27523 results about "Computer equipment" patented technology

The invention discloses a computer equipment fault monitoring system and method based on artificial intelligence, and relates to the technical field of computer equipment fault monitoring. The system comprises a data access module, a semantic analysis module, a knowledge graph construction module, a dynamic semantic association module, a data fusion processing module, a decision output module and an adaptive optimization module. The data access module collects and standardizes hardware, software and network data; the semantic analysis module extracts and enhances semantic tags; the knowledge graph construction module forms a data semantic relation network; the dynamic semantic association module screens potential semantic relationships; the data fusion processing module generates a multi-dimensional feature vector; the decision output module triggers fault early warning; and constructing a feedback knowledge graph of the self-adaptive optimization module. According to the method, through event-driven interpolation, dynamic weight fusion, closed-loop feedback optimization and the like, the problems of multi-source data alignment, semantic fusion and dynamic adaptation are solved, the fault monitoring accuracy and the system adaptability are improved, and the method is suitable for fault monitoring and early warning of computer equipment.

The invention relates to the technical field of servo motor control, and discloses an intelligent servo motor control method and device and computer equipment. The method comprises the following steps: carrying out multi-dimensional data acquisition and preprocessing on a position loop, a speed loop and a current loop of the intelligent servo motor to obtain standard structured parameter data; identifying a working condition characteristic parameter set of the intelligent servo motor; establishing a two-stage optimization architecture comprising a motor parameter layer and a control algorithm layer, and generating a dynamic parameter adjustment mapping table; a depth deterministic strategy gradient network is trained based on the characteristic parameter set and the dynamic parameter adjustment mapping table, and an optimal control strategy model for different working conditions is generated; and the optimal control strategy model is utilized, control parameters are automatically switched according to the working condition types recognized in real time, and smooth transition control of the intelligent servo motor among different working conditions is achieved. Smooth switching among different working conditions is achieved, system oscillation caused by sudden change of control parameters is avoided, and the disturbance influence in the working condition switching process is greatly reduced.

The present disclosure generally relates to a computer device, method and system utilizing machine learning for capturing and analyzing profile data communicated across a computing environment including but not limited to: each user's profile, online behaviors and career progression path and provides dynamic recommendations of online actions to be performed to reach a desired target state.

The invention relates to the technical field of data processing, and discloses a bridge displacement remote intelligent monitoring method and system and computer equipment. The method comprises the following steps: setting a multi-stage trigger threshold to perform grouped polling sampling to obtain displacement data; adopting dynamic weight fusion to denoise and identify abnormities, and utilizing a Kriging method to generate a digital twinborn model; extracting features to classify and distinguish environment and structure response displacement; calculating a deviation index to estimate fatigue damage to form a health report; calculating a risk entropy value and setting an early warning generation disposal scheme; and evaluating improvement rate update configuration to complete closed-loop optimization. According to the method, the environment data and the historical monitoring data are fused, a self-adaptive sampling strategy and an intelligent analysis processing mechanism are established, displacement changes caused by environment factors and structural factors are accurately distinguished, and accurate risk quantification and automatic decision support are carried out.

The invention relates to the technical field of wind power generation, and discloses a wind turbine generator gearbox fault diagnosis method and device and computer equipment, and the method comprises the steps: collecting multi-source data of a wind turbine generator gearbox; respectively extracting a time domain feature, a frequency domain feature, a temperature feature and a working condition feature of the multi-source data; performing weighted feature fusion on the time domain feature, the frequency domain feature, the temperature feature and the working condition feature to obtain a real-time fusion feature; obtaining historical health data of the wind turbine generator gearbox, establishing a health degree reference of the wind turbine generator gearbox based on the historical health data, and constructing a health degree model based on the real-time fusion features and the health degree reference; and performing fault diagnosis on the wind turbine generator gearbox by adopting the health degree model. According to the method, multi-source data of the gearbox of the wind turbine generator is combined, and gearbox health state evaluation and fault type identification are realized through dynamic feature fusion and deep learning modeling.

