8995results about "Software deployment" 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.

Cloud compliance monitoring for a cloud compute environment managed by a container orchestrator may be performed by a data platform. For example, the data platform may obtain cluster configuration data from a control plane of a cluster that is managed by a container orchestrator within a cloud compute environment, obtain node configuration data from one or more compute nodes associated with a data plane of the cluster that is managed by the container orchestrator, and generate a compliance statement based on the cluster configuration data and node configuration data. The compliance statement may indicate a configuration posture of the cloud compute environment according to a compliance benchmark. Corresponding methods, systems, and products are also disclosed.

Various examples, systems, and methods are disclosed relating to a model customization pipeline. A first computing system can receive at least one customization of at least one artificial intelligence (AI) model corresponding to a base instance. The first computing system can generate a customized instance of the at least one AI model by updating the base instance of the at least one AI model based on the at least one customization. The first computing system can generate a software component configured to perform at least one operation using the customized instance of the at least one AI model. The first computing system can package the software component and the customized instance of the at least one AI model into a first container instance. The first computing system can deploy the software component within a runtime environment.

The invention provides a legacy system-oriented large-model-driven API document automatic generation system, belongs to the crossing field of artificial intelligence and software development, and provides a multi-modal data fusion and closed-loop verification mechanism aiming at the defects of a traditional API document generation method in the aspects of semantic comprehension, dynamic context capture and multi-technology stack adaptation. A code static feature and a dynamic track during operation are analyzed through a multi-source data acquisition module, and an interface semantic feature is extracted in combination with a field self-adaptive large model of a semantic enhancement analysis module; deducing an implicit service rule by fusing static / dynamic characteristics through a graph neural network, and generating a standardized document conforming to an OpenAPI specification through a parameterized template generative adversarial network (PT-GAN); and finally, performing three-level verification and closed-loop optimization through a sandbox environment. The method supports a heterogeneous system of 16 programming languages such as Java / C + + / Python, interface version changes can be automatically recognized, document patches are generated, the problems of missing and outdated system documents and low maintenance efficiency are solved, and maintainability and integration efficiency of enterprise-level systems are remarkably improved.

Systems, methods, and devices that relate to assessing uncertainty associated with entities are disclosed. In one example aspect, the method receives artifacts relating to an entity and categories for assessing uncertainty. For each category, a generative model retrieves and standardizes data points from the artifacts. A rule-based model inputs the standardized data points to output a rating. The generative model then generates an assessment of the rating and data points according to a predefined structure. The method outputs a summary, rating, and standardized data points for each category. These outputs can be used by other systems for assessing the uncertainty of the entity and taking action based on the assessment.

The invention discloses an intelligent file service system generation method based on a low-code development mode. The method comprises the following steps: constructing a file service core data model, defining file metadata fields, a storage structure and an association relationship, and forming a standardized data architecture. According to the method, the core data model, the visual page component library and the drag-and-drop workflow template are constructed, linkage binding of data-component-process is achieved in combination with a low-code engine, and the efficiency bottleneck of a traditional development mode is broken through; through a three-way real-time synchronization mechanism, real-time hot repair and dynamic branch judgment, page layout and flow rule adjustment can take effect immediately through visual configuration, and the problem of dynamic demand response lag is solved; through the intelligent resource scheduling logic compiled through environment adaptation, a differentiation scheme is generated for different deployment environments, the configuration difference influence under a private cloud and server mixed deployment scene is remarkably reduced, the deployment failure rate is greatly reduced, and the system online stability is improved.

The invention discloses an AI model distribution and deployment system and method oriented to cloud edge collaboration, belongs to the technical field of artificial intelligence and edge computing crossing, and aims to solve the problems of low AI model distribution efficiency and poor deployment adaptability in a cloud edge collaboration scene. The method comprises the steps of collecting multi-dimensional attribute data of edge nodes and converting the data into multi-level capability tags, and triggering tag differentiation updating according to data change amplitude; constructing a model demand hierarchical description framework by relying on capability labels, converting distribution demands into quantitative query conditions, and adjusting and screening a target edge node set through a three-level progressive matching mechanism in combination with dynamic weight; coding data blocks corresponding to redundancy are generated based on the network state of a target node, adaptive cache nodes are screened by using a distributed cache mechanism, the data blocks are pre-stored, and a cache network is established and transmitted to the target node; according to the invention, efficient distribution and accurate deployment of the AI model in the cloud edge collaborative environment are realized, and the model transmission reliability and the node adaptability are improved.

