11 results about "Software work" patented technology

Systems and methods are described that allow for updating of software applications during testing of the application to detect errors as a result of execution of portions of the software application that prevent downstream portions of the software application from being evaluated. In an example, systems are described that are configured to detect errors during application execution. When an error is detected, the system obtains and executes specific script sets to debug the application. Based on the results of these debug operations, the system generates error reports that indicate issues within the software workflow. This ensures that errors in one part of the software do not hinder the evaluation of subsequent parts, allowing for a more efficient and thorough testing process.

Systems, methods, and computer-readable media for generating a contextual tutorial for a software workspace within a Software as a Service (SaaS) platform. Systems, methods, devices, and non-transitory computer-readable media may involve at least one processor configured to: access within the SaaS platform a customized software workspace including a plurality of software components therein; analyze the customized software workspace to determine a functional context of the plurality of software components; generate, using a generative artificial intelligence (AI) model, a contextual tutorial based on the determined functional context, wherein the tutorial includes a sequential narrative explaining the function and interrelationship of each of the plurality of software components within the customized software workspace; and output for presentation the generated contextual tutorial to a SaaS platform user via a user interface.

A human-led multi-surface AI workflow system includes a simulation domain for AI modelling, a distinct human-operated execution domain, and a mental-model engine that mimics human reasoning and authenticity evaluation. The AI is confined to a sandboxed surface where it proposes actions, visual guidance, predictions, warnings and authenticity judgments, but cannot directly trigger operations in the execution domain. A non-permeable control barrier ensures only human‐authorized execution of approved actions. The system spans multiple screens, mirrored or distributed surfaces, AR / VR overlays, projections, vehicle displays, kiosks, mobile devices and remote terminals. By enforcing human-led execution, transparent and auditable AI reasoning, and high-precision guidance across automotive, medical, industrial, administrative, diagnostic, authenticity and software workflows, the architecture ensures safety, compliance and prevents uncontrolled autonomous machine operation.

Various embodiments relate to a computer-implemented method involving a processor system for brokering dynamic workload-based virtual private network (VPN) connections. The method includes validating a first endpoint, which comprises a software workload executing on a remote computer system, by at least verifying a cryptographic credential associated with the software workload. Upon successful validation, the method initiates the establishment of a VPN connection between the first endpoint and a second endpoint. Following the establishment of the VPN connection, the method further initiates the destruction of the VPN connection between the first and second endpoints. This approach ensures secure and controlled VPN connectivity by validating software workloads and managing the lifecycle of VPN connections.

According to one implementation of the present disclosure, a method includes: receiving, by a hardware design generation circuit, a plurality of input signals of a software workload on a processing unit; training a power prediction model based on a toggling of the input signals accumulated over a training interval range; determining, by the hardware design generation circuit, a plurality of prediction proxies and respective weightings for the plurality of prediction proxies based at least partially on the trained power prediction model, wherein the plurality of weighted prediction proxies correspond to a power output of the hardware design generation circuit; and generating an updated circuit design of the processing unit based on the power output.

The invention relates to a multi-signal system fused DMI display method and system, DMI equipment is respectively connected with vehicle-mounted hosts of various signal systems, and DMI software and middleware software corresponding to the signal systems are installed on the DMI equipment. Firstly, middleware software is started to monitor a connection request; the DMI software is started in sequence, the DMI software is actively connected with the middleware software after being started, and a hardware control request is sent to the middleware software to set the initial state of the peripheral; the DMI software periodically obtains the control right state of the vehicle-mounted system from the vehicle-mounted host of the corresponding signal system, and if the vehicle-mounted system has the control right, a control right request is sent to the middleware software; and the middleware software judges the ownership of the control right based on the control right request, and executes a display switching operation when the control right changes. Compared with the prior art, the method has the advantages that the DMI hardware is shared by various signal systems, and meanwhile, various DMI software works independently, so that the flexibility of engineering application is ensured, and the influence between systems is avoided.

