1080 results about "Operating environment" patented technology

The invention provides a business process construction method combining a workflow engine and a dynamic form, which relates to the technical field of low-code platforms, and comprises the following steps of: establishing a data table according to a business, selecting and importing the data table in a code generation module, generating a basic code package, re-operating a main program compiling code package and loading the code package to a JVM (Java Virtual Machine) operating environment; creating a dynamic form on the basis of the entity class information of the code package through a visual form designer of the dynamic form module, and outputting dynamic form configuration in a JSON (JavaScript Object Notation) format; according to the method, the dynamic form and the process engine are combined, highly flexible business process design can be realized, a user can dynamically design the form and the process model as required to meet different business requirements, and the user-defined component can be designed according to a business data structure and common functions, so that the form can better adapt to a specific business scene, and the user experience is improved. And the user experience and the working efficiency are improved.

The invention discloses a reinforcement learning-based virtual power plant response optimization scheduling system and method, and relates to the technical field of virtual power plant intelligent scheduling. The system comprises an environment modeling module, an intelligent agent module, a multi-agent coordination module and a self-adaptive optimization module which are respectively used for constructing a multi-dimensional state space and a layered action space, generating and optimizing an action strategy based on an Actor-Critic network, executing a scheduling instruction through a layered multi-agent structure and realizing conflict consensus, and dynamically adapting to state space change in combination with incremental learning and meta-learning mechanisms. The system and the method have the advantages of fine state modeling, efficient action response, adaptive strategy updating, stable agent coordination and the like, and can keep the continuity, the stability and the optimality of a scheduling strategy in an operation environment in which multi-source heterogeneous power resources participate in scheduling cooperatively, market rules change frequently and load fluctuation is violent.

The invention discloses a multi-device cooperative management system based on cloud edge cooperation, and relates to the technical field of Internet of Things communication, and the system comprises a device management module which is used for connecting edge devices, carrying out the centralized management of the edge devices, and carrying out the optimal scheduling based on the functions and distribution positions of different edge devices; and the data acquisition module is connected with the equipment management module. According to the multi-device collaborative management system based on cloud edge collaboration, a comprehensive and accurate data basis is provided for multi-device collaborative management, and information exchange and collaborative work between devices are promoted; the optimization decision-making unit can formulate an equipment optimization management strategy according to the operation scheme generated by the big data analysis unit and the prediction result obtained by the prediction analysis unit, and continuously performs optimization adjustment according to the real-time operation condition of each piece of edge equipment; therefore, the system can adapt to continuously changing equipment operation environments and task requirements, and more efficient multi-equipment collaborative management is realized.

The invention discloses an edge computing node dynamic load balancing method based on a multi-agent system, and relates to the technical field of node load balancing. According to the edge computing node dynamic load balancing method based on the multi-agent system, the current computing power communication data and the operation environment data of each edge computing node are collected, the operation load index of each node is analyzed, and whether an overload node exists or not is judged; if an overload node exists, a plurality of nodes adjacent to the overload node are obtained, a task migration candidate set is screened out, finally, a target node is determined from the candidate set, and task migration operation is executed; the operation load index of the edge computing node is dynamically quantified by collecting and comprehensively analyzing multiple parameters such as the CPU utilization rate, the memory occupancy rate, the communication delay, the temperature rise rate, the I / O blocking rate, the power consumption fluctuation rate, the cache write-in waiting time and the vibration disturbance amplitude in real time. Compared with a traditional single index or fixed threshold mode, the method can identify the node overload state more accurately and timely.

The invention relates to the technical field of artificial intelligence and the field of financial science and technology, and discloses an SQL (Structured Query Language) cross-database conversion method, which comprises the following steps of: acquiring SQL data from a plurality of database systems, performing vectorization processing, storing the SQL data into an SQL vector database, and performing fine tuning training on a preset model by utilizing the data to obtain an SQL conversion model. And receiving a to-be-converted SQL statement and a target database type, analyzing the to-be-converted SQL statement through the SQL conversion model, converting the to-be-converted SQL statement into a natural language description, and generating a target SQL statement corresponding to the target database type by using the natural language description. Through the automatic SQL conversion process, the dependence on manual intervention is reduced, the accuracy and efficiency of cross-database SQL statement conversion are remarkably improved, the method is suitable for a complex multi-database operation environment, and the consistency and reliability of data processing are ensured.

