760 results about "Software system" patented technology

Techniques for leveraging a large language model (LLM) in software development are described. A description of an error message associated with a software system hosted by the multi-tenant provider network is received. An LLM is prompted with an analysis prompt to generate an analysis of the error message, the analysis prompt including the description of the error message, and the analysis of the error message is received from the LLM. The LLM is prompted with a suggested resolution prompt to generate a suggested resolution to a cause of the error message, the suggested resolution prompt including the description of the error message and the analysis of the error message, and the suggested resolution is received from the LLM. A change to resolve the cause of the error message is sent to an originator of the received description, the change based at least in part on the suggested resolution.

Techniques for leveraging a large language model (LLM) in software development are described. A description of a software development task is received from a user. Data associated with the software system is obtained from a data source. An LLM is prompted to identify at least one aspect of the task which requires clarification from the user, at least partly by providing the obtained data to the LLM and asking the LLM to identify a question for the user which remains unanswered by the obtained data. The question is presented to the user. An answer to the question is received from the user. The LLM is prompted to respond to propose an implementation of the task at least partly based on the data associated with the software system and the answer received from the user. The proposed implementation is received from the LLM and caused to be displayed to the user.

Techniques for displaying a graphical user interface of a software development system are described. An initial description of an initial structure of a software system is obtained, the initial description including an identification of interconnections amongst provider network resources that form the initial structure. A modified description of a modified structure of the software system is obtained, the modified structure based on at least a change to the initial structure of the software system described in the initial description. A difference between the initial description and the modified description is determined. An electronic device is caused to display a graphical user interface including a visual depiction of the initial structure of the software system and an indication of the difference, the visual depiction based at least in part on the initial description.

A method of diagnosing a software system of a vehicle includes receiving data related to the software system of the vehicle, identifying an anomalous event based on a pattern of the received data, and collecting contextual information related to the anomalous event. The method also includes inputting the anomalous event and the contextual information to a machine learning model, determining a root cause of the anomalous event by the machine learning model, and based on determining that the anomalous event corresponds to the malfunction, performing a mitigating action.

The invention belongs to the technical field of software system optimization and intelligent operation and maintenance, and discloses a shared platform autonomous upgrading method based on a graph neural network, and the method comprises the following specific steps: S1, component state perception and feature coding; s2, constructing a heterogeneous dynamic evolution diagram; s3, dynamic graph neural network embedding modeling; s4, state prediction and conflict evaluation; s5, constructing an upgrade game diagram and strategy reasoning; s6, executing an upgrading plan and real-time feedback acquisition; and S7, graph structure reconstruction and strategy closed-loop optimization. According to the method, the heterogeneous dynamic evolution diagram is constructed, the time sequence label and the state quantification model are introduced, real-time sensing and standardized modeling can be carried out on the running state of each service component in the sharing platform, and compared with a traditional upgrading method depending on static configuration and a predefined template, the upgrading efficiency is greatly improved. According to the mechanism, inductive expression of multi-dimensional features such as node resource occupation, access frequency and response fluctuation is completed in the stage before upgrading.

The invention relates to a public multi-mode cloud network resource software security enhancement method based on neural symbol fusion reasoning, which comprises the following steps: an intermediate representation generation stage: generating and optimizing an intermediate representation of a program through a fine-tuned large language model, and establishing semantic mapping from a source code to a structured logic representation; in the symbol language conversion stage, the intermediate representation generated by the large language model is converted into a domain-specific language fact set which can be recognized in the symbol logic reasoning stage, and formal and logic expression of program semantics is achieved; and a symbol logic reasoning stage: matching the fact set with the rule base through a symbol logic reasoning engine, performing detection and verification according to the safety rule, generating a structured report, and feeding back a result for optimization. The method is suitable for security enhancement of various core software systems in a public cloud network multi-modal network environment, high-precision security analysis is carried out on cross-modal and cross-subsystem fragmented codes in a compiling-free environment, and verifiable technical support is provided for public cloud network security control.

The invention provides a multi-dimensional operation parameter analysis fault detection method and system, and relates to the technical field of fault detection, and the method comprises the steps: building an inspection task system and a fault triggering system, and forming a task basic data set; generating an inspection task instance object; analyzing to obtain monitoring index data; effective fault report information is extracted, and a software system inspection result set is formed; operating parameters of the hardware system are collected, threshold detection and fluctuation analysis are executed, and a hardware system inspection result set is generated; and extracting multi-dimensional features, inputting a fault scoring function, calculating a comprehensive fault score of each device or system, and automatically generating an inspection report and a fault report sheet. According to the method and the device, the problems that fault evaluation depends on manpower and judgment standards are not uniform in the prior art can be solved, automatic risk grading and result generation based on comprehensive scores are realized, and thus the accuracy and the diagnosis efficiency of fault detection are improved.

