107 results about "Data efficiency" patented technology

The invention discloses a visual reasoning adjustment method based on mixed training and electronic equipment, and belongs to the technical field of data processing.The method comprises the steps that a visual reasoning data set is determined, and the supervision fine tuning reasoning ability of a visual language model is activated based on the visual reasoning data set, so that the visual language model can complete a target reasoning process; and utilizing a group relative strategy optimization algorithm to optimize the supervised fine tuning reasoning capability of the visual language model. According to the method, the dependence on a large amount of annotated data is reduced, and the data efficiency is improved; and through a dynamic optimization mechanism of reinforcement learning, the method can better adapt to cross-domain tasks, and the generalization ability of the model is remarkably improved.

A system and methods for integrated data processing and protocol adaptation using dyadic distribution-based compression. The system transforms input data into a dyadic distribution, enabling efficient compression through either variational autoencoders or Huffman encoding. A novel protocol appendix generator creates transformation rules for adapting the compressed data to various network protocols. The system interleaves transformation information with the compressed data, enhancing security and ensuring comprehensive data transmission. An enhanced codeword decoder, employing a hybrid neural network architecture, decodes the data and adapts it to target protocols. The system features a protocol handler using meta-learning techniques for adapting to unfamiliar protocols. Continuous learning mechanisms optimize performance over time. This integrated approach offers significant advantages in data efficiency, security, and protocol flexibility, making it particularly suitable for complex, heterogeneous data environments such as IoT networks, cloud computing, and big data analytics.

The invention discloses an electronic device parasitic parameter network analysis method based on point cloud deep learning and a related device, and the method comprises the steps: obtaining physical characteristic data in a laser-induced quartz material process, and the physical characteristic data comprises laser wavelength, pulse width, photon energy and material characteristics; and inputting the physical characteristic data into the trained neural network model, and predicting the damage threshold of the laser-induced quartz material. According to the machine learning method fusing physical information, unification of data efficiency and physical consistency is achieved, prediction precision and model expandability are remarkably improved, and the method is used for accurately predicting the damage threshold value of the quartz material under the specific laser condition, so that the laser processing technology is optimized, and the processing precision and efficiency of the quartz material are improved.

Imitation learning, or artificial intelligence-based learning from demonstration, aims to acquire an agent policy by observing and mimicking the behavior demonstrated in expert demonstrations. Imitation learning can be used to generate reliable and robust learned policies in a variety of tasks involving sequential decision-making, such as autonomous driving and robotics tasks. However, current imitation learning solutions are limited in their ability to generalize states or goals unseen from the expert's demonstrations. The present disclosure integrates a diffusion model into generative adversarial imitation learning, which, in terms of prior solutions, can provide superior performance in generalizing to states or goals unseen from the expert's demonstrations, provide data efficiency for varying the amounts of available expert data, and capture more robust and smoother rewards.

The invention discloses a drug combination patent intelligent identification method and device based on a large language model, and the method comprises the steps: obtaining a standard data set built based on drug combination labeling information of patent data, carrying out the data expansion of the standard data set, and generating an initial training set; a parameter efficient fine tuning technology is adopted, the initial training set is utilized to train the base large language model, and an initial recognition model is generated; screening and identifying difficult samples in the unlabeled patent data by using the initial identification model, and adding the difficult samples into the initial training set to form an enhanced training set; performing iterative training on the initial recognition model by using the enhanced training set and adopting a multi-task learning framework in combination with a comparative learning mechanism to obtain a drug combination patent recognition model; the to-be-recognized patent data is input to the drug combination patent recognition model, and a drug combination information recognition result is obtained.The accuracy, data efficiency and model interpretability of drug combination patent recognition can be remarkably improved, and the computing resource requirement and the manual review cost are reduced.

