1727 results about "Template based" patented technology

Techniques are described for a computing system configured to obtain a plurality of candidate logs for a plurality of layers of a computing infrastructure. The computing system may, for each candidate log of the plurality of candidate logs, map the candidate log to a log template of a plurality of log templates, wherein each log template to which a candidate log is mapped is a mapped log template. The computing system may rank mapped log templates based on properties of the mapped log templates. The computing system may select, based on the ranking, one or more candidate logs as critical logs. The computing system may output at least one of (1) an indication of the critical logs to determine a potential root cause associated with a performance issue of a network application or (2) an indication of the potential root cause associated with the performance issue of the network application.

The invention discloses a cross-border e-commerce compliance intelligent auditing platform and a multi-language contract analysis method, and relates to the field of cross-border contract compliance auditing. In the multi-modal data access step, customs codes, laws and regulations and other multi-source data are collected, and 18 kinds of language contract texts are analyzed; in the cross-language semantic alignment step, a knowledge graph is constructed, and multi-language legal concept mapping is achieved; in the compliance risk reasoning step, a rule engine and an agent cooperatively check a contract, and the compliance conclusion confidence is calculated; the dynamic risk assessment step adopts an LSTM network to analyze historical data and predict a risk trend; in the multi-language report generation step, a multi-format bilingual or multilingual report is generated based on a template engine, encrypted and archived. According to the invention, cross-border contracts are audited efficiently and intelligently, dynamic adaptation laws and regulations are analyzed in multiple languages, compliance risks are identified accurately, and a multi-language report is generated quickly; therefore, the checking efficiency is improved, the manual workload is reduced, the compliance risk is reduced, and the enterprise cross-border business competitiveness and the risk response capability are enhanced.

The invention relates to the technical field of continuous integration and continuous delivery, and discloses a Jenkins continuous integration method based on Kubernetes. According to the method, a Jenkins server is deployed in a Kubernetes cluster to serve as a control plane, a construction environment is created through a preset multi-container Pod template, and a Pod containing a plurality of containers is dynamically created based on the Pod template, so that each container executes different construction tasks. Containers in the Pod realize collaboration through sharing a network namespace and a storage volume, and a Jenkins server monitors task execution states of the containers in the Pod and performs dynamic scheduling according to task priorities and cluster load states. And after the task is completed, automatically destroying the Pod and releasing cluster resources. According to the method and the device, dynamic expansion and contraction of a construction environment and efficient parallel execution of tasks are realized, the problem that a traditional Jenkins static resource pool is difficult to deal with load changes is solved, the resource utilization rate is improved, the expandability of the system is enhanced, and an efficient and reliable solution is provided for an enterprise-level continuous integration system.

Implementations utilize a smaller LLM to generate content responsive to a user query and cause a portion of the generated content to be rendered as an immediate response to the user query. Implementations further utilize a larger LLM to generate content that starts with the portion of the generated content and that includes a refined portion succeeding the portion of the generated content. The refined portion can be rendered succeeding the portion of the generated content. In some implementations, instead of using the smaller LLM, alternatively, the portion of the generated content rendered as the immediate response can be generated based on a default text string or a template, where the template can be determined / selected from a plurality of predefined templates based on a natural language understanding of the user query.

Systems and methods are described for video coding using generalized bi-prediction. In an exemplary embodiment, to code a current block of a video in a bitstream, a first reference block is selected from a first reference picture and a second reference block is selected from a second reference picture. Each reference block is associated with a weight, where the weight may be an arbitrary weight ranging, e.g., between 0 and 1. The current block is predicted using a weighted sum of the reference blocks. The weights may be selected from among a plurality of candidate weights. Candidate weights may be signaled in the bitstream or may be derived implicitly based on a template. Candidate weights may be pruned to avoid out-of-range or substantially duplicate candidate weights. Generalized bi-prediction may additionally be used in frame rate up conversion.