The invention discloses a program fault processing method, device and equipment of a software system and a storage medium, relates to the technical field of computers, can be applied to financial science and technology and medical health business scenarios, and comprises the steps that a fault knowledge graph of the software system is acquired, the fault knowledge graph is dynamically constructed based on multi-source heterogeneous data, and the fault knowledge graph is stored in a database; wherein a diversified atlas relationship is stored; performing graph neural network driven association analysis based on the system component dependency relationship and the first association relationship, mining a hidden fault propagation path on a component dependency link, and determining a fault association component and a fault root cause corresponding to the program fault data based on the hidden fault propagation path; based on the fault association component, the fault root cause and the second association relationship, determining a plurality of candidate repair schemes corresponding to the program fault data; and calculating evaluation scores of the plurality of candidate repair schemes through the utility function, sorting according to the evaluation scores, and determining an optimal repair scheme of the software system about the program fault in the plurality of candidate repair schemes.

The invention provides a software system intelligent development method and system based on an AI technology, and relates to the technical field of software development. Through natural language demand analysis, intelligent component recommendation, code-free generation and continuous optimization closed loop, the problems that a traditional development mode is low in efficiency, insufficient in intelligence and rigid in system are solved, and the method has the advantages that the development threshold is lowered, the development efficiency is improved, continuous optimization of the system is achieved, and data security is guaranteed.

The invention discloses an AI software system defect repairing method, device and equipment of a government affair platform and a medium, and relates to the technical field of computers, and the method comprises the steps that target data of a current platform AI software system in the running process is obtained through an eBPF probe loaded by a K8s cluster of the government affair platform, and defect fingerprints are determined based on the target data; determining a target defect classifier based on the graph neural network, the teacher model, the student model, a preset distillation mechanism and a KL divergence loss function, determining a target strategy composer by using reinforcement learning and a preset strategy library, and determining a target defect repair strategy based on the defect fingerprint, the target defect classifier, the business data and the target strategy composer; and performing defect repair on the current platform AI software system by using the target defect repair strategy, verifying the repaired AI software system, and if the verification is passed, completing the defect repair operation. And the AI system defect automatic detection and repair efficiency is improved.

The invention provides an intelligent test data generation and verification method and system based on a hierarchical architecture, and relates to the technical field of data generation, and the method comprises the steps: constructing a cross-layer constraint dependency graph by analyzing each layer of data mode and constraint and conversion logic of a hierarchical architecture software system; generating candidate test data meeting the constraint based on the dependency graph in a back-stepping manner, and verifying the candidate test data through projection; and iteratively optimizing the dependency graph and the test data by monitoring the difference between the actual execution and the prediction form. According to the method, cross-layer data consistency verification is realized, the test coverage rate and the defect detection efficiency are improved, and the quality risk of a software system is reduced.

The invention discloses a method and device for determining the number of test cases and electronic equipment. The method comprises the following steps: generating an initial orthogonal table of a target software system; determining a target service operated by the target software system, obtaining a complexity index variable, and determining a service influence index variable of the target service; determining a complexity factor value according to the complexity index variable and the service influence index variable; acquiring historical operation information of the target software system, acquiring an error rate index variable from the historical operation information and the software code, and determining an error rate factor value of the target software system according to the error rate index variable; and adjusting the preset number according to the complexity factor value and the error rate factor value to obtain a target number of the test cases of the target software system. By means of the method and device, the problem that in the related technology, due to the fact that the accuracy of determining the number of supplementary cases of the orthogonal table is low, the accuracy of software testing according to the orthogonal table is low is solved.

The invention discloses a transformer winding deformation diagnosis software system based on frequency domain transfer function analysis, relates to the technical field of transformer winding deformation diagnosis, and is used for realizing accurate online diagnosis of winding deformation. The frequency response data quality control module performs fast Fourier transform and adaptive filtering on the original frequency response signal uploaded by the distributed detection terminal to generate a transfer function data set of which the signal-to-noise ratio reaches the standard; the transfer function multi-dimensional analysis module performs longitudinal deviation and transverse consistency analysis on the data set, identifies resonant frequency deviation, amplitude change and quality factor drift, and generates a deformation suspicious degree index; the deformation trend prediction module calculates a change trend slope based on a historical suspicious degree sequence and identifies a sensitive frequency band; and the fault mode identification module carries out spectrum refinement analysis on the sensitive frequency band, matches the sensitive frequency band with a typical fault feature library, and outputs a fault type and confidence evaluation. Multi-terminal synchronous monitoring, dynamic prediction and intelligent fault identification are realized, and technical support is provided for predictive maintenance of the transformer.