The invention discloses a VLA task space control method and system for a multi-degree-of-freedom mechanical arm, and relates to the technical field of intelligent robot control, visual information and language instructions when the mechanical arm executes a task are obtained, and a trained VLA model is adopted to obtain a target pose of an end effector in a task space; an IK solver corresponding to the mechanical arm configuration is adopted, and according to the target pose, multiple sets of joint angle solutions of all joints of the mechanical arm are obtained; selecting an optimal joint angle of each joint from the multiple groups of joint angle solutions by adopting a preset optimization criterion; and after the optimal joint angle sequence of the mechanical arm is subjected to track smoothing processing, the mechanical arm is driven to move. Mechanical arm motion control is carried out in combination with a VLA model and an IK solver, the problems that the generalization ability between different mechanical arm configurations is poor, the data collection cost is high and the model interpretability is insufficient are solved, and the generalization ability and data efficiency of mechanical arm control are improved.

The invention provides an autonomous operation obstacle avoidance method and system for a low-altitude aircraft, and the method is executed by the low-altitude aircraft, and comprises the following steps: S1, data acquisition: collecting data through a multi-source heterogeneous sensor group carried by the low-altitude aircraft, airspace control information, real-time meteorological data and collaborative sensing data of other aircrafts sent by the ground sensing base station are received through the air-ground communication interface; preprocessing the collected original data, wherein the preprocessing comprises data format standardization, abnormal value elimination and time synchronization; and S2, data fusion processing: carrying out credibility evaluation on the preprocessed multi-source heterogeneous sensing data and collaborative sensing data. According to the invention, through three-dimensional credibility evaluation and an air-ground data compensation mechanism, the perception precision is greatly optimized, the perception data efficiency is improved in complex environments such as low illumination, strong electromagnetic interference, rainstorm and the like, and obstacle avoidance decision errors caused by data distortion of a single sensor are avoided.

The invention discloses an excavator dynamic load calculation method based on physical information constraint deep learning, belongs to the technical field of load calculation, is used for excavator dynamic load calculation, and comprises the steps that an excavator dynamic model and a neural network model are constructed, and the neural network model comprises an input layer, a conversion layer, a dynamic model learning layer and an output layer; taking the excavator kinetic model as a constraint term of a kinetic model learning layer; the input data of the input layer comprises three angles, angular velocity, angular acceleration and hydraulic pressure variables of the three-section arm, totally 12 variables. According to the method, the sensitivity of the model to unavailable parameters is reduced based on hierarchical configuration of physical constraints, the interpretability of the model is enhanced through mechanical constraint explicit integration exported through a dynamic equation, the data efficiency is optimized by utilizing a dynamic relationship constraint solution space, the network structure design is improved, and the network performance is improved. Dependence on hyper-parameters such as the number of layers and the number of neurons determined manually is reduced, and actual application requirements are better met.

The invention discloses a multi-source heterogeneous knowledge injection type prompt learning method for legal criminal name prediction. The method comprises the following steps: step 1, obtaining and coding fact elements; 2, knowledge matching and prompt injection; 3, reasoning the legal language model; and 4, legal crime name category mapping. The method has the advantages that prediction accuracy is improved; the problem of input length limitation is solved; the field adaptability is enhanced; the data efficiency is improved; the interpretability of the model is enhanced; multi-task learning is supported; and intelligent development of laws is promoted.

The invention discloses a system and method for improving data configuration efficiency of a test machine, and relates to the technical field of board card testing, the system comprises an FPGA PCIE module, a DMA address data analysis module and a data transmission module, a general data frame is divided into an independent 32b address field and an independent 32b data field through the arranged DMA address data analysis module, then bit [31] and 0 of an 32b address are analyzed to represent write operation, and the data transmission module is connected with the FPGA PCIE module. 1 represents read operation, an identifier is independently extracted and embedded into a subsequent instruction packet, then a bit [27: 0] of a 32b address is extracted, a target daughter card number is determined according to a preset address mapping table, and data is packaged after determination, so that data distribution of different address fields is realized, the data transmission module receives the data and then sends the data to each post-stage daughter card module in parallel, and data read-write is realized. Therefore, the read-write speed of the test machine is greatly increased, and the read-write efficiency is further improved.

The invention discloses a report generation method based on a large model, electronic equipment and a computer program product, and relates to the technical field of artificial intelligence, the report generation method comprises the following steps: identifying a logic structure, a placeholder and a task dependency relationship in a preset report template, and initializing a context state carrier based on the logic structure, the placeholder and the task dependency relationship; obtaining a report generation requirement, converting the report generation requirement into SQL query data, and generating a cue word based on the SQL query data; and inputting the cue word into a preset generation model to obtain filling content, updating the filling content to the context state carrier, and filling placeholders of the report template based on the context state carrier to obtain a final report. According to the method, the defect of low efficiency of manual data arrangement is overcome, and the problem that a simple template filling tool cannot process a complex logical relationship is solved, so that the report generation efficiency is improved.

