43 results about "Core component" patented technology

The invention provides a relay core component double-layer production line AGV scheduling strategy considering task completion time and energy consumption optimization, and aims to optimize task completion time and AGV energy consumption distribution of a double-layer relay welding production line and improve the operation efficiency of an AGV in a complex production environment. The method comprises the following steps: firstly, constructing an environment model of a double-layer production line, extracting operation constraints and task requirements of an AGV, and planning an initial path by using an A * algorithm; secondly, whether dynamic or static position conflicts exist is judged according to the real-time position of the AGV, obstacle avoidance is performed in combination with an improved BUG2 algorithm and a priority strategy, and an AGV route after obstacle avoidance is updated; and finally, combined optimization is carried out on the path planning and task allocation of the AGV by using mixed integer programming in combination with an adaptive weight adjustment strategy so as to ensure that the AGV can complete the established task under the lowest energy consumption. The method can be widely applied to the fields of intelligent manufacturing, automatic logistics, unmanned warehousing and the like, the production efficiency is improved, and the operation cost is reduced.

The invention relates to the technical field of cloud native technologies and digital platforms, in particular to a method and system for deploying a hyper-converged all-in-one machine based on a console program, and the method comprises the steps: receiving a deployment instruction inputted by a user through the console program; analyzing the deployment instruction through a control unit, wherein the control unit comprises a command line analyzer, a deployment engine, a configuration manager and a log recorder core component; the command line parser parses the deployment instruction into an executable operation; the method has the beneficial effects that by providing the preset deployment template and script and the automatic deployment tool, the deployment complexity is remarkably reduced, the consistency and efficiency of the deployment process are ensured, and the deployment of the large-scale cluster becomes simple and rapid.

The invention discloses an equipment scheduling method and device, computer equipment and a readable storage medium. The method comprises the steps that equipment state parameters are defined, and actual values of the state parameters are continuously tracked and recorded; establishing a mapping relation between the part characteristics of the part and the state parameters; defining a part feature baseline of the part and an associated satisfaction rate function according to the part feature of the part; task strategies are distributed to all the tasks; according to the current state parameter of the equipment and the task demand, estimating the state parameter after the equipment executes the task; screening out the equipment of which the estimated state parameters accord with a mandatory strategy to obtain residual equipment; acquiring a satisfaction rate function according to the conformity strategy; inputting the values of the state parameters of the remaining devices into a satisfaction rate function to obtain satisfaction degree values of the devices; and sorting the satisfaction degree values of the devices, and selecting the corresponding device to execute the task according to the sequence after sorting. By means of the method, decentralized management of equipment is achieved, and the overall service life of core components is prolonged.

The invention relates to the technical field of software frameworks, and provides a system and a method based on dynamic registration and management of a heterogeneous AI function interface, the system comprises a core layer which comprises a characteristic registry and a characteristic manager, the characteristic manager is used for obtaining a characteristic definition list available in a current environment through a preset mechanism, registering the characteristic definitions in the characteristic definition list into a characteristic manager; the message rendering subsystem comprises a core message renderer, a message component registry and a message component; and the input area subsystem comprises a plug-in manager, an input area core component, an input plug-in registry, an input plug-in and a plug-in context module. According to the method, the integration process is greatly simplified, the iteration period is accelerated, the maintenance difficulty and risk are effectively reduced, changes can be flexibly adapted without redeployment, the components and the plug-ins are efficiently multiplexed and support independent evolution through standardized interfaces and registration mechanisms, and the expandability and maintainability of the system are further improved.

The invention discloses a pulse compression radar accelerator generation method based on model driving, and relates to the technical field of pulse radars, and the method comprises the steps: based on a pulse compression radar signal processing flow, splitting operators according to calculation, interconnection and storage dimensions, and extracting features; defining 9 types of core components to form a parameterized component library; the components and the connection relation are combined, and an architecture description graph in a JSON format is generated; analyzing the ADG through an architecture interpreter to obtain a standardized parameter, and generating an RTL code based on a Chisel hierarchical architecture; performing simulation verification on the RTL code, pre-estimating the resource if the simulation verification is passed, and returning an error if the simulation verification is not passed; injecting expert configuration and combining with Latin hypercube sampling to obtain an initial solution set, iteratively searching a high-quality solution cluster by a TPE algorithm, resetting a parameter boundary and then switching to an ILS algorithm, and combining with a hardware rule to obtain a global optimal solution; and repeatedly executing the steps until the end conditions that the resources reach the standard and iteration reaches the upper limit are met, and outputting the accelerator architecture with the optimal resources under the target application constraint and the complete RTL code.