Disclosed in the present invention is a fully automated log analysis and fault handling system and method based on an NLP large model. The method comprises: collecting a service state, an operating environment, and log files; extracting and generating log summaries from the log files, extracting key information from the log summaries to obtain state information of a service process; if an error is fed back, the NLP large model performing fault analysis on the error by using the service state and the operating environment as prior knowledge, and recording upstream and downstream key information involved in the error; on the basis of the upstream and downstream key information involved in the error, finding a fault cause and providing a fault handling action; and then executing a plan on the basis of the fault handling action. Log collection of the present invention is not limited to a prescribed format, and an NLP large model using a service state and an operating environment as prior knowledge is introduced for fault analysis.

Systems and methods for mathematical modeling of the hardware and software stack of commodity computer platforms are provided, enabling provable guarantees on memory, device, and program execution. This approach addresses the technical problem of reliance on system agents that rely on implicit trust in the operating environment, which can be exploited by sophisticated attackers using complex threats such as memory access exploits and code / data integrity exploits. The solution provides a proactive, mathematically-backed security solution that eliminates entire classes of cyberattacks by design, ensuring realizable guarantees on commodity computer platforms running hardware and software stack elements at the lowest operating level. This approach has significant advantages over current reactive cybersecurity methods, including reduced complexity and overhead, and increased confidence in the integrity of the system. The solution's main uses include providing mathematically-backed security and availability guarantees for critical infrastructure, financial institutions, and other organizations vulnerable to cyberattacks.

The invention discloses an operating system health assessment method and system based on multi-dimensional index dynamic weighting, and the method comprises the steps: collecting the state of each hardware module, calculating the index, carrying out the weighted summation to obtain the score of the hardware module, adjusting the weight parameter of each hardware module through combining with the real-time load characteristic or business priority, and carrying out the real-time evaluation of the health of each hardware module. Performing weighted summation on the scores of the hardware modules to obtain a hardware health score; software state information in the operating system is collected, scores of all software modules are calculated, the software modules comprise key components, key services and key processes, and the scores of all the software modules are weighted and summed to obtain software health scores; and performing weighted summation on the hardware health score and the software health score to obtain a health score of the operating system so as to determine the health state of the operating system. The invention aims to solve the systematic defect in the existing health monitoring technology system, improve the comprehensive evaluation capability of the health evaluation of the operating system, and dynamically adapt to the change rule of the actual operating environment.

The invention provides a security verification method for a large model service operation environment, a medium, equipment and a product, and relates to the technical field of computers, large model services are deployed in a container group created in a virtual machine, and the virtual machine is deployed in a hardware-based trusted execution environment, so that the security verification of the large model service operation environment is realized. The method comprises the following steps: acquiring environment measurement corresponding to a running environment of a large model service through a remote attestation service deployed in a hardware-based trusted execution environment, the environment measurement at least comprising a first measurement corresponding to the trusted execution environment in which a virtual machine is located and a container measurement under a container stack dimension corresponding to a container group; the remote attestation service generates a remote attestation report according to the environmental metrics to enable a user to validate the security of the operating environment through the remote attestation report. According to the invention, not only can the attack from the IaaS layer be shielded, but also the credibility verification under the dimension of the virtual machine and the credibility verification under the dimension of the container stack can be provided, so that the credibility of the user to the service provided by the service provider is improved.

A method for displaying an operating environment of a robot. The method includes determining an object type of each of one or more objects in the operating environment of the robot based on sensory information acquired by the robot; constructing a three-dimensional (3D) virtual environment of the operating environment based at least in part on the object type of each of the one or more objects; and displaying the 3D virtual environment of the operating environment via a display interface.