The invention discloses a fast decoding method suitable for multi-component Can bus data, which can be used for fast data analysis of a weapon system which is complex in composition and adopts Can bus communication during semi-physical simulation, system joint test and external field test, and the data analysis efficiency is improved. The decoding method has the characteristics of simplicity in use, rapidness in decoding, convenience in analysis and the like. The design key points of the decoding method comprise the following steps of: setting a Can bus data decoding protocol (which is required when software is only used for the first time or a communication protocol is changed); setting a path where the Can bus file to be decoded is located; parameters such as data formats needing to be stored are set; reading a Can bus file which needs to be decoded; all the components are decoded in sequence, decoded data are stored, and a decoding software workflow chart is shown in a figure 1 (not containing Can bus data decoding protocol setting).

This invention discloses a BIM-based method for automatically updating the lightweight model of a gravity energy storage device. The method involves creating a cloud disk project folder for the gravity energy storage project on an enterprise cloud server, copying the software working environment within the cloud disk, configuring a storage folder for the BIM design files of the gravity energy storage project, loading the lightweight software to generate a lightweight model link file, saving the lightweight model file as a link file, pointing the working environment of the design software on the working computer to the software working environment on the enterprise cloud server, automatically copying and updating the BIM design files and the lightweight model file, saving the NWF link file as an NWD format file, and opening the NWD format model data file of the gravity energy storage project to complete the automatic update of the lightweight model file. This invention achieves automatic updating of the lightweight model of the BIM design files for gravity energy storage projects, significantly improving the efficiency of lightweight model updating for BIM design files of gravity energy storage projects.

The embodiment of the invention provides a CC2 signal awakening and online power-off control circuit and a control method, and belongs to the technical field of electric vehicle charging control. According to the control circuit and the control method, automatic power-off can be completed under the condition that the charging gun is not pulled out after the whole circuit is awakened by arranging the CC2 awakening circuit, the power-off power consumption and the awakening cost when the vehicle stands are greatly reduced, meanwhile, after power-off, the MCU does not need to work in an electrified dormancy mode, the software workload is reduced, and the system reliability is improved.

A system for effort estimation in agile software development using multi-stage evolutionary stacking, consisting of: a data acquisition module configured to retrieve software effort records from one or more data set repositories containing historical data from software development projects with characteristics and actual effort values; a data preprocessing module that is operationally connected to the data acquisition module and is configured to receive the aforementioned software effort data sets from the data acquisition module, cleans the received data by removing inconsistencies with missing target values, and normalizes numerical input characteristics to a common range; a first-level ensemble module connected to the data preprocessing module, wherein the first-level ensemble module comprises a variety of heterogeneous basic learners, including a Random Forest model, a Support Vector Regression model, and an Extreme Gradient Boosting model, which generate predictions from each of the heterogeneous basic learners using the preprocessed data sets received from the data preprocessing module; A genetic algorithm optimization module connected to the first layer's ensemble module, configured to: encode weights as a normalized real-valued vector for each of the heterogeneous base learners; apply a fitness function to minimize the mean squared validation error and derive an optimal weight vector; assign optimized weights to the predictions of each of the heterogeneous base learners; and generate weighted predictions based on the optimized weights. a second-level meta-learning module connected to the optimization module of the genetic algorithm, configured to receive the weighted predictions from the optimization module of the genetic algorithm, processes the weighted predictions using a deep multilayer perceptron neural network to learn complex patterns and nonlinear interactions, and generates a final effort estimate for the software; an output processing module connected to the second-level meta-learning module, configured to receive the final effort estimate for the software and process and visualize the data to improve user understanding; and a user interface connected to the output processing module to receive the processed final effort estimate for the software, wherein the user interface is configured to display the processed and visualized final effort estimate for the software.

The application belongs to the technical field of software interface interaction, and provides a two-dimensional CAD software interface interaction method based on customized event distribution, which completely retains the general keyboard operation commands of the CAD software which have been conventionally accepted in the industry, so that the user can input commands according to the existing habits and quickly work. Through the redesign of the structure process of each function, the interface options are constant during the use of each function and are tiled on the interface, so that the user can quickly understand all the capabilities of the function. When performing specific operations, the function options can be directly triggered from the interface, which simplifies the triggering steps of the function options. At each step, the user can select keyboard commands or button clicks according to his own ability level and existing habits to promote the process, thereby solving the problem that the existing CAD software cannot coexist in high efficiency and easy learning for beginners, and improving the efficiency of the original operation mode to a certain extent.