23120 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 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 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 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.

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 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 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.

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 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 provides a process parameter optimization method and system, an in-situ online detection device, computer equipment and a storage medium. The process parameter optimization method comprises the following steps: acquiring process parameters and measurement data in a preset window; based on the measurement data, extracting characteristic parameters representing a wafer surface growth physical state; inputting the characteristic parameters and the process parameters into a pre-trained prediction model to obtain prediction results representing the growth state trend and the photoelectric characteristic change trend; and optimizing process parameters based on the prediction result, wherein the optimized process parameters are used for the epitaxial growth process of the wafer in the next preset window. By the adoption of the scheme, the feature parameters representing the growth physical state are extracted from the original measurement data, based on the feature parameters, the method does not need to depend on a prior model and parameters, the high-dimensional relation among the multiple parameters is automatically analyzed, the globally optimal process window is found, the generated prediction result is consistent with the actual growth mechanism, and the prediction efficiency is improved. And the accuracy of the technological process and the product yield are effectively improved.

The invention discloses a multi-modal data retrieval method and device, a storage medium and computer equipment. The method comprises the following steps: collecting multi-modal original data; on the basis of description information of metadata of original data, all metadata belonging to the same associated items and logic relations among all the metadata are obtained, a metadata chain is constructed, a distributed graph database is constructed on the basis of the metadata chain, the metadata chain is expressed in the distributed graph database in the form of a graph, the graph comprises nodes and edges, the nodes represent the metadata, and the edges represent the metadata. The edge represents a logical relationship between the metadata; and when a data retrieval instruction is received, traversing each node in the metadata chain along the logical relationship of the metadata chain in the distributed graph database, obtaining a target node matched with a data retrieval requirement corresponding to the data retrieval instruction, and returning original data corresponding to metadata represented by the target node. Multi-modal data dynamic association retrieval can be realized, and cross-modal information mining efficiency and accuracy are improved.

The invention discloses an abnormal transaction prevention and control method and device, computer equipment and a readable storage medium, relates to the field of artificial intelligence and financial science and technology, and can realize real-time monitoring and prevention and control of abnormal transaction behaviors. The method comprises the steps of obtaining a plurality of historical transaction behaviors of a target user, constructing a historical transaction behavior model, and adjusting an abnormal threshold value based on spatial-temporal feature distribution of the historical transaction behavior model; obtaining a to-be-detected transaction behavior, determining a current transaction scene, and adjusting an abnormal threshold according to the transaction scene; based on a preset abnormal transaction detection model, comparing the transaction characteristics of the to-be-detected transaction behavior with a historical transaction behavior model, generating an abnormal score, and comparing the abnormal score with the adjusted abnormal threshold value to determine a detection result; and if the detection result indicates that the to-be-detected transaction behavior is an abnormal transaction, determining a risk level of the to-be-detected transaction behavior, and executing a prevention and control strategy corresponding to the risk level.

The invention discloses an automatic SQL tuning method and system combined with a large language model and a database optimizer, computer equipment and a storage medium, and the method comprises the steps: generating a query fingerprint according to an SQL query statement input by a user, detecting whether there is an optimization result matched with the query fingerprint in a cache, and only when there is no optimization result, executing the step 1; if yes, the SQL query statements input by the user and the optimization instruction are input into the large language model, and a plurality of candidate SQL query statements are generated; performing semantic equivalence verification on the candidate SQL query statements and an SQL query statement input by a user, and screening out semantic equivalence candidate SQL query statements; submitting the screened candidate SQL query statements to a database optimizer, and obtaining an execution plan and cost information of each candidate SQL query statement; calculating an evaluation score of each candidate SQL query statement, and selecting the candidate SQL query statement with the highest evaluation score as an optimization result; and writing the optimization result and the related execution data into a cache, and returning the optimization result and the related execution data to the user.