The present application relates to a retrieval model training method and apparatus and a computer device. The method comprises: acquiring a knowledge document, and performing text segmentation processing on the knowledge document to obtain a text block set (501); on the basis of the text block set and summary information comprised in each text block, writing a question for each text block to obtain one or more question texts corresponding to each text block, separately combining the one or more question texts corresponding to each text block with the corresponding text block to obtain one or more data pairs corresponding to each text block and, on the basis of the one or more data pairs corresponding to each text block, generating a data pair set (502); and using the data pair set to train a first retrieval model so as to obtain a second retrieval model (503).

The invention discloses a code generation method and device based on LLM multi-agent cooperation and computer equipment. The method comprises the following steps: acquiring a user programming demand; analyzing the user programming demand based on LLM and decomposing a programming task to generate a task tree; selecting a to-be-allocated sub-task from the task tree, and evaluating the matching degree of an intelligent agent in an intelligent agent pool and the to-be-allocated sub-task based on LLM to generate a task allocation instruction; sending the task allocation instruction to a corresponding target agent, so that the target agent executes the to-be-allocated sub-task, and feeding back an execution result; and outputting the execution result. By implementing the method provided by the invention, the programming efficiency and quality can be improved, the resource allocation can be optimized, and the efficient development of complex requirements and large projects can be supported.

The present invention provides an inverter control method, device, computer equipment and storage medium, belonging to the field of power electronics. The method comprises: determining a frequency control equation of the inverter under a grid-connected condition when the inverter is in a VSG control mode; determining a relationship between a virtual electromotive force of a virtual synchronous generator in a reactive state and an output voltage of the inverter; constructing a voltage-current double closed-loop control structure of the inverter under the VSG control mode, and determining a current reference value and a voltage reference value of the virtual synchronous generator under dq axes through the frequency control equation and the relationship; constructing a fuzzy control rule of a virtual damping coefficient and a virtual inertia according to the current reference value and the voltage reference value; and controlling the virtual damping coefficient and the virtual inertia through the fuzzy control rule, thereby improving the stability and performance of the inverter during the grid-connected process, reducing the harmonic disturbance of the power system, and helping to improve the adaptability and reliability of the entire photovoltaic power generation system.

The invention belongs to the technical field of stirring control, and particularly relates to an intelligent speed regulation stirring control method and system and computer equipment, and the method comprises the steps: determining a target control value of stirring control according to the requirements of a stirring technology; various sensors are installed on the stirring equipment, it is ensured that key parameters in the stirring process are accurately collected in real time, the data collection frequency is set, and it is ensured that dynamic changes in the stirring process are captured; filtering processing is carried out on the collected sensor data, noise interference is removed, the sensor data is corrected, and the accuracy of the data is ensured; key features in the stirring process of the combustion furnace are extracted through a data analysis method, and a mathematical model of the stirring process is established for prediction and control; a closed-loop control system is established, the stirring speed of the combustion furnace is adjusted according to data monitored in real time, the stability and accuracy of the stirring process are ensured through an intelligent algorithm and real-time parameter monitoring, insufficient stirring or excessive stirring is avoided, and therefore the product quality and the production efficiency are improved.

The invention relates to the technical field of kiln temperature control. The kiln temperature consistency control method comprises the steps that kiln internal environment information is input into a temperature change prediction model, temperature fluctuation trend information is obtained, kiln temperature control optimization parameters are generated according to the temperature fluctuation trend information, and the kiln temperature consistency is controlled according to the kiln temperature control optimization parameters. The current operation parameter is kept if the adjusted environment information conforms to a kiln temperature control target value, and the adjusted environment information is input into the temperature change prediction model again if the adjusted environment information does not conform to the kiln temperature control target value to generate a new kiln temperature control optimization parameter. And on the basis of the new kiln temperature control optimization parameter, the operation parameter in the kiln is adjusted again until the adjusted environment information accords with the kiln temperature control target value. The method has the effect of ensuring the consistency of the internal temperature of the kiln.

