12results about How to "Quick verification" patented technology

The invention belongs to the related technical field of vehicle charging scheduling, and provides a vehicle charging dynamic scheduling method and system for a charging pile fault in order to solve the problem that a charging device cannot respond in time after a fault occurs, and the method comprises the following steps: monitoring the operation state of each charging pile in the execution of an initial charging scheduling scheme in real time; identifying a target vehicle set influenced by the charging pile fault; calculating an emergency priority score of each target vehicle according to the residual electric quantity and the planned departure time of the target vehicle; constructing a dynamic resource profile of the available charging pile based on the residual available power of the available charging pile, and screening a feasible candidate pile set; and according to the vehicle priority queue, reallocating available charging piles to each target vehicle in sequence by taking the minimum cost as a target, and generating a rescheduling scheme. According to the invention, charging of the vehicle at the valley point of the electricity price can be guaranteed to the greatest extent, and the reliability, economy and adaptive capability of charging are remarkably improved.

The application relates to an FPGA prototype verification method and system of a smart meter chip, and belongs to the field of integrated circuit testing and electric power metering. The method aims to solve the problems of ASIC design transplantation to an FPGA platform, large difference between a verification environment and a real power grid, and lack of a real-time closed-loop verification mechanism. A technical scheme efficiently transplants the design to the FPGA by optimizing ASIC front-end synthesis constraints and adopting a netlist-level IP replacement strategy; a hardware-in-the-loop system is constructed, which comprises a real power grid signal source, a separation device simulation front end, an FPGA verification board, a high-precision standard electric energy meter and a host computer. The system can generate a real power grid excitation and realize synchronous collection of prototype output and the standard meter, real-time error analysis and dynamic adjustment of parameters, thereby forming a closed-loop verification. The application significantly improves code transplantation efficiency, provides a highly consistent real power grid verification environment, and greatly improves verification iteration and defect positioning speed.

The invention provides a vehicle power system suitable for lunar polar region exploration and a verification method, and relates to the technical field of deep space exploration, the system comprises a configurable energy module, a driving motor transmission mechanism, a power conversion management system, a thermal control subsystem and an autonomous driving controller; the energy module comprises a space fuel cell module, a wide-temperature lithium ion battery module, a solar cell panel module and a radioactive isotope battery module, and one or more of the modules can be selected to be combined according to task requirements. According to the invention, full-scene energy supply coverage of the monthly day, the monthly night and the permanent shadow area can be realized, uninterrupted propulsion of detection and transportation tasks is supported, and the task execution efficiency and the detection coverage range are greatly improved; meanwhile, it can be ensured that the driving power is accurately matched with the driving requirement of the complex terrain in the polar region of the moon, the driving power calculation deviation is effectively reduced, and the terrain passing capacity and operation stability of the vehicle are remarkably improved.

The application provides a local three-dimensional tile data browser loading method and system based on ServiceWorker interception, and belongs to the technical field of browser offline loading schemes of 3DTiles format tile data. The method comprises the following steps: listening to a browser page loading event, registering a ServiceWorker and waiting for an active state, performing a query operation on a CacheAPI after activation, detecting whether a three-dimensional tile set entry file cache entry exists, and generating cache detection result data; a browser security mechanism restricts direct access of a webpage to a local file, and a three-dimensional engine (such as Cesium) requests a tile through an HTTP protocol. Through the technical combination of ServiceWorker interception of an HTTP request, CacheAPI caching of a local file and virtual URL path mapping, the three-dimensional engine can access the local file through a standard HTTP request format, and the sandbox restriction is broken while no native layer bridge is introduced.

The application discloses a deep learning-based line vector map data zero watermark method, comprising the following steps: learning deep features of line elements based on a transformer network, and extracting high-dimensional features of line elements in a line vector map data set; aggregating the high-dimensional features of all line elements into a global feature through a feature aggregation network; converting the global feature into a binary hash code through forward propagation of a hash layer; generating a zero watermark based on XOR operation between the binary hash code and a copyright image, and storing the zero watermark in a database of a copyright center. The application learns deep features of data as digital fingerprints in a manner of simulating human cognition, realizes strong differentiation, high robustness of copyright authentication of line vector map data under the premise of ensuring zero distortion of original data, and has the advantages of absolute non-loss, strong differentiation ability, excellent robustness, high security and good practicability.

The invention discloses a three-phase circuit breaker mechanical state intelligent diagnosis and fault prediction method and system, and relates to the technical field of power equipment state monitoring and intelligent operation and maintenance. The method comprises the following steps: synchronously acquiring three-phase vibration and acoustic signals in a same opening / closing window, performing stage segmentation after preprocessing, constructing stage fusion features, and obtaining three-phase characterization vectors of all stages through a shared characterization encoder; inter-phase comparison self-supervised learning is carried out only by using health data, and inter-phase consistency constraints are introduced. Inter-phase mismatch is calculated on line, a stage-phase anomaly score and a total anomaly score are obtained based on a health statistical model, an abnormal phase and an abnormal stage are output when a threshold value is exceeded, and key time slice / frequency band evidence is generated; and constructing a health index sequence according to the total abnormal score and predicting the residual life. According to the invention, fault label dependence can be reduced, abnormal positioning interpretability and cross-working-condition stability are improved, and predictive maintenance is supported.