The invention relates to the fields of artificial intelligence, finance and medical treatment, and discloses an intelligent question and answer accuracy improving method and device, computer equipment and a storage medium, and the method comprises the steps: converting structured supervision fine tuning training data into a high-dimensional vector; constructing a multi-dimensional joint index for the high-dimensional vector, and storing the indexed high-dimensional vector in a distributed vector database; receiving a current dialogue text input by a user, and retrieving an associated question set from the distributed vector database through a similarity matching algorithm; dynamically splicing the retrieved associated questions, the corresponding answers and preset template instructions to generate enhanced cue words containing knowledge contexts; inputting the enhanced cue word into a large language model, and generating N candidate answers in parallel; and carrying out quantitative evaluation on the N candidate answers by adopting a multi-dimensional reward model, and selecting the answer with the highest score and returning the answer to the user. According to the scheme, the answering accuracy of the dialogue robot can be improved, so that the user experience is improved.

The invention relates to the technical field of mobile communication networks, and discloses a network recovery method and device, computer equipment, a storage medium and a program product. The method comprises the following steps: acquiring a network operation parameter of a current network node; analyzing the network operation parameters, and judging whether the current network node has a network fault or not based on an analysis result; if the analysis result represents that the current network node has the network fault, determining target fault information of the network fault; determining a target recovery strategy corresponding to the target fault information according to a mapping relation between the fault information and a network recovery strategy; and performing network recovery on the current network node according to the target recovery strategy. By implementing the technical scheme of the invention, multi-dimensional real-time fault detection and intelligent automatic recovery of the network can be realized, the fault response time is remarkably shortened, the stability and service continuity of network communication are improved, and the operation and maintenance cost and the manual intervention requirement are reduced at the same time.

A path planning method and apparatus, a computer device and a storage medium. The method comprises: acquiring an initial work position of a robot (S202); acquiring a current map, the current map comprising a plurality of candidate work regions (S204); determining within a passable region of the current map an initial transition path corresponding to each candidate work region, the initial transition path being used for indicating a path from the initial work position to the candidate work region (S206); on the basis of the region area of each candidate work region, and the path length and the deflection angle of the initial transition path corresponding to each candidate work region, determining a region arrival cost, so as to obtain the region arrival cost corresponding to each candidate work region (S208); on the basis of the region arrival costs, determining a target work region of the robot from the candidate work regions (S210); and continuing to determine a next target work region of the robot from a latest map until a first termination condition is satisfied, and terminating path planning (S212).

The invention belongs to the technical field of network security, and relates to a network attack behavior chain construction and active defense method and device and computer equipment, and the method comprises the steps: collecting full-flow data and a multi-source log from a network environment, and carrying out the preprocessing of the full-flow data and the multi-source log; storing the preprocessed full-flow data and multi-source logs, and establishing an associated index; through a deep learning algorithm and an unsupervised model, abnormal traffic and attack behaviors are identified from the full-traffic data and the multi-source logs; reconstructing the fragmented attack events into a complete behavior chain through a graph neural network and a visualization mode; based on the AI model, a dynamic defense strategy is generated, and a response action is automatically executed; through time sequence prediction and a deep learning model, a future attack trend is predicted, and active defense is realized. The method improves the unknown attack detection capability, optimizes the traceability efficiency, enhances the defense initiative, guarantees the real-time performance and accuracy of network attack prediction, and has compliance adaptability.

The invention discloses a multi-modal data fusion method and apparatus, and a computer device. The method comprises the steps of obtaining to-be-fused sensing data; performing credibility calculation on each piece of to-be-fused sensing data to obtain first credibility data corresponding to each piece of to-be-fused sensing data; determining target weight data corresponding to each piece of to-be-fused sensing data on the basis of the first credibility data corresponding to the to-be-fused sensing data; and according to the to-be-fused sensing data and the target weight data corresponding to the to-be-fused sensing data, carrying out weighted fusion to obtain fused data. Therefore, the reliability of the to-be-fused sensing data of each modal is dynamically evaluated and the weight is allocated, so that the reliability of a multi-modal data fusion result is effectively improved.