The invention discloses a construction site worker behavior intelligent identification and early warning system, which collects multi-directional video streams in real time through a front-end video collection module, guarantees timely data updating, and facilitates real-time monitoring of worker behaviors. The early warning and display module displays a field picture, a worker skeleton diagram, a behavior state and duration time in real time, early warning is immediately carried out when abnormity occurs, an instant feedback mechanism of the early warning and display module enables safety management personnel to rapidly know the abnormity and take measures, accidents caused by information lag are avoided, and the defect that the real-time early warning capability is insufficient is overcome; the behavior recognition module adopts an EnhanceST-GCN model, can better capture a space-time relation of skeleton key points, accurately recognize a complex behavior mode, improve recognition precision and solve the problem of low recognition precision, and meanwhile, the model is high in video data processing efficiency, rapid in analysis, feature extraction and classification, reduces unnecessary calculation steps and meets the real-time monitoring requirement of a construction site.

The invention relates to the technical field of computer vision and medical instrument management, and discloses a medical instrument intelligent identification method, device and equipment and a medium for solving the problem that the accuracy of surgical instrument identification is insufficient. And a dual-polarization acquisition and pixel difference technology is combined to accurately separate a mirror surface highlight area from a diffuse reflection area, so that the image definition is improved. Through the first analysis model, the determined and uncertain identification areas are rapidly determined, and the second image of the uncertain area is focused and complementarily collected, so that redundant collection is reduced, and the data efficiency and accuracy are improved. The image subjected to glare-removing tuning processing has clearer feature information, and the recognition accuracy of the uncertain area can be remarkably improved after re-analysis. And finally, fusing the first and second determination identification results to comprehensively improve the accuracy and reliability of medical instrument identification, thereby effectively solving the technical problem of insufficient accuracy of existing medical instrument intelligent identification.

The invention belongs to the technical field of intelligent damper modeling, and discloses a magnetorheological damper modeling method based on a physical information neural network, comprising the following steps: initializing the physical information neural network; introducing physical constraints into the physical information neural network to obtain a magneto-rheological damper prediction model; obtaining experimental data of the magneto-rheological damper, and jointly training the magneto-rheological damper prediction model through the experimental data and a loss function; and obtaining model parameters of the trained magnetorheological damper prediction model, and updating the model parameters by using an adaptive optimization algorithm. In conclusion, the physical information neural network serves as a basic framework, physical constraints such as the electromagnetic equation, the heat conduction equation and the yield stress equation are introduced, the physical consistency, data efficiency optimization and online self-adaptive capacity of the model are improved, and then the method is flexibly suitable for high-precision dynamic control over the magnetorheological damper.

The invention relates to the technical field of block chains, in particular to a block chain tourism data privacy protection method and system, and the method comprises the steps: constructing a distributed cache layer which is disposed between a block chain node and a user terminal, and is used for dynamically capturing the newly-added tourism data of a user, and taking a preset time window or a data size threshold value as a unit; and aggregating the newly added tourism data into a data block group. According to the method, the distributed cache layer is constructed between the block chain node and the user terminal, the newly-added tourism data of the user is aggregated into the data block group according to the preset time window or the data volume threshold value, the situation that a complete privacy protection process is independently executed on each piece of newly-added data is avoided, and repeated operation and verification operation are reduced; double-layer encryption is adopted for a data block group, so that the basic data efficiency is guaranteed through lightweight symmetric encryption, and the group-level metadata security is enhanced through asymmetric encryption; and the fragmented parallel verification is started and the lock-free operation is realized by means of the message queue, so that the verification time is shortened.