The invention discloses a gallery integrated internal production software system based on a modular low-coupling architecture, which is characterized in that a system function is divided into a plurality of modularized modules and the modularized modules are packaged and deployed by a uniform interface, so that the coupling degree between the modules is remarkably reduced, and the problem of global code change caused by module dependence in a traditional single architecture is avoided; direct calling is replaced by an event-driven mechanism, asynchronous communication and decoupling cooperation between modules are realized, and the stability and maintainability of the system during function change or addition are improved; the configuration driving mechanism enables the business process to be dynamically adjusted, different business requirements can be met without modifying a program source code, the plug-in management mechanism provides a standardized third-party extension access channel and supports function loading according to requirements, and the limitation that a traditional system is fixed in function and closed in development is broken through; according to the method, the software can run consistently in Windows, Linux, MacOS and other environments, and the key problems that in the prior art, the coupling degree is high, the expansibility is poor, configuration is not flexible, and the platform adaptation capacity is weak can be effectively solved.

The invention discloses a code vulnerability detection method, a code vulnerability rule configuration method, a static single assignment SSA code acquisition method, devices corresponding to the methods, and electronic equipment. The code vulnerability detection method comprises the following steps: acquiring a plurality of source code files and code vulnerability rules of an application program; generating an abstract syntax tree of the source code file, and obtaining a function call relationship; and traversing the abstract syntax tree, and obtaining a cross-function risk propagation path according to the function call relationship and the code vulnerability rule. By the adoption of the processing mode, the function calling relation and the abstract syntax tree are fused to conduct cross-function code vulnerability detection, cross-function vulnerability detection of context sensitivity, flow sensitivity and domain sensitivity is achieved, and vulnerability false report and missing report are avoided; therefore, the accuracy and recall rate of vulnerability detection can be effectively improved.

The invention relates to the technical field of power monitoring system network security and real-time micropatch hot deployment, and discloses a power disaster recovery system-oriented micropatch non-inductive deployment engine, a resource scheduling method, a system, equipment and a medium, and the method comprises the steps: capturing system events through a kernel eBPF probe, and carrying out feature extraction and model reasoning; generating and transmitting an encrypted scheduling token; loading and verifying a patch fragment by a patch agent, inserting a jump instruction through a kernel interface to redirect an execution stream, and maintaining multi-kernel cache consistency; fusing multi-source telemetry data to carry out fusing judgment, realizing network isolation and calling a key service to cancel a key; and collecting runtime indexes and performing trend prediction, triggering a recovery or rollback operation according to a result, and storing an operation result and data through a block chain. According to the method, through combination of deep fusion of multi-source heterogeneous data, dynamic reasoning of a knowledge graph and strategy optimization of reinforcement learning, efficient perception and defense of a complex attack scene of a digital power grid are realized.

The invention relates to the technical field of computers, in particular to a dynamic configuration initialization and version synchronization method and system for multi-agent service, and the method comprises the following steps: obtaining a database update lock, constructing a database migration script dependency relationship, determining a current version of a database mode, upgrading and deploying the database mode, and verifying agent versions. The method has the beneficial effects that a rigorous database updating lock control mechanism, perfect migration script dependency chain verification, agent version management, port availability detection, idempotence and transaction safeguard measures are introduced, so that the processes of database structure upgrading and agent version initialization in a distributed deployment environment can be effectively supported; and the data consistency, the operation stability and the service continuity of the system are ensured.

The invention provides a low-code application logic generation method and system based on a large language model, and the method comprises the steps: obtaining a natural language demand and an operation environment snapshot of a user, and constructing a prompt project context of the large language model based on the natural language demand and the operation environment snapshot; based on the context of the prompt project, performing retrieval by utilizing a large language model to obtain a logic component; processing the logic component based on a pre-constructed agent to generate a logic DSL text; analyzing and rendering the logic DSL text to obtain a visual logic flow graph, and updating the logic DSL text in response to a modification operation of a user on the visual logic flow graph; based on the logic DSL text, verifying application logic corresponding to the logic DSL text in the sandbox environment, and deploying the application logic to the current application after verification is passed. According to the invention, complex business requirements can be met, and logic development thresholds are reduced.