The invention discloses an extension equipment function configuration method and device, computer equipment and a storage medium, and relates to the technical field of data processing. According to the method, a hardware and firmware collaborative dynamic configuration framework is constructed, and a receiving queue is adopted at an extension equipment end to trigger a central processing unit firmware processing mechanism; in combination with virtualization design of a shadow configuration space and a shadow register, dynamic reconstruction of configuration parameters and an address mapping strategy during operation is realized; a host end supports configuration space access, base address register remapping and memory data transmission interaction through a transaction generation and routing mechanism driven by an event type. Hardware solidification limitation of traditional extension equipment is broken through through software and hardware decoupling design, the problems that function extension capacity is insufficient, resource dynamic allocation is difficult and protocol updating and virtualization scenes cannot be adapted are solved, and the technical effects of improving configuration reconstruction efficiency and address space utilization rate and supporting virtual function creation are achieved. And the adaptability of the equipment in cloud computing and edge computing environments is enhanced.

The invention relates to the technical field of intelligent vehicle control, in particular to a loader vehicle control method and system, computer equipment and a storage medium. The method comprises the following steps: collecting multi-modal data of the loader, wherein the multi-modal data comprises working environment data, dynamic load state data and vehicle attitude data of the loader; constructing a three-dimensional operation scene digital twinborn model based on the multi-modal data, and predicting vehicle stability influence data of the influence of the load change on the vehicle stability based on the dynamic load state data; based on the digital twinborn model of the three-dimensional operation scene and the vehicle stability influence data, a dynamic control strategy is generated by utilizing a reinforcement learning algorithm, and cooperative actions of steering, lifting and walking mechanisms are synchronously optimized; according to operation environment data and a dynamic control strategy, hydraulic system pressure and motor torque distribution are adjusted in a self-adaptive mode, and operation efficiency and safety are ensured.

An icing detection method based on tension and tilt angle sensors. Two kinds of sensor data are comprehensively utilized, cross validation and mutual compensation are realized, and the precision of an icing monitoring result is improved; compared with a single sensor, data fusion of a multi-source heterogeneous sensor improves the robustness and the anti-interference ability of ice volume assessment, and improves the reliability of icing detection; an uncertainty analysis model for icing thickness is established, a confidence interval of a monitoring result is given, and more sufficient reference information is provided for related decisions; by setting a scientific and reasonable monitoring and early-warning mechanism, intelligent monitoring and early-warning of an icing condition are realized, and the intelligent level of the technology is improved; and by establishing an icing monitoring model, the icing condition of wires can be accurately predicted, and the early-warning and monitoring capabilities are improved to ensure the safe operation of a power grid. Also disclosed are an icing detection system based on tension and tilt angle sensors, a computer device and a computer-readable storage medium.

The road crack detection method based on the improved RT-DETR comprises the following steps: shooting a road at a preset flight height by using an unmanned aerial vehicle to obtain an original road image containing a crack; a pre-trained crack detection model is utilized to carry out crack detection based on an original road image to obtain crack parameters, and the crack detection model is obtained through improvement and training based on an RT-DETR (Real-Time Detecting Transformer) model; the method for improving the RT-DETR model to obtain the crack detection model comprises the following steps: replacing a basic residual block at the tail end of a ResNet18 backbone network in the RT-DETR model with a dynamic snakelike convolution residual block (DSCRBlock); a cross-scale feature fusion module (CCFM) in a hybrid encoder in an RT-DETR model is replaced by a bidirectional diffusion focusing pyramid network (BDFPN), and the bidirectional diffusion focusing pyramid network comprises a primary focusing sub-network and a secondary focusing sub-network. The method can efficiently and accurately identify the road crack, can be applied to the unmanned aerial vehicle, and is easier to implement.