The embodiment of the invention provides a storage strategy optimization method and device based on data popularity and data consanguinity, and dynamic optimization of a storage strategy is realized through innovatively designing a multi-dimensional popularity calculation model and through node attribute analysis and a graph attention network. A directed graph structure is constructed, and a reliable dependency relationship management system is established in combination with blood relationship analysis. A hierarchical storage strategy is introduced, and the storage efficiency is ensured through heat division and dependency relationship optimization. According to the method, the defects of the traditional technology in the aspects of popularity evaluation, blood relationship analysis, storage optimization and the like are effectively overcome, and a technical guarantee is provided for software system storage management.

The invention provides a test case generation and test method and device, equipment and a storage medium, relates to the technical field of software testing, and aims to understand logics and requirements of different software systems and automatically generate adaptive test cases. According to the specific technical scheme, a test task request is received; determining a target agent from the plurality of agents through a priority strategy, a load strategy, a capability matching strategy and an adaptive strategy between the test task and the plurality of agents; obtaining a source code, a business requirement document and a historical test case of the software system associated with the test task through the target agent; reasoning and planning by using a large language model through the target agent based on the source code of the software system associated with the test task, the business demand document and the historical test case, determining a test plan, and generating a test case; and running the generated test case through the target agent according to the test plan, and generating a test result and defect information.

The invention discloses a multi-mode SLAM implementation method for intelligent garden supervision, a software system and a hardware device, and belongs to the technical field of intelligent garden supervision, synchronous positioning and map construction. According to the technical scheme, the multi-mode SLAM implementation method for intelligent garden supervision comprises the following steps: S1, sensor calibration: acquiring a camera internal reference matrix and distortion parameters by adopting a checkerboard calibration method; compensating motion distortion through a constant-speed model, cutting sub-frames, and calculating a transformation matrix from the radar to the IMU in combination with the output of a pure laser odometer and IMU data; generating a transformation matrix from the radar to the camera through feature extraction, matching and external parameter optimization; through multi-modal sensor fusion and space-time synchronization technologies and in combination with collaborative optimization of the laser-vision-inertial odometer, high-precision map construction with geometric structure and color information is realized in a complex garden scene, and the method has the advantages of improving positioning precision and map information integrity in a complex garden environment.

The invention relates to a building space truss construction method and system, a storage medium and electronic equipment, and the building space truss construction method belongs to the technical field of building manufacturing, and comprises the steps: obtaining an upper chord curved surface and a lower chord curved surface, determined by taking a roof curved surface as a reference surface, of a target building; taking the column grid size, the upper chord grid size and the lower chord grid size as input data of a parameterized model, and performing grid structure division on the upper chord curved surface and the lower chord curved surface in the parameterized model to obtain an upper chord, a lower chord and column node group vectors corresponding to the upper chord and the lower chord; performing crossover operation on the column node group vectors corresponding to the upper chord member and the lower chord member to obtain a net rack web member; the space truss of the target building is constructed on the basis of the upper chord member, the lower chord member and the truss web member, the whole process can be automatically carried out through a parameterized model and a corresponding software system, manual construction is not needed, and therefore material waste caused by manual construction is reduced, and the structural performance of the constructed truss is improved.

Methods, systems, and computer-readable storage media for a software dependency update system that processes computer-readable files (e.g., source code file, dependency description file) of a software project to generate an updated dependency graph using linear programming in view of a set of quality metrics for updating dependencies of the software project.

