algorithm are utilized to analyze and encode node connectivity and resource states, and a network structure is iteratively optimized. And monitoring and updating a network state, adjusting a resource allocation strategy, and generating a dynamic resource allocation model. According to the method, the data processing task is performed on the near side of the data source, so that the data transmission distance and delay are remarkably reduced, the processing speed is increased, and the application performance is directly improved. Simulation mapping and dynamic adjustment of resource states and demands enable resource allocation to be more suitable for actual demands, and resource waste is reduced. Self-adaptive adjustment of resource allocation is carried out according to network real-time data, and real-time performance and accuracy of resource allocation are guaranteed. The instant response and prediction capability optimizes the operation of the whole network.

The invention provides a fault processing device and method compatible with arc suppression coil operation, and relates to the technical field of power supply equipment, the device comprises an arc suppression coil module and an intelligent grounding module, and the arc suppression coil module and the intelligent grounding module are connected in parallel; the arc suppression coil module comprises a first front circuit breaker, a first grounding transformer, an arc suppression coil and a first current limiting resistor, the wire inlet end of the first front circuit breaker is connected with a bus of the power distribution system, the output line of the first front circuit breaker is connected with the wire inlet end of the first grounding transformer, and the neutral point of the first grounding transformer is connected with one end of the arc suppression coil. The other end of the arc suppression coil is connected with a current-limiting resistor I, and the other end of the current-limiting resistor I is grounded; the intelligent grounding module comprises a front circuit breaker II, a fault phase selection sub-module and a fault line selection sub-module, a bus of the power distribution system is connected with a wire inlet end of the front circuit breaker II, the fault phase selection sub-module is connected with the front circuit breaker II in series and then is grounded, and the fault line selection sub-module is connected with the fault phase selection sub-module in parallel.