The invention provides a long document semantic coherence processing method and device, a storage medium and computer device.The method comprises the steps that block coding is conducted on a long document through a sliding window converter, and the sliding window converter comprises a window dividing unit, a position coding unit and a converter coding unit; the window division unit adjusts block positions according to sentences and paragraph boundaries and sets overlapping areas between adjacent blocks; the encoded document content is stored and managed through a memory management module, the memory management module comprises a chapter-level memory pool and a gating memory unit, the chapter-level memory pool stores the document encoding content, and the gating memory unit maintains entities and event timelines; performing deep semantic understanding on the document content through a semantic processing module, and calculating a multi-dimensional attention weight capture dependency relationship between paragraphs; providing a retrieval result through a retrieval module; and generating semantically coherent output content through a content generation module. According to the scheme, the semantic coherence of long document processing is improved.

According to the knowledge text retrieval method and device, the storage medium and the computer equipment, when knowledge text retrieval is carried out, the retrieval statement and the target knowledge document corresponding to the retrieval statement are obtained firstly, the document structure tree of the target knowledge document is determined, the document structure tree is of a tree structure constructed by utilizing the directory structure of the target knowledge document, and the retrieval statement is stored in the document structure tree; according to the method, a target knowledge document is taken as a component of the target knowledge document, an index structure between each index node in the component of the target knowledge document and the target knowledge document is recorded, and leaf nodes in the index nodes are connected through pointers to form a linked list structure. The corresponding index node can be dynamically retrieved based on the association relationship between the index structure and the linked list structure of the tree structure, and the context content corresponding to the retrieval statement in the target knowledge document is intercepted by adopting different modes based on the type of the index node. And omission of unrelated contents of other chapters and key contents of the same chapter is avoided, so that the retrieval precision and quality are improved.

Provided is a defect detection method and device, computer equipment and a storage medium. The method includes: acquiring an RGB image, a depth image and a sample label of a detection object sample; performing feature map extraction and feature map fusion on the RGB image and the depth image by a feature extraction network of the defect detection model, to obtain a fused feature map; performing defect detection based on the fused feature map by a feature reconstruction network of the defect detection model, to obtain a defect score map, wherein the defect score map being obtained by fusing a global defect score map which is generated based on a global defect detection network with a local defect score map which is generated by a local defect detection network; and updating parameters of the defect detection model based on the defect score map and the sample label.

The invention discloses a thinking chain-based text2sql model implementation method and device, computer equipment and a readable storage medium, and the method comprises the steps: firstly obtaining a sample SQL query instruction text, carrying out RAG retrieval, constructing cue words, calling at least two open source large language models for output, carrying out the evaluation of a target large model to obtain a step-by-step and reward training data set, and carrying out the calculation of the step-by-step and reward training data set; and performing fine tuning training on the initial step-by-step and reward model. And after the target instruction input by the user is obtained, reasoning is performed in combination with the trained step-by-step and reward model to obtain the corresponding SQL statement, so that efficient and accurate text-to-SQL conversion is realized.

The invention relates to a battery fault diagnosis method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring operation data of a target battery in an operation process; determining a target operation characteristic of the target battery in at least one dimension according to the operation data; and based on a pre-trained fault classification model, according to the target operation characteristics of the target battery in at least one dimension, determining a target fault type of the target battery. By adopting the method, the accuracy, robustness and interpretability of battery fault early warning can be improved.

The invention discloses a processor, a chip product, computer equipment and a tensor processing method, and relates to the technical field of processors. A first tensor having a plurality of elements arranged according to a first arrangement order is stored in the first storage unit; the tensor storage engine is used for reading the plurality of elements from the first storage unit to the second storage unit according to a second arrangement sequence to obtain a rearranged tensor; the tensor calculation engine is used for executing logical operation on the rearranged tensor to obtain an operation result; the format conversion unit is used for converting the data format of each element in the operation result into a target data format to obtain an operation result after format conversion, and storing the operation result after format conversion into a third storage unit, the operation result after format conversion comprises the operation results of the multiple elements, and the third storage unit is used for storing the operation results after format conversion. A third arrangement sequence of the operation results of the plurality of elements in the third storage unit is the same as the first arrangement sequence. The development cost of the processor can be reduced.