The application discloses a software development package compiling method and device, and relates to the technical field of computers. A specific implementation of the method comprises the following steps: performing encapsulation based on a second programming language on a first type of module of any core component in a software development package, extracting a header file of the first type of module and a header file of a second type of module after the encapsulation to generate a common umbrella header file of the core component; and after compiling core component source code and upper layer component source code in the software development package into an executable file, processing the common umbrella header file of any core component in the software development package, so that the common umbrella header file only contains declaration content configured in advance for the core component. The implementation can effectively reduce the risk of core code leakage.

The invention discloses a few-sample fault diagnosis method based on TFCD fusing time sequence characteristics, and the method comprises the steps: inputting a preprocessing state signal into a trained fault diagnosis model, so as to enable the fault diagnosis model to carry out the fault diagnosis of a core part. The fault diagnosis model is obtained by training a plurality of training samples generated by a trained TFCD model, and the TFCD model extracts discriminative time sequence features by introducing a long short term memory network, and embeds the discriminative time sequence features as static condition vectors into a DDPM model. According to the method, the model can effectively capture the long-range dependency relationship and the dynamic mode characteristics in the vibration signal, so that the generated sample keeps good coherence in the time domain, meanwhile, the specific periodic impact mode of the fault is reserved, and the physical rationality of the generated sample is remarkably improved. Therefore, accurate diagnosis under cross-working conditions can be realized.

The application provides a condensing unit control system, relates to the technical field of refrigeration control, and comprises a multi-modal sensing module, an edge computing unit, a collaborative control hub, an execution module and a human-computer interaction module. The multi-modal sensing module integrates invasive and non-invasive sensors, synchronously collects core component operation parameters, environmental parameters, load parameters and component surface temperature / vibration characteristics; the edge computing unit processes multi-source data and realizes early fault warning; the collaborative control hub generates a globally optimal control strategy through a pre-stored multi-dimensional benchmark model-based basic energy efficiency library and a dynamic correction module; and the execution module adjusts the core component according to a hierarchical response mechanism. The application solves the problems of single sensing, response lag, fixed energy efficiency model and non-priority adjustment of the traditional system, improves the reliability of the unit, the energy efficiency under all working conditions and the adaptability under complex working conditions, and reduces operation and maintenance costs.

This invention belongs to the field of elevator technology, specifically relating to a method, equipment, and medium for elevator fault injection based on multiphysics operating mechanisms. It establishes digital models of the main elevator platform system and the isolation door system and defines interfaces. The method includes configuring a data acquisition and control module; initializing the multiphysics parameter set; determining the fault injection point; sending a fault excitation signal to drive co-simulation; collecting state response data; evaluating the fault impact; and generating a report. This method, based on multiphysics coupling mechanisms, achieves accurate simulation of the fault evolution process, quantifies the impact of faults on elevator performance, and accurately locates fault propagation paths. Accurate modeling based on multiphysics mechanisms covers fault injection in core components, and co-simulation realistically reflects fault evolution. It quantitatively assesses the impact of faults on operational performance, clarifies propagation paths, and provides data support for elevator fault diagnosis, preventative maintenance, and control system optimization, thereby improving operational safety and reliability.