This invention develops a control plane policy verification method based on GNN fault model learning: It acquires network topology information, uses K-Means clustering to divide equivalence classes, and generates corresponding fault models; designs a GNN fault learning model based on a message-passing mechanism; trains a message-passing-based GNN fault learning model for each equivalence class; constructs a network policy graph conforming to network topology characteristics; preprocesses the directed policy graph, extracts feature data, and inputs it into the trained GNN model to generate an accurate network environment; and verifies network policies according to their categories. This invention considers the existence of faults in real-world networks, using GNN-assisted fault models to infer the actual network environment corresponding to the network policy to be verified. It uses K-Means clustering to divide equivalence classes, reducing network size and improving verification speed. The addition of a data augmentation module gives the trained GNN a certain generalization ability, enabling accurate and rapid verification of multiple network policy types.

Embodiments of the present application disclose a chip verification method and device, system, electronic equipment and storage medium, and relate to the technical field of electronic design automation, which can give full play to the large-scale and rapid verification advantages of a hardware verification platform while facilitating defect detection. The method is based on a hardware verification platform and includes: running a hardware simulation program of a chip to be verified; sending behavior information and behavior result information of the chip to be verified in program running to a software verification platform according to a preset strategy, so that the software verification platform verifies and reviews the chip to be verified according to the behavior information and the behavior result information. The present application is applicable to various chip verifications.

The invention discloses a simulation system modeling lightweight substitution method for high-end equipment manufacturing whole process rapid verification. The method comprises the steps that 1, multi-field simulation data and test data of typical parts of high-end equipment are collected; 2, performing normalization and standardization preprocessing on simulation data, and constructing a data set; step 3, fusing model network architecture design and initialization training of a physical mechanism, introducing a physical information neural network and embedding a physical equation constraint operator; 4, reducing the model calculation amount based on a parameter compression and self-adaptive advanced exit mechanism of similarity evaluation; and step 5, constructing a lightweight simulation system substitution model, exporting and integrating the model into a system model through a standard format, and supporting rapid verification of the whole process of high-end equipment manufacturing. According to the method, deep learning and a physical mechanism are fused, the calculation time is remarkably shortened while the calculation precision and the generalization ability are improved, the system-level verification period is shortened, and agile development of aerospace and other high-end equipment is effectively supported.

The invention discloses an intelligent engineering network transaction data management system based on a block chain, and relates to the technical field of block chain technology application, and the system comprises a data processing module which obtains original transaction data and processes the original transaction data to generate a standard transaction data packet; the index construction module is used for processing the standard transaction data packet based on an improved Merkel-Parene tree algorithm and constructing a data index structure containing a state mapping relation; the block generation module is used for verifying and packaging the data index structure by using a preset consensus mechanism to generate a transaction data block; the network consensus module broadcasts the transaction data block to a block chain network node to trigger synchronous verification; and the account book storage module is used for persistently storing the transaction data blocks to the distributed account book and updating the global state after the consensus is reached. According to the system, efficient, safe and traceable management of intelligent engineering network transaction data is realized, and the state retrieval speed and the system processing efficiency are improved.

A method and apparatus for canary deployment of an application includes: sequentially performing canary deployment of the target application on node databases in each center of a distributed database; each time a canary deployment is completed on a node database, obtaining the execution status of a first business request; if the execution meets the corresponding preset requirements, selecting the next node database in a different center from the currently deployed node database, and continuing the canary deployment process; wherein, the first business request is a business request routed to the previously deployed node database and requiring data access from the currently deployed node database. The canary deployment method provided in this application embodiment can quickly verify the execution status of business requests between node databases in different centers, thereby promptly identifying problems in data access between databases for the deployed application, greatly reducing deployment risks.

The application provides a vehicle parameter identification method and system considering measurement error and wheelbase error, comprising the following steps: constructing a target function based on a mathematical model of a target vehicle, considering measurement error and wheelbase error; obtaining mechanical information of the target vehicle and setting calibration parameters; collecting motion information of the target vehicle in real time according to the calibration parameters; optimizing the target function based on the mechanical information and the motion information to obtain an optimization result; updating target vehicle parameters by using the optimization result; and repeating the step to perform rolling update on the target vehicle parameters. According to real-time measured data, the application constructs a residual equation containing sensor measurement error and vehicle parameter error, and uses a nonlinear least square method to solve the sensor measurement error and the vehicle parameter error, thereby improving the accuracy of vehicle parameter identification.