This invention provides a technique for microservice scheduling and improving data read / write efficiency based on runtime state analysis. Microservice systems have become one of the fundamental technology platforms for building large-scale, complex applications. By encapsulating modular information processing units in a lightweight virtual machine manner, high fault tolerance is achieved. Various application functions are orchestrated through service mesh to realize stable large-scale, complex application systems. Microservices often need to access multiple data sources or data lake systems, and the location of data has a critical impact on data read / write efficiency. For example, in a distributed storage system, having the execution node of a microservice coincide with or be close to the data storage node will greatly improve data processing efficiency. Addressing the efficiency problem of microservices in data-intensive scenarios in large-scale applications, this invention proposes a technique based on analyzing the runtime state of data read / write operations to determine the optimal location for microservice scheduling and execution. By shortening the data path, data I / O efficiency is effectively improved, enhancing the performance of building large-scale, data-intensive application systems based on microservices.

The application provides a data query method and device, equipment and a storage medium. It can be used in the field of big data technology. The method comprises the following steps: receiving a data query command sent by a control device; the data query command comprises target query information; the target query information reflects target query data; determining a corresponding index type according to the target query information; the index type is a Hash index or a B+ tree index; in response to the index type being a Hash index, directly querying position information corresponding to the target query data from preset mapping information according to the target query information to obtain target position information; the preset mapping information comprises preset data information and position information corresponding thereto; in response to the index type being a B+ tree index, querying position information corresponding to the target query data according to the target query information and a B+ tree index strategy to obtain target position information. The application can reduce the number of scans, speed up the response and improve the efficiency of querying target query data.

The invention discloses a semantic guidance multi-label image classification method based on hierarchical prototype injection. Input multi-label images are classified by adopting a classification model. The classification model is a hierarchical prototype visual converter (HP-ViT) model. According to the method, label co-occurrence statistics is analyzed offline, and a hierarchical semantic community which can reflect high-order semantic dependence and is from coarse to fine is mined; secondly, instantiating the semantic concepts into a group of learnable hierarchical prototypes, and performing hierarchical injection at different depths of a Transform encoder by adopting a coarse-to-fine strategy; and finally, the prototypes are supervised through an auxiliary classification task corresponding to each level under a multi-task learning normal form, so that dynamic and structured semantic guidance of the backbone network feature extraction process is realized. The method shows potential in the aspects of improving classification performance, data efficiency and model interpretability, and provides a new technical path for solving a complex multi-label classification problem.

The application provides a method, device and related product for adjusting a database. The method comprises: in response to a plurality of data usage request instructions continuously initiated by an application end, identifying a plurality of access behavior information corresponding to the plurality of data usage request instructions. When a plurality of single response times in the plurality of access behavior information continuously increase, determining a preset adjustment rule according to the plurality of access behavior information. Adjusting the database according to the determined preset adjustment rule. In this way, the continuously increasing plurality of single response times in the plurality of access behavior information indicates that the database processing data efficiency is declining. When the database processing data efficiency is declining, the preset adjustment rule is determined through the plurality of access behavior information, and the database is automatically adjusted according to the preset adjustment rule, thereby improving the data processing efficiency of the database. No manual intervention is required, and the adjustment process is not limited by the application scenario.

The utility model discloses a communication system. The communication system comprises a micro-control unit, a main communication module and a plurality of auxiliary communication modules, the main communication module is electrically connected with the micro-control unit, and each auxiliary communication module is electrically connected with the main communication module through a universal asynchronous transceiver; the micro-control unit sends a network communication protocol data packet to the main communication module; the main communication module sends a network communication protocol data packet to the auxiliary communication module, and the auxiliary communication module transcodes the network communication protocol data packet and sends the network communication protocol data packet through communication. Through the mode, the logic pressure of the micro-control unit can be reduced, and the data transmission efficiency of the micro-control unit and the single communication module can be improved.

The present invention belongs to the field of blockchain technology, solves the technical problem in the prior art of reducing data processing efficiency due to abnormal data in the delayed processing of interactive data at the edge, and provides a method, device, equipment and storage medium for asynchronous data processing of financial management agency sales business. The method includes: obtaining each to-be-verified file corresponding to the to-be-verified data; retrieving preset business verification information corresponding to each to-be-verified file according to each to-be-verified file; verifying each to-be-verified file according to each to-be-verified business verification information, and obtaining a verification file corresponding to each to-be-verified file to be processed with delay; wherein, the verification file includes a verification-passed file and a verification-failed file, and the file data of the verification file is consistent with the file data of the to-be-verified file. The present invention performs advance inspection on the files received by the financial management subsidiary and the agency sales bank, completes the abnormal file alarm in advance, and helps to improve the efficiency of delayed data.