The application relates to the technical field of software development tools, and discloses a micro application generation system and method based on natural language interaction, which comprises four core components of a natural language understanding module, a requirement modeling module, a code generation engine and a deployment and integration platform; the natural language understanding module generates a structured semantic representation through intent recognition, entity extraction and context analysis; the requirement modeling module constructs a data model, a process logic and an interface prototype based on the semantic representation, and optimizes model parameters by using reinforcement learning; the code generation engine generates cross-platform code of a micro service architecture through template matching and component assembly; and the deployment and integration platform realizes seamless integration of external systems and Internet of Things devices through standardized interfaces. The application can solve the problems of traditional low-code platforms, such as dependence on visual dragging operation, rough requirement analysis and insufficient cross-field adaptation.

To provide a core member having satisfactory collapsibility and capable of suppressing gas defects in a mold to be produced, and a core member production method.SOLUTION: A casting core member 30 having a hollow structure is formed of molding sand used for molding a cast 1 and a binder cured by combining the particles of the molding sand with each other, and comprises a gas vent hole 35 connecting a hollow side and a mold formation side of a wall part 32 forming the hollow structure. Further, the gas vent hole 35 is formed such that the hollow side is located above and the mold formation side is located below during casting.SELECTED DRAWING: Figure 1

The invention discloses a core component performance prediction method, system and device based on industrial Internet of Things, and a medium, and relates to the technical field. The method is implemented through a five-platform architecture including a user platform, a service platform, a management platform, a sensing network platform and a production object platform, multi-dimensional feature vectors of core components in an operation cycle are obtained in real time, feature alignment processing is executed, a clustering algorithm is adopted to cluster performance degradation modes of the core components, and the performance degradation modes of the core components are obtained. Prototype vectors reflecting different decline stages are generated, a knowledge graph of a performance decline evolution path is established in combination with equipment working condition labels, prediction results are calculated through an inference method of the Bayesian theorem in combination with the knowledge graph, meanwhile, relation edge weights and confidence coefficients are updated according to prediction and actual results, and performance decline of core components can be accurately predicted. The prediction accuracy and reliability are improved, the service life of equipment is prolonged, the maintenance strategy is optimized, and the non-planned downtime and the maintenance cost are reduced.

The invention discloses a robot pursuit game method based on a retrieval enhancement driving large language model. Firstly, a system model is established for a multi-agent pursuit game, and four core components of retrieval enhancement generation, a reward designer, a reward analyzer and evolution iteration are defined. Then, a large-scale language model is introduced to serve as a core engine of reward design and analysis, and a vectorization knowledge base containing domain expert knowledge is constructed; then, a mechanism for autonomously generating an initial reward function is provided; and finally, proposing a reinforcement learning framework which iteratively optimizes the weight, the structure and the component of the reward function by continuously evaluating the interaction performance of the intelligent agent in the environment and generating a feedback instruction until the reward function is converged to the optimal form. According to the method, a complex collaborative strategy can be autonomously found and designed, the learning efficiency and the final performance of the agents in various pursuit scenes are remarkably improved, and finally efficient and robust multi-agent collaborative pursuit is achieved.

This invention discloses a method, apparatus, device, and medium for acquiring feature points of core components of a switchgear. The method involves acquiring the current waveform of the core component during operation; wherein the current waveform characterizes the mechanical properties of the core component, and is determined based on a set of current sampling points, each including the sampling point location and its corresponding current value; smoothing the current waveform using a preset sampling algorithm to obtain a target curve; wherein the target curve is determined based on a set of current sampling points, each including the sampling point location and its corresponding sampling current value; and acquiring the set of feature points of the target curve using a Flink engine. This method effectively acquires representative feature points, enabling accurate characterization of the switch's mechanical properties and achieving the beneficial effect of comprehensively sensing the status of the core component of the switchgear.

The invention relates to the technical field of format conversion, in particular to a DWG-to-DXF automatic conversion method capable of reserving layer and component attributes, which comprises the following steps of: analyzing a DWG file to obtain layer structure data and component attribute data, identifying a key layer in the layer structure data and a core component in the component attribute data, and obtaining a key layer set and a core component set; determining a subordinate layer associated with the key layer and an affiliated component associated with the core component to obtain a subordinate layer set and an affiliated component set; and if the key layers and the subordinate layers form a layer nesting relationship, taking other associations except the associations among the key layers in the nesting relationship as candidate layer associations. According to the method, the key layers and the core components are accurately identified, the subordinate layers and the auxiliary components which are associated with the key layers and the core components are processed, it is ensured that the most important attributes and structural data are reserved in the conversion process, information of original design is reserved, and the accuracy of the attributes of the layers and the components is improved.