Provided is a process including: obtaining, with a computer system, access to a code base; decomposing, with the computer system, the code base into parts; classifying, with the computer system, the parts according to content type; selecting, with the computer system, processing templates based on the content types, with at least some different content types having different selected processing templates; and generating natural language documentation for the parts, with one or more generative language models, using the processing templates selected for the parts.

The invention relates to the technical field of chip verification, and discloses a chip verification method, test equipment and a chip verification system.The method comprises the steps that a template file is built, a verification environment and a test case are generated based on the template file, a configuration file is obtained, and parameters in the verification environment and the test case are updated through parameters in the configuration file; according to the method, the verification environment and the test case are updated, the updated verification environment and the updated test case are obtained, regression testing is carried out on the chip on the basis of the updated verification environment and the updated test case, the test result is obtained, the verification environment can be rapidly generated on the basis of the template file, parameters can be rapidly replaced, and the chip testing efficiency is improved.

The invention discloses a dialogue type enterprise knowledge question answering method and device, a storage medium and electronic equipment, and relates to the technical field of data analysis, and the method comprises the steps: obtaining user request information; obtaining a corresponding channel; reconstructing the question content to obtain the reconstructed question content; performing vectorization processing on the reconstructed question content to obtain vectorization information of the reconstructed question content; obtaining a plurality of first FAQ knowledge and second FAQ knowledge; obtaining a plurality of pieces of first document block knowledge and second document block knowledge, and obtaining documents corresponding to a plurality of document blocks; obtaining a corresponding prompt word template based on the dialogue intention and the channel; and constructing a large language model, and sorting to obtain corresponding answer information. Based on the question content of the user, the dialogue intention is obtained through analysis, the knowledge base is matched, the answer information is obtained through arrangement, the user question is automatically answered, and the method has the advantages of being simple, efficient and high in real-time performance.

The invention discloses an electrical work ticket intelligent generation method and device, a medium and equipment in the technical field of power systems, and aims to solve the problems of serious module decoupling, dependence on manual connection and insufficient intelligent generation capability in the prior art. The method comprises the following steps: generating a required digital knowledge base and an electrical work task description generation work ticket based on a pre-constructed work ticket by utilizing a multi-model collaborative generation architecture; the multi-model collaborative generation architecture comprises a rule driving module used for calling a corresponding template based on task element matching; the task execution unit is used for executing a specific subtask according to a pre-deployed lightweight model or rule engine; and the large language model is used for performing semantic understanding and reasoning according to the electrical work task description, calling the rule driving module and the task execution unit according to a semantic understanding and reasoning result, and generating a work ticket in combination with a digital knowledge base required by work ticket generation. The method has both the accuracy of structured knowledge recognition and the flexibility of natural language generation.

The invention provides an environment detection method and system based on a sample target detection model. The method comprises the following steps: synchronously acquiring an environment image, a video stream and physical parameters by using a multi-mode sensor; decomposing the data into image features and environmental parameter components through a dual-time sequence control signal, and realizing space-time alignment by adopting a linear phase filter; constructing a foreground region template based on the depth information, and generating target recognition feature representation containing an abnormal blurred target; adversarial training is carried out on the lightweight target detection network in combination with a transfer learning strategy, the network integrates convolutional features and a Transform attention mechanism, and the weight is dynamically adjusted through environmental parameters; fusing a target result and sensor data in real-time detection, and inputting a decision tree model for risk grading; and after the early warning is triggered, reconstructing a false detection sample through an online learning mechanism and iteratively optimizing the model. The system correspondingly comprises a multi-modal data acquisition module, a data enhancement and annotation module, a model training module, a real-time detection and fusion module and an early warning and optimization module. According to the invention, through multi-source data fusion, dynamic data enhancement and an adaptive compensation mechanism, the small target detection precision, the environmental adaptability and the real-time early warning capability are significantly improved.