According to the product public opinion analysis method and device, the storage medium and the computer equipment provided by the invention, the multi-modal features in the public opinion data are extracted and feature fusion is carried out to obtain the fused feature data. In this way, information in different forms can be effectively integrated, and the public opinion situation can be analyzed more comprehensively and accurately. And then extracting an entity relationship in the fused feature data to update a preset public opinion knowledge graph. And performing sentiment analysis on the fused feature data in three dimensions of text, video and voice by using a public opinion knowledge graph to obtain a public opinion sentiment score. Therefore, the emotional tendency of the public opinion information can be captured more comprehensively, and the accuracy of public opinion analysis is improved. Meanwhile, an early warning mechanism is triggered when a preset condition is met. And finally, reasoning and plan matching are performed in the public opinion knowledge graph based on the generated public opinion event features so as to determine a public relation plan. According to the process, historical experience and reasoning ability in the knowledge graph can be fully utilized, and the timeliness and accuracy of public opinion management are improved.

The invention discloses a data collection method and device, computer equipment, a storage medium and a program product. The method comprises the steps that a control handle in a current interface of a target application program is acquired; the control handle is at least used for determining a control type; wherein the control of which the control type belongs to the target type in the current interface is the target control; the target type comprises a list, a button and an input box; the target control in the current interface comprises at least one of a list control, a button control and an input box control; inputting the feature data of the target control into an acquisition strategy generation model to generate a data acquisition strategy corresponding to the control type of the target control; the feature data at least comprises a control type; and monitoring the target control in real time based on the data acquisition strategy so as to capture the interactive behavior data of the target control when monitoring that the state is changed.

The invention relates to the technical field of interaction based on visual earphones, and discloses an interaction method and device based on a visual earphone, computer equipment and a medium, and the method comprises the steps: judging whether a user wearing the visual earphone sends an interaction instruction or not; analyzing content in the interaction instruction to determine a user demand; in response to the user demand, controlling a camera to scan current content to obtain a scanned picture; performing data processing on the scanned picture based on the user demand to obtain a processing result; and feeding back the processing result to the user. The method has the beneficial effects that the user demand is determined through the interaction instruction, and intelligent and personalized services can be provided according to the user demand. Through the camera integrated with the earphone, a user can obtain environment information in real time without depending on external equipment, and obtain instant feedback through the earphone, so that the practicability and interaction experience of the intelligent wearable equipment in a complex environment are greatly improved.

The invention belongs to the field of high-performance computing, and relates to an integer parallel computing method and device based on distributed storage and computer equipment, and the method comprises the steps of collecting real-time resource indexes, dynamically identifying fault nodes, triggering task migration, and performing data verification and hard disk fault detection. The weight value of each node is calculated, the nodes are arranged according to the descending order of the weight values, and the nodes with high load capacity are selected to distribute tasks; dynamically distributing a data generation task to a computing node, executing parallel computing, and performing distributed storage on a result; obtaining an operand, converting the operand into a first-order tensor form of a basic operand, serializing tensor data, and sending the serialized tensor data to a parallel computing layer; distributing a search task to a computing node, retrieving storage data in parallel, reading effective data from a storage layer, and combining search results into a partial sum; and summarizing and then outputting. The system has dynamic resource management and fault-tolerant capabilities, and can realize efficient task allocation and load balancing.

The invention relates to the field of webpage design, and discloses a webpage generation method and system, computer equipment and a storage medium, the webpage generation method comprises the following steps: sending a webpage configuration request to a server at a client and receiving dynamic configuration data and business logic data returned by the server; a predefined node generation rule is matched according to the function module identifier to generate and mount an interactive page element set, and then a browser rendering engine is triggered to generate a visual webpage; meanwhile, when the user operates on the webpage and packages and submits operation data to the server, the server can update the business logic data and the configuration state and return the business logic data and the configuration state to the client again, and continuous dynamic adaptation and real-time updating of the webpage are achieved. Compared with the prior art, the method has the advantages that the compiling quantity of front-end adaptive and compatible codes is effectively reduced, redundant codes and complexity are reduced, the flexibility and expandability in a multi-environment and multi-device scene are enhanced, and the webpage rendering performance and the user interaction experience are remarkably improved.