The invention belongs to the technical field of computer software, and particularly relates to a heterogeneous database unified deployment management method based on a cluster management platform, which comprises the following steps: predefining metadata of various database services; the method comprises the following steps: pre-constructing a multi-version program package warehouse, and establishing a structured warehouse directory system; obtaining a chip architecture and an operating system type of the target node, and generating environment fingerprint information; dynamically selecting a database installation package matched with the current environment and a dependency item set of the database installation package by mapping a warehouse directory system; through a unified database adaptation layer, a standardized operation interface is called, and installation, configuration and start-stop operation of the database are executed; a visual deployment and arrangement function based on component roles is provided by expanding a Web service interface of the management platform; and according to a deployment arrangement result and the environment fingerprint information, calling a deployment script to complete automatic deployment and configuration of the database cluster. According to the invention, unified adaptation, deployment and full-life-cycle management of various domestic databases are realized.

Automated deployment of an anomaly detection framework, including: receiving data describing a deployment of an anomaly detection framework in a cloud computing environment; generating, based on the data, a bundle of configuration resources for deploying the anomaly detection framework in the cloud computing environment; and provide, in response to receiving the data, a reference to the bundle.

The invention discloses a deployment method and device of a large model agent, equipment and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: determining agent configuration information of a to-be-deployed large model agent after an agent deployment request is received, and determining a target large model and model metadata; determining an available resource condition of a preset computing resource pool, and performing query matching according to the available resource condition and the model metadata to determine a plurality of hardware-compatible candidate computing resource nodes; performing load prediction operation on the to-be-deployed large model agent after the large model agent is online to obtain a predicted load, and generating a target deployment decision scheme based on the predicted load, a preset scheduling strategy, the available resource condition and the candidate computing resource node; and adjusting the runtime parameters of the target large model according to the resource characteristics of the target computing resource node, and loading the adjusted target large model on the target computing resource node so as to deploy and start the agent instance of the to-be-deployed large model agent.

The invention discloses an emission reduction system for chloride ions in waste incineration fly ash based on machine learning, relates to the technical field of solid waste resourceful treatment, is used for data-driven fly ash multi-stage dosing collaborative removal, and is divided into fly ash pretreatment and data acquisition, online ion monitoring and dosing decision, multi-stage reaction and dynamic feedback, OTA upgrading and cross-plant migration. And removing the multi-dimensional pollutants synergistically. The dosing ratio and the reaction stage are optimized in real time through a gradient boosting tree-reinforcement learning model, and the concentrations of chloride ions, heavy metals and dioxin precursors are monitored on line. According to the system, monitoring and decision-making paths of all stages are stored in a cloud knowledge graph and can be upgraded through OTA, the chloride ion removal rate is increased by more than three percent, the agent dosage is reduced by 25%, the scheme can adapt to the difference of fly ash components in different factories, economy and environmental protection are both considered, and the system has remarkable popularization value in large-scale waste incineration and hazardous waste disposal.

The invention discloses a version updating method and device, equipment and a storage medium, and relates to the technical field of version updating, and the version updating method comprises the following steps: obtaining to-be-updated version information of to-be-updated software, intercepting an original structured query language (SQL), and generating a syntax tree corresponding to the original SQL, the to-be-updated version information comprises to-be-updated SQL and to-be-updated code blocks; according to the to-be-updated SQL, performing field updating on the syntax tree and then converting to generate a target SQL; according to the code task identifier of the to-be-updated code block, determining a code position of an original code block, and generating a target code; based on the target SQL and the target code, version updating and testing are conducted on the software to be updated, and a version updating task is completed. According to the method, a low-code platform can be helped to accurately identify data and code change ranges, codes and scripts are automatically combined, and the purpose of reducing version updating time is achieved.