Disclosed herein are systems, methods, and a software system that can be used for prognostic management of energy systems including smart grid power transformers. In one embodiment, a smart grid data management system is provided. An exemplary system can comprise a Transformer-based fault classification model and a natural language processor (e.g., NLP model), where the classification model is configured to classify faults in the smart grid system and the natural language processor or model is configured to output responses and information using the classified faults

The invention discloses a reinforcing steel bar strength measuring system for building steel structure construction, and belongs to the field of building material detection, the reinforcing steel bar strength measuring system comprises a tensile test device and a software system, the software system comprises a test control module, a data processing and analysis module and a visual display module, the test control module is used for setting test parameters and automatically controlling the operation of the tensile test device; the data processing and analyzing module is used for processing and analyzing acquired data; the tensile test device comprises a base, a traction mechanism and a measuring mechanism, the measuring mechanism can move in the axial direction of the reinforcing steel bar, and a photoelectric encoder can monitor full-length deformation of the reinforcing steel bar and particularly can accurately capture a certain part of seriously-deformed area. And meanwhile, the annular seat drives the photoelectric encoder to measure the same cross section at multiple angles, so that the one-sidedness of a single angle is avoided, more comprehensive data is obtained, and the measurement accuracy of the percentage elongation after fracture is ensured.

The invention discloses a multi-agent dynamic diagnosis method and system for a vehicle-mounted software system, and relates to the technical field of software diagnosis. Collecting multi-source operation data of the vehicle-mounted software in real time; performing document analysis and skill processing on the internal technical document of the original equipment manufacturer to obtain an executable skill set; constructing a dynamic dependency graph based on the multi-source operation data and the executable skill set; obtaining a current fault prompt, calling a multi-skill collaborative agent, substituting multi-source operation data and the fault prompt into the dynamic dependency graph, arranging action layer operation in an executable skill set through the agent, executing log analysis, dependency chain verification, abnormal link identification and fault scene restoration, and outputting abnormal associated data; based on the dynamic dependency graph, the abnormal associated data, the rule layer information of the executable skill set and the historical fault cases, root cause reasoning is executed to obtain an executable troubleshooting scheme, and the fault diagnosis accuracy and efficiency of the vehicle-mounted software system are improved.

The invention discloses a full-automatic life test system for an isolation device, which belongs to the field of reliability test of semiconductor devices, and comprises a system host used for providing a test environment and bearing a plurality of test channels; the high-low voltage isolation conversion module is arranged corresponding to each test channel and is used for switching between a high-voltage aging loop and an insulation resistance measurement loop of each channel; the real-time leakage current monitoring module is used for continuously monitoring the leakage current of each isolation device in the high-voltage aging process; the precision measurement and data acquisition module is used for carrying out insulation resistance measurement on the isolation device in a measurement mode; the central control and software system module is respectively connected with the high-low voltage isolation conversion module, the real-time leakage current monitoring module and the precision measurement and data acquisition module, and the central control and software system module is used for automatically triggering and controlling an insulation resistance measurement process according to leakage current data, judging the state of a device according to a measurement result, and outputting the state of the device. And aging and stopping of the test channel are managed.

The application relates to the technical field of product copying, in particular to a product copying software system for three-dimensional model import and conversion, and discloses the following scheme: a primary three-dimensional model is generated by acquiring a main view image and a sub-view image, extracting a first characteristic parameter and a second characteristic parameter; target parameter adjustment is carried out based on the bidirectional mapping relationship between the target parameter and an inferred parameter, and a parameter combination is generated; the response coefficient of the inferred parameter is corrected through reverse deduction, a bidirectional correlation matrix is optimized, and the consistency of the target parameter and the inferred parameter is ensured. Through shadow area analysis and surface fitting technology of multi-view images, the problem of bottom area data loss is solved, and accurate three-dimensional reconstruction of the object surface and the bottom area is realized.

For generating a bill of materials and a corresponding bill of processes for a product, a natural language processing module maps at least one document specifying a product into a latent representation, which is an embedding in latent space. A bill of materials generator receives the latent representation as input and generates a bill of materials for the product. A user interface detects user interactions with the generated bill of materials and creates a curated bill of materials depending on the user interactions. A bill of processes generator receives the latent representation and the curated bill of materials as input and constructs a bill of processes for the product. The method and system, or at least some of their embodiments, provide semi-automatic generation of the bill of materials and bill of processes for a product, using an AI engine. Compared to manually setting up a bill of materials and / or bill of processes within a software-system, the approach of using AI for generating this information can significantly speed up the process of calculating costs and sustainability values of a product. In this way the time from request for quotation until submitting the actual offer can be lowered significantly.

The invention discloses a chaos test scene supplementing method and device, equipment and a storage medium. The method comprises the steps that a software system is analyzed through a micro-service analysis tool, and a chaos test scene corresponding to the software system is generated in combination with a rule engine tool; executing the chaos test scene on a chaos engineering platform, and obtaining scene data when the chaos test scene is executed in combination with a code coverage rate tool; and when the current code coverage rate of the software system does not reach the expectation, inputting the scene data into a pre-trained machine learning model, and supplementing a new chaos test scene based on the machine learning model. According to the method, when the current code coverage rate of the software system does not reach the expectation, a new chaos test scene is supplemented through the machine learning model, and the code coverage rate of the chaos test scene can be improved.