The application provides a pantograph fault prediction method, system, medium, equipment and program product, relates to the field of data monitoring, and comprises the following steps: acquiring a pantograph dynamics model; taking key parameters in the pantograph dynamics model as design variables, taking pantograph-catenary contact force as a target value, calling a parameter inversion calculation process to iteratively calculate a pantograph key parameter combination satisfying the target value; determining corresponding pantograph core components based on the pantograph key parameter combination; constructing equivalent proxy models corresponding to the pantograph core components; and determining the fault probability of each pantograph core component according to the stress distribution output by the equivalent proxy models corresponding to all the pantograph core components. The application can realize fine analysis of the pantograph core components, and then simulate the stress distribution of the pantograph core components in actual operation, thereby providing a direct basis for the evaluation of the fault probability.

The integration software toolkit has four core components: “Control Cube”, “Discovery”, “Qualifier”&“Resolution”. The “Control Cube” core component primary capabilities are to transmit functional instructions and serve as gateways to interact with backend and frontend system functions. The “Discovery” core component correct data stores sensitive data elements. The “Qualifier” core component retains real-time sensitive data compliance policies, which will be used to determine whether an offshore, nearshore or onshore production support engineer can or cannot see sensitive data at the database field-level. The “Resolution” Reactive-AI core component main capability is to perform sensitive data error resolutions with no human involvements if offshore or nearshore support engineers are denied access. The integration software toolkit can be implemented at any industry (i.e., healthcare, finance, banks, retail & airline), which are storing sensitive data for daily operations; and seamlessly configurable to align with their specific use cases and data compliance requirements.

The invention discloses a Direct3D 12 descriptor compatible method based on address homologous mapping, which comprises the following steps of: when VKEXTpush descriptor is not supported, initializing core components such as first associated information, various mapping tables, layout fingerprint hash tables and the like, and requesting a kernel to create a double-space virtual descriptor heap of CPU (Central Processing Unit) and GPU (Graphics Processing Unit) address homologous mapping; when a D3D12 descriptor heap is created by application, mapping descriptor types by VKD3D, verifying the step length of a handle of the virtual descriptor heap, and applying for a virtual descriptor heap partition; when creating a root signature, converting a root parameter into a Vulkan pipeline layout, generating a layout fingerprint, and multiplexing or newly creating a descriptor set sub-pool; when the root parameters are bound, analyzing different types of root parameters to generate a descriptor push packet; when drawing and scheduling are executed, a descriptor set is obtained or distributed from the sub-pool, the descriptor set is updated and translated into a Vulkan command, and the Vulkan command is submitted to a GPU to complete rendering.

This invention relates to the field of printed circuit board (PCB) manufacturing equipment and process technology, specifically disclosing a material traceability and error-proofing binding method and system for PCB assembly. The invention first establishes a motherboard lifecycle traceability file, collecting and comparing the brand attribute information of core components to ensure correct material assembly. Then, using multi-stage torque assembly equipment, torque data is collected in real time and stress state assessment characteristics are generated to warn of potential assembly defects. Next, stress release and functional testing are performed using a timing-based opening and closing test fixture, dynamically adjusting the testing strategy to improve detection accuracy. Finally, based on quality interception judgment instructions, bidirectional barcode scanning and binding are executed at the final station, associating the system manufacturing information code with the media access control address code to generate unique and tamper-proof, full-chain error-proofing traceability closed-loop data. This invention improves the accuracy of material management, assembly quality, and product traceability in the PCB assembly process, providing an innovative solution for the electronics assembly field.