The invention provides a visualization system capable of synchronously displaying multiple types of data according to time. Comprising a distributed service cluster composed of a micro-service, a user management micro-service, a data transmission micro-service, a data storage management micro-service, a data extraction micro-service, a data visualization display micro-service, a data visualization template management micro-service and a resource monitoring micro-service; the micro-services are packaged in a containerized manner and interact by adopting a lightweight communication protocol, and automatic deployment and dynamic expansion are realized through a container arrangement tool; the system is provided with a basic parameter set with customizable micro-service configuration and a graphical configuration interface which are used for cooperatively generating a system operation environment configuration strategy. The user management micro-service establishes a multi-dimensional authority system based on a role access control model, and records an operation log to realize audit tracking; the data transmission micro-service integrates a plurality of heterogeneous data access modes, carries out transmission process verification and carries out secure channel transmission through the service gateway.

System and methods are disclosed for providing unforgeable telemetry on computer platforms. Mathematical modeling and theorem proving are utilized to guarantee the integrity of telemetry probe execution flow and trigger, thereby preventing circumvention and tampering of logged probe data. In contrast to current state-of-the-art solutions that rely implicitly on the operating environment, this approach provides a sound and complete assurance of telemetry output. The system enables organizations to map unforgeable telemetry probe data to industry and government cybersecurity regulatory controls, ensuring compliance therewith. This invention addresses the shortcomings of existing solutions, including their vulnerability to sophisticated attacks, operational complexity, and inability to provide unforgeable telemetry data, thereby providing a reliable and accurate monitoring output in the presence of cyberattacks on computer platforms.

The invention discloses a dynamic module operation management system based on a programming language, and the system comprises a module analyzer which manages the dependency relationship between modules in the system, applies a compiling optimization technology before the modules are loaded, and optimizes the code execution through code expansion and constant folding; operation environment establishment: optimizing execution speed and resource scheduling by integrating a just-in-time compiling JIT technology and an optimization measure during operation; the module warehouse is used for controlling the retrieval and loading process of the module by adopting an optimized organization and index strategy; data structure optimization: using a skip list to adjust data structure design, and controlling data processing efficiency; dynamic optimization: the system dynamically adjusts an optimization strategy according to performance feedback and an operation environment, and controls improvement of self-adaptive performance; garbage collection: dynamically adjusting a garbage collection strategy by adopting a self-adaptive garbage collection algorithm, and reducing the module retrieval time, accelerating the module loading process and improving the data access efficiency.

An apparatus for virtual integration environments, the apparatus including computing device configured to receive a software package for deployment, determine one or more recipients of the software package, select one or more operating environments as a function of the software package and the one or more recipients, wherein each operating environment of the one or more operating environments is a virtual representation of a system associated with each recipient of the one or more recipients, execute the software package within the one or more operating environments, generate performance data for each operating environment of the one or more operating environments, compare each performance data of the one or more performance data to one or more performance thresholds and graphically display at least the one or more performance data through a graphical user interface.

The invention discloses a data-driven floating type wind turbine generator model-free control method and system, and the method comprises the steps: obtaining the operation state and external environment parameters of a wind turbine generator, and constructing sample data; inputting the sample data into a pre-trained controller, and outputting a control instruction to control the wind turbine generator; the specific process of obtaining the controller comprises the steps that the simulation operation state / historical operation state and external environment parameters of the wind turbine generator are obtained, and an initial controller is constructed based on the simulation operation state / historical operation state and the external environment parameters; determining a control target, a wind turbine generator operating environment, an intelligent agent, a state space, an action space, a constraint condition and a reward function in the initial controller; and performing continuous interactive learning training on the intelligent agent and the operation environment of the wind turbine generator through a deep reinforcement learning algorithm to obtain a trained controller. The method has the advantages of reducing the modeling difficulty, dynamically correcting the control strategy, improving the power generation efficiency and the like.

The invention discloses a service node deployment method, system and device, equipment and a storage medium, and can improve the deployment and debugging efficiency of service nodes. The method comprises the following steps: acquiring MCP Server configuration information and resource configuration information of service resources; generating deployment parameters used for operating the MCP Server and the service resources; sending the deployment parameters to a target server, so that the target server deploys an operation environment corresponding to the MCP Server based on the deployment parameters; in response to a debugging instruction for the service resources, performing debugging operation based on the running environment to obtain a debugging result; under the condition that the debugging result indicates that the service resource meets the preset operation condition, a service node construction request is sent to a target server, so that the target server constructs an MCP Server node in a to-be-called state, and node metadata of the MCP Server node is generated; and receiving the node metadata sent by the target server, and registering the node metadata in the MCP Server management platform.