The invention provides a Text2SQL generation method and system driven by a large language model, and the method comprises the steps: recognizing key entities and relationships in a natural language question through the large language model, and carrying out the construction to obtain a query graph; constructing a hierarchical graph representation model based on the database mode of the target database; searching candidate sub-image sets which have the same structure as the query graph and are in semantic association with the query graph from the constructed graph representation model; and constructing a structured Prompt template based on the candidate sub-image set and the user question, generating an initial SQL statement, and outputting a final query result after grammar and semantic detection correction. According to the method, a traditional database is converted into the graph knowledge base, so that the SQL generation accuracy and performance are improved; guiding the large model to generate an SQL (Structured Query Language) meeting business requirements through knowledge graph storage and retrieval table and column information; and the accuracy of the SQL is continuously optimized by utilizing a self-adaptive feedback mechanism, so that the method is particularly suitable for a large-scale complex query scene, and self-improvement and customization of the model are realized.

The invention discloses a human resource data natural language query and SQL generation method and system, and relates to the technical field of SQL generation, and the method comprises the steps: preprocessing a natural language query text input by a user, and constructing a query semantic tree; traversing the query semantic tree to generate a candidate entity set, determining an unambiguous entity through a three-layer ambiguity resolution mechanism of semantic scoring, service verification and cross validation, identifying a composite intention, disassembling the composite intention into a sub-intention sequence, and generating an SQL operation instruction set in combination with a preset SQL operation mapping rule; based on the SQL operation instruction set and the human resource data association model, determining a data table set and an association path, generating a dynamic calculation logic and structured condition expression, and constructing a structured query skeleton; according to the method, the personalized SQL template is dynamically generated according to the query skeleton, after filling, grammar verification, strategy optimization and logic consistency verification are performed, and the optimized SQL statement is output, so that the query accuracy and performance are remarkably improved.

Systems, methods, and instrumentalities are disclosed for template based cross component linear model / multimode linear model (CCLM / MMLM) adjustment. In an example, a device, such as a video decoding device, or a video encoding device, may obtain a prediction model for predicting a coding block. The device may select an adjustment model, from multiple adjustment models, for adjusting the prediction model. The device may adjust the prediction model based on the selected adjustment model. The device may process (e.g., encode and / or decode) the coding block based on the adjusted prediction model.

The invention relates to an industrial vision adaptive illumination compensation system and method based on Bayesian optimization, and the system comprises an environment perception module which is used for collecting the multi-dimensional environment state data of an industrial site in real time, and collecting an original image; the intelligent preprocessing module is used for enhancing the original image according to the parameter combination output by the Bayesian optimization module to generate an enhanced image; the model reasoning module is used for identifying and detecting the enhanced image through a bimodal algorithm to generate a detection result; the Bayesian optimization module is used for performing objective function calculation to generate an optimization objective score, performing iterative optimization according to the optimization objective score and a historical observation data set, generating a candidate parameter combination, and generating a global optimal parameter and a configuration template based on the candidate parameter combination and a current environment feature; and the industrial interface module is used for transmitting the global optimal parameter, the detection result and the multi-dimensional environment state data to an industrial control system. According to the invention, the efficiency and adaptability of illumination compensation are improved, and the data calculation amount is reduced.

The invention discloses a short message template intelligent identification method and system based on semantic similarity calculation, relates to the technical field of information and communication, and realizes automatic matching of a request short message and a template library through semantic vectorization and similarity calculation; semantic extension, structural constraint and a dynamic feedback mechanism are combined, so that the matching accuracy and robustness are improved; an optimization process driven by a multi-level model and category features is introduced, and an identification-feedback-optimization closed-loop system is formed; the system structurally comprises a semantic modeling module, a similarity calculation module, a dynamic feedback module and a template optimization module, automatic and efficient short message template recognition and updating can be achieved, and the requirements for real-time performance and accuracy in a large-scale application scene are met.