The invention discloses an eye movement tracking method, an eye movement tracking device, eye movement tracking equipment, computer equipment and a storage medium. The method comprises the following steps: calculating calibration gaze information and a calibration pupil position according to first event data collected by an event camera; determining a region of interest based on the calibrated pupil position; processing second event data collected by the event camera according to the region of interest to generate region of interest data, the collection time of the second event data being later than the collection time of the first event data; according to the region-of-interest data, calculating a current fixation position offset; according to the calibration gaze information and the current gaze position offset, the current gaze information is determined, the calibrated pupil position is updated based on the current gaze position offset, the gaze direction, the moving direction and the moving distance of the pupil are accurately captured in real time by utilizing the characteristics of high time resolution and the like of the event camera, and the eye movement tracking effect is effectively improved.

The invention relates to a matrix storage operator optimization method and device, computer equipment and a readable storage medium. The method comprises the following steps: allocating memory resources for target data; determining a logic structure corresponding to the source data, establishing a first mapping relation between each logic index in the logic structure and a register address of the source data, and establishing a second mapping relation between each logic index in the logic structure and a memory address of the target data; and determining a corresponding relationship between a memory address of the target data and a register address of the source data based on the first mapping relationship and the second mapping relationship, and storing the source data stored in a register into a memory resource corresponding to the target data based on the corresponding relationship. By adopting the method, the generalization ability of the matrix storage operator can be improved.

The invention relates to an electric power internet of things operation and maintenance management method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining sensor data, carrying out the cleaning and time alignment of the sensor data, and obtaining standard operation data; based on a historical data window corresponding to the standard operation data, calculating a mean value and a standard deviation of the historical data, and further generating a dynamic detection threshold value; comparing the standard operation data of the current time node with a dynamic detection threshold value, and screening out potential abnormal data; calculating an abnormal trend factor corresponding to the potential abnormal data, and determining an abnormal development trend of the potential abnormal data according to the abnormal trend factor; and determining a corresponding causal influence weight based on a preset causal relationship network, and calculating a fault score according to the causal influence weight and the abnormal trend factor, so as to generate an early warning report according to the fault score. The method has the effect of improving the management efficiency of the power equipment.

The invention provides a short-term photovoltaic power prediction method and system, computer equipment and a medium, and belongs to the field of photovoltaic power generation output power prediction.The method comprises the steps that short-term photovoltaic power and meteorological working condition data samples are obtained, and a Gaussian mixture model is used for conducting multi-modal clustering processing on the meteorological working condition data samples to obtain membership probability embedded vectors; through a Pearson's correlation coefficient weighting and sliding window mechanism, extracting features from the data sample and the membership probability embedding vector, and constructing a multi-modal time sequence feature tensor; a multi-head attention mechanism in a traditional Transform network is replaced with a class domain fusion self-attention mechanism, and a class domain fusion attention model is formed; inputting a multi-modal time sequence feature tensor to train a class domain fusion attention model to obtain an initial prediction value; and residual error estimation is carried out on the initial prediction value by using a residual error learning error compensation strategy, a short-term photovoltaic power prediction result is output, and the accuracy and robustness of the model are improved.

The invention relates to a curved glass processing method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: generating a three-dimensional curved surface model corresponding to to-be-produced curved glass based on geometric parameters, mechanical property data and optical property data of the to-be-produced curved glass; according to the model data of the three-dimensional curved surface model, dynamic stress distribution information of the curved surface glass to be produced is determined; generating a forming control instruction set of the curved glass to be produced according to the model data and the dynamic stress distribution information; obtaining formed curved glass according to the forming control instruction set; carrying out annealing treatment on the formed curved glass to obtain treated curved glass; and obtaining first point cloud data of the processed curved glass and second point cloud data of the three-dimensional curved surface model, and determining a quality detection result of the processed curved glass according to the difference between the first point cloud data and the second point cloud data. By adopting the method, the processing accuracy of the curved glass can be improved.