The invention discloses a low-power-consumption quick start awakening method for an acquisition terminal. According to the scheme, the acquisition terminal comprises a main control module, an auxiliary control module and a power supply switching circuit, and a rechargeable low-leakage-current battery capacitor is adopted as a standby power supply; during normal power-on, the terminal is powered by the main power module, the main control module works in a high-performance mode, the clock module and the liquid crystal screen module are switched to the main control module through the multi-channel analog switch, and the auxiliary control module performs voltage sampling of the main power supply and the standby power supply and charging control of the standby power supply. The main control module and the auxiliary control module are simultaneously communicated with the metering chip to read data; when the power-off key is awakened, the multi-channel analog switch is switched to the auxiliary control module, and only a few modules such as the auxiliary control module work. Through cooperation of the main power module, the standby power module, the power supply switching circuit and the like, the problems of long key wakeup starting time, high power consumption and low data reading efficiency are solved, and rapid key wakeup starting, low power consumption and improvement of the data reading efficiency of the acquisition terminal are realized.

The invention discloses an information management net value mail cleaning and analyzing method and system based on a large model, and the method comprises the steps: obtaining a mail of a user mailbox, and extracting and storing the metadata of the mail; performing multiple identification on the mails based on a large model, confirming net value mails, and excluding non-net value mails; aiming at net value mail contents in different formats, adopting a corresponding extraction mode and carrying out data verification; standardizing the extracted net value data according to a preset term mapping table and a standardized data dictionary; performing automatic analysis based on the net value data after standardization processing; and carrying out visual display on the analysis result. According to the method, automatic identification and acquisition of net value mails are realized, net value data in mails in different formats can be extracted and automatically analyzed, and analysis results such as income trend and risk assessment are generated in real time. The method solves the problems that in the prior art, the efficiency of obtaining and analyzing the net value data in the asset management mail is low, errors are prone to occurring, format adaptation is difficult, and a dynamic analysis result is difficult to generate in time.

The application discloses a kind of quick conversion calculation method of aircraft center of gravity data, comprising the following steps: step one, according to the geometric relationship diagram of center of gravity conversion, the geometric relationship between the height h of aircraft center of gravity coordinate, the height of center of gravity when landing gear is fully extended, the distance a between the projection of aircraft center of gravity coordinate to ground point and front wheel contact point and the distance b between rear wheel contact point is established;Step two, the geometric relationship is packaged into Excel software;Step three, input the front wheel coordinate parameters and rear wheel coordinate parameters of the model that needs to be calculated, and then input the center of gravity coordinate, and the corresponding h, a, b value can be automatically converted;The application is aimed at the drawbacks of drawing method, combined with commonly used Excel software can automatically process a large number of formulas, realize the calculation of aircraft h, a, b value in each state, data processing speed is fast, solve the problem of low efficiency and easy to make mistake of traditional drawing method manual reading data.

The invention provides a zero sample anomaly detection method based on bimodal prompt enhancement, and the method comprises the following steps: S1, carrying out the input coding and multi-modal feature extraction: carrying out the prompt coding of an input image and a text into feature vectors, and achieving the preliminary alignment of the features of the image and the text; s2, dynamic vision-text prompt interaction: adopting a PIM module to realize layer-by-layer dynamic guidance of text prompt on visual features through a learnable projection matrix; s3, constructing a hierarchical abnormal prototype memory library: constructing a multi-granularity abnormal prototype library by adopting a PMB module, and enhancing fine-granularity representation through micro-texture and macro-component prototypes; s4, an uncertainty perception anomaly scoring mechanism; calculating an abnormal score by combining multi-granularity prototype matching and the uncertainty weight; and S5, anomaly detection and output: integrating the text-visual similarity and the PMB score to complete anomaly detection. According to the method, the modal semantic deviation is solved, the fine granularity detection precision is improved, and the data efficiency is high.