The invention relates to an error collaborative calibration method, system and device based on a high-precision rotary table, and the method comprises the steps: obtaining the installation positioning data of a tilt angle sensor, carrying out the installation offset calibration of the high-precision rotary table, and obtaining an installation parameter set; according to the installation parameter set, controlling the high-precision turntable to carry out partition inclination angle scanning on the inclination angle sensor to obtain calibration response data; performing multi-source error identification on the calibration response data to obtain a collaborative error parameter set; and obtaining a dynamic simulation instruction template according to the tilt angle sensor, and performing instruction construction on the dynamic simulation instruction template and the collaborative error parameter set to obtain a turntable swing instruction set. Tracking errors caused by static calibration parameters can be avoided, and the execution precision of the rotary table in complex motion control is improved.

The invention relates to the technical field of data processing, in particular to a training system and method for a task processing model and a related device, and the system comprises a comprehensive scheduling agent which is used for generating training configuration information, at least one piece of input configuration information and template configuration information based on task description data; the function scheduling agent is used for obtaining constraint information fed back by the scheduled function agent based on each piece of input configuration information; the template agent is used for generating an initial prompt template based on the template configuration information; the comprehensive scheduling agent is also used for adjusting the initial prompt template to obtain a target prompt template based on the at least one piece of constraint information; and the training agent is used for training to obtain a task processing model based on the target prompt template, the training configuration information and the sample set. In this way, intelligent decision making and judgment can be conducted in all links of the task processing model obtained through training, and the training efficiency and the training effect of the task processing model are improved.

The invention provides an air-ground cooperative unmanned aerial vehicle countering signal adaptive generation method and system, and relates to the technical field of unmanned aerial vehicle countering, and the method comprises the steps: obtaining a target unmanned aerial vehicle communication signal, and extracting a feature analysis protocol; a secondary coding structure is adopted, and parameters are optimized by using a genetic algorithm with a composite fitness function; selecting and adjusting an interference waveform template based on the protocol type; executing air-ground collaborative resource optimization allocation, and dynamically allocating power and computing resources by adopting a reinforcement learning algorithm; and controlling the air-ground countering equipment to generate an interference signal. According to the method, the interference effect can be improved, the energy consumption is reduced, the detection resistance is enhanced, and intelligent dynamic allocation of countering resources is realized.

The invention provides a ship trajectory optimization method and system based on a visual language model and multi-modal collaborative decision, and relates to the field of ship trajectory prediction.The method comprises the steps that a trajectory prediction model is established, and a ship trajectory optimization model is established based on the visual language model; establishing a ship trajectory optimization cue word template based on chain thinking; first multi-modal data is obtained, and the first multi-modal data at least comprises a track classification label; generating a basic trajectory prediction result according to the first multi-modal data through a trajectory prediction model; generating second multi-modal data according to the basic trajectory prediction result and the first multi-modal data; and through the ship trajectory optimization model, according to the ship trajectory optimization cue word template based on chain thinking and the second multi-modal data, an optimized trajectory prediction result is generated, and the ship trajectory prediction method has the advantage of improving the accuracy of ship trajectory prediction.

The invention discloses a digital twinning progress tracking and risk pre-control method for hydraulic engineering construction, and relates to the field of hydraulic engineering construction, and the method comprises the steps: obtaining mechanical positioning, material entering and environment monitoring data through a multi-source sensing terminal, and generating a time-space aligned construction situation data set; a progress weight template is activated based on the construction stage identifier, and a dynamic weight vector is generated in combination with the environment data; comparing the construction situation data with the reference model to calculate a comprehensive progress deviation index; when the index exceeds the limit, constructing a resource topological relation graph to generate an elastic scheduling instruction; outputting a derivative risk probability through the risk conduction model and generating an optimized scheduling instruction set; triggering a calibration instruction to feed back a correction parameter according to the execution data; by establishing a three-level calibration mechanism linked with the construction progress, the risk change and the environmental disturbance, layered accurate adjustment of data acquisition, environmental factors and model parameters is realized.