The invention relates to the technical field of Internet of Things electric power communication, and discloses a remote software upgrading method and system based on an HPLC high-speed power line carrier network. The remote software upgrading method is applied to software upgrading equipment, and specifically comprises the following steps that S101, a master control node of software to be upgraded is obtained, the master control node completes encryption of an upgrading instruction packet through HPLC network broadcasting, if the master control node is in an offline state, a response is triggered through a wake-up pulse signal synchronized with a power line power frequency period, and if the master control node is not in the offline state, the software to be upgraded is upgraded; and it is ensured that the whole network nodes complete instruction synchronization within the preset time. Omnibearing upgrading and optimization are achieved through three core technologies of dynamic fragmentation transmission, millisecond-level service continuity guarantee and resource limit adaptation, the packet loss rate of a high-noise environment is compressed to 0.1% based on a signal-to-noise ratio self-adaptive fragmentation strategy (128KB-512KB), the average transmission time is reduced to 17.4 minutes from 30 minutes in combination with a multi-path concurrent transmission and breakpoint resume protocol, and the transmission efficiency is improved. And 300ms whole-network instruction synchronization is realized with the assistance of a power frequency synchronization wake-up mechanism.

The invention relates to the technical field of robots, and discloses a remote upgrading method of a robot, the robot and a system. According to the method, equipment state information of the robot is obtained, under the condition that the equipment state information meets upgrading conditions, an upgrading request is obtained, the upgrading request is sent to a cloud server, an upgrading package sent by the cloud server is obtained, and the robot is remotely upgraded according to the upgrading package. The equipment state information of the robot is obtained, if the equipment state information meets the upgrading condition, it is determined that the robot is remotely upgraded, and therefore the success rate of remote upgrading of the robot can be increased.

The invention discloses a dynamic calibration method and system for cross-terminal detection score offset, relates to the technical field of dynamic calibration of score consistency of a multi-terminal detection system, and is used for solving the problem that detection scores are incomparable due to inconsistent time bases among terminals and segmented drift of score scales. Generating an event record containing an original detection score and a quality mark by solidifying a terminal identifier and a field specification; the unified timestamp is corrected through four-timestamp time service, the aligned event groups are merged according to the aligned time windows, and the optimal shift amount is solved; dividing the offset sequence into segments, establishing order-preserving linear mapping in the segments, and performing interpolation connection on a transition region; and aperture self-certification and version updating verification are realized by combining the micro-correction list and the residual offset.

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 discloses a multi-version upgrading method and device based on distribution, equipment and a medium, and relates to the technical field of distribution. The method comprises the following steps: in response to a version upgrading operation event of a target object, performing metadata marking, multi-modal verification and increment extraction on the target object to obtain version upgrading data and a version upgrading type of the target object; wherein the target object comprises system data, a second-open script and an application source code; acquiring a baseline version of the target object from a multi-mode version database according to a version identifier in the version upgrading data; based on the version upgrading type, determining a target version of the target object according to the version upgrading data and the baseline version; and performing compatibility verification on the baseline version and the target version to obtain a compatibility evaluation report of the target version, and deploying the target version according to the compatibility evaluation report. According to the technical scheme, the reliability and efficiency of system version iteration can be improved.

In some embodiments, a method for software application deployment across dusters may include receiving a request to deploy a software application with a first deployable component and a second deployable component. The method may further include determining, based on the request, an availability, within a first duster of the plurality of dusters, of first computing resources capable of executing the first deployable component, and an availability, within a second duster of the plurality of dusters different from the first duster, of second computing resources capable of executing the second deployable component. The method may further include deploying, based on the availabilities of the first and second computing resources, the software application across the plurality of dusters. For example, this deploying across dusters may include deploying the first deployable component to the first cluster and deploying the second deployable component to the second duster. Corresponding methods and systems are also disclosed.

The invention provides an automatic driving hazard scene detection method and system, electronic equipment and a vehicle, and relates to the technical field of automatic driving. According to the method, a multi-dimensional hazard scene detection rule system is established, so that accurate identification and response to key security events are realized. In the automatic driving operation process, the system continuously collects and compares a vehicle state signal with a preset triggering condition in real time, once any hazard scene condition is met, vehicle burying point data in a preset time window are collected and uploaded to a cloud end, and the vehicle burying point data are stored in the cloud end. Accident scene restoration, system risk assessment and fault root cause analysis are carried out based on the collected data, then a targeted safety repair scheme is generated, vehicle software is remotely upgraded in an OTA mode, closed-loop processing of safety problems and continuous optimization of the system are achieved, the safety, traceability and remote operation and maintenance capacity of the automatic driving system are effectively improved, and the safety and reliability of the automatic driving system are improved. And the running safety of the vehicle is ensured.