This invention relates to the field of automotive communication technology, providing a method and system for converting and managing automotive communication database formats. It aims to solve the problems of low efficiency, error-proneness, and difficulty in collaboration associated with traditional manual processing of multi-source heterogeneous communication data. This method automatically parses source data in multiple formats such as DBC and Excel, converting them into a pre-defined intermediate data model. It then automatically generates ARXML files conforming to the AUTOSAR standard and finally uploads the files to a cloud database, associating them with project information to achieve centralized storage, version management, and collaborative work. Through collaboration between local and cloud platforms, multiple verifications and version management can also be achieved. This invention efficiently and accurately converts scattered and heterogeneous communication data into industry-standard files through an automated process, and leverages the cloud to achieve centralized file management and project-based collaboration, effectively improving the efficiency, consistency, and traceability of in-vehicle communication data processing.

The application provides a point cloud instance segmentation method and system based on synthetic data assistance, and the application generates an indoor 3D scene containing rich objects by using a 3D object model data set and a 3D scene model data. On this basis, the camera trajectory during scanning of the scene point cloud is counted, the same scanning process is simulated in the synthetic scene according to the trajectory to obtain labeled synthetic point cloud data. In order to learn semantic rich feature representation from the synthetic point cloud data, the application performs supervised pre-training on the synthetic data, and takes the semantic segmentation task as a proxy task for feature learning. In the pre-training process, the application introduces unlabeled real scene data and gives the data pseudo labels to guide the migration process from synthetic data to real data. When the learned feature representation is used for the real point cloud instance segmentation task, the rich semantic knowledge and object shape knowledge contained in the pre-training model can be used to realize data efficiency of the real data.

The invention provides a construction method of a multi-source heterogeneous equipment collaborative energy scheduling model, a medium and a system, and belongs to the technical field of energy scheduling. A fault propagation prediction model based on a heterogeneous graph structure and an attention mechanism is constructed, and the uncertainty is quantitatively predicted by using a Bayesian neural network; solving the collaborative scheduling mathematical model by adopting a self-adaptive robust optimization method to obtain an initial scheduling scheme, dynamically adjusting charging and discharging power according to a comprehensive aging index, guiding active learning through a sampling priority value to carry out dense sampling on high-uncertainty nodes, and carrying out incremental training on the model; and adjusting the power limitation of the energy storage unit and the control strategy of the photovoltaic inverter according to the node fault probability distribution, thereby solving the technical problem of insufficient reliability of the scheduling scheme caused by the lack of an artificial intelligence model training mechanism with high data efficiency in the fault propagation scene of the multi-source heterogeneous equipment of the micro-grid system.

The invention relates to the technical field of network security, in particular to a heterogeneous social graph detection method and system fusing neighborhood perception and adaptive gating, a heterogeneous social graph is constructed, nodes represent users, and edges represent various social relations among the users; coding the node features to generate a low-dimensional dense initial feature vector; the multi-layer neighborhood perception graph neural network NANNN takes R-GCN as a backbone network to carry out node representation learning, explicitly models heterogeneity of different types of social relations, and introduces an adaptive gating module into each layer of the R-GCN; and based on the final node representation, predicting the probability that the node belongs to a robot or a human through a classifier, and optimizing all model parameters through end-to-end training. According to the method, through a triple perception adaptive gating aggregation mechanism, the remarkable effects of higher precision, lower parameter quantity and higher data efficiency are achieved in a social robot detection task.

The invention discloses a stateless robot motion control method and system based on vision, and belongs to the field of robot intelligent control, and the method comprises the steps: collecting multi-view visual observation data through a top main camera and double-wide-angle wrist cameras installed above and below an end effector; inputting the visual observation data into a pre-trained strategy network, wherein the strategy network outputs a relative pose increment of an end effector; and the current pose of the end effector is updated according to the relative pose increment, and the robot is controlled to execute corresponding actions. The strategy network is trained based on a stateless imitation learning framework, only visual observation data and corresponding expert action labels are used in the training process, and state information is sensed without depending on a robot body; according to the method, pure-vision-driven end-to-end control is realized, and the method has strong space generalization ability, high data efficiency and excellent cross-platform adaptability, and is suitable for complex operation tasks such as industrial manufacturing and service robots.