The invention relates to the technical field of computer data processing, provides a cross-border data compliance processing method, device and equipment, and is used for solving the problems of relatively low cross-border data processing efficiency and data processing strategy errors in related technologies. According to the embodiment of the invention, the multi-method domain and regulation knowledge graph is established in advance, laws and regulations of a plurality of regions are summarized, the multi-method domain and regulation knowledge graph can be updated in real time, and the cross-border scene template library and the data label template base library are established according to the multi-method domain and regulation knowledge graph. The cross-border scene template library and the data label template base library can be dynamically updated along with updating of the knowledge graph, and a cross-border gateway can process data by adopting a compliance strategy according to a data cross-border scene through the cross-border scene template library, so that related data management enterprises and institutions efficiently manage data outbound in a compliance manner; through the data label template library, the cross-border gateway can rapidly and accurately carry out data marking work, and the efficiency of data classification processing is improved.

The invention relates to the technical field of building templates, and particularly provides a building template flatness threshold partition detection method based on normal vector constraint RANSAC, which comprises the following steps: performing three-dimensional laser scanning on a building template to obtain high-density point cloud data; preprocessing operations such as denoising and downsampling are carried out on the collected point cloud data, so that the data quality is improved; fitting a reference surface of the template based on the preprocessed point cloud data by using an improved RANSAC algorithm, and introducing a unit normal vector to ensure the correct direction of the reference surface; calculating the vertical distance from each point cloud point to the reference surface to obtain a deviation value; independently setting a deviation threshold value according to a building specification, dividing the deviation value into different grades, and distinguishing and displaying the deviation value on the three-dimensional point cloud model through colors; and a visual result containing the three-dimensional point cloud model and the color identification is output, the flatness condition of the template is visually displayed, and the method has the effects of automatic measurement, high efficiency and high precision.

The invention belongs to the technical field of artificial intelligence, and relates to a data query method and device, computer equipment and a storage medium. Performing semantic analysis on the query information to obtain a semantic analysis result; performing data source matching and data field extraction on a preset metadatabase based on the semantic analysis result to obtain a data source positioning result and a data field extraction result; constructing statistical logic based on the semantic analysis result and the data field extraction result; obtaining a target retrieval statement template based on the semantic analysis result; performing parameter filling on the target retrieval statement template based on the semantic analysis result, the data source positioning result and statistical logic to obtain a big data retrieval statement; performing query processing on the business data based on the big data retrieval statement to obtain a query result; and sending the query result to the user. The method can be applied to data query scenes in the fields of finance, medical treatment and the like, and the processing efficiency and accuracy of data query are improved.

At least one example describes mechanisms generating real-time AI-driven financial reports and responses from financial data of a business entity. In at least one example, the financial data including transaction details and account information from different sources is received. Based on the financial data, a financial report template is generated including performance indicators (PIs) that are dynamically generated by a first one or more machine-learning (ML) models. A second one or more ML models generates a mapping strategy automatically linking the PIs to relevant financial data. In at least one example, based on the template and the mapping strategy, a financial report is generated including estimated values of the PIs for specified time. The financial report is evaluated by a third one or more ML models that generates, via a graphical user interface (GUI), a response including a description of valuable insights, anomalies, and patterns in a natural language format.

The invention provides an OCR (Optical Character Recognition) method and system for high-precision table data structuring. The method comprises the following steps: converting an original image into a grayscale image and preprocessing the grayscale image; extracting a table edge structure line and filling a fracture part; detecting longitudinal and transverse straight lines in the preprocessed image, calculating intersection points, and determining a table row-column structure; dividing a cell region and positioning to generate a cell coordinate matrix; pixels in the cells are divided into a frame influence area and an effective data area, and the frame influence area executes neighborhood mean filtering and weighted fusion operation; performing end-to-end detection on characters and symbols in the effective data area, and outputting an OCR recognition result with coordinates; and dynamically generating a table structure template based on the cell coordinate matrix and an OCR recognition result, speculating a strategy matching field type through a rule, processing and merging cell missing data based on adjacent cell information, and outputting structured data. The reliability and accuracy of the OCR technology are improved, and the requirement of automatic information processing for high-precision data extraction is met.