The embodiment of the invention discloses a method for processing equipment state information before starting of an extruder and a related device, the method is applied to a server in an extruder control system, the method comprises the steps that a production task list is acquired, target product information in the production task list is analyzed, and the target product information comprises a formula identifier; acquiring initial process parameters of the target product according to the formula identifier of the target product information; obtaining working parameters, detected by the sensor cluster, of the core part of the target extruder; determining a pre-starting state of a core component of the target extruder according to the working parameters; the initial process parameters are adaptively adjusted according to the pre-starting state of the core component; and controlling the target extruder to start up according to the adjusted initial process parameters. Therefore, systematized management and control of the startup process information of the extruder are realized, the information processing efficiency is improved, the comprehensiveness and accuracy of equipment state information acquisition before startup are improved, and the service life of vulnerable parts in the extruder is prolonged.

This application discloses a static program analysis method for the Spring Boot framework, including constructing an annotation model and a configuration property model; the annotation model is configured to parse annotations; the configuration property model is configured to provide a unified hierarchical key-value representation structure for configuration files of different formats; establishing a mapping set between annotations and configuration files and pointer analysis side effect handling logic; generating an intermediate representation in the form of a corresponding three-address code based on the target program to be analyzed; performing pointer analysis based on framework characteristics on the intermediate representation, and triggering the corresponding side effect handling logic according to the mapping set, completing missing pointer edges on the pointer flow graph, and completing implicit call relationships on the method call graph; obtaining a complete pointer flow graph and method call graph; and performing static program analysis based on the complete pointer flow graph and method call graph. This solves the problem of missing core components in static analysis due to auto-configuration in Spring Boot applications.

The application discloses a kind of continuous casting crystallizer vibration equipment remote monitoring and diagnosis system, including perception module, transmission module, storage module, service module and presentation module;The transmission module is used for the conversion of communication protocol between perception module and storage module, and the transmission module is communicated and integrated with different data collectors in perception module, and finally communicates with storage module with unified communication protocol.The application can predict equipment failure in advance by collecting the operating characteristic data of crystallizer vibration equipment, and reduce the occurrence of operating accidents;Through the prediction of the crystallizer vibration equipment failure, the system can optimize the point inspection and repair plan, make the maintenance cycle more reasonable, and reduce the labor intensity of maintenance personnel;Through the automatic evaluation of the design life, operation length, damage probability and operating characteristic data of continuous casting crystallizer vibration equipment, the life prediction of core components can be effectively carried out, the number of equipment spare parts is reduced, and the utilization rate of spare parts is improved.

This invention provides a method and system for inferring latent device faults by combining multi-source IoT data. First, it acquires a multi-source IoT data set containing operating parameters of core components of smart home devices, ambient environmental perception data, and data on interactions between components. Next, it extracts temporal evolution imprints from the data set to generate a multi-dimensional data evolution imprint set. Then, it constructs an initial structure for a fault tracing inference tree based on the device's physical operating logic. Next, it dynamically matches the multi-dimensional data evolution imprint set with the nodes of the initial inference tree structure, adjusting the branch growth direction and node weights to generate an adaptive fault tracing inference tree. Finally, it mines the latent correlation logic between imprints and nodes based on this inference tree to generate a conclusion on the latent device fault tracing. This invention can comprehensively and accurately infer latent faults in smart home devices, improving system operational stability.

An apparatus comprises a processing device configured to provide telemetry data from a core component of a scalable embedded application deployed on an information technology asset to a support platform, the telemetry data being utilizable for determining a current state of the information technology asset. The processing device is also configured to receive and parse, at the core component of the scalable embedded application, a component definition data structure to identify a subset of additional components to be run on the scalable embedded application based at least in part on a predicted future state of the information technology asset. The processing device is further configured to dynamically adjust which of the additional components are run as part of the scalable embedded application based at least in part on the parsing of the component definition data structure.

This application discloses an aging test method, apparatus, terminal, storage medium, and product, belonging to the field of software technology. The method is executed by a terminal, which includes a first system and a second system. The method includes: the first system sending a test command to the second system, the test command instructing the second system to perform aging tests on some or all of the core components included in the second system; the second system entering a test mode based on the test command, and performing aging tests on some or all of the core components in the test mode to obtain aging test results; and the second system exiting the test mode. In some embodiments of this application, for a dual-system terminal, the first system of the terminal instructs the second system of the terminal to enter a test mode to perform aging tests on the core components. Since performing aging tests on core components in test mode is more efficient, the aging test method provided in this application can improve the efficiency of aging tests.