The invention relates to the technical field of static application security detection, and discloses a training data generation method and system based on static application security detection, and the method comprises the steps: analyzing a source code of a target application program, obtaining a key condition variable and a use position, and introducing condition logic on the basis, an unsecure execution path is created that is triggered under a particular runtime condition. And then, according to the conditional logic, generating environment simulation information during operation, verifying the source code sample into which the conditional logic is introduced, determining whether a defect exists in combination with the environment simulation information, and finally generating a defect label. The method effectively solves the problem that an existing SAST tool is difficult to effectively capture the hidden security defect triggered under the specific runtime condition when facing a modern complex software system, especially a micro-service or distributed architecture application.

The invention discloses a business full-process generation system and method based on an enterprise knowledge graph enhancement model, and belongs to the technical field of intelligent software codes, and the system comprises a perception and understanding engine which is used for receiving and analyzing the multi-modal original data of an enterprise by using a preset industry knowledge enhancement model, obtaining analysis content, and generating an enterprise knowledge graph enhancement model; constructing a business knowledge graph based on the analysis content; the design and planning engine is used for performing automatic design of the system architecture based on the business knowledge graph to obtain a design scheme blueprint; and the construction and delivery engine is used for generating a plurality of code components based on the design scheme blueprint and assembling and deploying all the generated code components to obtain a software system. The system can improve the current situation of serious disjunction between service cognition and software implementation.

The invention discloses an intelligent fan control software system based on ROS2, and relates to the technical field of fan control, the intelligent fan control software system based on ROS2 comprises a distributed sensing layer, an intelligent decision-making layer, an execution control layer and a remote interaction layer, the distributed sensing layer encapsulates a multi-type sensor data acquisition module through an ROS2 node, and the intelligent decision-making layer encapsulates a multi-type sensor data acquisition module through an ROS2 node; and fan operation state parameters and environment variables are obtained in real time. The distributed sensing layer, the intelligent decision-making layer, the execution control layer and the remote interaction layer are matched with one another, ROS2 node communication, topic subscription and service calling mechanisms are combined, multi-level data seamless circulation is achieved, the problems that traditional fan control software is high in data transmission delay and poor in module collaboration are solved, meanwhile, a fault self-diagnosis module is arranged, and the fault self-diagnosis function is achieved. Abnormality is detected in real time based on the sensing data and the preset threshold value, three-level emergency response is triggered, and the equipment operation safety and the fault processing efficiency are greatly improved.

The invention relates to the technical field of data transmission, in particular to a shared data transmission system based on double virtual machines. The system is composed of a hardware environment with double virtual machines and shared storage and a software system running on the double virtual machines, and the software system running on the double virtual machines comprises a data collection subsystem used for extracting real-time meteorological data in the shared storage at a set time interval for preprocessing operation; the data distribution subsystem is used for starting a two-stage file concurrence process and respectively sending the preprocessed meteorological data to each receiving platform; the system monitoring subsystem is used for monitoring the concurrent process of the two-stage file in real time; and the log management subsystem is used for recording a sending record of the data distribution subsystem and a real-time monitoring result of the system monitoring subsystem, and generating a log. The data transmission system provided by the invention has full-link monitoring and log analysis capabilities, stable operation of the system is guaranteed, and full-time efficient data transmission is realized.

The invention discloses an increment testing method and device, a storage medium and electronic equipment, and the method comprises the steps: constructing a dependency graph for describing the dependency relationship between software components in a software system according to software system data, and carrying out the continuous integration of the software components on the basis of a code change set corresponding to a current continuous integration task; a minimum candidate influence range is determined on the dependency graph, a candidate test set is delineated, tests in the candidate test set are ranked according to the sequence of risk scores of all the tests from high to low, a target candidate test set is obtained, all the tests are processed through a greedy approximation algorithm, and a target candidate test set is selected under the constraint of resource budget; and the minimum and optimal test set of software system defects is found to the maximum extent. According to the method, the minimum candidate influence range is determined on the dependency graph based on the code change set, the greedy approximation algorithm is combined, intelligent and accurate selection of the test set under the resource budget constraint is achieved, it can be guaranteed that the defect detection rate is not reduced, and the continuous integration process efficiency can be improved to the maximum extent.