The invention relates to an ultra-high-speed large-flow Ethernet data generation system and method. The system comprises a software unit and an FPGA unit. The software unit is used for generating stream template data and issuing the stream template data to the FPGA unit; the stream template data comprises stream template configuration items and dynamic configuration parameters; the flow template configuration item comprises protocol header information, scheduling configuration information, frame length configuration information and load configuration information; the dynamic configuration parameters comprise dynamic jump configuration information, routing table configuration information and the like; and the FPGA unit is used for storing, reading and calculating the issued stream template data, realizing intra-frame jump value calculation, message and IP head checksum generation, Ethernet frame construction and transmission. Through combination of software and hardware methods, the demand quantity of hardware resources can be reduced, the consumption of logic resources of the FPGA unit can be reduced, the phenomena of high and low speed operation environments, network congestion and the like in the existing network can be simulated, and diversified simulation environments can be provided.

The invention belongs to the field of chip power consumption detection, relates to a data analysis technology, is used for solving the problem that optimization decision analysis cannot be carried out according to test data when the power consumption of a chip is abnormal in the prior art, and particularly relates to a chip power consumption evaluation method and system based on artificial intelligence and a storage medium. Comprising a static test module, a static analysis module, a dynamic test module, a dynamic analysis module and a database, the static test module is used for performing static test on the chip power consumption; the static analysis module is used for analyzing a static test result of the chip and marking a static test object i as a static normal object or a static abnormal object; according to the method, the basic power consumption performance of the chip in the static operation environment can be tested, then the static test result is subjected to data processing to obtain the static coefficient, then the static test object is subjected to differential marking according to the static coefficient, and data support is provided for the performance evaluation and analysis process.

The invention discloses a performance testing method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of performance testing, and the method comprises the steps: generating a target load of a current cycle according to the cycle sequence of the current cycle through a constructed load generation model; or, the target load of the current period is obtained through the PID controller according to the target load and the actual load of each historical period, the target load of each period can be dynamically generated, and the dynamic target load better fits the load of the real operation environment, so that the load of the current period can be dynamically generated. The problem that the stability of the server for deploying the AI accelerator cannot be effectively evaluated when the load with the highest power consumption is used for testing can be solved, and the technical effect that the stability of each performance index can be truly, effectively and accurately evaluated is achieved. In addition, the target test task is generated through the load of the period, the execution process of accelerated calculation of the target test task is relatively close to the real operation condition, and the obtained multiple performance indexes are relatively close to the real operation result.

The invention discloses a prospective preprocessing method and system in six-axis mechanical motion automatic control, particularly relates to the field of six-axis mechanical motion automatic control, and is used for solving the problem of insufficient track prediction and control precision in a load sudden change scene. The inertia tensor is updated in real time in combination with dual-channel Kalman filtering, dynamic inertia data are accessed, and the sensitivity of trajectory prediction to load change is improved; double-domain feature extraction of time-domain thermal stress density and frequency-domain energy dispersion is adopted, acceleration pulse layout is dynamically optimized by using a complementary integral mechanism, and the overload risk of a driver is reduced; meanwhile, the instruction dispatcher avoids cache overflow through advanced rhythm regulation and control, and continuity of instruction streams is guaranteed; the inertia model is continuously refreshed through closed-loop feedback, and synchronous evolution of inertia change and trajectory prediction is achieved; the overload shutdown risk caused by inertia lag of six-axis mechanical motion is reduced, and the adaptive capacity of the prospective preprocessing method in a dynamic complex operation environment is enhanced.