A system and method for optimizing the deployment of a cloud computing environment is presented. The method includes: inspecting the cloud computing environment; generating a representation of the cloud computing environment based on a result of inspecting the cloud computing environment; determining a resource cost associated with each entity of a plurality of entities represented in the generated representation of the cloud computing environment; generating an optimization action for an entity of the plurality of entities, wherein the optimization action reduces the determined resource cost; and initiating the optimization action in the cloud computing environment.

The invention provides a code retrieval method and device for AI programming and related equipment, and the method comprises the steps: constructing a double-layer embedded vector model which comprises a basic semantic coding layer for parameter freezing and a plurality of independent lightweight adaptation layers which coexist; monitoring implicit feedback data generated by interaction of a user in the target code library to extract positive and negative sample pairs, and mining difficult negative samples based on a current model to form a training sample set; when the cumulative number of the samples reaches a preset trigger threshold value, optimizing and updating at least one lightweight adaptation layer parameter by utilizing a preset comparison loss function, and applying elastic weight constraint to key parameters of the lightweight adaptation layer parameter to obtain an updated double-layer embedded vector model; and performing multi-dimensional performance evaluation on the updated model, and when an evaluation result meets a preset publishing condition, deploying the model to a code retrieval service. The problems that in the prior art, cross-domain is difficult, the adaptation cost is high, and catastrophic forgetting is likely to happen are effectively solved, and zero-labeling self-evolution of the model is achieved.

The invention provides a hot updating method, system and equipment for an automation program of semiconductor equipment and a medium. The method comprises the following steps: dynamically loading a new version script code into an isolated runtime environment created by an application domain or a class loader through a reflective API (Application Program Interface) so as to isolate new and old codes. And performing preheating initialization on the new code in an isolated environment, wherein the preheating initialization comprises execution of an initialization function and simulation of a production request test. After preheating is completed, a new request of the production environment is routed to the new version to be processed through atomic switching operation, and meanwhile, the current running version continues to complete existing in-process affairs. According to the method, shutdown is avoided through dynamic loading and isolation, stability is improved through preheating initialization, new requests and in-process transactions are considered in atomic switching, non-shutdown updating of the automatic program of the semiconductor equipment is achieved, production interruption is effectively eliminated, and the equipment utilization rate and operation and maintenance flexibility are improved.

The invention discloses a government affair knowledge graph updating and version control method and system, and belongs to the technical field of artificial intelligence and knowledge graphs, Git-based file change drives incremental updating of knowledge graphs, and the method is implemented by the following steps: change monitoring: integrating Git version management; capturing file change through double modes of timing scanning and event triggering, establishing one-to-one mapping between a file Git version and a map version, and transmitting difference contents; difference preprocessing: standardizing difference contents into an analysis unit containing positions, types, original contents and updated contents, and filtering meaningless differences through a G-NF algorithm; constructing a difference subgraph; carrying out difference atlas fusion; and version control: storing version archives, and providing a multi-dimensional query tracing function. According to the method, version closed-loop management is achieved, change traceability is accurate and efficient, incremental atlas updating is achieved, total processing resource consumption is reduced, meanwhile, multi-scene version switching is supported, and repeated construction and maintenance cost is reduced.

Access to log event data and corresponding source code is obtained and static code analysis is performed on the source code to produce analysis output. First vectors representing the log event data and second vectors representing the analysis output are generated. A similarity analysis is performed on the first vectors and the second vectors. A probabilistic relevance score associating a given log event with a segment of the source code is determined based on the similarity analysis. A visualization is generated for log events based on the probabilistic relevance score.

The present technology pertains to a system for generating automations based on a request / prompt from a user to a language model. The language model receives a prompt in a conversation thread with a user. When the prompt includes a request for a delayed action (i.e., an automation), the request is forwarded to an automations engine, which determines the delayed action from an action component of the request and a time at which to perform the delayed action from a temporal component of the request. A scheduling instruction based on the determined time is sent to a scheduler, which signals when the time occurs, triggering the automations engine to perform the delayed action, for example, by acting as the user's delegate and sending a delayed prompt to the language model to elicit a response and amending part or all of the resulting conversation to the conversation thread with the user.