The invention relates to the technical field of artificial intelligence and medical health, and discloses an agent action generation method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a visual signal of an environment and text information of a task instruction; inputting the visual signal and the text information into a multi-mode intelligent agent with a body to perform action generation so as to obtain a generation result; outputting a generation result; the training process of the multi-modal intelligent agent comprises the steps that a task instruction and expert demonstration data are generated through a large language model, and behavior simulation of the multi-modal intelligent agent is optimized through a DAgger-DPO algorithm in combination with a visual signal and text information input by a task. By implementing the method provided by the invention, the advantages of the large language model intelligent agent and the visual language model intelligent agent can be combined, and the large language model is improved; the adaptive capacity in a dynamic visual environment is improved; compared with a traditional visual language model, environment information can be captured more accurately, and more efficient environment interaction is achieved.

Disclosed is a battery health state assessment method, including performing discharge control on a cell until a cutoff voltage; performing charge control on the cell until a preset cell load rated capacity, and performing discharge control on the cell until the cutoff voltage, and generating cell discharge efficiency information; performing charge and discharge cycle control on the cell and acquiring corresponding charge and discharge cycle control efficiency information; using a cell load algorithm to calculate and generate a cell load actual capacity information; and generating a cell health state assessment information according to the ratio between the cell load actual capacity and the cell load rated capacity. The accuracy of the SOH assessment process can be improved by implementing the methods of the embodiments of the present application to optimize the battery performance assessment process and improve the battery charge and discharge efficiency assessment capability.

The invention discloses a hidden camera detection method and system based on a cat eye effect, computer equipment and a storage medium, and relates to the technical field of laser imaging, and the method comprises the steps: sensing ambient light, carrying out the initialization, and carrying out the parameter adjustment in real time; transmitting a signal through a multispectral light source, and receiving a reflected signal for multiband imaging; carrying out adaptive binarization processing on the image, and calculating a local gray scale probability distribution matrix of the image; and calculating a suspected degree for target detection, and giving an alarm when a camera is recognized. According to the hidden camera detection method based on the cat eye effect, through automatic switching of multi-band light sources, the types of the light sources are optimized, detection light of different wavelengths is emitted, the broad spectrum range is covered, the reflection characteristics of various hidden imaging devices are dealt with, detection is carried out according to imaging of different light sources, and the detection accuracy is improved. The method can be used for detecting the anti-reconnaissance hidden imaging equipment.

The invention relates to the technical field of solid state disk performance evaluation. The solid state disk performance evaluation method comprises the steps of obtaining solid state disk operation information, conducting exception analysis on the solid state disk operation information through a deep learning model to obtain solid state disk exception information, conducting correction and interpolation on the solid state disk exception information, and obtaining the solid state disk performance evaluation result. Performing feature extraction on the corrected operation information to obtain a plurality of performance features of the solid state disk, screening the plurality of performance features based on a feature selection method to obtain a target feature set required by performance evaluation, and analyzing the target feature set required by performance evaluation through a time sequence prediction model. Obtaining performance change trend information under different loads, predicting the residual life and the performance degradation condition of the solid state disk according to the performance change trend information, and obtaining a performance evaluation result. The method has the effect of improving the performance evaluation accuracy of the solid state disk.

The invention relates to an injection mold temperature control method and device and computer equipment. The method comprises the following steps: acquiring temperature data of a plurality of temperature measuring points of the injection mold to obtain a temperature data matrix; establishing a heating and cooling input matrix and a temperature response matrix, and solving to obtain a heat transfer path coefficient matrix; taking the temperature data matrix as state input, constructing a depth deterministic strategy gradient control network comprising a gating circulation unit and a full connection layer, and performing linear mapping to obtain a hybrid action space; inputting the heat transfer path coefficient matrix and the mixed action space into a training environment, performing iterative training on a depth deterministic strategy gradient control network based on an experience playback mechanism, and updating network parameters by calculating a reward function to obtain a temperature control model; and deploying the temperature control model to a target control system, and calculating a real-time control parameter set. According to the invention, coordinated control of the heating stage and the stabilizing stage is realized, the injection molding period is shortened, and the energy consumption is reduced.