The invention discloses a computing device parameter adjustment method, a product, a device, a server and a medium, relates to the field of servers, and aims to solve the problem that energy efficiency and delay of a computing device cannot be balanced. The method comprises the following steps: acquiring a current energy efficiency gradient map of the computing device; determining a target position point on the current energy efficiency gradient map according to the current batch processing size and the current kernel frequency of the computing device, and executing current adjustment operation on the computing device based on the energy efficiency and delay comprehensive performance value corresponding to the target position point; and in response to the condition that the new batch processing size and the new kernel frequency meet the delay and energy efficiency balance condition after the current adjustment operation is executed, controlling the computing equipment to continuously run according to the new batch processing size and the new kernel frequency. According to the method, the configuration can be automatically adjusted according to the actual operation condition of the computing equipment, different workloads and operation environments are adapted, the dynamic balance between the energy efficiency and the delay is realized, and the optimal operation state of the computing equipment between the performance and the energy efficiency is ensured.

The invention relates to an intelligent equipment function reliability verification method and device, equipment and a storage medium, and the method comprises the steps: obtaining operation environment parameters and equipment state data of intelligent equipment, and carrying out the construction evaluation of a redundancy verification architecture, and obtaining an initial verification reference; acquiring real-time operation data of the intelligent equipment, and performing function association analysis on the real-time operation data and the initial verification reference to obtain function abnormality features; acquiring state information of associated equipment of the intelligent equipment, performing cross validation on the state information of the associated equipment according to the function abnormality characteristics, and performing fault analysis on the state information of the associated equipment and the function abnormality characteristics to obtain an evolution fault injection scheme; and carrying out abnormal behavior identification on the real-time operation data to obtain an equipment repair requirement, and carrying out feasibility evaluation on the equipment repair requirement based on the function abnormal characteristics to obtain a dynamic repair strategy. According to the invention, a comprehensive guarantee mechanism can be provided for reliable operation of the intelligent equipment.

Described herein is an apparatus and method for evaluating software performance. An apparatus may include at least a processor; and a memory communicatively connected to the at least processor, wherein the memory contains instructions configuring the at least processor to receive a software package; identify a safety standard; select an operating environment as a function of the safety standard; deploy the software package on the operating environment; operate a communication channel between a user device and the operating environment, wherein operating the communication channel comprises determining a performance metric for the software package; and displaying, at a display device having a user interface, the performance metric to a user through the user interface.

The invention relates to the technical field of software code evaluation and conversion of heterogeneous multi-core chips, in particular to a code evaluation and conversion method and system applied to a heterogeneous multi-core chip and a medium. The method comprises the following steps: reading a configuration table to obtain hardware configuration information of equipment; constructing a simulation experiment environment in the corresponding configuration table; converting the software algorithm according to requirements; running a software algorithm in the simulation environment, enabling the algorithm to run on hardware with different hardware configurations through parameter setting, and obtaining technical indexes of algorithm running; analyzing and comparing performance indexes of the algorithm in different operation environments, and performing adjustment optimization and operation test on the algorithm; a software algorithm is deployed on specific hardware, and evaluation testing in an actual operation environment is carried out. According to the method, the performance of a software algorithm on a heterogeneous multi-core chip can be improved, the development cost is reduced, and the product development period is shortened.

The invention discloses a flight training digital twin system, which comprises a data acquisition module (for collecting real-time data of an aircraft and an environment to enable virtual and actual conditions to be consistent), a model and simulation module (for creating an aircraft, an operating environment and an airborne equipment simulation model), and an intelligent analysis and control module (for analyzing operation and equipment states through big data, and analyzing and analyzing the real-time data of the aircraft and the environment to enable virtual and actual conditions to be consistent). Feedback and optimization suggestions are generated), and an interaction and training module (based on real-time data scoring feedback and immersive interaction-aid operation). According to the system, states and behaviors of physical entities such as an aircraft, a pilot and an environment are mapped to a virtual space through a high-precision virtual simulation technology, and a real-time interaction and dynamic feedback simulation training platform is constructed. The system integrates multi-dimensional data, can be linked with physical equipment, and assists a pilot in improving skills and enhancing the coping ability of complex conditions in a safe and controllable virtual environment by simulating a real scene and providing a personalized scheme, intelligent decision and risk prediction.