Example methods and computer programs for performing drag and drop (DND) operations are disclosed. The method includes detecting, at an adapter component of a DND library, commencement of a DND operation on a selected draggable entity in a web browser, and communicating, by the adapter component, a message to a core component of the DND library indicating the commencement of the DND operation and including information about the selected draggable entity. The method further includes activating, by the core component, API event listeners to detect API events fired by a native DND API of the web browser, detecting the API events, and computing drop targets in the web browser for the selected draggable entity. The method further includes firing, by the core component, core events, detecting, by the adapter component, the core events, and performing operations by the adapter component, in response to the core events.

The invention provides a pantograph fault prediction method and system, a medium, equipment and a program product, and relates to the field of data monitoring, and the method comprises the steps: obtaining a pantograph kinetic model; key parameters in the pantograph kinetic model are used as design variables, pantograph net contact force is used as a target value, and a parameter inversion calculation process is called to iteratively calculate a pantograph key parameter combination meeting a target value; determining a corresponding pantograph core component based on the pantograph key parameter combination; constructing an equivalent agent model corresponding to the pantograph core component; and determining the fault probability of each pantograph core component according to the stress distribution condition output by the equivalent agent model corresponding to all the pantograph core components. According to the invention, refined analysis of the pantograph core component can be realized, and then the stress distribution condition of the pantograph core component in actual operation is simulated, so that a direct basis is provided for fault probability evaluation.

The application discloses a heterogeneous computing resource configuration optimization method for deep reinforcement learning model training, constructs a heterogeneous graph to describe the characteristics and data flow relationship of the core components of the model-based Muzero series algorithm, maps the characteristics of nodes and edges to the same hidden space based on a multi-head attention node-edge encoder, predicts the algorithm training time under different configurations by using the summarized global characteristics, and then optimizes the configuration by using the simulated annealing algorithm according to the predicted training time output by the model. CPU resources, GPU resources and memory resources are allocated according to the optimized configuration to perform a training task. According to the training result, a resource balance factor is calculated to evaluate the resource configuration scheme and continuously monitor and optimize. The method realizes the best balance between performance and resource consumption in the model training process, significantly improves the efficiency and effect of model training, and reduces the technical difficulty of resource configuration optimization in a complex scene.

This invention specifically relates to a system for predicting the remaining lifespan of core components of charging piles based on big data. This system, based on big data, comprehensively collects multi-dimensional data related to the lifespan of core components during the charging pile's operation at the data acquisition layer. The data preprocessing layer cleans, integrates, and performs feature engineering on the collected raw data. The model training layer trains a prediction model for the remaining lifespan of core components and optimizes the model parameters. The prediction application layer deploys the trained model to the charging pile operation and maintenance management platform, enabling real-time prediction of remaining lifespan and providing operation and maintenance guidance. This system, based on big data, achieves quantitative prediction of the remaining lifespan of core components, rather than just providing fault warnings. It can adapt to charging pile equipment with different operating environments and usage frequencies, reducing operation and maintenance costs, minimizing charging pile downtime, and improving the quality of charging services.

The invention discloses a heat dissipation optimization method for a server by a dynamic fan control technology based on partition management, which comprises the following steps of: acquiring temperature data of each partition in a system through a BMC (Baseboard Management Controller), inputting the data into an algorithm module, and comparing the deviation between the real-time temperature and the set target temperature by the algorithm module to obtain the heat dissipation of the server. The appropriate values for adjusting the fan speed and the air inlet amount are calculated, and when the temperature of the core component exceeds the set threshold value, the algorithm indicates the BMC to increase the air inlet amount of the fan in front of the core component area so as to improve air flow, and therefore the cooling effect is enhanced. Through the dynamic adjustment, the system can automatically optimize the working state of the fan according to the temperature change of the core component, and ensures that the heat dissipation capability of each partition is effectively improved, so that the overall temperature of the server is kept within a constant range.