The invention relates to a task execution method and device, computer equipment, a computer readable storage medium and a computer program product, and relates to the technical field of artificial intelligence chips. The processing efficiency of execution of the calculation task and the communication task on the artificial intelligence chip can be improved. The method comprises the steps of obtaining a task flow; in the same task flow, enabling a first processing unit of the artificial intelligence chip to execute a calculation task, and enabling a second processing unit of the artificial intelligence chip to execute a communication task; wherein the calculation task does not depend on the communication task; the communication task depends on a corresponding computing task, and the communication task does not depend on a non-corresponding computing task.

The invention discloses a BMC intrusion protection method and device, a BMC and computer equipment. The method is applied to the BMC in the computer equipment. The BMC comprises a service module and a security detection module which are in communication isolation, and storage media accessed by the service module and the security detection module are isolated. The business module is used for providing a management function for parts in the computer equipment, and the security detection module is used for implementing security detection on the BMC. The method comprises the steps that the security detection module obtains data stored in the storage medium associated with the service module, analyzes the security situation of the data sensing service module, and obtains a security detection result. And when the security detection result indicates that the BMC suffers from the APT attack, restarting the BMC in a degraded starting mode. Through a deep defense scheme, namely on the basis of defense, intrusion detection capability is added to the BMC, a dynamic defense chain of defense, detection, degradation and recovery is formed, the APT attack resisting capability of the BMC is improved, and the BMC is ensured to be safe and credible.

The invention relates to an aerial photography target detection method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring an aerial image acquired by an image sensor; inputting the aerial image into a pre-trained target detection model to obtain a target detection result; the target detection model comprises a backbone network, a neck network and a head network; wherein the backbone network is used for performing feature extraction on an aerial image to obtain image extraction features; the neck network is used for performing multi-scale fusion on the image extraction features to obtain a plurality of fusion features of different scales; the neck network comprises a bidirectional information transfer network, the bidirectional information transfer network comprises a separation and attention enhancement module, and the separation and attention enhancement module is used for generating fusion features corresponding to tiny targets; and the head network comprises a detection head corresponding to the tiny target and is used for obtaining a target detection result corresponding to the fusion feature based on the fusion feature. By adopting the method, the detection accuracy under the complex aerial image can be improved.

The invention relates to the technical field of image processing, in particular to an image data processing method and system, computer equipment and a storage medium, and the method comprises the following steps: obtaining image target boundary pixel data, calculating boundary gradient change data and local pixel density change data, judging the form offset direction and amplitude, and extracting an offset maximum region. And setting a local form adjustment weight, calculating a direction compensation value, adjusting an image target contour, and generating a form compensation data set. According to the invention, through boundary pixel extraction and gradient change calculation, local pixel density fluctuation is captured, target form adjustment is more accurate, illumination intensity gradient and spectral reflectivity extraction are combined, the optical matching degree of a target area is improved, the target contour separation precision is optimized in trajectory analysis, and based on a separation boundary value and form compensation data, the target form is more accurately adjusted. According to the overall scheme, the target contour precision, the optical adaptability and the classification stability are enhanced, and high recognition reliability is kept in a complex environment.

The invention relates to the technical field of data processing, and discloses a multi-source heterogeneous data semantic alignment method and device and computer equipment.The method comprises the steps that multi-source heterogeneous spatio-temporal data are collected through a center data node and a plurality of edge nodes, spatial attribute feature extraction and inter-unit spatial correlation calculation are carried out, and multi-source heterogeneous spatial data are obtained; obtaining a city data semantic association degree feature matrix; inputting the urban data semantic association degree feature matrix into a multilayer graph attention network for feature extraction to obtain a node feature vector matrix and a task association degree adjacency matrix; generating a distributed scheduling scheme of the data processing tasks according to the node feature vector matrix and the task association degree adjacency matrix; according to the method, data processing tasks are distributed to computing nodes according to a distributed scheduling scheme, alternate iterative optimization is carried out based on a city data semantic association degree feature matrix, a semantic mapping relation set is obtained, and intelligent scheduling of the data processing tasks and efficient semantic alignment in a distributed environment are achieved.