The invention provides a PI coating equipment motion control method and system based on reinforcement learning, and relates to the technical field of coating equipment control. Firstly, a virtual simulation platform for simulating the actual operation environment of PI coating equipment is built, and a training environment is provided for reinforcement learning. And creating and initializing a Q matrix, and designing a comprehensive reward function considering the process performance parameter weight of the PI coating equipment. Then, obtaining operation data through interaction with the virtual simulation platform, and training a Q matrix based on reinforcement learning by using the data; and in the training process, dynamically adjusting the weight of the reward function by using a feedback mechanism so as to optimize the learning process until the training is completed. And finally, inputting the current state of the PI coating equipment into the trained Q matrix to obtain an optimal action, and guiding the PI coating equipment to operate. Motion control over the PI coating equipment is achieved, and the coating quality and efficiency are improved.

The invention discloses a building equipment maintenance demand linkage detection and evaluation system and method, and relates to the technical field of intelligent operation and maintenance of building equipment, and the system comprises an isomorphic equipment cluster division module, a group data flow collection module, a group behavior baseline learning module, an individual deviation degree calculation module, and a maintenance decision generation module. Equipment with consistent function attributes and operation environments is classified into the same cluster, data are classified according to different working conditions on the basis of historical operation data provided by a group data flow acquisition module, group average operation parameters, parameter fluctuation ranges and probability distribution characteristics under each working condition are calculated, a dynamic group behavior baseline model is generated, and the dynamic group behavior baseline model is established. With the continuous updating of new data, the normal behavior mode change of the equipment under different working conditions can be reflected in real time, and when the individual deviation degree calculation module calculates the individual equipment deviation degree according to the model, the accuracy and timeliness of the calculation result can be ensured.

The invention relates to an operation guiding method based on user behavior analysis. The method comprises the following steps: analyzing to obtain a user operation purpose, and classifying and recording the user operation purpose as an operation guiding template; obtaining an interface coordinate of the interaction element in the current presentation interface based on the identification number; obtaining an operation purpose of a currently logged-in user, and searching and presenting personalized operation guidance; carrying out user behavior and related data collection on the currently logged-in user; key indexes of the user completing the operation are counted, and evaluation and suggestions of the user on operation guidance are recorded. According to the method, the user operation purpose is determined through multi-dimensional analysis, the operation guide template is constructed, and highly personalized operation guide is provided for the current login user; establishing a dynamic updating mechanism of unique identification numbers and interface coordinates of the key interaction elements, and ensuring the accuracy and stability of guiding; the adaptive templates are screened according to user roles, permissions and operation environments, proper guidance and prompts are provided for users with different proficiency degrees, and the operation efficiency of the users is improved.

The invention discloses a scheduling and approval process control method and device for digital product online leasing, and the method specifically comprises the steps: carrying out the cloud native operation environment preparation, building a process control system based on a workflow engine capable of being arranged, carrying out the system initialization, and carrying out the operation of a cloud server; comprising the steps of initializing a lease process engine, an equipment resource scheduling module, an approval strategy and credit engine and a performance monitoring and state management system; and then a lease application and process control method, an equipment scheduling and conflict detection method, an examination and approval execution and process driving method and a performance monitoring and state write-back method are designed in sequence. After a user submits a leasing request, generating an approval process and distributing the approval process to an approval node; executing resource allocation and locking after the approval is passed; an event-driven mechanism is adopted to manage state circulation of all stages of leasing, and reverse-driven scheduling adjustment of approval results is supported. According to the invention, the approval efficiency of online renting of digital products is improved, the manual intervention cost is reduced, and the rationality and traceability of equipment resource configuration are guaranteed.

The invention provides a control method of building energy equipment, a server, a medium and a storage product, and relates to the field of intelligent control. The method comprises the steps of firstly obtaining operation data of energy equipment in a plurality of buildings under different environment parameter combinations, establishing an equipment optimal state prediction model according to the operation data, then obtaining operation environment parameters of target equipment by utilizing an environment sensor in a target building, and inputting the operation environment parameters into the model to obtain optimal operation parameters of the target equipment under the environment. And then obtaining the current operation parameter of the target equipment, judging whether the deviation between the current operation parameter and the optimal operation parameter exceeds a preset threshold value or not, if so, generating an equipment regulation and control instruction according to the deviation, and finally sending the instruction to the target equipment to adjust the operation parameter, thereby ensuring the efficient operation of the equipment.