A computer system for building, simulating, and / or validating a predictive model. The computer system programmed to (i) analyze one or more predictive pricing sub-models to detect one or more issues; (ii) execute a GPT model on the one or more predictive pricing sub-models, the GPT model trained to identify differences between predicted pricing and actual pricing for certain predefined events; (iii) compare the one or more issues to one or more outputs of the GPT model; (iv) in response to the comparison, generate a new model software template including one or more code changes to the one or more predictive pricing sub-models based upon the comparison; (v) generate a simulation environment based upon a plurality of data; (vi) execute the new model software template in the simulation environment; and / or (vii) update the new model software template based upon one or more outputs of the execution.

The invention discloses a database anomaly diagnosis method and system based on a large language model. The method comprises the steps that a diagnosis document is split according to chapters to extract diagnosis knowledge fragments so as to construct a diagnosis knowledge base; clustering the diagnosis knowledge fragments according to a preset topic, and generating a corresponding prompt word template for each cluster; judging whether the monitored index value in the diagnosis knowledge base exceeds a preset threshold value or not, and matching related diagnosis knowledge from the diagnosis database according to the prompt word template based on a judgment result that the index value exceeds the preset threshold value so as to collect corresponding abnormal information; and inputting the exception information into a plurality of large language models for parallel processing to obtain corresponding database exception diagnosis results, and synthesizing the diagnosis results of the large language models through a tree search algorithm to carry out diagnosis path optimization. The method aims at effectively analyzing abnormal root causes of different databases in a real scene.

The invention discloses a microgrid stability control method and system based on output characteristic simulation. The method comprises the following steps: acquiring fluctuation characteristic data, meteorological condition data, operation data, load demand data and environmental parameter data; separating the fluctuation characteristic data to obtain a periodic track and a random disturbance component; performing correlation analysis according to the periodic trajectory, the random disturbance component and the meteorological condition data to obtain potential influence data; according to the potential influence data and the operation data, obtaining an adaptability index; if the adaptability index is lower than a preset index threshold value, the energy capacity and the frequency range are adjusted according to the random disturbance component, the operation data and the load demand data, and dynamic response configuration is obtained; according to the dynamic response configuration, in combination with a preset state switching loss parameter and a preset environment adaptability parameter, generating an initial adjustment instruction template; and generating a control instruction set based on the initial adjustment instruction template in combination with the environmental parameter data, the adjustment frequency and the power parameter.

The invention relates to a hot topic chatting method, system and device based on an intelligent agent and a storage medium. The method comprises the steps of extracting key intention information through a large language model according to current input data and historical dialogue data; constructing a topic vector database and automatically updating; retrieval is performed based on cosine similarity, and hot topics are screened out; the hot topics are integrated into a Prompt template, an auxiliary tool is called in real time to obtain auxiliary information, and a large model is used for generating reply content based on the Prompt template according to the current input data and the auxiliary information; and storing the current input data and reply contents, and updating the historical dialogue data. Personalized emotional appeals of a user are analyzed according to data input by the user through a large language model, key intention information is extracted, a topic vector database is constructed, topics most related to current user input are screened out, and by means of auxiliary tools, the real-time performance of hot topics can be guaranteed, and emotional resonance with the user can be enhanced.

A cloud misconfiguration remediation application (“remediation application”) has been created that generates a remediation action for a resource misconfiguration detected with a CSPM policy. The remediation application includes a conversation agent that interacts with the foundation model according to a chain of prompts / input sequences. The conversation agent constructs the chain of prompts based on a template, the CSPM policy, metadata about the CSPM policy and the misconfigured cloud resource, and responses from the foundation model. The foundation model is implemented with retrieval augmented generation (RAG) that uses an embedding database built with remediation documentation of the CSP. Prompts from the conversation agent are augmented based on the implemented RAG. The remediation application aggregates the responses into a remediation action that can either be automatically performed or presented for consideration by a user.